ISSN 2185-159X

erd

International Journal ofEnvironmental and Rural Development

Volume 11 Number 1

June 2020

International Society of Environmental and Rural Development

IJERD – International Journal of Environmental and Rural Development

erdInternational Journal ofEnvironmental and Rural Development
Official Journal of the International Society of Environmental and Rural Development(Accessible from J-Stage, EBSCO, Google Scholar, CABI International, etc.)

Aims and Scope:
The International Journal of Environmental and Rural Development, IJERD, is an interdisciplinary journal
concerning environmental and rural development research in the education for sustainable development, ESD.
The IJERD is devoted to publishing peer-reviewed research article, review article and technical report in thefields of environmental and rural development, such as education for sustainable rural development, ruraldevelopment, environmental management or agricultural systems. Every article or report in IJERD must be
accepted by at least 2 reviewers and an editorial member.
The aims of IJERD are to publish the latest research development and achievements, to strengthen
education systems, to promote cooperation in environmental and rural development and to contribute to theprogress in sustainable development. The editorial board of this journal covers a variety of scientificdisciplines.
Thematic Areas:
The following areas are envisioned in IJERD.
Education for Sustainable Rural Development:
Environmental Education, Food and Agricultural Education, Participatory Approach, Capacity Building,Community Empowerment, Agricultural Extension, etc.
Rural Development:
Marketing, Partnership, Value Added Product, Community Development, Access to Technology,
Cultural Preservation, etc.
Environmental Management:
Bio-Diversity, Soil Degradation and Land Conservation, Water Quality Conservation, Deforestation andSustainable Forest Management, Environmental Management, etc.
Agricultural Systems:
Organic Farming, Conservation Tillage, Mechanization, Irrigation and Drainage, Nutrient and Pest
Management, Cattle Breeding, Agro-Forestry, Indigenous Technology, etc.
Infrastructural Systems:
Water Resource Development, Land Reclamation, Road Construction, etc.
Editorial Board:
Editor in Chief:
Prof. Dr. Machito Mihara, Tokyo University of Agriculture, Japan /Institute of Environmental Rehabilitation and Conservation, Japan
Managing Editors:
Dr. Jeeranuch Sakkhamduang, Institute of Environmental Rehabilitation and Conservation,
Southeast Asia Office / Association of Environmental and Rural Development, Thailand
Dr. Shinobu Terauchi, Institute of Environmental Rehabilitation and Conservation, Japan
Dr. Narong Touch, Tokyo University of Agriculture, Japan
Editorial Advisory Board:
Prof. Dr. Mom Seng, Royal University of Agriculture, Cambodia
Dr. Insa Kuhling, University of Applied Sciences, Germany
Dr. Carlos Onan Tovar Mendoza, Honduras Embassy in Japan, HondurasProf. Dr. Noureddin Driouech, Mediterranean Agronomic Institute of Bari, Italy
Prof. Dr. Kiichiro Hayashi, Nagoya University, Japan
Prof. Dr. Kingshuk Roy, Nihon University, Japan
Dr. Yuri Yamazaki, Tokyo University of Agriculture, Japan
Prof. Dr. Merites M. Buot, University of the Philippines Los Baños, Laguna, Philippines /UP Open University, Philippines
Prof. Dr. Salvador Catello, University of the Philippines, Philippines
Prof. Tomas Reyes, Jr. Bohol Island State University, Philippines

IJERD – International Journal of Environmental and Rural Development

Dr. Julian Torillo, Jr. Bohol Island State University, Philippines
Dr. Venkatachalam Anbumozhi, International Centre for Trade and Sustainable Development,
Switzerland
Dr. Anchasa Pramuanjaroenkij, Kasetsart University, Thailand
Prof. Dr. Rajendra P. Shrestha, Asian Institute of Technology, Thailand
Dr. Somsak Srisontisuk, Khon Kaen University, Thailand
Dr. Vidhaya Trelo-Ges, Khon Kaen University, Thailand
Prof. Dr. Vo Quang Minh, Cantho University, Vietnam
Editorial Section in ISERD Secretariat:
Dr. Shinobu Terauchi and Dr. Takashi Ueno
Research Center, Institute of Environmental Rehabilitation and Conservation (ERECON)
2987-1 Onoji Machida-Shi, Tokyo 195-0064, Japan
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Webpage: www.erecon.jp

IJERD – International Journal of Environmental and Rural Development (2020) 11-1

Contents
Examination of Optimal Search Method of Unknown Parameters in Tank Modelby Monte Carlo Method
Koji Miwa, Kosuke Mukae, Hiromu Okazawa,
Tadao Yamamoto, Takashi Inoue and Emmauel Okiria

1

The Influence of Percolation Patterns on Copper and Cadmium Uptake, Growth and Yieldof Rice Plants in Copper- and Cadmium-polluted Stratified Paddy Fields
Yoshito Toikawa, Choichi Sasaki, Chihiro Kato,
Nobuhiko Matsuyama, Akira Endo, Jinhun Fan
and Takeyuki Annaka

8

Development of a Simplified Water Storage Operation Curve at Irrigation Ponds for Flood Mitigation-A Case from Tamatsu District in Tottori, JapanRyoma Kuwae and Katsuyuki Shimizu

18

Evaluation of Drought Tolerance for Improved Rice Lines in terms of Yield,
Chlorophyll content and Water Use Efficiency
Aung Naing Oo, Kyaw Ngwe and Nang Ohn Myint

25

Evaluation of Water Shortages in Agricultural Water Use in the Sangker River Basin, Cambodia
Chey Vanna, Tadao Yamamoto and Takashi Inoue

32

Influence of Meteorological Parameters on Population of Whitefly, Bemisia tabaci
on Greenhouse-grown Tomatoes
Kanchana Thinnabut, Duangrat Thongpak, Chutinan Choosaiand Ubon Tangkawanit

40

Effects of Crimping by Oggun Tractor-mounted Roller/crimper on Cover Crop Termination,
Soil Strength, and Soil Moisture in Upland Cambodia
Lyhour Hin, Borarin Buntong, Manuel R. Reyes and Lyda Hok

45

Evaluating Changes in the State of Organic Matter Present in Agricultural Soils
based on Loss on Ignition Method
Takahiko Nakamura and Narong Touch

51

Vegetable Farming Practices in Cambodia: Case study of Small-scale Vegetable Farmers
in Kandal, Kampong Chhnang and Battambang Provinces
Thira Pinn, Syden Reach, Borarin Buntong
and Antonio Acedo Jr.

57

Feeds and Feeding Practices for Dairy Cattle Farming in Selected Areas of Myanmar
Aye Aye Myint and Ei Ei Win Muang

63

Environmental Effects from Contamination of Agricultural Soils via Spraying and Dust Application
to Crops and Animals
Barry N Noller

69

Effects of Different Percentages of Sorghum Silage and Napier Grass on Nutrient Intake
and Growth Performance of GoatsEi Ei Win Muang and Khin San Mu

75

Bending Strength and Toughness Properties of Pavement Concrete Reinforced with Bamboo Fiber
Eiji Matsuo, Hiroto Shimizu, Umi Iwanaga,
Takenori Nagamatsu and Masaki Iida

81

Richness of Horticultural Crops with Emphasis on Mango Species in Selected Home Gardens
in Myanmar
Nyein Nyein Aye, Thanda Aung and Khin Thida Myint87i

IJERD – International Journal of Environmental and Rural Development (2020) 11-1

Impact of Forest Cover Change in Phnom Kulen National Park on Downstream Local Livelihoods
along Siem Reap River, Cambodia
Oum Somaly, Nophea Sasaki, Sok Kimchhin, Takuji W. Tsusaka,
Sangam Shrestha and Neang Malyne

93

Study of Farmers’ Use of Fertilizers and Methods of Application for Rice Cultivationin Central Myanmar
Swe Swe Mar, Naing Naing Moe, Kyaw Ngwe and Kazuo Ando100

Factors Causing Deficits in Traditional Rice Liquor Production in Rural Areas of Cambodia
Mitsuru Hamano, Tetsuo Matsumoto and Kasumi Ito

106

Evaluation of Bacterial Contamination Level in Pickles Sold at Wet Market in Cambodia - Part 1in Case of the Samples from Kampong Cham and Phnom Penh
Shuki Muramatsu, Masataka Uchino, Yuri Tanikoka,
Sokly Sorm, Daiki Oka, Yoshiki Muramatsu, Toru Nakajima,
Motoe Sekido, Takahiko Nakamura, Chim Chay
and Machito Mihara

115

Evaluation of Bacterial Contamination Levels in Pickles Sold at Wet Market in Cambodia -Part 2Detection of Several Food-poisoning Bacteria of 48 Samples from Phnom Penh
Shuki Muramatsu, Masataka Uchino, Sokly Sorm,
Yuri Tanikoka, Daiki Oka, Yoshiki Muramatsu, Toru Nakajima,
Motoe Sekido, Takahiko Nakamura, Chim Chay
and Machito Mihara

121

Study of the Relationship between different Soil Properties in Agricultural Fields,
Kyee Inn Village, Myanmar
Swe Swe Mar, Naing Naing Moe and Kyaw Ngwe

127

Monitoring of Crop Plant Height Based on DSM Data Obtained by Small Unmanned Vehicle
Considering the Difference of Plant Shapes
Ke Zhang, Osamu Tsuji, Masato Kimura, Toshimi Muneoka
and Kenichi Hoshiyama

133

Evaluation of Soil Ecosystem Health in Different Farming Systems
by Observing Diversity of Soil Arthropods
Marisol Terashima and Machito Mihara

140

Risk Assessment of African Swine Fever Virus in Pork in Phnom Penh, Cambodia
Vouchsim Kong, Phea Sum, Pheary Mol, Karolin Chan,
Socheata Ratha, Seavchou Laut and Vutey Venn

146

Ant Biodiversity under Different Fruit Agro-ecosystem in Nam Phong Watershed, Thailand
Duangrat Thongphak and Chuleemas Boonthai Iwai

153

Temporal and Spatial Distributions of Soil Apparent Electrical Conductivity in Paddy Rice Fields,
Khon Kaen, Thailand
Katsuyuki Shimizu, Naho Nohara, Tadao Yamamoto,Takashi Kume and Chuleemas Boonthai Iwai

158

Reducing Nitrogen Emission during Cow Manure Composting with Adding of Rice Husk Biochar
Savesh Maskey and Machito Mihara

163

Socioeconomic Conditions and Perception of Climate Change in Rice Varietal Selection
by Smallholder Farmers in Myanmar
Shwe Mar Than and Thadar Htwe

170

Estimation of Flooded Areas and Water Levels after the Heavy Rain in July 2018 in Okayama
Takahiko Kubodera and Shumpei Hiramichi

176

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IJERD – International Journal of Environmental and Rural Development (2020) 11-1

Cover Crop Mixtures Effects on Soil Physical and Chemical Properties in Japan
Toru Nakajima, Riku Nakamura, Ryuji Kawamura
and Machito Mihara

182

Does the Downward Trend of the Farming Population in Remote and Mountainous Areas
Increase the Average Farm Size? A Community Level Data Analysis in Tohoku Region, Japan
Shigekazu Kawashima, Asato Mizuki and Fusao Ito

187

Empowerment of Rural Community and its Resilience: A Case Study of a Small-Scale Rural
Community in a Mountainous Area in Japan
Kako Inoue

191

iii

IJERD – International Journal of Environmental and Rural Development (2020) 11-1

Research article
erd

Examination of Optimal Search Method of Unknown
Parameters in Tank Model by Monte Carlo Method
KOJI MIWA
Extension Center, Institute of Environmental Rehabilitation and Conservation, Japan

KOSUKE MUKAE
Hitonomori Corporation, Kanagawa, Japan

HIROMU OKAZAWA*
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, Japan
Email: h1okazaw@nodai.ac.jp

TADAO YAMAMOTO
Research Faculty of Agriculture, Hokkaido University, Hokkaido, Japan

TAKASHI INOUE
Research Faculty of Agriculture, Hokkaido University, Hokkaido, JapanEMMANUEL OKIRIA
Graduate School of Agriculture, Tokyo University of Agriculture, Tokyo, Japan
Received 9 April 2018

Accepted 5 March 2020(*Corresponding Author)

Abstract Recent years, flood disasters have become rampant due to climate change. For therisk mitigation, building up a hydrological run-off model which can be used accurately andquickly helps communities prepare effective disaster prevention measures. Tank Model is ahydrological run-off model proposed in Japan. On one hand, its calculation is relatively
simple. On the other hand, many unknown parameters have to be identified. Thus, the more
random number it generates at a time, the more it takes time for calibration. This research
examines the optimal method of identifying the unknown parameters by Monte Carlo method,
considering the improvement of efficiency for practical use. Montel Carlo method cangenerate the massive number of random samples and help us select the best combinations of
unknown parameters. Among the generated random numbers, the optimal parameters aresearched which can fit to the actual measured values with the minimal number of samples.
This research examines how many random numbers need to be generated at minimum toobtain the optimal parameters. The number (N) of random samples were seven kinds of 100,
1,000, 10,000, 100,000, 1,000,000, 10,000,000 and 100,000,000. Every time random
samples generated by Montel Carlo method, the maximum and minimum numbers of each
unknown parameter among the five best combinations were applied for the retrieval rangeof the next simulation. In this way, the simulation was repeated ten times for each kind ofsample number. As the result, the watershed with simple land-use could obtain the optimal
parameter with fewer samples (N = 1,000) than the watershed with complex land-use type
(N = 1,000,000). With these sample numbers, the prediction accuracy for both watersheds
were high. It is considered that the complex land-use watershed had lower accuracy rate
because the water runoff was influenced by the underdrainage facilities.
Keywords hydrological runoff model, Tank Model, Monte Carlo simulation, watershed

INTRODUCTION
Hydrological disasters such as storms, flood and drought have been rampant in many parts of theworld in the recent years. Especially floods have caused huge economic damages and humanⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2020) 11-1

casualties. To prevent or mitigate the damages, the improvement of the prediction accuracy and speed
on water-runoff by a hydrological model is one of the measures. Generally, the prediction failure ofa hydrological runoff model is attributed to errors in input data, model structure and parameter setting.
A hydrological run-off model, Tank Model is a lumped conceptual model to predict rainfallrunoff, developed by Sugawara (1985). It is considered a simple model but requires model
parameters to be identified for calibration. To identify optimal parameters, various methods havebeen studied. Tanakamaru (1995) identified the accuracy of the Stuffled Complex Evolution method
and the multistart Powell method for the parameter estimation. Tada (2007) examined the Particle
Swarm Optimization algorithm and discussed its effectiveness under certain conditions. Fujihara etal. (2003) investigated the selection of objective functions to be used for identification of theparameters. For other examples, Kalman Filter (Ichihara et al., 2000) and Monte Carlo method(Mukae et al., 2017) have been applied. Monte Carlo is a simple simulation which generates a huge
number of numeric random samples. However, the optimal number of samples to be generated has
not been examined.
OBJECTIVE
This research aims to clarify the optimal search method by Monte Carlo method to identify theparameters of the Tank Model quickly and accurately. In this study, the simulation of Monte Carlo
was implemented by a software, MATLAB developed by MathWorks Inc. The fewer samples, theless time it takes to simulate. Therefore, it is important to find the minimal number of samples todetermine the optimal parameters promptly and accurately. Through this examination, it is expected
to suggest a minimal number of samples to identify the parameters with expedition and accuracy in
the Tank Model.
METHODOLOGY
Study Area and Data Collection

Fig. 1 The Shubuto River Basin, Hokkaido, Japan (left), land-use of Igarashi River
Watershed (center) and Soebetsu River Watershed (right) (Okazawa, et al. 2002)
The study sites of this research are watersheds of the Igarashi River and the Soebetsu River. Both ofthem, in fact, are the size of the stream rather than rivers and tributaries of the Shubuto River, locatedin southwestern Hokkaido, Japan (Fig. 1). The Igarashi River watershed has the area of 6.9 km2 andthe river length is 7.3 km. This watershed consists of a complex land-use with 2.7 km2 of pasturelandin the downstream basin and 4.2 km2 of forestland in the upper and middle basin, which covers 31%
and 69% of the watershed respectively. This pastureland is mainly used for livestock and there iscropland in a part of the upper basin. Therefore, this watershed contains the land-use of agriculturaland forest lands. On the other hand, the watershed area of the Soebetsu is 11.5 km 2, and the river
length is 11.1 km. It is a simplex land-use only consisted of forest land. Since these watersheds areⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2020) 11-1

close each other, the meteo-hydrological data, such as precipitation amount and evapotranspiration
for both areas have the similarities.
This research used data of the daily record of river discharge and precipitation from June 1, 1998
to October 31, 1998 (153 days) observed by Okazawa, et al. (2002). The daily data of temperature,
wind speed and hours of sunshine which are required to calculate daily evapotranspiration, areobtained from AMeDAS in Kuromatsunai Town managed by Japan Meteorological Agency.
Hydrological Model: Tank Model
The tank model sets four tanks vertically and sequentially as shown in Fig. 2. In the top tank,precipitation amount is entered and evapotranspiration is subtracted. When there is no water in the
top tank, evapotranspiration is subtracted from the lower tanks in order. The water flows from theupper to lower tanks through the bottom outlet as percolation and infiltration or flows out of the side
outlets as runoffs which are set at a certain height in each tank. The runoff from the side outlets is
considered surface flow in the top tank, intermediate runoff in the second tank, sub-base runoff in
the third tank, and base flow in the fourth tank. The total of these runoff becomes the river runoff.
Until the water level reaches side outlets, the runoff does not start and the water keeps flowing
downward.
R

ET

Sa
a0

a1
a2 ha2
ha1
b1

Sb
b0

hb
c1

Sc

hc

c0
Sd

d1

Fig. 2 Concept of Tank Model
The water level of the top tank of simulation is calculated by Eq. (1).
𝑆𝑎 𝑖+1 = 𝑅 − 𝐸𝑇 + 𝑆𝑎 𝑖 − 𝑄𝑎1 𝑖 − 𝑄𝑎2 𝑖 − 𝑄𝑎0 𝑖(𝑖 = 1 ⋯ 𝑛)(1)

Where R is daily rainfall (mm), ET is daily evapotranspiration calculated by FAO Penman-Monteith
method. Qa1 is water volume discharged by the upper side outlet, Qa2 is water volume discharged by
the lower side outlet and Qa0 is water volume discharged to the second tank from the bottom, of the
top tank. i is elapsed day(s), the maximum of n is 153 (days). The side and bottom outlets arecalculated as Eqs. (2) to (6).
if 𝑆𝑎 𝑖 ≥ ℎ𝑎1 𝑖 , 𝑄𝑎1 𝑖 = 𝑎1 (𝑆𝑎 𝑖 − ℎ𝑎1 𝑖 )(2)if 𝑆𝑎 𝑖 < ℎ𝑎1 𝑖 , 𝑄𝑎1 𝑖 = 0(3)

if 𝑆𝑎 𝑖 ≥ ℎ𝑎2 𝑖 , 𝑄𝑎2 𝑖 = 𝑎2 (𝑆𝑎 𝑖 − ℎ𝑎2 𝑖 )(4)if 𝑆𝑎 𝑖 < ℎ𝑎2 𝑖 , 𝑄𝑎2 𝑖 = 0(5)

𝑄𝑎0 𝑖 = 𝑎0 𝑆𝑎 𝑖

(6)

Where ha1 and ha2 are the height of side outlet, a1, a2 and a0 are coefficient of discharge in each outlet
in the top tank. The second and third tanks are calculated in the same way as Eqs. (7) to (14).
The second tank:
𝑆𝑏 𝑖+1 = 𝑄𝑎0 𝑖 +𝑆𝑏 𝑖 − 𝑄𝑏1 𝑖 − 𝑄𝑏0 𝑖(7)

if 𝑆𝑏 𝑖 ≥ ℎ𝑏1 𝑖 , 𝑄𝑏1 𝑖 = 𝑏1 (𝑆𝑏 𝑖 − ℎ𝑏 𝑖 )(8)Ⓒ ISERD

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IJERD – International Journal of Environmental and Rural Development (2020) 11-1

if 𝑆𝑏 𝑖 ≥ ℎ𝑏1 𝑖 , 𝑄𝑏1 𝑖 = 0(9)

𝑄𝑏0 𝑖 = 𝑏0 𝑆𝑏 𝑖

(10)

The third tank:
𝑆𝑐 𝑖+1 = 𝑄𝑏0 𝑖 +𝑆𝑐 𝑖 − 𝑄𝑐1 𝑖 − 𝑄𝑐0 𝑖(11)

if 𝑆𝑐 𝑖 ≥ ℎ𝑐1 𝑖 , 𝑄𝑐1 𝑖 = 𝑐1 (𝑆𝑐 𝑖 − ℎ𝑐 𝑖 )(12)

if 𝑆𝑐 𝑖 ≥ ℎ𝑐1 𝑖 , 𝑄𝑐1 𝑖 = 0(13)

𝑄𝑐0 𝑖 = 𝑐0 𝑆𝑐 𝑖

(14)

As the forth tank is considered the bottom, it is calculated as Eqs. (11) to (13).
𝑆𝑑 𝑖+1 = 𝑄𝑐0 𝑖 +𝑆𝑑 𝑖 − 𝑄𝑑1 𝑖(11)

if 𝑆𝑑 𝑖 ≥ 0, 𝑄𝑑1 𝑖 = 𝑑1 𝑆𝑑 𝑖

(12)

if 𝑆𝑑 𝑖 = 0, 𝑄𝑑1 𝑖 = 0(13)

Calibration of water runoff in the tank model requires to calculate and identify the sixteen
parameters, which are “a1”, “a2”, “a0”, “b1”, “b0”, “c1”, “c0”, “d1”, “Sa 1”, “Sb 1”, “Sc 1”, “Sd 1”, “ha1”,“ha2”, “hb” and “hc” as seen in Fig. 2. a, b and c are coefficient of discharge from the side and bottom
outlets in the top, second and third tanks respectively. The bottom tank has only one side outlet, d. S
is water level of each tank. h is the height of the side outlets from the bottom of each tank. Only the
top tank has two side outlets.
As an evaluation function, the compatibility of the actual value of river discharge and theestimated value is evaluated by Nash-Sutcliffe efficiency (NSE) as Eq. (14). The closer the NSE value
is to 1.0, the higher the accuracy of the simulation is. Moriasi et al. (2007) divided NSE into ≤ 0.50,
0.50 to 0.65, 0.65 to 0.75 and > 0.75 and evaluated unsatisfactory, satisfactory, good and very good,
respectively. N stands for the total number of calculation time, Qobs(i) is the actual river discharge at
any time step of i, Qsim(i) is the estimated river discharge of at any time step of i, and Qav is the meanvalue of the actual river discharge. NSE value is calculated as Eq. (14).
N

NS = 1 − Q (i ) − Q (i )

2

i =1
N

obs

sim

 Q (i ) − Q 
i =1

2

obs

av

(14)

Calibration Approach
The flow of this research method is as shown in Fig. 3. The initial retrieval range for each parameter
was decided as shown in Table 1. The random numbers were created between the minimum andmaximum value for each parameter by Monte Carlo method. The numbers of random samples were
1.0x102, 1.0x103, 1.0x104, 1.0x105, 1.0x106, 1.0x107 and 1.0x108. These samples were calculated in
the tank model to obtain the NSE value for each sample. From the top five of highest NSE value, themaximum and minimum values of each parameter were confirmed and used as the next retrieval
range of maximum and minimum. This process was repeated nine times. In this way, the retrieval
range was narrowed down. The value which has the highest NSE value was selected as the parameterobtained from the 10th simulation.

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IJERD – International Journal of Environmental and Rural Development (2020) 11-1

Fig. 3 Flow of calculation method with Monte Carlo method
Table 1 Initial retrieval range of value in each parametera1Min. value
Max. value

a2
0
0.3

S a1
Min. value
Max. value

a0
0
0.3

S b1
0
30

b1
0
0.1

S c1
0
50

0
200

b0
0
0.1

S d1

c1
0
0.1

h a1

0
300

c0
0
0.1

h a2
15
50

d1
0
0.1

hb
0
20

0
0.1
hc

20
50

20
100

RESULTS AND DISCUSSION
The results of the simulation for Igarashi River and Soebetsu River watersheds are shown in Table
2. In general, the higher the sample number (N) is, the higher NSE value is obtained. For the Igrarashi
River watershed where land-use is the mix of forest and agriculture, the increase of NSE value slowed
down after N = 1.0x107 (NSE value = 0.801). However, since the increase from N = 1.0x106 (NSE
value = 0.799) to N = 1.0x107 is very slight and the value was rather decreased at N = 1.0x108 (NSE
value = 0.798), the minimal and optimal sample number was considered N = 1,000,000. The NSE
value was insufficient in N = 1.0x102 as the value was lower than 7.0. On the other hand, for the
Soebetsu River watershed where land-use is solely forest, the NSE value was high enough even for
N = 100 (NSE value = 0.848). The increase from N = 1.0x103 onward was limited. Thus, N = 1.0x103
is considered optimal.
Comparing both watersheds, the forest watershed could obtain the optimal parameter with much
fewer samples than the mixed-land use watershed. From these, it is considered that the simple landuse watershed has the simple water movement, thus could obtain the optimal parameter with fewer
sample numbers.
Table 2 NSE value in Igarashi and Soebetsu watersheds after the tenth simulation
No. of random sample numbers
NSE Value in Igarashi watershed
NSE Value in Soebetsu watershed

1.0 x 102
0.690
0.848

1.0 x 103
0.766
0.872

1.0 x 104
0.765
0.872

1.0 x 105
0.776
0.875

1.0 x 106
0.799
0.878

1.0 x 107
0.801
0.880

1.0 x 108
0.798
0.882

Hyetograph and hydrograph for each watershed are shown in Fig. 4 and 5. For the Igarashi River,the optimal parameters were obtained from N = 1.0x106 and for Soebetsu N=1.0x103. Comparing to
the actual river discharge, both simulation had high accuracy. However, in the Igarashi, although the
gap was little at the peak of discharge at the rainfall time, it was large when water is calm with little
rain (base flow condition of river). In addition, the value of simulation is higher than the actual value
in June when rainfall is low. On the other hand, the value of simulation was lower than the actual
value in July when there is almost no rainfall. Such occurrence is considered because of drainage
facilities such as underdrain in agricultural lands. Therefore, water movement becomes more

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IJERD – International Journal of Environmental and Rural Development (2020) 11-1

complicated in agricultural watershed than forest watershed. For this reason, the prediction accuracy
becomes lower in agricultural watersheds than forest watershed by four layers of tank model.

Fig. 4 Hyetograph and hydrograph with Tank model simulation in the Igarashi

Fig. 5 Hyetograph and hydrograph with Tank Model simulation in the Soebetsu
The above results showed that the larger number of sample is, the higher the NSE value tends tobe obtained up to a certain point. When the land-use is simple, the high accuracy is obtained withfewer samples than the case of complex land-use. Although there are several different methods toidentify parameters, by using Monte Carlo method with an appropriate software, it is able to predictthe rainfall-runoff for simple-land use area with a limited time and effort. In case of complex landuse, it is suggested to conduct the prediction considering the time required to conduct the calibration
a number of times.
CONCLUSION
This research showed the predictability of rainfall-runoff with the minimal number of samples toobtain the results as swiftly and accurately as possible. The results showed that the larger number ofsample numbers is, the higher the NSE value is obtained. However, the increasing rate is slowed
down at certain sample numbers and the further attempts seemed unnecessary for both watersheds.
In the research site for this study, the simple forest watershed required fewer number of random
samples, which was N = 1.0x103, compared with the complex land-use watershed with forest andagricultural lands, which was N = 1.0x106. It is assumed that agricultural watersheds have lower
accuracy in four layers of tank model as they require additional calculation in parameter setting as
the runoff is influenced by drainage facilities. In the future study, it is important to continue testingin different cases to generalize the optimal number of random samples to confirm the parameters byMonte Carlo method in the tank model. For the case of agricultural watersheds, it needs to consider
the water movement influenced by drainage facilities in the calibration process.

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ACKNOWLEDGEMENTS
This research was supported by the grant-aid from Tokyo University of Agriculture in Japan, and by
the joint usage/research program of the Institute of Materials and Systems for Sustainability (IMaSS),
Nagoya University.
REFERENCES
Fujihara, Y., Tanakamaru, H., Hata, T. and Tada, A. 2003. Study on selection of objective functions forcalibration of rainfall-runoff models. Transactions of the Japanese Society of Irrigation, Drainage andReclamation Engineering, 225, 423-435.
Ichihara, K., Toyokawa, K., Sawaguchi, I. and Kawashima, H. 2000. Real time runoff forecasting with tankmodels whose parameter values are identified by Kalman filter. Journal of the Japanese Forestry Society,82 (2), 125-131.
Moriasi, N.D., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D. and Veith, T.L. 2007. Modelevaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of
the American Society of Agricultural and Biological Engineers, 50 (3), 885-900.
Mukae, K., Miwa, K., Okazawa, H. and Fujikawa, T. 2017. Ecosystem service assessment in agricultural
watershed by using TOPMODEL. AGROFOR International Jounal, 2 (3), 27-36.
Sugawara, M. 1985. Tank model, For the derivation of river discharge from rainfall. Journal of Geography, 94(4), 1-13.
Tada, T. 2007. Parameter optimazation of hydrological model using the PSO algorithm. 20 (5), 450-461.
Tanakamaru, H. 1995. Parameter estimation for the tank model using global optimization. 178 (63-4), 103-112.

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Research article
erd

The Influence of Percolation Patterns on Copper andCadmium Uptake, Growth and Yield of Rice Plants in
Copper- and Cadmium-polluted Stratified Paddy Fields
YOSHITO TOIKAWA
The United Graduate School of Agricultural Sciences, Iwate University, Iwate, JapanCHOICHI SASAKI*
Faculty of Agriculture and Life Science, Hirosaki University, Aomori, Japan
Email: chsasaki@hirosaki-u.ac.jp

CHIHIRO KATO
Faculty of Agriculture and Life Science, Hirosaki University, Aomori, JapanNOBUHIKO MATSUYAMA
Faculty of Agriculture and Life Science, Hirosaki University, Aomori, JapanAKIRA ENDO
Faculty of Agriculture and Life Science, Hirosaki University, Aomori, JapanJINHUN FAN
Qingdao academy of agricultural sciences, Shandong, China

TAKEYUKI ANNAKA
Faculty of Agriculture, Yamagata University, Yamagata, Japan
Received 20 Jan 2020

Accepted 5 March 2020(*Corresponding Author)

Abstract Combined pollution in farmland soil is a recognized issue in Japan. The
measurement of combined pollution has been discussed because the interaction between
heavy metals is complex. To date, soil dressing has been the primary method employed totackle this problem. The objective of this study is to clarify how the percolation patterns ofpolluted subsoil affect Cu and Cd uptake, and the growth and yield of paddy rice plants. We
prepared six stratified paddy-field models to test growth with plowsole and subsoil in open
and closed percolation patterns. Each model consisted of a 12.5-cm-thick plowed layer andan upper plowsole made of a non-polluted soil dressing and underlying polluted plowsole
and subsoil. Three soils with three levels of Cu contaminations (100, 250, and 500 mg Cu kg1) were prepared by adding a Cu solution to Cd-contaminated paddy-field soils (1.7 mg Cdkg-1). Three of the six models were open systems, and the other three were closed systems.
During the tests, a constant water-ponding system was adopted. As a result, the polluted
plowsole and subsoil became oxidized in the open system and reduced in the closed system.
The Cu and Cd concentrations in the rice grains were 5% higher in the open models than in
the closed models, regardless of the original Cu concentrations in the polluted soils.
Interestingly, the Cd concentrations in the grains had an inverse relationship with the Cu
concentrations. No significant difference was observed in the growth and yield of the rice
plants among the models. We concluded that the Cu and Cd concentrations in rice plants wereaffected by the percolation patterns of polluted plowsole and subsoil, even though they werecovered with non-polluted soil-dressing layers.
Keywords copper, cadmium, soil dressing, percolation patterns, rice

INTRODUCTIONⒸ ISERD

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Heavy metal contamination became an apparent problem when severe damage was caused by Cu
contamination to paddy fields in the lower reaches of Watarase River near Ashio Copper Mine andCd contamination caused Itai-itai disease among people living near Jintsu River, Toyama prefecture(Asami, 2010). In the 1970s, the Agricultural Land Soil Pollution Prevention Act came into effect,
identifying Cu, Cd, and As as harmful substances.
Arao et al. (2010) reviewed the history of heavy metal contamination in agricultural soil in Japanand the countermeasures against contamination such as soil dressing, water management, andchemical washing. They pointed out that while soil dressing was very effective in reducing Cd
absorption by paddy rice, the biggest problem was its high cost.
When it comes to combined heavy metal contamination in agricultural lands, soil dressing has
mainly been applied for remediation (e.g., Ogoya Copper Mine, Ishikawa prefecture, Japan; Asami,2010). However, recently low-cost countermeasures have gradually been introduced such as multiple
heavy metal insolubilization treatments and heavy metal absorption materials (Kanamori et al., 2019).
Clarifying the complex interaction among multiple heavy metals and its effect on the growth andyield of crops is a lengthy process.
It has been pointed out that the contaminated lower soil layers of stratified paddy fields with soil
dressing are under oxidizing and reducing conditions in open and closed percolation patterns,
respectively (Fan et al., 2018a). Paul et al. (2011a, 2011b) and Sasaki et al. (2016a, 2016b) focusedon these phenomena and studied the effects of percolation patterns on root uptake of Cd and thegrowth and yields of crops using models of contaminated stratified paddy fields with a soil dressing.
They found that Cd concentration in brown rice was significantly higher when the contaminated layer
was in the open system percolation pattern than its closed system percolation pattern counterpart.
Paul et al. (2011a), who used Andosol in their experiments, found a significant difference in thegrowth and yield of paddy rice, between open and closed system percolation patterns. On the contrary,
Sasaki et al. (2016a), who used alluvial soil, did not find any such difference. Fan et al. (2018a,
2018b) conducted a growth experiment by using stratified paddy field models with a soil dressing on
a Cu contaminated soil layer under open and closed system percolation patterns. They reported that
Cu concentrations in brown rice were higher in the open system percolation pattern than those in theclosed system percolation pattern. They also found no significant difference in growth and yield
between the two percolation patterns.
In Japan, paddy fields with Cu and Cd combined contamination have been recognized
downstream of some mines for decades. There has been an introduction of countermeasures against
such contamination, soil dressing being the most popular. However, few studies have clarified thecharacteristics of heavy metal absorption by rice plants, the degree to which contamination varies
based on the percolation pattern used, nor the impact of the percolation pattern on the growth andyield of rice crops. The purpose of this study is to investigate the effects of percolation patterns on
heavy metal absorption (for Cd and Cu) and the growth and yield of rice plants in paddy fields withcombined heavy metal contamination.
The experimental devices and the stratified paddy field models adopted in this study were thesame as those in Sasaki et al. (2016a). Three soils with three levels of Cu contamination (100, 250,
and 500 mg Cu kg-1) were prepared by adding a Cu solution to Cd contaminated paddy field soils(approximately 1.72 mg Cd kg-1; Sasaki et al., 2016a). We found that Cd and Cu concentrations inbrown rice were higher in open system percolation patterns than those in closed system percolation
patterns. No significant difference was found in the growth and yield of the rice crop. As a result, weconcluded that the type of percolation patterns had a significant impact on Cd and Cu absorption but
little on the growth and yield of the paddy rice plants.
METHODOLOGY
Materials and Methods
In this study, we used stratified paddy field models and conducted a growth experiment in open andclosed percolation systems following Sasaki et al. (2016a). Table 1 details the physical and chemicalⒸ ISERD

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properties of the soils used in this study. Non-contaminated soil (light clay; International Union of
Soil Science) was sampled from the plow layer of the paddy field at the Kanagi Farm of Hirosaki
University, Aomori prefecture (hereafter referred to as “Kanagi soil”). Cd and Cu combined
contaminated soil was prepared by adding CuCl2・2H2O to Cd contaminated soil (clay loam),collected from the plow layer of Cd contaminated paddy fields in Japan (hereafter referred to as“contaminated soil”). The 0.1 M HCl extracted Cu concentrations in the original contaminated soil
were 12.2 mg Cu kg-1, and we made three concentrations of Cu contaminated soil; 100, 250 and 500
mg Cu kg-1. This meant that one was below the Japanese standard of 125 mg Cu kg-1, and the othertwo were above that. The Kanagi soil and gravel were 3.7 and 0.8 mg Cu kg-1, respectively.
Table 1 Physical and chemical properties of soil samples and gravels
Density

Soil
MgO
Texture(g/cm3)Kanagi Soil
2.62
Contaminated
2.44
Soil
Gravel
2.68

Na2O

CaO

K2O

Cd

Cu

T-C

T-N

C/N

OM

LiC

120

64

(mg/kg)
400
120

0.14

3.70

(%)
2.07 0.16

13.3

(%)
3.6

CL

640

128

2280

288

1.81

12.2

2.96

0.21

14.1

5.1

-

147

18

539

600

0.13

0.80

-

0.00

-

0.1

The Cu concentrations in the combined contaminated soils were 20 to 100 times higher thanthose in average non-contaminated soils of paddy fields in Japan (which are approximately 5 mg Cu
kg-1) (Asami, 2010). In this study, we prepared such highly Cu contaminated soils because of aregionally unique situation in the Aomori prefecture. In this prefecture, apple has long been the centerof the economy and culture, and apple orchards often use a Bordeaux mixture (i.e., a mixture ofcopper sulfide and calcium carbonate), as a pesticide. As a result, Cu concentrations in surface soil
have at times been found to be higher than 500 mg Cu kg-1 (Aoyama, 2009). Nowadays the number
of apple producers has been decreasing due to the lack of successors and parts of apple orchards havebeen converted into other land uses, including paddy fields. This is especially the case in lowlands,
where some of them were once used as paddy fields.
Notably, no significant change was found in soil Cu concentrations before and after the experiment.
Experimental Design
We adopted the design of Sasaki et al. (2016a), where we used two types of stratified paddy fieldmodels in our experiment; an open system percolation model and a closed system percolation model.
Each stratified field model consisted of a 12.5 cm soil dressing (12.5 cm Kanagi paddy soil), a 15cm soil polluted by Cd and Cu, and a 35 cm gravel layer. We defined O-100 as an open systempercolation model in which Cu concentration in the plowsole and subsoil was 100 mg Cu kg -1. C100 was the closed system percolation model in which the Cu concentration in the sub-layer(plowsole and subsoil) was 100 mg Cu kg-1. Similarly, O-250 and C-250 were defined as models
with 250 mg Cu kg-1 in the sub-layer whilst O-500 and C-500 were defined as models with 500 mg
Cu kg-1 in the sub-layer. Note that ‘O’ and ‘C’ stand for the open system percolation pattern and theclosed system percolation pattern, respectively. The two percolation patterns have been used by
Sasaki (1992). Each stratified paddy field model was constructed in an iron box (30 × 50 × 70 cm),filled with three layers of soil. The box has several holes on its side walls. The plow layer was 0 to
10 cm deep, with dry density in puddling condition of 1.04 g cm -3. The plowsole was 10 to 20 cm
deep with dry bulk densities of 1.23 and 0.89 g cm-3 at depths of 10 to 12.5 cm and 12.5 to 20 cm,
respectively. The subsoil was 20 to 62.5 cm deep. It had dry bulk densities of 0.89 and 1.40 g cm -3
at depths of 20 to 27.5 cm and 27.5 to 62.5 cm, respectively. These layers were formed by compaction.
The ground water levels of the open and closed system percolation models were controlled at 57.5
cm and 12.5-20 cm depth, respectively. In the closed system percolation models, the holes on theside walls of iron boxes were blocked to prevent the penetration of air. In the open system percolation
models, these holes remained open at the lower part of the plowsole and the upper part of the subsoilⒸ ISERD

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to enable aeration of these layers. After those two types of models were prepared, fifteen paddy
seedlings (plant length and leaf stage were about 18 cm and about 5 leaves, respectively), named‘Oryza sativa L., Tsugaru Roman’ were transplanted as per Sasaki et al., (2016a) and Fan et al.,(2018b). The paddy seedlings were transplanted in 10 cm intervals. The fertilizer used consisted of
2 g of N, 2 g of P2O5 and 2 g of K2O for each model. This fertilizer was mixed with the entire plowlayer before transplanting. During the cultivation period, a water ponding condition was adopted andthere was no mid-summer drainage. Paddy seedling transplants and harvesting was conducted at theend of May and the end of September, respectively. Our experiment was conducted in a greenhouseon the university campus.
Measuring Method
Examination of rice plants such as plant length, leaf stage, the number of stems and panicles, theweight of straw, the number of brown rice and its weight was done using the Iwate Agricultural
Experimental Station standard methods (1981). The quantitative analysis of Cu concentrations in ricegrains, stems and leaves, roots and soils extracted by HCl solution was measured using atomic
absorption spectroscopy (AAS) (MAFF, 1979). Other measurements were conducted with thestandard methods used in Japan. The Oxidation-Reduction Potential (ORP) meter (Central Kagaku
Co., Ltd., model UC-203), was used for measuring oxidation-reduction potential (Eh). An ORP
sensor was set in each soil layer.
RESULTS AND DISCUSSION
Oxidation-reduction Potential (Eh)600
Depth from soil
surface(cm)Oxidation

500
400

Eh1(-5.0cm)
Eh2(-15.0cm)
Eh3(-20.0cm)
Eh4(-27.5cm)
Eh5(-37.5cm)
Eh6(-47.5cm)

Eh (mV)

300

200
Reduction

100
0

-100

0

20

40

60

80

-200
-300

Elapse day

100

120

Period of three
weeks before andafter heading

Fig. 1 The Eh of the open system percolation model (O-250)
The trend of Eh in each soil layer showed similar changes from transplanting to harvest among O100, O-250 and O-500, and among C-100, C-250 and C-500. Figure 1 presents the Eh of the open
system percolation model (O-250) and Figure 2 presents the Eh of the closed system percolation
model (C-250). The oxidized layer in a paddy field is defined as the layer in which the Eh value isgreater than +300 mV (Yamane, 1982). In the open system percolation model (O-250), the Eh of theplow layer became a reduced layer (less than 0 mV) during cultivation of paddy rice. The Eh of theplowsole and the subsoil became oxidized layers (> +300 mV). On the other hand, in the closed
system percolation model (C-250), the Eh of all layers gradually declined and became reduced layers
after transplanting. In all open system percolation models (O-100, O-250 and O-500), the plowsoles
became oxidized layers during the cultivation period. In contrast, all layers of the closed systempercolation models (C-100, C-250 and C-500) became reduced layers during the cultivation period.
The absorption of Cu and Cd by rice plants was influenced by the redox potential of the soil
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(Matsunaka, 2014). Based on these results, we anticipated adverse effects of soluble Cu and Cd on
rice in the open system percolation models. The variation of Eh values in each layer was attributed
to non-uniform contact between the Eh sensor and soil particles.
500

Depth from soil surface(cm)400
Oxidation

Eh1(-5.0cm)

Reduction

Eh2(-15.0cm)

300

Eh (mV)200

Eh3(-20.0cm)
Eh4(-27.5cm)

100

Eh5(-37.5cm)
0
0

20

40

60

80

100

120

Eh6(-47.5cm)

-100
-200
-300

Elapse day

Period of three
weeks before andafter heading

Fig. 2 The Eh of the closed system percolation model (C-250)
Copper Concentrations in Rice Plants
The results of Cu concentrations in rice plants are listed in Table 2.
Rice grains: Cu concentrations in rice grains (n = 7) ranged from 2.5 to 5.4 mg kg-1. The Cu
concentrations based on different percolation patterns were O-100 > C-100, O-250 > C-250 and O500 > C-500. Any increase in Cu concentrations in the rice grains was not recognized despite
increased Cu concentrations in soil. The range of Cu concentrations in our brown rice approximatedto values indicated by Fan et al. (2018a, b) and Asami (2010). However, our figures were slightly
higher than the 2.7 mg kg-1 in the Japanese rice ingredient table (MEXT, 2019). Unfortunately, there
is an absence of studies applying the ‘percolation pattern’ concept other than Fan et al. (2018a, b)
and Paul et al. (2011b). In our study, Cu concentrations in brown rice were significantly higher (atthe 5% level) in the open system percolation models, than those in the closed system percolation
models. A significant difference was also observed in brown rice Cu concentrations in Paul et al.
(2011b) and Fan et al. (2018a, b). Moreover, Paul et al. (2011b) found that Cu concentrations inbrown rice ranged from 2.5 to 4.2 mg kg-1 despite low Cu concentrations in the soils (12 mg Cu kg1). This result suggests that the range of Cu concentrations in brown rice with the difference in Cu
concentrations in the lower layer, which ranged from 12 to 500 mg kg -1, was 2.5 to 5 mg kg-1,
evidence of the impact of the percolation pattern. One reason for this may be that the immobilization
effect is lower than that of Cd as pointed out by Kanamori et al. (2019). Based on these results, it
was inferred that the variation of Cu concentrations in brown rice was due to the difference between
percolation systems of the stratified paddy fields under combined pollution conditions.
Stems and leaves: The Cu concentrations in the stems and leaves ranged from 1.9 to 4.4 mg kg-1, andthere was a significant difference (at the 5% level) between the concentrations in the open systempercolation models and the closed system percolation models, where the values of the former werehigher than the latter. Fan et al. (2018a, b) and Paul et al. (2011b) also observed a similar concentrationdifference due to percolation systems. However, Fan et al. (2018a, b), did not observe significant
differences in Cu concentrations of 70 and 100 mg kg-1 based on the type of percolation system. Thiswas presumably because of the difference in soil type. This indicates that there may be no significant
difference in Cu concentrations in the stems and leaves when soil Cu concentrations in the lower layer
are less than 250 mg kg-1. However, when soil Cu concentrations are greater than 250 mg kg-1, thedifference in the percolation system may affect the Cu concentrations in the stems and leaves.

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Roots: The Cu concentrations in roots (n = 5) ranged from 12.7 to 18.6 mg kg-1. The difference inthe soil Cu concentrations in this study was 400 mg kg-1. However, the difference in root Cu
concentrations was approximately 6 mg kg-1. The Cu concentrations in the roots observed by Fan etal. (2018a, b) also ranged from 13.7 to 20.3 mg kg-1, and they could not identify whether thisdifference was due to the type of percolation system. However, there was no significant differencein Cu concentrations in the roots, providing clarity on the extent to which the type of percolation
pattern influences brown rice, and the stems and leaves. Based on these results, it is presumed that
there is a difference in Cu concentrations due to the type of percolation patterns in the transportmechanism of Cu in the above-ground biomass of rice plants.
The Cu concentrations in rice plants among the six models were in the order of roots > rice grains> stems and leaves on average, and the ratio was 5.7: 1.3: 1. This ranking was similar to that of the
paddy rice indicated by Shibuya (1979) and Paul et al. (2011b), and that of the soybeans indicatedby Li et al. (2017). This result is considered to be a result of the Cu transport mechanism in the rice
plant. It is surmised that Cu concentrations in rice plants tend to be maintained within a certain range,similar to Zn concentrations (Shibuya et al., 1979), despite an increase in soil Cu concentrations.
The results indicated that there was a significant difference in Cu concentrations in rice grains,
stems and leaves due to the type of percolation patterns. In contrast, such difference was notdiscernible for Cu concentrations in the roots even if the percolation pattern was different.
Table 2 Cu concentrations in rice grains, stems, leaves and roots in the plow layer
Model

Rice grains
n=7

O-100
C-100
O-250
C-250
O-500
C-500

4.77 ± 0.50c2.99 ± 0.34
a5.40 ± 0.64c2.94 ± 0.20b3.72 ± 0.30c2.53 ± 0.20

aRoots of plow layer
n=5

Stems and leaves
n=5

16.19 ± 1.19ab
17.82 ± 3.12a
18.56 ± 1.28a
15.06 ± 2.89ab
15.58 ± 1.42ab
12.72 ± 0.85b3.38 ± 0.31b
1.90 ± 0.10cd
4.35 ± 0.66a
1.89 ± 0.18cd
2.42 ± 0.52c
1.53 ± 0.41d

Note: Tukey-Kramer test was performed at the 5% level; letter indicates significant difference.
The numerical value of ± shows standard deviation. Unit: mg kg-1

Cadmium Concentrations in Rice Plants
The results of Cd concentrations in rice plants are listed in Table 3.
Rice grains: Cd concentrations in rice grains (n = 7) ranged from 0.01 to 0.16 mg kg-1. The Cd
concentrations associated with the different percolation systems were O-100 > C-100, O-250 > C250 and O-500 > C-500. This concentration range approximated the range from 0.03 to 0.17 mg kg1
found by Sasaki et al. (2016a, b). In addition, the figures were higher in the open system percolation
model (O-100 and O-250) than the 0.06 mg kg-1 in rice grains from Japan (MAFF, 2019). The
increase in Cd concentrations in rice grains due to the increase in Cu concentrations was not observedin C-100, C-250 and C-500. However, these Cd concentrations in the open system percolation models
were higher in the order of O-100> O-250> O-500. Until now there research on heavy metal
concentrations caused by different percolation systems is lacking, with the exception of Sasaki et al.
(2016a, b) and Paul et al. (2011b). In this study, Cd concentrations in brown rice were significantly
higher (P value < 5%) in the open system percolation pattern than in closed system percolation pattern,
except for 500 mg Cu kg-1. Similarly, the Cd concentrations in rice grains indicated by Paul et al.
(2011b) and Sasaki et al. (2016a, b) showed a significant difference due to the type of percolation
patterns. In addition, models with an open system percolation pattern in the lower layer, were foundto have Cd concentrations in rice grains that decreased in inverse proportion to the increase of Cu
concentrations in the lower layer ranging from 100 to 500 mg kg-1. One reason for this may be thatan increase in the Cu concentrations suppresses the uptake of Cd.

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Based on these results, the type of percolation pattern of stratified paddy field models under acombined pollution condition would create differences in Cd concentrations in brown rice.
Additionally, when contaminated soil was in the closed system percolation pattern, the uptake of Cd
was suppressed.
Stems and leaves: The Cd concentrations in the stems and leaves were range from 0.06 to 1.04 mg
kg-1, and a significant difference (at the 5% level) was confirmed in Cd concentrations between the
open system percolation models and the closed system percolation models, with concentrations in
the former being higher than the latter. The exception to this was for 500 mg kg -1. Sasaki et al.
(2016b) and Paul et al. (2011b) also observed similar results, where concentration differences were
due to the type of percolation pattern. In these studies the soils of the models were only contaminatedby Cd. This was presumably because the contaminated soils were in the open system percolation
models, which were oxidized, so the solubility of Cd increased compared to those in the closed
system percolation models (Arao et al., 2010).
These results indicate that the type of percolation patterns affects the Cd concentrations in thestems and leaves under combined contamination conditions when the Cu concentrations in thecontaminated soils are lower than 500 mg kg-1.
Roots: The Cd concentrations in the roots (n = 5) ranged from 1.00 to 1.97 mg kg-1. The Cd
concentrations in the contaminated soils in this study were approximately 1.72 mg kg -1. However,the biggest difference in the Cd concentrations among the roots was 0.97 mg kg-1. Regardless of thetype of percolation model, which was under the same condition (1.81 mg kg-1) as in Sasaki et al.
(2016), no significant difference was observed.
Cd concentrations in rice plants were in the order of roots > stems and leaves > rice grains on
average, and the ratio was 28:7:1. This ratio was slightly lower than 100:10:1, as found in Ito and
Iimura (1976).
Table 3 Cd concentrations in rice grains, stems, leaves and roots in the plow layer
Model

Rice grains
n=7

Roots of plow layer
n=5

Stems and leaves
n=5

O-100
C-100
O-250
C-250
O-500
C-500

0.16 ± 0.05a
0.01 ± 0.00c
0.08 ± 0.03b
0.01 ± 0.00c
0.04 ± 0.01c
0.01 ± 0.01c

1.60 ± 0.15ab
1.06 ± 0.13c
1.34 ± 0.23bc
1.00 ± 0.24c
1.97 ± 0.31a
1.65 ± 0.18ab

1.04 ± 0.35a
0.09 ± 0.02b
0.71 ± 0.24a
0.10 ± 0.02b
0.18 ± 0.05b
0.06 ± 0.02b

Note: Tukey-Kramer test was performed at the 5% level; letter indicates significant difference.
The numerical value of ± shows standard deviation. Unit: mg kg-1

Growth and Yield of Rice Plants
The results of this experiment for the growth and yield of rice plants are shown in Tables 4 and 5,
respectively.
Growth of rice plants: The average plant height (n=8) of each model was almost equal, and was
between 99.6 to 101.7 cm (Table 4). The leafage of each model ranged from 14.3 to 15.0 leaves,
showing very little difference among them. The total straw weight was 12.1 to 15.6 g hill -1. No
significant difference was observed in plant height, leafage and total straw weight regardless of thepercolation system. Paul et al. (2011a) conducted stratified paddy field model experiments using
Andosol and reported that the growth items above showed significantly higher figures in the closed
system percolation models than the open system percolation models. Sasaki et al. (2016a, 2016b)
and Fan et al. (2018a, b), used percolation systems that were clearly conditioned as per our
experiments, but did not observe any significant difference (at the 5% level) in the growth of rice
plants between the closed and open system percolation models. Shibuya et al. (1979) reported that
Cu concentrations in the Cu polluted soil layer had an influence on the growth of rice plants. However,Ⓒ ISERD

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in our study, any influence of the Cu and Cd concentrations on the growth of rice plants was notnoticeable, and may be a result of the soil dressing application.
Yield of rice plants: The weight of one panicle and the number of grains of brown rice per unit hill
were between 2.3 and 2.8 g panicle-1 and between 597 and 724 grains hill-1, respectively (Table 5).
In addition, the percentage of ripening and the 1000 grain weight of brown rice were between 85.5
and 97.6% and between 20.2 and 22.2 g, respectively. No significant differences (at the 5% level)
were found in any of the items of the models for the different percolation systems. Paul et al. (2011a)
reported that yield components of the closed system percolation model were significantly higher thanthose of the open system percolation model although their experiment was conducted using a
different soil type for Cd polluted soil layers. Sasaki et al. (2016a, b) and Fan et al. (2018a, b), usedpercolation systems that were clearly conditioned as per our experiments, and did not observe any
significant difference (at the 5% level) in the yield components between closed and open systempercolation models. Shibuya et al. (1979) reported that Cu concentrations had an influence on thenumber of panicles and the percentage of ripening. However, in our study, the mixed effect of the
Cu and Cd concentrations on the growth of rice plants was unremarkable, which may be attributed
to the application of soil dressing.
Transfer prevention of nutrients from the stems and leaves to grains (100 × weight of brown rice
/ weight of total straws) ranged from 82 to 120%. In the models of O-500 and C-500, rather low
values of 83 and 82% were obtained, respectively, suggesting influence from the higher Cu
concentration of 500 mg kg-1. Sasaki et al. (2016b) did not recognize any transfer prevention in theirexperiments using Cd contaminated soils and controlling percolation patterns in the plow sole andsubsoil. This suggests that Cu has much more influence on paddy rice growth and yield than Cd(Yamane et al., 1997).
Table 4 Parameters of rice plant growth
Model
O-100
C-100
O-250
C-250
O-500
C-500

Plant length(cm)
101.66 ± 5.52ab
99.60 ± 1.97b
100.48 ± 4.22ab
99.90 ± 6.03b
101.40 ± 1.54ab
106.15 ± 3.50b

Leaf age(leaf)
14.88 ± 0.35ab
14.25 ± 0.46c
14.63 ± 0.52abc
14.38 ± 0.52bc
15.00 ± 0.00a
15.00 ± 0.00a

Weight of dry straw(g hill-1)
12.61 ± 2.58a
12.13 ± 3.59a
13.15 ± 3.45a
13.51 ± 3.29a
14.45 ± 3.38a
15.61 ± 4.41a

Note: Tukey-Kramer test was performed at the 5% level; letter indicates significant difference.
The numerical value of ± shows standard deviation.

Table 5 Parameters of rice plant yield
Model

Weight of one
paniclePercentage ofripening

Number of brown ricegrains per unit hill

Weight of 1000 ricegrains

O-100
C-100
O-250
C-250
O-500
C-500

（g）
2.27 ± 0.50b2.59 ± 0.18ab
2.51 ± 0.36ab
2.71 ± 0.38ab
2.63 ± 0.27ab2.79 ± 0.27b（%）
93.17 ± 1.30ab89.69 ± 4.93bc
91.60 ± 2.74abc
85.50 ± 7.50c
95.97 ± 4.73ab
97.57 ± 0.82a（grains hill-1）
700.13 ± 143.73a
597.38 ± 134.56a
761.38 ± 217.21a
628.13 ± 127.81a
685.63 ± 133.67a
723.88 ± 136.85a（g）
20.23 ± 0.59b
20.24 ± 0.68b
21.01 ± 0.31ab
20.44 ± 1.50b
22.21 ± 0.77a
22.18 ± 0.79a

Note: Tukey-Kramer test was performed at the 5% level; letter indicates significant difference.
The numerical value of ± shows standard deviation.

CONCLUSIONⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2020) 11-1

Using six types of Cu and Cd combined in polluted stratified paddy field models, we conducted anexperiment to clarify the effects of percolation patterns in the sub-layer (plowsole and subsoil) on Cu
and Cd concentrations in rice plants and growth and yield. The models had a 15 cm thick Cu and Cd
polluted soil layer and a 12.5 cm thick non-polluted soil dressing with a Cu and Cd concentration of
3.7 mg kg-1 and 0.14 mg kg-1, respectively. The six polluted soils were prepared by mixing each with adifferent concentration of Cu (100, 250 and 500 mg kg-1) and a Cd concentration of 1.72 mg kg-1.
The results of our experiment show that in open system percolation models the sub-layers
became oxidized and in the closed system percolation models the sub-layers became reduced. Cu
and Cd concentrations in the rice grains of the open system percolation models were significantly
higher (at the 5% level) than those observed in the closed system percolation models. Cu and Cd
concentrations in the stems and leaves also showed a significant difference between the models fordifferent percolation systems. It was found that in the open system percolation models a higher Cu
concentration reduced the Cd concentration in the brown rice, suggesting a coexistent effect of Cu
and Cd. However, there was no significant difference in the growth and yield of rice plants between
the two percolation systems.
Under the above conditions, the difference in percolation systems of the stratified paddy fieldmodels did little to affect the growth and yield of rice plants. However, it had an influence on Cu andCd concentrations in the rice plants.
ACKNOWLEDGEMENTS
The authors appreciate the cooperation of Li Sontao, Fu Uzyou, Takahashi Hideki, Nakamura
Mikako and Nara Ikumi who cooperated in carrying out this research. In addition, we would like tothank everyone concerned, such as farmers who most willingly provided us with the soil sample.
REFERENCES
Aoyama, M. 2009. Characteristics of the apple orchard soils and the effects of compost. Open Lecture in
Teaching and Research Center for Bio-coexistence, Faculty of Agriculture and Life Science, Hirosaki
University, Textbook of Science of the apple, 35-42.
Arao, T., Ishikawa, S., Murakami, M., Abe, K., Maejima, Y. and Makino, T. 2010. Heavy metal contaminationof agricultural soil and countermeasures in Japan. Paddy and Water Environment, 8, 247-257.
Asami, T. 2010. Toxic heavy metal pollution in soils of Japan. Agune-Gijutsu Center, Tokyo, Japan, 70-95.
Fan, J., Sasaki, C., Kato, C., Matsuyama, N., Annaka, T., Endo, A., Li, S. and Sasaki, K. 2018a. Influence of
percolation patterns on copper uptake, and growth and yield with copper- polluted stratified paddy fields.
International Journal of Environmental and Rural Development, 9 (1), 101-108.
Fan, J., Sasaki, C., Kato, C., Matsuyama, N., Annaka, T., Endo, A. and Li, S. 2018b. Influence of percolation
patterns on copper uptake, and growth and yield with copper- polluted stratified paddy fields. Journal ofEnvironmental Science and Engineering, A 7, 180-188.
Ito, H. and Iimura, K. 1976. The absorption and translation of cadmium in rice plants and its influence on their
growth, in comparison with zinc. Bultein of the Hokuriku Agricultural Experiment Station, 19, 71-139.
Iwate Agriculture Experimental Station. 1981. Standard investigation method of field crops. Retrived fromhttp://2.pref. iwate.jp/~hp2088/library/chousa/chousa_index.html
Kanamori, T., Horino, H., Sakura, S., Nakagiri, T. and Nakamura, K. 2019. Availability of organic composts
as soil amendment to reduce the mobility of heavy metals in dune sand. IDRE Journal, 308, 37-43.
Li, S., Sasaki, C., Kato, C., Matuyama, N., Annaka, T., Endo, A. and Sasaki, K. 2017. Reducing cadmium andcopper uptake of soybeans by controlling groundwater level and its impacts on growth and yield.
Environmental Science and Engineering, 8, 77-84.
Matsunaka, T. 2014. Basic soil science. Nobunkyo, Tokyo, Japan, 327-333.
Ministry of Agriculture, Forestry and Fisheries, Japan. 1979. The foundation of environmental paddy field andinvestigation of paddy field, water quality and crop analysis method, National Conference of Paddy Field
Preservation, Tokyo, Japan, 113-115.
Ministry of Education, Culture, Sports, Science and Technology, Japan. 2019. Standard tables of foodcomposition in Japan, 2010, Retrived from https://foodb.mext.go.jp/result/result_top.pl?USER_ID= 11650.
Paul, S.K., Sasaki, C., Matsuyama, N., Noda., K. and Mitra, B.K. 2011a. Influence of percolation patterns on
growth and yield of the rice plants and uptake of cadmium from polluted paddy fields using soil dressingⒸ ISERD

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models, Pedologist, 222-229.
Paul, S.K., Sasaki, C., Matsuyama, N. and Kato, K. 2011b. Effect of percolation pattern on yields andaccumulation of copper and cadmium in the rice plants with soil dressing models. Environmental Scienceand Engineering, 5, 1464-1473.
Sasaki, C. 1992. On the dissolved oxygen content in seepage water of the open and the closed systempercolation in a stratified soil column. Transaction of the Japanese Society of Irrigation, Drainage, andReclamation Engineering, 159, 65-71.
Sasaki, K., Sasaki, C., Kato, C., Endo, A. Annaka, T., Moritani, S. and Matsuyama, N. 2016a. Studies on
reducing cadmium uptake of paddy rice (Oryza stative L.) by both soil dressing and mixing tillage.
International Journal of Environmental and Rural Development, 7 (1), 6-14.
Sasaki, K., Sasaki, C., Kato, C., Annaka, T. and Matsuyama, N. 2016b. Effects of the percolation patterns andthe thickness of soil dressing on reducing cadmium uptake and growth and yields of rice plants (Oryza
stative L.). Journal of Environmental Science and Engineering, A 1, 11-20.
Shibuya, M. 1979. Mechanism and analysis of soil pollution. Sangyo-tosho, Tokyo, Japan, 3-15.
Yamane, I. 1982. Soil science of paddy field. Nobunkyo, Tokyo, Japan, 132-133.
Yamane, I. Hamada, R., Yoshinaga, N. et al. 1997. Soil science. Bunneidou, Tokyo, Japan, 196-204.

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Research article
erd

Development of a Simplified Water Storage Operation
Curve at Irrigation Ponds for Flood Mitigation -A Casefrom Tamatsu District in Tottori, JapanRYOMA KUWAE
Graduate School of Sustainability Science, Tottori University, Tottori, JapanKATSUYUKI SHIMIZU*
Faculty of Agriculture, Tottori University, Tottori, Japan
Email: shimizu@tottori-u.ac.jp
Received 18 February 2020

Accepted 10 March 2020(*Corresponding Author)

Abstract In recent years, Japan has been receiving heavy rainfall, and the flooding ofirrigation ponds is a major concern. The irrigation ponds in the Tamatsu district in Tottori,
Japan, are small and overflow easily during heavy rainfall. Pre-releasing water from theseponds was suggested as a simple, safe, and low-cost solution. However, local governments
have prepared no clear guidelines or instructions on the amount of water that should be prereleased or regarding the desired water level after pre-release. Currently, the water volume
that is pre-released from the ponds depends entirely on the pond manager’s experience. To
mitigate this situation, we first observed the water level and discharge of the irrigation ponds
and collected rainfall data from the nearest meteorological station. We then assessed the water
storage operations of pond managers at three irrigation ponds in the Tamatsu district in 2018.
Subsequently, we prepared the height-volume curve (H-V curve) of each pond by level
survey and 3D image analysis, and a cumulative water demand curve based on the volume ofwater released from each pond during no rainfall periods. Considering the cumulative water
demand curve and the height of the intake tap of each pond, we prepared water storage
operation curves for the irrigation ponds. This will assist pond managers to determine theappropriate water level reduction during heavy rainfall events without causing an irrigationwater shortage. We confirmed the extent to which the pond management curve could copewith rainfall. Using the developed water storage operation curve, managers can easily
understand the drop of the water level in the pond. Moreover, the curves are easily constructedfrom the information on the beneficial area, precipitation, water level fluctuations of theirrigation pond during dry days, and the H-V curves of ponds. Finally, the effectiveness of
the water storage operation curve is evaluated for different rainfall events.
Keywords flood mitigation, disaster prevention, irrigation pond, pre-release of water

INTRODUCTION
In recent years, disasters caused by heavy rain have increased throughout Japan. The heavy rains inJuly 2018 caused heavy damage in western Japan, and in Hiroshima Prefecture, the pond broke down.
The pond is small and is in the study area, Tamatsu district, and the possibility of flooding during
heavy rain increases. Disaster prevention and mitigation measures for ponds, therefore, are urgently
needed. The government, after conducting an emergency inspection of ponds nationwide, took
measures such as selecting ponds for disaster prevention, preparing pond maps, developing an
emergency communication system, creating inundation potential area maps, and creating hazard
maps (MAFF, 2018). The government also suggested pre-release in preparation for heavy rainfall by
lowering the water level of the pond in advance and providing sufficient storage capacity. The
spillways of most irrigation ponds in Japan require repairs to safely pass the probability of flood
occurrence, once every 200 years, which is the design criteria set by the Japanese government (MAFF,
2015). Because of the large number of ponds and the high repair costs, extensive time, however, will
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IJERD – International Journal of Environmental and Rural Development (2020) 11-1

be required to repair the spillways of every irrigation pond. We, therefore, developed a low-cost prerelease of water. The advantage is that there is no need to build a special facility and no funding isrequired, but there is no specific guideline for the pre-release amount, which depends on theexperience of the manager.
OBJECTIVE
In this study, we evaluated water storage management during heavy rains in July and September
2018 and created a water storage operation curve to guide the pre-discharge at three ponds in Tamatsu
district of Tottori City in 2018. Furthermore, we investigated how much rainfall the ponds could
withstand without flooding when the water level was controlled by the water storage operation curve.
METHODOLOGY
Study Area
Figure 1 shows an overview of the study area. There are six irrigation ponds in the study area,
Tamatsu District, Tottori City, Tottori prefecture, Japan. There are three ponds currently functioning
for water intake management, namely the Yutani, Shinjoji, and Daigo irrigation ponds. These ponds
are stored only by runoff from their basins. A canal connects the Yutani and Shinjoji ponds to the
Daigo pond and can be switched between flowing into the Daigo pond and flowing into the river by
operating the weir. Table 1 shows the specification of each pond. The main crop is paddy, and thetotal paddy area is about 6 ha. In this area, there is a rule that water is drawn during the daytime, andeach farmer freely takes water from the ponds. The pond manager performs water pond maintenanceand the pre-release of water except for water intake.

Fig. 1 Study area
Table 1 Information of irrigation ponds
Height of dike（m）
Length of dike（m）
Storage（103 m3）
Catchment area（km2）
Width of spillway（m）
Height of spillway（m）

YutaniShinjoji

Daigo

4
50
3.8
0.08
0.8
0.5

6
88
19.0
0.44
5.8
1.2

4
123
18.0
0.05
0.5
0.5

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Hydrological Observation
To clarify the water level fluctuation of the pond, a pressure-type self-recording water level gauge
was installed at the bottom and water surface of the pond, and observations were made hourly. Usinga small UAV, the aerial view of the pond was created and a three-dimensional image was createdbased on the image. The water area for each pond’s water level was calculated to create a water levelvolume curve. From the water level of the pond and the water level-volume curve, the capacity ofthe pond was calculated. To clarify the intake status and the operation status of the weir, acapacitance-type self-recording water level gauge was installed in the canal, and the water level wasobserved at 5-minute intervals. The observation period was from late May to late September 2018,
and the mid-drainage period for paddy rice fields is from late June to mid-July. Precipitation data
were obtained from the Japan Meteorological Agency (Tottori Observatory).
Estimation of Pre-release of Water
As a method of quantitatively indicating the pre-release rate, we estimated the average daily waterrequirement during the irrigation period. This was based on the amount of storage change of eachpond during the continuous drought period (14 days from August 1 to 14) with precipitation of lessthan 5 mm/d. Using the average daily requirement, the cumulative required water volume wascalculated retrospectively to create a pond management curve. The two types of management curves
for each pond are shown based on the land-use status of the target area in 2018 and the assumptionthat all the farmland in the area is paddy rice, that is, with maximum water demand.
Estimation of Water Storage Operation Curve
Flood arrival times in 60-minute rainfall events were calculated for different probability year using
the formula of Talbot, Kadoya–Fukushima, as shown in Eqs. (1) ~ (3), and the water level rise fromfull and from each intake tap position was calculated. From the result, if the water level is maintained
according to the water storage operation curve, it is determined at which probability year the rainfall
will not exceed the top of the embankment until 60 minutes of rainfall.
To simulate the water balance and water level of the pond under heavy rainfall, 60-minute
rainfall events at return periods of 10, 20, 30, 40, 50, 60, 70, 100, and 200 years were estimated using
the Iwai method.
a
I=
(1)
t+b
tp = C∙A0.22 ∙re- 0.35

(2)

re = fp ∙I(3)

where I is the rainfall intensity (mm/h), t is the duration of rainfall (min), a and b are the constantparameters related to the meteorological conditions, tp is the peak flow arrival time (min), C is theparameter related to the land-use conditions, A is the catchment area (km2), re is the average effective
rainfall intensity (mm/h), and fp is the peak runoff coefficient.
The rational equation to determine the peak discharge from the catchment area and average
effective rainfall intensity are expressed in Eq. (4).
Q=

1
∙r ∙A
3.6 e

(4)

The discharge from the spillway was calculated using rectangular weir formulas according to
the water level. The formulas used in the calculation are expressed in Eq. (5).
3(5)

Qs = Cs ∙b∙H2Ⓒ ISERD

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IJERD – International Journal of Environmental and Rural Development (2020) 11-1

where Qs is the discharge from the spillway, Cs is the discharge coefficient for the rectangular weir,b is the width of the spillway (m), H is the water level of the spillway (m). Calculations continued
until the water level reached the top of the spillway.
The water balance and increase rate of the water level of the pond were calculated using thepeak runoff as the inflow element and the discharges from the spillway. Due to the assumption ofshort and heavy rainfall, evaporation and infiltration from the pond were ignored (Shimizu et al.,2016).
The created water storage operation curve of each pond is evaluated for the different rainfall
events. In our analysis, the extra height of each pond was defined as the height of the spillway in caseof seepage collapse of the dike. There are three main reasons for damage to irrigation ponds by heavy
rainfall, namely overflow erosion, sliding, and seepage failure. In the case of seepage failure, internal
erosion occurs from the upstream slope near the full water level. In the case of sliding, a failure starts
at the toe of an embankment. Overflow failure occurs at the crest above the gut of the foundation(Hori et al., 2002). We, therefore, set the upper limit to the height of the spillway.
RESULTS AND DISCUSSION
Evaluation of Water Storage Management during Heavy Rain
Figure 2 shows the water level fluctuation and precipitation of each pond. It is probable that predischarge was not performed at Shinjoji and Daigo ponds because the water level did not drop before
the heavy rain. Pre-discharge was not performed at the Shinjoji pond because the manager judged
there was no necessity to perform pre-discharge because the spillway of the Shinjoji pond was large
enough, as shown in Table 1. Pre-discharge was not performed in the Daigo pond because the water
storage rate of the pond was low, about 60%, and the weir was operated so that water from the Yutani
and Shinjoji Ponds did not enter the pond. Furthermore, the Daigo pond has a larger water storage
capacity for the catchment area than the Yutani and Shinjoji ponds. For a detailed description, Fig. 3
shows the water level fluctuation of the Yutani pond during heavy rains in July (308 mm) and
September (180 mm). Since the water level dropped before the heavy rainfall, it was confirmed thatthe pre-discharge was performed. The water level of the Yutani pond did not rise more than 30 cm
from the full water level, the peak of the water level was reduced, and the effectiveness of the predischarge was reduced.

Fig. 2 Water level fluctuation at 3 ponds

Fig. 3 Water level fluctuation at Yutani pond

Creating Water Storage Operation Curve
Table 2 shows the daily irrigation requirement based on the paddy rice area in 2018 and the daily
irrigation requirement assuming that paddy rice is grown in all the fields. Figure 4 (a) ~ (c) shows
the water storage operation curves of each pond. The break-line indicates the amount of water storedwith a full water level in each pond. When the water storage operation curve falls below the dottedⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2020) 11-1

line, pre-release is possible. The dotted line indicates the amount of water storage corresponding to
the position of the intake tap in each pond, and the water level can be lowered to the position of theintake tap by opening the intake tap immediately above the water storage operation curve at the timeof pre-release. Water storage operation curves created from the land-use situation in 2018 show thatthe water storage capacity of the Yutani and Daigo ponds is always lower than the water storage
capacity of the irrigation pond during the irrigation period. It was shown that pre-release was possible.
Regarding the water storage operation curve, assuming the maximum water demand in the area, themanagement curve of the Daigo pond (Fig. 4 (c)) has fallen below the full storage volume of thepond since June 13th. It was shown that there was room for pre-release from the stage. However, inthe management curves of the Yutani (Fig. 4 (a)) and Shinjoji ponds (Fig. 4 (b)), there is no room
for water storage until the end of the irrigation season because of the higher ratio of upland fields
and fallow land in the beneficiary areas at the Yutani and Shinjoji ponds. Because paddy rice fields
are usually irrigated, but upland crop fields are rain-fed, if the ratio of the paddy rice fields increased,
the irrigation water requirement also increased accordingly. The reservoir manager can open theintake tap based on this curve to keep the water level in the vicinity of the opened intake tap positionand prepare for heavy rainfall.
Table 2 Beneficial area
Name of pond
2018
Maximum paddy rice area

Yutani
0.15
1.84

Shinjoji
2.64
3.95

Daigo
2.50
2.90

2018

15.56

391.95206.74

Maximum water demand187.15586.30324.043

3(10 m )

Paddy rice area（ha）
Average water
demand per day（m3/d）

Fig. 4 Water-storage operation curve (red line)
Evaluation of the Effectiveness of the Water Storage Operation Curve
We investigated how much rainfall each pond could withstand without flooding when the water level
was controlled by the water storage operation curve. Table 3 summarizes the simulation results. In
the Shinjoji pond, the water level did not exceed the upper limit in any rainfall events because thespillway is large enough to pass the flood. In the Yutani pond, the rainfall over the 10-year probability
60-minute rainfall at the full water level exceeds the upper limit. When the water level, however, islowered to the height of the intake tap No.1, the pond withstands flooding with a 50-year probability
60-minute rainfall. The Daigo pond cannot withstand any rainfall at the full water level or when thewater level is at the height of water intake tap No.1. The pond can withstand a 40-year probability
60-minute rainfall if the water level is lowered to the height of water intake tap No.2. Since the waterlevel in the ponds cannot be lowered in a short period of time by opening the intake tap with a smallⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2020) 11-1

diameter, it is necessary to constantly lower the water level of the ponds or install an emergency
discharge device to increase the ability to lower the water level.
Table 3 Effectiveness of water storage operation curve
Yutani pond
Return period (y)Full water level

Intake tap No.1

Intake tap No.2

Intake tap No.3

10

○

○

○

○

20

×

○

○

○

30

×

○

○

○

40

×

○

○

○

50

×

○

○

○

60

×

×

○

○

70

×

×

○

○

100

×

×

○

○

200

×

×

×

×

Full water level

Intake tap No.1

Intake tap No.2

Intake tap No.3

10

×

×

○

○

20

×

×

○

○

30

×

×

○

○

40

×

×

○

○

50

×

×

×

○

60

×

×

×

○

70

×

×

×

○

100

×

×

×

○

×

×

×

Daigo pond
Return period (y)200×○: water level does not exceed the upper limit
×: water level exceeds the upper limit

CONCLUSION
The pre-discharge of ponds is a simple, safe, and cost-effective method against heavy rainfall in casethe spillway is not constructed, but the pond manager uncertain to what extent the water level of thepond can be lowered. Given this, a water storage operation curve was developed by considering
paddy rice irrigation, and its effectiveness was evaluated based on the probability of rainfall fordifferent years. The developed method is applicable to any ponds and the created curve makes it easy
for pond managers to visually understand how much to lower the water level at what time of irrigation
period. Further refinement of the curve is required by more accurate hydrological observation. Also,
not only short rainfall events but longer rainfall events such as one to two days long should beconsidered.
ACKNOWLEDGEMENTS
This research was supported by the Tottori Prefecture Environmental Science Research Promotion
Project (FY28-H30) and the Tottori University International Dryland Research and Education
Organization.

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REFERENCES
Shimizu, K., Fukuda, Y. and Yoshioka, Y. 2016. Development of an emergency discharging device and an
early warning system for floods at irrigation ponds. International Journal of Environmental and Rural
Development, 7 (1), 15-21.
Ministry of Agriculture, Forestry and Fisheries (MAFF), Japan. 2018. Initiatives based on heavy rain in July
2018, Retrived from https://www.maff.go.jp/j/nousin/bousai/bousai_saigai/b_tameike/ (in Japanese).
Ministry of Agriculture, Forestry and Fisheries (MAFF), Japan. 2015. Design criteria of land improvement
project Irrigation pond improvement. JSIDRE, 32, 165-166 (in Japanese).
Japan Meteorological Agency: Search past meteorological record: Retrived from http://www.data.jma.
go.jp/obd/stats/etrn/index.php (in Japanese).
Hori, T., Mohri, Y. and Aoyama, S. 2002. Causes and features of damage to small earth dams induced by heavy
rainfall. Irrigation, Drainage and Rural Engineering Journal, 218, 253-263 (in Japanese).

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Research article
erd

Evaluation of Drought Tolerance for Improved Rice Linesin terms of Yield, Chlorophyll content and Water Use
Efficiency
AUNG NAING OO*
Department of Soil and Water Science, Yezin Agricultural University, Myanmar
Email: dr.aungnaingoo@yau.edu.mm

KYAW NGWE
Department of Soil and Water Science, Yezin Agricultural University, Myanmar
Email: dr.kyawngwe@yau.edu.mm

NANG OHN MYINT
Department of Soil and Water Science, Yezin Agricultural University, Myanmar
Email: dr.nangohnmyint@yau.edu.mm
Received 29 January 2020

Accepted 25 March 2020(*Corresponding Author)

Abstract Rice (Oryza sativa L.) is an important target crop for water use reduction becauseof its greater input water requirement than other crops. The pot experiment was conducted toevaluate the effect of two different levels [full irrigation and deficit irrigation (50% Plant
Available Moisture)] on the growth, yield, chlorophyll content and water use efficiency of
improved rice lines. Preliminary test for drought tolerance for the one hundred improved ricelines was conducted and screened under pot experiments in the dry season of 2018. Afterscreening this test, the selected rice lines were grown at two different water levels in thescreen house during the rainy season of 2019. Each experiment was assigned as a randomized
complete block design with three replications at Yezin Agricultural University. The result of
the preliminary test in 2018 found that among the one hundred YAU improved rice lines,
seven lines were tolerant to deficit irrigation. In 2019, all selected rice lines were also tolerant
to deficit irrigation. These lines also produced the best performance of the plant growth, thegrain yield and yield component characters when compared with the checked variety.
However, deficit irrigation for all selected lines reduced these characters when compared withfull irrigation. The five rice lines under deficit irrigation produced the high chlorophyll
content and water use efficiency when compared with full irrigation. Thus, it can beconcluded that the highest drought- tolerant rice lines would be useful for plant breeding
program.
Keywords deficit irrigation, screening rice lines, water use efficiency

INTRODUCTION
Rice (Oryza sativa L.) is the most important food crop for more than half of the population of theworld. It is cultivated over 167 million hectares with the production of 780 million tonnes (FAO
STAT, 2017). It is grown under various environmental conditions in both upland and lowland rainfed
ecosystem. Crop productions are affected by many climatic and environmental factors, which can be
abiotic and biotic factors. Among them, drought is the most significant environmental factor to
impact on the growth and yield of crop in accordance with the global climate change in the world’s
agricultural lands. In Myanmar, especially in the central dry zone areas, rice yield is quite low dueto the salinization, drought, and low soil fertility (Oo et al., 2017). Among them, drought is anincreasingly severe problem in rainfed rice production. To increase crop yield in these areas, it isneeded to study the plant responses to conditions with the ultimate goal of improving crop
performance in areas where water scarcity has a problem. According to further improvement of rice
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IJERD – International Journal of Environmental and Rural Development (2020) 11-1

breeding program in national level, it offers doing research and providing rice varieties for farmer
needs are recently lined out. The water requirement for rice genotypes vary with different stages of
its establishment processes, and to determine the best tolerance on rice varieties in our country is oneof the advantages and encouraging rice production to climate adaptability.
OBJECTIVESThis study was to evaluate the most drought tolerance rice lines based on the effects of water stresson the growth, yield, chlorophyll content and water use efficiency of improved rice lines.
MATERIALS AND METHODS
Evaluation of Improved Rice Lines at Vegetative StageA population of 100 improved rice genotypes (provided from the Department of Plant Breeding,Physiology and Ecology, Yezin Agricultural University) with Yar-8 (tolerant check) were tested at
the Department of Soil and Water Science, Yezin Agricultural University, Myanmar.
Preliminary test for drought tolerance screening trial was conducted in a net house with 41/23°C
day/night temperatures and a minimum relative humidity of 75% during the day in the dry season of
2018. This trial was laid out in a randomized complete block design with three replications. Twowater levels were used as full irrigation and deficit irrigation (50% Plant Available Moisture). Fivekilograms of 2-mm-sieved air-dried soil was filled in each black plastic pot of 15-cm height and 20cm diameter. Pre-germinated seeds of 100 rice lines were sown at two-three seeds on soil surface ofeach pot. Each plot was well-watered (soil moisture close to the field capacity) before starting the
deficit irrigation which was started at 35 days after sowing. Daily soil water status was monitored byusing soil moisture meter. Fertilizer was applied as the recommended rate of urea, triple
superphosphate and muriate of potash (Buresh and Witt, 2006). Hand weeding and insecticide
spraying were done whenever necessary. Drought tolerance was rated using a modified standard
evaluation system (SES) in rating the visual symptoms of water stress (IRRI, 2013). This scoring
discriminates the susceptible from the tolerant and the moderately tolerant genotypes. Leaf rollingand dry scores were started at 10 days after starting the deficit irrigation.
Evaluation of Selected Improved Rice Lines at Various Stages
This experiment was conducted in a randomized complete block design with three replications in therainy season of 2019. The selected seven rice lines, after screening at vegetative stage, with Yar-8(tolerant check) were grown at two different water levels (full irrigation and deficit irrigation (50%PAM)) in the net house. The black plastic pots (30-cm height and 30-cm diameter) were filled with
twelve kilograms of 2-mm-sieved air-dried fertilized soil. Three to four pre-germinated seeds of test
entries were placed on soil surface of each pot. Two weeks after seeding, seedlings were thinned to
one per pot. Maintain soil water status daily and protect the plants from any pests and diseases were
performed. Fertilizer was applied as the recommended rate. Plant height (cm), the number of tillers
per hill, the number of panicles per hill, the number of spikelets per panicle, filled grain percent,
1000-grain weight, dry matter and grain yield were recorded at harvesting. Chlorophyll content wasmeasured by using chlorophyll meter (SPAD-502, Minolta, Japan) at vegetative and panicle initiation
stages. Water use efficiency was calculated as grain yield (usually the economic yield) divided bywater used to produce yield consumed by the crop during the growing season (Boutraa, 2010). In
order to identify the drought tolerant rice lines, drought susceptibility index (DSI) (Fischer andMaurer, 1978), stress tolerance (TOL) (Rosielle and Hamblin, 1981), mean productivity (MP)(Hosssain et al., 1990), stress tolerance index (STI) (Fernandez, 1992), yield index (YI) (Lin et al.,1986) were also taken into consideration.

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IJERD – International Journal of Environmental and Rural Development (2020) 11-1

Data Analysis
The collected data were examined statistically using Analysis of Variance (ANOVA) Techniques,
and means were compared by least significant difference (LSD) method at 5% level of probability.
All statistical analyses were done using Statistix 8.0 software and Excel program (2010).
RESULTS AND DISCUSSION
Evaluation of Improved Rice Lines to Drought Stress at Vegetative StageAll one hundred improved rice lines were grown strongly and showed uniform green colour underboth soil water levels. According to the leaf rolling and dry scores, the rice lines differed significantly
for drought tolerance at vegetative stage among the rice lines ranging from score 0 (highly tolerant)
and score 9 (highly susceptible) under deficit irrigation (data not shown).
The one hundred improved rice lines were classified into five groups from highly tolerant (score0) to highly sensitive (score 9) as 0 (highly tolerant), 10 (tolerant), 20 (moderately tolerant), 16(sensitive) and 54 (highly sensitive) at vegetative stage. The effects of water stress on rice plants
were complex and it was difficult to interpret the results. Hence, the selected seven rice lines fromten tolerant lines were evaluated for all stages in the net house.
Evaluation of Selected Improved Rice Lines to Drought Stress
Mean comparisons of selected improved rice lines in terms of plant height and number of tillers perhill under two different irrigation levels at harvesting are shown in (Table 1). The plant height wassignificantly different among the rice lines under deficit irrigation, whereas it was not different infull irrigation. The number of tillers per hill was not significantly different among the rice lines underfull irrigation, while it was significantly different under deficit irrigation at 5% level. Similarly, thenumber of panicles per hill was not significantly different among the rice lines under full irrigation,
whereas it was significantly different under deficit irrigation (Table 2). YAU-1211-82-1-1 and YAU1211-118-1-1 had the highest (16.7 and 15.7) values, respectively. The number of spikelets per
panicle was significantly different among the rice lines under full irrigation at 1 % level. In contrast,
it was not significantly different under deficit irrigation (Table 2).
Table 1 Mean comparison of plant height and number of tillers per hillin different improved rice lines at harvesting
Improved rice lines
YAU-1211-14-1-1
YAU-1201-90-2-4
YAU-1211-118-1-1
YAU-1211-195-1-1
YAU-1201-26-1-1
YAU-1201-26-1-3
YAU-1211-82-1-1
Yar-8 (Tolerance check)F-testC.V %Plant height
(cm)
FI
DI
118.5
105.0 b128.5
97.1 bc
125.3
90.5 c121.5
100.5 bc
126.7
101.6 bc
124.2
104.7 b117.5
106.7 b127.6
119.3 a
ns
**
5.2
6.2

D (%)
8.8
24.5
27.7
17.3
21.9
15.7
11.7
8.2

Number of tillers
per hillFIDI
20.0
11.3 d20.3
13.3 bcd
17.7
16.3 ab
17.0
15.0 abc
13.7
13.3 cd
17.0
13.0 bcd
19.0
17.0 a17.7
15.3 abc
ns
*
21.6
13.7

D (%)
30.0
34.4
7.5
11.8
2.4
23.5
10.5
13.2

Values in the same column followed by the same letter are not significantly different at the 5% level by the LSD test,(**) significantly different at P≤0.01, (*) significantly different at P≤0.05, ns − not significant,
FI: Full Irrigation, DI: Deficit Irrigation, D (%): Percentage decrease down

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The data regarding filled grain percent and 1000-grain weight of selected improved rice lines
under two different irrigation levels at harvesting are presented in Table (3). There was significant
difference in filled grain percent under full and deficit irrigation at 5 % and 1 % level, respectively.
YAU-1211-195-1-1 had the highest filled grain percent (79.1) among the rice lines. The filled grain
percent of this rice line under full irrigation was lower than that under deficit irrigation. 1000-grain
weight was significantly different among the rice lines under full irrigation at 1 % level, while it was
not different under deficit irrigation.
Table 2 Mean comparisons of number of panicles per hill and number of spikelets
per panicle in different improved rice lines at harvesting
Improved rice lines
YAU-1211-14-1-1
YAU-1201-90-2-4
YAU-1211-118-1-1
YAU-1211-195-1-1
YAU-1201-26-1-1
YAU-1201-26-1-3
YAU-1211-82-1-1
Yar-8 (Tolerance check)F-testC.V %Number of panicles
per hillFIDI
17.0
10.7 cd
19.3
9.7 d
17.3
15.7 a16.0
14.0 abc
12.3
12.0 bcd
15.7
10.7 cd
17.0
16.7 a16.3
15.0 ab
ns
*
19.8
15.0

D (%)
29.4
50.0
9.6
12.5
2.7
31.9
2.0
24.0

Number of spikelets
per panicleFIDI
169 ab
124
123 d
121
161 bc111
149 bc131
142 cd
106
189 a
125
154 bc118
186 abc
122
**
ns
8.9
10.8

D (%)
23.9
1.1
31.3
12.7
25.7
33.8
24.1
21.2

Values in the same column followed by the same letter are not significantly different at the 5% level by the LSD test,(**) significantly different at P≤0.01, (*) significantly different at P≤0.05, ns − not significant
FI: Full Irrigation, DI: Deficit Irrigation, D (%): Percentage decrease down

Table 3 Mean comparisons of filled grain percentage and 1000-grain weightin different improved rice lines at harvesting
Improved rice lines
YAU-1211-14-1-1
YAU-1201-90-2-4
YAU-1211-118-1-1
YAU-1211-195-1-1
YAU-1201-26-1-1
YAU-1201-26-1-3
YAU-1211-82-1-1
Yar-8 (Tolerance check)F-testC.V %Filled grain (%)
FI
80.9 ab85.7 a79.5 ab77.2 b85.6 a69.7 c85.1 a73.7 ab
*
5.2

D (%)

DI
68.5 bc
72.5 abc
76.4 ab79.1 a75.3 abc
58.0 d64.9 cd
76.6 ab
**
8.4

15.4
15.4
4.0
-2.4
12.0
16.8
23.6
5.4

1000-grain weight (g)FIDI
25.5 d24.4
31.7 a28.9
28.2 bc
25.7
24.7 d24.7
28.8 b
28.6
26.5 cd
25.8
29.2 b
25.2
29.5 b
25.5
**
ns
4.4
7.4

D (%)
4.1
9.0
8.7
0.2
0.6
2.5
13.9
13.7

Values in the same column followed by the same letter are not significantly different at the 5% level by the LSD test,(**) significantly different at P≤0.01, (*) significantly different at P≤0.05, ns − not significant
FI: Full Irrigation, DI: Deficit Irrigation, D (%): Percentage decrease down

In comparing the seven improved rice lines, the effect of drought stress on grain yield wassignificantly different under deficit irrigation, whereas it was not different under full irrigation (Table4). Yar-8 (Tolerance check) had the highest grain yield (38.8 g) than the rice lines and followed by
YAU-1211-195-1-1 which had the highest grain yield (35.4 g) among the rice lines. In contrast,
YAU-1201-26-1-3 had the lowest yield (19.9 g).
The evaluation of drought tolerant promising rice lines based on a single criterion index wasincongruous (Table 4). Lower DSI and TOL values indicated the more resistant to drought (Winteret al., 1988; Rosielle and Hamblin, 1981). In this study, regarding to DSI and TOL values, YAU1211-195-1-1 and YAU-1201-26-1-1 were the most drought tolerant rice lines, while YAU-1201Ⓒ ISERD28

IJERD – International Journal of Environmental and Rural Development (2020) 11-1

90-2-4 and YAU-1211-82-1-1 were the least relative tolerant lines. According to MP, YAU-1211118-1-1 was the most tolerant rice lines, while YAU-1201-26-1-1 was the least rice lines. Concerning
to STI, YAU-1211-118-1-1 was the best tolerant rice lines, whereas YAU-1201-26-1-3 was the worst
relatively tolerant lines. Regarding to YI, YAU-1211-195-1-1 was the greatest tolerant rice lines,
while YAU-1201-26-1-3 was the poorest relatively drought tolerant lines.
Table 4 Evaluation of improved rice lines based on drought tolerance indices
Improved rice lines
YAU-1211-14-1-1
YAU-1201-90-2-4
YAU-1211-118-1-1
YAU-1211-195-1-1
YAU-1201-26-1-1
YAU-1201-26-1-3
YAU-1211-82-1-1
Yar-8 (Tolerance check)F-testC.V %Grain Yield(g per hill)
FI
DI
58.9
25.3 de63.9
24.5 de63.4
33.2 abc
45.3
35.4 ab
41.7
29.3 bcd
54.4
19.9 e66.2
27.2 cd
66.6
38.8 a
ns
**
22.8
10.4

D (%)

DSI

TOL

MP

STI

YI

57.1
59.7
47.7
22.0
29.7
63.5
58.9
41.7

1.17
1.27
0.98
0.45
0.61
1.29
1.20
0.85

33.60
36.21
30.26
9.99
12.40
34.57
38.99
27.72

42.09
42.59
48.28
40.35
35.49
37.13
46.74
52.69

0.46
0.45
0.64
0.49
0.37
0.33
0.55
0.79

0.44
0.43
0.58
0.62
0.51
0.35
0.48
0.68

Values in the same column followed by the same letter are not significantly different at the 5% level by the LSD test,(**) significantly different at P≤0.01, ns − not significant,
FI: Full Irrigation, DI: Deficit Irrigation, D (%): Percentage decrease down

Table 5 Mean comparisons of chlorophyll content (SPAD reading) in different improved ricelines at vegetative and panicle initiation stage
Improved rice lines
YAU-1211-14-1-1
YAU-1201-90-2-4
YAU-1211-118-1-1
YAU-1211-195-1-1
YAU-1201-26-1-1
YAU-1201-26-1-3
YAU-1211-82-1-1
Yar-8 (Tolerance check)F-testC.V %Chlorophyll content
at vegetative stage
FI
DI
39.8
47.2
41.1
44.1
46.1
46.7
40.0
47.0
35.5
44.0
39.9
43.2
42.8
45.3
42.2
44.4
**
ns
5.1
5.5

D (%)
-18.6
-7.2
-1.3
-17.4
-24.0
-8.1
-5.8
-5.1

Chlorophyll content
at panicle initiation
FI
DI
38.9
39.6
33.3
40.6
42.5
39.7
34.8
41.8
30.7
37.2
31.4
39.6
37.9
39.3
36.1
39.7
**
ns
6.2
19.4

D (%)
-1.6
-21.9
6.6
-15.0
-21.3
-26.1
-3.6
-10.0

Values in the same column followed by the same letter are not significantly different at the 5% level by the LSD test,(**) significantly different at P≤0.01, ns − not significant
FI: Full Irrigation, DI: Deficit Irrigation, D (%): Percentage decrease down

The effect of drought stress on chlorophyll content was significantly different under full
irrigation among the improved rice lines, while it was not different under deficit irrigation for bothvegetative and panicle initiation stages. In comparing between two irrigations, chlorophyll content
of the most rice lines resulted from deficit irrigation was higher than that from full irrigation,
indicating that drought tolerant lines had a high chlorophyll concentration. Kalaji et al. (2016) statedthat chlorophyll content was increased in abiotic stress tolerant crops, although it was reduced instress susceptible crops. YAU-1211-195-1-1 had the highest chlorophyll content among the rice lines
under deficit irrigation and followed by YAU-1201-90-2-4 rice line.
Mean comparisons of selected improved rice lines in terms of dry matter production and water
use efficiency under two different irrigation levels at harvesting are presented in (Table 6). The resultⒸ ISERD

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showed that there were significant differences in dry matter production among the rice lines underboth irrigations. In comparing the seven improved rice lines, there was significant difference in WUE
under deficit irrigation, whereas it was not different under full irrigation. WUE ranged from 0.42 kg
m-3 to 0.75 kg m-3. The maximum WUE value (0.75 kg m-3) was found from Yar-8 (Tolerance check)and it was followed by YAU-1201-26-1-1 and YAU-1211-195-1-1, (0.69 kg m-3 and 0.68 kg m-3,
respectively) under deficit irrigation.
Table 6 Mean comparisons of dry matter production and water use efficiency (WUE)
in different improved rice lines at harvesting
Improved rice lines
YAU-1211-14-1-1
YAU-1201-90-2-4
YAU-1211-118-1-1
YAU-1211-195-1-1
YAU-1201-26-1-1
YAU-1201-26-1-3
YAU-1211-82-1-1
Yar-8 (Tolerance check)F-testC.V %Dry matter (g)FIDI
125.7 abc
70.2 d160.6 a72.9 cd139.4 ab78.9 bcd
110.3 bc
80.0 abcd
91.0 c85.3 abc
128.7 abc
84.8 abc
171.7 a87.1 ab146.1 ab92.4 a
*
*
20.3
9.2

D (%)
26.3
25.7
23.7
24.0
26.7
22.7
27.3
24.0

WUE (kg m-3)
FI
DI
0.62
0.42 d
0.67
0.59 abcd
0.67
0.63 abc
0.48
0.68 ab
0.44
0.69 ab
0.57
0.45 cd
0.69
0.53 bcd
0.70
0.75 a
ns
*
22.2
19.2

D (%)
32.5
12.6
6.2
-42.6
-57.3
21.8
23.9
-6.1

Values in the same column followed by the same letter are not significantly different at the 5% level by the LSD test,(*) significantly different at P≤0.05, ns − not significant, FI: Full Irrigation, DI: Deficit Irrigation, D (%): decrease
down

CONCLUSIONSIn conclusion, differences of the selected improved rice lines were observed for relative droughttolerance in terms of growth, grain yield, chlorophyll content and water use efficiency. From thisstudy, five tolerant rice lines would be useful in the areas where drought has a problem.
ACKNOWLEDGEMENTS
The authors would like to thank the JICA (Japan International Cooperation Agency), Project for
Capacity Development of Yezin Agricultural University, for financial support.
REFERENCES
Boutraa, T. 2010. Improvement of water use efficiency in irrigated agriculture, A review. Journal of Agronomy,
9 (1), 1-8.
Buresh, R. and C. Witt. 2006. Productivity and sustainability in irrigated rice, Myanmar (MY-Rice).
MYANMAR- 02. Interpretive Summaries.
Food and Agricultural Organization (FAO). 2017. FAO STAT. FAO, Rome, Retrived from http://faostat3.
fao.org/faostatgateway/go/to/down load/ Q/QC/E
Fernandez, J.C. 1992. Effective selection criteria for assessing stress tolerance. Proceedings of the International
Symposium on Adaptation of Vegetables and Other Food Crops in Temperature and Water Stress, Taiwan,
13-18 August 1992, 257-270.
Fischer, R.A. and Maurer, R. 1978. Drought resistance in spring wheat cultivars, I. Grain yield responses.
Australian Journal of Agriculture Research, 29, 897-912.
Hossain, A.B.S., Sears, R.G., Cox, T.S. and Paulsen, G.M. 1990. Desiccation tolerance and its relationship to
assimilate partitioning in winter wheat. Crop Science, 30, 622-627.
IRRI, 2013. Standard evaluation system (SES) for rice. 5th Edition, International Rice Research Institute, Los
Baños, Philippines.
Kalaji, H.M., Jajoo, A., Oukarroum, A., Brestic, M., Zivcak, M., Samborska, I.A., Cetner, M. D., Łukasik, I.,Ⓒ ISERD

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Goltsev, V. and Ladle, R.J. 2016. Chlorophyll a fluorescence as a tool to monitor physiological status ofplants under abiotic stress conditions. Acta Physiol Plant, 38, 102.
Lin, C.S., Binns, M.R. and Lefkovitch, L.P. 1986. Stability analysis: Where do we stand? Crop Science, 26,
894.
Oo, A.N., Ando, K., Khaung, T. and Khaing, M.T. 2017. Food security and socio-economic impacts of soil
salinization in the central dry zone of Myanmar, A case study. Journal of Agroforestry and Environment,
11 (1&2), 149-154.
Rosielle, A.A. and Hamblin, J. 1981. Theoretical aspects of selection for yield in stress and non-stressenvironment. Crop Science, 21, 943-946.
Winter, S.R., Musick, J.T. and Porter, K.B. 1988. Evaluation of screening techniques for breeding drought
resistance winter wheat. Crop Science, 28, 512-516.

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Research article
erd

Evaluation of Water Shortages in Agricultural Water Use in
the Sangker River Basin, Cambodia
CHEY VANNA
Graduate School of Agriculture, Hokkaido University, Sapporo, JapanTADAO YAMAMOTO*
Research Faculty of Agriculture, Hokkaido University, Sapporo, Japan
Email: tady@env.agr.hokudai.ac.jp

TAKASHI INOUE
Research Faculty of Agriculture, Hokkaido University, Sapporo, Japan
Global Station for Food, Land, and Water Resources, GI-CoRE, Hokkaido University, Sapporo,
Japan
Received 27 January 2020

Accepted 25 March 2020(*Corresponding Author)

Abstract The shortage of agricultural water in the Sangker River basin of Cambodia is
becoming a severe problem, and the river flow seems to have decreased. The present study
assesses the excess and deficiency of irrigation water resources in this river basin for
agricultural production. The Soil and Water Assessment Tool (SWAT) is used to analyze thewater balance at the main headwork in this river from 2014 to 2018. SWAT was used toestimate the available volume of stream water at the ungauged point of the Kang Hot
headwork, and the model performance was evaluated at a gauged point downstream by the
Nash–Sutcliffe efficiency (NSE). The model was calibrated manually and automatically byusing data from 2007 to 2013. Furthermore, the flow rates in the two main irrigation canals
were measured from June 2018 to October 2019 to obtain the actual irrigation water supply,
and the evaluation was made by checking the difference between supplied water volume andirrigation water demand. The model output showed good agreement between observed andsimulated monthly streamflow during the validation period (NSE = 0.69, RSR = 0.55). From
the results of water balance analysis, water shortage rates exceeding 20% of the monthly
water requirement occurred mostly between April and June from 2014 to 2017. Furthermore,
there was excess water supply in 2018 because of abundant river flow that year. The actual
irrigation water supply during the study period showed rates of water shortage of 52% and
41% in the left and right main canals, respectively, at the headwork. This study provides new
insights for field technicians to consider irrigation planning for present and future water
resources management and development for sustainable irrigation agriculture.
Keywords SWAT, water balance, irrigation, rice paddy, water supply and demand

INTRODUCTION
Many river basins worldwide are facing perceived water shortages because of increasing demands
for water from all sectors. In Cambodia, increasing water demands and competitiveness among
sectors and between upstream and downstream of the river system are also becoming crucial, and theconcern is that the country will face highly restricted water resources (Technical Service Center &JICA, 2014). Accordingly, the shortage of agricultural water is becoming a serious problem in theriver basins, and the streamflows appear to be decreasing. Contributing 22% of gross domestic
product collectively, agriculture, forestry, and fishing are essential to the growth of Cambodia’s
economy, and of the 80% of Cambodian people who live in rural areas, 65% rely on those threesectors (USAID, 2019). The Royal Government of Cambodia continues to enhance the managementof water resources, develop irrigation systems, and undertake repair and maintenance of the irrigation
infrastructure where required. Furthermore, headwork and multipurpose dams have been constructedⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2020) 11-1

in the river basins to meet the increased demand for water and flood control. The Sangker River basin
in northwest Cambodia is one of the largest agricultural production areas in the nation and faces
similar water-resource issues. An irrigation project has been implemented in this area, but improved
management is required to solve the water-deficit problems.
The state-of-the-art method for estimating water deficiency is to analyze the balance between
the water supply and the demand for irrigation water (IW). Masona et al. (2018) revealed a shortfall
in IW use by applying the relative water supply (RWS), the ratio of water supply to water demand;
an RWS of 1.6 showed that the targeted scheme received much more water than its IW demands. To
assess water deficits in the Heibe River basin of China, Ji et al. (2006) established a model of thebalance between water supply and demand based on meteorological, hydrological, land use, andsocio-economic data. They found that the water supply was insufficient to meet the water demandsof the various irrigation districts, and indeed the Pingchuan irrigation district has already experienced
a water supply crisis. By comparing the actual water supply with crop water requirements, Shakir etal. (2010) revealed an annual water shortage of more than 40% in the Upper Chenab Canal of
Pakistan. However, with regard to the spatial distribution of operational parameters, calculating the
supply-and-demand balance of water resources does not resolve the regional problem of waterresource utilization on a relatively large scale in an inland basin (Bormann et al., 1999). Therefore,
the present study considers the water balance of a regional scheme by incorporating a physically
based model.
In the present study, the Soil and Water Assessment Tool (SWAT) hydrological model is usedbecause of its widespread use in predicting runoff and sediment yield (Arnold et al., 1998). Herein,
SWAT is used to estimate the streamflow at the headwork of the main canals. These streamflow data
are then used to evaluate the deficit of water resources in the irrigation project area. The present
results could help to identify the root of the water shortages that occur frequently not only in theirrigation project area but also across the river basin.
OBJECTIVE
This study aimed to define the available stream water to the irrigation project area and identify theextent to which the water supply of the system is in deficit and/or excess. The specific objectives are(i) to assess the surplus (excess) and deficiency of IW from the Sangker River at the Kang Hot
headwork point and (ii) to evaluate the actual IW uses and the demand in the Kang Hot Irrigation
Project area.
METHODOLOGY
Study Site
The present study was conducted in the Sangker River basin situated in the Battambang province ofCambodia and with a total drainage area of 6,053 km2 (Department of Hydrology and River Works,n.d.). The elevation ranges from 13 to 1,400 m above sea level, and the annual rainfall ranges from
695 to 1,787 mm. The focus was on a 3,062-km2 area to the southwest of the city of Battambang thatcovers the middle and upper basin of this river system (Fig. 1). The Kang Hot Irrigation Project isonly one of the huge numbers of water uses in the study area, with a total command area of 72,000
ha. However, the water is taken from the Sangker River to the project area at the Kang Hot headwork
and transported to the paddy fields through the left and right main canals, where the irrigated areas
are only 11,200 ha and 42,000 ha (Kodoma, 2018), respectively (Fig. 2).
To assess the surplus and deficiency of IW, the balance between the available water supply fromthe river and the IW demand was analyzed at the Kang Hot headwork from 2014 to 2018. The actual
IW use in the Kang Hot Project area was also evaluated by comparing the difference between theactual volume of water intake from the river and the IW requirement from June 2018 to October
2019.

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IJERD – International Journal of Environmental and Rural Development (2020) 11-1

Fig. 1 Location map of study area

Fig. 2 Kang Hot Irrigation Project area

Availability of Stream Water
In the study area, the supply of agricultural water depends on irrigation from the river source. For
the latter, the available volume of stream water was estimated using the SWAT hydrological model.
SWAT Modeling
SWAT is a river-basin-scale model developed to predict the impact of land management practices
on water, sediment, and nutrient yield, including agricultural activity, in large, complex watershedswith varying soils, land use, and management conditions over a long period of time (Winchell et al.,2013). In the present study, SWAT was set up with the basin outlet at the Samdach Hun Sen Bridge
as a gauging station located in Battambang City (Fig. 1). The watershed and sub-basins weredelineated within the basin using 30-m-resolution DEM data (ASTER-GDEM 2) in the
ArcSWAT2012 interface. The 3,062 km2 of the catchment area accounts for 51% of the entire
Sangker River basin (Fig. 2). A sub-basin was defined as a drainage area with a threshold of 5,000 ha,
resulting in 26 sub-basins that were subdivided further into 123 hydrological response units based on
slope, soil, and land use, the data for which were retrieved from the FAO-UNESCO Soil Map andthe Mekong River Commission (MRC-2010), respectively. The available meteorological data ofdaily rainfall and temperature data at four stations obtained from the Ministry of Water Resources
and Meteorology (MOWRAM) for 14 years (2007-2018) had to be preprocessed by the model. The
observed streamflow data collected from MOWRAM at the Samdach Hun Sen Bridge between 2007
and 2018 were used for model calibration (2007-2013) and validation (2013-2018). The SWAT
model was calibrated manually by editing the most sensitive parameters based on recommendations
by Abbaspour et al. (2015). In addition, the SUFI-2 algorithm packaged in SWAT-CUP was used forautomatic model calibration and validation and sensitivity and uncertainty analyses (Abbaspour etal., 2007). The model performance was evaluated using the quantitative statistics of the Nash–
Sutcliffe efficiency (NSE) and the ratio of the root mean square error to the standard deviation of
measured data (RSR) suggested by Moriasi et al. (2007). After fixing the SWAT for the Sangker
River at the Samdach Hun Sen Bridge gauging station, the flow rate at the Kang Hot headwork was
extracted from the database of model results.
Irrigation Water (IW) Demands
The IW demands were calculated mainly for rice-based farming in the Kang Hot Project area. Basedon its cropping calendar, the unit IW requirement was estimated with a 5-d time step using
IR = (ETo × Kc + PR +Lp – ER) / IE,(1)Ⓒ ISERD

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IJERD – International Journal of Environmental and Rural Development (2020) 11-1

where (i) IR is the unit IW requirement, (ii) ETo is the crop reference evapotranspiration (calculatedusing the Penman–Monteith equation with different time steps using meteorological data), (iii) Kc is
the crop coefficient following the FAO’s guideline for the case of rice paddy fields (Allen et al.,1998), (iv) PR is the percolation rate (because of the absence of field measurement records, this wascollected from the Master Plan Study Team of JICA in 2007, which applied a dial gauge and openended-cylinder method in the study site), (v) Lp is the land preparation requirement [(needed tosaturate the root zone and depends on soil type and rooting depth, which was estimated by assumption(i.e., sandy loam = 250 mm, clay loam = 200 mm, clay = 170 mm)], (vi) ER is the effective rainfall
calculated by following empirical relationships developed in Japan (Dastane, 1974), and (vii) IE is
the irrigation efficiency, which refers to the conveyance efficiency of the distribution system and wascollected from MOWRAM and the Master Plan Study Team of JICA in 2009. The IW demand comesfrom the unit IW requirement multiplied by the area to be irrigated, which gives a volume of IW
needed per unit time. The IW demand is computed as
ID = IR × A,(2)

where ID is the IW demand and A is the area.
Estimation of Actual IW Supply
The actual continuous flow rate for each of the main canals was estimated by three steps. (1) Waterlevel loggers were installed in the left and right main canals near the Kang Hot headwork during thestudy period from June 2018 to October 2019. (2) Each canal flow rate was calculated directly fromthe mean canal-flow velocity (using a current meter over ten time differences) and the flow canalcross-sectional area. The water level in each canal at the moment of measurement was also recorded.
These measurements were then integrated into a rating curve (H-Q curve) of the water level and thecanal flow rate for each canal (Buchanan and Somers, 1976). (3) By using this H-Q curve, the recordsof the daily water level were converted into the daily canal flow rate and aggregated at a monthly
volume.
RESULTS AND DISCUSSIONSWAT Calibration and Validation
Figure 3 shows the results of the observed and simulated streamflow at the Samdach Hun Sen Bridge
gauging station in Battambang City (Fig. 1) with a monthly time step in the periods of calibration(2007-2013) and validation (2014-2018). The graphs show that the SWAT model tracked theobserved data accurately both calibration, and validation periods. This was confirmed by NSE valuesof 0.58 and 0.69 in the calibration and validation periods, respectively (Moriasi et al., 2007). From
the SWAT results at the gauging station, the available water resources at the Kang Hot headwork
could be estimated for 2014-2018 from the database of the simulation results.
Analysis of Balance Between Available Stream Water and IW Demand
To evaluate the excess and deficiency of IW from the Sangker River at the Kang Hot headwork point,
Fig. 4 shows the monthly averages of available stream water and IW demand at the headwork, andFig. 5 shows the observed deficit rate for the 5-year study period (2014-2018). The results show thatthe availability of stream water varies over a year and from 1 year to the next. With regard to boththe irrigation area and cropping patterns in the Kang Hot Project area during this study period, they
were supposed that the IW demands did not change much. If the water shortage is less than 10-20%of the monthly requirement, then the production losses are not very serious (Brouwer et al., 1992).

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RainfallObserved

Simulated
0
100

600

200
500

Validation period
NS: 0.69/RSR:0.55
Good

Calibration period
NS: 0.58/RSR:0.64
Satisfactory

400

300
400
500

300

600

200

700
800

100

900

0

1000

Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13 Jan-14 Jan-15 Jan-16 Jan-17 Jan-18

Fig. 3 Monthly simulated and observed flow rate for calibration and validation period
in Samdach Hun Bridge gauging station of Sangker River
River Discharge

Irrigation Water Demand

600

0
100

500

200
400

300

300

400
500

200

600

100

Rainfall (mm.month-1)

Discharge volume ( 106 m3.month-1)

Rainfall700

0Jan-14800

Jan-15Jan-16

Jan-17Jan-18Fig. 4 Balance of available stream water and irrigationwater demand at Kang Hot headwork
Deficit rate (%)
0

90

100

80

200

70
60

300

50

400

40

500

30

600

20

700

10
0
Jan-14800
Jan-15Jan-16

Jan-17Jan-18Fig.5 Monthly water deficit rate at Kang Hot headworkⒸ ISERD36

Rainfall (mm.mo-1)

water deficit rate (%)

Rainfall100

Rainfall (mm.month-1)

Streamflow ( 106 m3.month-1)

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IJERD – International Journal of Environmental and Rural Development (2020) 11-1

The water balance calculation in Fig. 4 indicates that water deficit always occurred during theApril–May dry season every year from 2014 to 2017, with shortage rates of 50-99% of monthly
requirements. In the dry year of 2015, the area faced 5 months of subsequent water scarcity (withdeficiency rates of 30-99%). Each year, the stream water was surplus in the July-November wet
season. In 2018, heavy rainfall increased the streamflow volume, which means that the stream water
could meet the IW demand in a given area for the whole year.

( 106 m3.month-1)

Water supply volume

Irrigation water demand

60
50
40
30
20
10
0
Jun-18

Sep-18Dec-18Mar-19Jun-19Sep-19Fig. 6 Actual irrigation water (IW) in right main canal
Water supply volume

Irrigation water demand( 106 m3.month-1)

20
15
10
5
0

Jun-18

Sep-18Dec-18Mar-19Jun-19Sep-19Fig. 7 Actual irrigation water (IW) in left main canal
Shortage rate at Right Main Canal

Shortage rate at Left Main Canal

Shortage rate (%)

100
80
60
40
20
0

Jun-18
Sep-18Dec-18Mar-19Jun-19Sep-19Fig. 8 Water shortage rate in right and left main canals

Evaluation of Actual IW Use
The actual volume of water supply from the Sangker River and the IW demand are shown in Figs. 6
and 7 for the right and left main canals, respectively, as monthly averages from June 2018 to October
2019. Figure 8 shows the water shortage rate in each month for both main canals.
The results for the actual water supply and demand balance for the right main canal (Fig. 6)
show a maximum supplied water volume of around 57 × 106 m3⋅month−1 in 2018, decreasing to 40Ⓒ ISERD

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IJERD – International Journal of Environmental and Rural Development (2020) 11-1

× 106 m3⋅month−1 in 2019. Meanwhile, the highest IW demand was approximately 60 × 106 m3⋅
month−1, corresponding to the extreme lack of water in 2019. As shown in Fig. 8, large water
shortages occurred in both 2018 (70-78% from September to November) and 2019 (over 77% from
April to June). In those 2 years, the water supply was always less than the water demand in September
and October, with the shortage rate exceeding 50%. However, the IW demands in every July andAugust during this study period were met. Overall, we conclude that the right main canal can supply
around 59% of the required monthly IW.
Figure 7 shows that the maximum supplied water volume by the left main canal was around 5 ×
106 m3⋅month−1 in 2018, increasing to 17 × 106 m3⋅month−1 in 2019. Meanwhile, the maximum water
demand was around 16 × 106 m3⋅month−1. Similar to the right main canal, water was also scarce in
September and October for those 2 years, with shortage rates of 56-74% (Fig. 8). Figure 7 also showsthat water deficiency occurred between June and November, with 50-78% in 2018 and over 80%
from April to June in 2019. By 2018, the water supply in July and August was inadequate, with ashortage rate of around 51%. However, compared to that in 2019, it is increased at 7 × 106 m3⋅
month−1 and 12 × 106 m3⋅month−1, thereby meeting the water demands of those 2 months. The water
volume of the left main canal can supply only 48% of the requirement.
Cropping Pattern
To minimize the water deficiency, an analysis is conducted by modifying the existing cropping
pattern against the available IW. In the revised cropping pattern, the cultivated area is changed asfollows: (i) early-wet-season rice is reduced from 40 to 10%; (ii) transplanting-wet-season rice isincreased from 60 to 80%; and (iii) direct sowing-wet-season rice is increased from 10 to 20%. The
results of this analysis show that the water shortage rate was reduced from 20 to 100% between April
and May (2014–2017), which is at the beginning of the irrigation season.
CONCLUSION
The results of this study show that SWAT performed well in predicting the streamflow by providing
good agreement between the observed and simulated monthly flow, which is based on the statistics
of NSE = 0.58 and NSE = 0.69 in the calibration and validation periods, respectively. With such
good performance, SWAT can be used to estimate the river discharge at the Kang Hot headwork
point as needed.
The balance between the available volume of stream water and the IW demand at the Kang Hot
headwork indicates that the water shortage rate was 50-99% of the monthly requirement, which
occurred mostly in April–June, and the excess water was in August–November. This surplus should
be controlled to compensate for the deficiencies in other months. By revising the current croppingpattern calendar, the water deficits could be diminished from 20 to 100% of the monthly shortage
rate in those shortfall months.
The actual IW use during the study period was 52% and 41% have experienced the lack of waterin the left and right main canals, respectively. Also, water deficits occurred in September and Octoberin both 2018 and 2019.
The results of this study will be useful for policy makers and field technicians for irrigation
planning and for developing and managing the water resources in the river basin, in particular for thesustainability of agricultural development.
ACKNOWLEDGMENT
The authors would like to thank the Ministry of Water Resources and Meteorology of Cambodia forproviding data and the Japan International Cooperation Agency (JICA) for its support of this study.

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REFERENCES
Allen, R.G., Pereira, L.S., Raes, D. and Smith, M. 1998. Guidelines for computing crop water requirements.
Irrigation and Drainage Paper, 56, FAO, 300.
Arnold, J.G., Srinivasan, R., Muttiah, R.S. and Williams, J.R. 1998. Large-area hydrologic modeling andassessment, Part I Model development. Journal of the American Water Resources Association, 34 (1), 7389.
Abbaspour, K.C., Yang, J., Maximov, I., Siber, R. Bogner, K. Mieleitner, J. Zobrist, J. and Srinivasan, R. 2007.
Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. J. Hydrol.,
333, 413-430.
Abbaspour, K.C., Rouholahnejad, E., Vaghefi, S., Srinivasan, R., Yang, H. and Klove, B. 2015. A continentalscale hydrology and water quality model for Europe: calibration and uncertainty of a high resolution largescale SWAT model. J. Hydrol., 524, 733-752.
Buchanan, T.J., Somers, W.P. 1976. Discharge measurements at gaging stations. Techniques of Water
Resources Investigations of the United States Geological Survey, Washington, D.C., USGS. Book 3,
Chapter A8.
Brouwer, C., Hoevenaars, J.P.M, van Bosch, B.E., Hatcho, N. and Heibloem, M. 1992. Irrigation water
management, Training manual No. 6, Scheme irrigation water needs and supply. Food and Agriculture
Organization of the United Nations, Roma.
Bormann, H., Diekruger, B., Renschler, C. and Richter, O. 1999. Regionalization scheme for the simulation ofregional water balance using a physically based model system. Phys. Chem. Earth, 24, 43-48.
Dastane, N.G. 1974. Effective rainfall in irrigated agriculture. FAO Irrigation and Drainage Paper, Roma.
Department of Hydrology and River Works. (n. d.). Hydrology station network. Retrieved from www.dhrwcam.org/index.php/en/
Ji, X., Kang, E., Chen, R., Zhao, W., Xiao, S. and Jin, B. 2006. Analysis of water resources supply and demandand security of water resources development in irrigation regions of the middle reaches of the Heihe Riverbasin, Northwest China. J. Integr. Agri., 5, 130-140.
Kodoma, M. 2018. Practical use of water balance models in the Sangker and Prekthnoat River basins. The
Project for River Basin Water Resources Utilization, Phnom Penh, Ministry of Water Resources andMeteorology, Cambodia.
Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Binger, R.L., Harmel, R.D. and Veith, T.L. 2007. Modelevaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE,
50, 885-900.
Masona, E., Shimizu, K. and Yoshioka, Y. 2018. An evaluation of water demand and supply for a small scale
irrigation scheme in Zimbabwe. Int. J. Environ. Rural Dev., 9-2, 41-46.
Shakir, A.S. Khan, N.M. and Qureshi, M.M. 2010. Canal water management, Case study of upper Chenab
canal in Pakistan. Irrig. Drain., 59, 76-91.
Technical Service Center / JICA. 2014. Background of the project for river basin water resources utilization.
Phnom Penh, Ministry of Water Resources and Meteorology, Cambodia.
USAID. 2019. CAMBODIA: Agriculture and food security. Retrieved from http://www.usaid.gov/cambodia/
agriculture-and-food-security
Winchell, M., Srinivasan, R., Diluzio, M. and Arnold, J. 2013. User’s guide for ARCSWAT interface for
SWAT 2012. Blackland Research and Extension Center, Temple, Texas.

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Research article
erdInfluence of Meteorological Parameters on Population ofWhitefly, Bemisia tabaci on Greenhouse-grown Tomatoes
KANCHANA THINNABUT
Department of Entomology and Plant Pathology, Faculty of Agriculture, Khon Kaen University,
Khon Kaen, Thailand

DUANGRAT THONGPAK
Department of Entomology and Plant Pathology, Faculty of Agriculture, Khon Kaen University,
Khon Kaen, Thailand

CHUTINAN CHOOSAI
Department of Entomology and Plant Pathology, Faculty of Agriculture, Khon Kaen University,
Khon Kaen, Thailand

UBON TANGKAWANIT*
Department of Entomology and Plant Pathology, Faculty of Agriculture, Khon Kaen University,
Khon Kaen, Thailand
Email: Ubonta@kku.ac.th
Received 23 January 2020

Accepted 25 March 2020(*Corresponding Author)

Abstract Whiteflies (Bemisia tabaci (Gennadius)) are important vectors of diseases oftomato crop production. Outbreaks of whiteflies can cause decreased productivity andtransmit pathogens. The purposes of this study were to compare the degree of white fly
infestation in two varieties of tomatoes, Shiny Queen and Ninmanee, and to investigate
whitefly population responses to weather-related environmental parameters. The study was
conducted in a green house. Whitefly population was counted every two days on threepositions of the plant; the upper part, middle part and lower part. The results indicated that
the Shiny Queen variety was infected with significantly higher numbers of white fly than was
the Ninmanee variety. Whitefly numbers observed on Shiny Queen variety were significantly
related to temperature, relative humidity, solar radiation and dew point. A mathematicalmodel was developed to predict the relationship of environmental conditions to white fly
population.
Keywords weather parameters, abiotic factors, Aleyrodidae

INTRODUCTION
The whitefly Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is an important pest on tomato.
It can cause direct damage to the crop by puncturing the leaf tissue and feeding on the phloem
resulting in reduced growth rate and yield. In addition, it can transmit tomato yellow leaf curl virus(TYLCV) (Liu et al., 2013). The common cultivated tomato (Solanum esculentum) is very
susceptible to TYLCV. Infected tomato plants show symptoms of yellowing, curling, and cuppingof leaves. Additionally, this virus causes a severe stunting and abortion of flowers and fruits, leading
to 100% yield loss (Yan et al., 2018). The production of honeydew by the whitefly can causes
stickiness and promotes growth of mold. B. tabaci, eggs nymphs and adults are found on theunderside of leaves where they are protected from applications of insecticides. Production ofvegetables in greenhouses is practiced worldwide and it can protect crops from abiotic factors suchas wind, rain, and temperature that may damage to the crop. It may protect crops from many diseases,
and some common field pests. The largest area of greenhouse cultivation occurs in Asia, with China
having 55% of the total world’s plastic greenhouse acreage (including large plastic tunnels) and over
75% of the world’s small plastic tunnels (Peet and Welles, 2005).
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IJERD – International Journal of Environmental and Rural Development (2020) 11-1

Because of seasonal climatic variations in agricultural areas, insect pests show varying trends
in their incidence in nature. There some reports that the population of whitefly depends upon certain
abiotic factors such as temperature, relative humidity, rainfall and sunshine hours (Janu and Dahiya,
2017; Jha and Kumar, 2017). Therefore, if the correlation of whitefly abundance and abiotic factors
is understood, that knowledge can applied in designing a pest management program for tomato
production in the green house.
OBJECTIVE
The objectives of this study were to compare the degree of white fly infestation in two varieties oftomatoes, and to investigate how whitefly population changed with meteorological parameters.
METHODOLOGY
The experiment was conducted in a greenhouse at the agricultural experimental area at Khon Kaen
University. Seedlings (20 days old) of the tomato varieties Shiny Queen and Ninmanee weretransplanted from nursery to the field. Meteorological data, temperature (ºC), relative humidity (%)
wind speed (km/h), rainfall (mm), solar radiation (wat/m 2) and dew point (ºC) were recorded dailyusing a 2000 series Watch Dog weather station.
The population of whitefly was first observed when plants were 25 days old. The number ofwhiteflies was directly counted every other day between 16:00 and 17:00 from 23 tomato plants ofeach variety. Whiteflies were observed on three leaves (on upper, middle and lower parts of plant)
using hand len of 10x magnification. Analysis of variance was conducted for testing the significanceif one of the weather variables was linearly related to the whitefly population. The null hypothesis
states that 1=2=……i = 0. If alternative hypothesis is accepted ( 0), then, a mathematicalmodel of biting black flies can be analysed by logistic regression.
RESULTS AND DISCUSSION
Population dynamics of whiteflies from the two tomato varieties were similar during the vegetativeand flowering stages. In the vegetative stage, the number of whitefly from both tomato varieties waslowest. Then, whitefly population gradually increased in the late flowering stage. Abundance
increased by up to 18.3 and 2.39 insects/plant in Shiny Queen and Ninmanee, respectively. During
fruiting and harvesting stage, the number of whitefly gradually increased in Shiny Queen variety, but
it decreased in Ninmanee (Fig. 1). There were significant differences between the mean number ofnymphs and adults of whitefly on the two tomato varieties. Shiny Queen was more infected withwhitefly than was the Ninmanee variety (Table 1). The results of this study corresponded with thereports of Arnal et al (1998) and Patra et al. (2016) who found that whitefly were present throughout
the growing period in tomato.
Abiotic parameters were separately analysed by logistic regression for each tomato variety(Table 3). The results revealed that the whitefly abundance on the Ninmanee variety was not related
to abiotic parameters whereas it was on the Shiny Queen variety. This result may related to the small
number of whitefly infections in the Ninmanee variety. When each abiotic parameter was separately
analysed by logistic regression for the Shiny Queen variety, four of six parameters; temperature,
relative humidity, solar radiation and dew point, were significantly related to the whitefly population
with r2 0.31-0.47 (Table 3). The results indicated that solar radiation and temperature were negatively
related to the abundance of whitefly populations. These results were corroborated with the previous
studies of Jha and Kumar (2017) who reported that sunshine hours was negatively correlated whitefly
population on tomato. There was a positive relationship between whitefly abundance and dew point,and dew formation is a meteorological and hydrologic phenomenon which can provide an importantwater input. There is a report indicating that nutrients in dew can be assimilated by certain insectⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2020) 11-1

pests (Xu et al., 2015). The results differ from some previous studies where maximum temperature
was found to be positively correlated with whitefly abundance (Bala et al., 2019).
Umar et al. (2003) have reported that the correlation of whitefly population abundance andtemperature, relative humidity and precipitation was negative for whitefly populations on somevarieties of cotton. However for certain other varieties, a positive correlation of abundance withtemperature and relative humidity was observed. Janu and Dahiya (2017) found that weather
parameters from different years show different levels of correlation. Therefore, the difference of hostplant varieties or the duration of the experiment gave different results in respect to weather
correlation. During this experiment, there was no rain and no wind speed because the experiment
was done in a controlled greenhouse.
The regression equation of whitefly population with all weather parameters; temperature,
relative humidity, wind speed, rainfall, solar radiation and dew point was analysed. Statisticalanalyses indicate that no weather variable was related to the number of whiteflies in the Ninmaneevariety. However, there was statistically strong evidence that at least one of weather variables was
related to the number of whiteflies in Shiny Queen. The regression model revealed a significantly
positive association with only dew point, (p < 0.05), but not with any other of the weather variables(p > 0.05). The following regression model was selected as shown in Table 3 with r2 = 0.66. This
result differed from the previous report of Sharma et al. (2013) that six weather parameters played asignificant role in whitefly abundance on tomato; maximum and minimum temperature, sunshine
hours, maximum and minimum relative humidity, and rainfall. The differential results may dependon environmental conditions such as plant varieties, natural enemies, and cultural management.
Table 1 Mean number of nymphs and adults of whitefly on tomato varieties
Shiny Queen and Ninmanee
Varieties*
Shiny Queen
Ninmaneeadult
2.93 ± 3.01a
0.95 ± 0.6b

Mean ± SD**nymph
4.56 ± 5.6a
0.50 ± 0.80b

nymph + adult7.49 ± 8.03a
1.46 ± 1.24b* n=23 (for each tomato varieties)** Mean within a column followed by different small letters differ significantly (p < 0.05)

NinmaneeFig. 1 Population dynamics of whitefly on tomato leaves during a= vegetative stage,b=flowering stage, c=fruiting stage, and d= havesting stage(arrow indicates insecticide application)Ⓒ ISERD

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IJERD – International Journal of Environmental and Rural Development (2020) 11-1

Table 2 Meteorological parameters data from tomato greenhouse during the experiment
Checkeddate

Temperature (C)

RH (%)

Solar Rad
(wat/m2)

Dew Point(C)

Rainfall
(mm)

Wind
Speed
(km/h)

1

28.87

56.77

67.33

21.53

0

0

2

30.75

39.8

141

19

0

0

3
4

32.4
31.21

33.6
40.87

144.33124.33

18.67
20.2

0
0

0
0

5
6

33.12
32.71

34.3
30.47

113.33158.33

18.83
17.87

0
0

0
0

7
8

33.45
34.04

27.77
28.07

138
161.67

17.77
18.53

0
0

0
0

9

33.86

27.93

170.33

18.73

0

0

10
11

30.86
29.21

45.13
49.63

76.33
114.33

20.8
21.63

0
0

0
0

12
13

27.94
32.72

55.93
27.63

78
150

21.9
17.3

0
0

0
0

14

29.73

43.3

138

19.9

0

0

15
16

28.32
30.63

48.67
32.13

128.67158.67

19.5
15.7

0
0

0
0

17
18

31.54
33.71

38.47
32.53

152.67
163

19.17
17.4

0
0

0
0

19
20

30.3
31.54

60.57
45.7

71
108.67

22.67
21.33

0
0

0
0

21

29.2

64.7

66.67

23.37

0

0

22
23

31.49
31.83

55.23
40.33

127.33
92.67

22.5
19.2

0
0

0
0

24
25

33.15
31.28

48.53
40.7

102.33100.33

20.9
19.07

0
0

0
0

26

30.22

61.33

95

23.27

0

0

27
28

30.58
27.88

65.27
69.57

60.67
81.33

22.5
23

0
0

0
0

29
30

30.32
28.73

58.43
60.67

69.33
61

22.83
21.93

0
0

0
0

31
32

16.88
28.52

66.7
65.4

27.33
59.33

67.47
74.1

0
0

0
0

33

30.8

52.87

90.33

72.7

0

0

34
35

30.88
30.25

64.33
54

28
106

72.9
71.43

0
0

0
0

36
37

32.13
30.94

38.9
46.93

117
119.33

66.3
67.97

0
0

0
0

38

32.44

36.57

129.67

64.8

0

0

39

31.42

46.1

117.67

68.27

0

0

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IJERD – International Journal of Environmental and Rural Development (2020) 11-1

Table 3 Regression equations of weather variables related to whitefly population
Shiny Queen

Temperature (ºC)
Relative humidity (%)
Solar radiation (wat/m2)
Dew point (ºC)
Wind speed (km/h)
Rainfall (mm)Pooled weather
parameters

2

Regression EquationR𝑦 = 56.54 − 1.59𝑋
𝑦 = −12.2 + 0.42𝑋
𝑦 = 23.30 − 0.15𝑋
𝑦 = 0.06 + 0.24𝑋
-

0.31
0.44
0.47
0.38
-

𝑦 = 15.36 + 0.16𝑑𝑒𝑤 𝑝𝑜𝑖𝑛𝑡

0.66

Ninmanee
Regression
Equation-

R2-

CONCLUSION
This experiment provided information on the population of whitefly infesting two varieties of tomato;
Shiny Queen and Ninmanee. The statistical significance of whitefly numbers indicated that the Shiny
Queen was more infested by whitefly than the Ninmanee variety. Some weather variables;
temperature, relative humidity, solar radiation, and dew point, were related to whitefly population in
Shiny Queen. Those parameters were used to create a mathematical model for white fly populationin the greenhouse.
ACKNOWLEDGEMENTS
This research has been supported by Khon Kaen University. We would like to thank Dr. Chanon
Lapjit who provided plants and green house for this research.
REFERENCES
Arnal, E., Debrot, E., Marcano, R.M. and Manlagne, A. 1998. Population fluctuaion of whitefly and its relation
to tomato yellow mosaic in one location in Venezuela. Fitopatologia Venezoloana, 6, 21-26.
Bala, S.Ch., Nihal, R. and Sarkar, A. 2019. Population dynamics of whitefly (Bemisia tabaci, Genn.) and Thrips(Scirtothrips dorsalis, Hood.) in Bt cotton. J. Entomol. Zool. Stud., 7, 1020-1024.
Janu, A. and Dahiya, K.K. 2017. Influence of weather parameters on population of whitefly, Bemisia tabaci in
American cotton (Gossypium hirsutum). J. Entomol. Zool. Stud, 5, 649-654.
Jha, S.K. and Kumar, M. 2017. Effect of weather parameters on incidence of whitefly, Bemisia tabaci(Gennadius) on tomato. J. Entomol. Zool. Stud., 5, 304-306.
Liu, B., Preisser, E.L., Chu, D., Pan, H., Xie, W., Wang, S., Wu, Q., Zhou, X. and Zhang, Y. 2013. Multiple
forms of vector manipulation by a plant-infecting virus: Bemisia tabaci and tomato yellow leaf curl virus.
J. Virol., 87, 4929-4937.
Patra, B., Alam, Sk.F. and Samanta, A. 2016. Influence of weather factors on the incidence of whitefly, Bemisia
tabaci Genn. On tomato in Darjeeling Hills of West Bengal. JANRM., 3, 243-246.
Peet, M. and Welles, G., 2005. Greenhouse tomato production. In: Heuvelink, E.P. (Ed.), Crop Production
Science. Horticulture Series.
Sharma, D., Maqbool, A., Ahmad, H., Srivastava, K., Kumar, M., Vir, V. and Jamwal, S. 2013. Effect of
meteorological factors on the population dynamics of insect pests of tomato. Veg. Sci., 40, 90-92.
Umar, M.S., Arif, M.J., Murtaza, M.A., Gogi, M.D. and Salman, M. 2003. Effect of abiotic factors on the
population fluctuation of Whitefly, Bemisia tabaci (Gen.) in nectaried and nectariless genotypes of cotton.
Int. J. Agri. Biol., 5, 362-363.
Xu, Y., Yan, B. and Tang, J. 2015. The effect of climate change on variations in dew amount in a paddy
ecosystem of the Sanjiang Plain, China. Adv. Meteorol, Article ID 793107, 9.
Yan, Z., Pérez-de-Castro, A., Díez, M.J., Hutton, S.F., Visser, R.G.F., Wolters, A.A., Bai, Y. and Li, J. 2018.
Resistance to tomato yellow leaf curl virus in tomato germplasm. Front, Plant Sci., 9, 1198.

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Research article
erd

Effects of Crimping by Oggun Tractor-mounted
Roller/crimper on Cover Crop Termination, Soil Strength,
and Soil Moisture in Upland Cambodia
LYHOUR HIN*
Faculty of Agricultural Engineering, Royal University of Agriculture, Phnom Penh, Cambodia
Email: hlyhour@rua.edu.kh

BORARIN BUNTONGDivision of Research and Extension, Royal University of Agriculture, Phnom Penh, Cambodia

MANUEL R. REYES
Department of Agronomy, Kansas State University, Manhattan, KS 66506, the United States

LYDA HOK
Center of Excellence on Sustainable Agricultural Intensification and Nutrition and Faculty ofAgronomy, Royal University of Agriculture, Phnom Penh, Cambodia
Received 14 January 2020

Accepted 25 March 2020(*Corresponding Author)

Abstract Rolling/crimping technology for terminating cover crops is essential to ensure thatthe rolled plant residue benefits both soil and succeeding cash crops. Cambodia has also
adopted this technology, but it is still at the initial stage due to limited rolling services. The
objectives of this study were to compare different roller-crimpers with the Oggun-mounted
USDA roller-crimper in terminating sunn hemp (Crotalaria juncea L.) and to determine theireffects on soil strength and soil moisture. The experiment was conducted in Rattanak Mondul,
Battambang province in the wet season of 2019, using a randomized complete block design
with three treatments having four replicates, each of which was 14 m x 34 m and spaced 5
m. The treatments consisted of (i) a USDA roller-crimper, mounted on 19-hp Oggun tractor,(ii) a Cambodian made roller-crimper with elliptic bars, and (iii) a disc-plow, both pulled by
a 75-hp tractor. Plant height and biomass before rolling; soil strength at 0-10 and 10-20 cm
depths before and after rolling; field operations; and soil volumetric moisture content (VMC)
evaluated on the day of rolling, and then one, two, and three weeks after rolling, along withtermination rate were analyzed. The results show that the height and biomass of sunn hemp
were not significantly different among the treatments, being 164 cm and 2.57 t ha-1. In Oggun
crimping, speed and field efficiency were lowest, being 3.5 km h-1 and 0.8 ha h-1, but fuel use
was highest. Under plow-based management, soil compaction was slightly higher andtermination rate was more efficient, when compared to other treatments. Significant
difference was not observed for VMC between the equipment used. However, crimping canbe beneficial for cover crop termination, compared with disking that may have long-term
effects on the soil.
Keywords conservation agriculture, plant biomass, sunn hemp, termination rate

INTRODUCTION
Cover crops are an integral part of conservation agriculture (CA) and are planted to benefit the soil
in numerous ways (Clark, 2012). Unlike CA, conventional tillage (CT) modifies the soil, adversely
affecting soil physical, chemical, and biological properties (Acar et al., 2018). Technically, CT
involves plow-based practices, leaving less than 30% of crop residue on the soil surface (Vian et al.,2009). In contrast, CA is defined as a farming system whose key principles are minimal soil
disturbance, permanent plant cover, and crop diversification (FAO, 2016). It helps reduce soil erosion,
run-off, soil temperature, and soil compaction, retain soil moistures, increase nitrogen in the soil, andⒸ ISERD

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suppress weed (Balkcom et al., 2018; Mitchell et al., 2019). Because of its benefits, CA has been
adopted in Cambodia since 2004 to improve soil fertility for upland crops; nevertheless, it is still
considered an early stage at which rolling services remain inadequate.
There are many kinds of cover crop available in Cambodia, and sunn hemp is highly preferred.
Sunn hemp (Crotalaria juncea L.) is a tropical leguminous crop native to India and Pakistan. It is adrought-tolerant crop that can reach 183 cm and grow on soil pH 5 to 7.5 (USDA, 2012). It produces
1 to 9 tons ha-1 of biomass and 122 kg ha-1 N in 45 to 90 days (Price et al., 2012). Seeding rates varyfrom 17 to 34 kg ha-1 (Balkcom et al., 2011). Mature sunn hemp is manageable by crimping, or
herbicide, but herbicide is harmful to soil and human health (Kornecki et al., 2012).
Kornecki (2015) defined a roller-crimper as functioning to flatten cover crops on the soil surface,to kill or injure them, to create mulch through which cash crops are seeded and grow. Crimping iseffective when legumes are at flowering stage, or when grass cover crops reach anthesis stages(Frasconi et. al, 2019). Cover crop termination should be done no more than three weeks before
planting cash crops (Kornecki, 2009, 2012 and 2015). Crimpers consist of one or two rollers.
Crimpers with one roller may have elliptic or straight bars, and the roller itself can be solid or hollow.
Crimpers with one smooth roller are also common but need spontaneous operation with herbicide
spray to kill the cover crop (Kornecki et al., 2012). Besides that, crimpers with two rollers use thefront roller, which is usually smooth, to flatten cover crops and the rear one to crush them. Recent
crimping technology is trapping and applying tractor engine heat to kill the cover crop, so thecrimping mechanism is not roller-based, but like an iron (Fasconi et al., 2019).
OBJECTIVE
The objectives of this study were (1) to compare different roller-crimpers with Oggun-mounted
USDA roller-crimper in terminating sunn hemp (Crotalaria juncea L.) and (2) to determine theireffects on soil strength and soil moisture.
METHODOLOGY
In the wet season of 2019, the experiment was conducted on clayey soil, called Mollisol, with pH 7
to 7.13 in Rattanak Mondul District, Battambang Province, Cambodia. The average day-timetemperature and monthly rainfall were 29℃ and 148.6 mm (Climate.data.org, 2019). Sunn hemp wasplanted on May 12, 2019 at equal rate of 22 kg h-1 and terminated at the blossom stage.
Materials
The roller-crimpers tested in this experiment were a patented two-stage roller-crimper developed at
the National Soil Dynamics Laboratory (NSDL), the United State Department of Agriculture(USDA), and a Cambodian made roller-crimper with elliptic bars. The USDA roller-crimper(Kornecki, 2011) had two drums with a width of 1.35 m. The first drum was smooth and the second
drum had six straight crimping bars used. The Cambodian made roller-crimper had one roller a widthof 2.0 m. designed its elliptic crimping bars. A disc-plow had six discs and was 2.0 m. long,
functioning to cut and incorporate the cover crop into the soil, and turn over the soil. In addition, thetractors utilized to pull these implements were different. The USDA roller-crimper was mounted on
the Oggun tractor; the Cambodian made roller-crimper and the disc-plow on a Ford 6600 tractor.
Oggun is manufactured as an open-system tractor by the Cleber LLC located in Alabama, USA. It is
hydraulically operated, weighs 0.8 ton, and has a 19-hp gasoline engine. The Ford 6600 tractor is
powered by a 75-hp diesel engine, weighing 2.56 tons.
Design and Sampling Methods
Research design was based on the studies by Kornecki (2009, 2012 and 2015), but slightly modified
to suit the Cambodian condition. A randomized complete block design (RCBD) was used comprisingⒸ ISERD

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three treatments, each with four replications. Each plot was 34 m long and 14 m wide toaccommodate at least five times the roller-crimper’s widths and spaced 5 m for tractor turning. The
three implements were evaluated on the same day. Before rolling, sunn hemp height was measured
at 9 randomly chosen locations in each plot. Sunn hemp biomass was collected from three locations
within each plot area using a 1.0-m2 area wire frame (1.0 x 1.0 m). The biomass samples were driedfor 15 days at 50 to 65℃ using a solar dryer parabola dome. Before and after rolling, bulk density
and soil penetration resistance were evaluated at 0-10 and 10-20 cm depths from three locations
within each plot. The soil samples were dried for 24 hours at 105℃ using an oven (Universal Oven
UN55 Memmert). Soil penetration resistance was evaluated using a cone penetrometer with 0-100
reading scales (model S086 proving ring penetrometer). Termination rate was evaluated 7, 14, and21 days after rolling, using a visual method. Each rolled, or disked plot was split into five andevaluated based on 0-100% scales for mortality rate. However, the rolled residue was sprayed withglyphosate two weeks after rolling, to plant corn. Due to herbicide effects, data were collected only
two weeks and then averaged. Volumetric soil moisture content (VMC) was measured using a
portable TDR moisture meter with 12 cm long rods (Spectrum Technologies, Plainfield, IL) on therolling day and then 7, 14, and 21 days after rolling in each plot.
The data were analyzed performing analysis of variance (ANOVA) by using the R-software
6.3.1 available online. Fisher’s protected LSD test at α = 0.05 probability level was used to show
significant difference and to determine interactions between treatment means, periods before andafter rolling, and soil depths.

a

b

c

Fig. 1 (a) USDA roller-crimper, mounted on Oggun; (b) Cambodian made roller-crimper,
pulled by 75-hp diesel tractor; (c) disc-plow with six discs
RESULTS AND DISCUSSION
Sunn Hemp Height and Biomass
Significant differences in the plant height (P-value = 0.462) and biomass (P-value = 0.823) were notobserved among the treatments, being 164 cm and 2.57 t ha-1. The reason was that sunn hemp wasplanted using the same method, but crimping/disking was applied afterwards at blossom. The plantheight of sunn hemp in this study was acceptable, compared to the data by USDA (2012) andBalkcom and Reeves (2004), indicating it within the range of 120 to 180 cm. However, the plantbiomass was slightly low, compared to the average value of 5.6 Mg ha-1, due to prolonged drought.
Balkcom et al. (2011) recognized the weather as the main factor. His findings showed that the plantbiomass was even lower than 2.0 Mg ha-1 despite different planting dates and seed rates applied.
Field Operations
There were significant differences in speed, fuel consumption, and field efficiency (P-value < 0.001)
among the treatments. Disking speed was the fastest, operated at 5.21 km h-1, followed by the
Cambodian made roller-crimper at 4.5 km h-1 and the USDA roller-crimper at 3.5 km h-1. The Oggun
consumed 21.35 liter ha-1, greater than the 75-hp tractor that used 11.13 liter ha-1 for disking and12.44 liter ha-1 for crimping. Oggun crimping achieved low field efficiency at 0.79 ha h-1, whileⒸ ISERD

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disking and crimping with elliptic bars accomplished more work, equal to 1.10 and 1.27 ha h-1. Low
Oggun performance is most likely associated with the engine size, weight, attached equipment,rolling method and land condition. Likewise, Kornecki et al. (2009 and 2015) studied greater speed
and attributed the field efficiency to speed and numbers of rolling passages.
Table 1 Sunn hemp height, biomass, and field operations by crimping/disking
Treatment
USDA-crimper
Cambodian crimper
TillageSE
CV (%)
T-test probability

Plant height(cm)
166.42 a167.42 a
159.75 a
6.628
9.87
0.462

Plant biomass(t ha-1)
2.50 a2.66 a2.54 a
0.260
24.8
0.823

Speed
(km h-1)
3.50 c4.50 b
5.21 c0.269
14.96<0.001***

Fuel use(l ha-1)
21.35 c12.44 b11.14 a1.030
13.75
<0.001***

Field efficiency(ha h-1)
0.79 c1.09 b
1.27 a0.092
9.95<0.001****** significant at p < 0.001 and letters in the same column refer to the difference between treatments.

Fig. 2 Bulk density and soil penetration resistance (mean± SD) examined at different depths
before and after rolling, with respect to the equipment used. Means with different
letters are significantly different (LSD-Test, p < 0.05)
Bulk Density
Bulk density was examined at 0-10 and 10-20 cm depths, and then compared before and after rolling.
No significant interactions between the equipment used and soil depth in both periods (P-value =0.265). However, significant differences were observed among the treatments with respect to depths
and rolling periods (P-value = 0.043). After rolling, bulk density increased by 0.1 to 0.2 g cm -3. In
disking, no significant difference was found since the soil became loose, when disked and turned
over at less than 30 cm depth. Higher bulk density was observed at deeper subsoils, regardless ofthe equipment used. In this study, bulk density averaged 1.2 g cm-3, which was categorized by Rowell(1994) and Duruaha et al. (2007) as little impacts on plant root development.
Soil Penetration ResistanceNo significant interactions between the equipment and soil depth were observed in both periods (Pvalue = 0.917). Although crimping/disking was not applied, soil compaction still increased as soil
depth increased (P-value < 0.001). Soil compaction was even greater after utilizing the equipment(P-value = 0.025), indicating the effects of the equipment on the soil. Before rolling, soil penetration
resistance measured at the 0-10 cm depth was similar in all treatments, being approximately 1.1 MPa,but increased to 3 MPa after rolling. At the 10-20 cm depth, soil compaction became greater whencrimping/disking was applied, but was not significantly different among the equipment used. As aresult, it increased from 2.7 MPa before rolling to 4.4 MPa after rolling. These values fell into therange that Bartzen et al. (2019) classified as medium limitations on plant root growth, but exceeded
the threshold value of 2 MPa, that might restrict corn root elongation (Unger et al., 1994).
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Fig. 3 Effects of crimping/disking on soil volumetric moisture content (VMC) and sunn hemp
termination rate (mean % ± SD) evaluated with respect to time. Means with different
letters are significantly different (LSD-Test, p < 0.05)
Volumetric Soil Moisture Content (VMC)
Soil moisture was examined on the day before rolling and after first, second, and third week, withrespect to the equipment (Fig. 3). There were significant interactions between treatments and weeks(P < 0.001); thus, the data were analyzed weekly. On the day before rolling, significant differencesin VMC were observed among the treatments (P < 0.001), being 21.6% in Oggun crimping, 19.8%
in crimping elliptic bars, and 17.2% in disking. One week after rolling, no significant differenceswere detected (P < 0.525) and VMC averaged 7.3%. In week two, there were significant differencesin VMC (P < 0.001), being 20.1% in both crimping types and 17.6% in disking. However, VMC
were not significantly different in week three (P < 0.188), measured at 14.7%. It was observed thatVMC fluctuated over time, but a sharp decrease in VMC was observed in week one after cover crop
termination, due to sunn hemp decomposition that could not prevent water losses. In weeks two andthree, VMC increased because of successive rains that replenished the soil with water. With respect
to the equipment used, Effects of soil water losses caused by disking were not clearly seen due to theinterference of rainfalls and low cover crop biomass production.
Sunn Hemp Termination
Mechanical termination was evaluated, analyzed and compared in two weeks only. There weresignificant interactions between treatments and weeks (p < 0.001); thus, the data were compared by
week. In both weeks, significant differences in sunn hemp termination rate were observed among thetreatments (p < 0.001). In week one, termination rates for oggun crimping, crimping with elliptic
bars, and disking were 30.5, 44.1, and 71.5%, respectively. The value increased sharply in week two,but remained significantly lower in Oggun crimping, compared to operation by the 75-hp tractor. In
that week, Oggun crimping obtained the termination rate at 67.8%, tractor crimping at 74%, anddisking at 92.9%. Highest termination rate was found in disking because sunn hemp was subject todamage, when cut, crushed and buried in the soil. The termination rate obtained in this study werelower than the recommendations by Kornecki (2015), reporting that achieving 90% in two weeks is
critical for succeeding cash crops and profitable.
CONCLUSION
Two different roller-crimpers were compared with disking in terminating sunn hemp in upland
Cambodia. The USDA roller-crimper consumed more fuel amounts but had lower field efficiency,
when compared to other treatments. Termination rate by Oggun crimping was also lower than 75-hp
tractor operations. Tractor weight, engine size, rolling method, and soil condition tend to be the maincause. Bulk density, soil penetration resistance, and VMC in all treatments seem not to be different.
Despite that, it can be observed that the disked plots had disturbed soil with less crop residue, leaving
it exposed to the sun and prone to moisture losses, when compared with crimping.
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ACKNOWLEDGEMENT
This research is made possible by the generous support of the American People provided to theCenter of Excellence on Sustainable Agricultural Intensification and Nutrition (CE SAIN) of theRoyal University of Agriculture through the Feed the Future Innovation Lab for Collaborative
Research on Sustainable Intensification at Kansas State University funded by the United States
Agency for International Development (USAID) under Cooperative Agreement No. AID-OAA-L14-00006. The contents are the sole responsibility of the authors and do not necessarily reflect theviews of USAID or the United States Government. Many thanks also go to Conservation Agriculture
Service Center (CASC), Cleber LLC Company, and the National Soil Dynamics Laboratory (NSDL),
the United State Department of Agriculture (USDA) for technical support.
REFERENCES
Acar, M., Celik, I. and Günal, H. 2018. Effects of long-term tillage systems on aggregate-associated organiccarbon in the eastern mediterranean region of Turkey. Eurasian Journal of Science, (1), 51-58.
Balkcom, K.S. and Reeves, D.W. 2004. Sunn-Hemp utilized as a legume cover crop for corn production.
American Soiciety of Agronomy, Agron. J., 97, 26-31.
Balkcom, K.S., Kornecki, T.S. and Price, A.J. 2018. A simple guide for conservation systems in the southeast
Auburn, AL. United States Department of Agriculture, Agricultural Research Service, National Soil
Dynamics Laboratory Conservation Systems Resesarch.
Balkcom, K.S., Massey, J.M., Mosjidis, J.A., Price, A.J. and Enloe, S.F. 2011. Planting date and seeding rate
effects on sunn hemp biomass and nitrogen production for a winter cover crop. Hindawi Publishing
Cooporation, International Journal of Agronomy, doi:10.1155/2011/237510.
Bartzen, B.T., Hoelscher, G.L., Ribeiro, L.L.O. and Seidel, E.P. 2019. How the soil resistance to penetration affects
the development of agricultural crops? Journal of Experimental Agriculture International, 30 (5), 1-17.
Clark, A. 2012. Managing cover crops profitably (3rd Ed.). College Park, MD, Sustainable Agriculture
Research and Education.
Climate.data.org. 2019. Climate Battabang. Retrieved 11 05, 2019, Retrived from https://en.climate-data.org/asia/cambodia/battambang/battambang-3145/
Duruoha, C., Piffer, C.R. and Silva, P.A. 2007. Corn root length density and root diameter as affected by soil
compaction and soil water content. Irriga, Botucatu, 12 (1), 14-26. ISSN 1808-3765.
FAO. 2016. Conservation agriculture. Retrieved from Food and Agriculture Organization of the United States :http://www.fao.org/3/a-i7480e.pdf
Frasconi, C., Martelloni, L., Antichi, D., Raffaelli, M., Fontanelli, M., Peruzzi, A., Benincasa, P. and Tosti, G.
2019. Combining roller crimpers and flaming for termination of cover crops in herbicide-free no-till
cropping systems. PLOS ONE, 1-19.
Korneck, T.S. 2011. Multistage crop roller. U.S. Patent# 7.987.917 B1. August 02, 2011.
Kornecki, T.S. 2015. Rye termination by different rollers / crimpers developed for no-till small-scale farms.
American Society of Agricultural and Biological Engineering, 31 (6), 849-856.
Kornecki, T.S., Arriaga, F.J. and Price, A.J. 2012. Roller type and operating speed effects on rye termination rates, soil
moisture, and yield of sweet corn in a no-till system. HortScience, 47 (2), 217-223.
Kornecki, T.S., Price, A.J., Raper, R.L. and Arriaga, F.J. 2009. New roller crimper concepts for mechanicaltermincation of cover crops in conservation agriculture. Renewable Agriculture and Food Systems, 24 (3),165-173. doi: 10.1017/S1742175009002580.
Mitchell, J.P., Reicosky, D.C., Kueneman, E.A., Fisher, J. and Beck, D. 2018. Conservation agriculture systems.
CAB Reviews, doi: 10.1079/PAVSNNR201914001.
Price, A.J., Kelto, J. and Mosjidis, J. 2012. Utilization of sunn hemp for cover crops and weed control intemperate climates. Rijeka, Croatia: InTech Europe, University Campus STeP Ri.
Rowell, D.L. 1994. Soil science methods & applications. Longman Group UK Limited., New York.
Unger, P.W. and Kaspar, T.C. 1994. Soil Compaction and Root Growth: A Review. Agronomy journal, 759-765.
United States Department of Agriculture. 2012. Plant guide: Sunn hemp (Crotalaria juncea L.). Cape May, NJ.
08210: USDA-Natural Resources Conservation Service, Cape May Plant Materials Center.
Vian, J.F., Peigne, J., Chaussod, R. and Roger-Estrade, J. 2009. Effects of four tillage systems on soil structureand soil microbial biomass in organic farming. Soil Use and Management, 25, 1-10.

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Research article
erd

Evaluating Changes in the State of Organic Matter Present
in Agricultural Soils based on Loss on Ignition Method
TAKAHIKO NAKAMURA
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, Japan

NARONG TOUCH*
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, Japan
Email: nt207118@nodai.ac.jp
Received 5 December 2019

Accepted 25 March 2020(*Corresponding Author)

Abstract Loss on ignition (LOI) method is widely regarded as a quick, inexpensive method
for evaluating organic matter characteristics in terrestrial soils and littoral sediments.
Previous studies examined the LOI method alone to evaluate the state of organic matter,
finding that changes in the state of organic matter in littoral sediments could be represented
by LOI method-based indices related to combustion temperature: (LOI300−LOI200)/LOI600
and LOI300/LOI600. However, these new indices have not been applied to agricultural soils.
In this study, laboratory experiments were conducted to examine the validity of our proposed
indices for evaluating the state of organic matter in agricultural soils. Paddy soil, Andosol,and cow manure compost were allowed to decompose over three months. Measurements ofpH, redox potential (ORP), and weight LOI at 200, 300, and 600ºC were measured at one andthree months after the experiments started. Temporal decreases in ORP were observed,
highlighting the decomposition of organic matter present in each material. The(LOI300−LOI200)/LOI600 metric changed over time for all materials, whereas the
LOI300/LOI600 metric was almost constant. We concluded that (LOI300−LOI200)/LOI600 canrepresent changes in the state of organic matter in agricultural soils. Furthermore, we
discovered that LOI200/LOI600 can represent changes in the state of organic matter withreasonable accuracy, rather than the more complicated (LOI300−LOI200)/LOI600. This finding
should make LOI measurements easier and more affordable.
Keywords loss on ignition, organic matter state, decomposition, combustion,
agricultural soils

INTRODUCTION
Soil organic matter (SOM) is any plant or animal material, including leaf litter, microbial biomass,water-soluble organics, and humus at different stages of decomposition (Stevenson, 1994; Brady andWell, 1999). SOM has a major impact on the physicochemical properties of soils (Tisdall and Oades,
1982). For example, SOM has been shown to affect the cation exchange capacity, structure, water
infiltration rate, water-holding capacity, erodibility and conversion, and pesticide adsorption of soil
(Schulate, 1995; Ding et al., 2002).
Several methods have been proposed to measure the organic matter of terrestrial soils and littoral
sediments. For instance, the Walkley-Black method is a wet oxidation method used to determine theamount of organic matter from the organic carbon content of the soil (Swift, 1996; Kerven et al.,2000). Another method, loss on ignition (LOI), uses dry combustion method to directly determine
the amount of organic matter (Cambardella et al., 2001; Konen et al., 2002). Automated CHNS
analysis conducted on gas chromatographs has been used to quickly determine the elemental contents(i.e., C, H, N, and S) with high precision (Telek and Marshall, 1974). With advancements intechnology, it is now possible to accurately analyze the organic matter characteristics of a sample
based on molecular weight measurements, thermogravimetric analysis, and spectrometric analysis(Cuypers et al., 2002; Hong et al., 2010). To the best of our knowledge, the LOI method is the mostⒸ ISERD

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economical method that is suitable for rapidly analyzing large numbers of samples with theadvantage of time and cost savings, it does not require a measurement expert, and it is an accessible
method for developing countries.
To date, some authors have evaluated the amount and state of SOM on the basis of the LOI
method, alone. For instance, the ratio of weight losses on ignition at 300°C (LOI300) and at 600°C(LOI600) was found to describe the state of organic matter present in littoral sediment (Hibino et al.,2014). Subsequent refinement revealed that (LOI300−LOI200)/LOI600 metric more accurately
represents the state of organic matter in littoral sediments than LOI300/LOI600 metric, especially,changes in the state of organic matter due to decomposition (Touch et al., 2015; Touch et al., 2017).
However, these metrics have not been used to evaluate the state of SOM in agricultural soils, so theirvalidity should be tested in that setting.
OBJECTIVESThis study aims to examine the validities of LOI300/LOI600 and (LOI300−LOI200)/LOI600 metrics inevaluating changes in the state of organic matter present in paddy soil, andosol, and cow manurecompost. These materials are allowed to decompose over three months, and changes in their pH andredox potential (ORP) are measured for understanding the decomposition of organic matter present
in each material. Then, the association between temporal changes in LOI300/LOI600 and(LOI300−LOI200)/LOI600 metrics and the decomposition of organic matter is investigated for
examining the validities of these metrics. Furthermore, this study also aims to propose a more
economical metric for examining the state of organic matter present in organic agriculture materials.
METHODOLOGY
Materials and Experimental Procedures
The paddy soil used in experiments was collected from a rice field (Ebina, Kanagawa, Japan) during
the agricultural off-season. Approximately 150 mm of the surface soil was collected and transported
to the laboratory. Approximately 200 mm of the surface soil of an andosol was collected from afarmland (Nakai, Kanagawa, Japan) during the agricultural off-season. Finally, commercial cow
manure compost was purchased and used in this study. All of the materials were used in theexperiment without any pre-treatment. Each material was placed in the experimental device as shownin Fig. 1.

30 mm

120 mmDeionized water
1, 3 months

120 mm40 mm

Material➢ Paddy soil from rice field➢ Andosol from farmland➢ Cow manure compost

Surface soil
➢ pH
➢ Redox potential➢ Loss on ignition

Fig. 1 Diagram of the experimental device and some procedural details used in this study
The experimental device comprised a cylindrical bottle with an inner diameter of 120 mm and aheight of 150 mm. The bottle was filled with each material to a depth of 120 mm. The bottle wasthen filled the rest of the way with deionized water (Fig. 1), and placed under ambient conditions forthree months. After one and three months, the top 40 mm of the surface soil was collected from thebottle for measurements of pH, ORP, and weight LOI.
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Soil Analysis
Each collected sample (paddy soil, andosol, or cow manure compost) was mixed before pH and ORP
were measured. A D-50 pH/ORP meter (Horiba, Japan) was inserted directly into the sample. Afterthe pH and ORP measurements were completed, samples were prepared for the measurement ofweight LOI. Each sample was oven-dried at 50 ± 5°C for more than three days, and the dried weight
was then measured. A temperature of 50°C is suitable for drying samples before determination oforganic matter content (Takata et al., 2017). An electronic muffle furnace (Advantec, KL-420) wasused to burn each of the dried samples for 4 h at each of three temperatures: 200, 300, and 600°C
according to Saito et al. (1977). The sample was weight after burning at each temperature. LOI for
each stage was determined by comparing these values with the 50 °C oven-dried weight. The
measurements of LOI were performed in triplicate and had an error of ± 6 mg.g-1.
RESULTS AND DISCUSSION
Temporal Decomposition of Organic Matter
Figure 2 shows the temporal changes in pH and ORP for each material while under ambientconditions. The ORP values started at 304±4 mV, 318±1 mV, and 96±9 mV for the andosol, paddy
soil, and cow manure compost, respectively. The OPR of the paddy soil decreased to 93 mV overone month, and to −241 mV over three months. Decreases in the ORP of andosols and cow manurecompost were also found. After three months, the ORP was −226 mV for the andosol, and −437 mV
for cow manure compost.
400

Blue: 1 month, Red: 3 months
Andosols
Paddy soil
Cow manure compost

ORP (mV vs. Ag/AgCl)

300
200

100
0

R= 0.86

R= -0.99

-100
-200

R= -0.99

-300

-400
-500

5

6

7

8

9

10

pH (-)

Fig. 2 Temporal changes in pH and redox potential (ORP)
Variations in pH and ORP can represent the decomposition of organic matter present in littoral
sediments (Touch et al. 2015). A decrease in ORP indicates the decomposition of organic matterbecause oxidants are used in the decomposition processes. Thus, our results showed clear signs ofdecomposition of organic matter over time, as was found by Touch et al. (2015).
Validities of LOI300/LOI600 and (LOI300−LOI200)/LOI600
As revealed by Fig. 2, the decomposition of organic matter present in each material decomposed overtime. This means that a correlation between the proposed indices and operation time should beobserved. Figure 3 depicts the temporal patterns of LOI300/LOI600 and (LOI300−LOI200)/LOI600
metrics. Low correlation between LOI300/LOI600 metric and operation time was confirmed, with thismatric remaining nearly constant for each material over the span of experiment (Fig. 3a). This finding
suggests that LOI300/LOI600 metric is unable to reflect the decomposition of organic matter present inandosol, paddy soil, and cow manure compost.
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0.8

0.8

LOI300/LOI600 (-)

0.6

R= -0.55

0.5

R= 0.52

0.4
0.3
0.2
0.1

(LOI300-LOI200)/LOI600 (-)

R= 0.25

0.7

Andosols
Paddy soil
Cow manure compost

0.7
0.6
0.5

R= 0.990.4

R= 0.990.3
0.2

R= -0.99

0.1

0

0

0

1

2
3
Operation time (month)

40(a) LOI300/LOI600

1

2
3
Operation time (month)4(b) (LOI300-LOI200)/LOI600

Fig. 3 Temporal variations of LOI300/LOI600 and (LOI300-LOI200)/LOI600
On the other hand, the (LOI300−LOI200)/LOI600 metric for each material had a strong correlation(R = 0.99 for all samples) with operation time (Fig. 3b). This demonstrates that
(LOI300−LOI200)/LOI600 metric can indicate the decomposition of organic matter present in thesematerials. When combined with the findings of previous research (Touch et al., 2017), we concluded
that the (LOI300−LOI200)/LOI600 metric can represent the decomposition of organic matter either inlittoral sediments or in agricultural soils, including compost materials.
Interestingly, different tendencies in the variation of (LOI300−LOI200)/LOI600 metric wereobserved among the materials used. Specifically, (LOI300−LOI200)/LOI600 metric increased in theandosol and cow manure compost, while it decreased in the case of paddy soil (Fig. 3b). This reflects
differences in organic matter decomposition characteristics among these materials due to a differencein the initial pH-ORP (Fig. 2) and organic matter content (e.g., Fig. 3a) of each material.
New Index for Evaluating the Decomposition of Organic MatterTo explore the source of the difference in the performance between (LOI300−LOI200)/LOI600 metricand LOI200/LOI600 metric, Figure 4 shows the behavior of LOI200/LOI600 metric. This metric had astrong correlation (R > 0.87) with operation time for each material (Fig. 4a), but was not as strong as(LOI300−LOI200)/LOI600 metric (R = 0.99). Furthermore, it was observed that LOI200/LOI600 metric
had a strong correlation (R > 0.87) with (LOI300−LOI200)/LOI600 metric (Fig. 4b), suggesting that
LOI200/LOI600 metric can be used instead of (LOI300−LOI200)/LOI600 metric. This means that
LOI200/LOI600 metric can be viewed as a new index for evaluating the decomposition of organic
matter in these materials. This may provide an advantage in terms of cost and time savings, because
only the weight losses on ignition at 200 and 600°C must be measured for this simplified metric.
As noted earlier, we observed different tendencies in the variation of (LOI300−LOI200)/LOI600
metric (Fig. 3b). According to Cuypers et al. (2002), the burning temperature of organic matter
increases as the organic matter becomes more humified. For the cow manure compost,(LOI300−LOI200)/LOI600 metric increased (Fig. 3b), while LOI200/LOI600 metric decreased (Fig. 4a).
This suggests a release of the organic matter burned at 200−300°C (OM200−300) and the loss of theorganic matter burned at 50−200°C (OM50−200). The paddy soils, however registered a formation of
OM50−200 and a loss of OM200−300.
Generally, cow manure compost contains the organic matter that readily decomposes, while theorganic matter in paddy soils and andosols tends to be humified. Hence, these results reflect
decomposition of OM200−300, leading to the release of OM50−200 in paddy soils, while the humificationof OM50−200 leads to the formation of OM200−300 in cow manure compost. It can be said that the tendencyof the temporal change in each metric reflects the decomposition characteristic of organic matter.

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0.8

0.6
0.5

0.7
LOI200/LOI600 (-)

LOI200/LOI600 (-)

0.8

Andosols
Paddy soil
Cow manure compost

0.7

R= 0.960.4

0.3

R= -0.87

0.2

R= -0.91

0.1

0.6
0.5

0.4

R= -0.87

0.3
0.2

0.1

0

R= -0.97

R= -0.89

0

0

1

2
3
Operation time (month)

4(a) Operation time and LOI200/LOI600

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8(LOI300-LOI200)/LOI600 (-)(b) (LOI300-LOI200)/LOI600 and LOI200/LOI600

Fig. 4 Relations between operation time and (LOI300-LOI200)/LOI600 with LOI200/LOI600
CONCLUSION
Our laboratory experiments examined the validities of LOI300/LOI600 and (LOI300−LOI200)/LOI600
metric in evaluating changes in the state of organic matter present in paddy soil, andosol, and cow
manure compost. Temporal changes in LOI300/LOI600 metric were not observed, while(LOI300−LOI200)/LOI600 metric had a strong correlation with operation time. This reinforced the ideathat (LOI300−LOI200)/LOI600 metric can represent the decomposition of organic matter, either inlittoral sediments or agricultural soils, including compost materials. Furthermore, we found out that
LOI200/LOI600 metric had a strong correlation with (LOI300−LOI200)/LOI600, suggesting that thismatric can be used as a simpler proxy for (LOI300−LOI200)/LOI600 metric. Finally, the proposed
metrics, i.e., (LOI300−LOI200)/LOI600 and LOI200/LOI600, were used to identify the decomposition andhumification of organic matter. For example, the humification of organic matter caused a decreasein LOI200/LOI600 metric and an increase in (LOI300−LOI200)/LOI600 metric. The tendency of thetemporal change in each metric reflected the decomposition characteristic of organic matter.
ACKNOWLEDGEMENTS
The authors gratefully acknowledge the students of the Rural Environmental Engineering Laboratory,
Tokyo University of Agriculture, for their efforts in collecting data.
REFERENCES
Brady, N.C. and Weil, R.R. 1999. The nature and properties of soils. 12th Edition, Prentice Hall Publishers,
London.
Cambardella, C.A., Gajda, A.M., Doran, J.W., Wienhold, B.J. and Kettler, T.A. 2001. Estimation of particulate
and total organic matter by weight loss-on-ignition. In Assessment Methods for Soil Carbon, Lewis
Publishing, New York.
Cuypers, C., Grotenhuis, T., Nierop, K.G.J., Franco, E.M., De Jager, A. and Rulkens, W. 2002. Amorphousand condensed organic matter domains, The effect of persulfate oxidation on the composition ofsoil/sediment organic matter. Chemosphere, 48, 919-931.
Ding, G., Novak, J.M., Amarasiriwardena, D., Hunt, P.G. and Xing, B. 2002. Soil organic matter characteristics
as affected by tillage management. Soil Sci. Soc. Am. J., 66, 421-429.
Hibino, T., Tachiuchi, K., Touch, N. and Nakashita, S. 2014. Classification of the organic matter existing inlittoral sediments. J. Jpn. Soc. Civ. Eng. Ser. B2 (Coast. Eng.), 70 (2), 1101-1105 (in Japanese).
Hong, S.W., Kim, H.S. and Chung, T.H. 2010. Alteration of sediment organic matter in sediment microbial
fuel cells. Environ. Pollut., 158, 185-191.
Kerven, G.L., Menzies, N.W. and Geyer, M.D. 2000. Analytical methods and quality assurance: Soil carbon
determination by high temperature combustion: A comparison with dichromate oxidation procedures andⒸ ISERD

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the influence of charcoal and carbonate carbon on the measured value. Commun. Soil Sci. Plant Anal., 31,
1935-1939.
Konen, M.E., Jacobs, P.M., Burras, C.L., Talaga, B.J. and Mason, J.A. 2002. Equation for predicting soilorganic carbon using loss-on-ignition for north central U.S. soils. Soil Sci. Soc. Am. J., 66, 1878-1881.
Saito, J., Hosoya, Y. and Nishibayashi, K. 1977. Measurement on peat by ignition loss method. ObayashiGumi Gijutsu Kenkyu Shoho, 15, 89-94 (in Japanese).
Schulte, E.E. 1995. Recommended soil organic matter tests. In Recommended Soil Testing Procedures for the
North Eastern USA. Northeastern Regional Publication No. 493, Agricultural Experiment Station, Univ.
of Delaware, Newark.
Stevenson, F.J. 1994. Humus chemistry, genesis, composition, reactions. John Wiley & Sons, New York.
Swift, R.S. 1996. Organic matter characterization. In Methods of Soil Analysis. Part 3: Chemical Methods.
Soil Sci. Soc. Am. Inc., Madison.
Takata, H., Morimoto, Y., Touch, N., Nakashita, S. and Hibino, T. 2017. Proposal of differential thermal
analysis method for littoral sediments. J. Jpn. Soc. Civ. Eng. Ser. B2 (Coast. Eng.), 73 (2), 1201-1206 (inJapanese).
Tisdall, J.M. and Oades, J.M. 1982. Organic matter and water-stable aggregates in soils. J. Soil Sci., 33, 141163.
Telek, G. and Marshall, N. 1974. Using a CHN analyzer to reduce carbonate interference in particulate organiccarbon analyses. Mar. Biol., 24, 219-221.
Touch, N., Kinjo, N., Nakashita, S. and Hibino, T. 2015. Dry combustion-based method for evaluating
variation of organic matter state in sediment. J. Jpn. Soc. Civ. Eng. Ser. B2 (Coast. Eng.), 71 (2), 14171422 (in Japanese).
Touch, N., Hibino, T., Takata, H. and Yamaji, S. 2017. Loss on ignition-based indices for evaluating organic
matter characteristics of littoral sediments. Pedosphere, 27 (5), 978-984.

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Research article
erd

Vegetable Farming Practices in Cambodia: Case study of
Small-scale Vegetable Farmers in Kandal, Kampong
Chhnang and Battambang Provinces
THIRA PINN*Royal University of Agriculture, Phnom Penh, Cambodia
Email: pinnthira@rua.edu.kh

SYDEN REACH
Royal University of Agriculture, Phnom Penh, Cambodia

BORARIN BUNTONGRoyal University of Agriculture, Phnom Penh, Cambodia

ANTONIO ACEDO JR.
Mekong Institute, Thailand
Received 31 January 2020

Accepted 25 March 2020(*Corresponding Author)

Abstract Cambodia shifted from an agrarian-based to a service- and production-based
economy through its fast-economic growth. However, agriculture is still a backbone of theCambodian economy because 80 percent of the population lives in rural areas, and around 37
percent of the total workforce remains directly engaged in the agricultural sector. Even
though the Royal Government of Cambodia (RGC) considered the enhancement of the
agricultural sector as a high priority in its national development agenda for 2014 to 2018,
Cambodia remains a net vegetable importer. Approximately USD 200 million of vegetableproducts are imported informally from Thailand and Vietnam each year. Several researchstudies have been conducted to identify farmers’ constraints. However, challenges faced bysmall-scale vegetable farmers have yet to be clearly characterized. This research aimed to
identify the farming practices and challenges of small-scale vegetable farmers in Cambodia.
There were 40 households, selected by homogeneous purposive sampling method, surveyedfrom the Kandal, Kampong Chhnang and Battambang Provinces. The results of the study are
divided into three parts, with the first component focused on farming practices. There were
12 types of vegetables that were identified as being grown in the studied areas. However,
only 5 types of vegetables were commonly grown by farmers. Chinese mustard (55%) was
the most popular vegetable being grown in the areas, followed by green mustard (50%), pakchoi (45%), leafy mustard (37.5%) and Chinese kale (27.5%). Due to small production size,
almost all interviewed farmers harvested their products by sickle, knife, and/or scissors, andmanual harvest is still in practice by some farmers. Post-harvest activities such as grading,sorting and cleaning products after harvesting and before selling are not commonly practiced
by farmers. Second, the majority of vegetable products are sold to collectors; a small quantityof remaining products is sold to retailers, farmers’ groups/cooperatives and direct consumers
in the areas. Third, the study revealed three main challenges faced by farmers. Insect pests
and disease infestation (87.5%) was the most prominent issue of vegetable farmers, followed
by climate change (e.g. flooding, drought) (77.5%). Low selling price and considerable
fluctuation in prices were equally problematic (both 57.5%). Other notable issues included
post-harvest losses especially in peak seasons of oversupply (45%), lack of power in themarket chain (42.5%), and lack of pest control information (40%), while lack of information
about market prices (37.5%) and poor market information systems (37.5%) were the minor
issues in vegetable production.
Keywords vegetable farming, marketing, post-harvest, value chainⒸ ISERD

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INTRODUCTION
Cambodia has experienced rapid agricultural growth, among the fastest in the world. The annual
growth of agricultural gross production was 8.7 percent from 2004 to 2012. However, thecontribution of this sector in the Cambodian economy has been decreasing and is being replaced bymanufacturing and services (World Bank, 2015). Even though the Royal Government of Cambodia(RGC) considered the enhancement of the agricultural sector as a high priority in its national
development agenda (National Strategic Development Plan, 2014), the development of theCambodian agricultural sector has been slow. Cambodia is a net agricultural importer. Their imported
commodities include vegetables (about USD 200 million of vegetable products was informally
imported annually), agricultural inputs and finished products primarily from Thailand and Vietnam(USAID, 2019). Several issue which have been hampered the small-scale vegetable production arelack of agricultural techniques, unreliable supply and demand, and competition with informal
imported vegetables. Even though there have been various research studies conducted in thevegetable sector, the practices and challenges of small-scale vegetable farmers have remained
ambiguous.
OBJECTIVE
This research aimed to identify the farming practices and challenges of small-scale vegetable farmers
in Cambodia.
METHODOLOGY
Mixed methods, quantitative and qualitative, were used to conduct this study. The total sample was
40 households, selected by homogeneous purposive sampling method, from the Kandal, Kampong
Chhnang and Battambang provinces. The selection of study location based on two reasons. First,
these provinces are among Cambodian vegetable production’s zones. Second, characteristic ofsmallholders in these provinces represents the farmers in all location in the country. Ensuring
accuracy in sample selection, researchers reviewed literature, screened names in the list of vegetable
farmers provided by the local authority, and then discussed and finalized the selected sample withthe local authority. The collected data was analyzed by using descriptive statistics.
RESULTS AND DISCUSSION
Table 1 Household demographics
Items
Family members

N
40

Minimum
2

Maximum
9

Mean
4.85

Std. Deviation
1.545

Children ≤ 15 years old (male)Children ≤ 15 years old (female)
Adults 16 – 60 years old (male)
Adults 16 – 60 years old (female)
Elderly >60 years (male)
Elderly >60 years (female)
Valid N (listwise)

18
14
36
39
9
11
0

1
1
1
1
1
1

3
2
4
5
1
1

1.56
1.21
1.67
1.77
1.00
1.00

0.784
0.426
0.956
0.931
0.000
0.000

Source: Authors, *multiple answers will not add up with 100

To understand the current practices and various challenges faced by small-scale vegetable farmers in
Cambodia, 40 smallholder vegetable farmers were interviewed in 18 villages in 5 districts of Kandal,
Kampong Chhnang and Battambang provinces. The household demographics are shown in Table 1.
The gender and age distribution as well as the family size between the villages surveyed were similar.
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Of the respondents, 67.5% were male, 32.5% were female, and the average age of respondents was
47.30 years old. Ninety-five percent of respondents were able to read and write Cambodian language,
while 5% were illiterate. The majority of interviewed household (95%) were nuclear families1, while
5% were jointly family2. The average family size was 4.85 members.
Table 2 Household land use
Distribution of household land in hectares

KandalKampong Chhnang

Battambang

Main dwelling land
Other dwelling land
Agricultural land
Total owned cultivable area in 2018
Total rented cultivable area in 2018
Own areas under vegetable farming
Rented areas under vegetable farming
Total owned land

0.007
0.074
0.611
0.884
0.051
0.308
0.017
1.884

0.005
0.136
1.569
1.569
0.199
3.478

0.008
0.070
3.204
3.170
0.600
0.293
6.745

Source: Authors

The household land distribution is shown in Table 2. The household land size and agriculturalland size differed between provinces. Farmers in Battambang province owned on average 6.745
hectares, which was greater than Kampong Chhnang (3.478 hectares) and Kandal (1.884 hectares)
provinces. Similarly, farmers in Battambang had the most agricultural land (3.204 hectares),followed by 1.569 hectares in Kampong Chhnang province and 0.611 hectare in Kandal province. In
contrast, the average area under vegetable cultivation in Kandal province was 0.325 hectare, which
was higher than Battambang province (0.293 hectare) and Kampong Chhnang province (0.199
hectare).
Table 3 Types of vegetables grown in the studied areas
No
1
2
3
4
5
6
7
8
9
10
11
12

Name of Vegetable
Pak choi
Leafy mustard
Chinese mustard
Cabbage
Chinese cabbage
Green mustard
Lettuce
Spring onion
Crown daisy / Chrysanthemum green
Chinese Kale
Gallic chives
Culantro

Frequency
18
15
22
2
1
20
10
1
0
11
0
4

Percent
45.00
37.50
55.00
5.00
2.50
50.00
25.00
2.50
27.50
10.00

*multiple answers will not add up with 100; ** N=40.
Source: Authors

There were 12 types of vegetable identified as being grown in the studied areas (Table 3).
However, only 5 types of vegetable were commonly grown by farmers. Chinese mustard (55%) was
the most popular vegetable being grown in the areas, followed by green mustard (50%), pak choi(45%) and leafy mustard (37.5%). Chinese kale and lettuce were grown by 27.5% and 25% of farmers,
1 ‘’A nuclear family, elementary family or conjugal family is a family group consisting of two parents and their children(one or more). It is in contrast to a single-parent family, the larger extended family, and a family with more than two parents.
Nuclear families typically center on a married couple; the nuclear family may have any number of children’’ (Wikipedia ‘’
Nuclear family’’ accessed by January 22, 2020: https://en.m.wikipedia.org/wiki/Nuclear_family).
2 ‘’A joint family or undivided family is an extended family arrangement prevalent throughout the Indian subcontinent,
particularly in India, consisting of many generations living in the same household, all bound by the common relationship’’(Wikipedia ‘’ Hinu Joint Family’’ accessed by January 22, 2020: https://en.wikipedia.org/wiki/Hindu_joint_family).

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respectively. Culantro, cabbage, spring onion and Chinese cabbage were not commonly grown by
farmers in the studied areas. Post-harvest activities such as grading, sorting and cleaning products
after harvesting and before selling were rarely implemented by farmers. There are three main reasonsencounter the postharvest practices of farmers: interviewed farmers do not have knowledge andexperiences on postharvest activities; the required materials are not accessible in the target, and norequirements for grading, sorting and cleaning products from collectors, retailers, agricultural
cooperative, and customers.
0.38
17.00

0.25
1.63

5.75
75.00

Commission agent/middlemen/collectors
Retailers
Farmer's group/ cooperatives
OthersWholesalers
Processors
Direct consumers

Fig. 1 Vegetable distribution channels
The produced vegetables were distributed to 6 channels: commission agents/middlemen/collectors, wholesalers, retailers, processors, farmers’ groups/cooperatives, and direct consumers(Fig. 1). The majority of products (75%) were sold to collectors in the area and retailers (17%) in
Phnom Penh, Battambang, Kampong Chhnang and Siem Reap provinces, while some vegetables(5.75%) were sold to wholesalers in the areas. Vegetables were less commonly sold to consumers,
farmers’ groups and other channels because the quantity of product was small and some producers
have supplied contracts with collectors and retailers.
Table 4 Challenges of vegetable producers
No
1
2
3
4
5
6
7
8
9
10

Challenges
Insect pests and disease infestation
Climate change (e.g. flooding, drought)
Low selling priceConsiderable fluctuation in prices
High price of inputs
Post-harvest losses, especially in peak seasons of oversupply
Lack of power in the market chain
Lack of pest control information
Lack of information about market prices
Poor market information systems

Frequency
35
31
23
23
21
18
17
16
15
15

Percent
87.5
77.5
57.5
57.5
52.5
45.0
42.5
40.0
37.5
37.5

*multiple answers will not add with 100; ** other challenges consisted of: lack of technical skills, poor shelf life ofvegetables, differentiated price based on quality difference, labor shortages during critical times, lack of access to cold
chain facilities, lack of storage facility, poor coverage and quality of extension services, lack of consumer preference for
quality vegetables, high temperature especially at night, insufficient and lack of quality seed, lack of incentive for grading,lack of packaging, long distance to market, poor transport network, lack of access to credit, difficulty finding a buyer, andlack of quality seed ** N=40.

There were 10 prominent challenges faced by vegetable producers in the studied areas (Table4). Insect pests and disease infestation (87.5%) were the key issue of vegetable farmers, followed byclimate change (e.g. flooding, drought) (77.5%). Low selling price and considerable fluctuation in
prices were equally problematic (57.5%). Post-harvest losses, especially in peak seasons ofoversupply (45%), lack of power in the market chain (42.5%), and lack of pest control information(40%) were considered as important challenges; while, lack of information about market pricesⒸ ISERD

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(37.5%) and poor market information systems (37.5%) were the minor issues of vegetable farmers.
Sokhan et al. (2018), in their study on the effect of agricultural cooperatives on smallholders’incomes in Svay Chrum commune, Svay Rieng province, Cambodia, found that vegetable farmers
faced several similar challenges, including high production cost, disease infestation and prevention
techniques, and fluctuation of market price. More than half (59%) of interviewed vegetable farmers,
non-members of Svay Rieng Agro-Products Cooperative (SAC), did not know market information.
Therefore, price setting for their products depended on vegetable collectors. Ibeawuchi et al. (2015)
who conducted a study on Fruit and Vegetable Crop Production in Nigeria revealed that pest anddisease was one of important issues that affected fruit and vegetable in the field. More specifically,
it reduced shelf life and appearance of vegetable and fruits.
CONCLUSION
The study on current practices of small-scale vegetable famers was conducted in three provinces of
Cambodia to identify the farming practices and challenges faced by small-scale vegetable farmers.
The results of study found that twelve types of vegetables (pak choi, leafy mustard, Chinese mustard,
cabbage, Chinese cabbage, green mustard, lettuce, spring onion, crown daisy / chrysanthemum green,
Chinese kale, gallic chives and culantro) were being grown in the provinces. However, only five
types of vegetable were prominently grown, including Chinese mustard, green mustard, pak choi, leafy
mustard, and Chinese kale. As production size is small, the majority of farmers used sickle/knife/scissors
to harvest their products, and manual harvest is still being practiced by some farmers. Post-harvest
activities such as grading, sorting and cleaning products after harvesting and before selling are rarely
implemented by farmers. In terms of product marketing, majority of products are sold to collectors; while,a small quantity of remaining products is sold to retailers, farmers’ group/cooperatives and direct
consumers in the areas. The study also identified that three main challenges faced by farmers were insect
pests and disease infestation, climate change (e.g. flooding, drought), and considerable fluctuation in
prices. Other issues e.g. high input price, low price of products and post-harvest losses, especially in peakseasons of oversupply, also hamper the vegetable production in the studied provinces. Introduction ofcontract farming model is the best option to tackle the current issues of farmers. First, vegetablesmallholders need to integrate into the agricultural cooperatives in the areas. These cooperatives collect
all farmers’ produce and then contract with retailers and other marketing actors in and outside Cambodia.
Second, producer’s group need to form in order to monitor the farming practices of members. Finally,vegetable hand-on trainings are required for producers to ensure that required quality and quantity ofvegetable are produced.
ACKNOWLEDGEMENTS
The authors wish to acknowledge the contribution made by Dr. Antonio L. Acedo Jr, and Dr. Borarin
Buntong as research co-supervisor, supporting team for data collection and Molly Webb, managing
director Feed the future Food Safety Innovation lab for thoroughly checked for both English language
and written technique. This research is made possible by the generous support of the American
People provided to the Center of Excellence on Sustainable Agricultural Intensification and Nutrition(CESAIN) of the Royal University of Agriculture through the Feed the Future Innovation Lab for
Collaborative Research on Sustainable Intensification at Kansas State University funded by theUnited States Agency for International Development (USAID) under Cooperative Agreement No.
AID-OAA-L-14-00006. The contents are the sole responsibility of the authors and do not necessarilyreflect the views of USAID or the United States Government.
REFERENCES
Chen, S., Huon, T. and Serey, M. 2018. The effect of agricultural cooperative on income’s smallholders in
Svay Chrum commune, Svay Rieng province, Cambodia. Asian Journal of Agricultural and Environmental
Safety, 6.
Ibeawuchi, I.I., Okoli, N.A., Alagba, R.A., Ofor, M.O., Emma-Okafor, L.C., Peter-Onoh, C.A. and Obiefuna,Ⓒ ISERD

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J.C. 2015. Fruit and vegetable crop production in Nigeria. The Gains, Challenges and the Way Forward,
198.
Ministry of Planning. 2014. National strategic development plan 2014-2018. For Growth, Employment, Equity
and Efficiency, Royal Government of Cambodia, Cambodia.
USAID. 2019. Cambodia agriculture competitiveness opportunity assessment. Final Report - 9 January, 2019,
USA.
USAID. 2020. Agriculture and food security. Retrived from https://www.usaid.gov/cambodia/agriculture-and-food-security
Wikipedia. Nuclear family. Retrived from https://en.m.wikipedia.org/wiki/Nuclear_family
Wikipedia. Hindu joint family. Retrived from https://en.wikipedia.org/wiki/Hindu_joint_family
Word Bank. 2015. Cambodian agriculture in transition: Opportunities and risks. Economic and Sector Work,
Report No. 96308-KH, p.xii.

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Research article
erd

Feeds and Feeding Practices for Dairy Cattle Farming in
Selected Areas of Myanmar
AYE AYE MYINT*
Yezin Agricultural University, Naypyitaw, Myanmar
Email: ayeayemyint2006@gmail.com

EI EI WIN MUANG
Yezin Agricultural University, Naypyitaw, Myanmar
Received 22 January 2020

Accepted 10 April 2020(*Corresponding Author)

Abstract This study was carried out to evaluate the feeds and feeding practices of dairy cattle
farming in Yangon, Mandalay and Sagaing regions. The secondary data and questionnaires
were assessed from 60 farmers of 60 households in the study area and made discussion withthem. According to the findings of this study, zero grazing was the main production practices
used by smallholder dairy farmers in the study area. Most of farmers used locally available
feed resources such as cut and carry grass, forage crops, crop residue after harvesting andlocal feedstuffs. Common feedstuff used by dairy farmers in the studied areas are broken rice,
rice bran, wheat bran, beer cake, bean cake, oil cakes, cotton seed cake, sorghum, yellow corn,soybean meal, mineral block, and sugar syrup. Feeding input among the farmers in the studied
areas is similar. However, the availability and quality of these resources are varied, but
farmers are properly selected and combined according to their nutritional characteristics,
adequate and productive diets had been provided all year round for sustainable production
and productivity. There are generally two feeding practices: compound cattle feeding andhome-mixed feeding in the studied areas. Among the respondents, 84.77% preferred to use
compounded cattle feeding and others 15.23% used home- mixed feeding. Most of the
farmers in the study area used natural mating methods than artificial insemination method(AI). Over 90% of the respondents had knowledge on the signs for oestrus detection. The
average 7.45liter and 6.48liter of milk per cow per day was recorded in wet and dry seasonfrom the study area. Seventy three percent of dairy farmers used concentrate to their animals
based on milk production.
Keywords dairy, feedstuff, forage, fodder, feeding, grazing

INTRODUCTIONIn Myanmar, dairy farmers face many feed constraints such as inadequate feeding, quality andquantity of feed, price of feed, poor storage facilities for feed conservation as well as insufficientwater supply. Feed and feeding practices among dairy farmers are different due to lack of informationon composition and utilization of locally available feed resources, poor access to the market and highcost of feed inputs and low income from production. Dairy development in Myanmar has played avital role in improving the nutritional standards, generating employment opportunities and increasing
the level of income of dairy farmers in rural areas. Nowadays scarcity of feed and rising feedstuff
prices affect the dairy farming especially for small scale farmers. Farmers used seventy percent of
total production cost for feed and feeding practices. There is a limited access to adequate grazing
pastures and fodder production during the dry season. Many researchers have conducted feeding
trials to solve the feed problems to improve the utilization of crop residues and optimal feedingpractices. There are several approaches to improve the nutritive value of crop residues for dairy cattle
feeding including improved handling and processing to increase palatability, feed intake anddigestibility to release nutrients to animals. But the challenge to researchers is to work with farmers
to identify improved practices that improve productivity and are feasible, profitable and acceptablefor farmers to adopt. Selection of proper feeding standards, using the right combination of feeds inⒸ ISERD

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adequate quantity along with other related practices are some of the ways which will enable the farmers
to increase the milk production and make dairying more profitable. Therefore, the present study was
undertaken to ascertain the prevailing feeding practices of dairy animals in the selected areas.
METHODOLOGY
The study was conducted in Rangoon, Mandalay and Sagaing Regions. Two locations were randomly
selected from each region and 10 dairy farmers were randomly selected from each village, making
the total sample of 60 dairy farmers. Questionnaire was prepared in light of the objective. The data
was collected through interview method and personal observations for only one year i.e. 2018-2019.
The structured questionnaire was used to collect information on individual household’s
characteristics (e.g. age, gender, education and family size, etc.). Other information collected
included food and cash crop production, livestock herd structure, livestock production practices,husbandry activities, reproductive performance, livestock feeds and feeding practices, sources offeeds, major feeding constraints and milk yield were also recorded. Secondary data such as
Demographic, climate and feeding constraints, were collected using reports from Project of non governmental organization. The collected secondary data provided additional information tounderstand the dairy cattle performance in the study area.
Data Analysis
Statistical Package for Social Science (SPSS, 2003) was used to analyze the data obtained frombaseline survey. Descriptive statistics analysis for percentages, means, ranges, cross tabulation were
employed to assess household characteristics, feed and feeding practices, breeding and milk yield.
RESULTS
General Observations
Zero grazing was the main production practices used by smallholder dairy farmers in the study area.
Under zero grazing, farmers were confined dairy cattle in their backyard. Feeds and drinking water
were regularly offered to them. Most of farmers hired labour to do dairy farming activities as they
had other income generating activities i.e, crop farming, civil service and other businesses. Dairy
farmers who did not own land for forage cultivation, brought forage bundles from different feedsources such as communal grazing lands, around the road side and river banks. Farmers, who own
land, cultivated the grass and sorghum to feed their animals. During the dry season when there wasa shortage of forages, farmers used rice straw, grass hay, silage feed and beer cake as supplement tofeed dairy cattle. These crop residues were mainly offered to milking cows purposely for increasing
milk yield. However, during the dry season, these feeds were not enough for normal production
performances, since most of these roughages had low nutritive values due to high lignification.
Demographic Characteristics
Table 1 shows the household characteristics of smallholder dairy farmers in studied areas. The mean
age of farmers was 54 years. The respondents comprised of both male and female headed-households.
The level of education of dairy farmers varied from one household to another, ranging from primaryschool, middle school to high school level. Majority of heads of households had middle education,
followed by high school level. Information from the formal survey also indicated that 68% of the
farmers had training in dairy cattle husbandry. In the current survey, farmers had owned an averageof 0.96 ha. The land owned under established fodder by farmers was 0.94 hectare.

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Table 1 Demographic characteristics of sample households
Parameter
Household age of farmer
Household head’s education
Household head’s farming experience
Household’s family size
Farmers received training in dairy cattle
Farmers who did not received training in dairy cattle
Total land owned
Land owned under established fodder

Unit
Year
Year
Year
NumberNumber
Percent
HectareHectareAverage
54.00
7.00
8.00
6.00
1.20
31.67
0.96
0.94

Max.
74.00
14.00
12.00
8.00
3.00
0.00
3.20
1.60

Min.
45.00
1.00
4.00
4.00
1.00
0.00
0.40
0.20

Sources of Feeds for Dairy Cattle
Table 2 Sources of feeds for dairy cattle as indicated by respondents in studied areas
Type of feeds
Roughages
Local pasture grass
Fodder grass
Rice straw
SilageConcentrates
De Heus/CP
Cotton seed cake
Bean cakes
Bean and rice powder
Sorghum
Maize bran
Beer cake
Rice bran
Oilseed cakes (cotton, sesame)
Mineral block
Sugar syrup

Own Farm %
10
20
60
25

Sources
Outside %
90
10
40
0

Do not Used %
0
70
0
75

0
20
15
20
15
30
0
10
0
0
0

100
60
85
80
0
70
85
90
100
80
80

0
20
0
0
85
0
15
0
0
20
20

Table 2 shows the main sources of feed materials for dairy cattle. The main source of local pasturesin the study area was outside lands. Other sources were farmers’ own farms. The main sources ofingredients for concentrates and minerals were from shops and milling machines.
Feeding Practices of Dairy Animals Followed by Dairy Farmers in the Studied Areas
Table 3 showed the feeding practices of dairy animals followed by dairy farmers in the studied areas.
All the smallholder dairy farmers used zero grazing. Ninety two percent of farmers practiced
individual feeding while only 7.97% used group feeding. Among the respondents, 84.77% preferred
to used compounded cattle feed and others 15.23% used homemade mixture. In the study area,
65.57% of the respondents fed the animals thrice or more daily followed by 34.43% who fed theiranimals two times in a day. More than half of the respondents (78.21%) used rice straw, 10.06%
green fodder and 11.73% used maize straw as dry fodder. The dairy farmers (89.91%) fed concentrate
feed separately while 10.09% respondents mixed with fodder. Seventy three percent of dairy farmers
used concentrate to their animals based on milk production while remaining 14.82 and 11.87 percentof the respondents fed it on flat rate and body weight. Dairy farmer (62.54%) fed concentrate to theiranimals after milking followed by 28.76% during milking and 8.70 % before milking. More than halfof respondents 57.35% and 56 % did not feed mineral mixture and salt to their animals respectively.
Breeding System of Dairy Cattle
Breeding system of dairy cattle is shown in Table 4. Most of the farmers in the study area used
Natural mating methods than Artificial Insemination method (AI). There were some limitation of AI
methods, i.e. storage of frozen semen, transportation, communication and availability of frozenⒸ ISERD

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semen. Over 90% of the respondents had knowledge on signs for oestrus detection i.e. changes incows’ behaviour (shouting, mounting and restlessness) followed by mucus discharges. However,there were no records kept by farmers relating to reproductive performances.
Table 3 Feeding practices of dairy animals followed by dairy farmers in studied areas
No.
1.
a.
b.
2.
a.
b.
3.
a.
b.
4.
a.
b.
c.
5.
a.
b.
6.
a.
b.
7.
a.
b.
c.
8.
a.
b.
c.
9.
a.
b.
10.
a.
b.

Feeding Practices
Feeding system
Zero grazing
Grazing
Feeding of animal
Individual feedingGroup feeding
Frequency of feeding
Twice
Thrice or more
Fodder availability
Green fodder
Rice straw
Maize straw
Type of concentrate
Homemade mixture
Compound cattle feed
Methods of feeding concentrate
Mixed with fodder
Separately
Feeding standard based on
Milk production
Body weight
Flat rate
Time of feeding concentrate
During milking
After milking
Before milking
Feeding mineral mixture
Yes
No
Feeding salt
Yes
No

Percentage
100
0
92.03
7.97
34.43
65.57
10.06
78.21
11.73
15.23
84.77
10.09
89.91
73.31
11.87
14.82
28.76
62.54
8.70
42.65
57.35
44.00
56.00

Table 4 Percentage of respondents indicating breeding of dairy cattle
Breeding MethodAI
Natural mating
Use of signs of oestrus
Change in feed intake
Change in cows behaviour
Change in milk production
Mucus discharge

Unit
%
%

Yes
45
65

No
55
35

%
%
%
%

40
90
25
80

60
10
75
20

Milk Yield (litres/day/cow) of Lactating Cows as Reported by Respondents
The results for milk production, milk prices and feed cost reported by respondents during baseline
survey were summarized in Table 5. Average milk production was highest in Monywa industrial
zone (10.79 Liter) followed by Mingaladon (8.45 Liter) and Patheingyi (6.29 Liter). The availabilityof feed and feeding system was affecting the milk yield. It is consistent with the previous studyⒸ ISERD

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reported by Gillah et al., 2012, which said feeding systems and calving season were affect milk yield.
Milk prices were varied within and among the regions between US $0.42 and US $ 1.00 with anaverage of US $ 0.66/ Liter. At and around Yangon region was about 1.0 US$/ L. Price for raw milkaround Mandalay was 0.56 US$/ L. Raw milk price at and around Sagaing regions was about 0.42
US $/ L. Sagaing and Mandalay farmers were always complaining on the low milk price given by
the collectors in their regions. Value added technology and contract farming were urgently neededin the studied areas. The higher demand of milk in urban population, consumption pattern,seasonality and calving season will affect the price of milk. Feed costs used by farmers were different
between US $ 0.23 and 0.57 US $ with an average cost of US $0.38/liter milk. The feed cost andavailability of feed resources depends on season in the studied areas. Feeds are generally not available
in sufficient quantities owning to overgrazing of lands and uncertain weather conditions. However,concentrate feeds, crop residues and conserved forage are used both in wet and dry seasons.
Sintayehu et al.,(2008) said that feed shortage problems in terms availability of quality, quantity andcosts of feeds were also noted as major constraints in development of dairy production.
Table 5 Milk yield (litres/day/cow) of lactating cows as reported by respondents
Location

Mean Milk Yield
Liter/cow/day

Milk Prices
Liter / US $Mingaladon (Yangon)

8.45

1. 00

Patheingyi (Mandalay)

6.29

0.56

Feed Price
US $/day/cow

Feed Cost
US $/liter milk

Gross Margin
US $/liter milk

0.57

0.43

2.69

0.35

0.21

2.71

0.23

0.19

2.99
4.00
3.00

0.38
0.57
0.23

0.28
0.43
0.19

3.57

Monywa Industrial Zone
10.79
0.42
(Sagaing)
Average
8.51
0.66
Max
11.00
1.00
Min
6.00
0.42
Source: Own Calculation based on farmers from studied areas

DISCUSSION
The present feeding management practice adopted by dairy farmers are feeding on crop residue,
additionally grain by-products are provided only to the lactating animal during milking stage.
However, the nutritional availability for productive animals is much lower than the requirement.
Extension of fodder cultivation can contribute significantly to dairy farmer to feed their animals.
Integrated fodder production and feeding management demonstrations from NGOs and INGOs can
assist the dairy farmers to improve the feeding practice. The farmers’ expenditure on feeding of the
productive animal is influenced by the price of milk and the cost of concentrate feed. Cost effective
feeding practice for productive animal can be done by reducing the used of concentrates andincreasing fodder production. Therefore, demonstration plot such as nutrient rich Azolla production
farm, use of byproducts from crop farming, field training, and motivation of farmers to take up fodder
farming (i.e. providing seed and planting materials) in their own lands can assist nutrient availability
for optimum productivity. Education level of most dairy farmers had primary and middle school
education. It is also very important to farmers to handle the farm management practices to attend thetraining and to use the information regarding dairy cattle farming (Luhosi, 1998). Educated farmers
had the ability to keep record on the reproduction and production, the performance of dairy cattle
and identify area of weakness for improvement (Gimbi, 2006). In the current study, it was revealed
that dairy farmers practiced crop farming apart from dairy farming. Rice cultivation can provide not
only food for dairy farmers but also income generation for the family as a supplementary income
obtained from dairy farming. Husbandry practices such as feeding, collection of feeds, cleaning,
milking and health management were performed by both family and hired labour. The main sourcesof forages given to dairy cattle were Para grass, Mombasa grass, Mulato, Millet, Rhodes and Seteria.
Natural growing leguminous species such as Leucaena spp., Stylosanthes spp., Arachis spp., andSorghum and Centrosema pubescens were available in the study area. Feeding grasses alone withoutⒸ ISERD

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combining with other forage species do not meet the nutrient requirements of dairy cattle formaintenance and production. Similar observation was reported by Mlay (2001) that dairy farmers in
Morogoro were feeding their animals largely on natural grass and that these feeds were unable tomeet nutritional requirements of the animals for both maintenance and production. Feeding one typeof forage to dairy cattle attributed to lack of knowledge on proper feeding of dairy cattle for achieving
higher performance. It can happen true since most of respondents interviewed had no training indairy cattle husbandry. Training of these farmers on how to feed their animals properly could bringgreater improvement in animal performance. Therefore, regular training of farmers on various
methods of improving quality of crop residues could be one of the strategies of supplementing
nutrients to cattle during dry season and hence reduce the problem of underfeeding. Mlay, (2001)
and Mtamakaya, (2002) reported that molasses and urea supplementation and treating of the straws
using wood ash and urea are some of the feeding strategies to improve utilization of crop residues.
CONCLUSION
Farmers should establish enough fodder during the rainy season when there is adequate moisture andconserve them in the form of silage ready for being utilized in the dry season. Farmers know theimportance of feeding fodder grasses to cows especially during dry season area but they were neither
conserving forages nor natural grasses in form of silage or hay due to lack of enough knowledge andskills in silage and hay making and conservation methods. Therefore it can be concluded that optimal
feeding of dairy cattle in zero grazing is not attained probably due to lack of knowledge on proper
feeding. Feeding mixture of local pasture grasses and legumes, proper mixing of concentrates, proper
utilization of crop residues, use of fodder grasses and feed conservation could be the good strategies
to be used by farmers to improve the existing feeding practices for better animal performance.
Research and extension activities are recommended to find a way to reduce the problem of shortageof fodder during dry season.
ACKNOWLEDGEMENTS
The first and foremost special tribute goes to Japan International Cooperation Agency- TechnicalCooperation Project (JICA-TCP) for providing funds for data collection and publication andpresentation of research findings. We would like to express my deep appreciation to all ourcolleagues and staff of the Department of Animal Science for their academic, material, social andmoral support. Furthermore, our deep appreciation is extended to ward extension workers and theinterviewed smallholder dairy farmers.
REFERENCES
Gillah, K.A., Kifaro G. C. and Madsen J. 2012. Urban and peri urban dairy farming in East Africa: A review
on production levels, constraints and opportunities.Livestock Research for Rural Development, Volume24(11), Retrieved from http://www.lrrd.org/lrrd24/11/gill24198.htm
Mlay, P.N.S. 2001. Enhancement of small holder dairy production under tropical conditions throughsupplementation to optimize roughage intake, digestibility and microbial protein synthesis. Copenhagen,
176.
NRC. 2001. Nutrient requirements of dairy cattle, Seventh Revised Edition. National Academic Press,
Washington DC, 270.
Schmidt, G.H. and Van Vleck, L.D. 1974. Principles of dairy science. W.H. Freemen and Company, San
Francisco, 546.
Sintayehu Y., Fekadu B., Azage T. and Berhanu G. 2008. Dairy production, processing and marketing systemsof Shashemene – Dilla area, South Ethiopia. IPMS (Improving Productivity and Market Success) of
Ethiopian Farmers Project Working Paper 9. ILRI (International Livestock Research Institute), Nairobi,
Kenya, 62.

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Research article
erd

Environmental Effects from Contamination of Agricultural
Soils via Spraying and Dust Application to Crops andAnimals
BARRY N NOLLER*
The University of Queensland, St Lucia, QLD 4072, Australia
Email: b.noller.@.uq.edu.au
Received 17 February 2020

Accepted 10 April 2020(*Corresponding Author)

Abstract Salinity from spraying may be important with crop cultivation and pasture forgrazing. Soil guidelines can indicate salinity build up in soil. Effects on cultivated crops andsoil cumulative contaminant loading limit trigger values are generally available for salinity,
heavy metals and pesticides (kg/ha) for long-term application of irrigation water to soil. In
addition, metal levels in meat from livestock and fowl species can be compared against foodstandard guidelines. The air quality guidelines of the World Health Organization (WHO),
European Commission (EC) and United States Environmental Protection Agency (USEPA)
are based on studies of health effects undertaken globally. However, little detail exists on
effects of air particulates on terrestrial animals. Animals breathe air and may ingest depositedparticles on soil surface or via ingestion of grass or other plants. Soil contamination guidelines
are well developed for the ecological health case but may require extensive assessment forsoil and the native/wild species that are not characterized. The German metal deposition
guidelines (TA Luft) are useful in this case. In contrast to native/wild species, domestic
animal effects from soil contamination and particularly pasture grazing by cattle, sheep, pigs
and fowl are well understood. Such guidelines provide a basis for developing site specificcriteria for spray deposition to farm land. Particle dispersion and deposition for prediction ofenvironmental effects from spraying using risk assessment can be developed. Guidelines for
air particulates and fall out may be relevant to assess if spraying is significant to humans or
animals but limitations of guidelines for contaminants can occur. Risk-based assessment forenvironmental management of spray dispersion and deposition of agricultural wastewater canbe undertaken as such contaminants may be significant if they are dispersed at sufficiently
high concentrations.
Keywords crops, animals, spraying, contaminants, risks, management

INTRODUCTION
Particulate matter (PM) is defined by diameter (µm) or size based on its physical property (NEPC,
2016). PM10 (diameter 10 µm) and PM2.5 (diameter 2.5 µm) can be mixtures of solid particles andliquid droplets in air (NEPC, 2016). Particle sizes <10 µm and 2.5 µm can penetrate deep into thehuman lung and transfer to the bloodstream. PM2.5 approximate the fine mode particles giving
alveolar deposition, while PM10 is the thoracic aerosol component (Raunemaa, 2002). Particles frommechanical means have larger-sized particles (> PM2.5) compared with particulate matter generated
during combustion (high temperature) processes having a higher percentage of fine particles < PM2.5
to ultrafine particles. Particles generated from spraying activities can also be fine to ultrafine.
Particles that fall out of suspension under gravity are collected using a dust deposition gauge anddescribed as ‘deposited matter’. People exposed to particulate contaminants in food, air and water
receive background exposure primarily from food and water. The potential pathways for exposure ofhumans to contaminants are dermal, inhalation and ingestion (eNHealth, 2012). Only <PM250(diameter 250 µm) fall out material can be ingested by humans or animals (Ng et al., 2015) as
deposited larger particles on ground do not readily adhere to hands. Particle size dependence and oral
exposure have specific associations (eNHealth, 2012).
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Fall out guidelines in Australian are limited to nuisance soiling of property (Noller, 2018). The
German air pollution control regulation and guidelines (TA LUFT, 1990 and Annex 2, 1999) provide
metal and arsenic deposition guidelines for assessing deposition or spraying (Table 1). Higher
deposition rates require additional evaluation using Annex 2 (TA LUFT 1999).
Table 1 Arsenic and metal deposition guidelines for fall out
Arsenic or metal
Arsenic
Cadmium
Lead
Protection of human healthProtection of crop land integrity
Protection of grassland integrity
NickelStandard(µg/m2.day)
4
2
100 (1990)
185 (1999)
1900 (1999)
15

Averaging period
1-year1-year1-year1-year

Source: TA LUFT (1990, Annex 2 1999).

The Australian National Environmental Protection Measure (NEPM) soil contamination
guidelines are well-developed, risk-based Health and Ecological criteria for respective receptors, andan alternative to fall out monitoring (Tables 2 and 3). Deposition to soil is assessed by measuring
specified constituents in soil to a surface depth (e.g. 1 cm) per unit area and comparing concentrations
against the NEPM soil contamination guidelines (NEPC, 2013). Soil cumulative contaminant loading
limit (CCL) trigger values for metals and arsenic (kg/ha) (NWQMS, 2000) are applicable for longterm application of irrigation water to soil (Table 4). Excessive salinity from spraying or dust
deposition may affect crop cultivation and pasture for grazing (Table 5). Comparison with soil
guidelines can indicate salinity build up in soil and if soil cumulative contaminant loading limit
trigger values for salinity and heavy metals (kg/ha) are occurring for long-term application ofirrigation water to soil.
Table 2 Soil Health Investigation Levels for various land use
Arsenic
or metal

Arsenic
Cadmium
CopperLead
NickelZinc

HIL Level A
Urban residential
with garden
100
20
6,000
300
400
7,400

Health Investigation Levels (HIL) for soil (mg/kg)
HIL Level B Urban
HIL Level C
HIL Level D
residential without garden Public open space
Commercial
and industrial
500
300
3,000150
90
900
30,00017,000240,000
1,200
600
1,500
1,200
1,200
6,000
60,00030,000400,000

Source: NEPC (2013) Australian soil contamination guidelines for health risk assessments.

Table 3 Soil Ecological Investigation Levels in soil for various land uses
Arsenic or metal
and age ofcontamination(fresh/aged)
Arsenic
CopperLead
NickelZinc

Ecological Investigation Levels (EIL) for soil (mg/kg) and various land uses
Area of ecological
Urban residential/
Commercial andsignificancepublic open spaceindustrial
20/40
15-60/20-80
110/470
1-25/5-95
7-130/15-280

50-10030-120/60-230
270/1100
10-170/30-560
25-500/70-1300

80/16045-200/85-340
440/1800
20-350/55-800
15.64

Source: NEPC (2013) Australian soil contamination guidelines for ecological risk assessments.

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Table 4 Soil cumulative contaminant loading limits (CCL) and preferred level in soil
Element

Arsenic
Cadmium
CopperLead
NickelZinc

Soil cumulative contaminantloading limit (CCL) (kg/ha)(NWQMS, 2000)
20
2
140
260
85
300

Preferred level in soil
(mg/kg)(NSW Soil, 2011)
<20
<1
2-50
<35
1-20
1-200

Source: Soil cumulative contaminant loading limit (CCL) (NWQMS, 2000) and preferred level in soil (NSW Soil, 2011)

Table 5 Soil guidelines associated with salinity
Test
Available sulfur (KCl 40
test)
Total sulfur
Exchangeable Calcium
Exchangeable Magnesium
Exchangeable Potassium
Exchangeable Sodium
Soil salinity(dS/m)

Agriculture Victoria(2017)>8 mg/kg>5 meq/100g
65-80% Total Cation>1.6 meq/100g
10-20% Total Cation>0.5 meq/100g<1% Total Cation<0.1 meq/100g<1% Total Cation<0.2% dS/m

Rural Solutions SA(2016)>10 mg/kg

NSW Soil(2011)

10-20 mg/kg>5 meq/100g
65-75% Total Cation>1.6 meq/100g
10-15% Total Cation
3-8% Total Cation<6% Total Cation
Many plants affected
4-8 dS/mTolerant plants affected8-16 dS/m>0.5 meq/100g>0.5% Total Cation>1.0 meq/100g<0-1% Total Cation
Plants tolerant<8.6 dS/m
Tolerant plants affected8-16 dS/m

Source: Agriculture Victoria (2017), Rural Solutions SA (2016) and NSW Soil (2011) Soil guidelines for sulfur, calcium,magnesium, potassium, sodium and salinity

Contributions to human diet are assessed by comparing contaminant levels with food guidelines(FSANZ, 2009). Metal and arsenic levels in meat from livestock and fowl species can be comparedagainst food standard guidelines. Wild (game) animals for consumption as food can also be comparedsimilarly. However, wild species that are part of the ecosystem generally have no guidelines for
estimating exposure to specific contaminants. Although particulate matter effect is not normallyassessed for domesticated terrestrial animals, wild animals also breathe air and may ingest depositedparticles on soil surface or via grazing of grass or other plants (Arslan and Aybek, 2012; Serita, 1999).
Wild animals may also be exposed when air particulates drift over natural open land or forest(Isaksson, 2010). In contrast, domestic animal effects from soil contamination and particularly
pasture grazing by cattle, sheep, pigs and fowl are well understood.
OBJECTIVE
The Objective is to use a risk-based approach to examine contamination of agricultural activities inselected studies from air particulates and fall out produced from spray-base activities with irrigationwaters or other dust-generating agricultural activities, to provide a means to assess the significanceof effects to humans via uptake in crops, domesticated animals, and potentially for wild animals.
METHODOLOGYⒸ ISERD

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Details from published papers, reports and other sources were summarized. Selected data for salinity,
arsenic and heavy metals was used as case studies from Australian locations. Monitoring data for air
particulates and fall out for ambient air quality in Australia (NEPC 2016) is described (Noller, 2018).
Deposition of metals and arsenic to soil is assessed by collecting fall out and by measuring specified
constituents in soil for comparison against guidelines (Table 1). Soil requires sufficientcharacterization of the site (Standards Australia, 2005); selection of appropriate guidelines ensuresthat meaningful and appropriate comparison is made (Tables 1-4). Salinity from spraying may also
be important with crop cultivation and well as pasture (Table 5) and give rise to a buildup in soil
affecting cultivated crops. The tolerances of pasture and other plant species to various forms ofsalinity can be low but salinity build-up is likely when existing soil levels are high (Table 5). Datafor various livestock body weight, peak water intake and peak food intake is summarised(ANZECC/ARMCANZ, 2000 p 9.3-16). Peak food intake assumes importance with cattle and sheepfrom grazing and involuntary ingestion because (Thornton and Abrahams, 1983): (i) cattle ingest up
to 20% as soil (2 kg); and (ii) sheep ingest up to 30% as soil (0.7 kg). Spraying deposition over open
farmlands used for grazing may be significant if contamination occurs.
RESULTS AND DISCUSSION
Data from case studies is examined to demonstrate how deposition to soil by fall out or via irrigation
can be assessed using guidelines given in Tables 1-5. Background concentrations of metals andarsenic in the soil from natural mineralisation of the Line of Load at Broken Hill, NSW, Australia(AARC, 2011) are given together with fall out data for the deposition of lead in Broken Hill city,
nearby the Rasp Mine (Rasp Mine, 2010). Comparison of soil concentrations in Table 6 with the Soil
Health Investigation Levels (Table 2) shows that HIL Level A (urban residential) is exceeded forarsenic, cadmium, lead and zinc, and HIL Level C (public open space) is exceeded for lead and zinc.
Comparison with the Soil Ecological Investigation Levels for various land uses (Table 3) shows that
all categories of land use are exceeded for arsenic and all metals. Comparison of lead in fall out at
Broken Hill for both high and low deposition exceed the guideline for protection of crop land but notgrassland (Table 1). Thus the extent of contamination is indicated.
Table 6 Background concentrations of metals and arsenic in the soil with naturalmineralisation of the Line of Load at Broken Hill, NSW, Australia anddeposition of lead
Element
Arsenic
Cadmium
CopperLead
Zinc

Concentration (mg/kg)
Mean ± sd (n) (AARC, 2011)290±51 (9)89±45 (6)585±254 (6)8317±10161 (65)

Fall out (µg/m2.day)(Rasp Mine, 2010)

High deposition 822
Low deposition 274

45,510±76943 (20)

Source: AARC (2011) background concentrations of metals and arsenic in Line of Lode at Broken Hilland data for fall out in Broken Hill (Rasp Mine, 2010)

Potential build-up of salinity from a mining operation within a cattle station in Northern
Australia is given in Tables 7 - 8 (Noller, 2017). Total and water soluble sulfate, salinity based on
EC are all very low (Table 7). Similarly water soluble calcium, magnesium, potassium and sodium
are also very low (Table 8). The location is in the wet-dry tropics receiving high rainfall during theannual monsoonal wet season in excess of 1500 mm. Comparison with guidelines (Table 5) shows
the soil is highly leached by rainfall and shows no buildup of salinity. In fact the soil is deficient insulfur for pasture growth for cattle feed and requires addition of supplement.

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Table 7 Total and water soluble sulfate and electrical conductivity (EC) of water extractof soil from cattle station
Test

Total Sulfur(mg/kg)Soluble Sulfur(mg/kg)Electrical conductivity (1:5)(dS/m)

Within Mine Lease 1
Surface (0-10 cm)
Depth (100 cm)

<100
<100

<10
<10

0.025
0.0085

Within Mine Lease 2
Surface (0-10 cm)
Depth (100 cm)

130
<100

<10
10

0.01640.0089Property Lot 1
Surface (0-10 cm)
Depth (100 cm)

300
<100

<10
<10

0.03530.007

Property Lot 2
Surface (0-10 cm)
Depth (100 cm)

100
<100

<10
<10

0.03240.0573

Source: ANZECC/ARMCANZ, 2000 (p 9.3-16 and explanation) Electrical conductivity measured in a 1:5
water extract

Table 8 Water soluble calcium, magnesium, potassium and sodium in soil from cattle station
Test

Calcium(meq/100g)

Magnesium(meq/100g)

Potassium(meq/100g)

Sodium(meq/100g)

Within Mine Lease 1
Surface (0-10 cm)
Depth (100 cm)

<0.05
<0.05

<0.082<0.0820.033<0.026<0.043<0.043

Within Mine Lease 2
Surface (0-10 cm)
Depth (100 cm)

<0.05
<0.05

<0.082<0.082<0.026<0.026<0.043<0.043

Property Lot 1
Surface (0-10 cm)
Depth (100 cm)

0.10
<0.05

<0.082<0.0820.052<0.026<0.043<0.043

Property Lot 2
Surface (0-10 cm)
Depth (100 cm)

0.08500.15<0.082<0.082<0.026<0.026<0.043<0.043

Source: ANZECC/ARMCANZ, 2000 (p 9.3-16 and explanation) meq/100g = milli-equivalents per 100 grams soil.
Electrical conductivity measured in a 1:5 water extract.

CONCLUSION
The use a risk-based approach to examine contamination of agricultural activities from air
particulates and fall out in two dust-generating studies is able to provide a means to assess thesignificance of effects to humans via uptake in crops and domesticated animals. For terrestrial species,
NEPM soil contamination guidelines for ecological effects are able to provide a comprehensive
assessment procedure to follow. In contrast to native/wild species, domestic animal effects from soil
contamination and particularly pasture grazing by cattle and sheep are well understood. Thus
application of such guidelines provides a basis for developing site specific criteria for fall out fromdust and spray deposition to farm land.

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ACKNOWLEDGEMENTS
Phil McKenna and Raijeli Taga, both of Centre For Mined Land Rehabilitation, the University of
Queensland, provided assistance.
REFERENCES
AARC. 2011. Literature review of soil heavy metal contamination at Broken Hill. 29 July 2011. AustralAsian
Resource Consultants Pty Ltd.
Agriculture Victoria. 2017. Understanding soil tests - pastures. State of Victoria. Retrived fromhttp://agriculture.vic.gov.au/agriculture/farm-management/soil-and-water/soils/understanding-soil-testspastures
ANZECC/ARMCANZ. 2000. Australia and New Zealand guidelines for fresh and marine water quality,
Australian and New Zealand Environment and Conservation Council and Agriculture and Resource
Management Council of Australia and New Zealand, October 2000.
Arslan, S. and Ali Aybek, A. 2012. Particulate matter exposure in agriculture. Chapter 3 in Air Pollution - A
Comprehensive Perspective, INTECH, Retrived from http://www.intechopen.com/books/air-pollution-acomprehensiveperspective, 73-104.
eNHealth. 2012. Environmental health risk assessment. Guidelines for Assessing Human Health Risks from
Environmental Hazards, Canberra.
FSANZ. 2009. Food standards Australia New Zealand. User Guides to the New Food Standards Code, Retrived
from http://www.foodstandards.gov.au/foodstandards/userguides/
Isaksson, C. 2010. Pollution and its impact on wild animals: A meta-analysis on oxidative stress. EcoHealth 7,
342-350.
NEPC. 2016. Ambient air quality standards. National Environment Protection Council, Australian Government.
NEPC. 2013. National environment protection (Assessment of site contamination), Measure Guideline on
Investigation Levels for Soil and Groundwater, Amendment of the NEPM 1999. National Environmental
Protection Council. Canberra, Australia.
Ng, J.C., Juhasz, A., Smith, E. and Naidu, R. 2015. Assessing bioavailability and bioaccessibility of metals
and metalloids. Environ Science Pollution Research, 22, 8802-8825.
Noller, B.N. 2017. Unpublished report. The University of Queensland.
Noller, B.N. 2018. Evaluation of dust generation from animal farm activities. ISERD, International Journal ofEnvironmental and Rural Development, 9 (1), 91- 96.
NSW soil. 2011. Results interpretation. Retrived from http://www.dpi.nsw.gov.au/about-us/services/laboratory-services/soil testing/interpret
NWQMS. 2000. National water quality management strategy. Australian and New Zealand Environment andConservation Council, Canberra.
Rasp Mine. 2010. Rasp Mine zinc–lead–silver project application No 07_0018. Broken Hill Operations Pty
Ltd, Broken Hill, NSW.
Raunemaa, T. 2002. Urban PM10 and PM2.5 concentrations and traffic related exposure to fine particles.
Retrived from http://www.ktl.fi/sytty/abstracts/raune2.htm
Rural Solution SA. 2016. Standard soil test methods and guidelines for interpretation of soil results. Natural
Resources South east, Agricultural Bureau of South Australia Inc., Government of South Australia.
Retrived from www.naturalresources.sa.gov.au/files/sharedassets/south_east/get
Serita, F. 1999. An animal exposure system using ultrasonic nebulizer that generates well controlled aerosols
from liquids. Industrial Health, 37 (1), 82-87.
Standards Australia. 2005. AS 4482.1 - 2005 Guide to the investigation and sampling of sites with potentiallycontaminated soil - Non-volatile and semi-volatile compounds.
TA LUFT. 1990, Annex 2 1999. Technical instructions on air quality control (Technische Anleitung zur
Reinhaltung der Luft) Bundesministerium für Umwelt, Naturschutz und Reaktorsicherheit (Federal
Environment Ministry), Bonn, D-53175, Germany and Annex 2, Retrived from http://www.
umweltbundesamt.de and in the brochure Texte 37/02 Luftreinhaltung 2010, Umweltbundesamt.
(Technical Instructions on Air Quality Control – TA Luft). Dated 24th July 1999 Annex 2.
Thornton, I. and Abrahams, P. 1983. Soil ingestion – A major pathway of heavy metals into livestock grazing
contaminated land. Sci. Total Environ., 28, 287-294.

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Research article
erd

Effects of Different Percentages of Sorghum Silage andNapier Grass on Nutrient Intake and Growth Performanceof Goats
EI EI WIN MUANG*
Department of Animal Science, Yezin Agricultural University, Nay Pyi Taw, Myanmar
Email: eieiwinmaung@gmail.com

KHIN SAN MU
Department of Animal Science, Yezin Agricultural University, Nay Pyi Taw, Myanmar
Received 15 January 2020

Accepted 10 April 2020(*Corresponding Author)

Abstract This study was to evaluate the growth performance of goats fed different
proportions of sorghum silage and Napier grass. A total of fifteen local male goats weredivided into five groups on the basis of Completely Randomized Design (CRD). The dietary
treatments were: T1 (100% Napier grass); T2 (75% Napier grass + 25% sorghum silage), T3(50% Napier grass + 50% sorghum silage); T4 (25% Napier grass + 75% sorghum silage);
and T5 (100% sorghum silage).The experiment ran for 98 days. The average daily nutrient
intake of dry matter, crude protein, organic matter, neutral detergent fibre, acid detergent
fibre and cumulative live weight gain of the goats fed on T1, T2, T3, T4 and T5 diets were notsignificantly different (p > 0.05) from each other throughout the experimental period. Basedon these results, regardless of the inclusion of Napier grass in the diet, 100% sorghum silage
could be favourable feed for local goat in Myanmar especially dry zone area where goats are
populated and sorghum is available in planting.
Keywords sorghum silage, Napier grass, feed intake, growth performance, goats

INTRODUCTION
Ruminant production is largely limited by the availability and the high cost of quality feed. Low
quality feeds are considered to be a major constraint in farm animal production. The availability of
feed significantly decreases in the dry season when natural pastures are mature and highly fibrousand has low nutritive value due to low crude protein content (Moyo et al., 2012). Silage, anaerobicallyfermented green fodder, is widely valued as a source of animal feed. The primary goal of silage
making is to maximize the preservation of original nutrients in the forage crop for later food forlivestock (Stewart, 2011). Thus, surplus and cultivated quality forage is conserved during the wet
season for use during the dry season. Kung and Shaver (2001) reported that corn silage can be used
not only as a main source of feed for cattle but also in combination with other forage types, including
pasture grass. Silage, which is anaerobically fermented green fodder, is valued throughout the world
as a source of animal feed. Thus, feeding of ruminants with conserved forage has become an
important feeding strategy since this can be made available throughout the year. Napier grass(Pennisetum purpureum) is recommended as a basal forage for intensive cattle production becauseof its high biomass fresh dry matter yield of 40 t/ha compared to other grasses (ILRI 2001).Tesfaye
et al. (2016) observed that 100% Pennisetum purpureum silage had better feeding value as comparedto the natural grass hay fed ad libitum in crossbred lactating dairy cows. In Myanmar, locallyavailable crop residues such as rice straw, sorghum stover, maize stover, groundnut straw and pigeon
pea residues, sesame residues, butter bean residues, chick pea residues are used as feed resources forruminant in central dry zone area (CDZ) (Min Aung et al., 2015; Soe Min Thein et al., 2016). Amongthese crop residues, sorghum stover is used as common based feed mixture for ruminant becausesorghum stover production is higher than other crop residues. The dry sorghum stover production
was 800-3200kg/acre depend on soil type and nutrient quality in CDZ area of Myanmar. Farmers inⒸ ISERD

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CDZ area used three types of dried chopped sorghum stover based diets (50%, 60% and 70% dry
chopped sorghum stover) (Soe Min Thein et al., 2016). Feeding value and animal performance ofsorghum stover based diets were lower than that of Mombasa grass based diets because of low crude
protein contents and high fiber contents (Soe Min Thein et al., 2019). Nowadays, the farmers in dry
zone of Myanmar grew sorghum, which were fed to ruminant animals as both fresh and dry feed
mixture (Soe Min Thein et al., 2014). There is still need to do research related to effective usage offresh forage sorghum and sorghum silage for ruminant animal. There is a little information
concerning with the effects on different percentage of sorghum silage on growth performance ingoats.
OBJECTIVESThis study was undertaken to determine feed intake and growth performance of goats fed different
proportions of sorghum silage to Napier grass.
METHODOLOGY
Preparation of Napier Grass and Sorghum Silage
The sorghum forage and Napier grass were planted at the same time in the research field at Yezin
Agricultural University. The sorghum was harvested at the stage of maturity around 60 days.
Harvested and wilted green sorghum was cut into approximately 2-4 cm pieces, using a chaff cutter
machine. The chopped green sorghum chaff was compacted in the pit silo. After filling the pit silowith sorghum chaff, it was tightly covered with plastic covers and weighted down with sand bags tokeep the pit silo watertight and airtight. The silage remained sealed and in the pit silo, except to takefood once daily for the goats. The Napier grass was harvested after 90 days. This was then cut into
pieces around 2-4 cm by the chaff cutter machine and air-dried at room temperature until constantweight was obtained.
Experimental Animals and Diets
The experiment was carried out from April 2018 to August 2018 at the goat farm of Animal Science
Department, Yezin Agricultural University. A total of fifteen (locally bred) male goats at a similar
age of around six months and with weights ranging from12 kg to18 kg, were purchased from
Myanmar C.P Livestock Production Co. Ltd. These animals were randomly assigned into fivetreatment groups, with three goats each group. Initially, Ivermectin was administered by subcutaneous
injection to prevent internal and external parasites. The five treatment groups were based on thevarious proportions of Napier grass (G) and whole sorghum silage (SS): T1 (100% G); T2 (75% G +25% SS), T3 (50% G + 50% SS); T4 (25% G + 75% SS); and T5 (100% SS). The length of the feeding
trial to determine the relationship between feed intake and growth performance was 84 days, with an
initial adaptation period which lasted 2 weeks, in which no data was collected. The goats were fed
with the treatment diets ad libitum, with feed replenished twice daily, at 08:00 am and 4:00 pm.
Water was accessed freely by the animals. Feeding and drinking buckets were cleaned every morning
to prevent contamination with detrimental microorganisms such as yeasts, moulds and the spread ofundesirable bacteria that could have formed on the silage. During the feeding trial, the body weight(BW) of each goat was measured weekly, before that morning’s feed was offered.
Chemical Analysis
The chemical analysis determined the value of dry matter (DM), organic matter (OM) and crude
protein (CP) by the AOAC (1990) method. The levels of neutral detergent fibre (NDF) and aciddetergent fibre (ADF) were determined as outlined in Goering and Van Soest (1970). The feedⒸ ISERD

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samples were analyzed at the Department of Physiology and Biochemistry, University of Veterinary
Science, Yezin, Nay Pyi Taw.
Statistical Analysis
The chemical composition of Napier grass and sorghum silage were analyzed by Students’ t test. The
data were subjected to the one-way analysis of variance (ANOVA) using SPSS software (version 16,
SPSS Inc, Chicago. USA). Mean values were compared by Duncan’s Multiple Range Test (DMRT).
If P<0.05, the differences were considered as significance.
RESULTSThe chemical compositions of feedstuffs and experimental diets used in this experiment are shownin Tables1 and 2. The dry matter content of Napier grass was significantly higher (p < 0.05) thanthose of sorghum silage. The organic matter and crude protein of sorghum silage was significantly
higher (p < 0.05) than those of Napier grass respectively. There was no statistically difference
between Napier grass and sorghum silage for ADF, NDF and Ash. The feed intake and cumulative
live weight gain of goats fed the experimental diets containing different proportions of Napier grassand sorghum silage at 84 days are shown in Tables 3 and 4. The nutrient intake of dry matter, crude
protein, organic matter, neutral detergent fibre and acid detergent fibre by goats fed T1, T2, T3, T4
and T5 diets were not significantly different (p > 0.05) within the five treatments at 84 days. The
cumulative live weight gain of goats fed the experimental diets were not significantly different (p >0.05) among the treatment groups during the experimental period.
Table 1 Chemical composition of feedstuffs (DM basis) except DM
Item
Napier grass
Sorghum silage

DM %1)
56.63*
23.39

OM (%)
88.67
91.70*CP (%)
3.49
7.56*

NDF (%)
69.48
67.73

ADF (%)
41.83
39.07

Ash (%)
11.33
8.30

1) Fresh basis
All values except DM are on DM basis. DM = dry matter, OM = organic matter, CP = crude protein, NDF = Neutral
detergent fibre, ADF = Acid detergent fibre, *Comparison within each column and p<0.05 tested by Students’ t test

Table 2 Chemical composition of experimental diets (DM basis)
Parameters
Dry matter %Organic matter %Crude protein %Neutral detergent fibre %Acid detergent fibre %
Ash %

T1
94.38
88.67
3.49
69.48
41.83
11.33

T2
76.63
89.43
4.51
69.04
41.14
10.57

T3
58.89
90.19
5.53
68.61
40.45
9.82

T4
41.14
90.94
6.54
68.17
39.76
9.06

T5
23.39
39.07
7.56
67.73
39.07
8.30

T1; Napier grass: Sorghum silage (100: 0), T2; Napier grass: Sorghum silage (75: 25), T3; Napier grass: Sorghum silage(50: 50), T4; Napier grass: Sorghum silage (75: 25), T5; Napier grass: Sorghum silage (0: 100)

Table 3 Effect of experimental diets on average daily nutrient intake at 84 days of goats
Nutrient Intakes
Dry matter intake (kg/day)
Crude protein intake (kg/day)
Organic matter intake (kg/day)
Neutral detergent fibre intake (kg/day)
Acid detergent fibre intake (kg/day)

T1
0.54
0.02
0.48
0.38
0.23

T2
0.72
0.03
0.64
0.49
0.30

All mean values are not significant different 5% level (p>0.05).

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T3
0.80
0.04
0.72
0.55
0.32

T4
0.68
0.04
0.61
0.46
0.27

T5
0.44
0.03
0.17
0.29
0.17

SEM
0.03
0.002
0.05
0.02
0.01

p (value)
1.00
0.38
0.20
0.08
1.00

IJERD – International Journal of Environmental and Rural Development (2020) 11-1

Table 4 Effect of experimental diets on cumulative live weight gain at 84 days of goats
Live Weight Changes
Initial body weight (kg)
Final body weight (kg)
Total live weight gain(kg)

T1
18.33
24.07
5.64

T2
18.33
23.97
5.74

T3
17.33
23.98
6.65

T4
18.33
24.11
6.72

T5
16.33
23.36
7.03

SEM
0.87
0.58
0.35

p (value)
0.57
0.75
0.31

All mean values are not significant different 5% level (p>0.05).

DISCUSSION
The dry matter content of the Napier grass used in this experiment was 56.63%, which is higher thanthat of 23.4 % (Khaing et al., 2015). The CP content of Napier grass was 3.49% which is a lot lower
than that of 7.3% (Rahman et al., 2013). A possible reason for these variations might be that inprevious, trials the Napier grass was harvested about 45-60 days after planting, when the grass was
about 1m high. In this experiment, the maturation stage of Napier grass was around 90 days and thewhole plant was cut. The harvesting time is, therefore, an important factor for the nutritional
management of forage such as grasses and leguminous trees. The amount of OM in the Napier grass
was 88.67% which was in the same range with the values of 89.8% (Rahman et al., 2013) .The Ashcontent of Napier grass was 11.3% which is higher than 10.2 % (Rahman et al., 2013). The NDF and
ADF contents of Napier grass were 69.48% and 41.83% respectively. In this case, for these detergent
fibres, the values were lower than those of 74.8 % and 42.6 % (Khaing et al., 2015). In respect tothe sorghum silage, the dry matter (DM) content in this study was 23.39%, which is similar to Khaing
et al. (2015) at 23.30%, while lower than that of 56.3% in the (Muhamad et al., 2018) trial. The crude
protein (CP) value of sorghum silage was 7.56% which is lower than those of 12.6% in Muhamad etal. (2018) and 8.1% (Khaing et al., 2015). The contents of NDF and ADF in sorghum silage were
67.73% and 39.07%, which is higher than those of Khaing et al. (2015) at 58.60% and 35.10%, but
lower than those of Muhamad et al. (2018) at 81.4% and 42.5% respectively. The content of organic
matter in sorghum silage was 91.70% which is higher than the 88.5% obtained by Muhamad et al.
(2018) but lower than that of 96.10% reported by Khaing et al. (2015). In the current study, Ashcontent in sorghum silage was 8.30% which is lower than that of value 11.5% (Muhamad et al.,2018).Von Keyserlink et al. (1996) reported that chemical composition and mineral content offeedstuffs are affected by many factors such as growth, maturity, species or variety, drying method,
growth environment and soil type. Similar factors would explain the variation in this experiment.
The Napier grass contained higher NDF and ADF content than the whole sorghum silage althoughother chemical parameters were relatively similar. The inclusion of increased proportions of sorghum
silage relative to Napier grass has thus contributed to a reduction in NDF and ADF contents in theexperimental diets. Intake of dry matter, crude protein, organic matter, neutral detergent fibre andacid detergent fibre by goats fed T1, T2, T3, T4 and T5 diets were not significantly different (p > 0.05)
across the five treatments at 84 days. The goats fed the T2, T3, T4 and T5 diets showed high values forthe intake of dry matter, crude protein, organic matter, neutral detergent fibre and acid detergent fibre,
than those of the goats fed the T1 diet. The increase in the DM content of the feed might be due tothe chemical composition of the whole sorghum silage. This has a lower cell wall content resulting
in increased the dry matter composition of the sorghum silage compared to Napier grass. Okoruwa
et al. (2012) reported that the fermentation process by rumen microorganisms of high fibre diets takes
longer compared to low fibre diets. Similar results were obtained by (Khaing et al., 2015). They
reported increased growth performance of goats fed diets with a higher proportion of corn silage to
Napier grass, when the various proportion of Napier grass (G) and whole corn plant silage (CS) wereas follows: 100:0 G: CS, T2/75:25 G: CS, 50:50 G: CS, 25:75 G: CS and 0:100 G: CS and this was
fed to male Boer cross goats. This is because sorghum silage contains high amounts of fermentablecarbohydrate that increase nutrient intake by animals. The diets with a greater proportion of sorghum
silage have a lower content of indigestible fibre portion due to the fermentation process involved in
the silage production, compared to a diet solely of Napier grass. In contrast, Browne et al. (2004)
stated that voluntary intake and growth performance were negatively affected by the substitution ofhigh amounts of corn silage to the basal diet of the grass silage, due to the accumulation of high levelⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2020) 11-1of fermentation acid which depressed the activity of cellulolytic bacteria in the rumen. However, inthe current experiment, the basal diet was not grass silage but Napier grass. Widiawati and Thalib(2009) and Kariuki et al. (2001) stated that cell wall content of Napier grass degraded slowly in therumen and was more resistant to rumen microbial fermentation. Inclusion of corn silage to the grasssilage had a positive effect on feed intake as observed by (Browne, 2000). In addition, highconcentration of lactic acid in silage may be metabolized into propionic acid by rumen microorganisms(Abedol et al., 2013). The soluble carbohydrate can be used by the animal as an energy source formaintenance and production activities (Hariadi and Santoso, 2010). The goats fed the T2, T3, T4 andT5 diets showed higher numerical values for total weight gained than the goats fed T1 diet. The results
also mirror those of Khaing et al. (2015). They reported increased growth performance of goats fed
diets with a higher proportion of corn silage to Napier grass when the various proportion of Napiergrass (G) and whole corn plant silage (CS): 100:0 G:CS, T2/75:25 G:CS, 50:50 G:CS, 25:75 G:CS
and 0:100 G:CS fed to male Boer cross goats. Generally, the diets containing a higher percentage ofsorghum silage and which had higher CP content result in higher weight gain as the animal
performance is the product of nutrient concentration, intake and digestibility of absorbed nutrients.
CONCLUSION
According to this finding, 100% sorghum silage could be fed to goats without detrimental effect on
the growth performance. Since Myanmar farmers could not access Napier grass easily, making
sorghum silage for small ruminant will be advantages for small and large scaled farmers. Family
labour would be used to keep costs for feeding. Sorghum silage could be fed to form a balanced
ration. Therefore, it can be concluded that sorghum silage is a high potential feed comparable with
Napier grass and with positive growth response when fed to small ruminants.
ACKNOWLEDGEMENTS
The present research was financially supported by “Yezin Agricultural University – Japan TechnicalCooperation Project (YAU- JICA TCP)”.
REFERENCES
Browne, E.M. 2000. Maize silage- based diets for finishing beef cattle. Ph.D. Thesis. The University of Reading,UK.
Browne, E.M., Juniper, D.T., Bryant, M.J., Beever, D.E. and Fisher, A.V. 2004. Intake, live weight gain andcarcass characteristics of beef cattle given diets based on forage maize silage harvested at different stagesof maturity. J. Anim. Sci., 79, 405-413.
Goering, H.K. and Van Soest, P.J. 1970. Forage fibre analysis. Agricultural Hand Book, No. 379. USDA,
Washinton DC, 8-12.
Hafez, Y.H., Aedo, A.A. and Khalifa, E.I. 2012. Effect of microbial inoculation of whole plant corn silage on
growth performance and carcass characteristics of Rahmani lambs. Egypt. J. Sheep and Goat Sci, 7 (2), 1729.
Hariadi, B. and Santoso, B. 2010. Evaluation of tropical plants containing tannin on in vitro methanogenesis
and fermentation parameters using rumen fluid. J. Sci. Food Agri., 90, 456-461.
International Livestock Research Institute. 2001. ILRI 2000-2001: Deciphering the code of life to benefit thepoor. ILRI, Nairobi, Kenya, 22-25.
Kariuki, J.N., Tamminga, S., Byachuiri, C.K., Gitau, G.K. and Muia, J.M.K. 2001. Intake and rumen
degradation in cattle fed Napier grass (Pennisetum purpureum) supplemented with various levels of
Desmodium intortum and Ipomoea batatas vines. Afr. J. Anim. Sci., 31 (3), 149 -157.
Khaing, K.T., Loh, T.C., Ghizan, S., Halim, R.A. and Samsudin, A.A. 2015. Feed intake, growth performanceand digestibility in goats fed whole corn plant silage and Napier grass. Mal. J. Anim. Sci., 18 (1), 87-98.
Min Aung, M., Kyawt, Y.Y., Khaing, M., San Mu, K., Htun, M.T., Oo, L.N. and Aung, A. 2015. Nutritional
evaluation of conventional feedstuffs for ruminants using in vitro gas production technique. G.J.A.S.R., 3(2), 518-523.
Moyo, B., Patrick, J.M. and Muchenje, V. 2012. Effect of supplementing crossbred goat castrates with MoringaⒸ ISERD

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oleifera leaves on growth performance, carcass and non-carcasss characteristics. Trop. Anim. Health Prod.,44, 801-809.
Okoruwa, M.I., Igene, F.U. and Isika, M.A. 2012. Replacement value of cassava peels with rice husk for guinea
grass in the diet of West African Dwarf (WAD) sheep. J. Agri. Sci., 4 (7), 254-261.
Rahman, M.M., Abdullah, R.B., Wan Khadijiah, W.E., Nakagawa, T. and Akashi, R. 2013. Feed intake,digestibility and growth performance of goats offered Napier grass supplemented with molasses protectedpalm kernel cake and soya waste. Asia. J. Anim. and Vet. Adv., 8 (3), 527-534.
Soe Min Thein, Aung Aung, Kyaw Naing Oo, Nang Kham Hline, Win Myint Thein, Lwin Naing Oo, Zin Min
Latt, Tu Tu Zaw Win, Jenny Hanks and Werner Stur. 2014. Assessement of feed availability for cattle,
sheep and goats in two villages in the central dry zone of Myanmar. Proceedings of 16 th AAAP Animal
Science Congress, Gadjah Mada University, Yogyakarta, Indonesia, 10th -14th November, 915-918.
Soe Min Thein, Lwin Naing Oo, Khin San Mu, Moe Thida Htun and Aung Aung. 2019. Comparison on
performance between cattles fed on locally available forages and introduced forage in central dry zone areaof Myanmar. Proceedings of Myanmar Universities’ Research Conference, University of Yangon, 24 th 25th May, 124-137.
Soe Min Thein, Lwin Naing Oo, Moe Thida Htun, Khin San Mu, Dennis Poppi and Aung Aung. 2016.
Assessment of locally available feeds and feeding practices of ruminants in two villages in dry zone.
Proceedings of the Annual Conference of Myanmar Veterinary Association, Yangon, 27th -28th December,
51-59.
SPSS. 2007. Statistical product and service solutions. SPSS for version 16.0. Chicago, Inc., Chicago. USA.
Stewart, W.M. 2011. Plant nutrition today. From Scientific Staff of the International Plant Nutrition Institute(IPNI), Norcross, Georgia.
Tesfaye, M., Mengistu, U. and Getachew, A. 2016. Effects of different proportions of Pennisetum Purpureum
silage and natural grass hay on feed utilization, milk yield and composition of crossbred dairy cows
supplemented with concentrate diet. J. Bio. Agri. and Healthcare, 6 (11), 2224-3208.
Von Keyserlingk, M.A.G., Swift, M.L., Pchala, R. and Shelford, J.A. 1996. Degradability characteristics ofdry matter and crude protein of forages in ruminants. Anim. Feed Sci. Technol., 57, 291-311.
Widiawati,Y. and Thalib, A. 2009. Comparison of fermentation kinetics (in vitro) of grass and shrub legume
leaves: The pattern of VFA concentration, estimated CH4 and microbial biomass production. Indo. J. Agri.,2 (1), 21-27.

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Research article
erd

Bending Strength and Toughness Properties of Pavement
Concrete Reinforced with Bamboo Fiber
EIJI MATSUO*
Faculty of Architecture and Civil Engineering, Kyushu Sangyo University, Fukuoka, Japan
Email: ematsuo@ip.kyusan-u.ac.jp

HIROTO SHIMIZU
Yamau Co., Ltd., Fukuoka, Japan

UMI IWANAGA
Sasebo City Office, Nagasaki, Japan

TAKENORI NAGAMATSU
Faculty of Architecture and Civil Engineering, Kyushu Sangyo University, Fukuoka, Japan

MASAKI IIDA
Faculty of Architecture and Civil Engineering, Kyushu Sangyo University, Fukuoka, Japan
Received 16 January 2020

Accepted 10 April 2020(*Corresponding Author)

Abstract The problems of neglected bamboo forest have been actualized in association withthe aging of the people owning bamboo forest in recent years in Japan. Then it becomes
imperative to establish the methods of effective utilization of the bamboo. Some bamboo hasenough tensile strength as construction material, and studies and experiments on bamboo
have been increasing gradually. The purposes of authors are to make short fiber of bamboo(“BF” for short) from Moso- bamboo, one of most popular bamboo in Japan, by ourselves,
and to develop the concrete for pavement which matrix are reinforced with BF. A crosslinked effect between concrete surface on crack by BF had been expected as same as otherkinds of short fibers. The purpose of this paper is to confirm the reinforcement effect by BF,
and cement mortar was the object in order to simplify the experiments, in which the basic
properties were investigated. The experiment factors are the amount of BF, water cement
ratio, fiber length and distribution method of BF. These influence on compressive strength,
bending strength and bending toughness was investigated experimentally. As the results, thefollowings were obtained. The strength does not increase only by a cross-linked effect by BF.
The coefficient of toughness can be improved greatly by BF, and this effect is shown even inlittle quantity of fiber. Influence of water cement ratio on the toughness improvement is small.
In case of random mixing of BF, each strength become lower without depending on fiber
length, toughness coefficient can be improved. In case of spreading of BF, the strength andtoughness coefficient can be improved even in little quantity of fiber.
Keywords bamboo fiber, short fiber, bending toughness, strength

INTRODUCTION
In recent years, the problems of neglected bamboo forest have been actualized in association withthe aging of the people owning bamboo forest in Japan. Then it becomes imperative to establish themethods of effective utilization of the bamboo. Actually, bamboo has enough tensile strength asshown in Table 1 and studies and actions about the bamboo has been increasing gradually. Authors
has made short bamboo fiber (see Photo 1, BF for shot) by ourselves from Moso-bamboo, and it is
the final purpose to develop the concrete for pavement which matrix is reinforced by this BF. The
purpose of this study is to confirm the reinforcement effect by BF. Mortar was intended to simplify
the experiments, and its basic properties were investigated. The factors of experiment were fiberⒸ ISERD

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amount, water cement ratio, fiber length and casting method of fiber, and the influence of them tocompressive strength, bending strength and bending toughness was acquired experimentally.
Table 1 Tensile strength of Moso-bamboo
No.
1
2
3
4
5
6
7
8
9
10

width thickness cross section fracture load tensile strength
(mm)
(mm)
(N)
(mm2)
(N/mm2)
10.2
13.5
11.1
12.7
11.9
9.2
9.7
9.5
9.6
11.3

7.80
8.00
8.40
7.80
8.13
8.33
8.63
8.43
7.57
8.13
average

79.6
108.0
93.2
99.1
96.7
76.4
83.5
79.8
72.9
91.6

11400
12700
10000
13850
11950
9600
8150
10250
8100
11350
10735

143.3
117.6
107.3
139.8
123.5
125.7
97.7
128.4
111.1
123.9
121.8

Photo 1 Handmade bamboo fiber

METHODOLOGYa) Making of Bamboo Fiber and Materiasls
As a result of trial and error, the bamboo was molded into a fiber form by the next process. (1)split
by circular wedge (2)unfastened by compressor for concrete (3)struck by mallet and unfasted again(4)cut to predetermined length. The moisture state of bamboo was determined as that it had been
dried in laboratory over 1 year after the felling (air-dried state).
The materials were high early strength Portland cement, JIS standard sand, BF and tap water.
Size of specimen was 40×40×160mm and they were taken out of molds on the next day of the casting.
After curing in water, the tests were carried out at 7 days of material age.
b) Series I
The purpose in series I was to find the appropriate mixing ratio of BF which length was 20mm (aspectratio was 13.3). In previous experiment, W/C was 50%, mixing ratio of BF (outer percentage basedon the mass of mortar) was 0, 1, 3 and 5%. As the results, the workability decreased remarkably at
5%, so it was concluded that 3% was upper limit. Then, W/C was varied with 50, 55 and 60%, and
BF mixing ratio was varied with 0, 1, 2, and 3%. These mix proportions are shown in Table 2 as the
mixing weight. The compressive strength and bending strength of BF mortar was obtained inaccordance with “Cement Strength Test” to confirm the strength improvement effect by BF.
Furthermore, the bending toughness was calculated based on the JSCE concrete standard
specifications (See Figure1), expecting the toughness improvement effect by BF.
c) Series II and III
In series II, the BF length was changed to 30 and 40mm to increase adhesion. W/C was 55%, and BF
mixing ratio was 0, 1, 2, and 3%. Table 3 shows the mix proportion of series II.
A method to spread BF in tensile domain was adopted in series III. Because when BF is mixed
simply, the placement of BF becomes random in three-dimensionally and the two-thirds waste should
occur in a direction of stress. In actual construction, it will be effective to induce a direction of BF
by a container which has slits (see Photo 2). Firstly, specimens were made in the cover of 0mm,
mixing ratio of 0.5%, fiber length of 30, 60 and 90mm and the strength was evaluated by bending
strength. As the result, fiber length of 60mm, which had increased the bending strength most, wasⒸ ISERD

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adopted, and the specimens with the cover of 5 and 10mm were made in this fiber length. Table 4shows the mix proportion of series III.
Table 2 Mix proportion of series I
mixing weight (g)
JIS
W
C
BF
Sand
225

450 1350247.5

450 1350270

450 1350

Load (N)fiber
W/C BF
length（%） (%)
(mm)
0
1
50
2
3
0
1
55
20
2
3
0
1
60
2
3

0
7.7
15.5
23.2
0
7.9
15.9
23.8
0
8.1
16.2
24.4

Tb

δcd
Deflection (mm)Fig. 1 Calculation of Tb

Table 3 Mix proportion of series II

Table 4 Mix proportion of series III

mixing weight (g)fiber
W/C BF
lengthJIS(%) (%)
W
C
BF
(mm)
Sand
30
55 0.5 60 247.5 450 1350 4.0
90Photo 2 Casting of BF in series III
3000
BF=0%

2500
Bending load (N)mixing weight (g)fiber
W/C BF
lengthJIS（%） (%)
W
C
BF
(mm)
Sand
0
0
－
1
7.9
55
247.5 450 1350
30
2
15.9
3
23.8
1
7.9
55
40 247.5 450 1350 15.9
2
3
23.8

BF=1%
2000

BF=2%

1500

BF=3%

1000

500
0
0.0

1.0

2.0

3.0

Deflection (mm)Fig.2 Load and deflection curve of series I(W/C=55%)

Calculation of Toughness
Figure 2 shows the load-deflection curve in series I (W/C=55%). Bending toughness could be
evaluated by toughness coefficient, and when this is large, the toughness at destruction could be
improved, which leads the restraint of disasters and accidents.
The spans of beam specimens were 45cm and loads were applied at the 2 points of 3 equal parts.
In order to obtain the details of data for toughness coefficient, the loads were applied slowly until thedeflection became 3mm by the confirmation of data logger. After the measurement, load-deflection
curve was described in the procedure of JIS. The toughness coefficients were calculated by formula(1) using the area from 0 to 1mm of the deflection by the curves.
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=

Tb

 cd



L
bd 2

・・・・・・・・・・・（1）Here,σ: bending toughness (N/mm2)
Tb : Area to δcd（N･mm）δcd : deflection (=span/150; mm)
L: span (=120mm)
b: width of section (=40mm)
d: height of section (=40mm)
RESULTS AND DISCUSSIONa) Series 1
Figure 3 and 4 show the relationships between BF mixing ratio and compressive strength or bending
strength. Both strength decreased in comparison with non-mixing of BF. According to theobservation of the fractured section of mortar, every BF slipped out without being cut, which meansthat the adhesion of BF and mortar was not enough and the primary tensile strength of BF was notshown.
Figure 5 shows the relationship between BF mixing ratio and toughness. Improve of toughness
coefficient by BF can be confirmed. It is effective even in low BF mixing ratio, and the influence of
W/C on the effect is small. It is estimated that the cross-linked effect by BF increased the toughness
coefficient according to the above-mentioned observation results.

45

Bending strength (N/mm2)

W/C=50%
W/C=55%
W/C=60%

40

35
30
25

0%

1%
2%
BF mixing ratio (%)

1%
2%
BF mixing ratio (%)

Toughness coefficient (N/mm2)

Fig. 5 Relationship between BF mixing ratio
and toughness coefficient in series I

1%
2%
BF mixing ratio (%)

3%

Fig. 4 Relationship between BF mixing ratio
and bending strength in series I

W/C=50%
W/C=55%
W/C=60%0%

W/C50%
W/C55%
W/C60%

0%3%

Fig. 3 Relationship between BF mixing ratio
and compressive strength in series I
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0

8.5
8.0
7.5
7.0
6.5
6.0
5.5
5.0
4.5

Compressive stgrength (N/mm2)

Compressive strength（N/mm2）

50

3%

50
40
30
20

BF30mm
BF40mm

10

0
0%

1%
2%
BF mixing ratio (%)

3%

Fig. 6 Relationship between BF mixing ratio
and compressive strength in series IIⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2020) 11-1b) Series II and III

8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0

8
Bending strength (N/mm2)

Bending strength (N/mm2)

Figures 6 and 7 show the relationships between BF mixing ratio and compressive strength or bending
strength. Figure 8 shows the relationship between cover and bending strength in series III. Bending
strength and compressive strength decreased by mixing BF. However, in the case that BF was spreadwith little cover and the fiber length was 60mm, the effect of BF was shown most, and the bending
strength was stronger than non-mixing, nevertheless the mixing amount was only 0.5%. When BF ismixed at random, it is estimated that the effect as the fiber cannot be shown, even if fiber length andmixing amount are changed, because the adhesion between fiber and mortar are not enough.
Figure 9 shows the relationship between the mixing ratio and toughness coefficient with various
fiber length. The toughness coefficient improved with the increase of mixing ratio generally in seriesII, however, the effect by the change of fiber length could not be confirmed. Figure 10 shows therelationship between cover and toughness coefficient in series III. The toughness coefficient of themix proportion with the fiber length of 60mm and the cover of 5mm improved most even the mixing
amount was only 0.5%.

BF30mm
BF40mm0%

1%
2%
BF mixing ratio (%)

3%

7
6

Bending strength offiberless (6.7N/mm2)

5
4
3

30mm
60mm
90mm

2
1
0
0

4
6
cover (mm)810Fig. 8 Relationship between cover andbending strength in series III
2.0

2.0

Toughness coefficient (N/mm2)

Toughness coefficient (N/mm2)

Fig. 7 Relationship between BF mixing ratio
and bending strength in series II

2

1.6
1.2
0.8
BF40mm
BF30mm

0.4
0.0
0%

1%
2%
BF mixing ratio (%)

3%

1.5

Toughness of fiberless(0.62N/mm2)

1.0
0.5
0.0
0

Fig. 9 Relationship between BF mixing ratio
and toughness coefficient in series II

5
Cover (mm)10Fig. 10 Relationship between cover andtoughness coefficient in series III

CONCLUSION
The summary of this study on the concrete for pavement which short bamboo fiber was mixed withis as follows:(1) The strength does not improve only by a cross-linked effect.
(2) Toughness coefficient greatly improves, and the effect is shown even if the amount of fiber islittle.
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(3) The influence of W/C on the effect of toughness improvement is small.
(4) When bamboo fiber is mixed at random, each strength decreases, and toughness coefficientincreases regardless of fiber length.
(5) In case of spreading construction of bamboo fiber, both strength and toughness coefficient
improves even by little amount of fiber.
(6) As a contrivance to align the same direction of bamboo fiber easily in large scale on actual road,the sieve with slit in one direction could be effective, practicable and economical.
REFERENCES
Suzuki, M., Hasegawa, H., Mutsunobu, K. and Yamamoto, T. 2006. Spreading and ecology of bamboo groves
distributed in Yamaguchi prefecture. Journal of JSCE-G, 62 (4), 445-451.
Matsuo, E. and Takami, K. 2009. Applicability of bamboo as reinforcement for concrete. Journal of JSCE-F,
65 (2), 190-195.
Terai, M. and Minami, K. 2012. Experimental study on mechanical properties of bamboo fiber reinforced
concrete. Proceeding of the Japan Concrete Institute, 34 (2), 1279-1284.
Kimura, S., Saito T. and Demura, K. 2012. Flexural behavior of bamboo-reinforced cement mortars using
emulsion treated reinforcement. Proceeding of the Japan Concrete Institute, 34 (1), 1456-1461.
Yagi, S., Saito, T. and Demura, K. 2014. Effect of emulsion treatment for bamboo reinforcement on freezing
and thawing resistance of bamboo-reinforced cement mortars. Cement Science and Concrete Technology,
Vol. 67, Japan Cement Association, 501-506.
Japan Society of Civil Engineers. 2017. Standard specifications for concrete structures, Materials andConstruction, 265-275.
Matsuo, E., Suenaga S., Terasaki A. and Nagamatsu, T. 2019. Treating method and bond properties of thebamboo in BRC beam. Proceeding of the Japan Concrete Institute, 41 (1), 1523-1528.

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Research article
erdRichness of Horticultural Crops with Emphasis on Mango
Species in Selected Home Gardens in Myanmar
NYEIN NYEIN AYE*
Yezin Agricultural University, Nay Pyi Taw, Myanmar
Email: nyeinnyeinyau52@gmail.com

THANDA AUNG
Yezin Agricultural University, Nay Pyi Taw, Myanmar

KHIN THIDA MYINT
Yezin Agricultural University, Nay Pyi Taw, Myanmar
Received 28 January 2020

Accepted 10 April 2020(*Corresponding Author)

Abstract This study aims to explore the relationship between home gardens’ sizes andspecies richness of horticultural crops and to study the distribution of mango varieties in theselected area. Sixty home gardens were studied from September, 2018 to October, 2019 in
Nay Pyi Taw Union Territory, Myanmar. Drone was used to generate geographicalinformation. Coordinate points of each mango variety was collected. Information oncompound dimensions and crop plants namely local names, growth stages, numbers ofindividual, leaf attitudes and characters were collected. Shannon-Wiener’ index was
employed to determine species richness. Distribution maps of mango varieties weredeveloped. The compound areas could be classified into three classes as small, medium andlarge; were not correlated with species richness of all horticultural crops, but there hadsignificant correlation with richness of mango varieties (p = 0.01, r = 0.32). Potential areafor home garden development was highest in large gardens (41%) followed by medium (16%)
and small (9%). Out of total 480 species, richness was highest in ornamentals (46%) followedby vegetables (10%), fruits (8%), shade-trees (8%), medicinal-plants (6%), spices (3%) andunidentified species (20%). Among fruit trees, mango was the most dominant species and
90% of home gardens were growing a total 361 plants (young 37% and adult 63%) of 15 local
varieties namely Ma Chit Su (39%), Sein Ta Lone (24%), Sein Sar Thee (20%), Yin Kwe(9%), Ma Naw Nwe (3%), Waso, Padamyar Nga Mauk, Mya Kyauk and 4 unknown varieties(1% each) and Wet Ta Kaut, Pan Swae and Pyo Ta Ngone (0.3% respectively). Three typesof leaf attitude and 8 different leaf characters of mango varieties were identified. Diversity
index of total species was 1.57 and 1.40 for mango varieties. It was concluded that species
distribution was sufficient; however, there is remaining available space to extend homegardening in the study area.
Keywords drone, species diversity, potential area

INTRODUCTION
Home gardens (HG) are sites of plant species diversity and also serve to conserve different species
and varieties. Richness of horticultural crops species can be attained not only for self-sufficiency infood production but also nutritional status by diversification to those crops. Saving and exchanging
seeds/planting materials from home gardens with neighbours are able to conserve a considerable
amount of crop species. Being labour-intensive, horticultural crops provide job opportunities, besides
having great export potential and therefore create more income for rural people. In order to promote
the growth of horticulture sector, there are necessities to document the existing horticultural crops,
to calculate the richness of those crops species and also to assess potential area for further expansion.
To know the richness of horticultural crops species and the potential areas for home gardens
development, the existing kinds of crops species and sizes of home gardens are needed to investigate.
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The potential area for home garden development is the basic unit of species conservation in rural
sustainable development. Therefore also the exploration of relationship between the home gardens
areas and species richness has becoming importance for developing of the home gardens. Scientificinvestigations on richness of species and sizes of home gardens are severely lacking in the country.
In a statement of Helen Keller International/Asia-Pacific program, 2010, “the vegetables andfruits in the home gardens contribute considerably to increased consumption of these types of foods
in many Asian countries”. Among fruit trees, mango is one of the most important commercial crops
in Myanmar and peoples’ choicest fruit due to its agronomic and cultural value. Myanmar mangoes
are assumed to be traditional varieties as they has historical origins, distinct identity, are geneticallydiverse, locally adapted and associated with traditional farming systems (Hirano et al., 2011). The
total planted area of mango in Myanmar was 93,890 hectares and was 30% of total cultivated areafor fruits (MFFVPEA, 2013). There are about 300 different mango varieties and 20 kinds of mango
species in Myanmar. Leaf morphology of the mango species is highly variable depending on thecultivar. There is broad genetic diversity of mangoes in Myanmar that needs to be identified (Hirano
et al., 2010). Research based on genetic resources of mango is very scarce in Myanmar. Moreover,there have been no reports of detailed information on spatial varietal distribution and identificationon land-usage. By analysing the spatial distribution, the current land use of this area can be identified
and has great potential in efficient utilization of land (Sana Iftikhar and Hafiz Zahid Mahmood, 2017).
The determination of spatial distribution will help in determining the ecological suitability of an areafor fruit growing. Geospatial technology such as Global Positioning System (GPS) and Geographical
Indicating System (GIS) is one of the most widely used tool to provide updated information in
identifying suitable sites for various crops and mapping for fruit trees distribution (Singh et al., 2017).
OBJECTIVE
The aims of this study were to explore the relationship between the size of home gardens’ and species
richness of horticultural crops and to study the distribution of mango varieties in the selected area.
METHODOLOGY
The study was conducted in 60 selected home gardens out of total 370 in Kyee-Inn village, Nay Pyi
Taw Union Territory, Myanmar from September, 2018 to October, 2019. Drone (DJI Phantom
4PRO) was flown above 50m to generate geographical information. The location of each selected
home garden and mango varieties were recorded by Global Positioning System (GPS) GARMAN,
GPSMAP 62 device. Drone photos were merged and arranged with Pix 4D software to determine thebase map of the study area. ArcCatalog and ArcMap softwares (10.3.1 version) of ArcGIS were used
to map the distribution of selected home gardens and mango varieties.
The composition of each home garden such as compound and building areas were measured;
residential (social) and crops covered areas were visually estimated by drone images and the potential
areas for home garden development were calculated. Primary data were gathered by both direct
observations to acquire information on all horticultural crops especially mangoes: including local
names, growth stages and number of individuals, and by interviewing people with semi-structured
questionnaire sets. Descriptive statistics of the Statistical Package for Social Scientists (SPSS),
version 23 was used to examine the relationship between the home gardens areas and species richness.
The Shannon-Wiener’ index was employed to determine the species richness by use of the following
equation.
𝑠𝐻′ = − ∑ 𝑝𝑖 𝑙𝑛 𝑝𝑖
𝑖=1

where H′equals diversity index, s equals the number of species and pi equals the ratio of individuals of
species i divided by all individuals N of all species.
For growth stages of mangoes, less than 5 year-old trees were regarded as young and those above 5
years were regarded as adults. Finally, three representative leaf samples of each mango variety wereⒸ ISERD

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collected and leaf attitudes and leaf characters such as shapes of blades, apexes, bases and margins, were
evaluated using mango descriptors (International Plant Genetic Resources Institute, IPGRI, 2006). Leaf
lengths and widths were measured manually.

Fig. 1 Distribution of selected 60 home gardens (small, medium, large) in the study area
RESULTS AND DISCUSSION
Figure 2 shows three groups of home gardens (n=20) categorized from small (1400-4000 ft2),
medium (4001-6400 ft2) to large (6401-21,500 ft2). Potential area for home garden development was
highest in large home gardens group (41%) followed by medium (16%) and small (9%).

Fig. 2 Different areas that comprised home gardens in the 3 categories (n= 20)
The observed horticultural species were categorized into 7 groups. Of the 480 species that wereobserved, species richness was highest in ornamentals (46%) followed by vegetables (10%), fruits(8%), medicinal plants (8%), shade trees (6%), spices (3%) and unidentified species (20%) (Fig. 3).

Fig. 3 Classification of horticultural crops identified in selected home gardensⒸ ISERD

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Among fruit trees, mango species were the most dominant and 90% of home gardens weregrowing a total of 361 trees which included 15 local varieties: namely, Ma Chit Su (39%), Sein Ta
Lone (24%), Sein Sar Thee (20%), Yin Kwe (9%), Ma Naw Nwe (3%), Waso, Padamyar Nga Mauk,Mya Kyauk and four unknown varieties (1% each) and Wet Ta Kaut, Pan Swae and Pyo Ta Ngone(0.3% respectively). These respective proportions in each group of home gardens are presented inFig. 4 (a). A base map of the village was produced and then the distributions of the 15 mango varieties
are shown in Fig. 5. Mango varieties were well distributed over the whole study area. Out of a total
361 mango trees, mostly propagated by seeds, 37% were at the young stage and the remaining 63%
were adult (Fig. 4 (b)). Almost in all varieties, the juvenile period usually ended after 4th years andflowering started in the 5th year. This finding is consistent with the report by Mukherjee and Litz,(2009) who stated that the juvenile period of seedling trees usually ranged from 3 to 7 years.

(b)

(a)

Fig. 4 Fifteen mango varieties in 3 groups of home gardens (a) and growth stages (b)Fig. 5 Distribution map of 15 mango varieties
The evaluated leaf characters of 11 known and 4 unknown varieties are shown in Table 1. Threetypes of leaf attitudes and leaf characters (blade, apex, base) were observed. Both wavy and entire
margins were found in all varieties. Lengths and widths of leaves ranged from 16.5-25.0 cm and 3.04.4 cm, respectively. Figure 6 shows the comparison of different leaf characters of the observed
mango varieties.

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Table 1 Leaf descriptors of 11 mango varieties and 4 unknown varieties
No. Variety

Attitude

Length Width(cm) (cm)

Blade

Apex

Base

Marginoblongacute

obtuse

wavy

19.5

4.0

oblongacute

obtuseentire18.0

4.1

2 Unknown 2

semidropping
horizontal

3 Unknown 3

horizontal

lanceolate

acuminate acute

wavy

21.0

3.0

4 Unknown 4
5 Yin Kwehorizontal
semi-erectlanceolate
elliptic

acuminate acute
acute
acute

wavy
wavy

24.0
19.3

3.2
3.8

6 Ma Chit Su

semidropping

lanceolate

acuminate acuteentire20.5

4.0

7 Ma Naw Nwehorizontal

1 Unknown 1

oblongacute

obtuse

wavy

16.5

4.4

semidropping
9 Pa Damyar Nga horizontal
Mouk
10 Pyo Ta Ngone horizontal
horizontal
11 Mya Kyauk

lanceolate

acuminate obtuse

wavy

18.3

3.6

lanceolate

acuminate obtuse

wavy

21.0

3.8

lanceolate
oblongacuminate acuteentireobtuseacuminate entire

25.0
20.8

3.4
4.0

12 Sein Ta Lonesemi-erect

oblongacute

obtuse

wavy

21.5

4.0

13 Wat ThaKout

horizontal

lanceolate

acuminate acuteentire19.5

3.6

14 Sein Sar Thee
15 Wasohorizontal
horizontal

lanceolate
oblongacuminate acutewavy
obtuseacuminate entire

23.0
20.8

3.2
4.0

8 Pan Swe(o)
5 cm
Fig. 6 Leaf characters of (a-d) 4 unknown varieties, (e) Yin Kwe, (f) Ma Chit Su, (g) Ma Naw
Nwe, (h) Pan Swe, (i) Pa Damyar Nga Mouk, (j) Pyo Ta Ngone, (k) Mya Kyauk, (l) Sein
Ta Lone, (m) Wat Tha Kout, (n) Sein Sar Thee and (o) Waso
(a)

(b)

(c)

(d)

(e) (f)

(g)

(h)

(i)

(j)

(k)

(l)

(m) (n)Fig. 7 Relationship between area of home gardens and number of species for all horticultural
crops (a) and number of mango trees (b)Though the sizes of home gardens had no correlation with species richness of all horticultural
crops (p = 1.24, r = 0.20), there had a significant correlation with mango varieties (p = 0.01, r = 0.32).
Fig. 7 shows correlation coefficient for species richness of all horticultural crops (r2 = 0.04) andmango trees (r2 = 0.08) which mean only 4% of the variance in species richness and 8% of mango
trees is explained by the change in home garden areas. Furthermore, the slope is 0.001 in both cases,Ⓒ ISERD

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indicating that for every unit increase in areas, number of species increases by only 0.001. The
calculated diversity indices were H’ = 1.57 for all species and 1.40 for mango varieties. Typicalvalues of diversity index are generally ranged between 1.5 and 3.5 (Magurran, A.E. 2004) and thecalculated indexes of the study area were reached nearly this range.
CONCLUSION
Although the results suggest that indeed, plant species richness of home gardens in the study area ishigh, there were more potential areas to extend home gardening especially for large home gardens(41%) followed by medium gardens (16%) and small ones (9%), respectively. While we found nocorrelation between species richness and the home gardens sizes, these sizes were signiﬁcantly
correlated to richness of mango varieties with a higher species richness in larger than in smaller-sizedhome gardens. This might be because large-sized gardens tend to focus on fruit tree type species andallocate more of their land to food crops, while small gardens can afford to include different types ofcrop species. Mango varieties were well distributed and adaptable with agro-ecology of the study
area; therefore, the gardeners should try to replace with the marketable and exportable quality mango
varieties in spite of existing local varieties. Home gardeners need to be made localized efforts toconserve rare native species by promoting more widespread cultivation.
ACKNOWLEDGEMENT
We would like to acknowledge our appreciation to Japan International Cooperation Agency (JICA)
for their financial and technical support for this study. Similarly, we would like to express our sincere
thanks to authorized persons from Yezin Agricultural University for kind permission andencouragement to conduct this study. Finally, but not the last our great and sincere thanks goes tohome gardeners for their contribution and integrity.
REFERENCES
Helen Keller International/Asia-Pacific. 2010. Homestead food production model contributes to improved
household food security, nutrition and female empowerment, Experience from scaling-up programs in Asia
(Bangladesh, Cambodia, Nepal and Philippines). 8 (1), 1-8.
Hirano, R., Htun Oo T. and Watanabe, K. 2010. Myanmar mango landraces reveal genetic uniqueness overcommon cultivars from Florida, India and Southeast Asia. Genome, 53, 321-330.
Hirano, R., Ishii, H., Than Htun Oo., Gilani, S., Kikuchi, A. and Watanabe, K.N. 2011. Propagation
management methods have altered the genetic variability of two traditional mango varieties in Myanmar.
Plant Genetic Resources, 9 (3), 404-410.
International Plant Genetic Resources Institute, IPGRI. 2006. Descriptors for mango (Mangifera indica L.).
Rome, Italy, 30-33.
Magurran, A.E. 2004. Meausuring biological diversity. Australia, 72-98.
Mukherjee, S.K. and Litz, R.E. 2009. The mango. 2nd Edition, Botany, Production and Uses. Calcutta
University, Calcutta, India, 1-18.
Myanmar Fruit, Flower and Vegetables Producers and Exporters Association, MFFVPEA. 2013. Fruit calendar.
UMFCCI, Yangon, Myanmar.
Iftikhar, S. and Mahmood, H.Z. 2017. Spatial distribution of agricultural resources and food security: Punjab
Pakistan. Cogent Food & Agriculture, 3: 1357265, 1-26.
Shannon, C.E. and Wiener, W. 1949. The mathematical theory of Communication. Unversity of Illions Press,
Urbana, Illinois. 6-18.
Singh, V., Patel Ajay, N., Dalwadi, A., Kathota, J., Suthar, J. and Kalubarme, M.H.M. 2017. Horticultural fruit
crop plantations mapping using geo-informatics technology in Gujarat State, India. International Journalof Advanced Remote Sensing and GIS, 6 (2), Article ID Tech-709, ISSN 2320 - 0243, 2033-2049.

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Research article
erd

Impact of Forest Cover Change in Phnom Kulen National
Park on Downstream Local Livelihoods along Siem Reap
River, Cambodia
OUM SOMALY*Royal University of Agriculture, Phnom Penh, Cambodia
Email: oumsomaly@gmail.com / osomaly@rua.edu.kh

NOPHEA SASAKI
Asian Institute of Technology, Bangkok, Thailand

SOK KIMCHHIN
Royal University of Agriculture, Phnom Penh, Cambodia

TAKUJI W. TSUSAKA
Asian Institute of Technology, Bangkok, Thailand

SANGAM SHRESTHA
Asian Institute of Technology, Bangkok, Thailand

NEANG MALYNE
Royal University of Agriculture, Phnom Penh, Cambodia
Received 31 January 2020

Accepted 10 April 2020(*Corresponding Author)

Abstract National parks play important roles in the conservation of biodiversity,
environmental protection, and provision of resources for local livelihoods. This research aims
at examining the effect of forest cover change in Phnom Kulen National Park (PKNP) on
water quality and downstream local livelihoods along the Siem Reap river. Forest coverchange in the park was collected from the United States Geological Survey from 2000 to2016 through the Multi-Temporal Satellite Imagery (MTSI). The study used livelihood
approach to interview with 120 households and four key-informants to elicit local perceptions
on effect of forest lost on their livelihoods. The research found the forest cover in PKNP
decreased by 22% from 2000 to 2016. The loss of forest cover has affected downstream local
people whose daily livelihoods depend on Siem Reap River. Scores on local perceptions were
low. Livelihood capital were performed low; only 0.48 score on natural capital, 0.44 score
on human, and 0.24 score on financial capital, in which correspondent perceived theirlivelihood did depend significantly on those capitals. However, we found that the physicaland social capitals were not significantly affected by the forest cover change because forest
clearing created stable access to infrastructure and social network. As forest cover declined
impacted on the declined of crop production, fish availability, water quality and access toclean water. As local needs to adopt new skills for living, human and financial capitals were
low. The study confirmed the forest cover change at national park had a serious impact on
the downstream local livelihoods.
Keywords Phnom Kulen National Park, forest cover change, sustainable livelihoods
approach, upstream-downstream issue, perception analysis

INTRODUCTION
Forest provides direct and indirect of good and services to human well-beings by ecosystem functions(Robert et al., 2002). Moreover, it was totally essential for water storage because when the naturalforest was damaged, rainfall would be declined, reduced infiltration of water, increased run-off, andⒸ ISERD

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reduced water quality (Robert et al., 2002). Existence of watershed areas in protected forests andnational parks played the vital functions for water purification and water resources for both upstream
and downstream livelihoods. National park was one of the great tools for forest and biodiversity
conservation. The PKNP was one of famous sacred tourist zones with immense spiritual, cultural,
historical value. Moreover, it was the main source of water provision for downstream people almost
the whole year. However, weak law enforcement and higher market demands on timber and otherforest products caused serious forest lost in Protected Areas (Sanchez-azofeifa et al., 2002).
Approximately 70% of downstream local people were semi-subsistence farmers and fishers who
heavily depended on river-based livelihood activities (Hayes et al., 2013). Lastly, present Siem Reap
river had faced water shortage, soil fertility problems, and water pollution (UNDP, 2014).
OBJECTIVES
In overall, the study aimed at investigating the perception of local people on impact of forest coverchange in PKNP on Siem Reap river and their livelihood. The specific objectives are i). analyzing
the trend of the forest cover change in PKNP between 2000 and 2016; and ii). assessing the livelihood
capital of downstream people affected by upstream forest cover change. The limitation of the study
did not access to measure economic comparation between before and after the forest cover change
due to uncertainly of household’s livelihood diversity.
METHODOLOGY
Study Area
Figure 1 showed PKNP, targeted area, located at Siem Reap province, Cambodia (Hayes et al., 2013).
Over centuries, the PKNP provided not only main source of water for the city use throughout fullyear, but also for underground water, which support Angkor Wat temple basement and its scenery(Gaughan, Binford, & Southworth, 2009). Recently, the water from the park contributed importantly
daily consumption and agriculture of provincial population. However, there were concerned on the
deforestation, led to cause on both water quality and quantity from the park.

Fig. 1 Phnom Kulen National Park boundaries and administration
Forest Cover Change Detection in PKNP between 2000 to 2016
This study utilized Landsat Satellite Imagery (LSI) from 2000 to 2016. Multi-temporal satellite data
were downloaded from the United Stare Geographical Satellite including Landsat 5, Landsat 7 ETM+,
and Landsat 8 OLI with a 30 meters resolution. ArcGIS software (ArcGIS 10.6) was utilized for
digital image processing, classification, and analysis as well as forest cover change maps generation.
Two types of land use and land cover (LULC) were classified including forest, and non-forest areas.
In addition, these satellite imageries were selected with less than 10 % of cloud. The band
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combination of mosaic and masking were used to analysis and merge the images and clip theboundary. Indeed, Regions of Interest (ROI) was used for supervising classification for forest change
detection. Band Combination false color of Landsat was used to take ROI which is forest or nonforest. Therefore, Band 4, 3, 2 was used for Landsat 5 from 2001 to 2011, and Band 7, 4, 2 was usedfor Landsat 7 ETM+ from 2000 to 2013 and Landsat 8 OLI from 2014 to 2016.
Accuracy Assessment
Accuracy assessment was conducted for all classification to prove the accuracy of each classified
image. Moreover, it used to compare the classification from one to another, assumed to be accurate
i.e. aerial photo (Parece et al., 2011). An error matrix was accomplished for this task. To assess theaccuracy of classification, there were four main steps: 1) create random point on image classified in
ArcGIS (100 sample), 2) convert layer to KML to assess the point in Google Earth, 3) insert the point
in Google Earth and compared the classification task and Google Earth, and 4) created the error
matrix table and calculate with the formula.
Cohen’s kappa co-efficient was an essential component of accuracy assessment, it provided theaccurate result how well our classification performed.
k̂=observed accuracy - chance agreement
1 - chance agreement

where, The KHAT value ranges from 0 to 1,0 indicates the classification is not any better than arandom assignment of pixels, 1 indicates that the classification is 100% improvement from random
assignment.
k̂=N∑ri=1 Xii - ∑ri=1 (Xi+2N- ∑ri=1 (Xi+

X+i )

X+i )

where, r number of rows in the error matrix, Xii number of observations in row i and column i (thediagonal cells), Xi+ total observations of row i, X+i total observations of column I, N total ofobservations in the matrix (Parece et al., 2011).
This study calculated the error matrix formulations of two years: 2010 and 2016 to verify theaccuracy of forest cover change analysis. After calculating the error matrix of supervisedclassification in 2010, the overall accuracy of image classification is 96% and the Kappa was 91%.
In 2016, the overall accuracy of the image classification was 91% in 2010 and 82% in 2016.
Assessment of Livelihood of Downstream Local People Affected by Upstream Forest CoverChange120 households were selected with purposive sampling for the questionnaire survey. Four keyinformants from forestry administration office, department of environment, department ofmeteorological and few rangers in Siem Reap province were interviewed for the confirmation of both
qualitive and quantitative survey.
Adapted from Qian et al. (2017), livelihood approach has five capitals including; Physicalcapital which had indicators: household fixed assets (Land, Machinery, etc.); Natural capital had
indicators: forest coverage (changing), necessary for forest protection (important of forest), forest
protection activities in National Park, and biodiversity (quantity of water, quality of water, crop
fishing in natural place); Human capital had indicators: skill and knowledge (improvement), healthstatus (illness or not); Financial capital has indictors: household income (income of agricultural
production, forest and NTFPs), and Social capital had indictors: community membership(participant), social network (relationship), and family decisions. The total scores of local livelihood
assets were calculated by:Ⓒ ISERD

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𝐶𝑃𝑐 + 𝐶𝑁𝑐 + 𝐶𝐻𝑐 + 𝐶𝐹𝑐 + 𝐶𝑆𝑐
5
where; LA Livelihood Assets, 𝑪𝑷𝒄 Physical capital, 𝑪𝑵𝒄 Natural capital, 𝑪𝑯𝒄 Human capital, 𝑪𝑭𝒄
Financial capital, 𝑪𝑺𝒄 Social capital.
LA =RESULTS AND DISCUSSION
Trend of Forest Cover in Phnom Kulen National ParkIn Fig. shown in 2000, PKNP covered by 31000 ha of forest area. Between 2009 and 20163, forest
was dramatically declined about 2800ha. The forest in PKNP had lost huge amount at the same trendof forest cover in whole (ODC, 2016). According to the interview with rangers reported the main
reasons of population growth (both people who migrated in and population growth naturally inside
the village), people expanded land for settlement, agricultural activities. Moreover, it was because of
inadequate knowledge of local people regarding forest conservation and forest management, illegal
logging. The perpetrators cut trees and killed the wildlife for selling at nighttime, which was difficult
for rangers to take action. In addition, limitation of logistic and incentive of ranger affected on lesspatrolling activities.

hectare

45000

30000

15000

0
2000

2005

2010

2015

Fig. 2 Land surface of forest cover change in Phnom Kulen National Park from 2000 to 2016
Between 2009 and 2013 is the period of creating the policy on economic land concession (ELC)by the government in the National Strategic Development Plan (NSDP). The purpose of creating the
ELC policy was to develop the industrial agricultural sector, sustain economic growth and reducepoverty (ODC, 2015). By contrast, our study found there was impact on the forest cover change in
Siem Reap province. Based on the NGOs reported ELC was a hot very topic for land conflict
including land grabs, evictions and exploitation of natural resources (ODC, 2016).
In a line, the result of forest cover from the LSI from 2000 and 2016 shown in Fig. , forest lost
about 22%, equal to 6780 ha and the rate of deforestation was 1.3%, account to big amount of forest
lost that had to be consideration of the Cambodian government and development agencies. Recently,
over 4,600 people had settled in PKNP, caused to forest land conversion to residential, crop cultivation
mainly rice and cashew nut and the area was mainly used for rice (A&D Foundation, 2018).
The analysis found that 65% of local people perceived that forest cover in the upstream of PKNP
had decreased, while 35% reported not. The score of forest coverage was 0.35, suggesting a

3

In 2008, forest cover increased because of the cloud problem which made some error in calculation. In 2012
and 2013, the imagery of land 7 was selected to use because it is the clearest one but there has many gaps on
the image so the research applied Shoreline Extraction (Landsat 7 ETM+) to fix it.
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significant decline. Our analysis of satellite imagery found that sharp decline in Banteay Srei district,
where local people depended mostly on timber and non-timber production for daily livelihoods.
The result shown in Table 1, the percentage of local people perceived the forest at upstream wasdeclined, water quantity in the region decline is 76.7% especially. Moreover, there are 23.3% ofcorrespondents reported water was not declined due to the forest cover change at upstream. The scoreof this water quantity was 0.23, which means deforestation impacted the decline of water quantity at
downstream area.

Fig. 3 Comparison of forest cover change in Phnom Kulen National in 2000 and 2016
Table 1 Percentage of correspondent on livelihood capitalStatement

No (%)

Do you know that forest in the upstream national park has
decreased?
Do you think that since forest from upstream declined, water
quantity in your region has also declined?

35
23.3

Do you think since forest from the upstream decreased, water
quality in your region also decrease?
Do you think since forest from the upstream decreased, your crop
production also declined?
Do you think since forest from the upstream declined, the amountof fish in the river also decreases?

28.3
40.8
25.0

Respondents (n=120)
Yes (%)
Score= 35*1+65*0
65= 35 % = 0.35
= 23.3*1+76.7*0
76.7= 23.3 % = 0.23
= 28.3*1+71.7*0
71.7= 28.3 % = 0.28
= 40.8*1+59.2*0
59.2= 40.8 % = 0.41
75.0

= 25.0*1+75.0*0= 25 % = 0.25

Scaling:
0.00 – 0.25
0.26 – 0.50
0.51 – 0.75
0.76 – 1.00

Largely decreased
Decreased
Increased
Largely Increased

Total score

0.30

Another result shown 71.7% of downstream people perceived the decreased forest at upstream
impacted on water quality. The score of water quality was 0.28. Correspondents reported last sevenyears ago before 2013, water in river and lake could be used for drinking, taking shower, and using
for daily. In addition, 59.2% of the downstream people perceived the decreased forest at upstream
impacted on a decrease of crops productions due to water shortage, badly in dry season.
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There were 75% of the downstream people perceive the decreased forest at upstream, impacted
on the decline of natural fish stock in the river. The decline of fishery significantly affected on income
and food insecurity of the local livelihood.
Livelihood Assets of Downstream Local People in Relation to Siem Reap River Affected by
Upstream Forest Cover Change
Although forest cover change occurred in upstream but physical capital was required to use for daily
life. Natural capital was reported decreasing in biodiversity with 0.48 score. Local people perceived
there was decreased in upstream forest cover led to decrease the water quality, water quantity,
impacted on yield decreasing of crops and fish production. People in Banteay Srei, Tbaeng Lech, Ta
Pen, and Samraong villages raised the same aspect on decline of water quality and quantity impacted
on reduction of crop production and fishery.
As shown in Fig. 4, the result indicated, physical capital was not impacted regarding the forest
cover change in upstream with score 0.61. Similar change of physical capital because people required
to use for their daily live.

Fig. 4 Downstream local perception in termof change of livelihood capitalFig. 5 Downstream local perception on current
situation of the livelihood capital

In Fig. 5, the result illustrated that the current situation shown score of financial capital was very
with score 0.24, local people mostly depend on agriculture. The mean score human capital and social
capital in term of current level was medium mean score 0.44 and 0.41 because people did not haveany serious case of their health and both of man and woman had decided together for decision making.
CONCLUSION
The research found forest cover in PKNP was declined 22% from 2000 to 2016 due to illegal logging,inadequate knowledge regarding forest conservation, population growth, increase in cashew nut, andrice fields. A strong link between natural capital and forest cover have found. As local people needto change to new skills in order to survive after decline of forest cover. Low scores on human capital
was observed, indicating the need for interventions of the local or national authority to assist local
people. Similarly, for the current situation of their financial capital had low income and highexpenditure.
Therefore, this study provided very important evident base of the forest cover change thought
the satellite imagery maps from 2000 to 2016 and result of perceptions of local people about the
impacts of the forest cover change in PKNM, which contributed direct and indirect impacts to natural
capital, human capital and influenced on the current situation of financial capital.
ACKNOWLEDGEMENTSFirst author thanks the Kurita Water and Environmental Foundation for providing a full scholarshipto pursue master’s degree at Asian Institute of Technology, Thailand, and full fund of this research.
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REFERENCES
A&D Foundation. 2018. Phnom Kulen program. Retrieved from http://www.adfkulen.org/a-complexenvironment.html
Gaughan, A.E., Binford, M.W. and Southworth, J. 2009. Tourism, forest conversion, and land transformationsin the Angkor basin, Cambodia. Applied Geography, 29 (2), 212-223.
Hayes, B., Mould, A., Khou, E.H., Hartmann, T., Calame, T., Boughey, K. and Yon, T. 2013. A biodiversity
assessment of Phnom Kulen National Park, With recommendations for management. August, 1-58.
ODC. 2015. Concessions. Retrieved from https://opendevelopmentcambodia.net/topics/concessions/
ODC. 2016. Forest cover. Retrieved from https://opendevelopmentcambodia.net/profiles/forest-cover/#ytplayer
Swinton, S.M., Lupi, F., Robertson, G.P. and Landis, D.A. 2006. Ecosystem services from agriculture: Looking
beyond the usual suspects. American Journal of Agricultural Economics, 88 (5), 1160-1166.
Sanchez-azofeifa, A.G.A., Rivard, B., Calvo, J. and Moorthy, I. 2002. Title of tropical deforestation aroundnational parks. 22 (4), 352-358. Retrived from https://doi.org/10.1659/0276-4741(2002)022
Parece, T., Campbell, J.B. and McGee, J. 2011. Remote Sensing in an ArcMap Environment. United States.
UNDP. 2014. Communities in action for landscape resilience and sustainability the comdeks programme.
United Nations Development Programme.

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Research article
erdStudy of Farmers’ Use of Fertilizers and Methods ofApplication for Rice Cultivation in Central Myanmar
SWE SWE MAR*
Department of Soil and Water Science, Yezin Agricultural University, Myanmar
Email: swesmooth@gmail.com

NAING NAING MOE
Department of Soil and Water Science, Yezin Agricultural University, Myanmar

KYAW NGWE
Department of Soil and Water Science, Yezin Agricultural University, Myanmar

KAZUO ANDO
Center for Southeast Asian Studies, Kyoto University, Japan
Received 14 January 2020

Accepted 10 April 2020(*Corresponding Author)

Abstract Fertilizers play an important role in the maintenance of soil fertility for riceproduction. The correct application of fertilizers can alleviate declining soil fertility. To
improve levels of rice production in central Myanmar, it is essential to assess the current
practices used in the application of fertilizers. The aim of this study is to examine farmers’
knowledge and attitudes to the use of fertilizers for rice production and examine theconstraints they face. A survey was conducted using structured questionnaires to interview
164 farmers, randomly selected from three villages in central Myanmar. The survey data wereanalyzed using descriptive statistics. Based on the survey results, commonly used fertilizers
were urea and compound fertilizers. It was clear that the majority of farmers used amounts belowthat recommended amount in their application of urea and levels of application of mineral
fertilizer varied greatly. About 23% of respondents used cow-dung manure and 12% applied
pulses residues as fertilizers for rice cultivation. Although the farmers’ accept that fertilizer
application is necessary for better rice growing, the majority of the farmers faced financial
difficulties with any increased use of fertilizers. Also, this study shows that the sampled
farmers’ limited knowledge on fertilizer use and the lack of access to the information related tofertilizer management, is a constraint preventing appropriate use of available fertilizers. Thus, clear
improvements to increase agricultural productivity and soil fertility levels would result fromthe improved application of organic and inorganic fertilizers.
Keywords rice cultivation, use of fertilizers, farmers

INTRODUCTIONIn Myanmar the agriculture sector is the backbone of the economy. This sector contributes to 37.8
percent of gross domestic product (GDP), accounts for 25 to 30 percent of total export earnings andemploys 70 percent of the labor force (FAO, 2018). Among the cereal crops, rice is the mostimportant source of national prosperity. Rice is not only the staple food source but also an important
export crop, so successful rice production is crucial. Correctly used, fertilizers can play an importantrole in ensuring successful rice production.
Although Myanmar has had a long history of low fertilizer use, the fertilizer market has
expanded rapidly since 2008. However, current practices in fertilizer use have resulted in theunbalanced application of nutrients. One keyway to improve fertilizer use is to enhance farmers’
knowledge of the specific crop and soil nutrient needs and the fertilizer products (nutrient
grades/formulations) that will best ensure the supply of these needs. Choosing which fertilizer to useⒸ ISERD

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is often one of the most important decisions a farmer has to make. However, Myanmar farmers have
limited knowledge of modern agricultural technologies, including fertilizers (IFDC, 2018).
The Department of Agricultural Development and Planning, Myanmar, reports that there is a
declining tendency in domestic use and demand for chemical fertilizers, and that many small holder
farmers have not adopted technology that ensures best use of both organic and inorganic fertilizers
application, nor were farmers fully abreast of the importance of their use (DADP, 2007). This is
despite the advantages of using inorganic fertilizers in boosting production and previous efforts
making these fertilizers more freely available to farmers. In contrast to chemical fertilizers, farmyard
manure has been used long term by Myanmar farmers to restore soil fertility. However, because of
the lengthy process in processing farmyard manure so that it is ready for use, and the limited
availability of animals kept by small holders, the amount of farmyard manure produced and used has
always been insufficient to needs.
Myanmar faces huge food supply challenges due to the increasing population, the limited
opportunities to increase arable land, and the declining yields associated with continuously declining
soil fertility. In order to improve the productivity of rice farming, particularly for small holder farms
in central Myanmar, it is critical to assess the current practices for the use of fertilizers, and to explore
the constraints encountered by farmers with respect to both the use of organic and inorganic fertilizers.
OBJECTIVES
The main objective is to study the current methods of fertilizer application for rice production in central
Myanmar, and to ascertain any problems and constraints associated with the farmers’ use of fertilizers.
METHODOLOGY
General Description of Study Area
Three villages were selected, with two from the Naypyitaw area (Kyee Inn and Thit Tat villages) andthe third, Chiba (Shwebo Township) from the mid-north of Myanmar. Data were collected using a
structured questionnaire, with the assistance of the village-head (Table 1).
Table 1 General description of Kyee Inn, Thit Tat and Chiba Villages
Variables
Population
Sown acre
Rice sown acre (Summer)
Rice sown acre (Monsoon)
Farmer population
Water availability
Name of Dam
Cropping pattern

Unit
acre
acre
acre

Kyee Inn

Thit Tat

Chiba

2160
1392
850
1000
Irrigated
Nga-like Dam
Rice-Pulses

1522
550
550
550
89
Irrigated
Yezin Dam
Rice-Rice

2828
1750
1750
1750
230
Irrigated
Tha Phan Seit Dam
Rice-Rice

Survey and Data Analysis
Interviews were conducted, and farmers taking part were selected using a simple random sampling
method. A total number of 164 farmers (66 from Kyee Inn, 63 from Thit Tat, 35 from Chiba) wereinterviewed using questionnaires which included questions on gender, marital status, farmers’ age,farming experience, location of residence, educational levels, and size of area cultivated for rice.
Questions specific to fertilizer use related to quantity, time of application, and type of fertilizer used
for rice cultivation. The survey also identified any problems and constraints faced by farmers inⒸ ISERD

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obtaining fertilizers, and what methods of application were used. All data were analyzed bydescriptive statistics with the Statistix 8th version.
RESULTS AND DISCUSSION
Socioeconomic Background of Sampled Respondents
Agriculture is undertaken by an aging male population at all sites (80% in Kyee Inn, and 89% in Thit
Tat and Chiba). This finding is in agreement with those of Lawal and Ayoola (2008). The results
indicate that the majority of the farmers are about middle aged and according to Yunusa (1999) thisis an economically active age for motivation and innovation.
Education facilitates the transfer and promotion of technologies meant to improve crop
production (Lawal and Ayoola, 2008). The education status of the respondents in the survey areas
varies greatly, with farmers who have no formal education to those who have tertiary education anduniversity education (in this survey university education is given a separate category to education
gained at other tertiary institutions). However, in general, the level of educational attainment issufficient to support the adoption of technology through information sharing and written material.
The percentage of farmers attaining a secondary education is highest in Kyee Inn (45%) followed by
Chiba (43%). Thit Tat village had the highest percentage of farmers (41%), who had attained, at
highest, primary education. These results correspond to those of Ifejika et al. (2008) who found thatthe majority of farmers had some formal education. There is a low percentage of farmers with tertiary
education in all villages. A significant number of farmers with no formal education (14%) is foundonly in Thit Tat village. The results clearly indicate that the farmers in the surveyed villages can, in
general, read extension leaflets, posters and magazines promoting innovative farming methods.
Given the small percentage of illiterate farmers it can be expected that knowledge of new methods
would be communicated widely in the farming community.
The amount of experience of farming relates as a key risk management factor, and Ridler andHishamunda (2001) showed that new farmers were at a higher risk compared to experienced farmers.
The survey identified five different levels of farming experience; from under 5 years, then 5 to 10
years, 10 to 15 years, 15 to 20 years and finally those with above 20 years of farming experience.
Thit Tat village had the highest percentage of farmers with experience of more than 20 years,
followed by Chiba and Kyee Inn. The data show that most farmers are very experienced and thiswould be expected to enhance effective fertilizer use.
The size of land holding of the respondents ranges from under 2 hectares, 2 to 6 hectares andabove 6 hectares. However, Olayide et al. (1980) classified small-scale farmers as those having 0.1
to 5.99 hectares of farm size. Farm size determines the number of inputs, such as fertilizer, that canbe purchased. Most of the respondent's farms can be classified as small scale, with this being
attributable to land fragmentation with land acquisition among family members.
Types and Brand of Fertilizers used by Farmers in the Survey Area
The data show that the most common types of fertilizers used by the farmers are urea (85%, 95% and100%) and compound fertilizers (97%, 81% and 71%) in the Kyee Inn, Thit Tat, and Chiba villages,
respectively. This indicates that farmers in these areas have good knowledge in the use of these typesof fertilizers, which can provide a greater rice yield than other types of fertilizers. However, there
was little use or knowledge of other types of fertilizers such as the Phosphorus and Potash, in thesurveyed villages. In respect to most popular brands, most farmers use a combination of brands with
Armo brand used by 87%, followed by Awba (57%), China brand (46%), Myanmar brand (8%), while
Tan Quen and Thailand brand have the same percentage use (5%), and Shwe Nagar (2%).
Methods and Time of Fertilizers ApplicationsⒸ ISERD

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According to the data in Table 2, broadcast fertilizer application methods are used by most farmers
across the surveyed villages, and basal application methods are seldom used. The broadcasting
method has a lower investment cost, and is popular as farmers lack the capital to invest inmechanization or even hire labor for alternate application methods. Most respondents used a top
dressing application at the tillering and flowering stages. Some of the respondents from Thit Tat andChiba, but very few from Kyee Inn, apply fertilizers after flowering.
Table 2 Methods and time of fertilizer application for rice crops
Method of application
Broadcast
BasalTime of application
Top dressing
Tillering
Flowering
After flowering
Total amount of urea
application (kg ha-1)

Kyee Inn
Frequency, (%)
57 (86)0 (0)Thit Tat
Frequency, (%)
50 (79)13 (21)Chiba
Frequency, (%)
34 (98)1 (2)63 (100)60 (95)3 (5)59 (94)55 (87)45 (71)35 (100)
33 (94)22 (63)125.20169.05105.8

Urea Application at Different Farm Size and Education Levels
In Fig. 1, it is clear that farmers on smaller farms applied relatively low amounts of urea fertilizers
in comparison to larger farms in the areas surveyed. This indicates that larger holdings relate
definitely with higher use of urea. The Kyee Inn village in particular, shows this pattern, with vastly
higher amounts of urea application for those areas greater than 6 hectares.
It can be seen that there is a visible progress in the amount of urea application with higher levelsof education for farmers in the Nay Pyi Taw area. However, in the Chiba (Shwebo) area there is aquite difference. Here a relatively lower amount of urea is applied by those with a university level of
education, while farmers who were educated to a primary level or by another tertiary institution
applied high amounts of urea. In contrast, university educated farmers from Kyee Inn and Thit Tat
villages apply higher amount of urea for their rice production. In Thit Tat village, farmers with noformal education or a primary level education applied the lowest amounts of urea (Fig 2.).
1000.00
Amount of Urea (kg)Amount of Urea (kg)1000.00
800.00600.00400.00200.00800.00600.00400.00200.00

0.00

0.00
Small

mediumLarge

No formal Primary Secondary Tertiary University
Education Level

Farm Size (ha)Kyee Inn

Thit Tat

Chiba

Fig. 1 Urea application and farm size

Kyee Inn

Thit Tat

Chiba

Fig. 2 Urea application and education levels

Problems and Constraints Faced by Respondents in Rice Cultivation
Farmers face many problems in growing rice. The main problem is insufficient water, followed byinadequate credit facilities, pest infestation, and problems related to seed and soil in the Nay Pyi Taw
area (that is Kyee Inn and Thit Tat villages). However, poor soil and pest infestation are clearly themain problems faced by Chiba village in their rice production (Fig 3.).
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Inadequate fertilizer

Lack of knowledge

Constraints to use fertilizers

Problems faced for rice production

Farmers in the survey area encounter many common shared constraints in their use of fertilizers
for rice production (Fig 4.). While farmers of Kyee Inn village are mainly constrained by high costof fertilizers, lack of capital, less in weight and poor quality fertilizer, the farmers in Chiba village
are more constrained by lack of capital, the high cost of fertilizers, lack of organic fertilizers, andnon-availability of fertilizers for timely application. Finally, in the Thit Tat area, the major constraints
on rice production are high cost of fertilizers and replacement of organic fertilizers.

Seed impurity
Inadequate information
Insufficient water
Poor Soil

Non-availability in time
Poor quality of fertilizer
Less in weight
Lack of capital

Infestation of pests

Replacement of organic
fertilizer

Inadequate credit

High cost of fertilizer
0

Chiba

Thit Tat

20

40
60
80
100
percent of respondents

Kyee Inn0
ChibaFig. 3 Problems faced by respondents
for rice production

Thit Tat

Kyee Inn

20
40
60
80
percent of respondents

100

Fig. 4 Constraints on the use fertilizer
for rice production

CONCLUSION
According to these results, respondents in Kyee Inn have the highest appreciation of the benefits ofusing fertilizers. They believe fertilizers are necessary for the crop to do well and can double yields.
To a lesser extent, Thit Tat village has similar attitudes regarding the use of fertilizers, but farmers
from Chiba village do not value fertilizers as much in rice production. Most farmers applied urea
with two to three times of split application to be more efficient for rice production. A majority offarmers use amounts of urea below those recommended (276 kg ha-1 for monsoon season and 414 kg
ha-1 for summer). In spite of the role that fertilizers play in improving rice production, lack of accessto information about fertilizer management, high prices and lack of capital constrain their usage. This
study shows that manure or organic fertilizer cannot be relied on to increase rice production andsustain agricultural productivity. Manure is scarce and transportation costs are high. As a result, thequantity of manure applied is likely to be low. Due to the lack of proper storage facilities the qualityof manure applied is likely to be very low in terms of nutrient supply. Lack of proper managementof fertilizers by farmers increases the cost of production and excessive application will probably haveadverse effects on aquatic and terrestrial ecosystems. Greater education on fertilizer use andmanagement through agricultural extension services, the media, and at the point of sale arerecommended to improve sustainable use of fertilizers for rice production in Myanmar.
ACKNOWLEDGEMENTS
This study was funded by a research grant from the Japan International Cooperation Agency
Technical Cooperation Projects (JICA-TCP) and facilitated by Yezin Agricultural University (YAU)
of Myanmar. We would like to express our gratitude to YAU students, the managers and staff of the
Department of Agricultural, as well as to all the farmers who participated in this study. Finally, we
also would like to extend our gratitude to the Center for Southeast Asian Studies Kyoto University(CSEAS) who actively supported this study.

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REFERENCES
Delta State Agricultural Development Programme. 2007. Ibusa & Ors v. Mr. Mike Iloukwu Ofonye, DADP
Annual Report.
FAO. 2018. Food and Agriculture Organization of the United Nations in Myanmar.
Ifejika1, P., Akinbile, L., Ifejika, L. and Olajide, J. 2018. The socio-economic effects on adoption of
aquaculture technology among fish farmers in Anambra State Nigeria. Journal of Agricultural Extension,
11 (01), 74-86.
International Fertilizer Development Center. 2018. Soil fertility and fertilizer management strategy for
Myanmar. PO Box 2040, Muscle Shoals, AL 35662, USA.
Lawal, B.O. and Ayoola, O.T. 2008. Farmers’ knowledge and use of pacesetter organic fertilizer for arable
crops in Oyo State, Nigeria. Journal of Agriculture and Rural Development, 2, 55-68.
Olayide, S.O., Eweka, J.A. and Bello-Osagie, V.E. 1980. Nigerian small farmers. CARD, University of Ibadan,
Benin-Owena River Basic Development Authority, Benin City.
Ridler, N. and Hishamunda, N. 2001. Promotion of sustainable commercial aquaculture in sub-Saharan Africa.
Policy Framework, FAO Fisheries Technical Paper, No. 408/1/Rome, 1, 15-17.
Yunusa, M.B. 1999. Not farms alone, A study of rural livelihoods in the middle belt of Nigeria. DAREASC
Working Paper 38.

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Research article
erd

Factors Causing Deficits in Traditional Rice Liquor
Production in Rural Areas of Cambodia
MITSURU HAMANO
Faculty of Agriculture, Shinshu University, Nagano, Japan
Email: hamano_m@shinshu-u.ac.jp

TETSUO MATSUMOTO
Nagoya University, Aichi, JapanKASUMI ITO*
International Center for Research and Education in Agriculture, Nagoya University, Aichi,
Japan
Email: kasumito@nagoya-u.ac.jp
Received 15 January 2020

Accepted 23 April 2020(*Corresponding Author)

Abstract Adding value to agricultural products is expected to play an important role inincreasing income and reducing poverty in rural areas of Cambodia. The promotion of agroprocessing is one of the key strategies for the value addition. A variety of processed foods
produced by individual small-scale farmers have helped generate income, with the exceptionof rice liquor which has been faced deficits and low profitability. This study aimed to clarify
the socio-economic status of rice liquor farmers in rural areas and to examine the factors
leading to deficits and surpluses in rice liquor production. Data on the economic status of rice
liquor farmers and non-rice liquor farmers were collected by conducting structured interviews
using questionnaires at the targeted areas in Takeo Province. Comparisons of the income
structures of the two groups revealed that the rice liquor farmers mainly depended for incomeon agricultural activities including rice liquor production and pig rearing, whereas non-riceliquor farmers depended on non-agricultural activities such as off-farm businesses, labor
work, and remittances. Rice liquor production was less profitable, and around 30% of rice
liquor farmers faced deficits in this activity. A comparison of the economic status between
deficit and surplus operations of rice liquor production revealed the key factors that caused
deficits, such as a low sales price, high cost of rice, low productivity, and high frequency of
production failure. Rice liquor farmers expected improvements in the production techniques,in the quality and productivity of liquor. Thus, modifying production techniques to improve
the quality and productivity of rice liquor, and to reduce the rate of production failure are thekey strategies to increase the profitability of rice liquor businesses.
Keywords agro-processing, added value, traditional product, business improvement,
Cambodia

INTRODUCTION
Adding value to agricultural products is expected to play an important role in increasing income andreducing poverty in rural areas. The promotion of agro-processing in rural areas has emerged as oneof the key strategies to add value to local agricultural products (Royal Government of Cambodia,
2006; Royal Government of Cambodia, 2013). In rural areas of Cambodia, individual small-scale
farmers have produced a variety of processed products, such as pickled vegetables, processed fish,
traditional sweets, charcoal, and rice liquor (sraa sar in Khmer). The production of these processed
foods, with the exception of rice liquor, has helped generate income for rural households; in contrast,
rice liquor production has faced deficits and low profitability (Yagura et al., 2010). Instead, suchproducers have obtained profit from pig farming by reducing the cost of feeding pigs by using theby-products derived from the distillation of fermented rice (Vathana and Takeya, 2004; Yagura etⒸ ISERD

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al., 2010). This production system kept pig farming profitable despite rice liquor operations staying
in the red (Yagura et al., 2010). However, recent sales prices of pigs have been fluctuated in the
Cambodian market due to increasing the imported pigs from Vietnam and domestic pig from largescale farms. (Tornimbene and Drew, 2012). This situation have made the small scale rice liquor
farmers with pig farming difficult to sustain their businesses in rural areas. It is significantly
important to examine the strategies to improve profitability of rice liquor production.
The cultural norms and general production methods of alcoholic beverages from rice inSoutheast Asian countries such as Vietnam and Laos have been clarified (Kozaki et al., 2002; Kozaki
et al., 2005). The production methods of traditional brewed and distilled rice liquor in Cambodia
were revealed by Kozaki (2007). Yamamoto and Matsumoto (2011) identified the production
methods and raw materials of starter cultures for rice wine and rice liquor in Cambodia. However,there have been no studies which examined the economic issues and possibilities of the
improvements in rice liquor productions.
OBJECTIVE
This study aims to clarify the socio-economic status of rice liquor farmers in rural areas and to
examine the factors that lead to a deficit or surplus to come up with the strategies of the development
in rice liquor production.
METHODOLOGYThis study attempted to clarify the socio-economic status of rice liquor farmers by comparing thefarmers who were engaged in rice liquor production (rice liquor farmers) and those who were not(non-rice liquor farmers). Then, a detailed analysis was conducted by focusing on rice liquor
production, including its economic and technical issues, to examine the factors that lead to deficit
and surplus operations based on the economic analysis and the farmers‘ awareness.
In September 2008, structured interviews were conducted to understand the socio-economic
status of farmers in six targeted communes consisting of 93 villages in Takeo Province, one of theareas where rice cultivation is most popular, given the region’s high productivity and quantity of
produce (Hamano et al., 2013). The most popular communes in rice liquor production were selected
as surveyed areas according to the key informant interview results to the officers of the provincial
department and the district offices, commune chiefs, and village chiefs. These structured interviews
were conducted using questionnaires. All rice liquor farmers in the six communes were interviewed.
They were identified based on the information provided by key informants, such as village andcommune chiefs, since the accurate official information on the number and locations of rice liquor
producers were not found in governmental institutes. In all of the 93 target villages, a non-rice liquor
farmer was randomly selected in each village for comparison in this study.
One of the questionnaires required rice liquor farmers and non-rice liquor farmers to identify
their socio-economic status. The questionnaire sought the following information: details about thehead of the household and his/her spouse; businesses operated by the farmers; and income generated
from the businesses, including other agricultural and non-agricultural economic activities to identify
the main bearers and sources of a household’s income. The other questionnaire was devised
exclusively for rice liquor farmers in the region, and it consisted of questions designed to elicit
detailed information about the production and sale of rice liquor to identify the factors leading to its
deficit and surplus. The questions were designed to elicit the following information: the experienceof producing rice liquor; the production process and frequency; the costs of raw materials andequipment needed for rice liquor production; production failures; sales price and amount of the
product; and existing issues and areas of improvement in rice liquor production. Closed-ended
questions were used to elicit answers regarding these issues and areas of improvement. The
frequencies of keyword appearances as used by the farmers during their responses were also factored
into the analysis. The farmers’ awareness was also examined to ascertain whether there were issues
that significantly affected the production and business of rice liquor farming.
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The Statistical Package for Social Science Student Version 16.0 was used to conduct the Mann–
Whitney test to compare the averages of the parameters between rice liquor farmers and non-riceliquor farmers. The deficit and surplus operation groups involved in the production of rice liquor
were also compared in the same way.
RESULTS AND DISCUSSIONInterviewees and Features of the Households
Table 1 shows the numbers of farmers interviewed for this study and the numbers of their valid
responses that were factored into the analysis. The 93 villages (in the six targeted communes) in theregion consisted of 13,548 households (National Institute of Statistics 2009). A total of 166 rice
liquor farmers, spread across 56 villages, were interviewed, and 117 valid responses were obtained.
In each village in the target areas, a non-rice liquor farmer was randomly sampled for the interviews,
and 87 valid responses were obtained from them. Table 1 shows that the average age of the husbandsof rice liquor farmers was 39.9 years old and the average age of the wives was 38.5 years old. Theseaverages are approximately six years younger than the average ages of the non-rice liquor farmers.
There was, however, no difference in terms of the size of the household. Table 1 also shows theaverage sizes of the paddy fields and rice yields of the rice liquor farmers and non-rice liquor farmers.
Most of both rice liquor and non-rice liquor farmers (96.6%) produced rice as the main staple food.
Rice liquor farmers cultivated rice in 1.12 hectares of paddy field and harvested 1.78 tons of rice
yield on average, which are larger by 31.8% and 27.1% than the corresponding values of non-riceliquor farmers (0.85 hectares and 1.40 tons), respectively.
Table 1 Interviewees
Rice liquor farmers2 Non-rice liquor farmers3 Sig. 4
Valid responses1
117
87
Average age (years old) of husbands (n)
39.9
(110)
46.5
(78)
0.00**
Average age (years old) of wives (n)
38.5
(116)
44.8
(85)
0.00**
Average number of household members (n)
6.0
(117)
6.2
(87)
0.63
Average size (ha) of the paddy fields in wet season (n)
1.12
(113)
0.85
(84)
0.02*
Average rice yield (t) in wet season (n)
1.78
(113)
1.4
(84)
0.02*
Note: 1Overall, 166 rice liquor farmers were interviewed; 117 valid responses were obtained. Moreover, 93 non-riceliquor farmers were interviewed; 87 valid responses were obtained.
2Rice liquor farmers: 7 female household heads and 1 male household head did not have a souse.
3Non-rice liquor farmers: 9 female household heads and 2 male household heads did not have a spouse.
4The Mann–Whitney test was used to compare the averages between the rice liquor farmers and the non-rice liquor
farmers. The average difference is significant at * 5% and ** 1% levels.

The Economic Status of Rice Liquor Farmers and Non-rice Liquor Farmers in Rural Areas
Figure 1 shows the average annual incomes derived from all economic activities by both groups: therice liquor farmers and non-rice liquor farmers. The average income of rice liquor farmers was foundto be 5,601,000 Riel (R) (4,000 R = 1 U.S. Dollar), which was lower than the average income of nonrice liquor farmers of 5,980,000 R by 379,000 R (6.3%). A large proportion (73.8%) of the income
obtained by rice liquor farmers came from agricultural activities, of which 26.5% came from agroprocessing including rice liquor, 38.7% from animal husbandry, 7.2% from rice milling, and 1.4%
from crop/vegetable productions. On the other hand, non-rice liquor farmers obtained 73.8% of theirincome through non-agricultural activities, of which 43.9% came from off-farm business enterprises
such as grocery shops, food stalls, and small restaurants; 17.4% came from the paid work
(employment) such as agricultural work, construction, and public services; and 12.5% came fromremittance provided by family members who lived away from home. The higher average income ofnon-rice liquor farmers was a direct result of the difference of average remittance that the two groupsⒸ ISERD108

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received, which was 608,000 R. Rice was cultivated by both groups mainly for self-consumption,even though some of the rice was sold on the market. In addition, the rice liquor farmers fed rice totheir pigs. As a result of this, the sales revenue could not cover the production costs. The riceproduction resulted in a negative income of minus 315,000 R for rice liquor farmers and minus
147,000
R for non-riceAgro-processing
Animal liquor
raising farmers.
Rice mill Crop/vegetable cultivation Off-farm businesses Employment Remittance
Rice liquor
farmers(n = 117)

1,487

2,172

(26.5%)(38.7%)

Non-riceliquor282
909
farmers(n = 87) (4.7%) (15.2%)-0

401 76754

569 142(5,601 in total)(7.2%) (1.4%)(13.5%) (10.2%) (2.5%)(5,980 in total)

299 80

2,621

1,039

750

(5.0%)(1.3%)(43.9%)(17.4%)(12.5%)

1,000

2,000

3,000

4,000

5,000

6,000

(Thousand
7,000Riel)

Fig. 1 Average annual incomes derived from economic activities
Table 2 shows the detailed figures of the average income structure from the related businesses
practiced by rice liquor farmers and non-rice liquor farmers. The table shows details such as theirannual revenue and income from each business, as well as the numbers of farmer who were engaged
in. For the purpose of this study, the costs involved in family labor were not factored into the generalcosts. All 117 rice liquor farmers earned an average income of 1,364,000 R through the rice liquor
production. Moreover, 31 out of the 117 households earned an additional 463,000 R by producing
other agro-processed foods such as processed fish, palm juice and sugar, pickled vegetables, andtraditional sweets. Of the 87 non-rice liquor farmers, 19 households earned an average income of
1,291,000 R through their involvement in agro-processing practices. The profitability of rice liquor,
which was found to be 12.7%, was lower than the profitability of other agro-processing practices,which was found to be 39.8% for rice liquor famers and 46.5% for non-rice liquor farmers. All rice
liquor farmers raised pigs, and the average income from pig rearing was found to be 2,012,000 R,
and the profitability was found to be 55.6%. Most rice liquor farmers (107) also acquired an average
income of 174,000 R through other animal husbandry involving cattle, chicken, duck, and fish, andthe profitability was found to be 13.0%. A total of 26 non-rice liquor farmers obtained an average
income of 649,000 R by raising pigs and 78 farmers obtained one of 798,000 R from other animalhusbandry, the profitability of each of which was 54.1% and 33.6%, respectively. Forty rice liquor
farmers obtained an average income of 1,172,000 R through rice milling; the profitability of this wasfound to be 38.3%. On the other hand, four non-rice liquor farmers obtained an average income of
6,495,000 R through rice milling, the profitability of which was 66.1%. For the latter, operating therice mills was the main business, whereas for the former, rice milling was a secondary business.
Thirty-five rice liquor farmers and 31 non-rice liquor farmers sold surplus crops and vegetables
such as maize, cucumber, pumpkin, watermelon, mung bean, sugarcane, cassava, and sweet potato,
and their average incomes were 253,000 R and 225,000 R, respectively. Most rice liquor farmers andnon-rice liquor farmers produced rice mainly for self-consumption, although 21 rice liquor farmers(18.6%) and 25 non-rice liquor farmers (29.8%) sold some of their rice. The incomes of riceproduction were minus 327,000 R by 113 out of 117 rice liquor farmers and minus 152,000 R by 84
out of 87 non-rice liquor farmers. The rice liquor farmers used some of their rice for rice liquor
production and feeding their pigs. Since they spent most of their time on rice liquor production andrearing pigs within their daily activities, they tended to spend more on the hiring of extra labor during
the transplantation and harvesting of rice than the non-rice liquor farmers did. As a result, the deficit
for rice among the rice liquor farmers was greater than that by the non-rice liquor farmers, although
the rice fields of the former were 31.8% larger than those of the latter group (Table 1).
In terms of non-agricultural activities, 69 (59.0%) rice liquor farmers were engaged in nonagricultural activities, while the number was 80 (92.0%) for non-rice liquor farmers. In detail, 28(23.9%) rice liquor farmers managed off-farm businesses and gained an average income of 3,152,000Ⓒ ISERD

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R through them. The profitability of these businesses was found to be 19.6%. On the other hand, 37(42.5%) non-rice liquor farmers gained an average income of 6,163,000 R through off-farmbusinesses, the profitability of which was found to be 31.1%. In this context, the average income of
the latter was 96% more than the former’s income, and the latter’s profitability through thesebusinesses was 59% more than the former’s profitability. It was found that 32 (27.4%) rice liquor
farmers were employed as agricultural labor, construction labor, and government staff. Their average
income through these sectors was 2,082,000 R. Twenty-three (19.7%) rice liquor farmers received
an average remittance of 723,000 R from family members who lived away from home. On the other
hand, 36 (41.4%) non-rice liquor farmers received an average income of 2,510,000 R throughemployment, and 33 (37.9%) received an average remittance of 1,977,000 R. The latter’s income
through employment was 21% more than the former’s income. The average remittance received by
the latter was 173% more than the remittance received by the former.
In summary, these results indicate that rice liquor farmers obtain a large part of their income
through pig husbandry and rice liquor farming, although the profitability of rice liquor production is
low compared with that of their other businesses. On the other hand, non-rice liquor farmers dependon non-agricultural businesses more than rice liquor farmers.
Table 2 Income structures of rice liquor farmers and non-rice liquor farmers
Economic activities
Annual revenue
and income

No. of operating farmers
Rice liquor farmers
Non-rice liquor farmers
Sig. 1
Sig. 1
Rice liquor Non-rice liquor Average Average Profit- Average Average ProfitRevenue Income
farmers
farmers
revenue income2 ability3 revenue income ability3
No. (% 4)

Agriculture
Agro-processing
117 (100.0)
Rice liquor
31 (26.5)
OthersAnimal husbandry
117 (100.0)
Pig rearing
107 (91.5)
Others40 (34.2)
Rice milling
35 (29.9)
Crop/vegetable
113 (96.6)
Rice5
Non-agriculture6
69 (59.0)
Off-farm businesses 28 (23.9)
32 (27.4)
Employment
23 (19.7)
RemittanceNo. (% 4)

Thousand Thousand
Riels
Riels

19 (21.8)

10,728
1,164

1,364
463

26 (29.9)78 (89.7)
4 (4.6)31 (35.6)
84 (96.6)80 (92.0)
37 (42.5)
36 (41.4)
33 (37.9)

3,622
1,339
3,059
349
305
16,112-%Thousand Thousand
Riels
Riels

%

12.7
39.8

2,775

1,291

46.5

0.00** 0.05*

2,012
174
1,172
253
-327

55.6 1,199
13.0 2,374
38.3 9,819
72.5
258-107.2
286

649
798
6,495
225
-152

54.1
33.6
66.1
87.2
-53.1

0.00**
0.34
0.03*
0.72
0.89

3,152
2,082
723

19.6 19,846
-

6,163
2,510
1,977

31.1
-

0.39
0.04*
0.36
0.36
0.00** 0.00*

0.00**
0.04*
0.02*
0.87
0.09

Note: The interviews asked the information on the previous year. Questionnaires on agricultural businesses included costand revenue information such as the price and amount of the purchased materials and equipment for one operation cycle.
On non-agricultural businesses, information on daily sales and cost of products were asked. On employment andremittance, amount of salaries and remittances in one month or one year were asked.
1The Mann–Whitney test was used to compare the averages of the revenues and incomes between the rice liquor farmers
and the non-rice liquor farmers. The average difference is significant at * 5% and ** 1% levels.
2The farmer’s labor costs were not included. 3Profitability (%) = Average income ÷ average revenue × 100.
4Proportion for 117 rice liquor farmers and 87 non-rice liquor farmers.
5Numbers (proportions) of rice liquor farmers and non-rice liquor farmers who sold rice: 21 (18.6%) and 25 (29.8%).
6Numbers (proportions) of rice liquor farmers and non-rice liquor farmers who were not involved in non-agriculturalactivities: 48 (41.0%) and 7 (8.0%). Numbers (proportions) of rice liquor farmers and non-rice liquor farmers who
depended on a single non-agricultural activity: off-farm businesses for 17 (14.5%) and 19 (21.8%), employment for 20(17.1%) and 19 (21.8%), and remittances for 12 (10.3%) and 21 (24.1%). Numbers (proportions) of rice liquor farmers
and non-rice liquor farmers.

Characteristics of Deficit and Surplus Operations of Rice Liquor Production
Table 3 describes in detail the economic status of rice liquor production. The average income
obtained from rice liquor production was 5,210 R per production batch. The average sales revenue
was found to be 51,548 R, and the total production costs were found to be 46,338 R. The average
production costs include the costs of raw materials, equipment, and the equivalent value ofⒸ ISERD

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production failure. Rice, the main raw material in this process, accounted for 81.7% of the total costs,
and the starter culture (mee sraa), fuel, and equipment accounted for 5.5%, 4.0%, and 2.6%,
respectively. The production failure equivalent was found to be 6.2% of the total costs.
Table 3 Comparison of the economic status of surplus and deficit operations of rice liquor
production
Rice liquor farmers

Average
1) Income (Riel/time)2) Sales revenue (Riel/time) 2(1) Sales price of liquor (Riel/L)(2) Sales amount of liquor (L/time)3) Total production costs (Riel/time)(1) Rice cost (Riel/time) 3
a. Rice unit price (Riel/kg)
b. Rice amount (kg)/time(2) Starter cost (Riel/time)(3) Fuel cost (Riel/time)(4) Equipment cost (Riel/time)(5) Failure cost (Riel/time) 4
a. Failure frequency (time/year)
b. Production frequency (times/year)4) Experience (years)5) Age of husbands
Age of wives

Total farmers Deficit group Surplus group Sig. 1
n = 117n = 37n = 80(100.0%)(31.6%)(68.4%)
Amount %AmountAmount5,210-8,065
11,3490.00**
51,54845,35954,4110.00**
1,561
1,435
1,619
0.00**
33.2
31.4
34.0
0.09
46,338 100.0
53,42343,0620.00**
37,869 81.7
42,66035,6530.00**
1,890
20.2
2,550
1,850
1,204
2,865
9.7
211
7.1
39.9
38.5

5.5
4.0
2.6
6.2

1,994
21.6
2,895
2,532
1,252
4,084

1,842
19.6
2,391
1,534
1,183
2,301

0.00**
0.04*
0.94
0.04*
0.02*
0.05*

12.7
186
6.3
41.3
39.5

8.4
223
7.4
39.2
38.0

0.02*
0.07
0.32
0.23
0.34

Note: The interviews asked the questions on rice liquor production including production cost and revenue(sales) information such as the price and amount of the purchased raw materials, and sales amount and unit
price for one time production, equipment cost, production frequency, and experiences.
1The Mann–Whitney test was used to compare the averages between the surplus and deficit groups. The
average difference is significant at * 5% and ** 1% levels.
2The average sales revenue= 1 ∑N ab . 3 The average rice cost= 1 ∑N ab .
ii
N l̇=1N l̇=1
4 Farmers recognized production failure when retailers and consumers refused to purchase the liquor due
to its poor quality (Failure cost/time = Revenue/time × Failure frequency/year ÷ Production
frequency/year).

Thirty-seven households (31.6%) of the rice liquor farmers in the region faced a deficit of an
average of minus 8,065 R in their operation, whereas 80 households (68.4%) earned a profit, of
11,349 R on average. For the deficit group, the average revenue through sales was found to be 45,359
R, which is 83.4% of the revenue earned by the surplus group (54,411 R). The average total
production costs for the deficit group were 53,423 R, which is 12.4% more than the surplus group(43,062 R). In terms of revenue, the average sales price for the deficit group liquor was 1,435 R/L,
which is 11.4% less than the sales price of liquor produced by the surplus group (1,619 R/L). The
deficit group produced rice liquor at 31.4 L/batch, which is 7.6% less than the liquor amount of thesurplus group (34.0 L/batch). In terms of production costs, the cost of rice for the deficit group was
42,660 R (79.9% of the total product costs), which was 20% more than the amount paid by the surplus
group (35,653 R, 82.8% of the total product costs). The deficit group used 21.6 kg of rice per batch
and purchased rice at 1,994 R per kg; these amounts were 10% and 8% more than those by the surplus
group, respectively. The deficit group experienced production failure at a rate of 12.7 times per year,
which is 51% more than the failure rate by the surplus group. For the deficit group, the loss incurredfrom such failure was equivalent to 4,084 R, which is 77% more than the loss for the surplus group,and constituted the second highest cost. On average, the deficit group produced rice liquor 186 times
per year, which is 16.6% less than the value of the surplus group.
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These results indicate that a low sales price, high cost of rice, and frequent production failure
were the key factors that caused deficits for rice liquor farmers. Younger and more experienced rice
liquor farmers produced rice liquor more frequently and also tended to earn a greater profit as a result.
Table 4 shows the conditions that characterize production failures. Keywords that were
frequently encountered in responses pertained to “unsuccessful fermentation” characterized by acidic
and spoiled smell/taste, bubble expansion of the fermented rice, and a “burnt smell”. These results
indicate that the failures typically occurred during the fermentation and distillation stages.
Table 4 Characteristics of production failure as identified by the farmers
Criteria
Frequency1%
2
Unsuccessful fermentation
59
46.8
Burnt smell
59
46.8
Low amount of liquor
6
4.8
Uncooked rice
2
1.6
Total
126
100.0
Note: 1Frequent use of keywords in response to an open-ended question. Interviewees wereasked to identify situations of “production failure”; 126 keyword appearances by 100
respondents were analyzed.
2Unsuccessful fermentation was characterized by “acidic” and “spoiled” smell/taste andbubble expansion of the fermented rice.

Issues and Areas of Improvement Identified by Rice Liquor Farmers
The issues identified by the rice liquor farmers who participated in this study are shown in Table 5.
The areas in which they expected improvement are shown in Table 6. Closed-ended questions wereused to elicit information about the two most important issues and expected improvement areas fromthe farmers. In terms of issues, the respondents most commonly mentioned expensive rice (55.5%),
followed by the low quality of products (40.9%), low sales price (21.8%), scarcity and high cost offuel (20.0%), and low volume of alcohol (18.2%). In terms of expected improvements, therespondents most commonly mentioned the production techniques (79.8%), followed by the qualityof rice liquor (60.5%), sales price (23.7%), productivity (12.3%), and the cost of rice (4.4%). Amongthe 91 farmers who expected production techniques to be improved, 54 indicated that the quality of
the rice liquor should also be improved simultaneously. These results indicate that rice liquor farmers
place emphasis on improving production techniques, while also acknowledging the need to improve
the quality of their product.
Table 5 Issues related to rice liquor production
IssuesExpensive rice
Low quality
Low sales priceScarcity of fuel (expensive)
Low alcohol volume
Difficulties acquiring sanitary water
Late or incomplete payment by buyer
Production failure
OthersTotal

Answers
61
45
24
22
20
17
13
10
8
220

%
n = 110
55.5
40.9
21.8
20.0
18.2
15.5
11.8
9.1
7.2

Table 6 Improvements farmers expected inrice liquor production processes
Improvements
Production techniques
Quality
Sales price
Productivity
Cost of rice
Marketing and sales
Packaging/bottling
OthersTotal

Note: A closed-ended question was used to ask interviewees to
identify the two most important issues in rice liquor production.
Total respondents: 110. Unavailable answers: 7

Answers % (n = 114)
91
79.8
69
60.5
27
23.7
14
12.3
5
4.4
5
4.4
4
3.5
13
11.3
228

Note: A closed-ended question was used to ask
interviewees to identify the two most expectedareas of improvement. Total respondents: 114. No
answers: 1. Unavailable answers: 2

Table 7 shows the keywords typically used by the farmers to characterize low-quality rice liquor.
The most frequently used keyword was “acidic smell/taste” (37.9%), followed by “burnt smell/taste”Ⓒ ISERD112

IJERD – International Journal of Environmental and Rural Development (2020) 11-1

(22.0%). Other frequently used words included “watery taste,” “spoiled smell/taste,” “stimulus
smell/taste,” “cloudy,” and “addition of industrial alcohol.”Table 8 compares the sales prices and number of production failures of the 45 farmers who
answered that “quality” was one of the most important issues with those of the 65 farmers who did
not in Table 5. The sales price of rice liquor for the former was 1,495 R per liter, which is 6.7% lower
than the sales price for the latter. The former tended to experience more production failures. On
average, they experienced failures 12.2 times per year, whereas the latter experienced failures 8.6
times per year. These results show that the farmers who experienced more production failures tended
to recognize the poor quality of their products and, as a result, sold their rice liquor at a lower price.
Table 7 Characteristics of low-quality rice liquor Table 8 Comparison of the operational
status between the farmers who
Characteristics
Frequency1 %answered that quality was an
Acidic smell/taste
67
37.9
important issue and those who
Burnt smell/taste
39
22.0
did not
Watery taste (no smell/taste)
23
13.0
Spoiled/bad/strange smell/taste
Stimulus smell/taste
CloudyAddition of industrial alcohol
Others
Total

13
8
7
6
14
177

7.3
4.5
4.0
3.4
7.9
100.0

Note: The question aimed to identify the characteristics
of low-quality rice liquor, as perceived by the farmers.
Overall, 102 out of 117 respondents provided answers;
the keywords were mentioned a total of 177 times.
Fifteen respondents had no opinion. 1Frequent use of
keywords in response to an open-ended question.

Farmers’ recognition Farmers who Farmers who didof the poor quality as answered 1
not answered 2
an important issue(n = 45)(n = 65)

Sig. 3

Sales price (Riel)/L
Production failures(times)/year

1,495

1,592

0.15

12.2

8.6

0.26

Note: 1 Farmers who answered that “quality” was animportant issue in Table 5.
2 Farmers who did not answer that “quality” was animportant issue in Table 5.
3 The Mann–Whitney test was used to compare theaverages between the two groups.

Comparisons of the income structures between the rice liquor farmers and non-rice liquor
farmers revealed that the rice liquor farmers mainly depend for income on agricultural activities suchas rice liquor production and pig rearing, whereas non-rice liquor farmers depend for income on nonagricultural activities such as off-farm business enterprises, labor work, and remittances from family
members living elsewhere. However, producing rice liquor is not very profitable, and around 30%
of rice liquor farmers faced deficits in their rice liquor business. Improving the economic benefits ofproducing rice liquor may help economically uplift rice liquor farmers.
Analysis of the economic status of the rice liquor farmers revealed the key factors that cause
deficits in their rice liquor businesses. These factors included the low sales price of rice liquor, highcost of rice, and low productivity, such as more volume of raw rice, less volume of product, andhigher rate of production failure.
Rice liquor farmers in rural areas expected improvements in the production techniques used andin the quality and productivity of liquor that they produce. Improving the quality of rice liquor may
also enable the farmers to increase their sales price and reduce the frequency of production failure.
Gaining more experience in terms of technique and management could improve the operational statusof rice liquor farmers who are currently in a deficit in their business.
Thus, modifying production techniques to improve the quality and productivity of rice liquor
and to reduce the rate of production failure are key strategies to improve the economic benefits ofrice liquor production. Preventing production failures and improving productivity would directly
decrease the costs and increase profitability. Solving the issues during fermentation and distillationthat the farmers had noted could contribute to reducing production failures and improving the qualityof their liquor by reducing low-quality characteristics such as an acidic or burnt smell/taste. If farmers
had confidence in the quality of their product, they could increase its sales price, which would also
increase their revenue.
CONCLUSIONⒸ ISERD

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This study has identified the factors that lead to deficits in the rice liquor business in rural areas ofCambodia. Technical modifications to improve the quality and productivity of rice liquor, and to
reduce the likelihood of production failure, are key strategies to increase profitability.
ACKNOWLEDGEMENTS
We thank Royal University of Agriculture, Cambodia for supporting this survey. This study was
supported by Grants-in Aid for Scientiﬁc Research (No. 20405045) and Cooperation Bases System(34-G-G0003) from the Ministry of Education, Culture, Sports, Science and Technology of Japan.
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Kozaki, M., Iino, H., Thuoc, T.L., Ho, P.T. and Seki T. 2002. Rice wine of south highland of Annamese
Cordillera (Vietnam) –ruou can and ruou nep–. J. Brewing Soc. Japan 97, 327-337. (in Japanese)
National Institute of Statistics. 2009. General population census of Cambodia 2008. National Report on Final
Census Results, National Institute of Statistics of Ministry of Planning, Royal Government of Cambodia(Phnom Penh), 291.
Royal Government of Cambodia, 2006, National strategic development plan 2006-2010. Royal Governmentof Cambodia (Phnom Penh), 211.
Royal Government of Cambodia, 2013, National strategic development plan 2014-2018, Royal Governmentof Cambodia, Phnom Penh.
Tornimbene, B. and Drew, T. 2012. Characterisation of Swine Production systems in the Cambodian Mekong
lowland region. Royal Veterinary College, University of London. 30.
Yagura, K., Nishimura, Y., Keo, S. and Matsumoto, T. 2010. Roles and obstacles of agro-processing industries
in rural Cambodia. J. Agr. Develop. Studies, 20, 1-8. (in Japanese with English summary)
Yamamoto, S. and Matsumoto, T. 2011. Rice fermentation starters in Cambodia: Cultural importance andtraditional methods of production. Southeast Asian Studies, 49 (2), 192-213.
Vathana, T. and Takeya, H. 2004. Economic evaluation of local feed resources in fattening pigs in Cambodia.
Journal of Agricultural Development Studies, 15, 69-79Ⓒ ISERD

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Research article
erd

Evaluation of Bacterial Contamination Level in Pickles Soldat Wet Market in Cambodia - Part 1- In Case of the
Samples from Kampong Cham and Phnom Penh
SHUKI MURAMATSU*
Faculty of Life and Environmental Sciences, Showa Women’s University, Tokyo, Japan
Email: s-muramatsu@swu.ac.jp

MASATAKA UCHINO
Faculty of Life Sciences, Tokyo University of Agriculture, Tokyo, JapanYURI TANIKOKA
Faculty of International Agriculture and Food Studies, Tokyo University of Agriculture, Tokyo,
Japan

SOKLY SORM
Graduate School of Agriculture, Tokyo University of Agriculture, Tokyo, JapanDAIKI OKA
Faculty of Applied Bioscience, Tokyo University of Agriculture, Tokyo, JapanYOSHIKI MURAMATSU
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, JapanTORU NAKAJIMA
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, Japan

MOTOE SEKIDO
Department of Food and Nutrition, Yamanashi Gakuin Junior College, Tokyo, Japan

TAKAHIKO NAKAMURA
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, JapanCHIM CHAY
Faculty of Agro-Industry, Royal University of Agriculture, Phnom Penh, Cambodia

MACHITO MIHARA
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, Japan
Received 15 January 2020

Accepted 23 April 2020(*Corresponding Author)

Abstract Agriculture is the main industry in Cambodia and home-made agricultural products
for local Khmer people are commonly sold at wet markets. However, food poisoning andfood-related diseases frequently occur due to these home-made products. The improper
handling of food by vendors, inadequate storing conditions, and cross-contamination risk
while on display, were observed when inspecting wet markets. Sanitary conditions in the wet
markets were poor and the food handlers seemed to treat food based on their personal
experience. In this study, we aimed to investigate the hygiene level of food products sold in
wet markets in Cambodia. In Kampong Cham, a typical small town, processed foods are
consumed in the local area and are also transported to large cities, like Phnom Penh. Pickled
vegetables, which are a popular ready-to-eat food in Cambodia, were selected and assessed
for microbial contamination. A total of 17 pickles were collected from the local market in
Kampong Cham and we measured the Brix value, salt concentration, and pH in these samples.
Adenosine triphosphate (ATP) concentration was also measured, because the amount of ATPⒸ ISERD

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in a food sample indicates the approximate microbial contamination level. The five samples
with the highest ATP values were used for a quantitative evaluation of microorganism
contamination. In addition, five other samples with relatively low ATP concentrations were
tested for comparison. An assessment of colony number and morphology indicated that nineof the samples showed initial putrefaction or putrefaction. There was one coliform-positive
sample. Similarly, three pickles purchased in Phnom Penh showed putrefaction and two
samples from these three pickles were coliform-positive. These results indicated that foodsanitary conditions are unsafe and there is a critical need for improving food safety at wet
markets in Cambodia.
Keywords food safety, contamination, wet market, pickles

INTRODUCTION
Agriculture is the main industry in Cambodia and home-made agricultural products for local Khmer
people are commonly sold at wet markets. However, food poisoning and food-related diseases occur
frequently due to these home-made products. Many consumers are not aware of the likelihood of
spoilage of the purchased product when it is stored at an unsuitable temperature for a long period(Sanlier, 2009; Odeyami et al., 2019). Therefore, food handlers and service employees have a
responsibility to provide safe food products. However, the lack of personal hygiene consciousness is
a common cause of foodborne illness (Taulo et al., 2009). There are several reports describing a low
level of knowledge regarding food hygiene among food handlers (Osaili et al., 2013; Baluka et al.,2014; Emmanuel et al., 2015). In Cambodia, the major reason for foodborne illness is that the vendors
have little knowledge of food hygiene and safety. Large-scale foodborne outbreaks are monitored byevent-based surveillance in Cambodia, but there are no formal statistics regarding individual foodpoisoning cases (Vandy et al., 2012). In addition, most foodborne diseases that occur at home areunder-diagnosed and/or under-reported (Redmond et al., 2003; Keegan et al., 2009; Vrbova et al.,2012). Therefore, the current status of hygiene conditions in Cambodia requires clarification. Here,
we aimed to evaluate the hygiene conditions of the food sales environment and food products usedby the local Khmer people. These inspections will lead to practical ways of improving food safety.
Pickles are a very common ready-to-eat food in Cambodia. There are many types of vegetables
used to make pickles. The basic procedure for making pickles is to wash the vegetables and then add
sugar, salt, and other ingredients, such as fish sauce, soy sauce, garlic, chili, and rice. Most pickles
stored in jars are placed in the sun or in a room at ambient temperature for 1 to 4 days. Some of thesepickles are fermented spontaneously by lactic acid bacteria (Chrun et al., 2017). Althoughfermentation increases the shelf-life and the nutritional value of the product, the raw materials canbe sources of microbial contamination. Therefore, these home-made fermented foods are often risky(Anal et al., 2020) and food poisoning incidents are often caused by these pickled products. Since
these pickled products are consumed daily, they can be a critical hazard if not prepared hygienically.
Investigating the quality of pickled products requires the local Khmer people to change their way of
thinking about food safety.
Kampong Cham (KPC) is a typical small town and the food products produced there areconsumed in the local area and also transported to large cities like Phnom Penh (PP). Pickled
vegetables, which are a popular ready-to-eat food in Cambodia, were selected and examined for
microbial contamination.
OBJECTIVE
The objectives of this study were: 1) to investigate the safety levels and simple chemical propertiesof pickles sold in the wet market in Cambodia, 2) to discuss the cause of the unsanitary conditionsof these samples.

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METHODOLOGY
In this study, we assessed the safety conditions of food and performed bacterial contamination
inspections of pickles sold at wet markets in Cambodia.
Market Inspection
The market inspection was conducted through observation of equipment, facilities, and displaying
and storage condition of products. The results of the evaluation of these conditions were ascertainedby asking to seller.
Food Samples Used in This Study
Popular pickled and fermented vegetables were collected from local wet markets in KPC in August,2018. The products we selected by randomly but only the products from the vendors who are able totell the detailed information such as raw materials, shelf life and the process from the production toselling. In total, 17 food samples were purchased from four vendors at three different wet markets.
In PP, three food samples were purchased at a wholesale market. The food samples used in this study
are shown in Table 1. The properties of all food samples were measured within 2 hours after
purchasing. Ten KPC samples selected for bacterial inspection were placed in a sterilized storage
bag and kept at 4°C for 7 days until examination. PP samples were kept for 6 days under the same
conditions as the KPC samples.
Measurement of Food Properties
The sugar and salt content and pH of foods affect microbial growth and these values can be indicatorsof the preservability of the food. We measured Brix sugar content (soluble solids content), salt content,
and pH of food samples using a Brix refractometer (Atago, Tokyo, Japan), a salt meter (Horiba,
Kyoto, Japan), and a pH meter (Horiba), respectively. ATP content is an approximate indication of
the microbial contamination level of food samples. The standard ATP content value required to satisfy
the sanitary conditions of food-handling rooms and equipment is 36 RLU. Therefore, samples
showing greater than 100 RLU were selected as candidates for microbial investigation. Somesamples with lower ATP content were also investigated for comparison. ATP content was measured
using a luminometer and an ATP assay kit (SystemSURE Plus; Nitta, Osaka, Japan).
Sample Preparation and Bacterial Detection by Pour Plate Method
Ten grams of food sample was mixed with 90 mL of saline containing 0.1% peptone in a sterilized
storage bag. To make a homogeneous suspension, the sample was homogenized using a stomacher.
A series of sample dilutions was used for the detection of general viable bacteria, using standard agar
medium and coliform bacteria, using desoxycholate agar medium. The solidified plates wereincubated at 35 for 48 hours.
RESULTS AND DISCUSSION
Inspection of Wet Markets in Kampong Cham
To evaluate the hygiene level of food eaten on a daily basis in Cambodia, we inspected wet markets
in KPC in August, 2018. Most food products were stored at room temperature and therefore, those
products that do not have stable preservability at room temperature were susceptible to deterioration.
In the wet markets, retailers displayed their products close together and nearly all pickled products
were stored in a plastic bin at room temperature. The celling and the lights of the market were partly
covered with dust and some unattended raw garbage was observed in the passage. Because picklesⒸ ISERD

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were displayed without a lid, flies swarmed around them. From these inspection results, the wet
market did not appear to have appropriate conditions for storing food products.
When most vendors served the pickles into a takeout bag, they did not usually use gloves, but
served by bare hand. The risk of cross-contamination was also considered to be high for various
reasons. For example, cross-contamination could occur between pickled products and other raw
products and utensils, which can harbor microbes. The accumulation of waste and the manner inwhich it was disposed and stored may also result in the transport microbes and may lead to foodproduct contamination. Although vendors decided on an approximate expiration date, unsold
products were stored at room temperature. The hygiene conditions of the wet market meant that the
food being sold was highly susceptible to microbial contamination.
From the inspection of the wet market, we concluded that the food products being sold were nothandled in a hygienic manner. Therefore, we performed an assessment of food contamination levels
in pickle products from the wet market.
Chemical Properties of Pickles from KPC and PP Markets
Popular pickle products were selected and examined for quality and safety. Seventeen pickle samples
were purchased at the local wet markets in KPC and three pickle samples were purchased from a wet
market in PP. These samples were measured for Brix value, salt concentration, pH, and ATP content
immediately after purchase. The samples included several types of pickles. The Brix values ranged
from 5.7 to 49.5 °Bx and salt concentration ranged from 1.4 to >25%. The pH values ranged from
3.6 to 5.2 (Table 1).
Table 1 Chemical properties of pickles from KPC and PP markets
RegionVendorSample No.

Product nameATP (RLU)

Brix

pH

Salt (%)

Kampong
Cham

A

a-1

Young Pickled Onion

5

33.5

3.7

8.1

a-2

Slice Fermented Cucumber

4

32.6

4.2

5.5

a-3

Fermented Cucumber

5

30.8

4.6

12

a-4
a-5

Young Pickled Cucumber
Salty-sweet Raddish

378
94

10.7
16.0

4.3
5.2

3.8
5.6

b-1

Young Pickled Onion

21

31.8

3.6

7.8

b-2

Young Pickled Cucumber

542

40.9

4.3

1.4

b-3

Salty-sweet Radish

91

25.9

5.2

5.8

b-4

Fermented Cucumber

2

43.0

4.0

13

b-5

Fermented Green Mustard

184

16.9

3.6

3.0

b-6

Fermented Ginger

4

31.4

4.0

8.3

b-7

Fermented Papaya

400

49.5

4.9

9.1

b-8

Fermented Mustard

64

5.90

3.9

-

c-1

Fermented Papaya

148

48.3

4.3

9.6

c-2

Fermented Small Mustard

212

9.80

4.2

3.2

d-1

Fermented Sprout

5

14.3

3.9

4.4

d-2

Fermented Cabbage

1

16.8

4.0

3.8

E

p-1

Fermented Cucumber

1

40.5

4.1

7.2

F

p-2

Fermented Green Mustard

1,882

5.70

4.0

>25

G

p-3

Fermented Green Mustard

201

6.60

-

>25

B

C

D

Phnom
Penh

Generally, most bacteria required a minimum water activity (aw) of 0.88-0.91, most yeasts
require a minimum aw of 0.88 and regular molds need an aw < 0.88 (Matthews, et al., 2017). TheⒸ ISERD

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theoretical amounts of salt and sugar required to achieve an aw value of 0.88 (salt) and 0.92 (sugar)
are 17.8% and 62.6%, respectively. The minimum and maximum ATP content values were 1 and
1,882 RLU, respectively, with 55% of food samples showing ATP content values greater than 36
RLU.
Microbial Contamination Level of Food Samples from KPC and PP Markets
Of the 17 food samples from the KPC market, 10 were examined for general viable bacterial counts
and for coliform bacteria (Table 2). Four samples from the KPC markets and two samples from thePP market had bacterial counts greater than 7.00 log10 colony-forming units (CFU)/g. These samples
showed initial putrefaction and they were not suitable for consumption. Moreover, after observing
the colonies on the detection plates, 12 samples were found to contain various unwanted microbes.
One sample from the KPC market and two samples from the PP markets were found to be coliformpositive. Generally, coliform bacteria are not able to grow at pH values less than 4.0 (Besser et al.,
1993; Zhao et al., 1994), but some E. coli strains are reported to grow even at pH 3.8 (Presser et al.,
1997; Zhao et al., 1993). The pH values of the coliform-positive samples were 4.1 and 4.0 for samples
p-1 and p-2, respectively. It was not clear if E. coli could proliferate under these pH conditions, but
the contamination with coliform bacteria was considered to have occurred during the preparation ofthese products. The pickle samples with lower salt concentration, such as a-4, b-2, and c-2, tended toshow higher log10 CFU/g values. However, other samples with high salt and sugar concentrations
also contained unwanted microbes. The result of Table 2 showed that 55% of the sample from the
KPC market and all samples from the PP market were not enough microbial quality to eat. Taking
into consideration our inspection results, it is assumed that some steps lead to the contamination. The
causes of contamination are presumed by raw materials, the process of food processing, storage
condition. These steps might have contained complicated factors and there may multiple routes of
the contamination.
Table 2 Microbial contamination level of food samples from KPC and PP markets

Kampong Cham

Phnom Penh

Sample
No.

log10
CFU/g

Coliform

a-1

3.36

Negative

a-4

7.77

Negative

b-2

8.00<

Negative

b-3

3.30

Negative

b-4

3.68

Negative

b-7

5.48

Positive

b-8

6.88

Negative

c-1

6.27

Negative

c-2

8.00<

Negative

d-1

7.00

Negative

p-1

4.06

Positive

p-2

7.18

Positive

p-3

7.14

Negative

CONCLUSION
In this study, we evaluated the hygiene conditions of local wet markets in KPC and investigated thesafety level and chemical properties of pickled products sold at wet markets in KPC and PP. The
pickle samples were found to be highly contaminated with unwanted microbes and these productsⒸ ISERD

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were unsuitable for consumption. This result strongly suggested that the principle cause of foodcontamination is improper food handling. In Cambodia, food handlers and vendors seem to lacksufficient knowledge of food safety and do not have the opportunity to practice safe food handling.
Food safety education is considered to be one of the challenges for the future to reduce the incidenceof food poisoning.
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Anal, A.K., Perpetuini, G., Petchkongkaew, A., Tan, R., Avallone, S., Tofalo, R., Nguyen, H.V., Chu-Ky, S.
Ho, P.H., Phan, T.T. and Wache, Y. 2020. Food safety risks in traditional fermented food from South-East
Asia. Food Control, 109. https://doi.org/10.1016/j.foodcont.2019.106922
Baluka, S.A., Miller, R. and Kaneene, J.B. 2014. Hygiene practice and food contamination in managed foodservice facilities in Uganda. Afr. J. Food Sci., 9 (1), 31-42.
Besser, R.E., Lett, S.M., Weber, J.T., Doyle M.P., Barrett, T.J., Wells, J.G. and Griffin P.M. 1993. An outbreakof diarrhea and hemolytic uremic syndrome from Escherichia coli O157: H7 in fresh-pressed apple cider.
JAMA, 269, 2217-2220.
Chrun, R., Hosotani, Y., Kawasaki, S. and Inatsu, Y. 2017. Microbiological hazard contamination in fermented
vegetables sold in local markets in Cambodia. Biocontrol Sci., 22 (3), 181-185.
Emmanuel, A., Mangai, J.M, Kayong, E.A., Afoi, B.B., Goshit, J.D., Naman, K. and Innocent, O. 2015.
Assessment of practice of food safety and hygiene among food vendors within Jos North Local Government
Area of Plateau State. Nigeria. Int. J. Med. Health Res., 1 (2), 83-86.
Keegan, V.A., Majowicz, S.E., Pearl, D.L., Marshall, B.J., Sittler, N., Knowles, L. and Wilson, J.B. 2009.
Epidemiology of enteric disease in C-EnterNet’s pilot site – Waterloo region, Ontario, 1990 to 2004. Can.
J. Infect. Dis. Med Microbiol., 20 (3), 79-87.
Matthews, K.R., Knile, K.E. and Montville, T.J. 2017. Food microbiology, Fourth ed. American Society for
Microbiology, ASM Press, USA.
Odeyami, O.A., Sani, N.A., Obadina, A.O., Saba, C.K.S., Bamidele, F.A., Abughoush, M., Asghar, A.,
Dongmo, F.F.D., Macer, D. and Aberound, A. 2019. Food safety knowledge, attitudes and practices among
consumers in developing countries: An international survey. Food Res. Int., 116, 1386-1390.
Osaili, T.M., Jamous, D.O.A., Obeidat, B.A., Bawadi, H.A., Tayyem, R.F. and Subih, H.S. 2013. Food safetyknowledge among food workers in restaurants in Jordan. Food Control, 31, 145-150.
Presser, K.A., Ratkowsky, D.A. and Ross, T. 1997. Modelling the growth rate of Escherichia coli as a functionof pH and lactic acid concentration. Appl. Environ. Microbiol., 63, 2355-2360.
Redmond, E.C. and Griffith, C.J. 2003. Consumer food handling in the home: A review of food safety studies.
J. Food Prot., 66 (1), 130-161.
Sanlier, N. 2009. The knowledge and practice of food safety by young and adult consumer. Food Control, 20,538-542.
Taulo, S., Wetlensen, A., Abrahamsen, R.K., Narvhus, J.A. and Mkakosya, R. 2009. Quantification andvariability of Escherichia coli and Staphylococcus aureus cross-contamination during serving andconsumption of cooked thick porridge in Lungwena rural households, Malawi. Food Control, 20, 11581166.
Vandy, S., Leakhann, S., Phalmony, H., Denny J. and Roces, M.C. 2012. Vibrio parahaemolyticus ennteris
outbreak following a wedding banquet in a rural village – Kampon Speu, Cambodia, April 2012. WPSAR,
3 (4), 25-28.
Vrbova, L., Johnson, K. Whitfield, Y. and Middleton, D. 2012. A descriptive study of reportable
gastrointestinal illnesses in Ontario, Canada, from 2007 to 2009. BMC Public Health, 12, 970.
Zhao, T., Doyle, M.P. and Besser, R.E. 1993. Fate of enterohemorrhagic Escherichia coli 0157: H7 in apple
cider with and without preservatives. Appl. Environ. Microbiol., 59, 2526-2530.
Zhao T. and Doyle M.P. 1994. Fate of enterohemorrhagic Escherichia coli O157: H7 in commercial
mayonnaise. J. Food Prot., 57, 780-783.

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Research article
erd

Evaluation of Bacterial Contamination Levels in Pickles
Sold at Wet Market in Cambodia -Part 2- Detection of
Several Food-poisoning Bacteria of 48 Samples
from Phnom Penh
SHUKI MURAMATSU*
Faculty of Life and Environmental Sciences, Showa Women’s University, Tokyo, Japan
Email: s-muramatsu@swu.ac.jp

MASATAKA UCHINO
Faculty of Life Sciences, Tokyo University of Agriculture, Tokyo, JapanSOKLY SORM
Graduate School of Agriculture, Tokyo University of Agriculture, Tokyo, JapanYURI TANIKOKA
Faculty of International Agriculture and Food Studies, Tokyo University of Agriculture, Tokyo,
Japan

DAIKI OKA
Faculty of Applied Bioscience, Tokyo University of Agriculture, Tokyo, JapanYOSHIKI MURAMATSU
Faculty of Regional Environment Science, Tokyo University of Agriculture, JapanTORU NAKAJIMA
Faculty of Regional Environment Science, Tokyo University of Agriculture, Japan

MOTOE SEKIDO
Department of Food and Nutrition, Yamanashi Gakuin Junior College, Tokyo, Japan

TAKAHIKO NAKAMURA
Faculty of Regional Environment Science, Tokyo University of Agriculture, JapanCHIM CHAY
Faculty of Agro-Industry, Royal University of Agriculture, Phnom Penh, Cambodia

MACHITO MIHARA
Faculty of Regional Environment Science, Tokyo University of Agriculture, Japan
Received 15 January 2020

Accepted 23 April 2020(*Corresponding Author)

Abstract Some pickles transported from the local province are sold in the wet markets ofPhnom Penh, Cambodia. Therefore, products sold in Phnom Penh can be considered
representative samples that reflect the approximate levels of bacterial contamination in thefood supply chain of Cambodia. In this study, we conducted a microbiological inspection of
the food samples from Phnom Penh to investigate the seriousness of the unsanitary conditionsof food products. Eight kinds of pickles were purchased from three wholesale markets one
week apart on two separate occasions. A total of 48 samples were used for microbial testing,and the values of Brix, pH, and salt content of these samples were measured. Although all
food samples were purchased from the same markets and vendors, several pickles showed
different results in the measured values between the first and second purchases. To determine
the contamination level of the pickles, we targeted general viable bacteria, coliforms, fungi,Ⓒ ISERD

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Salmonella, Staphylococcus aureus, and Bacillus cereus. The total number ofmicroorganisms detected by the aerobic plate count showed that 15 of 48 samples contained
more than 7 log10 CFU/g of sample. Pickled onion and mustard showed relatively higher
number of microorganisms compared to other products. Of the 48 pickles, 58.3% werecoliform positive, 41.7% of the samples were Salmonella positive, and 83.3% and 37.5%
were S. aureus and B. cereus positive samples, respectively. These results indicate that the
products from the wholesale market of Phnom Penh are significantly contaminated withbacteria. In general, food contamination result from raw materials, cross-contamination, andpoor water quality. Thus, it is important to educate food handlers regarding the practicalknowledge to ensure proper food sanitation practices. The results of this study will be usefulto reveal the cause of food contamination and to develop appropriate countermeasures.
Keywords food contamination, food poisoning bacteria, wet market, pickles

INTRODUCTION
Pickles are ready-to-eat (RTE) products and are very popular to Khmer people in Cambodia.
However, due to the absence of heat sterilization prior to consumption, the safety of the products isa major concern. Our previous study had found that several pickles sold at the wet markets of
Kampong Cham and Phnom Penh (PP), Cambodia were unsanitary. In addition, the hygiene practicesof the local wet market were very poor, which subsequently lead to food contamination. We speculatethat these unsanitary conditions of food and food-processing and the selling environment were
derived from the food handlers who had limited awareness and knowledge on food hygiene.
The vegetables that are often the raw material for pickled foods can harbor pathogens such as
Escherichia coli and Salmonella (Francis, 1999; Matthews, 2017; Skočková, 2013). Oliveira et al.
(2011) reported that there were Salmonella and thermotolerant coliforms in RTE vegetables. Further,
there are several kinds of food spoilage microbes and foodborne pathogens that account for the
majority of foodborne diseases. The World Health Organization reported that the leading causes offoodborne deaths included Salmonella and enteropathogenic E. coli (WHO, 2015). B. cereus is awell-known food poisoning bacteria. B. cereus has caused food-borne outbreaks via a large variety
of foods in many countries (Tewari, 2015). S. aureus is a ubiquitous bacterium that causes potentiallyfatal infections (Kadariya, 2014). To assess the safety and quality of food products in Cambodia, theinformation on the actual hygiene practices for food products served to local Khmer people is very
important. However, scientific reports on food safety in regards to the pickle products in Cambodia
are nearly absent except for one report by Chrun et al. (2017). Therefore, it is necessary to accurately
assess the microbiological safety of food products in Cambodia. In order to determine the foodhygiene level in Cambodia, we investigated the major foodborne pathogens and food spoilage
microbes in pickled products. The level of microbe contamination in samples we tested reflect thesanitary conditions of the wet market. In this study, we investigated the pickle products sold in PP.
Because some products are transported from the local province to PP, the products sold in PP
represent a model of the food supply chain in Cambodia. Due to the large population of PP, microbial
food contamination leads to foodborne disease outbreaks.
OBJECTIVE
The objectives of this study were 1) to analyze the current status of food and microbiological qualityof pickles sold in PP, 2) to assess the substantial food hygiene problem in Cambodia.
METHODOLOGY
Evaluation of the Quality of Pickle ProductsⒸ ISERD

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Eight kinds of common pickle products were purchased from three major wholesale markets of PP.
The popular pickle products were selected based on interviews with Khmer people. They werecollected in March, 2019. In the following week, the same pickles were purchased from the same
shops. In total, 48 samples were collected and used in this study (Table 1). The samples were stored
at 4°C for one day. The values of Brix, salt concentration, and pH of the samples were measured.
Table 1 Pickles sample used in this study
MarketSampleProduct name

SampleProduct name

A/B/C

A
B
C
D

Fermented papaya (sliced)

E
F
G
H

Pickled mustard (small)Fermented cucumber
Pickled young onion
Pickled mustard (big)Pickled cucumber
Sweet salty radish
Kong chay

Microbiological Examination of Pickle Products
Ten grams of the food sample was mixed with 90 mL of Maximum Recovery Diluent (Merck KGaA,
Darmstadt, Germany) solution in a sterilized storage bag. To make a homogeneous suspension, thesample was homogenized by a stomacher. After stomaching, 1 mL of the sample suspension was
mixed with 9 mL of Maximum Recovery Diluent solution in a sterile tube. The serial dilution samples
were prepared and used for testing. For the detection of S. aureus and Salmonella, Buffered Peptone
Water (Merck KGaA, Darmstadt, Germany) solution was used for sample preparation. The standard
agar medium (AS ONE, Osaka, Japan) was used for the general viable count. Desoxycholate agar
medium (AS ONE, Osaka, Japan) was used for the detection of coliform. Potato Dextrose Agarmedium (AS ONE, Osaka, Japan) was used for fungi. CHROMagarTM Salmonella medium (KANTOCHEMICAL, Tokyo, Japan) was used for Salmonella. Mannitol salt agar medium with egg yolk
(KANTO CHEMICAL, Tokyo, Japan) was used for S. aureus. BACILLUS CEREUS AGAR BASE
(KANTO CHEMICAL, Tokyo, Japan) with Oxoid Polymyxin B Supplement (KANTO CHEMICAL,Tokyo, Japan) and egg yolk was used for B. cereus. 10 μL of the dilution samples aliquoted onto
plates. For general viable counts and the detection of coliform, S. aureus, plates were incubated at
35°C for 48 hours. For the detection of Salmonella and B. cereus, plates were incubated at 35°C for
24 hours. For the detection of fungi, plates were incubated at 30 ℃ for 5 days. All experiments wereconducted three times independently.
RESULTS AND DISCUSSION
Evaluation of the Quality Variation of Pickle Products
Eight kinds of popular pickle products for local Khmer people were selected and purchased fromthree major wholesale markets. These 24 food samples were the first samples used in experiments.
To examine the quality variation of the products, the same products from the same shop werepurchased in the following week. In total, 48 samples were collected and used for the experiments.
To evaluate the quality of pickle products, Brix, salt concentration, and pH value were measured
for the pickle products. Three samples were more than three times different in Brix values between
the samples purchased on the first versus second weeks. Seven samples were more than 1% different
in salt concentration values. Three samples varied in pH values greater than 1.0. The fact that thedifferent values were obtained between the same pickle products showed the quality of pickle
products were unstable and/or the way of food processing was varied (data not shown).
Contamination and Spoilage Level of Foodborne Pathogen and Microbe in Pickle ProductsⒸ ISERD

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All samples were subjected to microbiological investigation. We investigated the generally viablemicrobes, coliform, fungi, Salmonella, S. aureus, and B. cereus to determine the food contaminationand spoilage levels. The total number of generally viable microbes of 48 samples is shown in Table
2. Fifteen samples were over 7 log10 CFU/g, indicating initial putrefaction. The samples from market
B and C had relatively higher total CFU than those from market A. The sample C, pickled young
onion, sample D and E, and pickled mustard showed a higher number (6 log10 to 8 log10) of CFU(data are not shown).
Table 2 Total number of general viable microbes in pickle products
Market
log10
CFU/g

A

B

C

<3

1st
sample1

2ndsample1

1stsample1

2ndsample2

1stsample0

2ndsample
1

4

2

1

1

0

0

1

5

3

3

2

1

1

1

6

0

2

3

1

3

2

7

1

1

1

3

2

2

8

1

0

0

1

2

1

Next, we investigated major food spoilage microbes and food pathogens. Results are shown in
Table 3. Coliform is an indicator of whether or not the food was handled in a proper manner. Coliform
inhabit the human intestinal tract, the presence of them means likely fecal contamination (Madigan,2019). As a result, the total contamination rate of coliform was 58.3%. According to Ceuppens (2014),
the existence of coliform was correlated to the presence of E. coli and a sufficient amount of E. coli.
More than half of the pickles were coliform positive and had a significant risk of including pathogenic
E. coli such as EHEC.
Microbial spoilage of fresh vegetables is often caused by fungi (Madigan, 2019) that are able tosurvive at lower aw conditions. So, the contamination by fungi was examined. The total rate ofcontamination was 68.8%.
Salmonellosis is very common for bacterial food infection. This disease is typically caused by
Salmonella and Salmonella-contaminated food and animals. Of the first 24 samples, two samples
were Salmonella positive and in the second batch of samples, 18 samples were positive. In total,
41.7% of pickles were contaminated by Salmonella. Combined with the wet market conditions shownin our previous study, Salmonella contamination was most likely caused by cross-contamination ofchicken and egg products and by Salmonella-carrying animals such as dogs that come into contact
with the food.
S. aureus cause food poisoning and the produced toxins by S. aureus cause gastrointestinal
symptoms. These toxins can be superantigens that lead to lethal toxic shock syndrome. The presenceof S. aureus (83.3%) was surprisingly high in our tested samples. Chrun et al. (2017) reported thatthe contamination rate of fermented vegetables collected from the wet market in Phnom Penh wasless than 10%, but the samples used in this study exceeded 80%. S. aureus can grow in both aerobic
and anaerobic conditions and in many foods. S. aureus is often carried by humans. The interior of
nostrils and skin are associated with S. aureus and a pus-forming wound becomes a frequent cause(Madigan, 2019; Matthews, 2017). In our previous study, we observed that the vendors handled theirproducts with bare hands and utensils that were left out. Most staphylococcal contamination is causedby humans during food preparation (Matthews, 2017). It is assumed that the inappropriate manner offood handling increases the chance of food contamination.
B. cereus is widespread in nature and is frequently isolated from growing plants and soil. So,
this organism is easily spread if there is no equipment to prevent dust and dirt from the outside.
Foodborne illness by B. cereus is of two types—the emetic form results in vomiting and the diarrheal
form results in diarrhea. Our results showed that 37.5% of the total samples were B. cereus positive.
Upon microbiological examination, there were large variations in between different pickle productsⒸ ISERD

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and even within the same pickle products. Some pickles were considered to contain enough sugar andsalt to prevent spoilage; however, most products had some kind of microbiological hazard. Our
previous study revealed that the wet market was in a condition where cross-contamination can easily
occur. The cause of bacterial contamination could be from raw materials and cross-contamination. The
water used for food processing and cleaning may also be a source of foodborne pathogens (Steel, 2004).
Table 3 The rate of microbiological contamination in pickle products
Number of positive / Number of total
Microbial sp.
First sample

Second sample

Total rate(%)

Coliform

14/ 2414 /24

58.3

Fungi
Salmonella

13 / 24

20 / 2468.8

2 / 2418 / 24

41.7

S. aureus

19 / 24

21 / 2483.3

B. cereus

12 / 24

6 / 24

37.5

CONCLUSION
In this study, we investigated the level of food spoilage and the rate of food contamination of pickle
products. The results indicated that significantly poor sanitary conditions and unsafe food products
were provided at the wholesale market in PP. It is necessary that food handlers as well as consumers
receive practical knowledge on food hygiene to prevent the spread of bacterial food contamination.
In the future, we will focus on the cause of food contamination and give countermeasures against it.
ACKNOWLEDGEMENTS
The measurements of food properties and microbial examination were conducted at the Faculty ofAgro-Industry, Royal University of Agriculture, Cambodia.
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Chrun, R., Hosotani, Y., Kawasaki, S. and Inatsu, Y. 2017. Microbiological hazard contamination in fermented
vegetables sold in local markets in Cambodia. Biocontrol Sci, 22 (3), 181-185.
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vegetables. J. Food Sci. Technol, 34, 1-22.
Holvoet, K., Sampers, I., Seynnaeve, M. and Uyttendaele, M. 2013. Relationships among hygiene indicatorsand enteric pathogens in irrigation water, soil and lettuce and the impact of climatic conditions on
contamination in the lettuce primary production. Int. J. Food Microbiol, 171, 21-31.
Kadariya, J., Smith, T.C. and Thapaliya, D. 2014. Staphylococcus aureus and staphylococcal food-borne
disease: an ongoing challenge in public health. Biomed Res. Int, 2014, Article ID 827965, http://dx.doi.org/10.1155/2014/827965
Madigan, M.T., Bender, K.S., Buckley, D.H., Sattley, W.M. and Stahl D.A. 2019. Brock biology of
microorganisms, Fifteenth ed. Pearson Education, USA.
Matthews, K.R., Knile, K.E. and Montville, T.J. 2017. Food microbiology, Forth ed. American Society for
Microbiology, ASM Press, USA.
Oliveira, M.A., Souza, V.M., Bergamini, A.M.M. and Martinis E.C.P. 2011. Microbiological quality of readyto-eat minimally processed vegetables consumed in Brazil. Food Control, 22, 1400-1403.
Skočková, A., Karpíšková, R., Koláčková, I. and Cupáková, Š. 2013. Characteristics of Escherichia coli fromraw vegetables at a retail market in the Czech Republic. Int. J. Food Microbiol, 167, 196-201.
Steele, M. and Odumeru, J. 2004. Irrigation water as source of foodborne pathogens on fruit and vegetables. J.
Food Prot, 67 (12), 2389-2489.
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Tewari, A. and Abdullah, S. 2015. Bacillus cereus food poisoning: International and Indian perspective. J Food
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World Health Organization. 2015. WHO Estimates of the Global Burden of Foodborne Diseases.

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Research article
erdStudy of the Relationship between different Soil Properties
in Agricultural Fields, Kyee Inn Village, Myanmar
SWE SWE MAR*
Department of Soil and Water Science, Yezin Agricultural University, Myanmar
Email: swesmooth@gmail.com

NAING NAING MOE
Department of Soil and Water Science, Yezin Agricultural University, Myanmar

KYAW NGWE
Department of Soil and Water Science, Yezin Agricultural University, Myanmar
Received 15 January 2020

Accepted 23 April 2020(*Corresponding Author)

Abstract An understanding of the physical and chemical properties of soils is necessary due
their relationship with productivity in agricultural fields. A study was conducted in 480
hectares of agricultural fields at Kyee Inn, Pyinmana Township, Naypyitaw, Myanmar todetermine the relationship between different soil properties (bulk density, soil moisture, soil
pH, cation exchange capacity, organic matter, total nitrogen, total phosphorus, and total
potassium) using Geographic Information Systems (GIS). Soil samples were collected on agrid method (300 m × 300 m) from three places in each grid at a depth of 0-15 cm. Global
Positioning System (GPS) was used to determine the coordinates of sampling points. The
collected samples were composited to 80 soil samples and analyzed. To compute therelationship between soil properties, simple linear correlation was performed using statistix
8th version. Finally, the relationship among the soil properties was shown by overlay mappingin Arc Map 10.5 with the spatial analytical function of ArcGIS software. The results revealed
soil pH was significantly and negatively correlated with total nitrogen (r = -0.412), andsignificantly and positively correlated with total phosphorus (r = 0.248). Total potassium
content was highly significant and positively correlated with soil moisture (r = 0.782). The
relationships that are commonly found among soil properties are evident in this study with apositive correlation of soil organic matter and total nitrogen (r = 0.058), a negative correlationof bulk density with soil organic matter(r = -0.191) and soil moisture (r = -0.066), a positive
correlation of cation exchange capacity with total potassium (r = 0.204), and a negative
correlation of cation exchange capacity with bulk density (r = 0.018), but these were notstatistically significant. The observed information from this study can provide a clear
understanding of the relationship among soil properties, which positively or negatively affectnutrient availability.
Keywords GIS, GPS, relationship among soil properties

INTRODUCTION
The overall productivity and sustainability of a given agricultural sector are functions of fertile soils
and productive lands (Buol et al., 2003). Food productivity and environmental quality is dependent
on the physical and chemical properties of soil, so it is very important to have a detailed knowledgeof properties of soil (Tale and Ingole, 2015).
The productive capacity of Myanmar soils is an increasing concern. Many years of poor
agriculture and land management practices has led to serious land degradation. As elsewhere, theagricultural sector’s performance in Myanmar is highly dependent upon soil quality (IFDC, 2018).
Soil quality is controlled by the physical, chemical and biological properties of soil and theirinteraction (Papendick and Parr, 1992). Therefore, it is important to maintain soil health for foodsecurity and to allow for increasing agricultural production. An understanding of physical andⒸ ISERD

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chemical conditions of any soil is essential for the proper implementation of other management
practices (Tale and Ingole, 2015). Most physical and some chemical characteristics of soils allow aranking, indicating whether an area has a high potential for agricultural production (Lelago andBuraka, 2019).Therefore a physico-chemical study of soil is very important because both theseproperties affect soil productivity (Tale and Ingole, 2015).
OBJECTIVE
The main objective of this research is to determine the relationship between different soil properties
in the agricultural fields of the study area.
METHODOLOGY
Site Description and Soil Sampling
The study was conducted in agricultural fields of Kyee Inn Village, Pyinmana Township, situated in
mid-Myanmar. The study covers a total area of 480 hectares, situated between 1942'30''-1943'40''
N and 9613'30''-1915'30'' E (Fig. 1). This area receives a mean annual rainfall of about 1420 mm
and an average temperature of 26.8°C and monsoon rice and pulses are the main crops. Soil sampling
was on a grid method (300 m × 300 m). There was a total 80 grid plots, and soil samples werecollected from three places for each grid at 0-15cm depth using GPS to determine the coordinates ofthe sampling points. All samples were taken after the harvest of the previous crops and before theland preparation for the next season to ensure base line conditions for the analysis.

Pyinmana Township

Kyee Inn Village fields(480 hectares)

Fig. 1 Location of the study site
Laboratory Analysis
Table 1 Soil parameters and analytical methods adopted for laboratory analysis

PhysicalSoil Parameters

Unit

Analytical Methods

Bulk Density

g cm-3

Core sampler method (Black, 1965)

Soil Moisture%

Gravimetric method (Black, 1965)+

ChemicalSoil pH-log [H ]1:5 (soil: water) pH meter (Hesse, 1971)-1
Cation Exchange Capacity meq100 g soil Bascomb’s method (Bascomb, 1964)
Organic Matter
Total Nitrogen
Total Phosphorus%%%

Total Potassium

mg kg-1

Walkley and Black method (Walkley and Black, 1934)
Modified Kjeldahl Digestion method(Ohyma et al., 1991)
Molybdivanado phosphoric acid method(Spectrophotometer)
Atomic Absorption Spectrophotometer (AAS) (Flame)Ⓒ ISERD

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The collected soil samples were composited for each grid. The samples were air-dried, ground, andsieved. The soils’ pH levels, organic matter content, cation exchange capacity, bulk density, soil
moisture, total content of nitrogen, phosphorus, and potassium were measured. Analysis was done at
the laboratory of the Department of Soil and Water Science, Yezin Agricultural University. The
analytical methods used for conducting this analysis are described in Table 1.
Statistical Analysis and Overlay Mapping
The laboratory results of all soil properties were subjected to correlation analyses to detect functional
relationship among soil parameters using statistix (8th version). Firstly, the interpolation maps weregenerated using an inverse distance weighting (IDW) method in ArcGIS software version 10.5 andthen some of the more significant relationships among soil properties were able to be shown by
overlay mapping.
RESULTS AND DISCUSSION
Correlation Matrix of Soil Properties
The correlation coefficient values of soil parameters viz; soil pH, cation exchange capacity, bulk
density, soil moisture, soil organic matter, total content of nitrogen, phosphorus and potassium were
determined for the surface soils (0-15 cm) and are presented in Table 2.
Table 2 Correlation between the different soil parameters under study
pH
CEC
OM
TN
TP
TK
BD
SM

pH
1
0.014
-0.025-0.416**
0.242*
0.047
0.052-0.047

CEC

OM

TN

TP

TK

BD

1
0.035
0.145
-0.0360.204-0.0180.352**

1
0.058-0.1070.238*-0.191
0.162

1
0.216
0.045
0.092
0.182

1
-0.217
0.096-0.1171-0.161
0.782**

1-0.066SM

1*Correlation is significant at the 0.05 level; **Correlation is significant at the 0.01 level
CEC: cation exchange capacity, OM: organic matter, TN: total nitrogen, TP: total phosphorus, TK: total potassium,
BD: bulk density, SM: soil moisture

Relationships between Soil Parameters
In Fig. 2 (a), the correlation coefficient reveals soil pH shows a highly significant but negative
correlation with total nitrogen content (r = -0.416). This suggests that pH accounts for about 16.96%
of the total variability in total nitrogen. Similarly, with an increase in pH, total nitrogen decreases
progressively and vice-versa. In addition to this, with an increase on soil pH by one unit, total
nitrogen decreases by 0.098 unit and vice-versa. According to major crops growing in this area,pulses may take much cations and thus may cause lowering soil pH, but growing of pulses on every
year may also lead to encourage total nitrogen content by fixing nitrogen of pulses.
Similar results were obtained by Singh and Mishra (2012). Similarly, Khadka et al. (2016) statedthat total nitrogen content was significantly and negatively correlated with soil pH. Xue et al. (2013)
also reported that the correlation between the soil nitrogen forms and soil pH was negative. However,a significant and positive correlation between soil pH and total nitrogen was obtained by Athokpam etal. (2013), and a non-significant relationship was obtained by Dhamak et al. (2014).
Figure 2 (b) illustrated that soil pH is significantly and positively correlated with total phosphorus(r = 0.242). For these parameters, the results are in harmony with the findings of Athokpam et al.
(2013). But non-significant correlation between them was observed by Ogaard (1994).
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According to the literature, soils with inherent pH values between 6 and 7.5 provide ideal
conditions for phosphorus availability, while pH values below 5.5 and between 7.5 and 8.5 may limit
phosphorus availability to plants due to fixation by aluminum, iron, or calcium. The observed pH
values of this study ranged from 5.48 to 7.58, and therefore, the soil test phosphorus content would
be expected to be significantly and positively correlated with this observed pH range.

Fig. 2 Relationship between soil pH with (a) total nitrogen and (b) total phosphorus
The total potassium content is positively correlated with soil moisture, to a highly significant
level, expressed in the correlation matrix of r = 0.782 (Fig. 3 (a)). The results were in conformity
with those of Singh and Singh (2004), and Zeng and Brown (2000) who found a positive correlation
between moisture content and potassium content in soils. Total potassium also shows a significant
positive correlation with soil organic matter and a significant negative correlation with total
phosphorus. In addition, it shows a non-significant positive correlation with soil pH, total nitrogen,cation exchange capacity, and a negative correlation with soil bulk density. This means that the total
potassium content may increase with increases in pH, cation exchange capacity, soil organic matter,
total nitrogen, and may decrease with increases in bulk density and total phosphorus and vice versa.

Fig. 3 Relationship between soil moisture with (a) total K and (b) CEC
The commonly found relationships between soil properties is also observed in non-significant
correlations, such as the positive correlation of soil organic matter and total nitrogen (r = 0.058), thenegative correlation of soil bulk density with soil organic matter(r = -0.191) and soil moisture (r = 0.066), and the positive correlation of the soil’s cation exchange capacity with total potassium (r =0.204) and the negative correlation of cation exchange capacity with bulk density (r = -0.018). A
significant and positive correlation between cation exchange capacity and soil moisture content (r =0.352) is also observed (Table 2 and Fig. 3 (b)). According to topographic position of this area,
relatively higher soil moisture content was found on slightly lower elevation of southwest and also
northeast portion as located nearby a small stream on this side. The cation exchange capacity andtotal potassium were obviously increased with high moisture content portions of this area.
The negative correlation between bulk density and soil moisture content suggests two
possibilities that soil moisture content declines as bulk density increases due to less storage space orⒸ ISERD

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that soil moisture content is sufficient for the soil to resist compaction (Carter and Shaw, 2002). Thisis consistent with studies that have examined bulk density and soil moisture content (Hill and Sumner,1967). Many researchers (Askin and Ozdemir, 2003; Morisada et al., 2004) obtained the relationshipbetween organic matter and bulk density of soils all indicating a strong correlation between them.
Bulk density tends to decrease as a soil’s organic matter concentration increases (Curtis and Post,
1964).
Overlay Mapping of Significant Relationships of Soil Parameters
Figure 4 (a) and (b) display an overlay map showing significant relationships of soil pH with the total
contents of nitrogen and phosphorus in the study area. It can be seen clearly that the areas with higher
total nitrogen content correspond to areas largely occupied ‘Moderately acidic’ soil pHs in the study areaand vice versa. The total phosphorus content, however, is positively distributed in all classes of soil pHof the study area. The overlay map of the significant positive relationships of soil moisture content with
CEC and total potassium are shown in Fig. 5 (a) and (b). It is obvious that the variation of CEC and total
potassium is positively coincidental with areas with a higher amount of soil moisture content.

Fig. 4 Overlay maps of soil pH with (a) total N and (b) total P

Fig. 5 Overlay maps of soil moisture with (a) CEC and (b) total K
CONCLUSION
Results from correlation analysis show that many of the soil parameters are interrelated with eachother but often these relationships are non-significant. Since this study area occupies agricultural
fields and thus some soil properties can be reflected by many practices of crops growing such as
tillage systems, application of inputs, cropping patterns and so on. The results of this study havediscussed and suggested with farmers as field day for proper implementation of their soil
management practices. Finally, this study clearly illustrates that an understanding of soil properties
and their inter-relationship, would prove to be useful for the development of an effective soil
management plan allowing for efficient utilization of limited agricultural land resources.

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ACKNOWLEDGEMENTS
We would like to gratefully acknowledge the Japan International Cooperation Agency (JICA), who
supervised the Project for Capacity Development of Yezin Agricultural University, and who haveprovided technical and financial support for this study.
REFERENCES
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content. Poljoprivreda / Agriculture, 9, 52-55.
Athokpam, H., Wani, S. H., Kamei, D., Athokpam, H. S., Nongmaithem, J., Kumar, D. and Devi, L. 2013. Soilmacro-and micro-nutrient status of Senapati district, Manipur (India). African Journal of Agricultural
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Bascomb, C.L. 1964. Rapid method for the determination of cation exchange capacity of calcareous and non
calcareous soils. Journal of Science of Food and Agriculture, 15 (12), 821-823.
Boul, S.W., Southard, R. J., Graham, R.C. and McDaniel, P.A. 2003. Soil genesis and classification. 5th ed.
Iowa State University Press. Ames, USA.
Buraka, T. and Lelago, A., 2016. Physico-chemical properties and agricultural potentials of soils of Tembaro
Woreda, Kembata Tembaro Zone, Southern Ethiopia.
Carter, E.A. and Shaw, J.N. 2002. Correlations and spatial variability of soil physical properties in harvested
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Agriculture, USA. ASA-CSSA-SSSA, 677, South Segoe Road: Madison, WI.
Curtis R.O. and Post, B.W. 1964. Estimating bulk density from organic matter content in some Vermont Forest
soils. Soil Sci. Soc. Am. Proc., 28, 285-286.
Dhamak, A.L., Meshram, N.A. and Waikar, S.L. 2014. Identification of major soil nutritional constraints in
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Hesse, P.R. 1971. A textbook of soil chemical analysis. John Murray, London.
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International Fertilizer Development Center. 2018. Soil fertility and fertilizer management strategy for
Myanmar. Muscle Shoals, AL 35662, USA.
Khadka, D., Lamichhane, S. and Thapa, B. 2016. Assessment of relationship between soil pH andmacronutrients, Western Nepal. Journal of Chemical, Biological and Physical Sciences, 6 (2), 303-311.
Lelago, A. and Buraka, T., 2019. Determination of physico-chemical properties and agricultural potentials of
soils in Tembaro District, KembataTembaro Zone, Southern Ethiopia. Eurasian Journal of Soil Science,
8(2), 118-130.
Morisada, K., Ono, K. and Kanomata, H. 2004. Organic carbon stock in forest soils in Japan. Geoderma, 119, 21-32.
Ogaard, A.F. 1994. Relationships between the ratio of plant-available phosphorus (p-al) to total phosphorus
and soil properties. Acta Agric. Scandinavica, Sect. B, Soil and Plant Science, 44, 136-141.
Ohyma, T., Ito, M., Kobayashi, K., Araki, S., Yasuyoshi, S., Sasaki, O. and Ikarashi, T. 1991. Analyticalprocedures of N, P, K contents in plant and manure materials using H2SO4-H2O2 Kjeldahl digestion method.
Bull. Fac. Agric. Niigata Univ., 43, 111-120.
Papendick S.B. and Parr, J.F. 1992. Soil quality: Attributes and relationship to alternative and sustainableagriculture. American J. Alter. Agri, 7 (1-2), 2-3.
Sing, G. and Sing, B. 2004. Influence of soil water regime on nutrient mobility and uptake by Dalbergia sissoo
seedlings. Tropical Ecology, 45 (2), 337-340.
Singh, R.P. and Mishra, S.K. 2012. Available macronutrients in the soils of Chiragaon block of district
Vanarasi in relations to soil characteristics. Indian J. Sci., 3 (1), 97-100.
Tale, K.S. and Ingole, S. 2015. A review on role of physico-chemical properties in soil quality. Chem Sci. Rev.
Lett. 2015, 4 (13), 57-66.
Walkley, A. and Black, I.A. 1934. An examination of the Degtjareff method for determining soil organic matter,and a proposed modification of the chromic acid titration method. Soil Sci., 37 (1), 29-38.
Xue, Z., Cheng, M. and An, S. 2013. Soil nitrogen distributions for different land uses and landscape positions
in a small watershed on Loess Plateau, China. Ecological Engineering, 60, 204-213.
Zeng, Q. and Brown, P.H. 2000. Soil potassium mobility and uptake by corn under differential soil moistureregimes. Plant and Soil, 221 (2), 121-134.

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Research article
erd

Monitoring of Crop Plant Height Based on DSM Data
Obtained by Small Unmanned Vehicle Considering theDifference of Plant Shapes
KE ZHANG
Obihiro University of Agriculture and Veterinary Medicine, Hokkaido, JapanOSAMU TSUJI*
Obihiro University of Agriculture and Veterinary Medicine, Hokkaido, Japan
Email: tsuji@obihiro.ac.jp

MASATO KIMURA
Obihiro University of Agriculture and Veterinary Medicine, Hokkaido, JapanTOSHIMI MUNEOKA
Obihiro University of Agriculture and Veterinary Medicine, Hokkaido, JapanKENICHI HOSHIYAMA
Zukosha Co. Ltd, Hokkaido, Japan
Received 13 January 2020

Accepted 30 April 2020(*Corresponding Author)

Abstract Unmanned Aerial Vehicle (UAV) has been recognized as a potential tool to obtainspatial information of crop state in small-scale farming fields. The objectives of this study
are to assess the availability of UAV photographs to determine the plant height (PH) for threecrop varieties (barley, oat, and perilla); and to evaluate the effect on the accuracy of theestimated PH caused by different plant shapes. The UAV photography and ground surveys
were conducted weekly from May 31 to September 3, 2019, at the examination field of
Obihiro University of Agriculture and Veterinary Medicine. The estimated PH was obtained
from the differential value (D_value) of Digital Surface Models (DSMs) generated from UAV
photographs through the Structure from Motion (SfM) process. The results demonstrate thatthe D_value of DSMs produced the highest estimation accuracy (R2 = 0.97, RMSE = 9 cm)
for perilla, which is a herb crop with luxuriant spade-shaped leaves, and a moderate
estimation accuracy (R 2= 0.87, RMSE = 23 cm) for oat, which is a grain crop making small
spikes during the heading time with lanceolate leaves. The estimated PH produced the lowest
accuracy (R 2= 0.34, RMSE = 28 cm) for barley, which makes outward spikes with longprickly awns above the ear of grain. However, a higher accuracy (R2 = 0.67, RMSE = 17 cm)
for barley was obtained after a lodging of the spikes caused by rain on July 2. Furthermore,
the accuracy of the estimated PH increased in both barley and perilla fields after corrected byplant coverage of the field. These findings suggest the D_value of DSMs obtained from UAV
photographs can provide an accurate PH estimation for crops with luxuriant leaves likedicotyledons, but comparably less accurate estimation for crops with long and sharp leaves
like monocots. Additionally, the elimination of the effect of unrecognizable factors such as
long awns and non-plant factors such as the ground can also increase the accuracy of theestimated PH.
Keywords unmanned aerial vehicle, digital surface model, plant height estimation,
plant shape

INTRODUCTION
Plant height (PH) is an important indicator for farmland management as it directly relates to crop
growth status. However, traditional methods of PH measurement are time- and labor-consumingⒸ ISERD

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because of its requirement for mass sampling. In the past decade, the rapid development of
Unmanned Aerial Vehicle (UAV) has provided an efficient, labor-saving, and precise approach tomonitor crop biophysical parameters such as Normalized Difference Vegetation Index (NDVI) andPhotochemical Reflectance Index (PRI) (Berni et al., 2009). Due to its ability of frequent aero
photography, UAV has been also considered a suitable tool for crop PH monitoring. The method todetect crop PH using multi-temporal crop surface models (CSMs) generated with UAV-based
imaging has been proved available by Bendig et al. (2013). This method has later been applied inseveral studies using different crops such as paddy rice (Tilly et al., 2014), summer barley (Bendig
et al., 2014), and winter wheat (Lu et al., 2019).
However, no study has yet evaluated the estimated PH for crops with significant differences inplant shapes using UAV photographs taken under same flight conditions, and no study has discussed
the effect on estimation accuracy of PH caused by different crop plant shapes. Thus, the objectivesof this study are: (1) to determine the PH estimation accuracy for three subjects with different plantshapes; and (2) to evaluate the effect on PH estimation accuracy caused by plant shapes.

Fig. 1 Position and detail of the study site
METHODOLOGY
Surveys were carried out at the experimental field of Obihiro University of Agriculture andVeterinary Medicine in the city of Obihiro in Hokkaido, the largest and northmost prefecture of Japan(Fig. 1). The experimental field with a spatial extent of 180 m×160 m was divided into 22 small
fields (average area: 700 m2) and was cultivated with 19 different crop varieties. Barley field, oat
field, and perilla field were used as the measuring areas to measure PH in this study. To provide
accurate position information for the UAV photographs, ground control points (GCPs) were evenly
settled at seven positions across the experimental field. Geographic coordinates of the GCPs weretaken using a HiPer V RTK-GNSS receiver (TOPCON, Japan).

Photo. 1 Self-made PH measureⒸ ISERD

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The UAV used in this study was a Phantom 4 Pro (DJI). The flights were carried out weekly
from May 31 until September 3 (15 times) using an autopilot application known as Pix4D Capture(Pix4D). The UAV photographs were taken at 50 m above ground level on a flight path of double
grid, with both forward and side overlap of 80%. The camera angle was adjusted to 70° from thehorizon. Ground surveys to obtain the measured PH were conducted after each flight. Approximately
4-10 measuring points were settled and marked with pink marking tapes at each measuring area. A
self-made PH measure was used to obtain the optimal value of PH under natural state (Photo. 1).
The UAV photographs were processed using a structure-from-motion (SfM) software, known
as Agisoft Metashape 1.5.1 (Agisoft), to generate GCP-georeferenced DSMs (ground resolution: 5.6cm) and orthomosaic photos (ground resolution: 1.6 cm). According to the method provided by
Bendig et al. (2014), the differential value of DSMs (D_DSM) used for PH generation was calculated
by subtracting the bare ground model from the DSM for each survey. The calculation of DSMs andextraction of PH value were conducted using ArcGIS Pro 2.3.1 (Esri). The measuring points marked
with marking tapes were determined on the orthomosaic photos, and a circular polygon with adiameter of 20 cm was created at each measuring point. The maximum D_DSM value for eachpolygon was extracted and used as the estimated PH, which was compared to the measured PH. The
coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate the PH
estimation accuracy. In addition, within the UAV photographs of a field where the stems of crop
were not fully extended, there was a considerable area of ground surface exposed through the leaves
that may affect the value of the estimated PH. To find out the effect caused by the exposed groundarea, the vegetation cover (VC) of barley field, where the VC increased significantly during thesurvey period, and perilla field, where the VC changed slightly, were calculated and used for thecorrection of estimated PH. A raster graphic editor software, Adobe Photoshop Creative Cloud 14.0
(Adobe Inc., USA), was used to extract and tally up the pixels of vegetation within a field plot to
calculate the VC.
RESULTS
Figure 2 shows the comparisons of the time series between the measured and estimated PH for thethree crop varieties. The measuring surveys were carried out weekly from June 19 until August 15
for barley, June 13 until September 3 for oat, and July 31 until September 3 for perilla. In the barley
field, a lodging of spikes caused by rain happened on July 2 and continued until harvest. In the oat
field, a soiling, which refers to the cutting of the leaves of crops for green feed in the middle of thegrowth stage, was carried out on July 24. In the perilla field, a thinning, which refers to the removal
some plants of crop to make room for the others, was carried out on July 26. These natural or manmade phenomena were also reflected in the time-series of both estimated and measured PH.

Fig. 2 Comparison of time series between measured and estimated PH
The relationship between the measured and estimated PH for each crop are shown in Fig. 3. A
low correlation (R2 = 0.43) and an RMSE of 27.9 cm were observed in the barley field. A high
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correlation (R2 = 0.87) and an RMSE of 23.1cm were found in the oat field. An extremely highcorrelation (R2 = 0.97) and a much lower RMSE of 9.2 cm were perceived in the perilla field. In viewof the above, the estimated PH based on DSMs performed most satisfactorily for perilla, which is adicotyledon crop. Between the other two varieties which are both monocotyledon crops, theestimated PH performed better for oat than for barley.

Fig. 3 Relationship between measured and estimated PH
The VC value and the dates of the UAV photographs by which the VC was calculated are shownin Table 1. In the barley field, the VC increased pronouncedly after the lodging from 88.9% (June26) to 97.1% (July 3), while in the perilla field, the VC increased gradually from 90.4% (July 31) to
100.0% (September 3). The relationship between the measured PH and the estimated PH corrected
by VC is shown in Fig. 4. In the barley field, the performance of the estimated PH after correction
by VC (R2 = 0.57, RMSE = 20.9 cm) was shown to be more accurate than before correction. In theperilla field, the estimation accuracy was slightly increased (R2 = 0.97, RMSE = 7.9 cm) after it wascorrected by VC.
Table 1 Vegetation Cover (VC) during survey period(a) Barley Field
Date
VC(b) Perilla Field
Date
VC

June 19
87.3%

June 26
88.9%

July 3
97.1%

July 10
98.8%

July 17
98.8%

July 24
99.2%

July 31
87.3%

August 7
88.9%

August 14
97.1%

August 21
98.8%

August 29
98.8%

September 3
99.2%

Fig. 4 Relationship between measured and estimated PH corrected by VCⒸ ISERD136

July 3195.5%

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DISCUSSION
The method to manually obtain the optimal measurement of PH under natural state has been adifficult issue due to the variety of PH in real farmland, and the individual difference between
measurers (Bendig et al., 2014; Tilly et al., 2014). The problem of determining the representative
mean of PH of a field plot was also discussed in an earlier study (Bendig et al., 2014). In addition, it
is time- and labor-consuming even for skilled surveyors to set GCPs and obtain their precise
coordinates every time before the UAV is flown over the field.
To solve these problems, a self-made PH measure was used in this study to obtain themeasurement of PH properly and efficiently. A sliceable plastic plate with an area of 10 cm×20 cm
was installed on a 150 cm-long pole, on which a tape measure was stuck (Pic. 1). When measuring
the height, the plate was slid down slowly until there were most leaves reached it with the slightest
shape changing. Each measurement was carried out at a certain measuring point, which was marked
by a 100 cm-long pole (diameter: 0.5 cm) with a pink marking tape bound to it. These marking tapes
could be found on the orthomosaic photos and used for the determination of the measuring points.
Although there might still be a slight influence caused by a subjective judgement when adjusting theplate, it was expected to improve the accuracy of the measured PH through this method. Furthermore,in order to mitigate the cost of time and workforce caused by GCP settling and measuring, fixed
points of GCPs were used in this study. Seven wooden posts (length: 20 cm), each with a nail at thetop, were inserted into the ground at different positions in the experimental field. The geographic
coordinates were taken once by RTK-GNSS on May 28 and were used for the generation of DSMs
and orthomosaic photos ever since.
The comparison between the measured and estimated PH shows that the DSM-derived PH was
generally lower than the measured value for barley and oat, which are both monocotyledons. In thisstudy, each pixel of DSM represented the ground surface with an area of approximately 31.4cm 2 .
Although the maximum value of D_DSM of the polygon area of a measuring point was extracted, it
still represented the average height of several leaves including those in the lower position. Therefore,
for crops which has detectable distance among leaves such as monocotyledons, the problem ofunderestimation should be considered when estimating the PH using DSM data.
Between the two monocotyledons used in this study, the estimated PH of oat performed better
than barley. The reason for the low estimation accuracy of barley is considered to be the shape of the
spikes. Barley produces peculiar, outward spikes with long, prickly awn (10 cm-15 cm) above theear of grain. The height of the spikes was included in the measured PH. However, it is considered tobe ignored during the DSM generation because it was too small to be recognized. To verify thispossibility, the relationship between the measured and estimated PH before and after the lodging of
spikes were calculated which are shown in Fig. 5. A low correlation (R2 = 0.39, RMSE = 47.1 cm)
was observed before the lodging, while a medium correlation (R2 = 0.67, RMSE = 17.2 cm) was
observed after the lodging. The lodging, which is the elimination of the effect of spikes, was helpful
in increasing the PH estimation accuracy of the barley plant. Like barley, oat is also a poaceous crop.
However, oat produces scattered spikes which have little effect on both measured and estimated PH.
This is considered the reason why the DSM data showed a better performance in the PH estimation
for the oat plant.
The extremely high accuracy of the estimated PH for the perilla plant is considered to be due tothe luxuriant plant shape. Perilla is an erect, frontend crop that can grow to 0.6 m-2.0 m. The leaves
are broad shaped with a length of approximately 10 cm-15 cm and grow densely on the stem. Therewas barely detectable distance among the canopy leaves in the perilla field during the survey period.
This characteristic of the plant shape is considered as the reason why the estimated PH for perilla
was much closer to the measured PH than the other two subjects, and even became higher than themeasured PH at the late stage of the surveys. Although the underestimation problem of PH estimation
using DSM has been suggested by many studies (Bendig et al., 2014; Lu et al., 2019), overestimationof the estimated PH caused by the growth of crop has not been observed and discussed yet.
Considering this is probably a unique phenomenon of dicotyledon, future studies using otherdicotyledon crops such as potatoes, peanuts, and soybean are expected to verify this assumption.

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Fig. 5 Relationship between measured and estimated PH before and after lodging
Furthermore, correcting the estimated PH using VC was considered as a feasible method in thisstudy to improve the estimation accuracy. The results showed in Fig. 5 indicate that the accuracy ofthe estimated PH for both barley and perilla plants increased after they were corrected with VC. VC
is, thus, considered a potential indicator for PH estimation using UAV photographs.
CONCLUSION
In this study, the accuracy of estimating PH based on DSM data generated from UAV photographs
for three crops with different plant shapes were compared. Although the estimation accuracy forbarley was low during the whole survey period, it increased after the elimination of the effect of the
spikes. For both barley and oat plants, the estimated PH were lower than the measured PH, because
the DSM provided the average height of all factors within one pixel. However, the estimated PH for
perilla was more accurate than the other subjects and surpassed the measured PH late in the growthstage due to its luxuriant plant shape. Future study is expected to discuss and verify this differenceof estimation characteristic between monocotyledons and dicotyledons. Furthermore, VC was proved
to be helpful to increase the estimation accuracy of PH for both barley and perilla plant.
ACKNOWLEDGEMENTS
This research was partially supported by the cooperation business between Obihiro City and Obihiro
University of Agriculture and Veterinary Medicine. We would also like to express our gratitude to
the research assistance provided by the undergraduate students of Obihiro University of Agriculture
and Veterinary Medicine.
REFERENCES
Bendig, J., Bolten, A., Bennertz, S., Broscheit, J., Eichfuss, S. and Bareth, G. 2014. Estimating biomass ofbarley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote sensing, 6 (11),10395-10412.
Bendig, J., Willkomm, M., Tilly, N., Gnyp, M.L., Bennertz, S., Qiang, C. and Bareth, G. 2013. Very highresolution crop surface models (CSMs) from UAV-based stereo images for rice growth monitoring inNortheast China. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, 40, 45-50.
Berni, J.A.J., Zarco-Tejada, P.J., Sepulcre-Cantó, G., Fereres, E. and Villalobos, F. 2009. Mapping canopy
conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery. Remote
Sensing of Environment, 113 (11), 2380-2388.
Lu, N., Zhou, J., Han, Z., Li, D., Cao, Q., Yao, X. and Cheng, T. 2019. Improved estimation of abovegroundbiomass in wheat from RGB imagery and point cloud data acquired with a low-cost unmanned aerial
vehicle system. Plant Methods, 15 (1), 17.

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Tilly, N., Hoffmeister, D., Cao, Q., Huang, S., Lenz-Wiedemann, V., Miao, Y. and Bareth, G. 2014.
Multitemporal crop surface models: accurate plant height measurement and biomass estimation withterrestrial laser scanning in paddy rice. Journal of Applied Remote Sensing, 8 (1), 083671.

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Research article
erd

Evaluation of Soil Ecosystem Health in Different Farming
Systems by Observing Diversity of Soil Arthropods
MARISOL TERASHIMA
Graduate School of Agriculture, Tokyo University of Agriculture, Japan
Email: chamiter@hotmail.com

MACHITO MIHARA*
Faculty of Regional Environment Science, Tokyo University of Agriculture, Japan
Email: m-mihara@nodai.ac.jp
Received 22 January 2020

Accepted 30 April 2020(*Corresponding Author)

Abstract Organic or natural farming practices are known to promote soil fertility as well as
biological diversity. Organic matter recycling, multiple cropping and ecological corridors areavailable as reservoirs for biological control agents such as predators or parasitic insects.
These practices allow ecosystem services to reduce the presence of phytophagous insects andmicroorganisms. The objective of this study is to analyze the effect of the different farming
systems on the diversity of arthropods. The sampling of arthropods was performed by pitfall
trap method. The traps buried at surface level of the ground were set at 3.5 m intervals for 24hours in the line of the cultivated crops. In addition to this method, Tullgren funnel method
is applied in this study. In the experimental results, there were no significant differences inrichness and abundance of observed arthropods. In the natural farming system, taxonomic
group of Araneae, Acari and Coleoptera was observed. In the conventional farming system,
the group of Formicidae was dominant. Using the Simpson’s (inverse) index and ShannonWeaver’s index, the diversity was calculated. The results indicated there was more diversityof arthropods in the natural farming compared to conventional farming system. It is evident
that agricultural practices (natural or conventional) may affect the diversity of arthropods
within an agroecosystem.
Keywords beneficial arthropods, pitfall traps, pest management

INTRODUCTION
Soil health is associated with biological diversity and stability. Plant and animal diseases outbreaks
can be considered as indicators of instability and poor ecosystem health. Therefore, there is likely
also to be a link between soil health, the ability of the biological community to suppress plant
pathogens, the population density of plant pathogens in soil, and ultimately disease incidence andseverity (van Bruggen and Grunwald, 1996). The organic and natural farming is a production systemthat promotes processes which improve soil fertility and biological activity. Pest population density
is related to factors such as climate and population of predators. The climate generates immediate
and drastic changes in the population of the pests, but the control through natural enemies is more
complex. In a successful biological control of pest, the natural enemy reduces the pest to a level that
does not cause harm, but does not eliminate it completely, since the natural enemy requires aminimum population of pest for its survival (Nicholls, 2008). The idea of biological control is tokeep the natural community in balance, and unlike chemical control it is safe, selective, efficient andworks for long term. Those practices such as buffer zones, rotation and intercropping system,
promote biodiversity and it would provide more shelter, food and reproductive possibilities, allowing
the natural enemies to establish themselves as a tool for biological control of pests. And thesepractices also will make the production system more stable and productive in the medium and longterm. Therefore, this study focuses on analyzing the richness and abundance of arthropods in two
different production systems. By measuring the diversity of arthropods, it could indicate the stability
and health of the soil ecosystem of the farms.
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MATERIALS AND METHODS
The study was conducted at a natural farm in Saitama prefecture and a conventional farm in Tokyo
City, both located in Kanto region of Japan. The difference between them is that the natural farm
does not use chemical products and uses a system of rotation and intercropping. Which means thatto control pests and diseases they have a complex vegetation structure in the farm to distract insects
and prevent them from attacking their production. The macro or meso arthropods were collectedusing pitfall traps and microarthropods were extracted using Tullgren funnel method. The samples
were preserved in 70% ethyl alcohol and were identified at taxonomic order.
Data Analysis
For all the statistical analyses EstimateS 9.1.0 (Colwell, 2013) was used. For estimates of species
richness, Abundance based Coverage Estimator of species richness (ACE) was used (Chao and Lee,
1992; Chao et al., 1993). ACE is based on relative abundance data, those species with ≤ 10
individuals in the sample (Chao et al., 1993; Chazdon et al. 1998). Ace takes the form
Srare

F1

ace

ace

Sace=Scommon+ C + C γ2ace(1)where Scommon is number of common species, Srare is number of rare species, Cace is the sample
abundance coverage estimator and ϒace is the estimated.
And for indices of species diversity, Shannon – Weaver’s coefficient of variation of F1 for rare
species. This estimator has been found to give good results and is highly recommended (Chazdon etal. 1998; Hortal et al. 2006). diversity index (H´) and Simpson´s (inverse) diversity (D) index wereused.
𝐻ʼ = − ∑𝑠𝑖=1(𝑝𝑖)(ln𝑝𝑖)(2)where Hʼ is species diversity index, s is number of species and 𝑝𝑖 is proportion of the total sample
belonging to 𝑖 th species. Shannon-Weaver’s index (H´) considers the relative abundance andnumber of species and expresses the uniformity of the values of importance across all the species inthe sample. In addition, it assumes that individuals are randomly selected, and that all species are
represented in the sample (Magurran, 2004).
𝑛𝑖(𝑛𝑖−1))
𝑁(𝑁−1)

𝐷 = ∑((3)

where ni is the number of individuals in the i th species and N is the total number of individuals. As
D increases, diversity decreases. Simpson’s index is therefore usually expressed as 1-D or 1 ⁄ D(Magurran, 2004). This index is heavily weighted towards the most abundant species in the sample,
while being less sensitive to species richness. For comparing both diversity indices, t-test was used
(Hutcheson, 1970). This test was developed to compare the diversity of two communities samples
for Shannon-Weaver’s index and Simpson’s index.
RESULTS AND DISCUSSION
Richness and Abundance of Species between Natural and Conventional Farming System
The arthropods collected in conventional system are represented by 7 taxa of arthropods. They were:
Acari, Araneae, Collembola, Coleoptera, Hymenoptera, Orthoptera and Thysanoptera. In naturalagroecosystem in addition to those named, 6 taxa more were recorded: Dermaptera, Diptera,
Chilopoda, Hemiptera, Homoptera and Lepidoptera. In the conventional agroecosystem, 21 species
and an abundance of 174 individuals were found, whereas in the natural agroecosystem 47 species
and 270 individuals were registered. Figure 1 shows the distribution of total abundance among thedifferent taxonomic groups registered in both farming systems. The most abundant taxonomic groupin natural farming were Formicidae (33%), Orthoptera (14%) and Acari (12%). In the conventionalⒸ ISERD141

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one the Collembola (45%), Orthoptera (16.7 %), Formicidae (18.4 %) and Other Hymenoptera(11.5 %) were the taxa with greater abundance of individuals.

Fig. 1 Percentage of abundance of individuals per taxonomic group for both systems
With these results, we could discuss the significance of the different taxonomic groups inagroecosystems. Ground beetles predators (Carabidae and Staphylinidae) were found because they
are usually found in arable lands. These species as well as spiders (Araneae) are non-specificpredators, unlike parasitic wasps that are specific predators. Mites (Acari) are important in the soil
as fungivores, bacterivores and nematode predators (Largerlof and Andren, 1988). In addition, they
are related with the decomposition of organic residue in the soil.

Fig. 2 Number of families per order for both systems
Figure 2 shows the distribution of the total richness among the taxonomic groups recorded inboth evaluated sites. In natural agroecosystem the taxa with the highest number of families were:
Coleoptera (17%), Araneae (17%) and Other Hymenoptera (15%). In contrast, in the conventional
agroecosystem the taxa with the highest percentage of species were: Formicidae (29%), Araneae(19%), Other Hymenoptera (19%) and Collembola (14%). An advantage of species richness is thatit provides a broad measure of the complexity of communities and perhaps their resilience to change.
Its disadvantage lies in the practical difficulty of distinguishing invertebrate species and how little itreveals about species interactions. However, species richness has the potential to tell us more aboutⒸ ISERD

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invertebrate communities and soil quality than straight biomass, density or abundance (Stork andEggleton, 1992).
Diversity Functional Group between Natural and Conventional Farming System
Although there was no significant difference in richness and abundance in both cropping systems, inan agroecosystem, we can discuss the function that has each arthropod. From an ecological system
it is important to know this since not all invertebrates that we find in the system could have different
functions. That is, although there is more diversity, it could not mean that there are more functionalgroups. In Fig. 3, we can see that in the two systems, we could find a higher percentage of predators
abundance in the natural system than in the conventional one.

Fig. 3 Percentage of the abundance for functional group in both systems
Species Accumulation Curves (ACE) between Natural and Conventional Farming System
In order to understand the correlation between the observed and the estimated, Fig. 4 could be used
to analyze the scenarios. In this figure we can see that the effort of the samples was insufficient inboth systems. But we could conclude that despite that, in both systems the natural one is more diverse
than the conventional one, since it is estimated that in the natural system, there are more than twice
as many species as in the conventional one.

Sobs: observed species, ACE: estimatedFig. 4 Species accumulation curves in natural system (A) and conventional system (B)Diversity Indices between Natural and Conventional Farming SystemⒸ ISERD

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Both diversity indices indicate a higher value in natural system than conventional system. Diversity
in agroecosystem, or agro biodiversity, promotes the presence of beneficial fauna, optimizing theecological processes that favor stability, and consequently favoring their sustainability (Altieri and
Nicholls, 2000). But it should be clarified that it could not be quantified how an agroecosystem isonly considering the relation that exists in the same space and time by a determined number of species.
This is because the interaction between the same amounts of species in different conditions can vary
(Ewel, 1986, 1999). The diversity indices give us an indication of the stability of the system,
indicating that the farm of natural agriculture is more stable. And this indices at the same time
coincides with the abundance and richness of the arthropods found in the two systems. A strong pointof Simpson’s index is that it considers rare species. These arthropods could indicate that they are
specific to the system. In other words, rare arthropods include predators with general habits andparasitoids that are more specific, such as the parasitoid wasps which in total collected 11 species, 7of which were in natural farm and 4 in conventional farm. Predators such as spiders and beetles
recorded 18 species in natural agriculture and 4 species in the conventional.

(*) denotes significant difference at P<0.05(**) denotes significant difference at P<0.01

Fig. 5 Simpson Inverse Index in both
farming system

Fig. 6 Shannon – Weaver Index inboth farming system

These results would support the hypotheses of Altieri and Letourneau (1984) and Altieri and
Nicholls (2000) that diverse systems stimulate a greater diversity of arthropods. The first hypothesisis that more complex systems mean more diversity of species. And the diversity of species andstructural plants would be associated with the diversity and abundance of insects because it would
generate more food resources and temporary shelters. Increased abundance of predators andparasitoids in diverse plant associations reduces prey / host density (Root, 1973), so competition
between herbivores is reduced, which in turn allows for the addition of new species of herbivores
that support more species of natural enemies. And finally, they assume that the productivity ofpolycultures is greater, stable and predictable than in monocultures. Increased productivity andheterogeneity of the agroecosystem would mean that in a temporal and spatial environment, more
species of arthropods could coexist.
CONCLUSION
There was no significant difference in richness and abundance, but more predators were observed in
the natural system. Among the predators, the spiders and beetles population were dominant. The
diversity indices were higher in the natural system, which means the distribution and abundance inthe community is more equitable.
The farming system influences the diversity of arthropods in an agricultural environment.
Therefore, if a system cannot conserve or increase the biodiversity of the farmland, it will be more

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unstable and with poor soil ecosystem health. However, more research is needed to better understandthe interaction and roles of arthropods in a soil ecosystem.
ACKNOWLEDGEMENTS
I would like to express my gratitude to Nakamura farm as well as the people of the NPO Tagayasu,
who gave me the opportunity to take the samples from their farms.
REFERENCES
Altieri, M.A. and Letourneau, D.K. 1984. Vegetation diversity and insect pest outbreaks. CRC Critical Reviewsin Plant Sciences, 2, 131-169.
Altieri, M.A. and Nicholls, C.I. 2000. Agroecology and the search for a truly sustainable agriculture. 1, 14-21.
Chadzon, R.L., Colwell, R.K., Denslow, J.S. and Guariguata, M.R. 1998. Statistical methods for estimating
species richness of woody regeneration in primary and secondary rain forest of northeastern Costa Rica. In
Dallmeier F. and Comiskey J.A. (Eds.), The Parthenon Publishing Group, París. Eds. Forest Biodiversity
Research, Monitoring and Modelling, 285-309.
Chao, A. and Lee, S.M. 1992. Estimating the number of classes via sample coverage. Journal of the American
Statistical Association, 87, 210-217.
Chao, A., Ma, M.C. and Yang, M.C.K. 1993. Stopping rules and estimation for recapture debugging withunequal failure rates. Biometrika, 80, 193-201.
Colwell, R.K. 2013. Estimates: Statistical estimation of species richness and shared species from samples.
Version 9, User's Guide and Application, Retrived from http://purl.oclc.org/estimates
Ewel, J.J. 1986. Designing agricultural ecosystems for the humid tropics. Ann. Rev. Ecol. Syst., 17, 245-271.
Ewel, J.J. 1999. Natural systems as models for the design of sustainable systems of land use. Agrofor. Syst. 45,
1-21.
Hortal, J., Borges, P. and Gaspar, C. 2006. Evaluating the performance of species richness estimators:
Sensitivity to sample grain size. Journal of Animal Ecology, 75, 274-287.
Hutcheson, K. 1970. A test for comparing diversities based on the Shannon formula. Journal of Theoretical
Biology, 29 (1), 151-4, Doi: 10.1016/0022-5193(70)90124-4.
Largerlof, J. and Andren, O. 1988. Abundance and activity of soil mites (Acari) in four cropping systems.
Pedobiologia, 32, 129-145.
Magurran, A.E. 2004. Measuring biological diversity. Blackwell, Oxford, UK, 235.
Nicholls, C.I. 2008. Control biológico de insectos: un enfoque agroecológico. Ed. Universidad de Antioquia.
Root, R.B. 1973. Organization of a plant‐arthropod association in simple and diverse habitats: The fauna ofcollards (Brassica Oleracea). Ecological Monographs, 43 (1), 95-124.
Stork, N.E. and Eggleton, P. 1992. Invertebrates as determinants and indicators of soil quality. American
Journal of Alternative Agriculture, 7 (1-2), 38-47.
Van Bruggen, A.H.C. and Grunwald, N.J. 1996. Tests for risk assessment of root infection by plant pathogens.
In Doran, J.W., Jones, A.J. (Eds.), Methods for Assessing Soil Quality, Soil Science Society of America,
Madison, WI, 293-310.
Van Bruggen, A.H.C. and Semenov, A.M. 2000. In search of biological indicators for soil health and diseasesuppression. Applied Soil Ecology, 2000, 15, 13-24.

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Research article
erd

Risk Assessment of African Swine Fever Virus in Pork in
Phnom Penh, Cambodia
VOUCHSIM KONG*
Department of Agro-Industry, Ministry of Agriculture, Forestry and Fisheries, Phnom Penh,
Cambodia
Email: kongvouchsim@gmail.com

PHEA SUM
M’s Pig ACMC (Cambodia) Co., Ltd, Phnom Penh, Cambodia

PHEARY MOL
M’s Pig ACMC (Cambodia) Co., Ltd, Phnom Penh, Cambodia

KAROLIN CHAN
Faculty of Veterinary Medicine, Royal University of Agriculture, Phnom Penh, Cambodia

SOCHEATA RATHA
Faculty of Veterinary Medicine, Royal University of Agriculture, Phnom Penh, Cambodia

SEAVCHOU LAUT
Faculty of Veterinary Medicine, Royal University of Agriculture, Phnom Penh, Cambodia

VUTEY VENN
Department of Veterinary Paraclinical Science, Royal University of Agriculture, Phnom Penh,
Cambodia
Received 25 January 2020

Accepted 30 April 2020(*Corresponding Author)

Abstract African swine fever (ASF) is an acute infectious and deadly viral disease that
affects domestic and wild pigs of all breeds and ages. ASF virus (ASFV) can spread vastly
to non-infected pig population, but it cannot be transmitted from pigs to humans. In
Cambodia, first ASF outbreak was transmitted from bordering countries and reported in fiveprovinces. While estimated 70% of ASF widespread all over the country, local farmers
experienced a greater economic loss. Among the four components of risk analysis, the study
determines the risk assessment, and hazard identification plays a crucial step in risk
assessment. The objectives of this study aimed to detect the presence of ASFV in pork on
local markets through qualitative risk assessment approach and propose possible measurable
recommendations to prevent ASF outbreak. The study was conducted during the period of
ASF outbreak in August 2019, and the qualitative detection of ASFV was conducted on pork
tissue samples selected from wet markets and supermarkets in Phnom Penh. Sample
extractions were isolated from 30 pork tissue samples and detected virus by iiPCR. The
qualitative result on detection of ASF virus is confirmed by PCR technique. The ASFV isfound in pork tissue samples in wet markets and supermarkets. Of the 30 samples, 21 (70%)
were found positive with ASFV, 6 in 9 (20%) tissue samples from supermarkets and 15 in 21
tissue samples from wet markets (50%) confirmed the presence of ASFV. With this result, it
indicates that likelihood of the ASF virus transmission would be very likely to occur and the
spread of ASF virus in pork tissue samples in wet markets and supermarkets is significantly
prevalent, and the virus is likely to spread quickly. Scientifically, there is no vaccine toprevent ASF, and as recommended by FAO, the influenced policy-based implementation isrequired in place to minimize further production losses. The implementations must be
strengthened through strict farm biosecurity guideline and slaughter of infected pigs, strict
import regulation (border and movement control of live pigs) and heavy penalty to illegal
import of live pigs. Based on this result, it may contribute to bring consumers’ and relevantⒸ ISERD

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stakeholders’ awareness to reduce high risk through early detection of ASFV at theslaughterhouses and markets by risk assessment approach. Further studies on risk
management and risk communication to complete the risk analysis of ASFV in pork arehighly recommended.
Keywords African swine fever virus, risk assessment, pork, markets, Cambodia

INTRODUCTION
African swine fever (ASF) is an acute infectious and deadly viral disease that affects domestic andwild pigs of all breeds and ages, and caused by a DNA virus belonging to Asfarviridae family(Guberti et al., 2018). ASF virus (ASFV) can spread vastly to non-infected pig population, but it
cannot be transmitted from pigs to humans (FAO, 2008). Although humans are not susceptible to
ASF, its outbreak causes noticeable socio-economic consequences to infected countries (Bellini,
Rutili and Guberti, 2016). In Cambodia, first ASF outbreak transmitted from bordering countries wasreported in Ratanakiri province by the Ministry of Agriculture, Forestry and Fisheries, MAFF (FAO,2019). ASFV has continued its transmission and spread quickly to Tboung Khmum, Svay Rieng,
Takeo and Kandal provinces. While estimated 70% of ASF widespread all over the country, local
farmers in these five areas experienced a greater economic loss. Majority of small- and mediumscale pig farms stop to operate temporarily, and a small number of commercial farms are being
operated (Siem Reap PDAFF, 2019).
In such cases, risk analysis, the process composed of hazard identification, risk assessment, risk
management and risk communication, is required. FAO/WHO (2006) defined risk assessment as theterm that is generally used to describe the entire process of making a public health decision regarding
a specific drug or agent and it is the scientific evaluation of known or potential adverse effects
resulting from human exposure to food borne hazards. According to Venn et al. (2014) as cited in
the Codex Alimentarius Commission (CAC, 1999), risk assessment is defined as integrated elementsof the structure of risk analysis and to be based on the following steps as shown in Fig. 1 and Fig. 2.

Fig. 1 Structure of risk analysis (Venn et al., 2014 cited in CAC, 1999)

Fig. 2 Risk assessment (Venn et al., 2014 cited in CAC, 1999)Ⓒ ISERD

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ASFV can be transmitted and spread from infected pigs directly to uninfected populations. The
cause of transmission is associated with virus-containing matter (blood, faeces, urine or saliva from
infected pigs) that contact directly with brought-to-farm infected pork, farm inputs and in-farm usedmaterials (bedding, pig cage, feed, equipment, clothing and footwear, means of transportation(vehicles), studied by Thomson (1985). In the research of Mellor et al. (1987), it has shown that ASFV
can be airborne within a short distance, approximately less than 2 kilometers.
ASFV has a remarkable ability to survive for long periods in a protein environment, and therefore
meat from pigs slaughtered in the infective stages of ASF or die naturally of the disease provides agood source of virus. Pork slaughtered from infected pigs with ASF provides a favorable protein
environment for the virus to survive in the tissues for a long period of time. The virus is strongly
resistant to high temperatures. The studies of McKercher et al. (1978) and Plowright et al. (1994)
confirmed that fresh, frozen, processed, or preserved (salted and dried) pork may contain the infective
virus. As ASFV is transmitted in a closer distance, it may be possible to prevent the occurrences byapplying strict biosecurity rules and observation. While taking precautionary measures in use, it isnecessary to limit accesses for people/ farm workers/ visitors and vehicles to enter the areas or farms
where pigs are kept. This is also ensuring that the farm managers/ owners and veterinarians are
disinfected before entering the critical areas/ farms with protective clothing and footwear, and free frominadvertent feeding of leftovers and pork (Penrith and Vosloo, 2009).
OBJECTIVE
This study aims to detect the presence of ASF virus in pork in Cambodian markets through risk
assessment approach, and propose possible measurable recommendations to prevent ASF outbreak.
METHODOLOGY
The study was conducted during the period of ASF outbreak, and the qualitative detection of ASF
virus was conducted on pork tissue samples using PCR technique certified by OIE approval number
20130108 for registration of diagnostic kits (GeneReach Biotechnology Corp., 2019). The samples
were selected from 7 wet markets and 3 supermarkets in Phnom Penh in August 2019. Primary
information was gathered by interviewing the key informants of the city slaughterhouses and sellers
in both markets. The distribution of live pig movement and slaughtered pork was from smallholder
famers, middlemen and traders who source for pigs from the small-scale farms and neighboring
countries.
Sample extractions were isolated from 30 pork tissue samples and detected virus. The analysis
samples were evaluated based on the insulated isothermal PCR technique that automates sample lysis,
nucleic acid extraction and amplification and detection of the target sequences. Pork tissue samples
were pretreated by weighting 40 mg of each pork tissue sample and placing them into a clean 1.5 ml
microcentrifuge tube with 0.5 ml of Sample Storage Solution atc-lysis. The pork tissues were
homogenized in the grinder and spined the tube for 1 minute in mini-centrifuge before transferring
200 µl of supernatant to an Extraction Cartridge. Then, Transfer Cartridge was prepared by placing
one Premix and one Transfer Cartridge for each sample; labeling the sample ID and Premix ID on
the side of Transfer Cartridge; removing Transfer Cartridge Cap; turning the notched side of the
cartridge away, and snapping the Premix vial into well #3 of the Transfer Cartridge. The Extraction
Cartridge was prepared by placing one Extraction Cartridge (B) for each sample; removing theExtraction Cartridge from the aluminum pack, labeling the sample ID on the side of Extraction
Cartridge; slowly peeling off the aluminum film; turning notched side, and loading 200 µl of
homogenized sample to the sample well of the Extraction Cartridge. After the preparations ofTransfer Cartridge and Extraction Cartridge were completed, the Extraction lot number of theExtraction Cartridge was entered into the PCR; then the loaded Extraction Cartridge was place into
the selected slot before the Reagent lot number of the Premix Reagent was entered. The Transfer
Cartridge to the selected was loaded into the slot and the analysis were ready to run in the PCR.

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RESULTS AND DISCUSSION
The qualitative result on detection of ASF virus is confirmed by PCR technique. The ASFV is foundin pork tissue samples in wet markets and supermarkets. Of the 30 samples, 21 (70%) were foundpositive with ASFV, 6 in 9 (20%) tissue samples from supermarkets and 15 in 21 tissue samples
from wet markets (50%) confirmed the presence of ASFV (Table 1). With this result, it indicates thatlikelihood of the ASF virus transmission would be very likely to occur (OIE, 2010) and the spreadof ASF virus in pork tissue samples in wet markets and supermarkets is significantly prevalent.
Guberti et al. (2018 and 2019) confirmed that the virus is present in the meat of sick pigs and cansurvive for more than 3 months in the meat and offal, about one year in dry meat and fat, and morethan one year in the frozen meat.
Table 1 Percentage of sample detection
Sample detection
Supermarkets (9 tissue samples)
Wet markets (21 tissue samples)

Positive number (ratio%)
20% (6/9)
50% (15/21)

Negative number (ratio%)
10% (3/9)
20% (6/21)

Source: Results from pork tissue samples detection of ASFV confirmed by PCR technique

Based on this early detection of ASFV, risk assessment as scientific-based approach identifies
four components including hazard identification, hazard characterization, exposure assessment, andrisk characterization are described in Table 2.
Table 2 Risk Assessment of ASF virus
Components of Risk
Assessment
Component 1.
Hazard Identification
Component 2.
Hazard Characterization

Component 3.
Exposure Assessment

Component 4.
Risk Characterization

Identifications
African Swine Fever Virus (ASFV), is a DNA virus in the Asfarviridae
Family. The causative agent of the disease, the ASF virus (ASFV), is the only
member of the Asfaviridae family, genus Asfivirus.
- Human consequence: ASF virus cannot be transmitted from pigs to humans.
- Economic consequence: In Cambodia, first ASF outbreak was transmitted
from bordering countries and reported in April 2019 in five provinces.
While estimated 70% of ASF widespread all over the country, local farmers
estimated in greater losses. The pig price is increasing. Infected pigs’ weight
50kg loss approximately 90% of selling price, while pigs’ weight 80kg loss
about 45%. Majority of small- and medium-scale pig farms stop to operate
temporarily, and a small number of commercial farms are being operated.
Spread of the disease related vehicles and staff movements at the farms andoperation of kitchens inside the farms (bring-in infected pork/contaminatedfoodstuffs with ASF virus), and contamination of vehicles and staff
movements and carcass at the slaughterhouse.
Combining the results of the preceding three steps and advice for decision
making (to continue conducting the study on risk management).

Source: Results from pork tissue samples detection of ASFV confirmed by PCR technique

As shown in Fig. 3, a timely detection and identification of an incursion of initial event (Event is
NOT likely to occur) of infected pig in the farms can remove/ cull the sources of ASFV and break theroutes of risk outcomes in slaughterhouses and markets (NO RISK). However, the result indicates that
70% of samples were found positive with ASFV and the contaminated pork are distributed in the markets
that would be very likely to occur (RISK outcome of interest DOES Occur).
Economically, the sources of infected pigs may not be removed/ culled eventhough early
detection is positive in farms which are still slaughtered and distributed in the markets. It seems the
supply chain of pigs is less identical. There are a number of actors in the pig supply chain; in which,mostly buyers, middlemen and traders are those who source for pigs in the local villages. Sometimes
farmers may contact buyers, middlemen and traders directly to sell their pigs. The purchases areⒸ ISERD

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operated daily, and pigs are transported by motorcycles or trucks from farms directly to theslaughterhouses. The research studies conducted by Thomson (1985) and Thomson and Tustin(1994) confirmed that ASFV can be transmitted and spread from infected pigs directly to uninfected
populations. The transmission is associated with virus-containing matter (blood, faeces, urine orsaliva from infected pigs). The infected pigs may be in contact directly with brought-to-farm infected
meat, farm inputs and in-farm used materials such as bedding, pig cage, feed, equipment, clothing
and footwear, and other means of transportation (motorcycles and vehicles). Mellor et al. (1987)
noticed that ASFV can be airborne over in a short distance approximately less than 2 kilometers that
may give a higher chance for the virus to spread in the affected populations/areas.

Fig. 3 A scenario tree outlining the biological pathways leading to swine selected from aninfected herd, and being either accepted or rejected after it has been PCR tested
The pork is sold raw and distributed to local markets. Some semi-commercial farms havearranged the contracts with village famers and the farms will purchase the pigs when they reach acertain market weight (Mutua et al., 2011 and Levy et al, 2013). A main cause of concern linked tothe spread of AFS is a typical practice of buyers, middlemen and traders visit villages and households
regularly. Farmers may sell their infected pigs to the buyers and the spread of ASFV may get incontact with other pigs. After slaughter those infected pigs, ASFV can survive for long periods in a
protein environment, and meat from pigs slaughtered in the infective stages of ASF or that die
naturally of the disease provides a good source of virus and a favorable protein environment for thevirus to survive in the tissues for a long period of time (FAO, 2010; Plowright et al. 1994 and
McKercher et al. 1978). Bellini et al. (2016) emphasized on the improvement of efficiency of thedisease control measures by early detection of ASF that the infected pigs may be identified at thefarms/slaughterhouses/markets.
CONCLUSION
The results show that the spread of ASF virus in pork tissue samples in wet markets and supermarkets
in Phnom Penh is significantly prevalent, and the virus is more likely to spread quickly in Cambodia.
Scientifically, there is no vaccine to prevent ASF. Technically, influenced policy-based
implementation is necessary to address to minimize further production losses. The implementations
must be strengthened through strict farm biosecurity guideline and slaughter of infected pigs, strict
import regulation (border and movement control of live pigs) and heavy penalty to illegal import oflive pigs. Based on this result, it may contribute to bring consumers’ and relevant stakeholders’awareness to reduce high risk through early detection of ASFV at the slaughterhouses and marketsⒸ ISERD

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by risk assessment approach. Further studies on risk management and risk communication tocomplete the risk analysis of ASFV in pork are highly recommended.
ACKNOWLEDGEMENTS
The authors would like to express a sincere appreciation and thank to M’s Pig ACMC (Cambodia)
Co., Ltd for providing supports in conducting this study. The authors would like to extend theappreciation for the diligent, motivated and dedicated work done by research team, and theproofreader, Ms. Marianne Gudmundsdottir Ernst.
REFERENCES
Bellini, S., Rutili, D. and Guberti, V. 2016. Preventive measures aimed at minimizing the risk of African swine
fever virus spread in pig farming systems. Acta veterinaria Scandinavica, 58 (1), 82. Doi: 10.1186/s13028016-0264-x
Codex Alimentarius Commission. 1999. Procedural manual, tenth edition. Joint FAO/WHO Food Standards
Programme, Food and Agriculture Organization of the United Nations, 1997. Definitions for "riskmanagement" and "risk communication" were adopted as revised texts at the Twenty-third Session of theCodex Alimentarius Commission.
Food and Agriculture Organization of the United Nations. 2008. African swine fever in the Caucasus. EMPRES
Watch, 1-4. Retrived from http://www.fao.org/3/a-aj214e.pdf
Food and Agriculture Organization of the United Nations. 2010. Good practices for biosecurity in the pig sector.
FAO Animal Production and Health Paper, No. 169, Rome, FAO. Retrived from http://www.fao.org/3/ai1435e.pdf
Food and Agriculture Organization of the United Nations. 2019. ASF situation in Asia update. Retrieved fromhttp://www.fao.org/ag/againfo/programmes/en/empres/ASF/situation_update.html
FAO/WHO. 2006. Food safety risk analysis. A guide for national food safety authorities. FAO Food andNutrition, Paper 87, Food and Agriculture Organization, Rome, Italy.
GeneReach Biotechnology Corp. 2019. POCKIT™ central nucleic acid analyzer. Retrived from http://www.
genereach.com/index.php?func=product&action=view&product_no=13
Guberti, V., Khomenko, S., Masiulis, M. and Kerba, S. 2018. Handbook on African swine fever in wild boar
and biosecurity during hunting. FAO Animal Production and Health Manual. Rome, FAO and OIE.
Guberti, V., Khomenko, S., Masiulis, M. and Kerba, S. 2019. African swine fever in wild boar ecology andbiosecurity. FAO Animal Production and Health Manual, No. 22, Rome, FAO, OIE and EC.
Levy, M., Dewey, C., Weersink, A., Mutua, F. and Poljak, Z. 2013. Pig marketing and factors associated withprices and margins in Western Kenya. Journal of Agricultural Economics and Development, 2, 371-383.
McKercher, P.D., Hess, W.R. and Hamdy, F. 1978. Residual viruses in pork products. Applied Environmental
Microbiology, 35, 142-145.
Mellor, P.S., Kitching, R.P. and Wilkinson, P.J. 1987 Mechanical transmission of capripox virus and Africanswine fever virus by Stomoxys calcitrans. Research in Veterinary Science, 43, 109-112.
Mutua, F., Dewey, C., Arimi, S., Ogara, W., Githigia, S., Levy, M. and Schelling, E. 2011. Indigenous pig
management practices in rural villages of Western Kenya, Livestock Research for Rural
Development. Retrived from http://www.lrrd.org/lrrd23/7/mutu23144.htm
Plowright, W., Thomson, G.R. and Neser, J.A. 1994 African swine fever. In Coetzer J.A.W., Thomson, G.R.
and Tustin, R.C. (Eds.), Infectious Diseases of Livestock, with Special Reference to Southern Africa, 1.
Oxford University Press, Cape Town, 568-599.
Penrith, M.L. and Vosloo, W. 2009. Review of African swine fever: Transmission, spread and control. Journalof the South African Veterinary Association, 80 (2), 58-62. Retrieved from http://www.scielo.
org.za/scielo.php?script=sci_arttext&pid=S1019-91282009000200001&lng=en&tlng=en.
Siem Reap Provincial Department of Agriculture, Forestry and Fisheries. August 2019. Strengthening theprevention of infectious diseases, especially the African swine flu. Retrieved from https://www.siemreap.
maff.gov.kh/post/5d638a2e4834b
Thomson, G.R. 1985. The epidemiology of African swine fever: The role of free-living hosts in Africa.
Onderstepoort Journal of Veterinary Research, 52, 201-209.
Thomson, G.R. and Tustin, R.C. (Eds.). 1994. Infectious diseases of livestock, with special reference toSouthern Africa. Vol. 1, Oxford University Press, Cape Town, 568-599.

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Venn, V., Vouchsim, K., Borarin, B., Thong, K., Loinda, R.B. and Sopheareth, M. 2014. Risk assessment ofveterinary drug residues: The dietary exposure assessment of benzylpenicillin and tetracycline residues inpork consumed on Filipino market. Journal of Life Sciences, 8 (11), 872-879.
World Organisation for Animal Health. OIE. 2010. Introduction and qualitative risk analysis. Handbook on
Handbook on Import Risk Analysis for Animals and Animal Products, Vol. 1, 2nd Ed.

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Research article
erd

Ant Biodiversity under Different Fruit Agro-ecosystem in
Nam Phong Watershed, Thailand
DUANGRAT THONGPHAK*
Department of Entomology, Faculty of Agriculture, Khon Kaen University, Thailand
Email: duathg@kku.ac.th

CHULEEMAS BOONTHAI IWAI
Department of Soil Science and Environment, Faculty of Agriculture, Khon Kaen University,
ThailandReceived 23 January 2020

Accepted 5 May 2020(*Corresponding Author)

Abstract Ants are one of significant components of ecosystems. They play important roles
in agro-ecosystem functioning and provide ecological services as bioindicator, pollinator andbiological control. Ants are diverse taxonomic groups. The study of ant diversity in economic
crop areas including guava, mango, custard apple and banana gardens in Nam Phong
Watershed, Khon Kaen province was conducted during November 2017 to August 2018. Ants
were collected using three random sampling methods: manual catching, plant litter siftingand syrup trapping. The result revealed that 5 subfamilies, 13 genera of ants were found in
this study. Five subfamilies found in the studied sites comprised of Dolichoderinae,
Formicinae, Myrmicinae, Ponerinae and Pseudomyrmecinae. The most common species of
ant with wide distribution in the study area were Anoplolepis and Diacamma. The ShannonWiener’s species diversity index revealed that the diversity was highest in guava (0.36)
followed by mango (0.35), custard apple (0.34) and lastly the banana garden (0.26). There
was no dominant species of ant in the study areas. The β-diversity score, using Sorensen’ssimilarity coefficient to determine the similarity in community composition, was foundhighest between guava and custard apple garden at 62%, follow by between mango and guava
garden at 46%, and between mango and the banana garden at 38%, the results indicated thatant species diversity was varied in the four studied sites. Therefore, the results from this study
suggests that ant species diversity can be used to support agro- ecosystems management.
Keywords ant diversity, ecosystem service, economic crops, Nam Phong Watershed

INTRODUCTION
Ants may function as bio-indicators, due to their complex ecological interaction within their, and totheir sensitivity to disturbances (Read and Andersen, 2000). It is possible to analyze variations in thecommunity of ants under the concept of functional groups, defined in accordance with their tolerance
to disturbance and environmental stress, as well as with their ability for competitive interactions(Andersen, 1995). Ants play diverse and important ecological roles. Because of their ecologicalsignificance in ecosystems, ants are considered to be the suitable bio-indicator species forbiodiversity studies (Alonso, 2000). Moreover, ants have been used as biological agents of insect
pests in agriculture in several countries include Malaysia (Khoo and Chung, 1989) and Thailand(Kritsaneeapiboon and Saiboon, 2000). Although ants have relatively low species diversity, they are
the single most important arthropod group by their dominance in animal biomass (Alonso and Agosti,
2000). Environmental changes have an impact on macro-arthropod abundance (Pearson and Derr,
1986; Adis and Latif, 1996). Overall, common species, most habitats are likely to have specialized
species, which occur in sufficient species diversity and abundance as to be able to serve as suitableterrestrial indicator species of habitat quality and changes.

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OBJECTIVE
The objective of this research was to investigate biodiversity of ants in four different agroecosystems; guava, custard apple, mango and banana gardens in Nam Phong Watershed, Khon Kaen
Province, Thailand.
METHODOLOGY
Study sites were located at Namphong Watershed, Khon Kaen, Thailand. Four different fruit
agroecosystem included guava, custard apple, mango and banana gardens were chosen to investigate
ant diversity. Guava garden with the area of 6 Rai (0.96 ha) was located at Kok Tah Village, Mueang
District, (16.5° 38' 81.3" N, 102.90° 12' 75" E). There was not any management system in this garden.
Custard apple garden in area of 2 Rai (0.32) was located at Tha Kra Some Village, Nam Phong
District, (16.62° 22' 47" N, 102.87° 73' 75" E. Mango garden in area of 2.5 Rai (0.4 ha) was locatedat the same village of the custard apple garden (16.60° 59' 83" N, 102.87° 61' 40" E), both gardens
had no management system. Banana garden in area of 3.7 Rai was located at Nong Ngu Luam Village,
Mueang District, (16.5° 149' 74" N, 102.93° 11' 17" E), herbicide and chemical fertilizer (N-P-K-1515-15) were applied in banana garden. All four studied sites were in Khon Kaen Province.

Fig. 1 Study sites
Ant samples were collected from November 2017 to August 2018. The seasons of collecting
data were divided into wet season and dry season. Ant specimens were collected by using threerandom sampling methods: manual catching, plant litter sifting and syrup trapping. The ecologicalfactors were measured at the same time. Identification of ants to family, genus and species was basedon the keys by Bolton (1994), Hölldobler and Wilson (1990), and Wiwatwitaya and Jaitrong (2001).
The Shannon-Wiener’s diversity index (Krebs, 1999), was used to calculate ants diversity collected.
The formula of the Shannon-Wiener’s diversity index used is presented belowsH′ = Σ (pi) (lnpi)
i=1
where H′ = species diversity index, s = number of species, pi = proportion of the total sample
belonging to ith species.
The Sorensen’s similarity coefficient (Krebs, 1999) was used to measure the beta diversity or
the similarity between two study sites as follows:Ⓒ ISERD

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2aS =2a + b + c
where S = Sorensen’s similarity coefficient, a = number of species in site A and site B, b = numberof species in site B but not in site A, c = number of species in site A but not in site B.
The evenness index (Krebs, 1999) was calculated to determine the equal abundance of ants ineach study site as follows:H′
Evenness =H′MAX
where H′ = observed index of species diversity, H′MAX = maximum possible index of diversity.
RESULTS AND DISCUSSION
Table 1 Number of genera and family of ants in all sites at Nam Phong Watershed
Study area
Subfamily

Genus

Total

Guava

Custard
apple

Mango

BananaAmblyoponinae

Amblyopone

7

0

2

0

9

Dolichoderinae

Ochetellus

1

0

1

0

2

Iridomyrmex

0

0

0

1

1

Anoplolepis

329

112

8

11

460

Camponotus

3

16

0

0

19

Nylanderia

7

5

1

10

23

Oecophylla

76

43

195

4

318

Diacamma

10

26

0

1

37

Odontoponera

3

33

0

0

36

Pheidole

9

11

0

0

20

Cardiocondyla

0

0

0

1

1

Monomorium

23

42

136

130

331

Crematogaster

25

0

0

0

25

Number of genus

10

8

6

7

13

Number of specimens

503

296

349

165

1295

Formicinae

Ponerinae

Subfamily

Myrmicinae

20

Myrmicinae

15

Ponerinae

10

Formicinae

50Guava Mango Custard Banana
apple
Study area

Fig. 2 Comparison of ant diversity in each crop areasⒸ ISERD

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Total of 23 ant genera distributed among five subfamilies were identified from four difference agroecosystems (Table 1, Fig. 2). With the comparative ant communities between the four agroecosystems, the highest number of specimens was recorded in guava garden followed by custard
apple, banana and the lowest in mango garden. Ten genera and five subfamilies were found in guava
garden follow by eight general and four subfamilies from custard apple, seven genera and four
subfamilies from banana garden. Meanwhile there are only six genera and four subfamilies frommango garden. Four species of ants included Anoplolepis, Nylanderia, Oecophylla, Monomorium
were found in all four agro-ecosystems, whilst other species, Iridomyrmex and Cardiocondyla were
found only in banana garden and one species of Crematogaster was found only in guava garden. At
the subfamily level of all sites, Formicinae and Mymicinae had the highest number of four genera.
The richness of genera found in the study is lower compared to the richness recorded in other studies
carried out in crop area in central Thailand (Weerapadtra et al., 2018).

Fig. 3 Comparison between Shannon index (A) and Evenness (B) of ant diversity
in each plantation
The Shannon-Wiener’s species diversity index (Fig. 3A) indicated that the year-round diversity
was the highest in guava garden, followed by mango, custard apple and lastly in banana garden.
Moreover, the highest value of the evenness index was found in guava garden followed closely bymango and custard apple gardens, whereas those for banana garden was markedly lower (Fig. 3B).
This result indicates that a relatively equal abundance of each ant species was present in guava andmango gardens whereas banana garden had an unequal abundance of some ant species.
The species similarity between guava and custard apple gardens, as evaluated by Sorensen’ssimilarity coefficient was the highest, whilst that of between guava and mango gardens wasintermediate and the lowest similarity was found between in custard apple and banana garden (Table2). The relatively high ant species diversity in guava garden may be caused by the correspondingly highdiversity in the plant community and as such would potentially reflect the differences in the canopy
cover and leaf shedding. Hasin (2008) reported that the leaf litter, soil moisture content, and leaf litterbiomass in each study site would likely be affected by differences in each plant community. The leaf
litter provides both food and nest sites to many ant species, so it might be expected that an addition ofboth resources will produce a stronger response from litter-nesting ants (Armbrecht et al., 2006).
Table 2 The Sorensen’s similarity coefficient ants from the four sites
Sorensen's similarity

Study area
Guava

Mango

Custard apple

Banana

Guava

1

-

-

-

Mango

0.46

1

-

-

Custard apple

0.62

0.31

1

-

Banana

0.38

0.38

0.31

1

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CONCLUSION
In conclusion, diversity of ants was different in four crop gardens, which may reflect the different
ecological service they provide in different land uses of the studied sites. However, more detailed
study needs to be done to confirm include potentially factors influenced ant community species,
diversity, and composition, as somewhat intuitively expected but not to date ascertained for thesehabitats. Some species were found in all four crop areas, whilst other species were more specifically
being found only in specific microhabitats in the studied areas. If the understanding of microhabitats
used by specific ant species can be developed along with the key trophic interactions, then thepotential of using ants as terrestrial indicator species for detecting environmental changes canpotentially be reliably and easily (low cost and time) performed compared to some other indicatorspecies. Future research on the roles of ants in an ecosystem and their contribution to ecosystemservice need to be investigated.
ACKNOWLEDGEMENTS
This research was funded by the Khon Kaen University Funding 2018.
REFERENCES
Adis, J. and Latif, M. 1996. Amazonian arthropod respond to El Nino. Biotropica, 28, 403-408.
Alonso, L.E. 2000. Ants as indicators of diversity. In Agosti, D., Alonso, L.E., Majer, J.D. and Schultz, T.R.
(Eds), Ants: Standard Method for Measuring and Monitoring Biodiversity, Smithsonian Institution Press,
Washington, 80-88.
Alonso, L.E. and Agosti, D. 2000. Biological studies, monitoring, and ants: An overview. In Agosti, D., Alonso,
L.E., Majer, J.D. and Schultz, T.R. (Eds), Ants: Standard Method for Measuring and Monitoring
Biodiversity, Smithsonian Institution Press, Washington, 1-8.
Andersen, A.N.1995. A classification of Australian ant communities, based on functional groups which parallel
plant life-forms in relation to stress and disturbance. Journal of Biogeography, 22, 15-29.
Armbrecht, I., Perfecto, I. and Silverman, E. 2006. Limitation of nesting resources for ants in Colombian forestand coffee plantations. Ecological Entomology, 31, 403-410.
Bolton, B. 1994. Identification guide to the ant genera of the world, Harvard University Press, Cambridge, 222.
Folgarait, P.J. 1998. Ant biodiversity and its relationship to ecosystem functioning: A review biodiversity andconservation, 7, 1221-1244.
Hasin, S. 2008. Diversity and community structure of ants at Sakaerat Environmental Research Station, Nakhon
Ratchasima province. Department of Forest Biology, Faculty of Forestry, Kasetsart University, 94. (inThai).
Hölldobler, B. and Wilson, E.O. 1990. The ants. Belknap Press, Cambridge, 732 pp. Krebs, C.J. 1999.
Ecological Methodology, Addison- Educationall Publishers, California, 581.
Khoo, K.C. and Chung, G.F. 1989. Use of black cocoa ant to control mirid damage in cocoa. The Planter,
Kuala Lumpur. 65, 370-383.
Krebs, C.J. 1999. Ecological methodology. Addison-Educationall Publishers, California, 581.
Kritsaneepaiboon, S. and Saiboon, S. 2000. Ant species (Hymenoptera:Formicidae) in longkong (Meliaceae:
Aglaia dookkoo Griff.) plantation. Songklanakarin Journal of Science and Technology, 22 (3), 393-396.
Lamine Diamé, L., Rey, J., Vayssières, J, Grechi, I., Chailleux, A. and Diarra, K., 2018. Ants: Major functional
elements in fruit agro-ecosystems and biological control agents. Sustainability 2018, 10, 23, Doi:10.3390/
su10010023, 1-18.
Read, J.L. and Andersen, A.N. The value of ants as early warning bioindicators: Responses to pulsed cattle
grazing at an Australian arid zone locality. Journal of Environments, 45, 231-251.
Weerapadtra, D., Ruttana, N., Wandee, W. and Touchkanin, J. 2018. Diversity of ants in mango orchard
Phitsanulok province. PSRU Journal of Science and Technology, 3 (1), 1-8.
Wiwatwitaya, D. and Jaitrong, W. 2001. Identification guide to the ant genera of Khao Yai National Park,
Kasetsart University, Bangkok, 110. (in Thai)
Yahya, B.E.B. 2000. The use of three insect groups as biological indicators in three ecohabitats of Sabah.
School of Science and Technology, University Malaysia Sabah, Malaysia, 182.

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Research article
erd

Temporal and Spatial Distributions of Soil Apparent
Electrical Conductivity in Paddy Rice Fields, Khon Kaen,
Thailand
KATSUYUKI SHIMIZU*
Tottori University, Tottori, Japan
Email: shimizu@tottori-u.ac.jp

NAHO NOHARA
Hokkaido University, Hokkaido, JapanTADAO YAMAMOTO
Hokkaido University, Hokkaido, Japan

TAKASHI KUME
Ehime University, Ehime, JapanCHULEEMAS BOONTHAI IWAI
Khon Kaen University, Khon Kaen, Thailand
Received 15 January 2019

Accepted 5 May 2020(*Corresponding Author)

Abstract The temporal and spatial distribution of soil apparent electrical conductivity (ECa)was measured to elucidate the process of soil salinization in rainfed paddy rice fields in Khon
Kaen, Thailand. Soil ECa was measured in 2.3 ha of paddy fields using a ground conductivity
meter, utilizing the principle of electromagnetic induction. The average soil ECa wasmeasured at 150 cm and 75 cm depth at 10 m intervals. Observations were conducted every
two weeks for two months, from mid-May 2018 to mid-July 2018. The collected data wereinterpolated using an inverse distance weighted method with 1 m intervals and then analyzed
in terms of temporal and spatial distributions. The results showed that (1) higher soil ECa
was observed in the northern part of the study area near a swamp throughout the monitoring
period; (2) the average soil ECa at a depth of 0–75 cm ranged from 233 to 282 mS/m, while
it ranged from 236 to 268 mS/m at a depth of 0–150 cm; (3) the average soil ECa in the upper
layer was slightly higher than that of the lower layer in May, but this difference became
smaller over time and the situation reversed in mid-July; and (4) a larger spatial distribution
was observed in the upper layer compared with the lower layer based on the difference of acoefficient of variation of soil ECa. A soil salinization process was clearly observed.
Keywords electric conductivity, soil salinization, rainfed paddy fields, wet season, resilience

INTRODUCTION
In Thailand, saline soil covers an area of about 2.3 million ha, especially in inland areas (Pongwichian
et al., 2013), and the accumulation of salt occurs in rainfed paddy fields. A major source of the salt
is the Rock Salt Member of the Maha Sarakham Formation, which consists mainly of rock salts,
wherever it is exposed or lies close to the surface. However, other potential sources of salt wereformerly classified as salt-free strata (Wongsomsak, 1986). The management of these soils is very
problematic and strictly depends upon the degree of salinization and the mode of the salt-forming
processes. As general countermeasures, the management of these soils involves leaching, proper
drainage control, land leveling, surface mulching, organic amendments, deep plowing, and the useof salt-tolerant plant varieties (Land Development Department). However, before taking any
countermeasures, it is important to understand to what extent fields are affected by salt. ToparkNgarm et al. (1990) conducted physical and chemical analyses of soil samples collected from a depthⒸ ISERD

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of 30 cm at 5 cm intervals in Ban Thum, Khon Kaen. Hanchai et al. (2010) monitored changes inbiotic activity in relation to the chemical and physical properties of salt-affected soil before and after
tree planting. However, the high-resolution temporal and spatial distributions of salt accumulation
have not been well studied in the Khon Kaen Province.
OBJECTIVE
This study aimed to elucidate the high-resolution temporal and spatial distribution of soil apparent
electrical conductivity (ECa) in paddy fields and areas adjacent to them in Khon Kaen, Thailand.
When combined with groundwater level fluctuation, rainfall, and microtopography data, themonitoring results should contribute to the understanding of the soil salinization mechanism.
METHODOLOGY
Measurement and analysis of soil ECa
Although there are many saline-damaged farmlands in northeastern Thailand, a rain-fed paddy rice
block of Ban Pasan village in Khon Kaen was selected as a study site because of its clear spatial
distribution of the degree of salinity. The study area, in Ban Pasan, Khon Kaen, encompassed paddy
fields and an adjacent forest and swamp (N16.296° W102.612°; elevation 243 m). The difference inelevation between the south and north ends of the monitoring area is 28 cm and the area is quite flat(< 1/1,000). The groundwater level in the study area ranged from 140 cm below the ground surface
in April to 0 cm in September. Soil ECa was measured using a ground conductivity meter (EM38MK2; Geonics Limited), which simultaneously measured both quad-phase (conductivity) and inphase (magnetic susceptibility) components within two distinct depth ranges, without any
requirement for soil-to-instrument contact (GEONICS). With a maximum effective exploration depthof 150 cm, this meter is used for agricultural, archaeological, and general soil science purposes e.g.
Kume et al. (2003), Zukemura et al. (2016), and Rafael et al. (2011).
N

100 m

Area of soil EC

Fig. 1monitoring
Study area
The study area is shown in Fig. 1. The yellow dotted line indicates the domain of the ECa survey.
Rice had been planted in a rainfed paddy in the southern part of the domain, with a fallow area in thecenter. A swamp was located to the northeast, and a tree plantation was located to the west. Soil ECa
measurements were conducted at – 10 m horizontal intervals, with about 150–160 points, at two
different depths, 75 cm and 150 cm vertically. Measurements were collected five times between 16Ⓒ ISERD

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May 2018 and 11 July 2018, every two weeks. The collected soil ECa data were interpolated using
an inverse distance weighted method with a 1 m mesh.
RESULTS AND DISCUSSION
Temporal Distribution of Soil ECa
The average soil ECa in the upper soil layer (0–75 cm) and the deeper layer (0–150 cm) aresummarized in Tables 1 and 2, respectively. The average soil ECa between 0 and 75 cm ranged from
233–282 mS/m, while it ranged from 236–268 mS/m between 0 and 150 cm. The average soil ECa
in the upper layer was slightly higher than that of the lower layer in May, but this difference reduced
over time, with the situation being reversed in mid-July. The average ECa peaked in late May, withan average ECa of 282.3 mS/m at 0–75 cm and 268.0 mS/m at 0–150 cm. The highest median ECa
and the highest coefficient of variation (CV) occurred in late May at both depths.
Table 1 Summary of soil ECa in the shallow layer (0–75 cm)
Date
2018/5/16
2018/5/30
2018/6/14
2018/6/27
2018/7/11

Average
247.8
282.3
243.0
242.0
233.1

Max
928.6
989.1
982.9
971.4
895.9

Min
32.6
35.2
26.1
24.1
27.2

Median
214.5
217.2
203.8
217.4
214.5

mS/m
STD
141.9
198.0
149.7
150.5
133.0

CV
0.57
0.70
0.62
0.62
0.57

mS/m
STD
103.3
155.7
120.8
120.2
112.2

CV
0.42
0.58
0.51
0.50
0.47

Table 2 Summary of soil ECa in the deeper layer (0–150 cm)
Date
2018/5/16
2018/5/30
2018/6/14
2018/6/27
2018/7/11

Average
243.3
268.0
236.1
241.2
237.6

Max
769.7
987.3
928.4
773.0
802.2

Min
52.7
39.2
35.1
38.2
41.2

Median
214.0
225.2
211.0
223.4
216.5

Fig. 2 Average soil apparent electrical conductivity (ECa) in the upper (0–75 cm)
and lowest layers (75–150 cm) and daily rainfallⒸ ISERD

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The average soil ECa in the lowest layer (75–150 cm) was then calculated, as shown in Fig. 2.
The difference between the ECa in the upper soil layer (0–75 cm) and the lowest soil layer (75–150cm) was greatest during May, but the difference reduced, and the ECa of the lowest layer was higher
than that of the upper layer from late June. It is generally understood that saline water moves upwards
by capillary action, and the increasing difference in ECa in the upper and lowest soil layers can be
explained by capillary action. The reversal of the difference between the upper and the lowest layers
might have been due to rainfall during late June and there was actually about 100 mm of rainfall (atthe Khon Kaen airpoirt which is the nearest whether station from the study site) during the three days
from June 25-27, 2018.
Spatial Distribution of Soil ECa
Figure 3 shows the spatial distribution of soil ECa at different depths. Higher soil ECa at both depths
was observed in the northern part of the study area near the swamp throughout the monitoring period.
There were some spot high ECa values in the southern paddy fields in May; however, these werelower in June and July. At a glance, the ECa values were highest during May, then decreased in June
and July. The ECa in and around the rainfed paddy field was about 200 mS/m. The FAO states thatrice is a salt-sensitive crop, with an ECa threshold of 300 mS/m (FAO, 2002). As shown in Figure 3,
the ECa in the central and northern areas exceeded this threshold; therefore, these areas were left
fallow due to the high salinity. The reason of high salinity of the central and northern part of thestudy area is to be discussed by considering spatial and temporal distribution of groundwater level
fluctuation, soil type, and inundation of ground surface.
N

16-May30-May14-Jun27-Jun11-JulHorizontal (average 0–75 cm)

16-May30-May14-Jun27-Jun11-JulVertical (average 0–150 cm)

Fig. 3 Temporal and spatial distributions of soil apparent electrical conductivity (ECa)Ⓒ ISERD

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CONCLUSION
A high-resolution investigation of soil ECa over a large area was conducted using the electromagnetic
induction method. The ECa of two soil layers (0–75 cm and 0–150 cm depth) was measured and asoil salinization mechanism discussed. The results can be summarized as follows: (1) higher soil ECa
was observed in the northern part of the study area near the swamp throughout the monitoring period,(2) the average soil ECa at a depth of 0–75 cm ranged from 233 to 282 mS/m, while it ranged from
236 to 268 mS/m between 0 and 150 cm, (3) the average soil ECa in upper layer was slightly higher
than that of the lower layer in May, but the difference between them was reduced over time and thesituation reversed in mid-July, and (4) a larger spatial distribution was observed for the upper layercompared with the lower layer, judged by the difference of the CV of the soil ECa. Soil salinization
during the wet season was clearly observed. A comprehensive analysis of the soil salinization process,considering the fluctuation of the groundwater level, rainfall, and microtopography is planned for the
future.
ACKNOWLEDGEMENTS
This work was supported by JSPS KAKENHI Grant Numbers JP17H04630 and International
Platform for Dryland Research and Education, Tottori University.
REFERENCES
FAO. 2002. Agricultural drainage water management in arid and semi-arid areas. Rome, Italy: FAO Irrigation
and Drainage Paper 61. Retrived from http://www.fao.org/docrep/005/y4263e/y4263e00.htm#Contents
Hanchai, S., Iwai, C.B., Topark-Ngarm, B. and Bunterng, P. 2010. Soil biota activities relation with soil
characteristics in the improved salt-affected area by tree plantation. International Journal of Environmental
and Rural Development, 1 (2), 54-57.
Kume, T., Nagano, T., Watanabe, T. and Mitsuno, T. 2003. Salinity measurement of homogeneous soil using
electromagnetic induction method. Transaction of JSIDRE, 22, 105-111. (in Japanese)
Kume, T., Yamamoto, T., Shimizu, K. and Jalaldin, A. 2015. Non-uniform distribution of soil salinity along atransect of an irrigation field in an arid region. International Journal of Environmental and Rural
Development, 6 (1), 181-187.
Pongwichian, P., Kohno, E., Kingshuk, R. and Sasada, K. 2013. Actual situation of management and future
problems of reclamation on inland salt-affected soils in Thailand. Environmental Information Science, 27,
305-310.
Tatsuno, J., Hashimoto, Y., Tajima, K., Oishi, T., Dissataporn, C., Yamclee, P. and Tamaki, K. 2005.
Reclamation of saline soil in northeastern Thailand using a solar-powered pump to control groundwaterlevels. Environ. Control Biol., 43 (3), 155-163.
Topark-Ngarm, B., Patcharapreecha, P., Goto, I. and Kimura, M. 1990. Studies on saline soils in Khon Kaenregion, northeast Thailand, II. Seasonal changes of physical and chemical properties. Soil Sci. Plant Nutr.,
36 (2), 289-298.
Santos, V.R.N. and Porsani, J.L. 2011. Comparing performance of instrumental drift correction by linear andquadratic adjusting in inductive electromagnetic data, Journal of Applied Geophysics, 73, 1-7.
Wongsomsak, S. 1986. Salinization in northeast Thailand. Southeast Asian Studies, 24 (2), 133-153.
Zukemura, C., Haraguchi, N., Miyamoto, T. and Nakaya, T. 2016. Determining high salinity soil layers depth
using electromagnetic induction sensor EM38-MK2. IDRE Journal, 301, Ⅰ_23-Ⅰ_30. (in Japanese)Ⓒ ISERD

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Research article
erd

Reducing Nitrogen Emission during Cow Manure
Composting with Adding of Rice Husk Biochar
SAVESH MASKEY
Graduate School of Agriculture, Tokyo University of Agriculture, Tokyo, Japan
Email: smaskey27@gmail.com

MACHITO MIHARA*
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, Japan
Email: m-mihara@nodai.ac.jp
Received 15 January 2020

Accepted 5 May 2020(*Corresponding Author)

Abstract Nitrogen losses in the forms of ammonia and nitrous oxide emission are major
causes for low nitrogen content in cow manure compost. In addition, nitrogen losses decrease
the agronomic value of compost. In this study, rice husk biochar was added in cow manure
to assess its effects on reducing nitrogen losses. Rice husk biochar is a product ofthermochemical reaction in limited oxygen supply resulting in a product with micro-pores
and large surface area with negative charge. A small scale composting experiment was
designed and conducted using cow manure for a period of 60 days. Gases released were
measured periodically with static gas chamber method. In addition, total nitrogen content andC/N ratio were analyzed. The experiment results showed that the loss of ammonia which isin peak during initial stages were lower in the treatments added with biochar. The cumulativeammonia emission was lowered by 12.6%, 14.5% and 23.2% in 5%, 10% and 15% biochar
added treatments respectively, as compared to control. The cumulative gas flux result of N2O
showed similar trend to that of NH3. N2O emission was lowered by 40.0%, 46.4% and 60.4%
in 5%, 10% and 15% biochar added treatments respectively, as compared to control.
Furthermore, total nitrogen content and C/N ratio had better results in biochar added
treatments. The obtained results may be attributed to the fact that micro-pores and negative
charge in rice husk biochar adsorbed the nitrogenous compounds. Additionally, biochar
increased retention of moisture and nutrients enhancing microbial activity for better
degradation and humification of organic matter. The results indicated that biochar could be agood medium in reducing the nitrogen loss and increasing agronomic value duringcomposting of cow manure.
Keywords cow manure composting, nitrogen emission, rice husk biochar, agronomic value

INTRODUCTION
Composting is a sustainable method for waste management of cow manure. It is a naturally occurring
process where various distinctive microorganisms break down organic matter to produce humus like
substance, which has high agronomic value. On the other hand, composting produces harmful gases
like ammonia (NH3) and nitrous oxide (N2O), which causes environmental problems. High nitrogencontent of cow manure causes the release of NH3, which is one of the principal malodorouscompounds produced during composting. Aerobic degradation of organic material in cow manurecauses intensive NH3 generation and emission. Ammonia volatilization leads to the loss of nitrogenand effects compost nutrition content (Pagans et al., 2006). N2O is released from nitrification or
denitrification (Fig.1) of nitrogenous compounds and has a high global warming potential (Houghton
et al., 1992). Nitrous oxide is considered 265 times more potential GHG than carbon dioxide (IPCC,
2013). Emission of these gases causes loss of nitrogen, which decreases the agronomic value ofcompost. Loss of nitrogen through ammonia volatilization can be as high as 70% (Martin and Dewaes,
1992; Eghball et al., 1997; Beck-Friss et al., 2001) and 0.62 to 1.07% in the form of N2O in manure(Hao et al., 2001). Nitrogen transformation during of manure composting is a complex mechanismⒸ ISERD

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and is shown in Fig. 1 (Wu et al., 2012). It is of great concern on developing measures for reducing
nitrogen losses during composting process to minimize the emission of hazardous gases and increase
the agronomic value of compost.
Recently, biochar amendment has been used in limiting N2O emission in manure compostingand soil (Kamman et al. 2015; Wang et al. 2013). Biochar is a carbon rich material derived frompyrolysis of biomass characterized by high number of micro pores and large surface area withnegative charge. In this study, it is speculated that these properties of biochar can reduce the emissionof NH3 and N2O when composted with animal manure as biochar can effectively retain NH3, NH4+
and NO3- in animal manure (Steiner et al., 2010).

Organic nitrogen
A
M
V
Compostingmaterial of
manureNH4+-NN

N
NO2- N

NH3

D

N20
DN

DN

N2

L
NO3--NL

Leachate

Fig. 1 Nitrogen transformation during manure composting (adopted from Wu et al., 2012)
A: Ammonification; I: Immobilization; M: Mineralization; V: Volatilization;
D: Dissolution; Nf: N-fixation; N: Nitrification; DN: Denitrification; L: Leaching loss

Therefore, this study concentrates to evaluate the effects of composting cow manure with rice husk
biochar amendment to reduce the emission of NH3 and N2O and increase the agronomic value of compost.
METHODOLOGYDesigning of Compost Box and Gas Chamber
A compost box was designed using glass container of dimension 30 cm3. The glass box was covered
with styro-foam sheets of thickness 4 cm on five sides for minimizing the heat loss (Fig. 2).

Fig. 2 Schematic diagram of composting box
and gas flux analysis

Fig. 3 Apparatus used during gas flux
measurementⒸ ISERD

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A static gas chamber was designed to measure the gas flux. A digital thermometer and a fanwere attached inside the chamber for measuring temperature and uniform circulation of air during
measurement of gas respectively (Figs. 2 and 3). The volume of air inside the chamber was calculated
as 0.017705 m3.
Conditions of the Experiment
The composting experiment was conducted from 8 August 2019 to 7 October 2019 in Laboratory ofLand and Water Use Engineering, Tokyo University of Agriculture. The average temperature was
23.0 C̊ during the experiment. Cow manure, rice straw and litter was used as main components forcomposting. The nitrogen content in cow manure and rice husk biochar was 23695 mg/kg and 1241
mg/kg respectively at the start of the experiment. Rice husk biochar was added in three different
variations (Table 1). The initial water content of the composting material was set at 70±2% for all
the treatments.
Table 1 Composition of treatments
Composition

Control

Treatment with 5%
added biochar

Treatment with 10%
added biochar

Treatment with 15%
added biochar

Cow manure (g)

3200

3200

3200

3200

Litter + rice straw (g)

1800*

1800*

1800*

1800*

250*

500*

750*

Rice husk bio-char (g)Note: * represents air-dry weight basis

Measurement of Gas Emission
Gas flux was measured using static gas chamber method with photoacoustic spectrometer. The
spectrometer used in this study was INNOVA 1412 Photoacoustic Field Gas Monitor (Fig. 3). Gas
flux was calculated using linear aggression method showed in Equation 1 (Minamikawa et al., 2015).
𝑉
𝐴

𝐹 =𝜌× ×

△𝑐
273
×
△𝑡
𝑇

(1)

where F is gas flux (mg m-2 hr-1), ρ is density of gas (kg m-3) where density of NH3 is 0.772 kg m-3
and of N2O is 1.96 kg m-3, A is bottom surface area of chamber (m2), V is volume of air inside thechamber (m3), △c/△t is average increase rate of gas density inside the chamber (10-6 m3 m-3 hr-1), T
is average temperature inside the chamber (K).
Compost Sampling and Analysis
Compost was sampled at 5, 10, 20, 30, 40 and 60 days to analyze its agronomic value. Temperatureof the compost pile was measured using Custom CT-0580 data logger and digital thermometer on
daily basis. Water content of the sample was analyzed using gravimetric method, where samples
were kept at 105°C for 24 hours. Carbon content was determined by using the formula C=0.580* IL.
Total nitrogen was analyzed by absorption spectroscopy using HC-1000 (Central Science Corp.) as
measurement device (Mihara and Ueno, 2000).
RESULTS AND DISCUSSION
Biochar Effects on Composting Temperature
Temperature of composting process is widely considered as significant factor in gas emission during
cow manure composting as microbial metabolism and activities are all temperature sensitive andⒸ ISERD

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dependent. NH3 is released when the temperature is high and are carried by thermophiles. Whereas,
N2O is released after temperature declines and mesophiles are responsible for it. In this study, all thetreatments showed a similar pattern of temperature evolution with a rapid activation of compostingprocess carried by intensive microbiological degradation of organic matter. Biochar added treatments
had increased temperature profile during the thermophilic phase compared to control (Fig. 4). The
result coincides with previous studies using biochar as a co-substrate for manure composting (Jindo
et al., 2012; Wang et al., 2013; Wei et al., 2014). It is speculated that higher temperatures in biochar
added treatments were attributed to the structure of biochar, which provided suitable habitat
conditions with positive effects on substrate properties such as porosity, surface area and moisture
content. These factors promoted microbial activity, explaining higher temperatures.
60
Control55

5%50

Temperature (

)

10%
15%

45

Ambient temp.
40
35
30
25
20
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60

Days

Fig. 4 Periodic changes in temperature with time
Biochar Effect on Ammonia Emission
Ammonia emission is inevitable during cow manure composting which has high content of nitrogen.
Generally, emission is high during early stage of composting where temperature and pH are high. In
this study, emission was high during the first week of composting which lowered gradually as thetemperature decreased for all the treatments (Fig. 5) with negligible emission after 20 days ofcomposting. The emission pattern of the results of this study coincides to that of Osada et al. (2000)
and Kuroda et al. (1996). The total ammonia emissions were significantly lower in biochar amended
treatments with 15% added treatment having the lowest emission.
0.12
5%
0.1
10%

0.08

15%

0.06
0.04
0.02
0

Cumulative gas emission (mg)0.35
Control!

Gas flux( mg mˉ² hˉ¹ )

0.14

(

denotes significance difference p<0.01)

0.3

A**

0.25
0.2

Control
5%
10%
15%

B**
C**

0.15
0.1

D**

0.05
0

1

2

3

4

5

10

15

20

0

Days

5

10

15

20

Days

Fig. 5 Periodic changes in ammonia flux

Fig. 6 Cumulative ammonia emission

The cumulative gas emission was lowered by 12.6%, 14.5% and 23.2% in 5%, 10% and 15%
biochar added treatments respectively, as compared to control (Fig. 6). The reduced emission can be
attributed to the microscopic porous structure having negative charge adsorbing ammonium ions byelectrostatic attraction (Montes-Moran et al., 2004; Nguyen et al., 2017).
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Biochar Effect on Nitrous oxide Emission
Composting of high organic content materials has been shown to produce N2O by nitrification anddenitrification under aerobic and low oxygen conditions respectively. Fig. 7 shows the periodic
change in emission between the treatments. The cumulative gas flux resulted in least emission in
15%, 10% and 5% biochar added treatments followed by control (Fig. 8). The cumulative gas
emission was lowered by 40.0%, 46.4% and 60.4% in 5%, 10% and 15% biochar added treatments
respectively, as compared to control. The result observed is supported by that of Jeffery et al., 2015,
which states that addition of biochar can decrease the emission of nitrous oxide.
Cumulative gas emission (mg)

Gas flux （ mg m-2 hr-1 ）

0.25
0.2
Control

0.15

5%
0.1

10%
15%

0.05

0.9

(

0.8

denotes significance difference p<0.01)

A, A**

Control

0.7

5%

0.6

10%

0.5

15%

A, B
A, B

0.4
0.3

B, B**

0.2
0.1
0

0
1

5

10

20

30

40

50

0

60

10

20

Days

Fig. 7 Periodic changes in nitrous oxide flux

30

Days

40

50

60

Fig. 8 Cumulative nitrous oxide emission

Biochar Effects on Agronomic Value of Compost

25000

40
(

(

denotes significance difference p<0.01)

Controldenotes significance difference p<0.01)

Control

22000

B**
B**

5%

19000

C**

10%

A**

15%

16000

35

C/N ratio

Total nitrogen (mg/kg)

Nitrogen content and C/N ratio are oftenly used as a criteria for determining the agronomic value ofcompost. Nitrogen is the most esential plant nutrient for growth and development of plants. Compost
which has high nitrogen content is considered having high agronomic value. The experimental results
showed that total nitrogen content of biochar added treatments were higher than control (Fig. 9) at
60 days of composting with 99% significant difference. The high nitrogen content in biochar added
treatments can be due to the result of reduced emission of NH3 and N2O.

5%
10%

30
A**

B**
B**

20

13000
10000

15%

25
B**

15

1

5

10

20

40

5

60

10

20

40

60

Days

Days

Fig. 9 Periodic changes in nitrogen content

Fig. 10 Periodic changes in C/N ratio

In addition, biochar may have enhanced aeration, water holding capacity and microbial activity
increasing humification of organic material. This resulted in high nitrogen content and concurrs tothat of Akdeniz (2019), El-Naggar et al. (2019) and Godlewska et al. (2017). C/N ratio is used as anindicator of compost stability and nitrogen availability. The periodic change of C/N ratio showed
better values in biochar added treatments compared to control (Fig. 10). The C/N value of biochar
added treatments at 60 days of composting had siginificant difference at 99%, compared to control.
This can be explained as increased surface area and moisture content favoured compost
microorganisms for degradation and humification of organic material. Also, biochar addition
increased sorption of nitrogen compounds in the microspores providing microorganism withⒸ ISERD

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sufficient nitrogen for their metabolism.
CONCLUSION
This study discusses the effects of composting cow manure with rice husk biochar in reducing theemission of NH3 and N2O and increasing the agronomic value of compost. A small scale laboratory
experiment was conducted where rice husk biochar were composted with cow manure The results
showed that addition of biochar is effective in reducing emission of NH3 and N2O with 15% treatment
being the most efficient. This can be attributed to the structure of biochar with high number ofmicropores and surface area that enables absorption / adsorption of NH3, N2O, water soluble NH4+
as well as NO3-. The result also showed better nitrogen content and C/N ratio in biochar-amended
treatments. The author speculates that adding of biochar helped in increasing oxygen content andretention of water enhancing better microbial metabolism and mineralization. The author believes
that composting biochar with cow manure is encouraging method for reducing emission andincreasing better quality compost. However, further researches are needed to understand the role ofbiochar and interaction of manure, biochar and microbes during composting process.
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Jeffery, S., Abalos, D., Spokas, K.A. and Verheijen, F.G.A. 2015. Biochar effects on crop yeild. In Lehmann,
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Kammann, C.J., Schmidt, H.P., Messerschmidt, N., Linsel, S., Steffens, D., Muller, C., Koyro, H.W., Conte,
P. and Joseph, S., 2015. Plant growth improvement mediated by nitrate capture in co-composted biochar.
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Martins, O. and Dewes, T. 1992. Loss of nitrogenous compounds during composting of animal wastes.
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Research article
erdSocioeconomic Conditions and Perception of Climate
Change in Rice Varietal Selection by Smallholder Farmers
in Myanmar
SHWE MAR THAN*
Advanced Center for Agricultural Research and Education, Yezin Agricultural University,
Yezin, Myanmar
Email: shwemar@gmail.com, dr.shwemarthan@yau.edu.mm

THADAR HTWE
Department of Agricultural Economics, Yezin Agricultural University, Yezin, Myanmar
Received 24 January 2020

Accepted 5 May 2020(*Corresponding Author)

Abstract Rice is an economically, socially, politically, and culturally important crop grownin diverse agro-ecological conditions all over Myanmar. Climate change impacts affect riceproduction directly and link in farming decisions for cropping systems. As the population
keeps growing and climatic variabilities and extreme weather events keep increasing themarginal productivity of land would not be negligible for the food security and sustainableagriculture. This study was conducted to meet the objectives of to overview thesocioeconomic characteristics of farmers, find out the serious problems faced by the farmers
in rice production, know the most practicing coping strategy to adopt the weather variability
and determine farmers’ preferred characteristics of rice and perception on climate change inrice variety selection. A sample of 203 rice farmers from Thazi Township were randomly
selected and conducted interview. Descriptive analysis and weighted average method were
employed. Rice varieties were very diverse and Manawthukha was the most favorite variety.
The newly released salt tolerant variety, Pyi Myanmar Sein was adopted by only one percent
of respondents. The farmers are quite aware of climate change and they claimed the extreme
weather conditions are the most stressful to rice production and they want shorter duration
variety in order to make sure the enough soil moisture because majority of them do not haveaccess to irrigation water. Changing rice variety was the most cited coping strategy to climatechange adaptation followed by changing crop, changing sowing time, practicing crop
rotation, changing cultivation methods and managing fertilizer application. The most
preferred trait was high yielding variety followed by high quality, resistance to disease, pests
and drought.
Keywords climate change, strategy, adaptation, stress, traits, coping strategy

INTRODUCTION
Myanmar has been experiencing various features of climate change conditions such as frequent andstrong storms, floods, late onset of monsoon, early departure of monsoon, lower minimum
temperature and higher maximum temperature, erratic rainfall patterns, frequent falls of hales andthunder storms, and sea level rise, etc. According to the long-term climate risk index (CRI) publishedby Germanwatch, Myanmar stood as the third most affected country in the world on annual average
from 1998-2017. Increased intensity of storms has taken a toll on small island states and poor
countries since 1997, and over 520,000 people have been killed by more than 11,000 extreme weather
events (Burck et al., 2017). Myanmar is the second most vulnerable country revealed by global
climate risk index. Extreme weather events have a strong negative impact on economic developmentof a developing country. Besides, their main economic sector is still agriculture and it is extremely
vulnerable to weather variabilities. Furthermore, more than two third of farming society aresmallholders and they grow rice in rain fed and/or irrigated lowland area in Myanmar. Therefore,Ⓒ ISERD

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this study was conducted to learn more about how socioeconomic background of farmers and theirperceptions on climate change affect their choice of rice varieties.
OBJECTIVE
The study was conducted to meet the objectives for to overview the socioeconomic characteristics offarmers, find out the serious problems faced by the farmers in rice production, know the mostpracticing coping strategy to adopt the weather variability and determine farmers’ preferredcharacteristics of rice and perception on climate change in rice variety selection.
METHODOLOGY
Description of Study Area
This study was conducted in Thazi Township located in Meiktila District of Mandalay Division,
Myanmar. Thazi Township is situated in North Latitudes 20˙ 50' and East Longitude 95˙ 59', and its
area is 2,039 km². It is located in Central Dry Zone of Myanmar and has a tropical climate. According
to the Department of Population (2014 Census, 2015), its total population was 202,680 - males were
95,463 and females were 107,217. In Thazi Township, about 182,119 (89.9%) of its population livein rural area and about 20,561 (10.1%) live in urban area.
Data Collection
For this study, both quantitative and qualitative data were used. Pilot survey was done in November
and main survey was conducted in December 2016. Simple random sampling method was used tocollect primary data and 203 sample farmers from 19 villages in Thazi Township were interviewed
by using structured interview schedule. Secondary data were collected from the Township
Department of Agriculture Office.
Methods of Analysis: Descriptive Analysis
Descriptive analysis was used to describe demographic characteristics of the household such as age,education, family size, farming experience, farm size and agricultural land utilization and to observe
the problems faced by the rice farmers.
Weighted Average
Weighted average method was used to investigate farmers’ preferences on rice varietal
characteristics. Farmers’ preferences were categorized into three groups: production characteristics,
stress tolerant characteristics and grain quality characteristics. Farmers were requested to score on
the given rice varietal traits. If they assumed that the given trait was extremely important or important,
the trait was scored as 3 or 2, respectively. If the trait was assumed as not important, it was scored as
1.
Weighted Average =Sum of Weighted Terms
Total Number of Terms

The score values were calculated by using weighted average method to rank farmers’
preferences on rice varietal trait.
RESULTS AND DISCUSSION
Socioeconomic Conditions of FarmersⒸ ISERD

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The demographic characteristics such as age, farming experience, family size, education and farm
size the sample farmers are presented in Table 1. In Thazi Township, the average farmer age was
55.6 years, and ranging from 28 to 86 and most farmers are quite old. Agriculture is mainly threatenedby climate change and becoming less profitability. Therefore, young people from rural area are notinterested in agriculture and moving out of rural areas. That is becoming an important issue for
Myanmar Agriculture. The average farming experience was 29.6 years with the range of 2-70 years.
Although farmers have many years of farming experience with a lot of knowledge and experience inagriculture, they may rarely adopt new agricultural technologies. The average family size was 5.5,
ranging from 1 to 14 family members. In case of education, the average schooling years was 7.3, andranging from 3 to 16 years. Some farmers were graduated, and thus they can adopt new technologies
and can manage their farm very well. The average farm size was 4.1 hectares with a range between
0.4 and 14.9 hectares, respectively.
Table 1 Demographic characteristic of the respondents in Thazi TownshipItems
Age of household’s head
Farming experience of household’s head
Family size
Education of household’s head
Farm size
Low land (70.2 %)Upland (29.2 %)Orchard (0.6 %)

Unit
Year
Year
No.
Year
ha
ha
ha
ha

Average
55.6
29.6
5.5
7.3
4.1
3.0
1.7
2.8

Minimum
28
2
1
3
0.4
0.4
0.2
2.4

Maximum
86
70
14
16
14.9
13.0
8.1
3.1

In Thazi Township, the average farm size of lowland, upland and orchard were 3.0, 1.7 and 2.8
hectares, respectively. Lowland area ranged from 0.4 to 13.0 hectares while upland area ranged from
0.2 to 8.1 hectares. Orchard was the smallest agricultural land area, ranging from 2.4 to 3.1 hectares.
Among the agricultural land areas, lowland occupied 70.2%, and rice was the major crop in Thazi
Township. Upland growing area was the second largest, and it occupied 29.2%. Orchard was thesmallest area and it was only 0.6% of total agricultural land of the respondents.
Table 2 Annual household incomes in Thazi Township (MMK in million)
Types of Income
Crop Income
Livestock income
Off-farm income
Non-farm income
Total annual household income

N
203
61
16
106
203

Average
3.51
1.68
0.84
2.36
5.32

Minimum
0.08
0.18
0.03
0.02
0.38

Maximum
33.70
6.40
4.73
8.64
38.70

Annual household incomes earned by respondents in Thazi Township are shown in Table 2.
Crop income was the main income source for sample farmers. Average crop income was 3.51 million
MMK per year for farmers, ranging from 80,000 MMK to 33.7 million MMK per year. Livestockincome was earned by 61 respondents, and it amounted to 1.68 million MMK in average per year
while maximum and minimum livestock incomes were 0.18 million MMK to 6.4 million MMK per
year, respectively. Only 16 respondents had off-farm income, and the average off-farm income was
0.84 million MMK per year. The maximum off-farm income was 4.73 million MMK per year, but
the minimum income was only 30,000 MMK per year. Non-farm income was earned by 106 sample
households, and the average non-farm income was 2.36 million MMK per year ranging from 20,000
MMK to 8.64 million MMK per year. As a total annual household income, the average annual
household income was 5.32 million MMK per year. The minimum and maximum total annual
household incomes were 0.38 million MMK and 38.7 million MMK per year.
Problems Faced by Rice FarmersⒸ ISERD

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As lowland was the largest area, rice was the major crop in Thazi Township. Therefore, stress or
problems in rice production faced by the respondents were collected. It was found that six mainstresses for rice farmers were climate change, insects, disease, salinity, rats and weeds. According tothe opinions of farmers, the largest group of respondents (48.3%) assumed that climate change is oneof the biggest stresses for rice production. The second (31%) and third (15.3%) biggest group of
farmers said that insects and diseases were major serious problems for rice production. In addition,
4.4% of respondents thought that soil salinity was one of the problems for rice production. Incidenceof rats and weed problems were faced by 3.4% of respondents. Moreover, 6.9% of respondents faced
other stresses such as scarcity of labor and high price of inputs, especially fertilizer in their riceproduction (Fig. 1).

Fig. 1 The stresses in rice crop production in Thazi Township
Climate Change Coping Strategies
Table 3 Adaptation strategies to climate change in Thazi TownshipResponse to climate change
Change varieties
Change crop
Change sowing time
Crop rotation
Change cultivation methods
Use fertilizer
Use underground water
Put F.Y.M
Use traditional varieties
Use quality seeds

Frequency
88
57
18
15
10
10
4
4
3
2

Percent (%)
41.7
27.0
8.5
7.1
4.7
4.7
1.9
1.9
1.4
0.9

Rank
1st
2nd
3rd
4th
5th
5th
6th
6th
7th
8th

Since climate change was the major stress according to farmers’ opinion, adaptation strategies of
local farmers to climate change were observed. According to the results, changing crop varieties was
the strategy used by most of the respondents (41.7%) to combat climate change. It was followed bychanging crop as the second rank and used by 27% of respondents. Changing sowing time, practicing
crop rotation, changing cultivation methods and using fertilizer were the strategies used by 8.5%,
7.1%, 4.7% and 4.7% of respondents, respectively. Using underground water, putting F.Y.M andusing traditional varieties were used by 1.9%, 1.9% and 1.4% of respondents. Using quality seeds
was the least strategy used by respondents (0.9%) to adapt to climate change (Table 3).
Farmers’ preferences on production characteristics of rice are shown in Table 4. Majority offarmers preferred the trait of high yielding most. It is clear that a variety to be yielding high is themost important factor for rice growing farmers. Moreover, farmers preferred rice variety trait of highyield with less inputs, and it was ranked as the second. However, few of them think rice variety withthat trait is not that important. Rice variety producing high number of tiller ranked as third factor
according to farmers’ preferences. Most of farmers assumed that the more the tiller numbers, thehigher the rice yields. Resistance to lodging and shorter growing duration were ranked as fourth andⒸ ISERD

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fifth factors, respectively. Heavy rains and floods caused lodging of rice plants causing the yield loss.
If the farmers were able to grow lodging resistant variety, they could reduce yield loss. Short duration
variety can avoid extreme climatic conditions relative to traditional long duration variety. In addition,
farmers preferred shattering resistant variety as the sixth rank. It is clear that shattering leads to bothpre-harvest and post-harvest yield losses. Therefore, shattering resistant variety is also important forfarmers. High rice straw production was ranked as the last according to farmers’ preferences. Farmers
mostly used rice straw for cattle feeding. During recent years, cattle have been scarce in the study
area, and some of farmers did not prefer to get high rice straw.
Table 4 Farmers’ preferences on production characteristics of rice
Production characteristics
High yieldhigh yield with less inputs costs
High tillering
Resistant to lodging
Shorter growing duration
Resistant to shattering
High straw production

Extremely
important
3
198
191
181
164
130
115
82

Important

Not important

2
4
7
17
29
48
38
47

1
1
5
5
10
25
50
74

Weighted
average

Rank

2.97
2.92
2.87
2.76
2.52
2.32
2.04

1st
2nd
3rd
4th
5th
6th
7th

Farmers’ preferences on stress tolerant characteristics of rice are presented in Table 5. Majorityof farmers preferred pests and diseases resistant varieties, and ranked them as first and second,
respectively. Actually, pests and diseases are the main important stresses or problems for farmers,
thus farmers preferred pest and disease resistant varieties most. Since the study area is in the dryzone, farmers preferred drought and salinity tolerant varieties as third and fourth ranks, respectively.
In addition, some farmers preferred rice variety tolerant to cold injury, and it ranked fifth. Flood orsubmergence was also one of the stresses for some farmers, and it ranked sixth.
Table 5 Farmers’ Preferences on Stress Tolerant Characteristics of RiceStress tolerant characteristics
Resistant to pests
Resistant to diseases
Tolerant to drought
Tolerant to salinity
Tolerant to cold injury
Tolerant toflood/submergence

Extremely
important
3
184
183
173
76
81
66

2
15
14
15
43
30

Not
important
1
4
6
15
84
92

24

113

Important

Weighted
average

Rank

2.89
2.87
2.78
1.96
1.95

1st
2nd
3rd
4th
5th

1.77

6th

Rice varieties Grown in the Study Area
There were about twenty varieties of rice grown in Thazi Township. The farmers are still growing
the local varieties which still suit the weather and soil type. During the survey period, there was nosummer paddy cultivation because of no supply of irrigation water from the dams. Therefore, thestudy included the only monsoon season rain-fed rice. Local cultivars are the primary source forfarmers to cope with changing environments. Genetic diversity gives a species or a population that
can have the ability to adapt to changing environments (Sthapit et al., 2007). Manaw Thukha was
the most popular rice variety among others and mainly grown by nearly half of the respondents(49.8%) (Table 6).

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Table 6 Rice varieties grown in Thazi Township
No.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.

Rice
Varieties
Manaw Thukha
90 Days
Sat Thukha
Thukha Tun
Khun War
Yezin Thukha
Pale Thwe
Shwe Pyi Htae
100 Days
Shwe PoePyi Myanmar sein

F

%

No.

101
29
17
16
6
4
4
4
3
3
2

49.8
14.3
8.4
7.9
3.0
2.0
2.0
2.0
1.5
1.5
1.0

12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.

Rice
Varieties
Shwe Manaw
Thee Htet Yin
Byawt Tun
Mann Nga Sein
Shine NatTun Thiri
Taung Htate Pan
Shwe Manaw
Yadana Thukha
Byawt Thukha
Shwe Thwe Yin

F

%

2
2
2
1
1
1
1
1
1
1
1

1.0
1.0
1.0
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5

CONCLUSION
Poverty prone rural dry zone still depends on rice cultivation and it is at the high risk under climatechange. Farmers change the rice varieties with shorter days to secure the moisture to get filled grains
in rain fed low land area. Nevertheless, they still prefer the high yielding variety most to reap highprofit. On the contrary, they are facing problems with so much weather variability resulted in poor
yield and profit. That is why more suitable variety for climate change resilient one, 90 Days variety
was gradually accepted by many farmers. It is recommended that income diversification is one of thegood strategies to combat the climate change impacts that more than half of farmers have non-farmincomes and many of them have income from livestock raising.
REFERENCES
Burck, J., Marten, F., Bals, C., Höhne, N., Frisch, C., Clement, N. and Szu-Chi, K. 2017. Climate change
performance index (CCPI). Germanwatch, Retrived from https://germanwatch.org/sites/germanwatch.
org/files/publication/20503.pdf
Department of Population. 2015. 2014 Population Census Report.
Eckstein, D., Künzel, V., Schäfer, L. and Winges, M. 2020. Global climate risk index 2020, Who suffers mostfrom extreme weather events? Weather-related Loss Events in 2018 and 1999 to 2018. Germanwatch, 142.
Sthapit, B., Rana, R., Eyzaguirre, P. and Jarvis, D. 2008. The value of plant genetic diversity to resource-poor
farmers in Nepal and Vietnam. International Journal of Agricultural Sustainability, 6 (2), 148-166. Doi:
10.3763/ijas.2007.0291Ⓒ ISERD

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Research article
erd

Estimation of Flooded Areas and Water Levels after the
Heavy Rain in July 2018 in Okayama
TAKAHIKO KUBODERA*
Faculty of Science and Engineering, Toyo University, Saitama, Japan
Email: kubodera@toyo.jp

SHUMPEI HIRAMICHI
Faculty of Engineering, Osaka Sangyo University, Osaka, Japan
Received 19 February 2020

Accepted 23 May 2020(*Corresponding Author)

Abstract Paddy fields in the Osaka Prefecture were heavily flooded by heavy rain in July
2018 due to levee breaches. To estimate the flooded area and water level, it was necessary to
combine aerial- and ground-based surveying. Accordingly, aerial photographs of the flooded
area were taken on the day after the heavy rain. In this study, we developed a digital surface
model (DSM) of the flooded area using the structure-from-motion (SfM) software, which can
produce three-dimensional (3D) models based on multiple aerial photographs and multiple
ground control points (GCPs). Using the global navigation satellite system (GNSS), we
surveyed the target area to observe the 3D coordinate values of the GCPs. The DSM of theflooded area could be made by the SfM with the aerial photographs and GCPs. The DSM was
input into the geographic information system to analyze the flooded area and water level. The
water levels were calculated from the differences between the DSM and the digital elevation
model (DEM). We used airborne laser surveying to build the DEM because it provides higher
accuracy than the SfM software. To check the accuracy of the DSM, we compared theelevations of the DSM on the ground points of non-flooded areas with those of the DEM, andthe differences were found to be less than 0.2 m. Then, the water levels on the actual waterlevel observation points were compared to verify their accuracy, and the differences were
found to be less than 0.4 m. The flooded area was found by overlaying the water level on theexisting map.
Keywords structure-from-motion, digital surface model, digital elevation model,
geographic information system, paddy field

INTRODUCTION
Heavy rain in July 2018 seriously affected the Okayama Prefecture in the west of Japan (particularly
the paddy fields) due to levee breaches. As a result, nearly half of harvest of the paddy fields was lost.
A combination of aerial- and ground-based surveys was necessary for a prompt understandingof the complete picture of the damaged area (Kubodera et al., 2017). Therefore, we conducted anaerial photogrammetry and a global navigation satellite system (GNSS) survey. Aerial
photogrammetry is a surveying technology that utilizes three-dimensional (3D) ground-based models,
which are constructed from aerial photographs, camera coordinate values, and coordinate values ofground control points (GCPs). Based on the aerial photogrammetry, we also built orthophoto mosaics
and a digital surface model (DSM), which captures the land surface elevations, including the heightsof buildings and vegetation. An orthophoto mosaic is a single aerial photograph on which several
orthographically projected aerial photographs are assembled. To obtain the coordinate values of the
GCPs in the devastated areas, we conducted a GNSS survey (Yamazaki et al., 2019), which is amethod to determine the positions using signals from positioning satellites, such as the global
positioning system (GPS), global navigation satellite system (GLONASS), and Galileo. The
accuracy of these satellites ranges from several meters to several millimeters. In this research, we
conducted a network-based real-time kinematic (RTK) GNSS survey to determine the coordinate
values of the GCPs with an accuracy of several millimeters. We used the geographic informationⒸ ISERD

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system (GIS) to determine the difference between the DSM and digital elevation model (DEM),
which captures the ground elevations (excluding the heights of buildings and vegetation), to analyze
the flooded area and water level in the paddy fields quantitatively.
We considered that aerial photogrammetry by airplane would be preferable than by satellite ordrone for the analysis of the flooded area and water level. The aerial photographs used were captured
from an airplane immediately after the heavy rain. The Geospatial Information Authority of Japan(GSI) has built a detailed database of Japan’s land surface from photographs taken from airplanes andhas released the DEM (5 m mesh) from airborne laser surveying throughout Japan. Furthermore, the
GSI has released aerial photographs of Okayama taken immediately after the heavy rain in July 2018.
METHODOLOGY
Figure 1 shows the target area of the survey and analysis. The Okayama Prefecture is located to thewest of Osaka and Kyoto. Of the large area damaged by the heavy rain, we selected Mabi City tofocus on the damage to paddy fields. Figure 2 shows the levee breach at the Oda river in Mabi City
that caused the flooding of the paddy fields. The first author took the picture on November 10, 2018.

Fig. 1 Target area in Okayama

Fig. 2 Levee breach at Oda River in Mabi City
The first author took the picture in Novemeber 10, 2018.

Aerial photo

GCP
Paddy fields

Oda river

Point of observation of thewater level

Fig. 3 3D model by aerial photogrammetry

Fig. 4 GNSS surveying

We used the photogrammetry structure-from-motion (SfM) software PhotoScan Professional,
which allows the aerial triangulation based on multiple aerial photographs and multiple GCPs. It is
used to build DSMs through image matching and create orthophoto mosaics automatically. Figure 3Ⓒ ISERD

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shows the 3D model built in this study. The Oda river and paddy fields are in the center of Fig. 3;
the point of water level observation, which was performed every hour, is in the right of Fig. 3. The
twelve blue rectangles in the sky show the 3D positions from which the aerial photographs weretaken. The 15 circles on the ground surface indicate the 3D positions of the GCPs where we
conducted the GNSS survey. AS coordinate system, we used the Japanese geodetic datum (JGD)
2011 plane-rectangular coordinate system Area 5 (Matsumura et al., 2004; Tsuji and Matsuzaka,
2004; Imakiire and Hakoiwa, 2004; Geospatial Information Authority of Japan, 2011). We
successfully set a group of 3D points on the ground surface through aerial photogrammetry. The
aerial photographs were taken at 12:00 on July 9, 2018, immediately after the heavy rain with a wellcalibrated digital aerial camera. Using the RTK GNSS surveying and inertial measurement unit
(IMU) technology, we set the coordinate axes and altitudes of the digital aerial camera with anaccuracy of several tens of millimeters.
As shown in Fig. 4, we conducted a network-based RTK GNSS surveying at GCPs in Okayama
on November 10, 2018. We used the Trimble R10 for the GNSS surveying, and three types ofsatellites: GPS, GLONASS, and the quasi-zenith satellite system (QZSS). In addition, we used two
frequency bands: L1 (1575.42 MHz) and L2 (1227.60 MHz). We used the geoid model GSIGEO2011(Miyahara et al., 2014; Kubodera et al., 2016). For the GNSS survey at the GCPs, we selected thefifteen points enclosing the flooded area and the point of observation of the water level, sites
accessible by car, and pavements to obtain clear photographs.
We conducted aerial triangulation based on the 3D coordinate values of the camera and the
GCPs. Table 1 shows the standard deviation (SD) values of the residual errors of the most probablevalues of fifteen GCPs. The SD values for the X and Y coordinates and elevation H were lower than
0.10 m, indicating high accuracy.
We created a DSM with 0.4 m mesh to determine the differences between the DSM and DEM.
Furthermore, we created orthophoto mosaics to obtain more information on the flooded area.
RESULTS AND DISCUSSION
Specifying the Position of Levee Breaches with Orthophoto Mosaics
Figure 5 shows the orthophoto mosaic developed on the GIS. For the aerial photogrammetry, weused the coordinate axis JGD2011 Plane-rectangular Coordinate System Area 5. The GIS software
used was ArcGIS 10.7. By comparison with an existing map, we confirmed that the mosaic wasdeveloped on the designated positions correctly. The black areas in Figure 5 were either not in theaerial photograph field of view or covered by cloud or water, because SfM could not create the clouds
and water. As shown in Figure 6, we successfully specified the exact positions of the levee breaches,
and the orthophoto mosaic indicates that the paddy fields were flooded.
Table 1 SD values of the residual errors of the most probable valuesof fifteen GCPs

SD

X [m]

Y [m]

H [m]

0.097

0.061

0.078

DSM accuracy verification
We compared the elevation values of the DSM to those of the actual water surface at the point ofwater level observation to verify the accuracy of the constructed DSM. Figure 7 shows a photographof the instrument used to observe the water level in Yakatabashi, which observed the actual waterlevel every hour during the heavy rain in July 2018. The first author took the picture on October 29,
2018. Figure 8 shows the DSM of the area around Yakatabashi. The areas of not-heat-map were thosethat PhotoScan Professional could not create the DSM.
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Out of aerial photographfield of view

Cloud

Water

Fig. 5 Orthophoto mosaic developed from the GIS

Flooded area
Water flow
Not analyzed
Water flow

Levee breach
Water flow

Fig. 6 Levee breaches and flooded paddy fields based on the GIS
Table 2 shows the elevation values in the DSM around Yakatabashi at 12:00 on July 9, 2018.
The aerial photographs were taken at 12:00 on July 9, 2018. So, this DSM was at 12:00 on July 9,
2018. The elevation values of DSM were measured at 10 points in the water surface of theverification area of Fig. 8. The average and SD of the ten elevation values were 8.208 and 0.096 m,
respectively. Table 3 shows the elevation values of the actual water surface at Yakatabashi on July
9, 2018. As shown in the table, the elevation values of the actual water surface at 12:00 was 8.167
m. Therefore, the difference between the elevation values of the DSM and those of the actual water
surface was +0.041 ± 0.096 m.

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Vegetation
Verification area
Not analyzed“Yakatabashi”

Fig. 7 The water level observation in
Yakatabashi
The first author took the picture on
October 29, 2018.

Fig. 8 DSM around Yakatabashi
Table 2 Elevation values in the DSM around Yakatabashi at 12:00 on July 9, 2018
No.

1

2

3

4

5

6

7

8

9

10

Ave.

SD

Elevation [m] 8.122 7.923 8.514 8.220 8.125 7.731 8.024 8.318 8.799 8.302 8.208 0.096

Table 3 Elevation values of the actual water surface at Yakatabashi on July 9, 2018
Time

09:00

10:00

11:00

12:00

13:00

14:00

15:00

Elevation of water surface [m]

8.507

8.357

8.247

8.167

8.107

8.047

7.987

Not analyzed

Flooded area

Levee breach
Building

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Differences between DSM and DEM
To build the DEM before the heavy rain, we used the results (5 m mesh, elevation value in units of
0.01 m) obtained from the GSI. We deducted the elevation values of the DEM before the heavy rainfrom those of the DSM after the heavy rain to obtain the differences. Figure 9 shows the results of
the analysis in the same area as that shown in Fig. 6. The black areas indicate areas for which the
PhotoScan Professional software could not create the DSM. We whited out the objects with a height
difference greater than 3 m because these were assumed to be buildings or vegetation. The analysis
results indicate that the vertical water level was 0−2 m and vertical levee breaches levels were
between -5 and -6 m. Based on the analysis of the differences between the DSM and DEM, we
successfully clarified the vertical water level both dimensionally and quantitatively.
CONCLUSION
The contributions of this study are as follows:(1) The exact levee breach in flooded paddy fields was specified based on the orthophoto mosaic.
(2) We compared the elevation values of the DSM and those of the actual water surface at the pointof water level observation to verify the accuracy of the constructed DSM. We determined that thedifference between the elevation values of the DSM and those of the actual water surface was +0.041±0.096 m.
(3) The analysis of the differences between the DSM and DEM indicated that the vertical water level
was 0−2 m and the vertical levee breaches levels were between -5 and -6 m.
ACKNOWLEDGEMENTS
We express our gratitude to the Information Provision Support Team of the Major Disaster
Management Headquarters of the GSI for providing the aerial photographs used in this research inorder to support the recovery project implemented by the Okayama Prefecture.
REFERENCES
Geospatial Information Authority of Japan. 2011. Retrived from http://www.gsi.go.jp/ENGLISH/page_e
30030.html
Imakiire, T. and Hakoiwa, E. 2004. JGD2000 (vertical), The new height system of Japan. Bulletin of the GSI,
51, 31-51.
Kubodera, T., Okazawa, H., Hosokawa, Y., Kawana, F., Matsuo, E. and Mihara, M. 2016. Effects of surveying
methods between GNSS and direct leveling on elevation values over long distance in mountainous Area.
International Journal of Environmental and Rural Development, 7 (1), 62-69.
Kubodera, T., Suzuki, T., Masaharu, H. and Matsuo, E. 2017. Analysis on surface deformation and cracks inpaddy fields by 2016 Kumamoto earthquake using GNSS and photogrammetry. International Journal ofEnvironmental and Rural Development, 8 (1), 85-92.
Matsumura, S., Murakami, M. and Imakiire, T. 2004. Concept of the new Japanese Geodetic System. Bulletinof the GSI, 51, 1-9.
Miyahara, B., Kodama, T. and Kuroishi, Y. 2014. Development of new hybrid geoid model for Japan,
GSIGEO2011, Bulletin of the GSI, 62, 11-20.
Tsuji, H. and Matsuzaka, S. 2004. Realization of horizontal geodetic coordinates 2000. Bulletin of the GSI, 51,
11-30.
Yamazaki, Y., Okazawa, H., Sekiyama, A. and Fujikawa, T. 2019. Accuracy verification of UAV-SfM surveyof terrace paddy fields in a hilly and mountainous area. International Journal of Environmental and Rural
Development, 10 (1), 153-159.

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Research article
erd

Cover Crop Mixtures Effects on Soil Physical and Chemical
Properties in JapanTORU NAKAJIMA*
Faculty of Regional Environment Science, Tokyo University of Agriculture, Japan
Email: tn206473@nodai.ac.jp

RIKU NAKAMURA
Faculty of Regional Environment Science, Tokyo University of Agriculture, Japan

RYUJI KAWAMURA
Faculty of Engineering, Kasetsart University, Bangkok, Thailand

MACHITO MIHARA
Faculty of Regional Environment Science, Tokyo University of Agriculture, Japan
Received 6 December 2019

Accepted 23 May 2020(*Corresponding Author)

Abstract Agricultural management practices such as cover crop could restore soil physicaland chemical properties, and in turn positive effect of crop productivity. However, it can varywith soil type, climate, and type of cover crop species. In addition, different cover crops
provide different benefits to agricultural land. For instance, legumes (e.g. clover (Trifolium
repers L.)) fix atmospheric nitrogen (N) for their own use. After terminating the legume cover
crop, it will release N into the soil as the residues decompose and provide available N to themain crops. Some of cover crop (e.g. phacelia (Hydrophylloideae)) are able to suppress weeds
or prevent insect damages by competition, shading, and allelopathy. Recently, researchersreported that cover crop mixture may provide greater diversity of benefits than monoculture
cover crop. Therefore, the objective of this study was to demonstrate soil physical andchemical properties assessment under cover crops (clover, phacelia, and their mixture)
vegetation.
Keywords cover crop, mixture, soil organic carbon, soil physical and chemical property

INTRODUCTION
Cover crops have numerous benefits for agriculture practices (Murrell et al., 2017). According to theSoil Science Society of America (SSSA), cover crops are defined as a “close-growing crop thatprovides soil protection, seeding protection, and soil improvement between periods of general crop
production, or between trees in orchards and vines in vineyard, and when plowed under andincorporated into the soil, cover crops could be referred to as a green manure crop” (SSSA, 2008).
In addition, cover crops have the potential to enhance ecosystem services such as 1) increasing food,feed, fiber, and fuel productivity 2) soil organic carbon and other nutrient and water cycling, and 3)
soil, water, and air quality/health improvement (Blanco-Canqui et al., 2015). Cover crop may bealternative for chemical fertilizer and pesticide. While actively growing, cover crops increase solar
energy harvest and carbon flux into the soil, providing food for soil microorganisms, while
simultaneously increasing evapotranspiration from the soil (Dabney et al., 2007). Moreover, covercrops have been proposed in the United State to increased soil organic carbon (SOC) stock of erodedand degraded soils and sequester SOC and according to the research, the result showed that covercrop management could increase SOC by 1.12 Mg-C ha-1 yr-1 with no-tillage agricultural practices(Olson et al., 2014).
Different cover crops provide different benefits to agricultural land. For instance, legumes (e.g.
clover (Trifolium repers L.)) fix atmospheric nitrogen (N) for their own use. After terminating thelegume cover crop, it will release N into the soil as the residues decompose and provide available NⒸ ISERD

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to the main crops. Some of cover crop (e.g. phacelia (Hydrophylloideae)) are able to suppress weeds
or prevent insect damages by competition, shading, and allelopathy. Cover crop may be alternative
for chemical fertilizer and pesticide.
Recently, researchers reported that cover crop mixtures could provide greater diversity of
benefits than monoculture cover crop (Murrell et al., 2017; Tribouillois et al., 2016). Cover crop
mixtures increase plant species diversity, in turn, potentially increasing ecosystem services that
directly or indirectly improve main crop productivity (Zhang et al., 2007; Clark et al., 1994; Murrell
et al., 2017). Yet the effects of cover crop mixtures on soil physical and chemical properties vary by
climate, soil type, management practices, and a species of cover crops. Thus, there is a need for site,soil, and cover crop species-specific research on cover crop mixtures.
OBJECTIVE
The objective of this study was to demonstrate soil physical and chemical properties assessment
under cover crops (clover, phacelia, and their mixture) vegetation.
METHODOLOGY
Experimental Design
The experiment was carried out at the field research plot, Tokyo University of Agriculture(35°38’29’’N, 139°37’56’’E, 40.3m elevation) during July to October in 2018 (Fig. 1). On average
this site received 1464 mm of precipitation annually and mean annual temperature was 15.0 ℃,
respectively. Treatments included four cover crop systems: clover (Trifolium repers L.), phacelia(Hydrophylloideae), their mixture (clover and phacelia), and control (without cover crop) using
Wagner pot (diameter = 256 mm, height = 297 mm, 1/2000a-scale). In this study, soils were takenfrom subsoil under arable land and were relatively the sterile soils. Soil are classified as Andosol andsilty loam (sand 69.1 %, silt 19.9 %, and clay 11.0 %) by IUSS (International Union of Soil Sciences)classification.
2nd July

4th September

25th September
2 weeks

2 months

Crop Growth

Harvest and Soil Sampling

Decomposition

3 weeks

Planting Jp. Mustard Spinach

Plowing into Soil

Planting Cover Crop

Cover Crop Growth

17th October

Soil Moisture Sensor

Fig. 1 Experimental schedule
Soil Analysis
Soil pH and electrical conductivity (EC) were determined using 1:1 soil to water ratio (Glendon andDani, 2002). Concentration of SOC and total nitrogen (TN) were measured following the dry
combustion method (960℃) using an automatic nitrogen-carbon analyzer (Sumika Chemical
Analysis Service, Ltd., Tokyo, Japan). Briefly, the soil samples preparation was as follow; ovendried (below 60℃) samples were ground with a mortar and pestle to allow passage through a 250
μm sieve, and then roller ground to yield a fine and homogeneous sample. Soil hardness was measureusing yamanaka soil hardness tester (Daiki Rika Kogyo Co., Ltd, Tokyo, Japan). Saturated hydraulicconductivity (Ks) was measured with a mini disk infiltrometer (METER Group Inc., USA). TemporalⒸ ISERD

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changes of water content were measured by TDR (time-domain reflectometer) (5TE, METER Group
Inc., USA).
Statistical Analysis
Analysis of variance was conducted comparisons between clover, phacelia, mixture, and control. The
data were analyzed statistically using Tukey’s test in R-Studio (Studio 2012). Statistical significancewas computed at p ≤ 0.05, unless otherwise stated.
RESULTS AND DISCUSSION
Soil Physical and Chemical Properties
Data of soil physical and chemical properties of different cover crop treatments (clover, phacelia,
and mixture, and control vegetations) are shown in Table 1. Cover crop vegetation did not
significantly affect pH. A similar trend in EC was observed at different cover crop treatments. Covercrop treatments significantly affect SH (p < 0.05). Cover crop with Mixture can alleviate soil
compaction by penetrating compact layers and acting similar to tillage tools or bio drill. BlancoCanqui et al (2015) stated that cover crop can alleviate compaction and reduce the susceptibility ofthe soil compaction. this benefit depends on the length of cover crop, and the amount andcharacteristics of the length and the size of roots. In addition, cover crop may improve soil
compatibility by improving soil aggregation and increasing SOC concentration. Cover crop
treatments significantly affected the Ks (p < 0.05). Ks under Control treatment was higher than othertreatments. Cover crop may have intact bio-channel (e.g. root) which increase the subsurface channel
network and may enhance the Ks. Cover crop mixture can positive impacts on Japanese mustard
spinach. However, weather conditions (e.g. precipitation and temperature) and soil type are also
important factors impacting vegetable yields response to cover crop management. The cover crop
treatment especially Mixture improved the overall soil physical and chemical properties and Japanesemustard spinach in this study.
Table 1 Effects of cover crops on soil, pH, electrical conductivity (EC), soil hardness (SH),saturated hydraulic conductivity (Ks), and yield of Japanese mustard spinach
pH

EC

SH

Ks

-

mS cm-1mm

cm s-1

Japanese mustard
spinachMg ha-1

Clover6.82a

0.129a19.5b2.36×10-3

13.6abPhacelia

6.83a

0.136a19.5b2.51×10-3

8.52b

Mixture6.87a

0.131a20.8ab

3.69×10-3

15.6aControl

6.80a

0.125a26.5a

6.63×10-4

10.8ab*Means with different letters among before vs. after logging soil for each depth are not significantly different at p ≤ 0.05.

The data in Fig. 2 show the soil organic carbon and total nitrogen contents under clover, phacelia,
their mixture, and control treatments. The largest SOC concentration was observed in Mixture
treatment, while the lower SOC concentrations were observed in Control and Clover. Similarly, thegreatest TN concentration was obtained in Mixture treatment, while lower TN concentrations wereobtained Clover, Phacelia, and Control. In general, cover crop residues and root accumulate on thesoil surface layer. It leads to increase SOC concentration. Moreover, belowground biomass such as
root inputs may particularly be important to increase SOC concentration (Blanco-Canqui et al., 2015).
In turn, for the long-term strategy, cover crop mixture increases the SOC concentration, and it may
improve the vegetable production.

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23.0

1.80

(a)

(b)
1.75

Total Nitrogen (g kg- 1)

Soil Organic Carbon (g kg- 1)

22.5

22.0

21.5

21.0

20.5

1.70

1.65

1.60

1.55

20.0

1.50

CloverPhacelia

MixtureContolCloverPhacelia

Treatment

MixtureContolTreatment

Fig. 2 Soil organic carbon (a) and total nitrogen (b) contents under clover, phacelia, their
mixture, and control treatments
Means with same letter within treatments do not differ significantly with p ≤ 0.05.
Error bars represent ± one standard deviation.

Figure 3 shows that temporal changes of water content under clover, phacelia, and their mixture
treatments. Phacelia and Mixture had higher the volumetric water content than Clover during theexperiment. In general, cover crop roots and surface residues improve soil macro-porosity which
increase water retention capacity (Blanco-Canqui et al., 2015). Cover crop also can improve soil
hydraulic properties such as water infiltration, water retention capacity, and saturated hydraulicconductivity through improved the soil structure, especially, soil aggregation.
Volumetric Water Content (m3 m-3)

0.30
CloverPhacelia
Mixture

0.25
0.20
0.15
0.10
0.05
0.00
0

5

10

15

20

25

30

35

40

Day

Fig. 3 Temporal changes of water content under clover, phacelia,
and their mixture treatments
CONCLUSION
The hypothesis that that the cover crop mixtures could provide greater diversity of benefits than
monoculture cover crop and increase plant species diversity, in turn, potentially increasing ecosystemservices that directly or indirectly improve main crop productivity is proven. The data also support
following conclusions: 1) Cover crop with Mixture can alleviate soil compaction by penetratingⒸ ISERD

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compact layers and acting similar to tillage tools or bio drill 2) Cover crop mixture can positive
impacts on Japanese mustard spinach 3) Cover crop also can improve soil hydraulic properties suchas water infiltration, water retention capacity, and saturated hydraulic conductivity through improved
the soil structure, especially, soil aggregation.
ACKNOWLEDGEMENTS
The authors greatly acknowledge Dr. Yoshiyuki Ohshima at Department of Agricultural Chemistry,
Tokyo University of Agriculture for his laboratory assistance and invaluable discussions.
REFERENCES
Blanco-Canqui, H., Shaver, T.M., Lindquist, J.L., Shapiro, C.A., Elmore, R.W., Francis, C.A. and Hergert,
G.W. 2015. Cover crops and ecosystem services: Insights from studies in temperate soils. Agronomy
Journal, 107 (6), 2449-2474.
Chen, G. and Weil, R.R. 2010. Penetration of cover crop roots through compacted soils. Plant and Soil, 331(1-2), 31-43.
Clark, A.J., Decker, A.M. and Meisinger, J.J. 1994. Seeding rate and kill date effects on hairy vetch-cereal rye
cover crop mixtures for corn production. Agronomy Journal, 86 (6), 1065-1070.
Dabney, S.M., Delgado, J.A. and Reeves, D.W. 2001. Using winter cover crops to improve soil and water
quality. Communications in Soil Science and Plant Analysis, 32 (7-8), 1221-1250.
Murrell, E.G., Schipanski, M.E., Finney, D.M., Hunter, M.C., Burgess, M., LaChance, J.C. and Kaye, J.P.
2017. Achieving diverse cover crop mixtures: Effects of planting date and seeding rate. Agronomy Journal,
109 (1), 259-271.
Olson, K., Ebelhar, S.A. and Lang, J.M. 2014. Long-term effects of cover crops on crop yields, soil organiccarbon stocks and sequestration. Open Journal of Soil Science, 4 (08), 284.
Soil Science Society of America (SSSA). 2008. Glossary of soil science terms 2008. ASA-CSSA-SSSA.
SAS. 2007. SAS user’s guide: Statistics. SAS Inst, Cary, NC.
Tribouillois, H., Cohan, J.P. and Justes, E. 2016. Cover crop mixtures including legume produce ecosystemservices of nitrate capture and green manuring: Assessment combining experimentation and modelling.
Plant and soil, 401 (1-2), 347-36.
Zhang, W., Ricketts, T.H., Kremen, C., Carney, K. and Swinton, S.M. 2007. Ecosystem services and disservices to agriculture. Ecological economics, 64 (2), 253-260.

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Research article
erd

Does the Downward Trend of the Farming Population in
Remote and Mountainous Areas Increase the Average Farm
Size? A Community Level Data Analysis in Tohoku Region,Japan
SHIGEKAZU KAWASHIMA*
Department of Food Resource Development, Faculty of Food Industrial Science,
Miyagi University, Japan
Email: kawashim @myu.ac.jp

ASATO MIZUKI
Department of Environmental and Resource Economics, Graduate School of Agricultural
Science, Tohoku University, JapanFUSAO ITO
Department of Environmental and Resource Economics, Graduate School of Agricultural
Science, Tohoku University, Japan
Received 20 January 2020

Accepted 23 May 2020(*Corresponding Author)

Abstract Over the last several decades, Japan’s farming population has gradually decreased
and the average age of farmers has risen. The recent agricultural census 2015 indicated that
there were 2.09 million people engaged in agriculture, which is a reduction of 60% comparedto three decades earlier. The decline in farming populations is robust and is taking place inmany agricultural communities in Japan. However, the impacts of the declining farming
population differ significantly on the basis of the conditions of farming land. Retirement of
aged farmers provides farming opportunities for young, local farmers, consequently leading
to an expansion in the size of farms. In fact, the average farm size per household in Japan
doubled from 1.05 ha in 1985 to 2.20 ha in 2015. This represents a positive aspect of aging
and the decreasing farming population. On the other hand, farmland abandonment has beenprominent in the last 30 years, accounting for nearly 10% of total farmland. In the remote andmountainous areas of Japan, maintaining agricultural activities is increasingly difficult,thereby casting doubt on the sustainability of agricultural production and rural communities.
In such a case, the average farm size does not increase, weakening rural economies andcommunities. This study uses approximately 14,000 community-level data in the Tohoku
region of Japan and examines the land conditions that induce an increase in average farmsize. The land conditions we consider are (1) steepness, (2) elevation, and (3) time-distancefrom a city. Quantile regression analysis is carried out to identify land conditions and othersocio-economic variables associated with average farm size. Our regression analysis
indicates that farm size does not necessarily increase where farm sizes are initially small,even in the case of declining farming population. Discussion within agricultural communities
to build trust and consensus on distributional conflicts are considered to be an effective
countermeasure to increase farm sizes in remote and mountains areas. The empirical evidence
suggests the need for varied policy interventions in accordance with land conditions.
Keywords farm size, agricultural community, quantile regression, farmland abandonment

INTRODUCTION
Family farmers in different locations face different constraints to agricultural development. In Asia,
it can be generalized that land scarcity has been the most serious constraint, although this can beaddressed through increasing land productivity at an initial stage of agricultural development (FAO,
2014). As land productivity increases with the use of fertilizer, pesticide, irrigation, and the use ofⒸ ISERD

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modern technology, labor scarcity becomes the next most serious constraint, to which mechanicaltechnologies are adapted to reduce labor cost. As labor moves from the agricultural sector to nonagricultural sectors, increasing labor productivity of the remaining farmers becomes vitally important
to maintain farm incomes. This is the second phase of agricultural development. Many Asian
countries, including Japan, are now in the second phase of agricultural development (FAO, 2014).
Priority has shifted from increasing land productivity to increasing labor productivity and farmers’
incomes, which requires increasing farm size. The question to be asked is whether the average farm
size has been increasing in accordance with the downward trend of the farming population.
The recent agricultural census 2015 indicated that 2.09 million people engaged in agriculture,
which represents a decrease of 60% compared to three decades earlier (MAFF, 2018). The decline
in the farming population is robust and is taking place in many agricultural communities in Japan. A
declining farming population has both positive and negative impacts on rural communities. On thepositive side, there are increased opportunities to expand the farming size because the retirement of
aged farmers generally provides farming opportunities with neighborhood and/or young farmers,
consequently leading to an expansion in farming size. In fact, the average farm size per household inJapan doubled from 1.05 ha in 1985 to 2.20 ha in 2015 (MAFF, 2018). However, farmland
abandonment has also been prominent in the last 30 years, accounting for nearly 10% of total
farmland (MAFF, 2018). When farmland is not taken over by farmers of the next generation,
maintaining agricultural activities becomes increasingly difficult, thereby casting a doubt on thesustainability of agricultural production and rural communities. It is highly important to preserve thefarm and possibly to increase the farm size. Only a few studies have been carried out to examine thecorrelation among farm size, socio-economic variables, and community variable (Akimowicz. et.al,2013), empirical analysis with larger datasets is still insufficient. Therefore, this study examines thesocio-economic conditions under which farmers in rural communities increase farm sizes. Utilizing
GIS datasets for the Tohoku Region of Japan, including elevation, slope, and time-distance to thecity, along with other community variables such as the number of community meetings and thepercentage of rural population over 65, we investigate the determinants of farm size.
MATERIALS AND METHOD
Data and Agricultural Community
Table 1 Basic statistics
Variablenotation

Mean

s.d.

min

max

Farm size in 2015

y2015

2.38

1.68

0.075

25

Farm size in 2010

y2010

2.12

1.42

0.1

25

Elevation (100m)

z1

1.425

1.357

0.004

9.055

Slope

z2

2.57

2.58

0

20

Time-distance: 30 to 60 minutes

z3

0.26

0.44

0

1

Time-distance: 60 to 90 minutes

z4

0.04

0.20

0

1

Time-distance: over 90 minutes

z5

0.01

0.08

0

1

# of community meetings (CM)

x1

14.14

12.02

0

99

# of issues discussed at CM

x2

4.42

1.70

0

6

% of rural population over 65

x3

0.34

0.08

0

1

# of incorporated farm

x4

0.07

0.30

0

5

Average Farm Size

GIS Variables

Community Variables

This study uses Agricultural Census Datasets for 2015 and 2010 for the Tohoku Region of Japan (seeTable 1). The total sample is 14,253. The datasets include average farm size, elevation, slope, timeⒸ ISERD188

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distance to the nearest city, and community variables. Community variables are included to identify
socio-economic characteristics that induce an expansion in farming size. We hypothesize that thatthe number and quality of community meetings, as well as mutual trust within the community, makeland transactions easier, thereby contributing to increases in average farm size.
Table 2 shows the average farm size by elevation and slope. It indicates that the average farm
size is larger at lower elevations and where farmland is flatter. It also shows smaller increases inaverage farm size for farmland located in steeper mountainous area. This implies that farmland
located in the remote and mountainous areas is more likely to be abandoned rather than handed to
the next generation.
Table 2 Change in average farm size by elevation and slope from 2010 to 2015
Slope (θ): z2(flat)(high)A:
Farm Size (ha) in2015

Elevation(m)：z1(steep)

2.59

2.14

1.59

1.38

2.54

1.83

1.68

1.44

2.08

2.23

1.88

1.63

(low)

2.76

2.34

1.94

1.82

(high)

2.35

1.98

1.54

1.31

2.26

1.69

1.57

1.31

1.84

2.03

1.74

1.53

(low)

2.40

2.07

1.84

1.57

(high)

10.2

8.1

3.7

5.8

12.3

8.4

6.6

10.2

13.3

10.1

7.9

6.9

14.9

13.0

5.5

16.5

B:
Farm Size (ha) in2010（A-B)/B*100(% Change)(low)

Statistical Analysis
The distribution of farm sizes across agricultural communities is highly skewed and asymmetrical.
Therefore, the results from a standard regression analysis (i.e. ordinary least squares) are biased andcannot provide a comprehensive picture of the effect of the independent variables on the dependent
variable. In this study, quantile regression analysis is employed to observe the different effects ofeach explanatory variable along the distribution of the dependent variable. The estimated equation isdefined as (1).
𝛼 is a constant term, and 𝛽 captures the effect from average farmland size in 2010. 𝛾𝑖 measures theeffect of GIS variables on the average farmland size in 2015. 𝑥𝑗 represents socio-economic
community variables. These are included to examine socio-economic characteristics that areassociated with farmland size. Since community variables might be considered as endogenous
variables, we report the estimation results with and without the community variables.
RESULTS AND DISCUSSION
Table 3 shows the results of the quantile regression analysis. The effects of average farm size in 2010
on the average farm size in 2015 are dependent on the quantile estimated. With a smaller farm size
(quantile 25%), the estimated coefficients turn out to be 0.96-0.97, less than 1.0, meaning that
average farm size is more likely to decrease by 3 to 4% every five years. On the other hand, with alarger farm size (quantile 75%), the estimated coefficients are more than 1.0, which indicates that the
average farm size increases by about 22% when assuming other variables are constant. Thus, theⒸ ISERD

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aged population and general decrease of the farming population contributes only to agriculturalcommunities where the average farm size is already high. In other words, agricultural communities
in the remote and mountainous areas, often with their smaller farm sizes, do not make use ofadditional farming opportunities, weakening overall agricultural activity.
Table 3 Results of quantile regression
Dependent variable: Average farm size in 2015
With community variable

Without community variable

Quantile

Quantile

Variable

25%

50%

75%

25%

50%

75%

Average farm
size in 2010

0.960***

1.081***

1.222***

0.967***

1.083***

1.223***

Elevation
Slope
Time-distance:
30 to 60
Time-distance:
60 to 90
Time-distance:over 90# of meetings(177.06)(187.98)(160.64)(166.13)(188.37)(161.94)-0.007***-0.009***-0.013***-0.008**-0.009***-0.013***(-2.79)(-3.24)(-3.03)(-2.46)(-3.02)(-3.18)-0.008***-0.006***-0.007***-0.009***-0.007***-0.006***(-7.03)(-4.78)(-3.14)(-6.4)(-4.64)(-3.11)0.008-0.005-0.0070.005-0.007-0.004(1.02)(-0.58)(-0.57)(0.6)(-0.84)(-0.32)-0.007-0.001

0.016-0.031-0.0030.031(-0.29)(-0.08)(0.51)(-1.26)

-0.19

1.06

0.017

0.033

0.045

0.019

0.037

0.054

(0.36)(0.93)(0.64)(0.52)(0.7)(0.66)

0.002***

0.001***

0.0005(8.71)(3.69)(0.90)# of issues
discussed

0.006***

0.002

0.001

(2.80)(0.91)(0.37)% of population
over age 65-0.239***-0.0630.092(-6.25)(-1.65)(1.34)# ofincorporated
farmland0.025*

0.012

0.006

(1.85)(0.75)(0.36)

0.046***

0.011

0.008

0.009

0.015

0.049***(2.62)
0.561(0.61)
0.617(0.26)
0.632(0.81)
0.559(1.40)
0.616(3.03)
0.632

Constant
Pseudo R2

N
14,235
14,235∗∗∗, ∗∗, and ∗ indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

This study investigates geographic and socio-economic conditions that induced an increase infarm size in rural Japan. Community level data analysis indicates that the average farm size does notnecessarily increase as the farming population decreases, particularly where the average farm size isinitially small. Some farmland in the remote and mountainous areas of Tohoku Region has beenabandoned rather than passed down to the next generation. Farmland on steeper slopes at higher
elevations is more expensive to operate than in the lowland, meaning that consolidating farmland
does not provide sufficient economic benefits in mountainous areas. Our regression analysis also
shows that frequent community gatherings to discuss local issues and the establishment ofincorporated farmland within the community would help increase the farm sizes. Fostering trust
through engagement reduces transaction costs and encourages the establishment of cooperatives orincorporated farms, which may account for an increase in farm size.
Furthermore, the effects of elevation and slope on average farm size are always negative andⒸ ISERD

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significant, particularly at the 25% quantile (smaller farm size). Land condition represented bysteepness and a mountainous area always impair the expansion of farm size. Lower elevation andflatter farming areas are key conditions that accelerate land transactions, thus resulting in higher
average farm sizes.
The sign of the percentage of people above 65 years of age is positive at the 75% quantile andnegative and significant at the 25% quantile. The retirement of aged farmers generally provides
opportunities for younger farmers. Such opportunities are not taken advantage of where the averagefarm size is generally smaller (25% quantile). The sign of the number of incorporated farms turns
out to be positive and significant only at the 25% quantile, indicating that the small number ofincorporated farms in the highlands may contribute to the expansion in farm size throughconsolidating farmland within the community.
Both the number of community meetings and the number of issues discussed in these meetings
have a positive effect on the average farm size at the 25% quantile. It is important to note that
discussion within the agricultural community allows for building mutual trust and mitigating
distributional conflict, thus leading to a reduction of overall transaction costs.
CONCLUSION
This study investigates geographic and socio-economic conditions that fostered an increase inaverage farm sizes in rural Japan. Community level data analysis indicates that the average farm size
does not necessarily increase as the farming population decreases, particularly where average thefarm size is still small. Some farmland in the remote and mountainous areas have been abandoned
rather than handed to the next generation. Farmland on higher slopes at higher elevation is more
expensive to operate than in the lowlands, meaning that consolidation of farmland does not provide
sufficient economic benefit. Our regression analysis also shows that frequent community gatherings
to discuss local issues and establishment of incorporated farms within the community would help
increase the farming size. Fostering trust through discussions has reduced transaction costs andencouraged the establishment of cooperatives or incorporated farm, possibly resulting in an increasein farming size.
ACKNOWLEDGEMENTS
This work was supported by JSPS KAKENHI Grant Number JP17K07986.
REFERENCES
Tasso, A. and Restuccia, D. 2014. The size distribution of farms and international productivity differences.
American Economic Review, 104 (6), 1667-97. Retrived from https://doi.org/10.1257/aer.104.6.1667
Akimowicz, M., Magrini, M.B., Ridier, A., Bergez, J.E. and Requier‐Desjardins, D. 2013. What influences
farm size growth? An illustration in southwestern France. Applied Economics Perspectives and Policy, 35(2), 242-269. Retrived from https://doi.org/10.1093/aepp/ppt008
Food and Agriculture Organization of the United Nations. 2014, The state of food and agriculture: Innovationin family farming. Roma.
Lowder, S.K., Skoet, J. and Raney, T. 2016. The number, size, and distribution of farms, smallholder farms,
and family farms worldwide. World Development, 87,16-29. Retrived from https://doi.org/10.1016/j.
worlddev.2015.10.041
Masters, W.A., Djurfeldt, A.A., De Haan, C., Hazell, P., Jayne, T., Jirström, M. and Reardon, T. 2013.
Urbanization and farm size in Asia and Africa: Implications for food security and agricultural research.
Global Food Security, 2 (3), 156-165. Retrived from https://doi.org/10.1016/j.gfs.2013.07.002
Ministry of Agriculture, Forestry, Fisheries of Japan. 2018. 2015 census of agriculture and forestry in Japan
report and data on the result. Retrived from https://www.maff.go.jp/e/data/stat/
Wiggins, S., Kirsten, J. and Llambí, L. 2010. The future of small farms. World Development, 38 (10), 13411348. Retrived from https://doi.org/10.1016/j.worlddev.2009.06.013Ⓒ ISERD

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Research article
erd

Empowerment of Rural Community and its Resilience:
A Case Study of a Small-Scale Rural Community in a
Mountainous Area in Japan
KAKO INOUE*
University of Miyazaki, Miyazaki, Japan
Email: kako.inoue@gmail.com
Received 15 January 2020

Accepted 27 May 2020(*Corresponding Author)

Abstract Japanese rural communities have experienced an aging population with a drastic
decline in its size while more economic functions and workplaces have become concentrated
in urban areas. Some rural communities in remote mountainous areas even face the further
serious problem of extinction of their own community. Even with such a difficult situation,some rural communities have been successful in achieving the migration of their young
generation back to their rural areas, and even in reviving their energy year by year. The
objective of this paper, therefore, is to demonstrate how rural communities in remotemountainous areas in Japan can become empowered and resilient regardless of the serious
threat of further depopulation and marginal settlement through a case study of a small-scale
rural community in a remote mountainous area of Miyazaki Prefecture, Japan. The paper first
overviews how rural communities have experienced depopulation over time together withchanges in livelihood along with changes in lifestyle. Then, by describing how a small
community in the study area was challenged to implement a series of projects by themselves,utilizing their local resources and cultures, it explains how a depopulated community could
revive and gain confidence in themselves while receiving a lot of attention from visitors fromoutside. The study results show that the aged female population in the community is the key
human resource as they can actually play an important role in the rural community, producing
local valuable products and services. Moreover, the study points out the importance of
maintaining and passing on rural communities’ identities to the next generations, while
explaining why some of the young population now choose to live in such remote rural areas‒ a shelter from the overly globalized and industrialized modern society.
Keywords community empowerment, resilience of rural community,
small-scale community in mountainous area, aged population

INTRODUCTION
This study seeks to stimulate the frank discussion that is required if researchers, practitioners andpolicymakers of any nation under the economic development stage are to work effectively with ruralcommunities in responding to the possible transitional changes impacting rural declines over time.
This paper describes the experience of, and lessons learned from, a small-scale rural community thatexperienced serious declines in the last 50 years but struggled to sustain the community.
The notion of rural decline in Japan since the 1960s (i.e. due to the rapid economic growth andchange in industrial structure that happened around the 1960s) is firmly established in the literature(Ono, 2005; Odagiri, 2009), and is supported by demographic trends that depict many small ruralcommunities in remote, hilly and mountainous areas gradually shrinking in population, making thecommunity age as well. Odagiri (2009) pointed out that the “decline” has been particularly witnessedin three dimensions, namely i) population reduction, ii) abandoned agricultural and forestry land, andiii) weakened community functions, leading to a loss of pride in one’s own community. An economic
and population imbalance has also been found between large cities and smaller remote ruralcommunities in Japan. There are similar concerns about the fate of small rural communities asⒸ ISERD

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economic and demographic pressures have also been noted in many developed countries in Europe,
North America and Australia (Forth and Howell, 2002; McManus et al., 2012).
What this paper wants to focus on is not the decline itself but critical factors involved insustaining the rural communities regardless of the serious threat of their extinction with gradualemptying and aging at a high rate. To explore the possible efforts made by rural communities to react
to the pressures of rural declines, this paper pays close attention to the key concepts of “resilience”
and “community empowerment” by analyzing the real case of one small-scale rural community in a
mountainous area in Japan.
“Resilience” and “Community Empowerment”
Japan’s depopulating rural communities received significant attention when a report, the so-called“Masuda Report,” was released in May 2014. The report predicted that almost half of themunicipalities might disappear due to a population decline and weakened administrative functions.
“Municipalities at risk of extinction,” mostly located in rural areas, have been listed and the urgent
need to cope with rural community issues cannot be avoided, with words like “revitalization” and“renewal” being used in the policy arena in Japan. Throughout this debate, Japan has experienced
other kinds of negative impacts brought about exogenously that have often hit rural communities(such as huge natural disasters, for example the Great East Japan Earthquake), and the concept of“resilience” has been debated (Itonaga, 2012; Hattorie et al., 2013).
Resilience is a complex concept, being used as an ecological term (i.e. the ability of a system toregain the status quo after a major shock), and extended to other domains and applied in social
contexts where resilience is understood as the capacity to adapt seamlessly to largely exogenous
events (McManus et al., 2012). The critical feature of the notion of resilience is the idea that there
are some factors about a rural community that enable it to cope with changing circumstances. Such“factors” can be social capital (Putnam, 1993) or, more specifically, trust and norms of reciprocity(Bridger and Alter, 2006), but they cannot be simplified and based on a single factor. Rather, thispaper suggests that communities are empowered through a number of events involving theparticipation of all community members, increasing confidence and the mutual respect of people
living in a depopulated and aged rural community.
The concept of empowerment originates from the effort of a local community or marginal
people to gain power, leaving their voiceless or powerless conditions behind (Sianipar et al., 2013;
Sutawa, 2012). To discuss how a rural community can cope with a changing environment, especiallywhen the weakening function of the community is under serious threat, this paper attempts todemonstrate how a community can be empowered even under the situation of depopulation and aging.
OBJECTIVE
The objective of this paper is to demonstrate how ruralcommunities in remote mountainous areas in Japan canbecome empowered and resilient regardless of the serious
threat of further depopulation and marginal settlement
through a case study of a small-scale rural community inthe remote mountainous area of Miyazaki Prefecture,
Japan (Fig. 1). By looking closely at the case of a smallscale rural community of less than 100 people, this paper
tackles the following research questions: 1) How has the
depopulation of the community evolved over time?; and 2)
What triggered the community to revive themselves, andhow has the community been empowered? By analyzing
the results, this paper attempts to discuss critical factors
for resilience in fighting back from rural declines.
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Community
Power
Pressure of declines

Community
Empowerment

Triggering resilienceTime

Fig. 1 Rural declines, resilience andcommunity empowerment

IJERD – International Journal of Environmental and Rural Development (2020) 11-1

RESEARCH METHOD AND THE STUDY TARGETThe study is conducted based on a series of field observations carried out since 2016, interview
surveys held in August 2016, July 2019 and September 2019, and documents collected from thetarget community and village.
When the land of Japan is classified into the four categories of i) urban areas, ii) flat farming
areas, iii) hilly farming areas and iv) mountainous farming areas, in accordance with the guidelinesof the Ministry of Agriculture, Forestry and Fishery in Japan, hilly and mountainous areas (i.e. iii
and iv) account for about 36% of the total farming area and output (MAFF, 2017). The study target
community is located in the village of Nishimera , Miyazaki Prefecture, in the Kyushu region ofJapan. Approximately 88% of the prefectural land is hilly and mountainous and about 37% of theprefectural population reside there (Miyazaki Prefecture, 2019). Among the 26 municipal cities,
towns and villages of Miyazaki Prefecture, Nishimera village has the smallest population with 1,132
people (580 households) as of October 2019, and 96% of the land is categorized as mountainous
areas. The village has eight traditional
communities, and this study focuses on oneof those, “Ogawa Community,” which islocated about 40 minutes from the village
center by car, and it takes more than 1.5 hours
to reach the nearest commercial area. The
Ogawa Community has a population of 92
people. This community has experienced
drastic population declines over the last 50
years and has been implementing a numberof projects to revive its own community for
20 years. The study focuses on the case of
Ogawa Community to demonstrate thecritical factors required for a small-scale ruralcommunity to be resilient regardless of theFig.2 Location of Ogawa Community,
long-term socio-economic change brought
Nishimera Village, Miyazaki, Japan
about from outside the community.
RESULTSHistory and Transitional Change of Ogawa Community (RQ1: How has Depopulation
Evolved Over Time?)
The Ogawa Community has a long history as the center of Nishimera Village with the feudal lords’
residence (i.e. castle) having been there for about 200 years in the Edo era with. The shrine of thecommunity is said to have been built about 500 years ago. At the time of the Meiji restoration in the1860s, the landlord (i.e. the feudal lord) had kindly allocated all his forest land to local dwellers
equally, making people’s livelihood quite stable. With such forestry resources, the community
traditionally had a unique way of living with two residences, one in the forest (called a Sakugoya)and the other in a residential community center. Table 1 shows the transitional changes experienced
in the Ogawa Community over the last 50 years. There was a drastic population movement since1960 from rural communities to urban areas due to changes in the industrial structure in the higheconomic growth period and the stagnant wood prices due to imported wood caused the forest
industry to shrink and the population in forest areas to decrease. The Ogawa Community also
followed the trend, and it has about 550 people left from the community in 50 years. As the workingage population migrated out of the community during the high economic growth period, the people
who were left in the village 50 years later became old, while less and less children were born in thecommunity, leading to school close, making difficult situation for younger generations to continue
to stay or to come back there.
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Table 1 Changes in the local situation along with modernization and forestry declines
Year: Population
1960: about 650

1980: about 250

2000: about120

Local situation, lifestyle, etc.
- People had a traditional lifestyle of staying at theircottage for forestry work, and coming back to theirofficial house to live in Ogawa Community.
- Neighboring communities had been submerged due
to reservoir construction. (Many construction
workers came to construct the dam.)- 1970s: Rapid forestry decline and forestry work did
not require many laborers anymore due totechnological improvements.
- Modern work style with one house, commuting bycars to fields, was commonly found.
-1980: % of population aged 65 or older in Nishimera
village: 13.6%.
-1989: Primary and Junior school closed due to lackof students (no high school in the village).
-1994: Public bus service to the community has
terminated its operation.
-2000: % of population aged 65 or older in Ogawa
Community and Nishimera village is 71% and 36%,
respectively. => 63% and 41% in 2010,
respectively.

Events and trends in Japan- Early 1960s: Crude oil
imports were liberalized, andpetroleum replaced coal as themain player in Japan’s energy.
- Timber self-sufficiency: 87%- 1970s: Rapid increase in thenumber of private cars andvehicles.
- Timber self-sufficiency: 32%.
- Continuous decline in timber
prices(importedtimberbecomes dominant in themarket).

- Timber self-sufficiency: 18%.

Note: Source of timber self-sufficiency rate is “Learning Museum of the Forest and Forestry,” and source of % of
population aged 65 or older is from Nishimera Village documents for Ogawa, and from e-stat.com for Nishimera village.

Community’s Initiative and Community Empowerment (RQ2: What Triggered theCommunity to Revive Themselves, and how the Community has been Empowered?)In 2000, Nishimera village officers came to explain about the estimated future population of the
Ogawa Community, informing dwellers: “[T]his community will be extinct if the current situation
continues.” People were shocked and held many meetings, discussing what they should do. Having
gained a consensus among all the dwellers of the community that they could not die without taking
any action, the local people decided to utilize what they had locally; specifically, they used theirhands to collect resources from the forest (i.e. edible wild plants). Having collected these forest
resources, they decided to hold a festival, serving the local resources to visitors. All the dwellers of
the community participated in the festival, and there were about 300 visitors in the first year. Due toits popularity, the number increased to more than 1000 in the second year. In receiving more visitorsfrom outside communities (10 times more visitors than its population), local people started to realizethat their traditional food, culture and even their lifestyle were unique in the view of outsiders, whoplaced high value on their culture, which led local people to grow in confidence. After several years,
they started to say: “Festivals are held once a year, but why can’t we hold something every day, andreceive more guests?” With this in mind, a preparatory committee to develop community projects
was first established among community members in 2005. This was followed by the community’s
official management organization established in 2007, and finally the launching of restaurants andguesthouses to share their traditional food and lifestyle with guests throughout the year, which were
to be operated and managed by local people in 2009. When people began to discuss what was to be
started up, they thought they didn’t have anything special, and presented the simple idea of opening
a restaurant serving popular food, like Udon noodles, which are available anywhere in Japan.
However, after consulting with various supporters, including village officers, people started torecognize that their traditional food and even their olden-days’ lifestyle were absolutely unique
outside their community, and therefore of value to the majority of guests coming from cities. With
such findings, people decided to name their initiative Sakugoya, their traditional work cottage(second house) in the mountains, thereby maintaining and cherishing their traditions and local
identities. With this philosophy, people decided to utilize the historical area of the Ogawa Community,Ⓒ ISERD

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creating a restaurant and guesthouses built in the traditional way as shown in Fig. 3. Moreover,community members were stunned by the results of their operation as the number of visitors rose as
high as 25,000 in 2012, and economic benefits were also brought to the community, as illustrated inFig. 4.
It should be noted that as the main community’s workforce comprised elderly female dwellers,
the restaurant was managed by 10 elderly women with an average age of 72.3 years as of 2016. Theseold women who used to work in the mountains and on farmland now started to receive guests frommany places, even from foreign countries, and they became very busy every day, cooking in therestaurant kitchen, chatting to visitors, coworking with many other neighbors as colleagues and being
appreciated by community members. Such people often say that they feel happy to contribute to theirown community. In addition to local people’s initiatives, the village office supported the community
by constructing houses to receive new dwellers, leading to the arrival of 26 migrants since 2009,
including three families with children. The peaceful style of traditional living surrounded by natureand forest land attracted a number of younger people who placed importance on quality of life. With
these new dwellers, the percentage of the population aged 65 or older was reduced to 58% in 2019.

Fig. 4 No. of visitors and sales

Fig. 3 Ogawa Sakugoya

Source: Data from 2005 to 2008 aretaken from village office document, anddata since 2009 from Ogawa Sakugoya.

Source: The author edited the picture
taken from https://www.ogawasakugoyavillage.com (accessed Jan. 09, 2020).

DISCUSSION AND CONCLUSION
From the experience and lessons learned from the case demonstrated above, at least two importantfactors are to be pointed out in relation to rural community resilience. When the socio-economic
situation changes over time as a result of a weakening forest industry along with progressing
globalization and a lifestyle shifted to the modern way, the rural communities can be marginalized
to a very small level if local people continue to make their livelihood through business as usual, butsuch communities can be empowered against such pressures when local people possess thewillingness to sustain their community. Such willingness can be turned into a collective power,
making the community as a whole change themselves in order to move forwards. The sense of crisis
shared unanimously triggers a series of actions taken by local dwellers. The first important factor isthe shared vision among all community members that they do not wish to see their community
become extinct but rather that their community succeeds into the next generation, as such a sharedvision drives the community as a whole to move forward in the same direction.
Second, there existed “unrecognized” local resources, which became the strength for thecommunity to gain power. For instance, local people did not recognize the edible wild plants
available in their forest and that their traditional healthy cuisine, which they ate every day, had suchvalue for people in cities. Moreover, there were many old and hard-working women who lived in thecommunity. As many of the female dwellers typically performed unpaid work or did a relatively lesswell-paid job in the forest, on farms and in houses growing trees, vegetables and cooking, many maledecision-makers in the rural community might have not recognize that such old female members
could bring such benefits to the community. Having seen the participation of all community members,Ⓒ ISERD

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the aged female population in the depopulated community is now recognized as a key humanresource as they can actually play an important role, producing local valuable products and services.
Last, but not least, the study results indicated that many people in cities were attracted by rural
traditional values, with even younger generations migrating back to rural communities in remotemountainous areas. Those young generations now seek to enrich their life by enjoying craftmanshiputilizing natural resources, and even being part of a community of members whose connection isbased on trust. Rural communities that still retain their local identities are opposed to standardized
and regulated norms of a globalized modern society. Such communities may offer places to stay forpeople who are looking for real richness of human life surrounded by a rich culture and natural
resources from which to self-supply their own food and satisfy their needs for their everyday life.
ACKNOWLEDGEMENTS
We would like to thank local residents of Ogawa community for their assistance with this research.
We are grateful that this research is supported by KAKENHI 19H03076.
REFERENCES
Bridger, J. and Alter, T. 2006. Place, community development, and social capital. Community Development,
37 (1), 5-18.
Forth, G. and Howell, K. 2002. Don't cry for me upper wombat: The realities of regional/small town decline.
Sustaining Regions, 2 (2), 4-11.
Hattori, T., Yanai, Y. and Saito, A. 2013. The meaning of the resumption of farming to return to the hometown.
Journal of Rural Planning, 32 (3), 400-403.
Itonaga, K. 2012. Resilience of rural community through emigration and return. Journal of Rural Planning, 30(4), 563-566.
Ministry of Agriculture, Forestry and Fisheries. 2017. Census of agriculture and forestry. Ministry ofAgriculture, Forestry and Fisheries, Japan.
Miyazaki Prefecture. 2019. Development plan of hilly and mountain areas. Miyazaki Prefecture. (in Japanese)
McManus, P., Walmsley, J., Argent, N., Baum, S., Bourke, L., Martin, J. and Sorensen, T. 2012. Rural
community and rural resilience: What is important to farmers in keeping their country towns alive? Journalof Rural Studies, 28 (1), 20-29.
Odagiri, T. 2009. Rural regeneration. Iwanami-hoten. (in Japanese)Ono, A. 2005. Introduction of environmental sociology of rural villages in mountains, Nousangyoson-bunkakyokai. (in Japanese)
Putnam, R. 1993. The prosperous community: social capital and public life. The American Prospect, 13, 3542.
Sianipar, C., Yudoko, G., Adhiutama, A. and Dowaki, K. 2013. Community empowerment through appropriatetechnology: Sustaining the sustainable development. Procedia Environmental Sciences, 17, 1007-1016.
Sutawa, G. 2012. Issues on Bali tourism development and community empowerment to support sustainable
tourism development. Procedia Economic and Finance, 4, 413-422.

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erdInternational Society ofEnvironmental and Rural Development

Philosophy of ISERD:
Recently, in developing countries, subsistence agriculture is being converted to export-oriented mono-culture,and the amounts of agricultural chemicals applied to the farmland are increasing every year. The appliedchemicals in farmland cause serious environmental problems downstream such as eutrophication, unusual
growth of aquatic plants, decrease in dissolved oxygen and accumulation of bottom mud in water resources.
Also, there seem to be many cases in which people apply agricultural chemicals without understanding its
impact to health and food safety. Therefore, it is necessary to promote and enhance understanding ofsustainable rural development among local stakeholders including farmers.
Sustainable rural development aims to meet human needs while preserving the natural environment. As
it should cover not only social and economic development but also natural environment conservation, no
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IJERD – International Journal of Environmental and Rural Development

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Prof. Dr. Nang Hseng Hom, Yezin Agricultural University, Myanmar
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June 2020