ISSN 2185-159X

erd

International Journal ofEnvironmental and Rural Development

Volume 12 Number 1

June 2021

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,
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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.
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Editor in Chief:
Prof. Dr. Machito Mihara, Tokyo University of Agriculture, Japan /Institute of Environmental Rehabilitation and Conservation, Japan
Managing Editors:
Dr. Narong Touch, Tokyo University of Agriculture, Japan
Dr. Shinobu Terauchi, Institute of Environmental Rehabilitation and Conservation, Japan
Dr. Jeeranuch Sakkhamduang, Institute of Environmental Rehabilitation and Conservation,
Southeast Asia Office / Association of Environmental and Rural Development, Thailand
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:
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Webpage: www.erecon.jp

IJERD – International Journal of Environmental and Rural Development (2021) 12-1

Contents
Assessment of Soil Properties using GIS Technologies in a Selected Area in Myanmar
Hla Moe Khaing, Kyaw Ngwe, Swe Swe Mar and Htay Htay Oo

1

Preparation of Effective Microorganisms Based Compost Using Some Selected Wastes for
Improvement of Plants Growth
Thidar Kahing, Nyo Nyo Win and Aye Mon Thida Nyo

8Spatial Variability and Mapping of Soil Properties Using GIS-Based Geostatistic in Myanmar
Tun Tun Hlaing, Swe Swe Mar, Chaw Su Lwin, Htay Htay Oo
and Kyaw Ngwe

15

Assessment of Insect Damage and Growth Performance of Dipterocarps Planted at Rainforestation
Demonstration Farm at VSU, Baybay City, Leyte
Kleer Jeann G. Longatang, Ma. Juliet C. Ceniza,
Christopher Jr. R. Galgo, Jimmy O. Pogosa, Dennis P. Pequeand Leonard Paolo Longatang

22

Spatial Variability of Soil Salinity and its Influence on Rice Yield in Salt-Affected Areas using Remote
Sensing Techniques
Aung Naing Oo and Lwin Bo Bo Thet

29

Evaluation of the Acceptability of Cashew Apple Jam in Cambodia
Sokly Sorm, Yoshiki Muramatsu, Daiki Oka, Yuri Tanikoka,
Masataka Uchino, Shuki Muramatsu, Motoe Sekido,
Takahiko Nakamura, Toru Nakajima, Eiichiro Sakaguchi,
Shotaro Kawakami, Chim Chay, Mari Arimitsu andMachito Mihara

35

Food Service Industry Development and its Effect to Distribution of Fresh Agri-products in
Vietnamese Suburban Area
Fumie Takanashi

42

The Influence of Packaging Materials and Storage Duration on Seed Quality of Sesame(Sesamum indicum L.)
Hnin Thida Nyo, Nyein Nyein Htwe and Kyaw Kyaw Win

48

Evaluation of Site Contamination from Storage of Fertilizer
Barry Noller

55

Growth Performance of Trema orientalis L. (Blume) Ulm. Seedlings in Response to Mineral Nutrient
Omission
Ken N. Florentino, Romel B. Armecin, Jimmy O. Pogosa
and Marlito M. Bande60

Potential Measurement as a Method for Monitoring the Soil Chemical Environment
Narong Touch and Takahiko Nakamura

66

Study on the Water Quality of Indawgyi Lake Affected by Surroundings
Moe Tin Khaing and Daung Hawng

72

Experimental Study of Extension Impact on Farmers’ KAP towards Sri Lankan Cassava Mosaic
Disease Prevention in Cambodia
Sovannara Moun, Hisako Nomura, Tamon Baba, Tha Than,
Kasumi Ito and Pao Srean

79

Evaluation and Preference Analysis of Improved Rice Genotypes in TharGaYa Village, Tharsi
Township, Myanmar
Nyo Mar Htwe, Su Latt Phyu and Khant Sanda Htet

88i

IJERD – International Journal of Environmental and Rural Development (2021) 12-1

Rainwater Harvesting as a Mean for Water Conservation in Ovche Pole Region, Macedonia
Ognen Onchevski and Machito Mihara

95

Marketing Strategies and Grape Farmers’ Welfare Improvement: Evidence from Afghanistan
Sayed Jalil Hashimi, Dan Yang, Zainullabuddin Hakimi andZaigham Kazimi

103

Effect of Temperature on the Cohort Life Table of Brown Planthopper (Nilaparvata lugens Stål)(Homoptera: Delphacidae)
May Thu Htet, Moe Hnin Phyu and Thi Tar Oo

108

Effects of adding Rice Husk Biochar on GHG Emission and Compost Quality during Cow Manure
Composting
Sarvesh Maskey and Machito Mihara

114

Conservation and Ecological Threats of Agarwood (Aquilaria sp.) on Leyte Island, Philippines
Lorraine Cristy E. Ceniza, Jimmy O. Pogosa, Suzette O. Lina
and Marlito M. Bande

122

Effects of Air Injection and Iron Oxide Pellet Addition on Hydrogen Sulfide Removal andBiogas Production
Lytour Lor, Machito Mihara, Bunthan Ngo, Lyhour Hin,
Dyna Theng, Chan Makara Mean, Chhorth Chhiengputheavy
and Bart Frederiks

129

Changing Spent Mushroom Substrate into a Quality Vermicompost
Supawadee Ruangjanda and Chuleemas Boonthai Iwai

136

Study on Use of Plant Based Material as a Fertilizer
Pa Pa San and Mya Yadanar Aung

142

Effect of Chicken Manure and Chemical Fertilizer Applications on Growth and Yield of Rice(Oryza sativa L.)
Kyi Kyi Shwe, Swe Swe Mar, Theingi Win, Win Yu Hlaing,
Hsu Myat Thwin, Kyaw Ngwe and Toru Sakai

149

Characterization and Diversity of Selected Maize (Zea mays L.) Genotypes Using Qualitative Traits
San Kyi, Kyaw Kyaw Win, Hla Than, Soe Win and
Nyo Mar Htwe

156

Biofunctool® Approach Assessing Soil Quality under Conservation Agriculture and Conventional
Tillage for Rain-fed Lowland Rice Systems in Cambodia
Veng Sar, Kannika Sajjaphan, Wutthida Rattanapichai,Alexis Thoumazeau, Vang Seng and Florent Tivet

162

Assessment of Biogas Production Potential from Commercial Pig Farms in Cambodia
Lyhour Hin, Bunthan Ngo, Lytour Lor, Suyheang Sorn,
Dyna Theng, Chanphakdey Dok, Sokhom Mech,
Chan Makara Mean, Sovanndy Yut, Makara Lay andBart Frederiks

172

Evaluation of Some Nutritional Values and Antioxidant Activities of Dried Tea Leaves in Seasonally
Chaw Su Hlaing, A Mar Wint, Ohn Mar Aye and
Myo Thida Hteik

181

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IJERD – International Journal of Environmental and Rural Development (2021) 12-1

Research article
erd

Assessment of Soil Properties using GIS Technologies in a
Selected Area in Myanmar
HLA MOE KHAING*
Dept. of Soil and Water Science, Yezin Agricultural University, Myanmar
Email: hlamoekhaing45@gmail.com

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

SWE SWE MAR
Dept. of Soil and Water Science, Yezin Agricultural University, Myanmar

HTAY HTAY OO
Dept. of Agronomy, Yezin Agricultural University, Myanmar
Received 24 December 2020

Accepted 12 May 2021(*Corresponding Author)

Abstract This study evaluates selected soil properties and maps these under different cropping
patterns of Sipintharyar village, Zeyarthiri Township, Myanmar. A total of 130 soil samples
were collected at a depth of 0-20 cm, with sample points selected using a Global Positioning
System. Soil fertility maps were created using Kriging interpolation in ArcGIS software 10.5.
Soil textures in the study area were loam, loamy sand, clay loam and sandy loam. The soils
were strongly acidic to moderately alkaline and contained a very low status of soil organic
matter (84%), available potassium (89%) while total nitrogen was at a medium (56.92%) level.
The coefficient of variation (CV) showed that soil pH was the least variable (9.91%) parameter
examined, with mean values ranging from 4.95 to 8.47, while available potassium was highlyvariable (86.95%) with content values ranging from 1 to 578 ppm. Other selected properties
such as bulk density, total nitrogen, CN ratio, electrical conductivity and soil organic matter
were found, respectively, to have the following variabilities; 11.68%, 33.84%, 34.86%, 72.23%
and 52.07%. Levels of soil organic matter were highly significant, and positively correlatedwith total nitrogen and available potassium. These variations in soil properties are probably
related to the different cropping patterns and fertility management practices employed in the
study area.
Keywords soil properties, global positioning system, Arc GIS

INTRODUCTION
The agriculture sector in Myanmar accounted for 30% of gross domestic product, 60% of employment,
29% of value addition, and 23% of exports in 2016 (Agriculture Guide, Myanmar, 2018). Therefore, anevaluation of the fertility status of soils of an area is an important aspect in promoting sustainableagricultural production (Singh and Mishra, 2012). In Myanmar, the major soil fertility issues areunderstood only at the higher level with limited information at local levels. Soil fertility andproductivity have a direct relationship in ensuring food security for the increasing world population.
The optimum plant growth and crop yield might be influenced by not only the total amount of nutrients
present in the soil at a particular time, but also on the availability of these, a process controlled byphysicochemical properties (Bell and Dell, 2008). In addition, nutrient properties vary over time andspace. Wakene and Heluf (2003) stated that the periodic evaluation of important soil properties andⒸ ISERD

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their responses to changes in land management is required to apply proper soil fertility management
techniques, and to improve and conserve the fertility and productivity of soils. Additionally, the spatial
variability of soil fertility and its classification can be mapped by applying GIS, to clearly show thespecific locations where attention is required with respect to management of plant nutrients (Jatav et al.,2013). Currently, there is little information on the spatial variability of these soil fertility parametersand very few efforts in generating soil fertility maps for the agricultural soils in Myanmar. Furthermore,
without detailed soil related information at a specific local level, sustainable crop production cannot be
achieved. From this, understanding the spatial variability of soil fertility in specific agricultural fields isessential in optimizing the application of agricultural inputs and ensuring maximum crop yields in
Myanmar.
OBJECTIVES
The main objective of this research is to evaluate the spatial variability of selected soil properties andto map soil fertility status under different cropping patterns in the study area.
METHODOLOGY
Study Area, Soil Sampling and Data Collection
Total study area is 0.6 km2 and it is located at Sipintharyar village (19˚44'43" N - 19˚45'22" N and96˚17'42" E - 96˚18'02" E), Kyidaung village Tract, Zeyarthiri Township, in central Myanmar (Fig. 1).
It receives a mean annual rainfall of about 1265 mm and has an average temperature of 26.8°C.
The grid map preparation was created using ArcGIS (Ver.10.5) software for the collection of soil
samples, in March 2020. The soil samples were collected after harvesting of crops and during thecultivation of horticultural crops, with samples from 300 m × 300 m grid points for determining soil
texture, and 75 m × 75 m for the other soil parameters, and at 0-20 cm depth by using auger and handhoe from the selected points (Fig. 2). Ten soil samples were taken from each grid to form a composite
a representative sample, with a 130 soil samples in total. After labelling and packing, samples were
tested at the laboratory of the department of Soil and Water Sciences, Yezin Agricultural University,
Myanmar. After air drying, the samples were gently crushed, sieved (2 mm) and properly stored foranalysis. Furthermore, interviews were conducted with farmers using structured questionnaires to
identify variations in management practices. Sixty farmers were selected as sample respondents andinterviewed. The data collected related to method of land preparation, fertilizer management and thecropping patterns practiced by the respondents in the study area.

Fig. 1 Loation of study area

Fig. 2 Soil sampling grid points

Laboratory AnalysisⒸ ISERD

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The composited soil samples were analyzed to determine soil texture, adopting a pipetted method(Ryan et al., 2001), with bulk density determined by gravimetric method. Soil pH and electricalconductivity were measured in an extract, with a soil: water (1:5) suspension, using a digital pH-meterand EC meter (Hesse, 1971). Total nitrogen was determined by the Modified Kjeldahl Digestion
method (Ohyma et al., 1991). Heanes (Rayment and Lyons, 2011) wet digestion method was used todetermine soil organic matter and available potassium was determined by Atomic Absorption
Spectrophotometer.
Statistical Analysis and Soil Fertility Mapping
The soil results were analyzed by using statistix (8th version). The coefficient of variation (CV) was
also determined to measure nutrient variability according to Ogunkunle (1993), where, soil properties
having a CV between 0 and 15% are considered least variable, those with 15 and 35%, moderately
variable, and larger than 35%, highly variable. These results were used to create soil fertility maps. The
soils were classified into different categories of fertility i.e., very low, low, medium, high, and very
high on the basis of the measured soil parameters. Nutrient index was also calculated by Ramamoorthy
and Bajaj (1969).
RESULTS AND DISCUSSION
Spatial Distribution in Soil Properties
The results of the descriptive statistics of soil samples are presented in Table 1. There is a large
variation in soil properties in the study area. Electrical conductivity shows the highest variability with
72.23% CV, followed by soil organic matter, with a CV value of 52.07%. However, least variability
across sample areas was found for bulk density and soil pH, with CV values of 11.68%and
9.91%respectively. Moderate variability occurred for the CN ratio and total nitrogen, which have CV
values of 34.86% and 33.84%, respectively.
Table 1 Descriptive statistics of soil physicochemical parameters
Unit*
Minimum
Maximum
Sand
%
24.44
78.80
Silt
%
7.72
33.36
Clay
%
13.06
42.20
Bulk Density
g cm-3
0.80
1.51
pH
4.95
8.47
Electrical ConductivitydS m-1
0.03
0.72
Organic Matter
%
0.31
3.55
Total Nitrogen
%
0.11
0.39
Available Potassium
ppm
1.00
578.00Carbon : Nitrogen (CN ratio)
1.19
7.85
SD: Standard Deviation, CV: Coefficient of Variation;* Units represent for the columns of minimum, maximum and mean in the table
Variables

Mean
56.38
17.04
26.58
1.21
6.94
0.17
1.41
0.23
87.08
3.43

SD
18.04
8.23
11.04
0.14
0.69
0.12
0.73
0.08
75.72
1.20

CV%
31.99
48.29
41.53
11.68
9.91
72.23
52.07
33.84
86.95
34.86

Soil Texture and Bulk Density (BD)
Sandy loam was the dominant soil textural class throughout the study area (Fig. 3), indicating asimilarity in soil forming processes and parent materials. According to USDA soil texture classification
system, six points in a 12 point of soils describes a sandy loam (58.33%) textural class; whereas twoⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2021) 12-1

points describe clay loam (16.67%) and loam (16.67%) and one point represents loamy sand (8.33%),
respectively. The spread of BD values ranged from 0.8 to 1.51 g cm-3, with a mean value of 1.21 g cm-3.
From the survey, all farmers use machine, animal and manpower from the land preparation to theharvesting process, for crop production and grow the different cropping patterns in every year.
Soil pH and Electrical Conductivity (EC)The pH values of the soil samples ranged from strongly acidic to moderately alkaline, (4.95 to 8.47)
with a mean value of 6.94. The spatial distribution map of soil pH is presented in Fig. 5 and the pH canbe seen as mostly neutral (47.69%), strongly alkaline (1.54%), moderately alkaline (26.92%), slightly
acidic (13.85%), moderately acidic (9.23%) with only 0.77 % strongly acidic. Gazey and Davies(2009) indicated that the pH values between 5.5 and 8.0 are considered as ideal for plant growth.

Fig. 3 Spatial variability of soil texture

Fig. 4 Spatial variability of soil bulk density

Fig. 5 Spatial variability of Soil pH

Fig. 7 Spatial variability of total N

Fig. 6 Spatial variability of EC

Fig. 8 Spatial variability of available KⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2021) 12-1

Therefore, the observed pH values are suitable for different crops and the availability of most ofplant nutrients should not be subject to restrictions within the observed pH range. The values of EC
range from 0.03 to 0.72 dS m-1 with an average of 0.011 dS m-1 (Fig. 6). According to the Soil Guide(Moore, 2001), these EC values are situated between a low EC value (0.03 – 0.47 dS m-1; 96.92%) anda medium level (0.52 – 0.72 dS m-1; 3.77%), which would have no effect on plant growth.
Total Nitrogen (TN) and Available Potassium (AK)
Variation in the TN content of the soil samples ranged from 0.11% to 0.39%, with a mean value of
0.23%. The study confirms that about 20.77% of the sampled area has low levels of TN, while 56.92%
has medium levels and 22.31% of the sampled area has high levels (Fig. 7). The areas of medium tohigh (79.23%) levels of TN would probably be as a result of the addition of nitrogen fertilizer and theintercropping with legumes. The AK levels appears to be low, with 89% of the sampled area being in
the lower range, whereas 9% and 2% of the samples showed medium and high levels (Fig. 8),
respectively. The low levels of AK may be due to infrequent application of potassium fertilizers intheir cultivation, and this only through the use of compound fertilizers.

Fig. 9 Spatial variability of SOM

Fig. 10 Spatial variability of CN ratio

Soil Organic Matter (SOM) and Carbon : Nitrogen (CN Ratio)
The spatial distribution of SOM is displayed in Fig. 9. The SOM content of soil ranges from 0.31% to3.55% with a mean value of 1.41%. Most (84%) of the study area has very low levels of SOM. Basedon the survey data, this may be as a result of the complete removal of crop residues, the failure to add
organic manures and the burning of crop residues after harvesting. This finding is in line with
Alemayehu, K. and Sheleme, B. (2013) who report that SOM recorded in cultivated fields as being
lower than for other land uses because of the effect of continuous cultivation and SOM oxidation.
There is a very low spatial variation of CN ratio throughout the study area, with ratios ranging from1.19 to 7.85, and with a mean value of 3.43 (Fig. 10).
Nutrient Index Value (NIV) and the Relationship among Soil Parameters
NIV of the SOM, CN ratio and AK content for the studied area clearly reveals these parameters are at
low levels, while soil TN was at a medium level. The ranking of nutrients according to NIV is total N >available K > Soil organic matter > CN ratio. According to the results, TN content was highly
significant and positively correlated with SOM and AK at a 1% confidence interval level, and CN ratio
at a 5% level, and SOM also shows a highly significant positive correlation with AK and the CN ratio(Table 2).

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Table 2 Correlation among the different soil parameters
BD
pH
EC
TN
SOM
C:N
pH
0.255 **
EC
0.225**-0.029 ns**
TN-0.437-0.490**0.018 ns
SOM
-0.333**-0.399**-0.081 ns
0.791**
ns
ns
ns
C:N
-0.034-0.095-0.0930.212*
0.730**
K-0.119 ns
0.003 ns
0.053 ns
0.431**
0.302**
0.059 ns
*Correlation is significant at the 0.05 level; **Correlation is significant at the 0.01 level(EC: electrical conductivity, BD: bulk density, SOM: soil organic matter, TN: total nitrogen, C:N: carbon nitrogen ratio,
K: Available potassium)

Crop Patterns and Soil Fertility Management Practices
The most common cropping patterns in the study area are rice-black gram, rice-fallow, maize-tomato,maize- tomato and lablab bean intercropping, okra-tomato and okra-Japanese mustard-onion. Moreover,some farmers grow mango, guava, banana and ambarella. Almost all farmers, except rice growers,
practice mixed cropping, crop rotation and intercropping systems for fertility management. According
to the survey data, 78% of farmers have no knowledge about the soil fertility testing or analysis,
whereas the remaining 22% know about this but cannot afford the cost of analysis. The most common
types of inorganic fertilizers used by the respondents were urea, foliar and NPK compound fertilizers.
CONCLUSION
The ranges of soil pH and EC measured should not be detrimental to crop cultivation. However, thedistribution of soil TN percentage are 50% of the study area at medium, 25% low and 25% at highlevels, whereas most of the levels for SOM, CN ratio and AK are at low levels. The study also showsthat NIV of TN and SOM are only at medium levels, while that of CN ratio and AK are at low levels.
Based on the survey results, farmers in the study area, in their crop management practice, use nitrogen(urea) fertilizer regularly, but without applying potassium fertilizer, apart from manure and NPK
compound. The spatial variability in soil properties appear to be largely due to the differences in crop
management practices, diverse cropping patterns and the variety of chemical fertilizers available.
Therefore, these soil fertility maps may greatly assist farmers in identifying nutrient levels for the
specific areas they crop and so help them to improve their crop fertility management programs, leading
to an increase in productivity and higher incomes.
ACKNOWLEDGEMENTS
I would like to gratefully acknowledge the JICA-TCP, Japan International Cooperation Agency,
through its Capacity Development program at Yezin Agricultural University, for providing the
research grant. My thanks also go to the supervisory committee, Staff at DOA, and to the farmers inthe study area who have provided technical and manual support through their participation in the study.
REFERENCES
Alemayehu, K. and Sheleme, B. 2013. Effect of different land use systems on selected soil properties in south
Ethiopia. Journal of Soil Science and Environmental Management, 4 (5), 100-107.
Bell, R.W. and Dell, B. 2008. Micronutrients for sustainable food, feed, fibre and bioenergy groduction (1st ed.).
IFA, Paris, France.Retrieved from https://www.fertilizer.org/Ⓒ ISERD

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Gazey, C. and Davies, S. 2009. Soil acidity: A guide for WA farmers and consultants. Department of Agriculture
and Food, Perth, Western Australia.
Hesse, P. 1971. A text book of soil chemical analysis. John Murray, London.
Jatav, M.K., Dua, V.K., Kumar, M., Trehan, S.P. and Kumar, S. 2013. Spatial distribution of soil available
nutrients in the potato growing pockets of Hoshiarpur district of Punjab. Potato Journal, 40 (2), 128-134.
Moore, G.A. 2001. Soil guide: A handbook for understanding and managing agricultural soils. Department ofPrimary Industries and Regional Development, Bulletin 4343, Western Australia, Perth.
Ogunkunle, A.O. 1993. Variation of some soil properties along two top sequences on quartile schist and banded
gnesis in southern Nigeria. Geoderma, 30 (4), 397-402.
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.
Ramamoorthy, B. and Bajaj, J.C. 1969. Available nitrogen, phosphorus and potassium status of Indian soils.
Fertilizer News, 14, 25-36.
Rayment, G.E. and Lyons, D.J. 2011. Soil chemical methods - Australasia. CSIRO publishing, Collingwood, VIC,
Australia.
Ryan, J., Estefan, G. and Rashid, A. 2001. Soil and plant analysis laboratory manual (2nd ed.). International
Center for Agricultural Research in the Dry Areas (ICARDA), Syria and National Agricultural ResearchCenter, Pakistan.
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.
The European Chamber of Commerce in Myanmar. 2018. Agriculture guide 2018. Myanmar.
Wakene, N. and Heluf, G. 2003. Forms of phosphorus and status of available nutrients under different land use
systems of Alfisols in Bako area, Ethiopia. Ethiopian Journal of Natural Resources, 5 (1), 17-37.

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Research article
erd

Preparation of Effective Microorganisms Based Compost
Using Some Selected Wastes for Improvement of Plants
Growth
THIDAR KAHING*
Department of Chemistry, Mandalay University, Myanmar
thidarkhaing7878@gmail.com

NYO NYO WIN
Department of Chemistry, Sagaing University, Myanmar

AYE MON THIDA NYO
Department of Chemistry, Mandalay Universtiy, Myanmar
Received 11 December 2020

Accepted 16 May 2021(*Corresponding Author)

Abstract Solid waste disposal is the most pressing problem facing mankind throughout theworld. The solid waste management plays a significant role to create a sustainable environment.
Some vegetable wastes such as rice husk, cotton husk, coconut husk, pigeon-pea husk andchicken manure were selected for chemical analysis. The effective microorganism (EM)solution was prepared from kitchen vegetable wastes except onion and garlic peels to ferment
for two months. The microorganisms that contain in prepared EM solution were studied byusing microscopic morphology. The pH of prepared EM solution was measured by using pH
meter. The compost was prepared from the vegetable waste materials and prepared EM solution
by using aerobic method. The yield percent of compost was determined by calculation method.
The yield percent of prepare compost was found to 55%. The mineral contents of compost andsoil sample were measured by using EDXRF spectroscopy. The physicochemical properties ofvegetable wastes, prepare compost and soil sample were determined. Planting the seedlings ofsome selected useful vegetables in various ratios of the prepare compost and soil sample. The
growth rates of plant rates were found to be effective by the planting experiment.
Keywords physicochemical properties, EM, compost, EDXRF, planting

INTRODUCTION
Compost is organic matter that has been decomposed in a process called composting. This process
recycles various organic materials otherwise regarded as waste products and produces a soil
conditioner. Compost is rich in nutrients. Compost is decomposed organic material, such as leaves,grass clippings, and kitchen waste. There are two composting types in which the difference is by thenature of the decomposition process; namely as aerobic and anaerobic method. In aerobic compostingthat oxygen is present, mainly to break the organic matter in the waste into a stable end product such ascarbon dioxide, ammonia, water and heat. Anaerobic composting developed an intermediate
compound such as methane, organic acid and hydrogen sulphide (Sita et al., 2016).
Composting is a technique which can be used to reduce the amount of organic waste throughrecycling and the production of soil fertilizers and conditioners. Compost is primarily used as a soil
conditioner and not as much as a fertilizer because it contains a high organic content (90-95%) but
generally low concentrations of nitrogen, phosphorus, potassium as well as macro and micro nutrientscompared to commercial fertilizers (Knight, 1997). Effective Microorganisms (EM) are mixed culturesⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2021) 12-1of beneficial naturally-occurring organisms that can be applied as inoculants to increase the microbial
diversity of soil ecosystem. They consist mainly of the photosynthesizing bacteria, lactic acid bacteria,
yeasts, actinomycetes and fermenting fungi. There is evidence that EM inoculation to the soil can
improve the quality of soil, plant growth and yield (Kengo and Hui-lian, 2000).
The main objectives were to examine the properties of compost from the vegetable waste andinvestigate the effect on plant growth. The vegetable wastes are very effective in soil nutrient. The
principal goal of nature planting is to produce abundant and healthy crops without the use of chemicalfertilizers and pesticides and without causing adverse effects on the natural environment.
METHODOLOGY
Sample collection:
The degradable vegetable wastes such as rice husk, cotton husk, coconut husk and pigeon pea husk
were collected from Samar village, Kyaukse Township, Mandalay Region. Chicken manure wascollected from Taungthamam village, Mandalay Region, Myanmar. Chicken manure was dried under
the sunlight. Soil sample was collected from Mandalay University Campus for planting. All selected
samples were ground with blender and used for throughout the experiment.
Determination of physicochemical properties of selected wastes:
Some physicochemical properties of selected vegetable wastes and chicken manure were determined(AOAC, 1990).
Preparation of effective microorganism solution:
The kitchen waste materials used for preparation of effective microorganism solution were collectedfrom the home kitchen. The mixture of vegetable wastes from home kitchen (10 kg) was made thesmall pieces and kept in 10 liters air tight container to ferment for two months. Effective
microorganism (EM) solution was prepared by using anaerobic digester method.
Isolation and microscopic morphology of prepare EM solution by using sub-micrometer:
Isolation of microorganism from prepared EM solutions was done at Department of Biotechnology,
Mandalay Technology University. The microscopic morphology of microorganism in prepare EM
solutions was also studied.
Determination of pH of EM solution:
The pH of EM solution was determined by pH meter (AOAC, 1990).
Preparation of compost by using aerobic digester method:
Compost was prepared by mixing of rice husk (0.25 kg), cotton husk (0.25 kg), coconut husk (0.25 kg),
pigeon-pea husk (0.25 kg), chicken manure (0.25 kg) and EM solution 10 liters in container by using
aerobic digester method.
Determination of yield percent of prepared compost:
The prepared compost was dried and the yield percent was determined based upon the total weight ofselected materials used.
Determination of mineral contents of compost and planting soil:
The mineral contents of prepared compost and planting soil were measured at department of Chemistry,
University of Monywa, by applying EDXRF (Energy Dispersive-X-Ray Fluorescence Spectroscopy).
Determination of pH of prepare compost and planting soil:
The pH of sample was determined by pH meter (AOAC, 1990).
Determination of moisture content of compost and planting soil:
The moisture content was measured by oven dry method (AOAC, 1990).
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Determination of ash content of prepare compost and planting soil:
The ash content was measured by oven drying method (AOAC, 1990).
Determination of organic matter:
The sample is ash at 500-600°C by placing a suitable weight (0.5-1.0g) of the sample in a silica
crucible and heating it in a muffle furnace for 4-6 h. Loss of weight on ignition can be used as a direct
measure of the Organic Matter (MO) (AOAC, 1990).
Estimation of available nitrogen by using alkaline permanganate method:
The amount of nitrogen released by alkaline permanganate solution was estimated by distillation
procedure. The distillate was collected in known amount of standard acid and the excess acid wastitrated against standard alkali solution by using methyl red as an indicator. The nitrogen so estimatedis designated as available nitrogen (Subbiah and Asija, 1956).
Determination of available phosphorus by using Olsen’s method:
2.5 g of sample was taken in a conical flask and 1.0 g of carbon black was added. 50 mL of 0.5 M
NaHCO3 solution was added to the flask and shaken for half an hour. It was filtered through the filter
paper. 5 mL of filtrate was pipette out into a 25 mL volumetric flask. 5 mL of molybdate reagent wasadded and washed down the steam of the flask and mixed. 1 mL of dilute stannous chloride solution
was added and made up to 25 mL with distilled water. The contents were mixed thoroughly. After ten
minutes the color intensity was read in the spectrophotometer by using read filter (or 700 nm). The
value (mg of phosphorus) was read from the standard curve (Olsen et al., 1954).
Determination of potassium by using atomic absorption spectroscopic method:
The potassium content of prepare compost and planting soil were determined by Atomic Absorption
Spectrophotometer at Department of Agriculture (Fishman and Downs 1966).
Planting:
The roselle, nannan, pumpkin, lady’s finger, cow pea and mustard were seedlings in pots. After 20thdays, all the seedlings of the same size were selected and planted in planting experiment. The threeplant-based filter were used in the different ratio of compost and soil sample. In the first plant-based
filter, no fertilizer was used. In the second plant-based filter, 1:10 of prepare compost and soil sample
were mixed. In the third plant-based filter, 1: 5 prepare compost and soil sample were mixed. All plantbased filter was regularly watered. The duration from the time of selected plants to the end of the study
period was 60 days. The growth rate of the plants was measured from selected plants from each plantbased filter and average values were recorded. The plant growth was measured at every ten days during
this research work.
RESULTS AND DISCUSSION
The Results of Physicochemical Properties of Vegetable Wastes
The results of physicochemical properties of vegetable wastes are shown in Table 1. According toTable 1, the pH values of samples were lie between 6 and 8. It values showed the slightly alkalinecondition. The organic matter content of cotton husk was found to be higher than the other samples.
Thus, all selected samples contain suitable inorganic matter such as N, P and K contents found to besuitable amount respectively (Dharmakeerthi et al., 2007).
Isolation of Microorganism and Bacterial Identification from Prepare EM Solution
The results of isolation of microorganism and bacterial identification from prepare EM Solution were
describe in Table 2.
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Table 1 Physicochemical properties of vegetable wastes
Sampledescription

Unit

pH
Moisture
Organic Matter
Available N
Available P
Available K

%
%
%
%
%

Chickenmanure
7.12
21.60
41.02
2.66
0.52
1.12

Rice husk
7.43
12.93
79.36
0.41
0.02
0.93

Coconut
hush
7.62
8.45
88.12
0.31
0.01
0.16

Cotton
husk
6.62
6.24
89.97
3.36
0.48
0.51

Pigeon pea
husk
7.21
10.18
20.50
2.15
0.28
1.25

Table 2 Cultural and microscopic morphology of isolated bacteria
Colony morphology

Sample
name

Size
(mm)

EM1

1

EM2

3- 4

EM3

3- 4

EM4

4-6

Color

Microscopic morphology

Elevation

Shape

Size (um)Gram’reactionShaperaisedround

1-2 × 2-4+

Small Rodflat

Irregular

1-2 × 2-4+

Rod (spore)raisedIrregular

2-3 × 3-4+

Rod (spore)raisedround

2-3 × 3-4+

Rod (spore)

Yellow(opque)White(opque)White(opque)White(opque)According to cultural and microscopic morphology, four bacterial strains were observed in liquidof EM solutions with different conditions. Four bacterial strains (EM 1, EM 2, EM 3, EM 4) fromprepare EM samples were isolated on Nutrient media. According to the cultural and microscopic
morphology, EM 1 predicted that positive gram stain bacterial from the family of Pseudomonadaceae,
and EM 2, 3 and 4 could be assumed that positive gram stain bacterial from the family of Bacillaceae(Khan et al., 2018).
The pH of Prepare Effective Microorganism Solution
The pH value of prepared effective microorganism solution was found to be 4.29. The value is towards
slightly acidic and to neutralized selected vegetable wastes (Khan et al., 2018).
The Yield Percent of Prepared Compost
The yield percent of prepare compost was found to be 55%.
Table 3 Relative abundance (%) of elemental composition of prepared compost and planting soil
Element
SiliconCalcium
Iron
Potassium
SulfurTitanium

Symbol
Si
Ca
Fe
K
S
Ti

Prepare compost (%)
40.41326.922
5.143
22.5962.997
0.933

Mineral Content of Prepared Compost and Planting Soil
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Planting soil (%)
56.573
4.410
11.110
5.546
2.225
1.127

IJERD – International Journal of Environmental and Rural Development (2021) 12-1

The relative abundance (%) of elemental composition of prepared compost and soil were shown in
Table 3. The elemental analysis indicated that the amount of silicon was highest value in preparecompost and planting soil. The calcium content in prepare compost was higher than planting soil. The
sulphur content of prepare composed and planting soil was nearly equal. The other mineral contents
were found to be small amount in prepare compost and planting soil (Pravina et al., 2013).
Results of Physicochemical Properties of Prepared Compost and Planting Soil
The results of physicochemical properties of prepared compost and planting soil were showed in Table
4. The pH value of prepare compost and planting soil was nearly equal and the ash content of preparecompost was higher than planting soil. The moisture content of planting soil was decreased compare to
the prepare compost (Clark et al., 1998).
Table 4 pH, moisture and ash value of prepared compost and planting soil
SamplepH

Moisture (%)

Ash (%)

Planting soil

8.81

2.24

10

Prepared compost

8.76

12.19

45

The available nitrogen value of prepared compost and planting soil were described in Table 5.
According to the results from the determination of available nitrogen content of prepare compost was
higher than planting soil. The phosphorus and potassium contents in prepare compost were higher thanplanting soil (Clark et al., 1998).
Table 5 Available nitrogen, phosphorus and potassium valueof prepared compost and planting soil
Chemical properties
Available Nitrogen
Available Phosphorus
Available Potassium

Prepared compost(% by dry mass)
2.08
1.32
1.02

Planting soil(% by dry mass)
0.12
0.46
0.44

Planting
Duration from the time of roselle, nannan, pumpkin, lady’s finger, cow pea and mustard were to theend of the study period was 60 days. The plants growth was measured at every 10 days during thisresearch work and average values were record.
Table 6 The growth rate of Roselle

Table 7 The growth rate of NannanⒸ ISERD

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Table 8 The growth rate of Pumpkin

Table 9 The growth rate of Lady's finger

Table 10 The growth rate of Mustard

Table 11 The growth rate of Cow pea

In this planting experiment contains three conditions. The first condition is blank, the second
condition is 1:10 ratio and the third condition is 1:5 ratio of prepare compost and planting soil.
According to these results, the third condition was found in the best growth rates of planting because
its uses the most amount in prepare compost.
CONCLUSION
This experiment was done in an effort to solid wastes management by investigating homemadecomposting with effective microorganism solution. From this analysis, vegetable waste materials wereused in evaluation provided a better environment for EM to grow produce quality compost. The
degradable vegetable wastes, cow dung and EM play a significant role in stabilizing the mixture andaccelerate the composting process. The pH value of EM was found to be 4.29 and it is slightly acidic
condition. According to microorganism morphology, four types of bacterial strains were observed in
EM solution. All are gram positive bacteria. The observed bacteria are degraded the vegetable wastematerials and creates the suitable environment conditions for decomposition of organic materials used.
The elemental analysis indicated that silicon was found to be the highest amount in prepared compost
and planting soil. Since silicon generates the resistance in many plants to disease and pests, it may
contribute to reduce the rate of application of pesticides and fungicides. The physicochemical
properties of vegetable wastes and prepare compost were lie within the limiting range and it is suitablefor planting. The selected useful vegetables were treated with different ratio of prepared compost andplanting soil. The highest amount of prepare compost used in planting is most growth rates in all
selected plants. Thus, the prepared compost was low cost, easily available and supporting theimprovement of growth rates of plants. According to the results of experimental work, compost fromkitchen waste product could be employed as an alternative, eco-friendly and low cost for supporting ofplants growing.

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REFERENCES
AOAC. 1990. Official methods of analysis (15th ed.). Association of Official Analytical Chemistry, 200-210,
Washington DC., USA.
Clark, M.S., Horwath, W.R., Shennan, C. and Scow, K.M. 1998. Changes in soil chemical properties resulting
from organic and low-input farming practices. Agronomy Journal, 90 (5), 662-671.
Dharmakeerthi, R.S., Indraratne, S.P. and Kumaragamage, D. 2007. Manual of soil sampling and analysis (1sted.). Araliya Printers, Kurunegala, 3-79.
Fishman, M.J. and Downs, S.C. 1966. Method for analysis of selected metals in water by atomic absorption. U.S.
Geological Survey, Water-Supply Paper, 1540-C, 36-8.
Kengo, Y. and Hui-lian, X. 2000. Properties and applications of an organic fertilizer inoculated with effective
microorganisms. Journal of Crop production, 3 (1), 255-268.
Khan, I., Sajjad, W., Zada, S., Rafiq, M., Ali, B., Jamil, S.U.U., Shah, A.A. and Hasan, F. 2018. Morphological,
biochemical and molecular characterization of culturable epilithic and endolithic bacteria from rocks of
Ayubia (Murree), lower Himalaya, Pakistan. Applied Ecology and Environmental Research, 16 (1), 563-577.
Knight, W. 1997. Compost convective airflow, N and C conservation with passive and active aeration. M. Sc.
Thesis, Agric. and Biosystems Eng. McGill University, Canada.
Olsen, S., Cole, C.V., Watanable, F.S. and Dean, L.A. 1954. Estimation of available phosphorus in soil byextraction with sodium bicarbonate. USDA Circular, USA.
Pravina, P., Sayaji, D. and Avinash, M. 2013. Calcium and its role in human body. Int. J. Res. Pharm. Biomed.
Sci., 4 (2), 659-668.
Sita, A., Muttalib, A., Sharifah, N.S.I. and Sarva, M.P. 2016. Application of effective microorganism in foodwaste composting, A review. Asia Pacific Enviromantal and Occupational Health Journal, 2 (2), 37-47.
Subbiah, B.V. and Asija, G.I. 1956. Rapid procedure for the determination of available nitrogen in soil. Curr Sci.,
25, 260.

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Research article
erd

Spatial Variability and Mapping of Soil Properties Using GISBased Geostatistic in Myanmar
TUN TUN HLAING*
Dept. of Soil and Water Science, Yezin Agricultural University, Myanmar
Email: thlaing83@gmail.com

SWE SWE MAR
Dept. of Soil and Water Science, Yezin Agricultural University, Myanmar

CHAW SU LWIN
Dept. of Soil and Water Science, Yezin Agricultural University, Myanmar

HTAY HTAY OO
Dept. of Agronomy, Yezin Agricultural University, Myanmar

KYAW NGWE
Dept. of Soil and Water Science, Yezin Agricultural University, Myanmar
Received 29 December 2020

Accepted 16 May 2021(*Corresponding Author)

Abstract Precise information on the spatial variability of soil is a crucial component for
productive intensive agriculture, sustainable development, and the management of natural
resources. The primary purpose of the study was to investigate the spatial variability of soil
properties of the study site at Yezin Agricultural University Field, Myanmar using geostatistics.
A total of 94 composite soil samples were collected from a depth of 0 to 20 cm, in a systematic
grid (50 x 50 m2) at the site in May 2019. Soil pH, electrical conductivity (EC), soil organic
matter (SOM), total soil nitrogen (TSN), available phosphorus (Ava-P), and available
potassium (Ava-K) were measured using standard analytical methods. Data were analyzed
geostatistically based on semivariogram. The exponential model best fitted the semivariogramfor pH, EC, Ava-P, and Ava-K; SOM was adapted from the Gaussian model while TSN was
adapted from the spherical model. The nugget/sill ratio showed a strong spatial dependence
exists for EC, Ava-P, and Ava-K and a moderate spatial dependence for pH, SOM, and TSN.
Most of the soil was found to be strongly acidic. It was also found that EC, SOM, and Ava-P
are very low in most of the study area. Most of the study area was found to have low TSN
levels, while Ava-K content was low over the entire area. With such an analysis, it is possible toplan better nutrient management practices for agricultural production and environmental
protection. Therefore, geostatistical analysis with ordinary kriging is a useful tool for studying
the spatial variability of soil properties.
Keywords spatial variability, soil properties, geostatistic, semivariogram, kriging

INTRODUCTION
An understanding of the distribution of soil properties is essential for ecological modelling,
environmental predictions, precise agriculture, and management of natural resources (Wang et al.,2009). However, soil properties vary spatially from a small to a larger regional area, and are affectedby intrinsic (parent materials and climate) and extrinsic factors (soil management practices, fertility
status, crop rotation) (Cambardella and Karlen, 1999). Therefore, demands for more accurateⒸ ISERD

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information on spatial variability of soils are significant for intensive agriculture, sustainable
development, and natural resource management (Karlen et al., 2011).
Many studies have used a classical statistical method to quantify spatial characteristics in soil
properties (Salehi et al., 2013). However, physico-chemical characteristics of soil often exhibit spatial
dependency, which cannot be recorded with classical statistical methods (Lin et al., 2005). To
overcome this problem, many researchers apply geostatistical interpolation methods to estimate thespatial variability of soil properties (Cambardella et al., 1994; Webster and Oliver, 2007).
Geostatistics is a set of statistical tools that can be used to investigate and predict the spatial
structure of georeference variables and generate soil property maps (Patil et al., 2011). Based on thegeostatistical analysis, several studies have been carried out to characterize the spatial variability ofvarious soil properties (Weindorf and Zhu, 2010). Among the various geostatistical methods, ordinary
kriging is widely used to map spatial variations in soil fertility because it offers a higher level ofpredictive accuracy (Song et al., 2013). Therefore, it is important to extend the availability of soil
resource information maps to allow the planning of appropriate soil management practices, including
fertilization for agricultural production and environmental protection.
OBJECTIVES
The objective of this research is to investigate the spatial variability and mapping of spatial distributionof selected soil properties status in the study area using GIS-based geostatistical analysis.
METHODOLOGY
Study Area, Soil Sampling and Laboratory Analysis
The study area was Yezin Agricultural University Field (1949'47''1950'21''N and 9615'32''9616'15''E), Zeyarthiri Township, Nay Pyi Taw Union Territory, in central Myanmar. It has anelevation ranging from 121.547 m to 125.205 m above sea level (Fig 1). The area of the research site is
18.19 ha. The study area has an average temperature of 26.8°C and a mean annual rainfall of about
1420 mm. Summer and monsoon rice are the main crops in the study area and these include both rainfed and irrigated rice cultivations.

Fig. 1 Georeferenced sampling sites of the research area
A total of 94 composite soil samples were collected at a depth of 0 to 20 cm based on a systematic
grid (50 x 50 m2) determined with the help of a hand-held GPS device, in May 2019. Soil samples were
air-dried and ground so as to pass through a 2-mm sieve. Soil pH and electrical conductivity (EC) wereⒸ ISERD

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measured in a 1:5 soil/water extract, soil organic matter (SOM) was determined by Heanes wet
oxidation method, total soil nitrogen (TSN) was analyzed by the Semi-micro Kjeldahl steam
distillation method, available phosphorus (Ava-P) was measured by the Olsen-P method, and available
potassium (Ava-K) was determined by extraction with 1M ammonium chloride (Rayment and Lyons,2011).
Geostatistical Analysis
The geostatistical analysis, consisting of semivariogram calculation, cross-validation, and mapping,
was performed with the Geostatistical Analyst Extension Tool of ArcGIS 10.7. The spatial variation ofsoil properties was analyzed using geographical semivariogram to quantify the spatial variation of aregionalized variables which derives important parameters used for ordinary kriging (OK) spatial
interpolation (Krige, 1951). The semivariogram is used as a fundamental tool to study the spatial
distribution structure of soil properties. The semivariogram analyzes were performed before theapplication of OK interpolation, as the semivariogram model determines the interpolation function(Goovaerts, 1997), defined as:2

 ( h) =1 N (h) z ( xi ) − z ( xi + h)
2 N (h) i =1

(1)

where,γ (h) is the experimental semivariogram value in a distance interval h (in meters or km), N (h)is the number of sample pairs that are located by a particular distance (h) from each other. z (xi) and z(xi+ h) are the values of a regionalized variable at location xi and xi+ h, respectively (Wang and Shao2013).
Theoretical semivariogram models fit the empirical semivariogram obtained from the data to
generate geostatistical parameters, including nugget variance (C0), structured variance (C), sill variance(C0+C), and distance parameters (A). The nugget/ sill ratio, C0/ (C0 + C) is calculated to characterize
the spatial dependency of the values. A nugget/ sill ratio is classified as strongly spatially dependent if
the ratio is less than or equal to 0.25, moderately spatially dependent if the ratio is between greater than
0.25 and less than or equal to 0.75 while it is classified as a weak spatial dependent if it is greater than
0.75 (Cambardella et al., 1994).
Several semivariogram models were evaluated to select the best fit with the data. The model,
spherical, Gaussian, or exponential that offers the best fit varies depending on the soil parameters(Ramzan and Wani, 2018). A cross-validation technique was used to evaluate and compare theperformance of the OK interpolation method. The lowest RMSE (Root Mean Square Error) value
indicates the best fit for the variogram model (Panday et al., 2018). The predictive maps of soil
properties are then created using a semivariogram model through OK.
Assessment of Accuracy of Interpolation Map
The effectiveness of interpolation was evaluated based on Goodness-of-Prediction Estimate (G) Eq. (2).
A “G” value of 100% indicates a perfect prediction, positive values (i.e., 0 to 100%) show that thepredictions are more reliable than the use of the sample mean, and negative values indicate that thepredictions are less reliable than using the sample means (Laekemariam et al., 2018).
N2  z ( xi ) − zˆ ( xi ) 
  100
G = 1 − i=1 N
2


ˆ


z
(
x
)
−
y

i


i =1

(2)

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Where, z(xi) is the observed value at location i, ž(xi) is the predicted value at location i, N is the sample
size, and ŷ is the sample mean.
RESULTS AND DISCUSSION
Geostatistical Analysis
The semivariogram parameters obtained from the best-fit model are in Table 1. An exponential model
produced the best fit to semivariogram for pH, EC, Ava-P, and Ava-K. This model is one of thestandard models used in the study of soil properties (Cambardella et al., 1994; Reza et al., 2016). The
spherical model was the best suited to the semivariogram of TSN, while a Gaussian model was the best
fit for SOM.
The range for all soil properties varies from 96.76 m to 276.68 m, and therefore the length of thespatial autocorrelation is much longer than the sampling interval of 50 m. According to Goovaerts(1997), the current sample design is appropriate for this study, and it is expected that the interpolated
map will display good spatial structure.
In the present study, the nugget/ sill ratio showed that EC, Ava-P, and Ava-K were strongly
dependent spatially whereas, pH, SOM, and TSN were moderately dependent spatially. The strong
spatial dependency suggests that intrinsic factors, such as climate, parent material, topography, soil
properties, and other natural factors, play important roles in spatial variability. The weak spatial
dependency indicates that the spatial variability is mainly caused by extrinsic factors, such as
fertilization, local farming practice, cropping systems, and other human activities. The moderatespatial dependency shows that spatial variability is caused by a mix of extrinsic and intrinsic factors(Bhunia et al., 2018; Cambardella et al., 1994).
The G-values are greater than zero for all soil parameters. This value indicated that spatial
prediction using semivariogram parameters is better than assuming that the mean of observed values isthe best value for an unsampled location. This result also shows that semivariogram parameters
obtained from fitting experimental semivariogram values describe the spatial variation reasonably(Reza et al., 2010).
Table 1 Geostatistical parameters of the fitted semivariogram models for soil properties
Parameters
Soil pH
EC
SOM
TSN
Ava-P
Ava-K

Model

NuggetSill

Exponential
Exponential
Gaussian
Spherical
Exponential
Exponential

0.10370.00000.09000.00050.08560.00000.1822
0.48220.28690.00080.6445214.0108Range(m)
253.3496.76
276.68227.30178.09108.58NuggetDSD/ Sill0.57 Moderate
0 Strong
0.31 Moderate
0.38 Moderate
0.13 Strong
0 Strong

RMSE

G (%)

0.41
0.03
0.34
0.03
6.71
12.26

6.11
13.25
47.43
25.08
25.66
39.90

DSD: Degree of Spatial Dependence, RMSE: Root Mean Square Error, G: Goodness of Prediction

Spatial Distribution of Soil Properties
The parameter of the fitted semivariogram models is used for OK to produce a spatial distribution mapof soil properties in the study area. The spatial distribution of soil properties such as pH, EC, SOM,
TSN, Ava-P, and Ava-K are shown in (Fig. 2a-e). The distribution of the predicted soil pH map (Fig.
2a) shows that 0.77%, 80.92%, and 18.31% of the study area were very strongly acid, strongly acid,and moderately acid, respectively. Most of the soils were strongly acid which may be caused by a
mixture of the nature of the soil mineralogy, the use of acidic fertilizers, low input of organic materials,Ⓒ ISERD

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and removal of base nutrients (Rawal et al., 2018). The predicted map of EC (Fig. 2b) shows that89.22% and 10.78% of the soil in the study area, can be described as very low and low, respectively.
According to the soil guide (Moore, 2001); low EC levels only have a minimal impact on plant growth.
(a)

(b)

(c)

(d)

(e)

(f)

Fig. 2 Spatial distribution maps for (a) pH, (b) EC, (c) SOM, (d) TSN, (e) Ava-P and (f) Ava-K
The distribution of SOM (Fig. 2c) ranged from very low (62.73%) to low (37.27%), but low levels
were most prevalent. The lower organic matter content in these soils can be attributed to the poor
management practices such as intensive cropping, the complete removal of crop residues, and lack ofaddition of organic fertilizer sources (Gebreselassie, 2002). The majority of the soils were low(97.75%) in total nitrogen content, whereas 2.25% of the study area is rated at a medium level (Fig. 2d).
The low nitrogen levels in most areas could be as a result of continuous cereal-based cropping, lower
external organic-N inputs (like plant residues, animal manures), N (nitrate ions) leaching problem, andaddition of a low amounts of SOM (Patil et al., 2011). The Ava-P map (Fig. 2e) shows that, in terms ofⒸ ISERD

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area coverage, 68.83%, 28.42%, and 2.75% of the study area has very low, low, and medium levels
respectively. This result indicates that, in general, the majority (97.25%) of the study area is deficientin phosphorus. The reason for low soil phosphorus levels may be due to the intensive cropping system,
low pH (acidic) soils, the imbalanced use of fertilizer, and nutrient mining (Sertsu and Ali, 1983). The
whole of the study area can be classified with a low rating for Ava-K content (Fig. 2f). The lowest
Ava-K in the study area might be due to the lowest SOM and the continuous removal of potassium by
cereal crops, as the field has been intensively cultivated for a long period.
CONCLUSION
Most of the soil was strongly acidic. The selected soil properties had a distribution showing low
content levels in most of the study area. The distribution of variability of the soil properties across thelandscape might be sufficient to construct fertility maps. The generated spatial distribution and fertility
maps can serve as a powerful tool for farmers, decision-makers, and planners to understand the existingsoil conditions and make sensible decisions to better manage the soil for sustainability and productivity.
These results show that geostatistical analysis with kriging is an effective tool for studying the spatial
variability of soil properties and that it will be useful technique for future soil sampling campaigns in
Myanmar.
ACKNOWLEDGEMENTS
It is a pleasure to express special gratitude to the supervisory committee and Australia Centre forInternational Agricultural Research (ACIAR) which supports research grant and technical support forthis study under the project title of “Management of nutrients for improved profitability andsustainability of crop production in central Burma (SMCN– 2014 -044)”.
REFERENCES
Bhunia, G.S., Shit, P.K. and Chattopadhyay, R. 2018. Assessment of spatial variability of soil properties using
geostatistical approach of lateritic soil (West Bengal, India). Annals of Agrarian Science, 16 (4), 436-443.
Cambardella, C.A. and Karlen, D.L. 1999. Spatial analysis of soil fertility parameters. Precision Agriculture, 1(1), 5-14.
Cambardella, C.A., Moorman, T.B., Parkin, T.B., Karlen, D.L., Novak, J.M., Turco, R.F. and Konopka, A.E.
1994. Field-scale variability of soil properties in central Iowa soils. Soil Science Society of America Journal,
58, 1501-1511.
Gebreselassie, Y. 2002. Selected chemical and physical characteristic of soils of Adet research center and its
testing sites in north-western Ethipoia. Ethiopian Journal of Natural Resources, 4 (2), 199-215.
Goovaerts, P. 1997. Geostatistics for natural resources evaluation. Oxford University Press.
Karlen, D.L., Wienhold, B.J., Kang, S., Zobeck, T.M. and Andrews, S.S. 2011. Indices for soil management
decisions, USDA-ARS/UNL Faculty, Paper, 1381.
Krige, D.G. 1951. A statistical approach to some basic mine valuation problems on the Witwatersrand. Journal of
the Southern African, Institute of Mining and Metallurgy, 52 (6), 119-139.
Laekemariam, F., Kibret, K., Mamo, T. and Shiferaw, H. 2018. Accounting spatial variability of soil properties
and mapping fertilizer types using geostatistics in southern Ethiopia. Communications in Soil Science andPlant Analysis, 49 (1), 124–137.
Lin, H., Wheeler, D., Bell, J. and Wilding, L. 2005. Assessment of soil spatial variability at multiple scales.
Ecological Modelling, 182 (3), 271-290.
Moore, G.A. 2001. Soilguide (soil guide) : A handbook for understanding and managing agricultural soils.
Department of Agriculture and Food, Western Australia, Perth. Bulletin 4343.

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Panday, D., Maharjan, B., Chalise, D., Shrestha, R.K. and Twanabasu, B. 2018. Digital soil mapping in the Bara
district of Nepal using kriging tool in ArcGIS. Plos One, 13 (10), e0206350.
Patil, S.S., Patil, V.C. and Gaadi, K.A.I.A. 2011. Spatial variability in fertility status of surface soils. World
Applied Sciences Journal, 14 (7), 1020-1024.
Ramzan, S. and Wani, M.A. 2018. Geographic information system and geostatistical techniques to characterizespatial variability of soil micronutrients including toxic metals in an agricultural farm. Communications inSoil Science and Plant Analysis, 49 (4), 463-477.
Rawal, N., Acharya, K.K., Bam, C.R. and Acharya, K. 2018. Soil fertility mapping of different VDCs of Sunsari
District, Nepal using GIS. International Journal of Applied Science, Biotechnology, 6 (2), 142-151.
Rayment, G.E. and Lyons, D.J. 2011. Soil chemical methods: Australasia, 3. CSIRO publishing.
Reza, S.K., Baruah, U., Sarkar, D. and Singh, S.K. 2016. Spatial variability of soil properties using geostatisticalmethod: A case study of lower Brahmaputra plains, India. Arabian Journal of Geosciences, 9 (6), 446.
Reza, S.K., Sarkari, D., Daruah, U. and Das, T.H. 2010. Evaluation and comparison of ordinary kriging andinverse distance weighting methods for prediction of spatial variability of some chemical parameters of
Dhalai district. Tripura, 12.
Salehi, M., Safaei, Z., Esfandiarpour-Borujeni, I. and Mohammadi, J. 2013. Generalisation of continuous models
to estimate soil characteristics into similar delineations of a detailed soil map. Soil Research, 51 (4), 350-361.
Sertsu, S. and Ali, A. 1983. Phosphorus sorption characteristics of some ethiopian soils. Ethiopian Journal of
Agricultural Sciences.
Song, G., Zhang, L., Wang, K. and Fang, M. 2013. Spatial simulation of soil attribute based on principles of soil
science. 21st International Conference on Geoinformatics, 20-22 June 2013. Kaifeng, China.
Wang, Y., Zhang, X. and Huang, C. 2009. Spatial variability of soil total nitrogen and soil total phosphorus under
different land uses in a small watershed on the Loess Plateau, China. Geoderma, 150, 141-149.
Wang, Y.Q. and Shao, M.A. 2013. Spatial variability of soil physical properties in a region of the Loess Plateau
of PR China subject to wind and water erosion. Land Degrad Dev., 24 (3), 296-304.
Webster, R. and Oliver, M.A. 2007. Geostatistics for environmental scientists. John Wiley & Sons.
Weindorf, D.C. and Zhu, Y. 2010. Spatial variability of soil properties at Capulin volcano, New Mexico, USA:
Implications for sampling strategy. Pedosphere, 20 (2), 185-197.

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Research article
erd

Assessment of Insect Damage and Growth Performance ofDipterocarps Planted at Rainforestation Demonstration Farmat VSU, Baybay City, Leyte
KLEER JEANN G. LONGATANG*
Visayas State University, Baybay City, Philippines
Email: kleerjeann.longatang@vsu.edu.ph

MA. JULIET C. CENIZA
Visayas State University, Baybay City, Philippines

CHRISTOPHER JR. R. GALGO
Visayas State University, Baybay City, Philippines

JIMMY O. POGOSA
Visayas State University, Baybay City, Philippines

DENNIS P. PEQUE
Visayas State University, Baybay City, Philippines

LEONARD PAOLO LONGATANG
Visayas State University, Baybay City, Philippines
Received 30 December 2020

Accepted 16 May 2021(*Corresponding Author)

Abstract Assessment of associated insects and leaf damage caused by insects is essential
concerning decisions to contribute to developing suitable rehabilitation techniques. Few studies
have been done to identify the insect species associated with dipterocarp species and determine
the damage caused by insects and their growth performance. Six species of dipterocarps,
namely: Dipterocarpus alatus (hairy leaf apitong), Hopea philippinensis (gisok-gisok), Shorea
malibato (malibato), Shorea assamica (manggasinoro), Shorea polita (malaanonang), andShorea almon, were studied. This study was conducted to determine the insect-associated faunausing the visual and handpicking method of insect collection, leaf damage assessment using the
Bioleaf app, and the morphological traits (i.e., basal diameter and plant height) on the growth
performance of dipterocarps. There were eight orders of insects associated with thedipterocarps: Coleoptera, Diptera, Hemiptera, Heteroptera, Hymenoptera, Lepidoptera, Odonata,and Orthoptera. There was a significant difference (p ≤ 0.05) in the leaf damage among the six
dipterocarps species after 25 months from planting. Shorea assamica had the highest leaf
damage (8.68% ± 0.09), and Shorea almon had the least leaf damage (2.57% ± 0.09). In termsof basal diameter, the species with the highest significant increment (p ≤ 0.05) was Shorea
polita (2.49 ± 0.67 mm), while Shorea almon had the least growth increment (0.98 ± 0.67 mm)
25 months after planting. Dipterocarpus alatus grows faster for the plant height than otherspecies with a significant increment (p ≤ 0.05) of 32.90 ± 0.19 cm, while Shorea assamica had
the least increment of (4.95 ± 0.19) cm. The study indicated eight orders of insects associatedwith the dipterocarps showing significant damage on the S. assamica. Despite the insect
association, the plants grow significantly with the rapid increase observed on D. alatus.
Keywords dipterocarp, defoliation, insect fauna, leaf damage, bioleafⒸ ISERD

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INTRODUCTION
Herbivorous species, mostly insects, are a significant global biodiversity component, comprising
approximately 25% of all described species. Insect herbivores are among the many biotic factors
known to help maintain forest diversity through selective predation on vulnerable tree species'
seedlings altering forest community composition (Norghauer and Newbery, 2013). Insect herbivores,
directly and indirectly, influence plant community composition by altering the recruitment, mortality,
or individual growth rates of plant species, as supported by Maron and Crone, 2006.
Dipterocarps are known worldwide because of their economic and ecological functions. It has agood timber quality exported to other countries in finished products such as plywood and sawn timber(Corlett and Primack, 2005). According to Langenberger (2005), some species of dipterocarps areindicators for site suitability in local reforestation programs. Moreover, insect herbivores increase tree
seedling recruits (Dyer et al., 2010). Annual rates of leaf damage are higher in tropical forests than intemperate broad-leaved forests (Coley and Barone, 1996). In the natural Dipterocarp forest, insects are
the primary source of damage as leaf feeders, borers, suckers, and gall formation (Appanah, 1998).
Despite the knowledge about the damages caused by the insects' attack, few studies have been
done in the Philippines to identify the insect species associated with Dipterocarpus alatus (hairy leaf
apitong), Hopea philippinensis (gisok-gisok), Shorea malibato (malibato), Shorea assamica(manggasinoro), Shorea polita (malaanonang), Shorea almon and its growth performance. These
species were chosen because of their conservation status according to the Updated National List of
Threatened Philippine Plants and Their Categories by the Department of Environment and Natural
Resources Administrative Order No. 2017-11. Assessing the damage caused by the insect is essential
concerning assisting experts to make better decisions to contribute to efforts of developing suitablerehabilitation techniques and to recommend favorable dipterocarp species that can stand insect
herbivory. Furthermore, the study used a novel approach in assessing leaf damage i.e., using the
Bioleaf app for the first time on dipterocarps which in the literature has been commonly used inassessing leaf damage on soybean (Machado, 2016)
OBJECTIVES
1. To identify the insect species associated with the dipterocarps planted in the dipterocarp germplasm
and;2. To assess the leaf damage of insects and growth performance of dipterocarps planted in the
dipterocarp germplasm of VSU, Baybay City, Leyte, Philippines.
METHODOLOGY
Location of the Study Site
The study site was located at the Dipterocarp germplasm of the Terrestrial Ecosystems Division (TED),
Institute of Tropical Ecology and Environmental Management (ITEEM) field laboratory establishednear the Rainforestation Research Training Center (RRTC), Visayas State University-Main Campus,
Baybay City, Leyte, Philippines (Fig. 1). It has a total area of approximately 1.80 hectares planted to
30 species of dipterocarps. The seedlings were randomly planted with a planting distance of 5 meters x
5 meters, constituting 21 seedlings per species. Six species of dipterocarp trees are randomly selectedamong the species of dipterocarps planted in the germplasm.

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Fig. 1 The study site is located in Visca, Baybay City, Leyte, Philippines.
Assessment of Insect Fauna Associated with Dipterocarps
To identify the insect species inflicting damage on the trees and affected their growth performance,
visual observation and handpicking was used as an insect collection method (Fig. 2). This included
documenting all insects encountered and their damage among the sample plants. Insects observed were
photographed, documented, and were collected by handpicking. They were brought to the laboratory tovalidate and identify the type of damage to the tree species. Insects that were collected were identified
according to Order level. Sampling was done every morning (6:00 to 8:00 a.m.) and afternoon (3:00 to
5:00 p.m.). Samples were separately placed in jars with a killing agent and brought to the laboratory forprocessing, identification, and recording. Insects collected from each sample tree were separately kept
for counting and sorting (Fig. 3). The different arthropod species were classified according to thefollowing categories (Wall work, 1976 as cited by Ceniza, 1995). Insects can be considered“accidental” if the species occurs in 1-24 % of samples; “accessory” if the species occurs in 25-49% of
samples; “constant” if the species occurs in 50-74% of samples; and “absolute” if the species occurs in
75-100% of samples.

Fig. 2 Visual andhandpicking

Fig. 3 Sorting of insects in labeled plastic wares
and vials

Assessment of Leaf Damage Using the Bioleaf App
The study followed the methodology on insect herbivory assessment by Herve et al. 2017, which issuitable for this study. Measures were made on a fixed number of leaves per tree, choosing two facing
opposite branches at the top and two facing opposite branches at the middle, and two facing opposite
branches bottom of the tree crown. Ten leaves were randomly chosen at the top of a branch, ten at theⒸ ISERD

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middle, and ten leaves at the bottom per tree per species. If there were not enough leaves on the branch,
one could choose another branch at the same height. If there are not enough branches, one can use themain axis. Assessed leaves were different from one assessment to the next because some may fell,
some may have appeared, and damage may have accumulated. Bioleaf foliar analysis was used toestimate the damage (Machado et al., 2016), a professional mobile application to measure foliar
damage caused by insect herbivory, developed by a Brazilian team of researchers released in 2016. It
then estimated the defoliation percentage related to the total area using images captured from thecamera or loaded from the photo gallery. Pictures of the leaf images taken from the field were loaded
into the bioleaf app, automatically reading the injured leaf regions caused by insect herbivory andestimating the total area's defoliation percentage (Fig. 4).

Fig. 4 Process flow for the bioleaf foliar analysis
Growth Performance of Dipterocarp Species
Ten sample plants per species of dipterocarp were measured every three months. The basal diameter(mm) was measured at the base of the stem using a Vernier caliper. The sample plants' base wasmarked with white ink to ensure a permanent measurement point from the present to the following datacollection (Fig. 5). Meanwhile, the plant height (cm) was measured using a meter stick. It was takenfrom the stem's base up to the stem's tip (Fig. 6).

Fig. 5 Measuring the basal diameter

Fig. 6 Measuring the total plant hight

Data Encoding and Statistical Analysis
All data gathered were collated, encoded, and summarized using an electronic spreadsheet editor,
Microsoft Excel 2013. The data were analyzed using the Statistical Package for Social Science (SPSS
version 20). The mean of basal diameter and plant height variability were analyzed using the one-way
analysis of variance (ANOVA). Moreover, in a case where the significant variations at p ≤ 0.05 wereidentified, Tukey and Least Squares Differences (LSD) were carried out to compare means.
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RESULTS AND DISCUSSION
Insect Species Associated with Dipterocarp Species
Table 1 shows the list of insect orders associated with the dipterocarps. It included eight insects,
namely, Coleoptera, Diptera, Hemiptera, Heteroptera, Hymenoptera, Lepidoptera, Odonata Orthoptera.
These groups were composed of chewers and suckers, the major groups causing damage to thedipterocarp species.
Table 1 List of insect order associated with the dipterocarp species
Dipterocarp species

Insect orders with constancy class

Dipterocarpus alatus

Orthoptera-(Ab), Coleoptera- (A), Hemiptera- (A), Lepidoptera (Ab), Odonata (A)Hopea philippinensis

Orthoptera-(C), Coleoptera- (Ac), Hemiptera- (Ac), Lepidoptera (Ab)

Shorea almon

Orthoptera-(A), Coleoptera- (A), Hemiptera- (A), Lepidoptera (Ab)

Shorea assamica

Orthoptera-(Ab), Coleoptera- (Ab), Hemiptera- (Ab), Lepidoptera (Ab), Heteroptera(Ab), Diptera (C), Hymenoptera (Ab), Odonata (Ac)

Shorea malibato

Orthoptera-(C), Coleoptera- (Ac), Hemiptera- (Ac), Lepidoptera (Ab)

Shorea polita

Orthoptera-(C), Coleoptera- (A), Hemiptera- (A), Lepidoptera (Ab)

Note: Accidental 1-24% Ac- Accessory 25-49% C- Constant 50-74% Ab- Absolute 75-100%

Leaf Damage
The leaf damage samples of six dipterocarp species were shown in Fig. 7. The statistical analysis
results showed significant (p ≤ 0.05) differences in the leaf percentage damage among the six
dipterocarps species during the data collection 25 months after planting. Fig. 8 shows the results on theleaf percentage damage of dipterocarp species in the three sampling periods. Shorea assamica had thehighest leaf damage (8.68% ± 0.087), and Shorea almon had the least leaf percentage damage (2.57%± 0.087).

Fig. 7 Leaf damage samples of six dipterocarp
speciesFig. 8 Percentage damage of leaves of six
dipterocarp species
Note: Values in the figure with different letters (a-b)
designation across treatments during datacollection periods are statistically significant at p ≤0.05. N = 10 in total for all treatments per species
per period.

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Basal Diameter and Plant HeightThe different dipterocarp species' growth performance showed increments and variations after a 3month sampling period, 25 months after planting. Increments are used as a measure of performance inour forest stands for a particular period (Assmann, 1970). This was calculated as the difference
between the initial growth of the height and basal diameter of the plant at 22 months and its growth
after 3months and 6 months.The most significant growth increment was the Shorea polita (2.49 mm ±0.67) regarding basal diameter. At the same time, Shorea almon had the least growth increment (0.98 ±0.67). However, it can be seen from the results that regardless of the dipterocarp species, there was anincrease in the growth of the basal diameter between the periods of the first three months and six
months (Fig. 9). At least one species in every Dipterocarp genus differed in terms of basal diameter
among other species. There was a significant increase in dipterocarp species for the plant height after
six months of data collection, 28 months after planting (Fig. 10). There is evidence that at least one
species in each Dipterocarp genus differs in plant height among other species. Notably, Dipterocarpus
alatus (32.90 cm ± 0.19) had a considerable increment in plant height, while Shorea assamica (4.95 cm± 0.19) had the least increment, 28 months after planting.

Fig. 9 Basal diameter increment of six
dipterocarp species

Fig. 10 Height increment of six
dipterocarp species

Note: Values in the figure with different letters (a-b)
designation across treatments during data collection
periods are statistically significant at p ≤ 0.05. N =10 in total for all treatments per species per period.

Note: Values in the figure with different
letters (a-b) designation across treatments
during data collection periods are statisticallysignificant at p ≤ 0.05. N = 10 in total for all
treatments per species per period.

CONCLUSION
There are eight orders of insects associated with dipterocarps: Coleoptera, Diptera, Hemiptera,
Heteroptera, Hymenoptera, Lepidoptera, Odonata, and Orthoptera, showing significant damage on the
S. assamica. There was a significant difference (p ≤ 0.05) in the leaf damage among the six
dipterocarps species during the data collection 25 months after planting. Shorea assamica had the mostsignificant leaf damage, and Shorea almon had the least leaf damage. The different dipterocarp species'
growth performance showed significant increments (p ≤ 0.05) and variations after a 3-month sampling
period, 25 months after planting. In terms of basal diameter, the species with the most significant
growth increment was Shorea polita. At the same time, Shorea almon had the least growth increment.
Dipterocarpus alatus grow significantly (p ≤ 0.05), which had the largest increment for the plant height
despite the insect association, while Shorea assamica had the least increment. The assessment ofassociated insects is more relevant in assessing the damage of insects. Thus, it is suggested that afurther detailed study and analysis on the family and species level of other groups should be conductedto enhance the recent findings.

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ACKNOWLEDGEMENT
This study was supported by the Environmental Leadership and Training Initiative (ELTI) and Visayas
State University (VSU).
REFERENCES
Appanah, S. and Turnbull, J.M. 1998. A review of dipterocarps: Taxonomy. Ecology and Silviculture.
Assman, E. 1970. Principles of forest yield study: Studies in the organic production, structure, increment andyield of forest stands. 207-433.
Ceniza, M.J. 1995. Arthropod abundance and diversity in different ecosystems of Mount Pangasugan, Baybay,
Leyte, Philippines with special reference to the Coleoptera and Hymenoptera. PLITS, 13 (3), 65-71.
Coley, P.D. and Barone, J.A. 1996. Herbivory and plant defenses in tropical forests. Annual Review of Ecology
and Systematics, 27 (1), 305-335.
Corlette, R. and Primack, R. 2005. Dipterocarps: Trees that dominate the Asian rainforest. Arnoldia, 63 (3), 3-7.
DENR. 2017. Department of Environment and Natural Resources Administrative, Order No. 2017-11.
Dyer, L.A., Letourneau, D.K., Chavarria G.V. and Amoretti, S. 2010. Herbivores on a dominant understory shrub
increase local plant diversity in rainforest communities. Ecology, 91, 3707-3718.
IUCN. 2020. IUCN Red list of threatened species. Retrieved from https://www.iucnredlist.org
Jactel, H., Moreira, X. and Castagneyrol, B. 2017. Tree div net insect herbivory assessment. Retrieved fromhttps://treedivnet.ugent.be/protocols/TDN_InsectHerbivoryAssessment.pdf
Langenberger, G. 2006. Habitat distribution of dipterocarp species in the Leyte Cordillera: An indicator for
species–site suitability in local reforestation programs. Annals of Forest Science, 63 (2), 149-156.
Machado, B.B., Orue, J.P., Arruda, M.S., Santos, C.V., Sarath, D.S., Goncalves, W.N. and Rodriguez Jr, Jf. 2016.
Bioleaf: A professional mobile application to measure foliar damage caused by insect herbivory. Computers
and Electronics in Agriculture, 129, 44-55.
Maron, J.L. and Crone, E.E. 2006. Herbivory: Effects on plant abundance, distribution and population growth.
Proceedings of the Royal Society B: Biological Sciences, 273:25752584.
Norghauer, J.M. and Newbery, D.M. 2013. Herbivores equalize the seedling height growth of three dominant tree.
Statistical Package for Social Sciences (SPSS). 2011. IBM SPSS statistics for windows. Version 20.0. Armonk,
NY, IBM Corp.
The Plant List. 2013. Version 1.1. Published on the internet, Retrieved from http://www.theplantlist.org/Ⓒ ISERD

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Research article
erd

Spatial Variability of Soil Salinity and its Influence on Rice
Yield in Salt-Affected Areas using Remote Sensing Techniques
AUNG NAING OO*
Yezin Agricultural University, Phyu Campus, Bago Region, Myanmar
Email: dr.aungnaingoo@yau.edu.mm

LWIN BO BO THET
Department of Agricultural Engineering, Yezin Agricultural University, Myanmar
Email: lwinbobothet@yau.edu.mm
Received 1 March 2021

Accepted 16 May 2021(*Corresponding Author)

Abstract Soil salinity has become one of the major constraints to sustain crop production in
Myanmar, especially in the dry zone areas. Salinity stress and its spatial distribution has been auseful for crop monitoring. Unevenness in early crop growth stage can support to identify yieldlimiting factors such as soil salinity, nutrient availability, and soil moisture. Remote sensing
techniques have been used to collect reflectance numbers from crop canopies and to analyze thevegetative index (VI). VI has been related with percentage of ground cover, chlorophyll content
of plant, and nitrogen use efficiency. This study aims to monitor the influence of salinity stresson the growth, yield and chlorophyll content of rice by using remote sensing techniques. This
study was conducted in the salt-affected soils at Myittha Township, the central dry zone of
Myanmar during the rainy season of 2019. Electrical conductivity (EC) and chlorophyll content
in rice plant were collected at early growth and tillering stages of rice. Sentinel-2 satellite
imagery was used in the analysis for those two growth stages. Total grain yield of rice was alsomeasured. The acquired images analysis was implemented with ArcGIS 10.7 software to
calculate vegetative indices. The results showed that significant relationships were foundbetween plant chlorophyll content and the normalized difference vegetation index (NDVI)
values under different salinity levels. A relationship was also observed between NDVI and ricegrain yield. Identifying the spatial distribution of salinity stress using the spectral vegetation
indices would be effective for increasing rice yield in the salt-affected areas.
Keywords salinity stress, Sentinel-2 satellite imagery, vegetation index

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 tones (FAO STAT,2017). It is grown under various environmental situations in both upland and lowland rainfed
ecosystem. Crop production in Myanmar is affected by many factors such as soil salinization, drought,
and low soil fertility (Oo et al., 2017). Among them, soil salinization is an increasingly severe problem
in rainfed rice production.
In Myanmar, salt-affected soils are found in coastal and inland regions. Coastal salinity is affectedby seawater intrusion/ infiltration during flood resulting salt accumulation in the top soil in the summer
season. It is commonly occurred in Ayeyarwady, Yangon, Yakhaing and Taninthari regions. Inland
salinity is commonly happened in dry zone areas of the central Myanmar such as Mandalay, Magway
and Sagaing regions. Salinization in central dry zone is becoming a prominent abiotic problem
declining rice production which little or lack of attention was paid in the past (Swe and Ando, 2017).
They reported that sodic/saline soils have been settled in certain areas, because of the excessiveⒸ ISERD

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application irrigation for several years continuously. The excessive applications of irrigation water
elevated the ground water level sufficiently to increase salts concentration through evaporation. It isrelated principally to the presence of sodium carbonate and sodium bicarbonate in these specific areas.
Inland salinity or irrigation salinity is due to over-watering, seepage from irrigation channel, impaired
natural drainage and high water table. In the low land soil from those salt affected areas, high rate ofevaporation and evapotranspiration of rice crop increase the capillary transport of water and solutes
from the groundwater to the root zone. When there is a condition of no or negligible leaching of thesesalts, the soils will be affected with salts within a few years.
The nutritional monitoring of plants using remote sensing is a vital component of precision
agriculture (Liaghat and Balasundram, 2010). Remote sensing has received increased interest as a nondestructive tool for determining the nutrient status of growing crops due to the time and expense
involved with traditional soil and plant analysis. Remote sensor can estimate chlorophyll content andnitrogen status by their reflectance in the visible region of the electromagnetic spectrum (Thomas andGausman, 1977). By using remote sensing images, normalized difference vegetation index (NDVI) is
essential parameter for the vegetation growth of crops as well as assessing plant nitrogen content.
The concept of precision agriculture is based on the fact that crop productivity varies spatially andtemporally within a field, depending on soil, environment and operational activities. One direction inprecision agriculture research is to identify the correct management plot based on the variability inyield limiting factors within a field, and to implement optimal management practices for each plot. The
effects of salinity stress on crops are complex and it is difficult to interpret the results if investigates are
not designed carefully and if suitable measurements are not made (Negrao et al., 2017). Therefore,
classifying the soil salinity variability and its influence to crop yield are essential for farmers to reclaim
the salt-affected soils.
OBJECTIVE
This study was to determine the influence of salinity stress on the growth, yield and chlorophyll content ofrice by using remote sensing techniques.
MATERIALS AND METHODS
Study Area
In order to determine the influences of salinity stress on the growth and yield of rice, the experiment was
conducted in the salt-affected area at Hteinkangyi village in Myittha Township, Mandalay Division where issituated in the dry zone area of Myanmar during the rainy season of 2019. Geographical coordinates are
between 21°14’16.36” N latitudes and 96°8’23.88” E longitudes. Its elevation is 106-111 m above sea level.
The total area of the study site is 40 hectares. The study site has been affected by salinity for 20 years. The
study location map is shown in Fig. 1.

Field Data Collection
The field data was collected during the rainfed rice growing season of 2019. Apparent electricalconductivity was collected by using a portable electrical conductivity meter (Field Scout (FS) direct soil EC
meter) before the rice cultivation. Electrical conductivity (ECe) in saturated paste extracts was calculated byusing the formula (2.7FS+0.8).
Chlorophyll content was measured randomly from three mature leaves in each sampling point using a
Minolta SPAD-502 leaf chlorophyll meter at the early growth and tillering stages of rice. Rice grain yield
was recorded at harvesting. Pawsan and manawthuka varieties were cultivated in the study area.
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Vegetation Index (VI) Calculation
The multispectral images from Sentinel-2A Satellite were used in this study. Sentinel-2A has fourbands, namely red (R), green (G), and blue (B) and near-infrared (NIR) at 10 meter spatial resolution.
The Sentinel-2 image was acquired at the time of early growth and tillering stages of rice. The
normalized difference vegetation index (NDVI) was calculated for those two growth stages of rice.
This index is widely used for measuring crop health - a healthy plant will absorb visible light(especially blue and red), while the fortified leaf structure will reflect a high amount of NIR. NDVI
calculation is simply (NIR–red)/(NIR+red), returning values between -1 and 1, ranging from nonvegetated (water or barren) to healthy plants (Rouse et al., 1974; Tucker, 1979).

Fig. 1 Map of study area (Hteinkangyi Village)
Data Analysis
The collected data were examined by descriptive statistics to attain the minimum and maximum values,
mean, standard deviation (SD), coefficient of variation (CV) of each parameter. All statistical analyses
were implemented using Statistix 8.0 software and Excel program (2010). The spatial distribution
maps of soil electrical conductivity, SPAD values and rice grain yield were accomplished by ArcGIS
10.7 software.
RESULTS AND DISCUSSION
Spatial Variability of Soil Salinity and Rice Grain Yield
The spatial variability of soil salinity is presented in Fig. 2. Soil salinity was found at three levels as
2.0-3.9 dSm-1 (low salinity), 4.0-6.0 dSm-1 (moderately salinity) and 6.1-7.6 dSm-1 (high salinity). This
distribution map can help the farmers to manage or remediate their salt-affected soils accordingly tosoil EC level.
Rice grain yield can be varied as affected by soil salinity. The result showed that rice grain yield
ranged from 3.00 to 4.83 ton ha-1 with the mean value of 4.11 ton ha-1 (SD=0.39) and CV of 9.46%.
Spatial variability of rice grain yield in the study area was obvious (Fig. 2). This might be due toⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2021) 12-1

differences in soil salinity become limiting. Previous study (Oo et al., 2019) reported that salinity stresson the growth of tested rice varieties was found seriously in 7.5 dSm-1 when compare with 3.7 and 5.7
dSm-1 salinity values in the Hteinkangyi Village.
Chlorophyll Content of Rice Plant
Interpolated maps of chlorophyll meter (SPAD) values at the early growth stage and tillering stage ofrice in the salt-affected soils are presented in Fig. 3. The results of this study showed that chlorophyll
content was observed relatively large variation within a farmland at the early growth stage (Fig. 3a). It
ranged from 23.50 to 38.90 SPAD reading. At the tillering stage, it ranged from 28 to 43 (Fig. 3b). The
distribution maps of SPAD values showed that soil salinity affected the chlorophyll content in plant.
The plant under low salinity level had the highest chlorophyll content indicating that it may not beaffected by salinity. In contrast, the rice plant under the high salinity level produced the low
chlorophyll content which was due to limitations of chlorophyll synthesis (Santos, 2004) and it resulted
in increased reflectance of active light for plant photosynthetic (Clay et al., 2006) and decreased
reflectance in the near-infrared light (Cui et al., 2009; Yoder and Pettigrew-Crosby, 1995).

Fig. 2 Interpolated maps of salinity level and rice grain yield in the study area
(b)

(a)

Fig. 3 Interpolated maps of chlorophyll meter (SPAD) readings in the salt-affected soils of
Hteinkangyi Village (a) early growth stage and (b) tillering stageⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2021) 12-1

(a)

(b)

Fig. 4 NDVI maps for Sential-2 spatial resolutions at (a) early growth stage and (b)tillering stage of rice in the salt-affected soils of Hteinkangyi Village
Vegetation Index (VI)The VI maps were produced at the early growth stage and tillering stage of rice using Sentinel-2
satellite imagery. The maps are shown in Fig 4. The NDVI map demonstrated the vast variation at
early growth stage (Fig. 4a). The decreasing NDVI values at early growth stage may be attributed todecrease in the number of rice leaves. The NDVI values largely rise up at tillering stage (Fig. 4b). The
high NDVI values (0.90) were observed in the tillering of rice when compared with the early growthstage. Scattered vegetation or death of rice plant was found under high salinity soils, indicating that theeffect of salinity stress on rice is the shrinkage of leaf size, which leads to damage of the leaf, andlastly the death of the plant (Volkmar et al., 1998).
Relationships among ECe, NDVI, SPAD Values and Rice Yield
In this study, there was negatively significant relationship ECe and NDVI values for both growth stages(Table 1), indicating the higher the ECe the lower chlorophyll meter in rice plant. ECe values was
negatively significantly correlated with rice yield (p<0.000), indicating the higher the EC e the lower
rice yield. The SPAD value was positively associated with the NDVI values for both growth stages.
Rice yield was positively associated with the NDVI values and SPAD values for both growth stages.
These results showed that NDVI and SPAD value was a strong predictor of rice grain yield. The resultsof this study can be useable information for the farmers in the study area.
Table 1 Relationships among ECe, NDVI, SPAD values and rice yield at two different growthstages in Hteinkangyi Village during the rice growing season of 2019
Variable
ECe

SPAD
Yield

NDVI values
NDVI values
Yield
NDVI values
NDVI values
NDVI values
NDVI values
SPAD values
SPAD values

Regression equation

R2

p-valueAcquired growth stage

Y = -0.0547x + 0.7347
Y = -0.043x + 0.9014
Y = -0.2308x + 4.1096
Y = 0.0251x - 0.152
Y = 0.0294x - 0.2964
Y = 1.5546x + 2.3764
Y = 2.5674x + 1.28
Y = 0.0458x + 1.4948
Y = 0.097x - 0.6633

0.342
0.452
0.679
0.237
0.255
0.267
0.245
0.140
0.453

0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.003
0.000

Early growth
Tillering
Harvesting
Early growth
Tillering
Early growth
Tillering
Early growth
TilleringⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2021) 12-1

CONCLUSION
This research evaluated the salinity and its influence on rice in the salt-affected areas in Hteinkangyi
village, Myittha township, Myanmar employing with remote sensing technique and geographic
information system (GIS). The maps created by ArcMap 10.7 presented virtual concept of salinity
management system which could be useful for the farmers in the study area. The vast variability of soil
salinity was found. Soil salinity was affected on the vegetation development and yield of rice under thehigh salinity area.
Sensor values and rice grain yields had positively significant relationships at all growth stages.
The distribution maps are very applicable to farmers for reclaiming the salinity level to their farmlands.
The results showed that vegetative index could be useful in detecting the salinity stress in salt-affected
area. Based on this study, it could be suggested that the Sentinel-2 satellite imagery would be
beneficial for monitoring crop development and managing the salt-affected soils.
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
Clay, D.E., Kim, K., Chang, J., Clay, S.A. and Dalsted, K. 2006. Characterizing water and nitrogen stress in cornusing remote sensing. Agronomy Journal, 98, 579-587.
Cui, D., Li, M. and Zhang, Q. 2009. Development of an optical sensor for crop leaf chlorophyll content detection.
Computers and Electronics in Agriculture, 69, 171-176.
Food and Agricultural Organization (FAO). 2017. FAO STAT. FAO, Rome, Italy, Retrieved fromhttp://faostat3.fao.org/faostatgateway/go/to/download/Q/QC/E
Liaghat, S. and Balasundram, S.K. 2010. A review: The role of remote sensing in precision agriculture. American
Journal of Agricultural and Biological Sciences, 5 (1), 50-55.
Negrao, S., Schmockel, S.M. and Tester, M. 2017. Evaluating physiological responses of plants to salinity stress.
Annals of Botany, 119, 1-11.
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.
Oo, A.N., Ngwe, K. and Myint, N.O. 2019. Evaluation of salt tolerance for improved rice lines in terms ofagronomic parameters. International Journal of Environmental and Rural Development, 10 (1), 62-67.
Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W. and Harlan, J.C. 1974. Monitoring the vernal advancements
and retro-gradation (green wave effect) of natural vegetation. Greenbelt, MD: NASA.
Santos, C.V. 2004. Regulation of chlorophyll biosynthesis and degradation by salt stress in sunflower leaves.
Scientia Horticulturae, 103, 93-99.
Swe, K.L. and Ando, K. 2017. Rice production in salt affected areas of central dry zone, Myanmar. The
Proceedings of 10th Agricultural Research Conference of Yezin Agricultural University, Nay Pyi Taw,
Myanmar.
Thomas, J.R. and Gausman, H.W. 1977. Leaf reflectance vs. leaf chlorophyll and carotenoid concentrations foreight crops. Agronomy Journal, 69 (5), 799-802.
Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing
of Environment, 8 (2), 127-150.
Volkmar, K.M., Hu, Y. and Steppuhn, H. 1998. Physiological responses of plants to salinity: A review. Can. J.
Plant Sci., 78, 19-27.
Yoder, B.J. and Pettigrew-Crosby, R.E. 1995. Predicting nitrogen and chlorophyll content and concentrations
from reflectance spectra (400 - 2,500 nm) at leaf and canopy scales. Remote Sensing Environment, 53, 199211.
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Research article
erd

Evaluation of the Acceptability of Cashew Apple Jam in
Cambodia
SOKLY SORM
Graduate School of Agriculture, Tokyo University of Agriculture, Tokyo, JapanYOSHIKI MURAMATSU*
Tokyo University of Agriculture, Tokyo, Japan
Email: y-murama@nodai.ac.jp

DAIKI OKATokyo University of Agriculture, Tokyo, JapanYURI TANIKOKATokyo University of Agriculture, Tokyo, Japan

MASATAKA UCHINOTokyo University of Agriculture, Tokyo, JapanSHUKI MURAMATSU
Showa Women’s University, Tokyo, Japan

MOTOE SEKIDO
Yamanashi Gakuin Junior College, Yamanashi, Japan

TAKAHIKO NAKAMURATokyo University of Agriculture, Tokyo, JapanTORU NAKAJIMATokyo University of Agriculture, Tokyo, JapanEIICHIRO SAKAGUCHITokyo University of Agriculture, Tokyo, JapanSHOTARO KAWAKAMITokyo University of Agriculture, Tokyo, JapanCHIM CHAY
Royal University of Agriculture, Phnom Penh, Cambodia

MARI ARIMITSU
Institute of Environmental Rehabilitation and Conservation, Tokyo, Japan

MACHITO MIHARATokyo University of Agriculture, Tokyo, Japan
Received 24 December 2020

Accepted 16 May 2021

Ⓒ ISERD

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IJERD – International Journal of Environmental and Rural Development (2021) 12-1

Abstract This study aimed to evaluate the acceptability of cashew apple jams based on asensory evaluation test in Cambodia. Sensory evaluation tests for 2 types of cashew apple jams
were conducted in Phnom Penh, Cambodia, and in Setagaya, Japan, in September 2019 for 70
persons. The jam raw materials were cashew apple, sugar, low-methoxyl pectin, and citric acid.
These materials were mixed, dissolved, condensed, and prepared to 50 °Brix sugar content
while heating to approximately 80°C. The jam heated to 80°C was filled in a glass bottle. We
called this sample a hot-pack jam. The hot-pack jam was reheated at 90°C for 20 min in asterilization process. After 20 min, the jam was kept at room temperature for cooling withsterilization. We called the sample with the sterilization procedure a reheated jam. The color,sweetness, acidity, taste, flavor, jelly state, smoothness, and overall score of each sample were
evaluated using a five-point hedonic scale (1: hate, 2: dislike, 3: neutral, 4: like, and 5: like verymuch) in the sensory test. The hot-pack and reheated jams were tested by 40 panelists of
Cambodian students and faculty members at the Royal University of Agriculture, Cambodia(RUA), and Tokyo University of Agriculture, Japan (TUA). Thirty citizens evaluated only
reheated jam at a supermarket in Phnom Penh. Almost all panelists answered that the reheatedjam was sweeter than the hot-pack jam. Because the scores of both jams given by panelists
ranged from 3 to 4, the cashew apple jam was considered to be acceptable and had a possibility
to become a new processed food in Cambodia.
Keywords cashew apple, jam, sensory evaluation, acceptability

INTRODUCTION
New processed food made from local sites is expected to increase income for farmers and support their
independence. The cashew (Anacardium occidentale L.) is originally from Brazil and is also cultivatedin tropical areas (Honorato and Rodrigues, 2010; Oliveira et al., 2020). The cashew nut is a part of the
cashew fruit, which contains an edible pedicel called the cashew apple (Janick and Paull, 2008).
In Cambodia, the cashew plant is an industrial crop. The total yield of cashew nuts was 136,094
tons in 2018, and the largest cultivation areas in Cambodia were the provinces of Kampong Thom
(58,624 ha), Ratanakiri (30,459 ha), Kratie (27,761 ha), and Kampong Cham (24,364 ha) (MAFF/GDA,2018). However, only cashew nuts are commercialized; and the rest of the cashew fruits (cashew
apples) become wastes. Providing a way to utilize cashew apple will support the economy of the
agricultural industry. The cashew apple is rich in vitamin C (five times more than that in oranges) andminerals and is good for health (Akinwale, 2000). The cashew apple exhibits antibacterial properties,
treating gastritis and ulcers in the stomach and preventing scurvy (Melo et al., 2003). Cashew apples
can be eaten fresh, cooked in curries, used for fermented vinegar, consumed as alcoholic andnonalcoholic drinks, and used to make preserved food, chutneys, and jam (Ogunsina et al., 2008).
Codex Alimentarius International Food Standards (CXS 296-2009, 2017) reported that jam is the
product brought to a suitable consistency, made from the whole fruit, pieces of fruit, unconcentrated
and/or concentrated fruit pulp or fruit puree. Jam is made of one or more fruits and is mixed withfoodstuffs with sweetening properties with or without water. There are many reports about methods ofpreparing jam from various fruits and their sensorial attributes. Ajenifujah-Solebo et al. (2011)
examined the possibility of producing jam from black plums and evaluated its physicochemical
properties, nutritional properties, and consumer acceptability. Ho et al. (2020) studied the proximate
composition, physicochemical characteristics, and sensory evaluation of reduced-calorie belimbi fruit(Averrhoa belimbi) jam with maltitol.
The appropriate procedure for processing fruit and making jam is different from the characteristics
of using fruit. Jam in this study is processed from cashew apple pulp, sugar, low-methoxyl (LM) pectin,
and citric acid. Some jam products are sold in Cambodia, but they do not appear to be common,Ⓒ ISERD

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IJERD – International Journal of Environmental and Rural Development (2021) 12-1

especially for local Khmer. Sensory evaluation is necessary to grasp whether a new cashew apple jam
is preferred in Cambodia.
In this study, we investigated the acceptability of cashew apple jams based on a sensory evaluationin Cambodia. The sensory analysis of two types of cashew apple jams was conducted in Phnom Penh,
Cambodia, in September 2019 with 70 persons. The jam is a gelled ready-to-eat product and has many
uses, for example, bread, milk-product, sweets, and so on. The jam is easy to make without the needfor any special devices and skills. The jam also can keep at room temperature. Therefore, we conducted
the sensory evaluation of the cashew apple jam.
OBJECTIVE
The objectives of this study were 1) to survey Cambodian citizens regarding the sensory evaluation of
cashew apple jams and 2) to evaluate the acceptability for the sensory characteristics for cashew apple
jams.
METHODOLOGY
Materials and Product Process of Jam
Cashew (Anacardium occidentale L.) fruit (cashew apple and nut) was purchased in Kampong Cham
Province, Cambodia, and was stored in a refrigerator at approximately -18 °C until processing into
jams. The cashew apple jam was made from 400 g of cashew apple paste, 300 g of sugar, 3.4 g of LM
pectin, and 2.4 g of citric acid. Fig. 1 shows the production process of 2 types of cashew apple jams.
Frozen cashew
fruit

Pretreatment

Cashew apple
paste
Sugar 100 g

Water 100 g
Sugar 100 g
LM Pectin 3.4 g

AddHeat dissolution

Hot pack jam

Citric acidAdd 2 times

Boiling

Sterilization(90 °C, 20 min)Add

Boil until 50 °BX

Filling(Fill while hot)

CoolingReheated jam(Room temperature)

Fig. 1 Product process of 2 types of cashew apple jams: a hot-pack jam and a reheated jam
In the pretreatment of cashew apple paste, the frozen cashew fruit was thawed at room
temperature. Then, the seed (cashew nut) and peer were removed from the cashew apple. The peeled
fruit pulp (cashew apple) was crushed with a food processor for 2-3 min because there was substantial
fiber in the pulp, and it did not fall apart even when boiled. During heat dissolution, LM pectin was
mixed in 100 g of sugar in advance and dissolved in 100 g of boiling water. As pectin is difficult todissolve in water, it must be mixed with sugar beforehand. After heat dissolution, 400 g of cashewⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2021) 12-1

apple paste was added to the sugar and LM pectin solution. Keeping boiling this mixture, 100 g ofsugar was added to this mixture twice. The boiling process took a total of 6-10 min. This mixture wasboiled and concentrated up to 50 °Brix. After stopping heating, citric acid dissolved in a small amountof water was added to this concentrate. While hot, the jam was filled into a glass bottle up to 3-5 mmfrom the bottle lid. After closing the lid of the bottle, the bottle was turned upside down. We call thissample “hot-pack jam” in this manuscript. The hot-pack jam was sterilized for 20 min in hot water at atemperature of 90°C. We call the jam with the additional sterilization process “reheated jam”. After
sterilization, the reheated jam was cooled to room temperature.
Fig. 2 shows photographs of the hot-pack and reheated jams. Each bottle contained 90 g of jam.
Considering the sanitary conditions in Cambodia, the sterilization process was added to the hot-packjam. As a result, as shown in Fig. 2, the browning phenomenon occurred in the reheated jam. The
component composition of these jams was the following: moisture, 47.1%; protein, 0.40%; fat, 0.1%;
ash, 0.2%; carbohydrate, 52.3% (sugar, 50.6%; dietary fiber, 1.7%).

Reheated

Hot-pack

Fig. 2 The hot-pack and reheated jams
Sensory Evaluation
Sensory evaluation plays an important role in assessing the acceptance of new food products andpreferences for different consumers (Banaś et al., 2018). The color, sweetness, acidity, taste, flavor,
jelly state, smoothness, and overall score of each sample were evaluated using a five-point hedonic
scale (1: hate, 2: dislike, 3: neutral, 4: like, and 5: like very much) at the sensory test (preference type).
The hot-pack and reheated jams were tested by 40 panelists consisting of Cambodian students andfaculty members at the Royal University of Agriculture, Cambodia (RUA), and Tokyo University ofAgriculture, Japan (TUA). These panelists are represented as the RUA group in this research. Thirty
citizens evaluated only reheated jam at a supermarket in Phnom Penh, Cambodia. The time and spacefor sensory evaluation of jam were limited in this case. The reheated jam received favorable evaluation
results in the RUA group. The reheated jam is safer than the hot-pack jam. Therefore, only reheatedjam was evaluated at the supermarket. The panelist ages of the RUA group and citizens were 19-55
years (average: 22) and 18-64 years (average: 27), respectively.
RESULTS AND DISCUSSION
The sensory evaluation results of both cashew apple jams from the RUA group are represented in Fig.
3. The average scores of sensory evaluation items for the hot-pack jam and the reheated jam ranged
from 2.7-3.0 and 3.3-3.9, respectively. The reheated jam had higher scores than the hot-pack jam
except for the color. The average values of all evaluation items for the hot-pack and reheated jams
were tested for differences between population means (paired t-test), and there was a significant
difference (5% significance level) between all items, except for acidity. Although the average value ofⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2021) 12-1

the color for the reheated jam was 3.3, this value was lower than that for the hot-pack jam (3.7). The
occurrence of the browning phenomenon in the reheated jam affected this result. The average scores of
the reheated jam were higher than those of the hot-pack jam except for the color. Because the score of
the reheated jam for color was over 3: neutral, we considered that the color of the hot-pack jam wasmore favorable than that of the reheated jam but not unacceptable in the RUA group. The average
sweetness, taste, and overall evaluation scores for the reheated jam were nearly 4.0, and the reheated
jam received a high evaluation from the RUA group, which means that the reheated jam was morefavorable. This result indicates that the addition of heat to the cashew apple jam after filling in thebottles influenced the final product characteristics and sensorial acceptance.

Fig. 3 Sensory evaluation results of the hot-pack and reheated jams in the RUA group
Fig. 4 shows the sensory evaluation results of the reheated jam from citizens, with a comparison
to the results for the RUA group. The average score of the sensory evaluation items by the citizens
ranged from 3.3 to 4.0, which was higher than that for the RUA group. Compared to the RUA group,
the citizens highly evaluated the color of the reheated jam, and there was a significant difference (5%significance level) between the evaluation results of the color for both groups. A t-test for differences
between population means for each evaluation item of the reheated jam (significance level of 5%)
showed no significant differences between citizens and the RUA group, except for color. The averagescore of the overall evaluation for citizens was 4.0, and citizens also gave the reheated jam highevaluation scores.
Almost all panelists answered that the reheated jam was sweeter than the hot-pack jam. One of thepossible reasons for this result is that the sweetness of the reheated jam increased because the sourness
decreased due to volatilization caused by heating (sterilization). The cost of cashew apple jam made inJapan will be approximately 80 Japanese yen, including the glass container and the lid, based on theassumption that cashew apples can be obtained for free, the loss of materials and/or products during
processing is negligible, and 600 units of jam (100 g per container) per 1 batch can be made. Because
RUA group and citizens gave relatively high evaluation of the hot-pack jam and reheated jam, theseⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2021) 12-1

cashew apple jams were acceptable and had the possibility to become new processed foods in
Cambodia. The sweeter jam was preferred in Cambodia. For improved food safety, we recommend
adding sterilization to the jam-making process. The potential of cashew apples in jam making, productdevelopment, reduction of postharvest losses, creation of job opportunities, and so on would contributeto Cambodia's development.

Fig. 4 Comparison of the sensory evaluation results of the reheated jam between the RUA
group and 30 citizens
CONCLUSION
A sensory evaluation test of two types of cashew apple jams was conducted in Phnom Penh, Cambodia,
in September 2019 with 70 persons. During the production of the hot-pack jam, the jam’s raw materials
were mixed, dissolved, condensed, and prepared to 50 °Brix sugar content while heating up to
approximately 80 °C, and this jam heated to 80°C was filled in a glass bottle. Another jam, i.e.,reheated jam, included a sterilization procedure to the hot-pack jam preparation process. The color,sweetness, acidity, taste, flavor, jelly state, smoothness, and overall score of each sample were
evaluated using a five-point hedonic scale (1: hate, 2: dislike, 3: neutral, 4: like, and 5: like very much)for the sensory test (preference type). Both jams were evaluated relatively highly by the 70 panelists.
The cashew apple jams were acceptable and had the possibility becoming new processed foods in
Cambodia. Such a development will reduce postharvest losses, which have been a challenge to
Cambodia's food technologists, as well as help create job opportunities and contribute to Cambodia's
development.
REFERENCES
Ajenifujah-Solebo, S.O. and Aina, J.O. 2011. Physico-chemical properties and sensory evaluation of jam madefrom black-plum fruit (Vitex doniana). African Journal of Food, Agricultural, Nutrition and Development, 11Ⓒ ISERD

40

IJERD – International Journal of Environmental and Rural Development (2021) 12-1(3), 4782-4784.
Akinwale, T.D. 2000. Cashew apple juice: Its uses in fortifying the nutritional quality of some tropical fruits.
European Food Research Technology, 211, 205-207.
Banaś, A., Korus., A. and Korus, J. 2018. Texture, colour, and sensory features of low-sugar gooseberry jams
enriched with plant ingredients with prohealth properties. Journal of Food Quality, 2018, 1646894.
CXS 296-2009. 2017. Standard for jams, jellies and marmalades. Codex Alimentarius International FoodStandards, 2-10.
Ho, L.H., Irisha Yasmira, S.R.R. and Norlia, M. 2020. Proximate composition, physicochemical characteristics
and sensory evaluation of reduced-calorie belimbi fruit (Averrhoa belimbi) jam with maltitol. Food Research,
4 (5), 1545-1553.
Honorato, T.L. and Rodrigues, S. 2010. Dextransucrase stability in cashew apple juice. Food BioproductsTechnology, 3 (1), 105-110.
Janick, J. and Paull, R.E. 2008. The encyclopedia of fruit and nuts. CAB International, Wallingford, UK.
MAFF/GDA. 2018. Annual statistics report of general department of agriculture, Cambodia. Ministry ofAgriculture, Forestry and Fisheries, Cambodia. Retrieved from http://www.maff.gov.kh
Melo-Cavalcante, A.A.G., Rubensam, J.N., Picada, E.G. da Silva, Moreira, F.J.C. and Henriques, J.A.P. 2003.
Mutagenicity, antioxidant potential and anti-mutagenic activity against hydrogen peroxide of cashew(Anacardium occidentale L.) apple juice and cajuina. Env. Mol. Mutagen, 41, 360-369.
Ogunsina, B.S. and Lucas, E.B. 2008. The development of a manually operated cashew juice extractor. Agri. Eng.
International: CIGR Ejournal, 10, Manuscript FP 07 037.
Oliveira, N.N., Mothé, C.G., Michelle, G.M. and Leandra, G.O. 2020. Cashew nut and cashew apple: A scientificand technological monitoring worldwide review. Journal of Food Science and Technology volume 57, 12-21.

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Research article
erdFood Service Industry Development and its Effect toDistribution of Fresh Agri-products in Vietnamese Suburban
I
Area
7
d
e
n
FUMIE TAKANASHI*tTokyo University of Agriculture, Tokyo,
Japan

Email: ft207429@nodai.ac.jp
4
Accepted
16 May 2021 (*Corresponding Author).
7
Abstract This research aims to
2 uncover how the emerging catering industry contingent toReceived 31 December 2020

deindustrialisation changes the value chain of fresh agri-product in the Vietnamese suburban
area. The results revealed that byentering both international and local businesses, the cateringw
industry is competitive. Firstly, while the catering business in the North are small and operateoon a local level those operating in the South are consolidated enterprises as they have differentrmarket conditions. Secondly, Fresh food ingredients provide direct contract with producers ordwholesalers. In the south, catering companies tend to contract food wholesalers to purchase inbulk and distribute them to theirs branch preparation kitchens located in surrounding provinces.
Third, the value chain created by catering comapny could make transactions and the volume of
products handled concentrated, this could extend the transport distance of produce. However,the vertical coordination for the entire chain is inadequate. Furthermore, contract transactions
are limited to only part of the chain, and the requirement for quality control is weak.
Keywords food service industry, fresh vegetable and fruits distribution, industrialization,
suburban area, Vietnam

INTRODUCTION
The food system in developing countries has transformed rapidly in the past several decades (Reardon
et al., 2019). The two main themes are in trends regarding food systems in developing countries. These
themes include, exports and imports with trade liberalization and globalization, and transformation in
the food systems (Reardon, 2015). Consolidation and globalization of supermarket chains and its affecton small holders are also examined in many countries (Berdegué et al, 2005; Minten et al., 2009;
Ruben et al.,2007; Wang et al.,2009). The supermarket plays an important role in food production with
their ‘own brand’ products and the setting of safety, quality and manufactured food. The value chain is
aimed at quality control of food governed by global retail chains effect on all the actors in the chain,from the input suppliers to the farmer and the food retailer to the consumer (Burch et al., 2013).
Furthermore, the transaction system is changed from spot markets to contract transaction, foodproducts are standardised and the area of food supplied is geographically expanding. The modern value
chain offered by supermarkets are gaining the attention of scholars, as well as detailed research andanalysis have been conducted (Maruyama and Trung, 2007; Mergenthaler et al., 2009; Moustier et al,.
2010; Ruben et al., 2007).
In contrast, research regarding the middle stream (processing, wholesaling and storage) indeveloping countries are limited. Reardon (2015) reviled middle-stream growth and transformation,
both in a “modern revolution” with large and often foreign companies, but also a “quiet revolution”
with a small and medial enterprise and a substantial investment made by them. However, the foodservice industry, especially in rural and suburban areas, has recently begun rising due to urbanizationand industrialization, which has not been considered in previous studies.
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With the rapid growth in the industrial zone in rural and suburban Vietnam, the food service
industry providing meals for labourers working in factories is emerging. With the development of theindustrial and rural labour markets, non-farm labour is rising continuously. In 2005, the number oflabourers in the country working in the primary sector was 23,563,000 (55.1%), with the number oflabourers in manufacturing numbering 5,031,000 (11.9%). However, the number of people working in
the primary sector dropped to 18,831,000 (34.4%), while the number of labourers in manufacturing
increased to 11,287,600 (20.6%) in 2019. Employers in the industrial sector (factories) contracted bycatering companies provide one to two meals per working day for employees as welfare support. While
supermarkets are suspended in relatively small channel for Vietnamese lifestyle (Sigrid C.O.
Wertheim-heck et al., 2015), as the emergence of the catering industry’s contingency toindustrialization and food demand growth in suburban area, the value chain of food is graduallychanging. Nevertheless, the framework of catering companies providing meals to the factories, how
they deal with food suppliers and how food value chains are restructured remains a matter of
uncertainty.
OBJECTIVE
This research aims to uncover how the emerging catering industry is industrialized and changes in thevalue chain of fresh agri-product (fruits and vegetables) in the Vietnamese suburban area.
METHODOLOGY
A semi-structured interview was conducted with 15 catering companies (3 international and 12 local
companies), which supply meals to factories in the industrial sector in northern and southern Vietnam,
particularly in Hanoi City, Hai Duong Province, Ho Chi Minh (HCM) City and the Tien Giang
Province. In addition to Hanoi and HCM, the centre of industrialization for North and South Vietnam,
Hai Duong and Tien Giang Province, respectively, are used as a fresh food producer for the former
cities, which are investigated to clarify changes in agri-food distribution.
This research answers the following questions:
1) What are the general characteristics of these catering companies and business conditions?
2) How do they procure and control the quality of fresh agri-products as ingredients?
3) How does the development or modernization of the food service industry affect agricultural
distribution in the surrounding areas of the industrial sector?
Sample companies are selected due to introduction by department of health in each province andcity, and the interviews were conducted in 2014 for North Vietnam and in 2019 for South Vietnam.
RESULTS AND DISCUSSION
General Characteristics of Catering Companies Supply Meals to Factories at Industrial Zone
Table 1 outlines each of the catering companies, with 15 companies divided into 3 groups by the scaleof business. Companies that provided over 20,000 meals/day were categorised as large scale, those thatprovided 10,000 to 20,000 meals/day as middle, and 10,000 meals/day as small scale. The size ofcatering companies varied, the largest cater (L1) provides 60,000 meals/day and has 600 labourers,
while the smallest (S5) distributes only 200 meals/day.

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Most of informants were started their business in the 2000s, especially in 2011-2015, when
foreign direct investment increased1. The largest company (L1) are jointly invested with a Japanesecatering company and the Vietnamese local company. Their Headquarter is in HCM, then spreading
their business into the north. Several former food wholesalers (L3, L4) started their catering business.
Local restaurants have also entered the catering industry, but their business scale is relatively small (S1,S2 and S4). Middle scale companies are comprised mostly of those that left other catering companies
to develop their own business. According to the informants, companies in the catering industry arecontinuously merging and splitting.
Two types of meal preparations were observed. These include those that set up the facilities2 to
prepare meals on spot, and those that prepare meals at a central kitchen location and then deliver themto each customer in the industrial sector. While the latter is recommended for hygienic reasons, most ofcompanies could adapt to either style, depending on their customers’ needs. Large catering companies
with facilities (L1, L3, L4 and M3) and small catering companies which distribute a small number ofmeals/day (S2, S4, S5) tend to prepare these in their own kitchen and then deliver them to each factory.
Of the 15 catering companies, 4 of them invested in a central kitchen that met ISO and HACCP
standards, yet quality certification is not popular for small scale companies. Furthermore, unstableannual contracts made middle and small scale catering companies hesitate to invest in new facilities.
Table 1 Outline of sample catering companies

Table 1 Outline of sample catering companies
Large scale

Middle scale

Small scale

L1

L2

L3

L4

M1

M2

M3

M4

M5

M6

S1

S2

S3

S4

S5

Location

HN

TG

HCM

HCM

HD

HCM

HCM

HCM

TG

TG

TG

HD

TG

HD

TG

Year established

2013

2011

1998

2011

2008

2017

2003

2015

2004

N.A.

2007

2014

2009

2014

2011

Num. of branches

2

16

5

4

5

7

2

8

4

5

2

1

20

1

background

JP+V

Num. ofmeals(1,000M/day)
note

N.A.

WS

WS

N.A.

600

N.A.

350

55

400

130

2500

60

13

20

20

15

13

11

HACCP,ISO2000

ISO22000

N

Num. of employees

split

ISO2200

out

FP

split

1
Sub

N.A.

N.A.

FS

FS

N.A.

FS

70

N.A.

N.A.

N.A.

21

N.A.

22

N.A.

9

6-11

5

1.9

1.5

1

0.6

0.2

out

con.

ISO9001,BRC,
HACCP* NH=Hanoi, TG=Tien Giang, HCM=Ho Chi Minh, HD=Hai Duong**N.A.=no answer, WS=Whole sealer, FP=Food Processor, FS=Food Service (Restaurant)***Shaded companies are in north.
Source: field survey in 2014-2019

As Table 2 shows, the contract between catering companies and customers (factories) are typicallyannually arranged3. Contract sometimes broken, and often not renewed. It made their business unstableand flexible so large companies often outsource meal preparation to local small catering companies. In
addition to existence of countless competitors, unstable business condition caused caters to continuallymerge and split.
Contract unit prices vary widely from 12,000-40,000 Vietnamese Dong (VND) per meal (Table 2).
As explained next, the food safety requirement for industrial workers meals are not high. This
minimum safety standard makes catering companies provide reasonably priced meals. According to theinterview with department of Health, the average price for primary school lunch in HCM was 35,000
1 Value of foreign direct investment to Vietnam was $428.5 million (US) in 1991, which increased to $2.4 billion (US) in

2000 and $17.5 billion (US) in 2017.
2 Depending on the contract, facilities are invested in by either the customers or catering companies. According to theinformant, it costs 1.5-2.0 billion VND for the facility with capacity for 1,000 meals/day.
3 It could also be every 3 months, 6 months, 12 months, 18 months or more. However, one year is the most common period

for the contract.
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IJERD – International Journal of Environmental and Rural Development (2021) 12-1

(VND) per meal, while the average price the informant provided for the factory was 15,800 (VND) per
meal. In most cases, the meals that catering companies provide for factories are segregated from those
prepared for school meals because the quality required for both differ greatly.
Table 2 Contents of contracts

Table 2 Contents of contractsNum.
customers

L1

L2

Large
L3

L4

M1

M2

Middle
M3
M4

M5

M6

S1

S2

Small
S3

S4

S5

N.A.

N.A.

50

35

N.A.

7

N.A.

8

4

5

1

3

1

1

1

Fac.,
office,school
15
40

Fac.,
schoolSchool,fac.

Fac.

office

Fac.

Fac.

Fac.

Fac.

Fac.

Fac.

Fac.

School, Fac.

20
35

12
15

15
30

20
30

14
20

20

16.5

15

14
17

15

13
-20

15

Main
customer.

Fac.

Fac.

Unit price(1,000VN
D)

18
20

N.A.

source: field research in 2014-2019

Supply Chain of Ingredients – Vertical CoordinationTwo main routes: contract with suppliers and spot purchasing, were observed for ingredient food(especially fresh food) procurement.
Table 3 Purchasing fresh produce

Table 3 Purchasing fresh produce
Large
L1
Supplier
Contractor*
required
standard

Pro
kitche
n
own
Stan.

L2
W
S
H
Q
―

L3

L4

WS

WS/Pro

HQ
－

HQ
V-GAP

M1

M2

Pro

Coop

kitch
en
own
Stan.

HQ
own
Stan.

Middle
M
M4
3
W
WS
S
H
HQ
Q
―

―

Small
M5

M6

Coop
kitche
n
VGAP

S1

S2

S3

WS

WS

Coop

WS

WS

Pro

HQ

N.C
.

N.C.

HQ

N.C
.

N.C
.

―

―

―

―

―

―

S4

S5

*N.C.=no contractsource: field research in 2014-2019

1) Direct contract with producers/producers’ coop (L1, M1, M2, M5, S2 and S5):
Catering companies purchase food directly from producers or producers’ cooperatives which obtained
the Food Safety and Hygiene Certification (FSHC) located near the preparation spot. The transaction
volume was relatively small and transportation capabilities were limited. Each kitchen had theinitiative to find suppliers, purchase process and maintain quality control. This purchasing system tends
to be employed primarily by catering companies in the north rather than the south. There are threeimportant reasons that direct purchasing is more common in north than in the south. Firstly, the meals
provided each day are limited since the industrial zone in the north is not as developed andconcentrated as in the south. Secondly, the consolidation of transactions are difficult because the food
wholesale business is also not well developed to provide standardised ingredients for preparation
locations. Lastly, even though catering companies can find food wholesalers with well-developed
networks, it is difficult to purchase from a distant area because the cold chain is not maintained.
2) Purchase from suppliers (L2, L3, M3, M4, M6, S1, S3 and S4):
Creating contracts with food suppliers are more prominent in the south. Several large catering
comapnies (L2, L3, L4, M3, M4 and M6) deal with food suppliers that are based on the Binh Dinh,
Hoc Mon and Tu Duc wholesale markets4. They aggregate orders for all their branch kitchens locatedin southeast and Mekong delta regions, then indicate suppliers to deliver to each kitchen. However, insome cases, each kitchen could be a contractor.
4 The Binh Dinh market is the largest wholesale market and mainly provides fish and fruits from the Mekong Delta. The Hoc

Mon market is in the west side of the city, mainly supplying meat. Finally, the Tu Duc market located in the east part of
HCM accepts vegetables from the central highland to distribute to the entire city.
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Using quality controlled or certified produce is not popular for caters. The minimum level ofsafety standard officially set by the government, only requires a FSHC provided by the department of
Health for each province. Furthermore, it supports caters provide reasonably priced meals and are
discouraged from forming tight relations with suppliers. In most of cases, the contract unit price is notaffordable to prepare certified products. While the response to their customers mainly comes fromoverseas, several catering companies (L4 and M5) supplying meals with certified ingredients havespecial practices for production (Good Agricultural Practice (GAP)), and tracing their products origin.
The companies, L4 and M5 needed to find producers who could produce V-GAP form a contract withthem to ensure quality control. Furthermore, L1, M2 and M2 have their own standard for purchasing
food. Caters’ behaviour for purchasing is determined by customers (factories) attribute for workers
welfare. However, most of the enterprises in the Vietnamese industrial sector do not pay close attention
to it.

Impact of the Value Chain on Agricultural Produces
The development of such large scale catering in the south could be the changing value chain of
agricultural products in the region. Food for urban areas has been supplied from surrounding
production areas, and it has been clarified that the supply chain of fresh food in Vietnam was scatteredand relied on small scale distribution actors even in the 2000s (Cho, 2001). Delays of infrastructuraldevelopment, development of commercial capitals and procedure cooperatives, and the gradual growthof urban food demand due to the slow influx of migrants to cities have hindered the changes of
distribution systems. For instance, the industrial park surveyed by the Tien Giang Province is an area
where agricultural production is carried out and it used to be relatively large vegetables supplier not
only in Tien Giang, but also for cities like HCM and Bien Hoa5. They were suppliers of food to urban
area and to the local market. Food demand in Tien Giang was supplied by local produce. Thus, when alocal restaurant provides meals for workers in the Tien Giang province, locally produced food from thelocal market could be prepared. However, developing large scale catering that has a headquarters in
HCM employ different methods for efficient procurement of their ingredients. They purchase in bulk at
the wholesale market in HCM then deliver the ingredients to each preparation kitchen. In the former,
the food system is completed within the region, while in the latter, agricultural products produced in
the region are transferred to the HCM market where they are purchased and distributed by a large
collector, and then transferred again to the Tien Giang Province.
On the other hand, since the demand for quality food is not strong, development of the catering
industry did not promote vertical integration of the whole value chain. For this reason, contracts
between catering and wholesalers, which obtained FSHC, is popular.
CONCLUSION
This research empirically investigated how the emerging catering industry impacted value chain of
fresh food in the Vietnamese suburban area. The results suggest that first, both international and local
enterprises have entered the catering industry. In northern area, since industrial sectors are
geographically sparse, catering companies are also scattered, and meals are mostly prepared by local
small businesses so that the impact to conventional procurement value chain is limited. While in thesouth, since large industrial sectors invested more, catering companies developed and merged toprovide large number of meals to more extensive places. Second, the fresh food ingredients areprovided by producers, or wholesalers. In the south, more catering companies tend to contract food
wholesalers to purchase bulk amounts then distribute them to their branch preparation kitchens located
5 Central city of Binh Duong province, where a lot of industrial parks are concentrated.

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in surrounding provinces. Finally, the value chain created by caters could make transaction and volumeof fresh product handled more concentrated and extend the transport distance of produce. However,
because of low requirement for quality, the value chain is not integrated and coordinated strongly.
ACKNOWLEDGEMENTS
This work was supported by JSPS KAKENHI, Grant-in-Aid for Young Scientists (B), JP17K15337.
REFERENCES
Berdegué, J.A., Balsevich, F., Flores, L. and Reardon, T. 2005. Central American supermarkets’ private standardsof quality and safety in procurement of fresh fruits and vegetables. Food Policy, 30 (3), 254-269.
Burch, D., Dixon, J. and Lawrence, G. 2013. Introduction to symposium on the changing role of supermarkets inglobal supply chains: From seedling to supermarket: Agri-food supply chains in transition. Agriculture andHuman Values, 30 (2), 215-224.
Cho, K. 2005. Vietnamese agriculture and rural society under market-oriented economy, Tsukuba-Shobo (inJapanese).
Maruyama, M. and Trung, L.V. 2007. Supermarkets in Vietnam: Opportunities and obstacles. Asian Economic
Journal, 21 (1), 19-46.
Mergenthaler, M., Weinberger, K. and Qaim, M. 2009. The food system transformation in developing countries:
A disaggregate demand analysis for fruits and vegetables in Vietnam. Food Policy, 34 (5), 426-436.
Minten, B., Randrianarison, L. and Swinnen, J.F.M. 2009. Global retail chains and poor farmers: Evidence from
Madagascar. World Development.
Moustier, P., Tam, P.T.G., Anh, D.T., Binh, V.T. and Loc, N.T.T. 2010. The role of farmer organizations in
supplying supermarkets with quality food in Vietnam. Food Policy, 35 (1), 69-78.
Reardon, T. 2015. The hidden middle: The quiet revolution in the midstream of agrifood value chains indeveloping countries. Oxford Review of Economic Policy, 31 (1), 45-63.
Reardon, T., Echeverria, R., Berdegué, J., Minten, B., Liverpool-Tasie, S., Tschirley, D. and Zilberman, D. 2019.
Rapid transformation of food systems in developing regions: Highlighting the role of agricultural research &innovations. Agricultural Systems, 172, 47-59.
Ruben, R., Boselie, D. and Lu, H. 2007. Vegetables procurement by Asian supermarkets: A transaction cost
approach. Supply Chain Management, 12 (1), 60-68.
Sigrid, C.O., Werheim-heck, S.V. and Gert, S. 2015. Food safety and urban food markets in Vietnam: The needfor flexible and customized retail modernization policies. Food Policy, 54, 95-106.
Wang, H., Dong, X., Rozelle, S., Huang, J. and Reardon, T. 2009. Producing and procuring horticultural crops
with Chinese characteristics: The case of Northern China. World Development.

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Research article
erd

The Influence of Packaging Materials and Storage Duration on
Seed Quality of Sesame (Sesamum indicum L.)
HNIN THIDA NYO*
Department of Agriculture, Ministry of Agriculture, Livestock and Irrigation, Myanmar
Email: hninnyo@gmail.com

NYEIN NYEIN HTWE
Department of Agricultural Extension, Yezin Agricultural University, Myanmar

KYAW KYAW WIN
Pro-rector, Yezin Agricultural University, Myanmar
Received 1 January 2021

Accepted 17 May 2021(*Corresponding Author)

Abstract The process of seed deterioration can lead to some physiological changes, such as aprogressive decrease in germinability, an increase mean time for germination, an increase in thenumber of abnormal seedlings and a subsequent lower tolerance to adverse storage conditions.
This study was conducted to determine the seed quality losses associated with the use ofdifferent types of packaging materials used for storage under farm condition. The study was
conducted in collaboration with sesame farmers in Pwintphyu Township, from August 2016 to
March 2017 (pre-monsoon seed storage) and in Pakokku Township, from January 2017 toAugust 2017 (post-monsoon seed storage). The sesame seeds were stored in the farmers’ houses
using two types of packaging materials; woven polypropylene bags and pioneer super bags. The
data were recorded before storage and at two-month intervals during the eight month storage
period. The results show that the germination percentage and germination index means of the
pre-monsoon seeds stored in woven polypropylene bag are significantly higher than for thosestored in super bags. However, the packaging material used has no significant influence on thegermination percentage and germination index of post-monsoon sesame seeds. In regard to
storage duration, there was significant variation in the germination percentage and germinationindexes depending on storage method for both pre-monsoon and post-monsoon seeds. Although
the seedling vigour measures of index I and II for pre-monsoon seeds stored in woven
polypropylene bag are significantly higher than for those of seeds stored in super bags, there isno difference in seedling vigour indexes I and II for post-monsoon seeds with the use ofdifferent packaging methods. However, the seedling vigour indexes I and II of both pre- andpost-monsoon seeds varied significantly with duration of storage. Germination percentage andgermination indexes are different for the two packaging materials examined, for pre-monsoon
sesame storage. The germination percentage and germination indexes of stored seeds in superbags are significantly lower than that in woven polypropylene bags, whereas, this effect was notobserved in post-monsoon sesame. Black sesame cultivated in both pre-monsoon and postmonsoon seasons has a different dormancy period, and any effect of packaging materials on thisdormancy release time is not evident, for both seasons. Therefore, hermetic (super bag) storage
can be used in post-monsoon sesame without adverse effects on germination. However, without
further research, hermetic storage appears to not be suitable for pre-monsoon sesame, due to its
negative effect on germination.
Keywords sesame, germination percentage, germination index, seedling vigour indexⒸ ISERD

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INTRODUCTION
A seed’s storage environment greatly influences the period of seed survival. The type of packaging
used during the storage of seeds helps in lessening the speed of deterioration by maintaining the initialmoisture content of the seeds stored, and by diminishing the seeds’ respiration rate (Brooker et al.,1992). Longevity of seed viability in storage is influenced by the initial quality of the seed stored aswell as the storage conditions. However, irrespective of initial seed quality, unfavourable storage
conditions, particularly air temperature and air relative humidity, contribute to acceleration of seed
deterioration in storage (Walters et al., 2010; Hansen, 2011). Maintaining seed viability and vigour
during storage also depends on the type of packaging employed. Seeds in packaging which enables theexchange of water vapor with the environment can absorb water under high relative humidity, easily
decaying seeds (Filho, 1998).
OBJECTIVESThe study was conducted to determine losses in seed quality associated with the use of different typesof packaging materials used during storage and under farm conditions.

METHODOLOGY
Experimental Sites and Design
The study was conducted in collaboration with the sesame farmers in Pwintphyu Township, during theperiod August 2016 to March 2017 and in Pakokku Township from January 2017 to August 2017, withboth of these townships situated in Magway Region, Myanmar. The black sesame seeds (Samou Nei)
were stored in the farmers’ houses using two types of packaging materials, woven polypropylene bags
and pioneer super bags. The woven polypropylene bag is permeable bag and pioneer super bag is nonpermeable poly-ethylene bag with thickness of 0.078 mm. The pioneer super bags (which consists of a
two layer bag, with a bag inside a polypropylene outer), and as much air as possible was removed fromsuper bag before it was taped closed. The bags were stored together with other bags using different
storage methods. This storage treatment was undertaken in the houses of six farmers in each township.
The data were recorded before storage and at two-month intervals during the eight month storage
period. Germination tests (germination percentage, germination rate), vigour tests (seedling vigour
index I, seedling vigour index II) were conducted to determine seed quality.
Germination Percentage
The germination test was performed using the top paper method. Four replications of one hundred
seeds, randomly distributed on wet filter paper in petri dishes 9 cm in diameter, were examined. Eachdish was placed into an incubator set to a constant temperature of 25°C throughout the testing period.
The germinated seeds (2 mm radicle elongation) were counted daily, up to the tenth day, to calculate
the germination rate (ISTA, 2004).
Germination Index
The germination index was computed using the following formula:
G.I =

N1
D1

+

N2
D2

+

N3
+
D3

+

Nn
Dn

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where,
N1, N2, …, Nn: Number of seedlings on day 1st, 2nd and nth day after sowing
D1, D2, …, Dn: Number of days after sowing
Seedling Vigour Index
This was calculated by measuring the germination percentage and seedling length of the same seedsample. The seedlings were grown by the rolled paper towel method. Fifty seeds each, in four
replications, were germinated in moist paper towel, in such a way that the micropyles were oriented
towards the bottom to avoid root twisting. The rolled paper towel mediums were kept in an incubator,
with temperature maintained at 25 °C. After 10 days, the paper towel substrates were removed and fivenormal seedlings were randomly selected. Their lengths were measured and a mean seedling length
calculated. To determine dry weight, the seedlings were removed from the substrate and dried in anoven at 100°C temperature for 24 hours, then cooled and weighed. The seed vigour indexes were
calculated by multiplying the germination percentage by seedling dry weight (g) (Index I) or by
seedling length (mm) (Index II). The seed lot showing the higher seed vigour index was considered tobe more vigorous (Adbdul-Baki and Anderson, 1973).
Vigour index -I: Germination (%) × total dry weight of seedling (g)Vigour index -II: Germination (%) × seedling length (mm)Statistical Analysis
All data were analyzed with analysis of variance (ANOVA) by using Statistix (version 8.0) andcomparison of treatment means was done using the LSD test at 5 % level of significance.
RESULTS AND DISCUSSION
Effect of Different Packaging Materials and Storage Durations on the Quality of Pre-monsoon
SesameGermination percentage:
The means of germination percentage of seeds stored in woven polypropylene bags was significantly
higher than that of those stored in super bags. These results indicate that woven polypropylene bags are
more suitable to store pre-monsoon sesame seeds to maintain good quality seeds. In hermeticallystorage, high moisture contents in stored grain can loss in germination and viability. The dryness ofstored grain in pre-monsoon may not be enough for safety storage in hermetical bags. The mean valueof germination percentage of pre-monsoon sesame varied with storage duration. The minimum
germination percentage for sesame seeds was found at the initial storage time and this percentage
increased significantly after two months storage and again after four months storage. The increase ingermination percentages were positively correlated with the duration of storage up to a period of six
months then decreased slightly. It can be said that the physiological quality of sesame stored for sixmonth did not decrease during storage. Therefore, the results show that the best storage duration forpre-monsoon sesame seeds, in relation to germination rates is six-month (Table 1).
These results compare with those reported by Prashant et al. (2016), whose study of the variabilityof germination percentage with the duration of seed storage for Seasamum indicum, found thatmaximum germination percentages occur after short term storage (68-75%), followed by mid-term, andminimum germination percentages occurred in seeds stored for long terms. The mean germination
percentage of pre-monsoon sesame seeds was significantly different, and was affected by packagingⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2021) 12-1

materials and storage duration (Table 1). However, it can be seen that the variations in the germination
percentage means during the storage periods depend primarily on the packaging material used forstorage. The best germination of pre-monsoon sesame could be achieved from seed stored in woven
polypropylene bags for six months (Fig. 1). This agrees with the study carried out by Lima et al. (2014)
that concluded that the physiological quality of sesame stored for six months does not decrease duringstorage.
Table 1 Mean effects of packaging materials and storage durations
on the quality of pre-monsoon sesame
Treatment

Germination(%)

Germinationindex

Seedling
vigour index I

Seedling vigour
index II

Packaging materials (P)Super bag
31.583 b
6.821 b0.055 b226.520 b
Woven polypropylene bag
51.708 a
12.197 a
0.086 a390.570 a
LSD0.05
6.207
1.610
0.012
50.666Storage duration (D)Initial storage
1.583 d0.720 e0.003 d14.010 d
2 months
22.979 c
6.264 d0.043 c181.910 c
4 months
38.208 b
9.885 c0.064 b228.050 c
6 months
74.604 a
16.625 a0.119 a637.790 a
8 months
70.854 a
14.052 b
0.124 a480.970 b
LSD0.05
9.814
2.546
0.019
80.109Pr > FP<0.0001<0.0001<0.0001<0.0001D<0.0001<0.0001<0.0001<0.0001
PxD
0.00100.00010.00990.0002CV (%)
28.66
32.57
32.08
31.58
Note: In each column, means with the same letter are not significantly different at 5 % level.

Fig. 1 Combination effect of packaging materials and storage durations
on germination percentage of pre-monsoon sesame
Germination index:
The maximum germination index is seen from seed stored in woven polypropylene bags followed byseeds stored in super bags. The mean germination indexes varied significantly with the duration ofstorage. It is the effect of dormancy of stored seed, the germination index of stored seeds increasedⒸ ISERD

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with storage duration due to dormancy release and reached the maximum after six months storage
when the dormancy fully release. Germination indexes significantly differed for the two packaging
materials, except at the two-month storage interval, and this difference was greatest at six months,
when seeds stored in woven polypropylene bag storage displayed the greatest germination index.
Seedling vigour index I and II:
The seedling vigour indexes I and II, for seeds stored in woven polypropylene bag were significantly
higher than for those seeds stored in super bags. Seedling vigour depends on the germination
percentage, seedling length and seedling dry weight. And maintaining the viability and seed vigor
during storage also depends on the type of packaging employed (Filho, 1998). The maximum reading
for seedling vigour index I is seen after eight months of storage, although this is not significantly
different to the index I measurement at the six-month storage interval. The pre-monsoon sesame seeds
stored for longer than four months show a higher seedling vigour index I when stored in the woven
polypropylene bag. In relation to seedling vigour index II, the lowest measurements occur in the initialstorage but these germination index rates increase with duration of storage. The results indicate that themost vigorous seedlings are those stored in woven polypropylene bags for six months.
Effect of Different Packaging Materials and Storage Durations on the Quality of Post-monsoon
SesameGermination percentage:
The mean values of germination percentages of post-monsoon sesame are statistically similar for thetwo packaging materials, super bags and woven polypropylene bags. The packaging materials do notshow an influence on the germination percentages of post-monsoon sesame seeds. However, thedifferences in the mean germination percentages of post-monsoon sesame are significant when storage
duration is considered (Table 2). The interaction effects between packaging materials and storage
duration exhibit non-significant effects on mean germination percentages for post-monsoon sesame(Fig. 2). It can be seen that mean germination percentages, which vary with storage duration, are notdependent on the packaging material used for storage. It can also be seen that water proof, air tightcontainers are a suitable packaging type to maintain germination of post-monsoon sesame above 90%
at the eight-month interval of storage.
Germination index:
Although the germination index for seeds is similar for the two packaging materials, these indexes
varied significantly with storage duration. However, the interaction effects between packaging
materials and storage durations were not significant in germination index measurements for postmonsoon sesame (Table 2). It can be said that the changes of germination index during storage are notinfluenced by the packaging material used for storage.
Seedlings vigour index I and II:
Seedling vigour indexes I and II for seeds stored in different packaging is not different, however, theseindex measurements varied significantly with storage duration. Seedling vigour indexes I and II at theinitial storage interval, exhibit minimum values and significantly differ to seeds stored for longer
durations. The combination effect of packaging materials and storage duration on seedling vigour
indexes is not significant (Table 2). This shows that the variations in seedling vigour indexes observed
over time are not governed by the packaging materials. Although germination percentage and thegermination index are numerically higher for seeds stored in super bags, seedling vigour indexes I and
II are higher for seed in woven polypropylene bags.

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IJERD – International Journal of Environmental and Rural Development (2021) 12-1

Table 2 Mean effect of packaging materials and storage durations
on the quality of post-monsoon sesame
Treatment

Germination(%)

Germinationindex

Seedling vigour
index I

Seedling vigour
index II

Packaging materials (P)Super bag
74.317 a
18.637 a
0.142 aWoven polypropylene bag
74.375 a
19.148 a
0.143 a
LSD0.05
5.226
1.626
0.016
Storage duration (D)Initial storage
26.750 c
8.800 c0.045 c2 month
65.333 b17.645 b
0.119 b4 month
94.625 a
22.585 a0.182 a6 month
94.521 a
23.412 a0.173 a8 month90.500 a
22.017 a
0.195 a
LSD0.05
8.263
2.572
0.025
Pr > FP
0.98220.52980.8417D<0.0001<0.0001<0.0001
P×D
0.77910.18490.6766
CV (%)
13.52
16.55
21.08
Note: In each column, means with the same letter are not significantly different at 5 % level.

618.290 a
620.640 a
45.080174.050 d
566.170 c
669.290 b
951.510 a734.290 b
71.2780.9167<0.0001
0.8691
13.99

Fig. 2 Combination effect of packaging materialsnstorage durations
on germination percentage of post-monsoon sesame
CONCLUSION
Black sesame seed cultivated in both pre-monsoon and post-monsoon seasons has different dormancy
periods. The maximum germination rates occur after 6 months storage for pre-monsoon sesame but at
4 months storage for post-monsoon sesame. There was no observed differences in the effects of thematerials used for storage on the dormancy release time for sesame seeds, for either pre- or postmonsoon seasons. Hermetic storage can be used for post-monsoon sesame without adverse effects ongermination. However, hermetic storage is not suitable for pre-monsoon sesame, as it has a negative
effect on germination. More research should be conducted to investigate the reasons for this.
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ACKNOWLEDGEMENTS
This study was supported financially and technically by the Japan International Cooperation Agency,
Technical Cooperation Program (JICA-TCP).
REFERENCES
Abdul-Baki, A.A. and Anderson, J.D. 1973. Vigour determination in soybean seed by multiple criteria. Crop Sci.,
13, 630-633.
Brooker, D.B., Arkema, F.W.B. and Hall, C.W. 1992. Drying and storage of grains and oilseeds. New York, Van
Nostrand Reinhold.
Filho, J.M. 1998. New approaches to seed vigour testing. Scientia Agricola, 55, 27-33.
Hansen, R. 2011. Sesame profile, Retrieved from http://www.agmrc.org commoditie_ products/grains_oilseeds/sesame_profile.cfm
International Seed Testing Association (ISTA). 2004. International rules for seed testing annexes. International
Seed Testing Association (ISTA), Zurich, Switzerland.
Lima, D.C., Dutra, A.S. and Camilo, J.M. 2014. Physiological quality of sesame seeds during storage. Rev. Ciênc.
Agron., 45, 138-145.
Prashant, K.M., Seema, P. and Ravi, P.M. 2016. Assessment of storage dependent physiological parameters of
sesamum indicum seeds. Int. J. Curr. Microbiol. App. Sci. 5 (1), 641-653.
Walters, C.T., Ballesteros, D. and Vertucci, V. 2010. Structural mechanics of seed deterioration. Standing the testof time. Plant Sci, 179, 565-573.

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Research article
erd

Evaluation of Site Contamination from Storage of Fertilizer
I
7d
BARRY NOLLER*e
The University of Queensland, St Lucia, Queensland, Australian
Email: b.noller@.uq.edu.aut
Received 14 January 2021

Accepted 18 May 2021 (*Corresponding Author)
4.
Abstract Australia has extensive agricultural
production of crops and livestock production.
Regular fertilizer application maintains
7 production rates of grain crops. Large quantities ofphosphorus-based fertilizers are produced
from phosphate rock by sulfuric acid extraction at2

designated production works at coastal locations and transported by train to distribution points
in selected agricultural areas. Bulk fertilizer
is stored in large sheds adjacent to railway linesw
and large or small trucks then makeo deliveries to farms. When such sites are disused or
abandoned, contaminated soil may remainfrom fertilizer constituents. Occasionally fertilizerr
storage sites may have experience accidental
contamination from railway engine or truckdhydrocarbons. The objective of this studyistoexaminethe extent of fertilizer contamination atsagricultural storage sites, identify environmental risks associated with the handling practices ofstorage sites, and distinguish from other contamination from transport vehicles. Soil samples
were collected at two locations using a grid-base sampling program to identify contaminants.
Following preparation soils were analysed for phosphorus, sulfate, sulfur, petroleumhydrocarbons (aliphatic and aromatic), heavy metals and pesticides using standard procedures.
A ‘within a target area’ was used to assess contamination levels by comparing the 95% Upper
Confidence Limit (UCL) of the average concentration of each analyte of concern against
respective ‘contamination’ criteria for industrial sites; exceedance of the 95% UCL against site
criteria identified contamination. Statistical analysis of the data for different analytes in soil at
fertilizer storage sites showed the relationship between contaminant levels and dispersion overrespective sites and extent of contamination from fertilizer handling activity and other vehiclerelated site contamination. In conclusion accurate estimation of contamination of sites can beprovided using the grid-base sampling approach and 95% Upper Confidence Limit (UCL)
criteria to distinguished transport vehicle-related contamination from phosphate-based fertilizer
on site.
Keywords fertilizer storage, transport vehicles, contamination, risk management

INTRODUCTION
Australia has extensive agricultural production of crops and livestock. Regular fertilizer application has
maintained production rates over many years (Fitzpatrick, 1968). Phosphorus-based fertilizers areproduced by sulfuric acid extraction at Australian coastal production works and transported by train to
distribution points. Bulk fertilizer is stored in large sheds adjacent to railway lines and large or small
trucks then deliver to farms. The storage sheds usually have bare soil floors and over periods of
decades fertilizer levels can build up. The disused or abandoned sites may leave soil contaminated withfertilizer phosphate, sulfate and sometimes sulfur (FMA, 1998). Occasionally fertilizer storage sites
have experienced contamination from railway engine derailment or truck hydrocarbons (NSW EPA,
1994). The Australian National Environmental Protection Measure (NEPM) soil contamination
guidelines are risk-based Health and Ecological criteria (Noller, 2020) and some guidelines may be
applicable for assessing fertilizer contamination.

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OBJECTIVE
The objective of this study is to examine the extent of fertilizer contamination from phosphate, sulfate
and sometimes sulfur at agricultural storage sites, identify environmental risks associated with thehandling practices of storage sites, and distinguish from other contamination due to transport vehicles.
METHODOLOGY
Soil samples were collected using a grid-base sampling program to identify analytes of concern (NSW
EPA, 1995) at two comparative fertilizer storage sites; one location (A) experienced contamination due
to a rail derailment following fertilizer storage ten years earlier and another location (B) was used for
fertilizer only storage for several decades (Noller, unpublished data). Following preparation, soils wereanalysed by Queensland Health Scientific Services laboratory, Coopers Plains, Queensland forphosphorus, sulfate, sulfur, petroleum hydrocarbons (aliphatic and aromatic), heavy metals andpesticides using standard procedures (ANZECC, 1992; NEPM, 1999; NSW EPA, 1994; NSW EPA,
1995).
The criteria from the NSW EPA (1998) Guidelines for the NSW Site Auditing Scheme were used,
together with the assessment criteria of the Australian National Environmental Forum health-based soil
investigation Levels for Soil and Groundwater NEPM (1999) at industrial sites. A ‘within a target area’
statistical evaluation was used to assess contamination levels by comparing the 95% Upper Confidence
Limit (UCL) of the average concentration of each analyte against respective ‘contamination’ criteria
for industrial sites; exceedance of 95% UCL against site criteria identified contamination using theassessment criteria of the NSW EPA Sampling Design guidelines (NSW EPA, 1995). “Hotspots” could
be identified when analytical results from individual sites were 250% of the relevant criteria value, andwhen the standard deviation of data was <50% of the criteria.
The 95% upper confidence limit (UCL) of the arithmetic average concentration is calculated using
Eq. (1) (NSW EPA, 1995) as follows:
UCL average = x̄ + tα,n-1 x sd/√n(1)

UCL average = Upper confidence limit of the arithmetic average concentration of the sampling area atthe 1-α confidence level.
α = The probability that the ‘true’ average concentration of the sampling area might exceed the UCL
average determined by Eq. (1).
n = Number of sample measurements.
n-1 = Ddegrees of freedomx̄ = Arithmetic average of all sample measurements.
tα,n-1 = The test statistic at an α level of significance and degrees of freedom of n-1 obtained from tableof values of Student’s t.
sd = Standard deviation of the sample measurements.
RESULTS AND DISCUSSION
Table 1 gives results for soil sulfate, total phosphorus and elemental sulfur concentrations at the two
fertilizer storage Locations (A and B). Both show extensive soil contamination, compared withcontamination criteria, from sulfate, phosphorus and sulfur (B only) with higher concentrations
observed at Location B due to extensive use over several years. Such contamination is quite different
to low level contamination of agricultural soils (Noller, 2020), but significant over long-term
accumulation in soil (Weissengruber et al., 2018). Comparison of the statistical evaluations using the
95% Upper Confidence Limit (UCL) of average concentration of each constituent (NSW EPA, 1995)Ⓒ ISERD

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and exceedance of 95% UCL against the respective contamination criteria against site criteria(ANZECC, 1992; NEPM, 1999) enabled the following features of ‘hotspots’ to be identified:
•
•
•
•
•

Location A Sulfate. The difference of samples below the contamination criteria from all
samples showed that 7 sites are contaminated and indicated the extent of the ‘hotspot’ forsulfate.
Location A Total phosphorus. The difference of samples below the contamination criteria fromall samples showed that 13 sites are contaminated and indicated the extent of the ‘hotspot’ fortotal phosphorus and was more extensive than for sulfate.
Location B Sulfate. The difference of samples below the contamination criteria from all
samples showed that 7 sites are contaminated and indicated the extent of the ‘hotspot’ forsulfate.
Location B Total phosphorus. The difference of samples below the contamination criteria fromall samples showed that all 10 sites are contaminated and indicated the extent of the ‘hotspot’
for total phosphorus was also more extensive than for sulfate.
Location B Sulfur. The difference of samples below the contamination criteria from all
samples showed that 2 sites are contaminated and indicated the extent of the ‘hotspot’ forsulfur, was much less than for sulfate.

Table 1 Soil sulfate, total phosphorus and elemental sulfur concentrations at fertilizer sites
Test

SulfateTotal phosphorus
Elemental sulfur
as S (mg/kg)
as P (mg/kg)
as S (mg/kg)
A. Once only storage site (600 m2) followed by train derailment contamination of soil
Number of samples (n-all)
20
20
Mean
4509
6132
Range
61-33400
130-25200
SD
8458
6935
Students tα=0.05
1.729
1.729
95% UCL mean
1239
9850
Contamination criteria
2000a
2000a
Number of samples
13
7(n<Contamination criteria)
Mean
527
926
Range
61-1600
130-1700
SD
532
631
Students tα=0.05
1.782
1. 943
95% UCL mean
792
1389
Contamination criteria
2000a
2000b
B. Storage site (5800 m2 used continuously for 40 y with only fertilizer contamination
Number of samples (n-all)
10
10
10
Mean
28542
10470
3615
Range
520-100100
2400-22700
100-32300
SD
33124
7277
10087
Students tα=0.05
1.833
1.833
1.833
95% UCL mean
47756
14691
9466
Contamination criteria
2000a
2000b
600b
Number of samples
30
8(n<Contamination criteria)
Mean
11732
225
Range
520-1600
100-500
SD
575
183
Students tα=0.05
2. 353
1. 8603
95% UCL mean
1954
345
Contamination criteria
2000a
600b
Source: a. ANZECC (1992) and b. NEPM (1999)Ⓒ ISERD

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Table 2 Soil sulfate, total phosphorus, cadmium, lead and zinc concentrations at 9 sites,
Location A
Site A(surface 0cm)

Sulfate-S(mg/kg)Cadmium(mg/kg)Lead(mg/kg)Zinc(mg/kg)33,4005,600
120
19,000
360
160
930
1,200
250

Total
phosphorus-P(mg/kg)20,800
10,200400
25,200
6,300
4,100
8,600
2,200
1,700

Site 1Site 2Site 3Site 4Site 5Site 6Site 7Site 8Site 9Contamination
criteria

9.7
2.2
<1
45
4.3
3
7.7
1.3
1.3

98
43
25
790
62
36
78
34
400

170
160
32
460
220
130
220
110
240

2,000a2,000b

100b

1500b

35,000b

Source a. ANZECC (1992) and b. NEPM (1999)

Table 3 Soil total petroleum hydrocarbon, polynuclear aromatic hydrocarbons, benzo(a)pyrene,
and organo phosphorus pesticides concentrations at 9 sites, Location A
Site(surface
0cm)
Site 1Site 2Site 3Site 4Site 5Site 6Site 7Site 8Site 9Contamination
criteria

Total
petroleumhydrocarbon(mg/kg)
142
194
48
336
108
23
46
13
67

PolyAromatichydrocarbons(total) (µg/kg)

Benzo(a)pyrene(µg/kg)

2,255
1,460
450
5,330
1,510
320
830
255
1,370

2,255
1,460
450
5,330
1,510
320
830
255
1,370

Organophosphorus
pesticides(µg/kg)
<10
<10
<10
<10
<10
<10
<10
<10
<10

28,000b100,000a

100,000a

10,000a

Source a. NEPM (1999) and b. NSW EPA (1994)

Tables 2 and 3 give comparative data for soil sulfate, total phosphorus, petroleum hydrocarbons(aliphatic and aromatic), benzo(a)pyrene, heavy metals (cadmium, lead and zinc), and organophosphorus pesticide concentrations for 9 sites at Location A with rail derailment contamination being
close to the railway line. Based on historical use of the agricultural storage site (Location A) for
fertilizer, the type and concentration of contaminants found should be consistent with storage ofphosphorus-based fertilizers. Therefore the presence of cadmium, lead, zinc, total petroleumhydrocarbon (TPH) and BTEX (all at detection limit; benzene <0.5 mg/kg, toluene <0.5 mg/kg,
ethylbenzene <0.5 mg/kg and xylene <0.5 mg/kg), polyaromatic hydrocarbons (PAHs) comprisingbenzo(g,h,i)perylene, dibenz(a,h)anthracene, indeno(123c,d)pyrene, benzo(a)pyrene, benzo(b+k)
fluoranthene, chrysene, benz(a)anthracene, pyrene, fluoranthene, phenanthrene, fluorene,
acenaphthalene, acenaphthene and naphthalene, benzo(a)pyrene itself as a key carcinogen from fuelcombustion, organo phosphorus pesticides, and are inferred collectively as not being consistent withthe presence of phosphate-based fertilizers; cadmium is also present in phosphate rock used in fertilizer
manufacture but is associated with zinc additives used in engine oils.
The data in Tables 2 and 3 is intended to show how fertilizer contamination can be displayed at
the same sites at Location A together with rail derailment contamination. Sulfate and total phosphorus
concentrations (Table 2) are consistent with the identification from Table 1 that some sites at LocationⒸ ISERD

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A are contaminated with fertilizer by comparison against the contamination criteria and comprise‘hotspots’. None of the cadmium, lead, zinc, total petroleum hydrocarbon (TPH) and BTEX,
polyaromatic hydrocarbons (PAHs), benzo(a)pyrene or organo phosphorus pesticides (OCPs)
concentrations given in Tables 2 and 3 exceed respective contamination criteria and cannot be
described as ‘hotspots’ at Location A or as being environmentally significant. However, the associationof engine fuel and oil constituents with a rail engine derailment is clear.
CONCLUSION
Accurate estimation of contamination of sites has been demonstrated using the grid-base sampling
approach and 95% Upper Confidence Limit (UCL) criteria to distinguish transport vehicle-related
contamination from phosphorus-based fertilizer at two locations. Statistical analysis of the data fordifferent constituents in soil at the fertilizer storage sites showed the relationship between contaminantlevels and dispersion over respective sites (ANZECC, 1992; NEPM, 1999; NSW EPA, 1994; NSW
EPA, 1995). Dispersion of constituents of phosphorus –based fertilizers can distinguish extent ofcontamination during the operational phases of fertilizer handling activity from other vehicle-related
contamination. “Hotspots” were identified when analytical results from individual sites were 250% of
the relevant criteria value, and when the standard deviation of the data set was <50% of the criteria.
ACKNOWLEDGEMENTS
Keith Halford provided assistance with the sampling study.
REFERENCESANZECC. 1992. Australian and New Zealand environment and conservation council, Australia and New Zealandguidelines for the assessment and management of contaminated sites. ACT, Australia.
FMA. 1998. Code of practice for the storage, handling and transportation of solid ammonium nitrate based
fertilisers. Fertiliser Manufacturers Association (FMA), Peterborough PE2 6XEUK, April 1998.
Fitzpatrick, E.N. 1968. The changing fertiliser industry. Journal of the Department of Agriculture, Western
Australia, Series 4, 9 (12), 554-555.
NEPM. 1999. Guideline on investigation levels for soil and groundwater. National Environment Protection
Measure for Assessment of Site Contamination. National Environment Protection Council, Canberra,
Australia.
Noller, B.N. 2020. Environmental effects from contamination of agricultural soils via spraying and dust
application to crops and animals. IJERD International Journal of Environmental and Rural Development, 11(1), 68-73.
NSW EPA. 1994. Guidelines for assessing service station sites. NSW Environment Protection Authority,
Chatswood, NSW, Australia.
NSW EPA. 1995. Sampling design guidelines. NSW Environment Protection Authority, Chatswood, NSW,
Australia.
NSW EPA. 1998. Guidelines for NSW site auditor scheme. NSW Environment Protection Authority, Chatswood,
NSW, Australia.
Weissengruber, L., Moller, K., Puschenreiter, M. and Friedel, J.K. 2018. Long-term soil accumulation ofpotentially toxic elements and selected organic pollutants through application of recycled phosphorus
fertilizers for organic farming conditions. Nutrient Cycling in Agroecosystems, 110, 427-449.

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Research article
erd

Growth Performance of Trema orientalis L. (Blume) Ulm.
Seedlings in Response to Mineral Nutrient Omission
KEN N. FLORENTINO
Visayas State University, Baybay City, Philippines
Email: kennoya.florentino@vsu.edu.ph

ROMEL B. ARMECIN
Visayas State University, Baybay City, Philippines

JIMMY O. POGOSA
Visayas State University, Baybay City, Philippines

MARLITO M. BANDE*
Visayas State University, Baybay City, Philippines
Received 31 December 2020

Accepted 18 May 2021(*Corresponding Author)

Abstract The Philippine forest land is among the top threatened biodiversity areas in the world
and one of the major causes is deforestation. In rainforestation, a reforestation approachdeveloped by Visayas State University (VSU) where pioneer tree species are planted first to
copy the natural flow of forest succession. Studies on early successional tree species are limited,
especially on determining the growth performance as affected by nutrient deficiency. The study
was conducted under screen house condition at the Terrestrial Ecosystems Division (TED),
Institute of Tropical Ecology and Environmental Management (ITEEM), VSU, Philippines.
This study aimed at evaluating the effect of nutrient omission on morphological andphysiological growth of Trema orientalis L. (Blume) Ulm at seedling stage. The experimental
design was randomized complete block design, with 7 treatments and 3 replications with 35
seedlings in each replication which constituted a total of 735 seedlings. The root collar
increment, plant height increment, number of leaves, leaf area, dry biomass and root shoot ratio
were evaluated. After six months of fertilizer application, results showed that there was anincreasing influence (p≤0.01) to the root collar diameter increment, plant height increment,
number of leaves and leaf area specially in –Ca treatment. The biomass production and rootshoot ratio was significantly affected (p≤0.01) in control and –N treatments. Stunted growth
and reduced leaf were exhibited in seedlings planted at -N and control treatments. Furthermore,
the result showed that the omission of calcium has positive effects (p≤0.01) while omitting
nitrogen showed negative effects (p≤0.01) on the morphology and physiology of Tremaorientalis. Therefore, the omission of macronutrients at early seedling stage of Trema orientalis
growth has positively (p≤0.01) and negatively (p≤0.01) affected the growth performance of thestudy plant.
Keywords biomass production, growth performance, nutrient omission, rainforestation

INTRODUCTION
Forest has a vital role in the environment and climate change since it functions as a carbon sink.
According to World Wildlife Fund (2018), without forest areas, excessive carbon would contribute to
the carbon present in the atmosphere and enhances the greenhouse effect. The tropics and theⒸ ISERD

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subtropics experienced the greatest loss in forest cover mainly due to extensive forestry land uses
where forests are converted into crop production areas (Hansen et al., 2013). The Philippine forest land
is among the top threatened biodiversity areas in the world (Apan et al., 2017). The country depends onthe forest for timber and other resources which leads to irresponsible logging activities, swidden
agriculture and mining (Perez et al., 2020). Deforestation also affects the country’s resistance to natural
disasters such as landslides and flush floods. A solution to this problem is by restoring forest covers
through planting of native trees. The value of the forest has become more important considering thethreats brought by climate change, as forests’ mitigate the effects by retaining water, carbon
sequestration and so on (FAO and UNEP, 2020). Rainforestation promotes the importance of native
species as it promotes biodiversity rehabilitation, conservation of remaining primary forests and natural
resources, and the development of a closed canopy and high diversity forest farming system (Milan andCeniza, 2009). Regeneration of forest largely depends on the survival and growth rates of native
species that are planted or have arrived on their own (D’Antonio and Meyerson, 2002). Rainforestation
provides environmental and economic benefits as well as enrichment of biodiversity. It also improves
chemical properties of soil and provides higher net income because of the availability of resources(Neidel et al., 2012).
In a disturbed and damaged ecosystem, the early successional tree species together with otherpioneer plants are the first to grow because they can adjust to poor soil conditions and adverse
environmental situations (Natividad, 2016). In reforestation, pioneer tree species are planted first to
copy the natural flow of forest succession which creates diverse forest and variations (Neidel et al.,2012). Soils in the tropics are fragile and the overuse of soil leads to unproductiveness and affects thegrowth of plants (Pessaraklim, 1999). Just like other tropical countries, the Philippines is also affectedby land degradation which is a major ecological problem (Asio and Milan, 2002). Land degradation ismainly due to anthropogenic activities that can lead to loss of soil nutrient, organic matter content,
addition of heavy metals, compacted and polluted soils and increased of the pH level of the soil (Asioet al., 2009).
Trema orientalis is a medium to large sized tree, growing in the average 20 m and attaining
diameters of 60 cm at breast height. A tropical tree belonging to family Ulmaceae that grows in poor
soil condition and chromite overburdens. This tree is an effective cover to avoid leaching of heavy
metals in the presence of mine waste dumps into neighboring environments.
Studies on early successional tree species are limited, especially on determining the growth
performance as affected by nutrient deficiency. Thus, this study will focus on early successional
species Trema orientalis and the effects of mineral nutrient omission, both physiologically andmorphologically at seedling stage. The hypothesis for this study is “nutrient element omission willaffect the growth performance of Trema orientalis.
OBJECTIVES
1. To evaluate the morphological performance of Trema orientalis seedlings as affected by nutrient
element omission under screen house conditions; and2. To determine the influence of nutrient element omission on the physiological performance ofTrema orientalis seedlings.
METHODOLOGY
Location of Study Site and Experimental Design
This study was conducted at the screen house in VSU, Baybay city, Leyte, Philippines. The study site
falls under type IV climate wherein rainfall is more or less distributed throughout the year.
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Randomized Complete Block Design (RCBD) was used as the experimental design with 7 treatments
and 3 replications for each treatment. Each replication consists of 35 seedlings with a total of 735
seedlings. The treatments were the following: Treatment 1 (T1) – without the application of nutrient
solution; Treatment 2 (T2) - nitrogen (-N) was omitted in the solution; Treatment 3 (T3) - phosphorus(-P) was omitted in the solution; Treatment 4 (T4) - potassium (-K) was omitted in the solution;
Treatment 5 (T5) - calcium (-Ca) was omitted in the solution; Treatment 6 (T6) - magnesium (-Mg)
was omitted in the solution; and Treatment 7 (T7) – complete (N, P, K, Ca &, Mg) nutrient solution.
Data Collection
The first destructive harvesting was done before treatment application. A total of 10 samples in which
the roots, stem, and leaves were separated and then measured. After measuring and air drying, thesamples were placed in a paper bag and were oven dried at 70 °C for 27 hours (Bande et al., 2013).
Then the dried samples were measured using digital analytical balance to acquire the biomass of theroots, stem, and leaves. Six months after treatment application (February 2019) random selection was
done and 15 samples were harvested per treatment. The morphology and physiology of the seedlings
were initially documented and recorded. This includes the initial root collar diameter (RCD), shootheight, and number of leaves. A correction fluid was used to mark the exact point of the root collar.
The root collar diameter was measured using the Vernier caliper (mm). This was done by placing thecaliper at the marker and the height was measured using a ruler (cm). The number of leaves wasdetermined through counting of functional leaves. After six months of fertilizer application, second
collection of data was done using the same methodology and instruments in their respective parameters.
Data Encoding and Statistical Analysis
Morphological (i.e. root collar diameter, height, and number of leaves) and physiological (i.e. root
biomass, stem biomass, and leaf biomass) data were encoded and graphed using MSExcel 2016. Test
was done for normality and homogeneity using Statistical Package for Social Sciences (SPSS) version
25. One-way ANOVA was used to determine significant effects of the treatments on root collar
diameter increment, shoot height increment, number of leaves, leaf area, dry biomass (i.e., leaves, stem,roots, and total) and root-shoot ratio. Duncan Multiple Range Test (DMRT) and Least Square
Differences (LSD) were used to compare treatment means and significant differences of independent
variables with significant variations at p≤0.05.
RESULTS AND DISCUSSION
Morphological Performance
Table 1 presents that the root collar diameter increment and shoot height of Trema orientalis was
significantly (p ≤ 0.01) affected by the nutrient element omission after six months of treatment
application. It is further observed that -Ca treatments had the highest increment in root collar diameter(2.76mm ± 0.09) and shoot height (34.34cm ± 0.70) as compared to the –N and Control (0.92mm ±0.03) treatments which showed the lowest growth increment of Trema orientalis after six months offertilizer application (Table 4). Nitrogen is important for the formation of protoplasm which is essential
for plant growth and development. Thus, omission of Nitrogen will result into stunted growth because
cell division is reduced (Uchida, 2000).

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Table 1 Root collar diameter (mm) and shoot height (cm) increments of Anabiong planted
under screen house condition six months after treatment application
Treatment
Control
-N
-P
-K
-Ca
-Mg
Complete

Root collar increment (mm)0.92 ± 0.08d
1.15 ± 0.08c
2.65 ± 0.08a
2.49 ± 0.08b
2.76 ± 0.08a
2.41 ± 0.08b
2.36 ± 0.08b

Shoot height increment (cm)8.54 ± 0.76d
7.38 ± 0.76d
25.50 ± 0.76b
26.03 ± 0.76b
34.34 ± 0.76a
26.61 ± 0.76b
22.25 ± 0.76c

Note: Values with different superscript letters (a-d) within columns are statistically significant at p ≤ 0.05.
Root Collar Increment n=720, Shoot Height Increment n=727

Similarly, statistical analysis showed that the number of leaves and leaf area of Trema orientalis
were significantly (p ≤ 0.01) affected by the nutrient element omission after six months of fertilizer
application (Fig. 1). The -N and Control treatments significantly had the least leaves produced after
this period and all treatments with Nitrogen produced significantly higher number of leaves. Results
indicate that Nitrogen is necessary for the production of photosynthetic plant organ. On the other hand,omission of Calcium resulted to the significant increase (p ≤ 0.01) in leaf quantity as well as the leaf
area of Trema orientalis after six months. In view of this observation, it is suggested that Ca maybe
regarded not necessary for the production of leaves in Trema orientalis under certain growth stage.
Results of the current study may also indicate inherent Ca content of the potting medium sufficientenough for the production of leaves.

Before fertilizer application

6 months later

Before fertilizer application

6 months later

Fig. 1 Number of leaves and leaf area before and after 6 months of fertilizer application
Note: Values with different letter (a-d) designation with the same color are significantly different at p≤0.05.
Number of leaves n=120, Leaf area n=15

Physiological Performance
The dry biomass as shown in Table 2 indicates that the –Ca treatment had the highest total biomass
after 6 months of fertilizer application while the –N had the least biomass. -Ca treatment significantly
had the highest root, stem and leaf biomass while –N and control treatments had the lowest increase inbiomass after 6 months of fertilizer application. Furthermore, it was observed that among the plantorgans roots had the most biomass allocation, regardless of what nutrient element was omitted.
Root Shoot Ratio
Overall observation shows that omission of N in the fertilizer solution resulted to the increase in the
root-shoot ratio (Fig. 2). This observation confirms the findings of Davidson (1969) in ryegrass that
decreasing the availability of N increased root: shoot ratio due to the increased sink strength of theⒸ ISERD

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roots compared to the shoot sinks. Under the N depletion, soil environment roots tend to increase its
length to aid the plant in obtaining more Nitrogen; hence, increasing its root biomass. The observationstrongly indicates the importance of these nutrients in translocating photosynthates from shoot for root
development.
Table 2 Biomass (g) of the roots, stem and leaves of Trema orientalis
after six months of treatment application
Treatment
Control
-N
-P
-K
-Ca
-Mg
Complete

Roots (g)Stem (g)b0.23 ± 0.02
0.26 ± 0.02b
1.00 ± 0.09a
1.18 ± 0.10a
1.16 ± 0.09a
1.06 ± 0.08a
1.04 ± 0.07a

Leaves (g)c0.13 ± 0.02
0.11 ± 0.01c
0.61 ± 0.06b
1.00 ± 0.20a
0.84 ± 0.06bc
0.77 ± 0.09bc
0.67 ± 0.06b

Total (g)c0.13 ± 0.02
0.11 ± 0.01c
0.72 ± 0.07b
1.10 ± 0.14ab
0.97 ± 0.07ab
0.95 ± 0.08ab
0.95 ± 0.07a

0.52 ± 0.05c
0.45 ± 0.04c
2.34 ± 0.18b
3.28 ± 0.41a
2.98 ± 0.17bc
2.77 ± 0.24bc
2.66 ± 0.19bc

Note: Values with different superscript letters (a-c) within columns are statistically significant at p ≤ 0.05, n=105

Fig. 2 Root shoot ratio of Trema orientalis after six months of fertilizer application
Note: Values with different letter (a-b) designation are significantly different at p≤0.05 root:shoot ratio n=15

CONCLUSION
Omission of different macronutrient elements has a significant effect (p ≤ 0.01) on the growthincrement of root collar diameter and shoot height, as well as on production of leaves and leaf area that
subsequently affected the allocation of biomass in any of the plant parts and the root shoot ratio. This isbecause the macronutrients have specific functions as involved in the metabolic processes of the plants.
Omission of calcium has positive effects on the morphology and physiology of Trema orientalis.
Furthermore, the consistent low biomass production in -N treatment of the Trema orientalis strongly
indicates that nitrogen primarily accounts for the growth rate of these plants. Therefore, the omissionof macronutrient at the early seedling stage of Trema orientalis has positively (p ≤ 0.01) and negatively(p ≤ 0.01) affected the growth performance of the study plant.c
ACKNOWLEDGEMENTS
We extend our gratitude to the Nature-Life International through ViSCA Forest Reserved CO2
Conservation Project for their generous financial support of the study.
REFERENCESⒸ ISERD

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Apan, A., Suarez, L.A., Maraseni, T. and Castillo, J.A. 2017. The rate, extent and spatial predictors of forest loss
(2000-2012) in the terrestrial protected areas of the Philippines. Applied Geography, Vol. 81.
Asio, V.B., Jahn, R., Perez, F., Navarette, I. and S. Jr. Abit. 2009. A review of soil degradation in the Philippines.
In Annals of Tropical Research, 70-86.
Asio, V.B., and Milan, P.P. 2002. Improvement of soil quality in degraded lands through rainforestation farming.
Sustaining Food Security and Managing Natural Resources in Southeast Asia.
Bande, M.M., Grenz, J., Asio, V.B. and Sauerborn, J. 2013. Fiber yield and quality of abaca (musa textilis var.
laylay) grown under different shade conditions, water and nutrient management. 70-77.
D’Antonio, C. and Meyerson, L.A. 2002. Exotic plant species as problems and solutions in ecological restoration:
A synthesis. Rest. Ecol. 10, 703-713.
Davidson, R.L. 1969. Effects of soil nutrients and moisture on root/shoot ratios in Lolium perenne L. andTrifolium repens L. Annals of Botany, 33 (3).
Hansen, M.C., Potapoy, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D., Stehman,
S.V., Goetz, S.J., Loveland, T.R., Kommareddy, A., Egorov, A., Chini, L., Justice, C.O. and Townshend,
J.R.G. 2013. High-resolution global maps of 21st-century forest cover change. Science, 342 (15), 851-853.
Milan, P. and Ceniza M.J. (Eds.) 2009. Rainforestation trainors' manual Baybay City, Leyte, Philippines: Instituteof Tropical Ecology, Visayas State University.
Natividad, R.A. 2016. Pioneer tree species: Their potential uses and harvesting constraints. Ecosystems &Development Journal, 6 (1), 32-36. ISSN 2012-3612.
Neidel, J.D., Consunji, H., Labozetta, J., Calle, A. and Vega, J.M. (Eds.) 2012. Mainstreaming native speciesbased forest restoration. ELTI Conference Proceedings, New Haven, CT, Yale University; Panama City:
Smithsonian Tropical Research Institute.
Perez, G.J., Comiso, J.C., Aragones, L.V., Merida, H.C. and Ong, P.S. 2020. Reforestation and deforestation in
Northern Luzon, Philippines: Critical issues as observed from space. Forests, 11 (10), 1071.
Pessaraklim. 1999. Nutrient uptake by plants under stress conditions. Handbook of plant and crop stress.
UNEP. 2020. The state of the world’s forests. Forests, Biodiversity and People, Rome.
World Wildlife Fund (WWF). Deforestation. Retrieved from https://www.worldwildlife.org/threats/deforestationⒸ ISERD

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Research article
erd

Potential Measurement as a Method for Monitoring the Soil
Chemical Environment
NARONG TOUCH*
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, Japan
Email: nt207118@nodai.ac.jp

TAKAHIKO NAKAMURA
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, Japan
Received 8 January 2021

Accepted 19 May 2021(*Corresponding Author)

Abstract Soil chemical environment (SCE) affects soil degradation and productivity, thus
understanding of the temporal changes of SCE is important for obtaining higher soil
productivity. Widely used indices of SCE included ion concentration, pH, and redox potential(ORP), but these cannot monitor SCE continuously. Previously, a study proved that continued
potential measurement can represent temporal changes in the water quality at the sea floor.
However, no report related to monitoring the SCE with continued potential measurement has
been found. Therefore, this study proposes a method for evaluating soil ORP using continued
potential measurement, and examine the method’s validity in representing changes in SCE due
to soil reduction, bacteria activation, and soil oxidation. Laboratory experiments wereconducted using soils from rice paddy fields. A brush-type carbon electrode was installed in thesoil layer that then connected to a reference electrode for measurement of the soil ORP.
Different soil conditions were created by mixing the paddy soil with cow manure compost or
potassium sulfate. The soil ORP was automatically recorded every 15 min using a voltage
meter. The ORP of paddy soil decreased temporally and stabilized at 50 days after the start ofthe experiment, suggesting that soil reduction occurred over those 50 days. When testing thepotassium sulfate-paddy soil mixture, the soil ORP rapidly decreased during the first day after
the experiment started. When testing cow manure compost-paddy soil mixture, a larger
decrease in soil ORP was observed compared to that in the paddy soil alone. These findings
suggest that soil reduction is promoted by adding potassium sulfate or cow manure compost.
Among the soil samples tested, there was a range of soil ORPs and trends in the potential
decreases. Based on the results of this study, it was found that continued potential measurement
is effective in evaluating soil ORP, which represents temporal changes in SCE due to redox
reactions after the addition of cow manure compost or potassium sulfate. In addition, the effectsof bacteria activation are revealed during continued potential measurement of soil ORP.
Keywords potential measurement, carbon electrode, redox reaction, metabolism reaction,soil reduction, soil oxidation

INTRODUCTION
Soil is a medium for plant growth and is one of the fundamental bases for agricultural productions, thus
agricultural productivity is strongly influenced by the use of soil fertilizer (Hatfielf, 2006). Over thelast few decades, the relationship between cropping and managing systems and the soil chemical
environment (SCE) has been discussed with regard to obtaining higher productivity by preventing soil
degradation or erosion. The understanding of SCE is useful for ensuring healthy soil that can sustain
crop production and environmentally friendly agriculture (Yan and Hou, 2018).
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It has been suggested that the transition from conventional to organic or low-input farming has
caused changes in the SCE and the processes related to soil fertility (Clark et al., 1998). As a decline insoil fertility results in a decrease in productivity (Buresh et al., 1997), management of soil fertility andsoil health plays an important role in realizing sustainable agriculture (Prasad and Power, 1997). Sincechemical reactions and nutrient circulation affect soil fertility, understanding changes in the SCE areessential in facilitating successful, high-productivity organic farming.
To date, parameters such as pH, organic matter contents, phosphorus availability, and cationexchange capacity have been widely used as indices for evaluating SCE. However, their measurements
cannot be conducted continuously, thus changes in the SCE over time are underdetermined. Therefore,
a method that can continuously monitor SCE is required. Potential measurement is a method that canmonitor the redox environment of soil over time. This method may play an important role in organic
farming. For obtaining a higher productivity of organic farming, the measuring SCE over time canprovide useful information for controlling or improving redox and biological conditions of soils. Thismethod was used by Nagama et al. (2018) when they suggested that water potential measurement can
describe changes over time in the water quality at the sea floor. However, no report related tomonitoring SCE with continued potential measurement has been found in the literature.
OBJECTIVE
This study was aimed at proposing a method for measuring soil ORP and at examining the method’s
validity in representing changes in SCE due to soil reduction, bacteria activation, and soil oxidation.
This was done by continued potential measurement to determine changes in the soil ORP of paddy soil.
The paddy soil samples studies were pure, mixed with cow manure compost, or mixed with potassium
sulfate.
METHODOLOGY
The experimental device comprised a cylindrical bottle with an inner diameter of 120 mm and a heightof 150 mm. The bottle was filled with paddy soil to a depth of 20 mm, and a brush-type carbon
electrode was placed on the soil layer. Then, another soil layer at a depth of 30 mm was placed on theelectrode. Finally, tap water was poured over the soil layers (Fig. 1). The paddy soil was collectedduring the agricultural off-season from a rice filed in Ebina, Kanagawa, Japan. Approximately 150 mmof the surface soil was collected and transported to the laboratory.

Reference electrodeTap water

Soil

Voltmeter

20 mm

50 mm

100 mmBrush-type electrode(100*100) mm2
120 mmCase 1: paddy soil, Case 2: Cow manure compost-paddy soil mixture,
Case 3: potassium sulfate-paddy soil mixture

Fig. 1 Experimental device and measurement methodⒸ ISERD

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The soil layer was prepared under different conditions to ensure differences in the SCE. For
example, certain paddy soil was mixed with cow manure compost, and another mixture had potassium
sulfate. The mixing ratio (mass basis) of paddy soil to cow manure compost or potassium sulfate was
9:1. The electrode material was carbon cloth (News Company, PL200-E), which was heated at 500°C
for 1 h prior to use to improve its performance, as was suggested by Nagatsu et al. (2014). The heatedcarbon cloth with a width of 100 mm and a length of 100 mm was separated into fibers to form abrush-type electrode (Fig. 1).
The bottles were then submerged in a container that was filled with tap water (Fig. 1). In thecontainer, the electrode in the soil layer was connected to a reference electrode (Toyo Co., RE-7A) for
measuring the electrode potential. This electrode potential was the soil ORP. The potential wasrecorded automatically every 15 min by a voltmeter (T&D Corp., VR-71). A plastic-coated copper
wire was used for all the connections in the measurements.
RESULTS AND DISCUSSION
Accuracy of Potential Measurement

Soil ORP of Case 1-1 (V vs Ag/AgCl)

To examine the accuracy of potential measurement, two paddy soil samples were made under the same
condition, and the soil ORP measurements of the two samples were compared (Fig. 2). The soil ORP
measurements taken during the first day were used because they nearly reflected the electrode potential.
As time increases, the SCE may differ from one sample to another, even though they were made under
the same condition because soil reduction (organic matter decomposition) may differ.
As demonstrated in Fig. 2, the maximum variance in potential was only +0.004 V (1.33%),
indicating this method’s high accuracy. Therefore, it can be said that the proposed method, i.e.,
potential measurement, can accurately represent temporal changes in the SCE owing to redox reactions
and bacteria activation in soils.
0.35

0.34

0.33

0.32

0.31

0.3

0.3

0.31

0.32

0.33

0.34

0.35

Soil ORP of Case 1-2 (V vs Ag/AgCl)

Fig. 2 Comparison of the measured soil ORP from two samples under the same condition
Potential Measurement for Representing Soil Reduction
Fig. 3 illustrates the comparison of temporal changes in soil ORP for each sample. The paddy soil used
was in an oxidized state as it was collected during the agricultural off-season. Thus, the initial potentialⒸ ISERD

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was 0.35 V which then temporally decreased and almost stabilized after 50 days. Approximately twomonths are required for complete soil reduction.
On day 50, the soil ORPs were −200 mV for the paddy soil, −260 mV for the potassium sulfatepaddy soil mixture, and −475 mV for the cow manure compost-paddy soil mixture. Previously, it wasreported that different redox reactions result in a difference in soil ORP (Liesack et al., 2000; Matocha,
2005). Specifically, soil ORP indicates redox reactions occurring in soils. The results of this study
indicate that different redox reactions occur because of different soil compositions. Compared withpaddy soil alone, a large decrease in soil ORP was observed when potassium sulfate was added to the
paddy soil. This was due to the reduction reaction of sulfate in the soil. Since methanogens are present
in cow manure, methane production occurred in the paddy soil the manure was mixed with, resulting ina large decrease in soil ORP. In summary, potential measurement is useful for understanding changes
in SCE due to redox reactions in soils.

Soil ORP (V vs. Ag/AgCl)

0.4
Paddy soil (PS)
PS+Cow manure compost
PS+Potassium sulfate

0.3
0.2

0.1
0

-0.1
-0.2
-0.3

-0.4
-0.5

0

5

10

15 20 25 30 35
M easurement time (day)

40

45

50

Fig. 3 Comparison of temporal changes in soil ORP

Soil ORP (V vs. Ag/AgCl)

0.5
Paddy soil (PS)
PS+Cow manure compost
PS+Potassium sulfate

0.4
0.3
0.2
0.1
0

0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.91

M easurement time (day)Fig. 4 Comparison of decreasing trends of soil ORP during the first day of the experiment
Potential Measurement for Representing Microbial Activation in Soils
Fig. 4 depicts a comparison of decreasing trends of soil ORP during the first day of the experiment.
Interestingly, large differences in decreasing soil ORP were observed during this time. Compared withpaddy soil, a large decrease in soil ORP was recorded when the soil was mixed with cow compostⒸ ISERD

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manure, and an even larger decrease in soil ORP was recorded when it was mixed with potassium
sulfate. These trends depend on the redox reactions occurring at the electrode and possible potential
losses due to electron transfer at the electrode surface.
Following the Nernst equation (Eq. 1), the electrode potential (E) depends on the standard
potential of redox couple (E0) and the concentration of ions (logarithm term).
E = Eo + (RT/nF) ln (Ox/ Re)(1)

R is the universal gas constant, T is the temperature in Kelvins, n is the number of electronstransferred in the reaction or half-reaction, F is the Faraday constant, Ox is the activity of the oxidized
form, and Re is the activity of the reduced form.
E0 value are different between redox couples; for example, E0 =1.057 V for the Fe(OH)3/Fe2+
redox couple and E0 =0.303 V for SO42-/H2S. Therefore, large changes of E will be observed when theredox reaction varies from one to another redox couple. Compared with a biological reaction, achemical reaction occurs rapidly, thus the observed rapid decrease of soil ORP in this study is due tothe change of the redox couple from iron reduction in the paddy soil alone to sulfate reduction in thepotassium sulfate-paddy soil mixture.
In addition, E depends on microbial activation at the electrode. According to Yamasaki et al.
(2018), potential loss (activation loss) occurs during the flow of electrons into an electrode. In apotential measurement, activation loss should be minimized to obtain the equilibrium potential in ashort time. Microbial activation at the electrode is one of the methods for minimizing activation loss.
Wang et al. (2009) succeeded in improving electrode performance by lowering the potential losses byactivating bacteria at the electrode. As seen in Fig. 4, the decrease of soil ORP when cow manurecompost was used was larger than that when paddy soil alone was used. This may be explained in thatmicrobial activation occurs in the cow manure compost. Therefore, changes in the redox couple and thebenefits of microbial activation in soil can be understood through potential measurement.
Potential Measurement for Representing Soil Oxidation

Soil ORP (V vs Ag/AgCl)

Measurement did continue after day 50 for the case of using paddy soil alone. Germination was
confirmed on day 50. From the measurement taken (Fig. 5), soil ORP started to increase on day 50,
indicating the generation of soil oxidation. On day 170, the soil potential reached 0.32 V which is close
to the initial potential measurement taken. Additionally. the plant growth had occurred (Fig. 5).
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3

Germination
170 days after theexperiment started

0

10 20 30 40 50 60 70 80 90 100
Measurement time (d)Fig. 5 Changes in soil ORP of paddy soil due to soil oxidationⒸ ISERD

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The increase in soil ORP was caused by soil oxidation owing to the oxygen supply from theoverlying water layer. Plant growth may have also facilitated the oxygen supply of the soil layer. As
the electrode was placed at a depth of 30 mm from the soil surface, the oxygen supply may haveaffected these long-term measurement results. In other words, the proposed method of potential
measurement may also predict soil oxygen levels.
CONCLUSIONSIn this study, a laboratory experiment was conducted to examine a method for monitoring SCE and to
examine its validity in representing changes in SCE due to soil reduction, bacteria activation, and soiloxidation. It was found out that the proposed method, i.e., potential measurement, had high accuracy(with a variance of 1.33%) for representing SCE. A difference in equilibrium soil ORP was observed
when mixing different fertilizers with paddy soil, indicating that redox reactions in soils can bepredicted through potential measurement. In addition, different trends in decreasing soil ORP wereobserved, suggesting that chemical and biological reactions in soil can be understood from potential
measurement. Finally, soil ORP started to increase on day 50 because of soil oxidation, indicating that
potential measurement can predict soil oxygen supply that causes soil oxidation.
ACKNOWLEDGMENTS
The authors gratefully acknowledge the students of the Rural Environmental Engineering Laboratory,
Tokyo University of Agriculture, for their efforts in collecting data.
REFERENCES
Buresh, R.J., Sanchez, P.A. and Calhoun. F.G. 1997. Replenishing soil fertility in Africa. Soil Sci. Soc. America,
Special Publication, No 51.
Clark, M.S., Horwath, W.R., Shennan, C. and Scow, K.M. 1998. Changes in soil chemical properties resulting
from organic and low-input farming practices. Agron. J., 60, 662-671.
Hatfield, J.L. 2006. Erosion: On-site and off-site impacts. Encyclopedia of Soil Science, 2nd ed. Taylor& Francis.
Liesack, W., Schnell, S. and Revsbech, N.P. 2000. Microbiology of flooded rice paddies. FEMS Microbiol. Rev.,24, 625-645.
Matocha, J.C. 2005. Oxidation-reduction of contaminants. In Hillel, D. (Ed.), Encyclopedia of Soils in theEnvironment, Elsevier, 133-140.
Nagama, K., Yamaji, S., Miyatsu, T., Touch, N. and Hibino, T. 2018. Electrode potential-measured method andits practicality for monitoring the formation of hypoxic water. J. Jpn. Soc. Civ. Eng. Ser. B2 (Coast. Eng.), 74(2), 1189-1194 (in Japanese).
Nagatsu, Y., Tachiuchi, K., Touch, N. and Hibino, T. 2014. Factors for improving the performance of sedimentmicrobial fuel cell. J. Jpn. Soc. Civ. Eng. Ser. B2 (Coast. Eng.), 70 (2), 1066-1070 (in Japanese).
Prasad, R. and Power, J.F. 1997. Soil fertility management for sustainable agriculture. Lewis Publishers, CRC
Press, New York.
Wang, X., Feng, Y., Ren, N., Wang, H., Lee, H., Li, N. and Zhoa, Q. 2009. Accelerated start-up of twochambered microbial fuel cells: Effect of anodic positive poised potential. Electrochem. Acta, 54 (3), 11091114.
Yamasaki, R., Maeda, T. and Wood, T.K. 2018. Electron carriers increase electricity production in methane
microbial fuel cells that reverse methanogenesis. Biotechnol. Biofuels, 11, 211 (10 pages), Retrieved fromhttps://doi.org/10.1186/s13068-018-1208-7
Yan, B. and Hou, Y. 2018. Soil chemical properties at different toposequence and fertilizer under continuous riceproduction, A review. IOP Conf. Ser.: Earth Environ. Sci., 170, 032107 (5 pages). Doi :10.1088/17551315/170/3/032107.

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Research article
erdStudy on the Water Quality of Indawgyi Lake Affected bySurroundings
MOE TIN KHAING*
Kalay University, Myanmar
Email: moetin.khaing@gmail.com

DAUNG HAWNG
Myitkyina University, Myanmar
Received 30 December 2020

Accepted 19 May 2021(*Corresponding Author)

Abstract Water quality assessment was carried out at Indawgyi Lake, the biggest lake of
Myanmar. The main aim of this research is to evaluate the current status of lake water quality
affected by the surroundings and man-made activities for three seasons (cold, hot and rainy) in
2019. Some physicochemical properties such as temperature, pH, ORP, EC, NTU, TDS and DOof surface and deep water samples from each point and totally four different points wereanalyzed in situ by water quality monitor U-53-G(Horiba). The concentrations of Cr, Mn, Fe,
Cu, As and Hg were also examined by atomic absorption spectrometer (AAS) and total
hardness, total alkalinity, salinity and chloride were measured by their respective methods.
Nitrate and phosphate were detected by LaMotte, SMART 3 colorimeter. The condition of
COD and BOD of the lake water were also investigated by standard incubating methods. In
addition, counting of coliform was performed to detect pathogenic microorganisms of lakewater. The resulting values were compared with the standard limits of World HealthOrganization (WHO) and it was concluded that the lake was urgently needed to sustain thelong-life existence and its water qualities due to the findings of some physicochemical,
biological, toxic and pathogenic affects produced by the surroundings.
Keywords physicochemical properties, lake water, concentration, pollution,
standard guideline value

INTRODUCTION
Water pollution becomes a serious threat and its affect to human and environmental is very wide.
Freshwater ecosystems contain rivers, streams, ponds, lakes and wetlands. Fresh water is a main
resource of habitat for organisms and its quality is closely related with surroundings. Now freshwater
biodiversity has been challenged by many kinds of anthropogenic affects. Indawgyi Lake is not only thelargest lake of Myanmar but also the third largest inland lake in Southeast Asia. It is located in the
Mizaram-Manipur-Kachin Rain Forest Ecoregin in which it is the subtropical moist broad leaf forest
biome. It is situated in the Mohnyin Township, Kachin State of northern Myanmar and 180 km awayfrom the south-west of Myitkyina. Its measures are 13 km long from east to west, and 24 km wide from
north to south. Lake water flows from the south to the north and only one water outlet, Indaw chaung at thenortheast of the lake which finally combines into the Ayeyarwady River. Indawgyi area was officiallyrecognized as a wildlife sanctuary on August 9th 2004 and it has also been regarded as a Ramsar site on
February 2nd 2016. The entire lake basin has been declared as Biosphere Reserve on June 15th 2017 by
UNESCO Man and the Biosphere Reserve Programme-MA (Indawgyi Wildlife Sanctuary, 2018). It
has total area 815 km2 including the lake and lakeside which is 259 km2 with grasslands and thesurrounding forested watershed. It has 546 feet (166 m) above the sea level. Over 400 species of birds,Ⓒ ISERD

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109 species of water birds and 93 species of fishes and shrimps rely on the lake (Indawgyi Wildlife
Sanctuary, 2018).
There are 38 villages around the lake and some villages are on the bank of the lake. Ethnic groups
living in nearby villages are mostly the Red Shan and the Kachin and they earned for their livings from theagriculture, fishing and mining. They grow rice, orange, mustard, pineapple and dog-fruit. Now the lakehas been facing many issues such as deforestation, disposal of mine tailings, poor management ofwaste, depletion of fisheries, climate change and reducing lake area.
Some water researches have doneon it but there has not been still found a
research that determines for both of
surface and deep water qualities fromeach site until now. Therefore the aimof this research was to study thequalities of Indawgyi lake water
affected by the surrounding not only
from surface but also from bottom as
the short term one year assessment in2019. Seasonal sampling date wereJanuary 13 (as cold season), May 1 (ashot season) and July 25 (as rainy season)
Fig. 1 Map of the Indawgyi Lake, Mohnyin Township,
in 2019 (Daung Hawng, 2019).
Kachin State, Myanmar
OBJECTIVE
The objective of this research is to evaluate the current status of physicochemical and biologicalproperties of Indawgyi lake water which is polluted by the surroundings for three seasons (cold, hotand rainy) in 2019 and to contribute the local people to get the awareness on the relation between water
quality and the environment.
METHODOLOGY
In this research, determination of physicochemical properties of Intawgyi lake water was carried out on
8 samples both of surface (sites 1, 2, 3 and 4 ) and bottom (sites 1(a), 2(a), 3(a) and 4(a) ) of the lakewater from 4 different sites. They were sampling site-1 (Near Nyaung bin Village), site-2 (Near
Nantmee laung Village), site-3 (Near Center of the Lake), site-4 (Near Mamon kaing Village)
respectively. Some villages are on the lakeside and near to the entrance of the permanent and seasonal
inflowing steams. The depths of the water are different along the lake in everywhere randomly though
the center is the deepest part, and so water-depth, location, collecting time and temperature wererecorded for every sampling time. Some physicochemical properties of lake water such as pH,
electrical conductivity (EC), nephelometric turbidity unit (NTU), dissolve oxygen (DO), total dissolve
solid (TDS) and oxidation reduction potential (ORP) were measured by situ experiment by applying U53-G (Horiba), (Trace20-HM 2000, Nanova). The concentrations of chromium (Cr), manganese (Mn),iron (Fe), copper (Cu), arsenic (As) and mercury (Hg) were also examined by atomic absorption
spectrometer (AAS) and total hardness, total alkalinity, salinity and chloride were measured by theirrespective methods. Nitrate and phosphate were detected by LaMotte, SMART 3 colorimeter. The
condition of chemical oxygen demand (COD) and biological oxygen demand (BOD) of the lake water
were also investigated by standard incubating methods. In addition, counting of coliform was
performed to detect pathogenic microorganisms of the lake water.

Ⓒ ISERD73

IJERD – International Journal of Environmental and Rural Development (2021) 12-1

Sampling site-1 (Near Nyaung bin Village)

Sampling site-2 (Near Nantmee laung Village)

Sampling site-3 (Near Center of the Lake)

Sampling site-4 (Near Mamon kaing Village)

Fig. 2 Sampling sites
RESULTS AND DISCUSSION
Table 1 Physicochemical and biological parameters of cold season
Site
1
7.41
0.13
46.8
8.01
86

Site
1(a)
7.17
0.13
50.32
7.17
85

Site
2
7.69
0.2
2.16
6.45
88

Site
2(a)
7.43
0.2
6.29
7.44
92

Site
3
7.44
0.14
1.45
6.53
91

Site
3(a)
7.1
0.14
1.19
6.46
91

Site
4
7.54
0.14
3.13
7.96
93

Site
4(a)
7.36
0.14
5.18
4.29
96

Mni

Max

Aver

SD

n

7.1
0.13
1.19
4.29
85

7.69
0.2
50.32
8.01
96

7.393
0.155
16.803
6.661
90.3

0.189
0.030
21.07774.518
3.694

3
3
3
3
3

ORP (mV)
223
Total Alkalinity
120
COD (mg/L)
7.9
BOD( mg/L)
4.2
Salinity0
Total hardness
60(mg/L)
Cl- (mg/L)
4.96
Mn (mg/L)
0.01
Fe (mg/L)
0.16
Cu (mg/L)
0.06
Hg (mg/L)
0.02
Cr (mg/L)
0.02
0.095
As ( g/L)
NO3- (mg/L)
0.62
PO43- (mg/L)
0.08
Depth (m)0.05
E-coli4(CFU/100mL)
Temperature(°C) 20.76
Time (hr)
12.29

153
100
8
4
0
60

243
200
7.8
5.5
0
68

162
140
7.7
3.9
0
64

271
140
8
4.3
0
64

293
140
8.29
4.3
0
64

265
120
9.4
3.7
0
66

154
120
10.3
5.9
0
66

153
100
7.7
3.7
0
60

293
200
10.3
5.9
0
68

221
138
8.539
4.54
0
64

56.94629.761
0.929
0.791
0
2.828

3
3
3
3
3
3

WHO
(2017)
6.5-9.2
1.5
56001000
10
6
500

3.19
0.01
0.3
0.06
0.03
0.02
0.04
0.51
0.09
1.45
4

3.19
0.01
0.03
0.06
0.05
0.02
0.024
0.48
0.06
0.15
4

1.42
0.01
0.16
0.05
0.01
0.02
0.003
0.23
0.08
2
4

4.96
0.01
0.02
0.05
0.04
0.02
0.016
0.15
0.09
0.55
4

3.19
0.1
0.03
0.05
0.02
0.02
0.011
0.33
0.01
5.25
5

3.19
0.01
0.03
0.06
0.02
0.02
0.141
0.4
0.08
0.3
5

1.42
0.01
0.09
0.05
0.04
0.02
0.065
0.31
0.15
3.54
5

1.42
0.01
0.02
0.05
0.01
0.02
0.003
0.15
0.01

4.96
0.1
0.3
0.06
0.05
0.02
0.141
0.62
0.15

3.19
0.028
0.103
0.055
0.029
0.02
0.044
0.353
0.072

1.338
0.032
0.099
0.005
0.014
0
0.048
0.155
0.039

3
3
3
3
3
3
3
3
3

200
0.4
0.3
2
0.006
0.05
0.01
50
-

19.89
12.27

23
2.22

22.65
2.24

22
3.14

21.65
3.17

22.9
4.48

21.66
4.52

ParameterspH
Ec (mmhos/cm)
Turbidity (NTU)
DO (mg/L)
TDS (mg/L)

0

Table 2 Physicochemical and biological parameters of hot season
Parameters(units)pH
Ec(mmhos/cm)
Turbidity (NTU)
DO (mg/L)
TDS (mg/L)

Site
1
9.04
0.12
6.02
9.43
80

Site
1(a)
8.49
0.12
7.29
9.4
80

Site
2
8.31
0.14
1.86
8.98
90

Site
2(a)
8.06
0.14
2.53
8.16
90

Site
3
7.9
0.14
2.06
7.36
90

Site
3(a)
7.66
0.14
1.81
6.96
90

Site
4
9.04
0.14
5.76
9.57
90

Site
4(a)
8.18
0.14
8.22
8.66
90

Mni

Max

Aver

SD

n

7.66
0.12
1.81
6.96
80

9.04
0.14
8.22
9.57
90

8.338
0.134
4.558
8.505
87

0.502
0.009
2.660
0.988
4.629

3
3
3
3
3

ORP (mV)

141

198

224

247

217

240

139

196

139

247

198.8

41.223

3Ⓒ ISERD

74

WHO(2017)
6.5-9.2
1.5
56001000-

IJERD – International Journal of Environmental and Rural Development (2021) 12-1

Total Alkalinity

140

120

160

160

160

160

160

160

120

160

150

14.8803-

COD (mg/L)
BOD( mg/L)
Salinity
Total hardness(mg/L)
Cl- (mg/L)
Mn (mg/L)
Fe (mg/L)
Cu (mg/L)
Hg (mg/L)
Cr (mg/L)
As ( g/L)
NO3- (mg/L)
PO43- (mg/L)
E-coli(CFU/100mL)
Depth (m)Temperature(°C)
Time (hr)

8.7
3.8
0
62

9.2
4
0
92

8
2.5
0
80

7.5
2.7
0
76

9.5
5
0
82

11
6
0
78

14
6.5
0
72

18
7
0
74

7.5
2.5
0
62

18
7
0
92

11.14
4.7
0
77

3.575
1.709
0
8.619

3
3
3
3

10
6
500

4.96 4.96
0.15 0.12
0.05 0.05
00000.005 0.005
0.013 0.014
0.42
0.6
0.03 0.07
4
4

3.19
0.01
0.01
0.001
0
0.02
0.027
0.08
0.05
5

7.09
0.09
0.01
0.001
0
0.02
0.044
0.2
0.11
5

10.64
0.13
0.01
0.03
0
0.005
0.062
0.24
0
5

4.96
0.1
0.01
0.003
0
0.009
0.074
0.12
0.03
5

8.87
0.14
0.03
0.17
0.01
0.005
0.075
0.12
0.14
5

7.09
0.14
0.03
0.17
0.01
0.007
0.086
0.07
0.08
5

3.19
0.01
0.01
0
0
0.005
0.013
0.07
0

10.64
0.15
0.05
0.17
0.01
0.02
0.086
0.6
0.14

6.559
0.104
0.026
0.05450.003
0.01010.0494
0.252
0.065

2.435
0.045
0.018
0.077
0.005
0.007
0.029
0.188
0.046

3
3
3
3
3
3
3
3
3

200
0.4
0.3
2
0.006
0.05
0.01
50
0

0.65
1
28.07 27.73
12.17 12.19

1.25
28.39
12.58

2
28.41
1

1.75
28.14
2.37

5.15
27.88
2.39

0.6
29.36
4.12

2.30
29.26
4.13

SD

Table 3 Physicochemical and biological parameters of rainy season
Parameters(units)pH
Ec (mmhos/cm)
Turbidity( NTU)DO (mg/L)
TDS (mg/L)

Site
1
6.9
0.09
96.9

Site
1(a)
7.46
0.09
12

Site
2
7.46
0.13
13

Site
2(a)
6.09
0.13
27.4

Site
3
8.15
0.14
7.91

Site
3(a)
7.85
0.14
9.83

Site
4
7.94
0.13
12.3

Site
4(a)
6.5
0.13
11.8

Mni

Max

Aver

6.09
0.09
7.91

8.15
0.14
96.9

7.259 0.732 3
0.121 0.021 3
29.595 30.086 3

4.4
60

4.4
60

4.5
80

4.2
80

4.6
80

3.8
80

4.3
90

5.9
90

3.8
60

5.9
90

4.58
77

ORP (mV)
390
Total Alkalinity 100
COD (mg/L)
9.8
BOD (mg/L)
7.8
Salinity0
Total hardness
52(mg/L)
Cl- (mg/L)
0.2
Mn (mg/L)
0.06
Fe (mg/L)
0.3
Cu (mg/L)
0.08
Hg (mg/L)
0.001
Cr (mg/L)
0.02
0.01
As ( g/L)
NO3- (mg/L)
0PO43- (mg/L)
0.03
E-coli5(CFU/100mL)
Depth (m)0.65
Temperature(°C) 27.82
Time (hr)
1.32

405
120
10.6
8.3
0
50

216
160
9.2
8.5
0
72

434
160
9.5
9.3
0
60

442
160
13.5
10
0
74

373
160
15
12
0
64

281
140
17
15
0
64

286
200
20
17.5
0
66

216
100
9.2
7.8
0
50

442
200
20
17.5
0
74

348.5 82.285 3
150 30.237 3
13.38 3.998 3
11.37 3.523 3
00362.6 8.548 3

0.2
0.06
0.3
0.15
0.001
0.02
0.011
0
0.03
5

0.09
0.02
1.12
0.05
0.003
0.05
0.012
0.1
0.03
5

0.09
0.02
1.12
0.05
0.003
0.05
0.003
0.11
0.03
5

0.15
0.06
0.12
0.12
0.002
0.03
0.01
0.03
0.05
5

0.12
0.09
0.12
0.11
0.002
0.03
0.01
0.03
0.05
5

0.1
0.1
0.08
0.09
0
0.03
0.029
0.14
0.13
5

0.1
0.1
0.08
0.09
0
0.04
0.05
0.15
0.13
5

0.09
0.02
0.08
0.05
0
0.02
0.003
0
0.03

0.2
0.134 0.0467
0.1
0.063 0.0320
1.12 0.444 0.450
0.15 0.094 0.034
0.003 0.0015 0.001
0.05 0.034 0.011
0.05 0.0188 0.0153
0.15 0.071 0.062
0.5
0.191 0.207

1.65
27.58
1.34

0.55
30.36
2.39

2.9
30.13
2.4

0.45
30.33
3.28

8.5
30.27
3.3

0.75
30.33
4.58

3.33
30.37
4.59

n

0.610 311.650 3

3
3
3
3
3
3
3
3
3

WHO
(2017)
6.5-9.2
1.5
56001000
10
6
500
200
0.4
0.3
2
0.006
0.05
0.01
50
0

Sampling time for three seasons was afternoon but significant temperature difference could beseen in (Tables 1, 2 and 3). The highest water temperatures were found in the rainy season with therange 27.58-30.37 °C while the hot season was moderate with variation 27.73-29.36 °C and the coldseason showed as the least temperature with change 19.89-22.9 °C. Ongoing from surface to the depthⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2021) 12-1

along a vertical column, most of water samples showed their decreased temperatures but the difference
was small. Therefore the whole lake water seems to be well mixed which result from stirring by
frequent water transportation. Precipitation could be only seen in the early morning of cold season.
Although alkaline pH values were found in the hot season with the range 7.66-9.04, the rainy season
showed some acidic pH within the range 6.09-8.15. The lake reached its lowest water level due to the
reduction of seasonally inflowing streams into the lake except annually streams and therefore lakewater alone remained with the alkaline pH. In the rainy season, the lake and its all wetland areas were
flooded and therefore possible mine disposal and sewage directly touched with the lake water. It wasleading to have acidic pH value. DO is an important index of physical phenomenon and it is influenced
by many factors that affect the oxygen solubility, temperature, water movement and salinity etc. DO
range in the cold season was 4.29-8.01 mg/L, 6.96-9.57 mg/L in hot season and 3.8-5.9 mg/L in therainy season respectively. It was denoted that the DO value declined in the depth water than top waterand the highest amount was determined in the hot season of site 4, 9.57 mg/L (Table 2) and the lowest
value was detected in the rainy season of site 3 (a), 3.8 mg/L (Table 3).
In the cold season, COD values of sampling points with the range 7.7-10.3 mg/L showed thelesser amount than WHO standard limit, 10 mg/L except for site 4 (a), 10.3 mg/L. Three site 3(a), 4
and 4(a) out of eight sites exceeded the standard limit in the hot season with the variation 7.5-18 mg/L
and five sampling points except site 1, 2 and 2 (a) were above WHO limit in the rainy season with therange 9.2-20 mg/L. All BOD values of the cold season with the range 3.7-5.9 mg/L were observed
under the WHO standard limit 6 mg/L though the hot season within the range 2.5-7 mg/L surpassed thestandard limit in two sites 4 and 4(a) and also the rainy season in all sites varied 7.8-17.5 mg/L. It
showed that lower BOD and COD of the lake water than WHO limits could be observed at cold season
due to the relation with low temperature while most of the sampling sites of hot and rainy seasonsindicated higher BOD and COD amount than the WHO limits due to the relation with their hightemperatures. Though the acceptable WHO guideline value of nitrate for drinking water is 50 mg/L,
excess of 0.2 mg/L in the lake water indicate the possible eutrophic condition (WHO,1992). Seven
sampling points except site 3 in the cold season with the range 0.15-0.62 mg/L and three sampling
points, site 1, 1(a) and site 3 expressed their exceeded levels in the hot season with the range 0.07-0.6
mg/L. All sampling points for the rainy season with the range 0-0.15 mg/L lie under 0.2 mg/L set by
WHO. Due to the literature, the concentration of phosphate ranges 0.005-0.020 mg/L in most surface
waters and higher than that support to the eutrophication (WHO, 1992). Most of the sampling points ofthree seasons (Tables 1, 2 and 3) showed exceeded phosphate level than WHO guideline limit. The
lake has potential extent of nitrogen and phosphorous because a large outbreak of toxin producing
cyanobacteria (blue green algae) in the whole lake, the excessive growth of aquatic plants, alkaline pH,
oxygen depletion (DO) in bottom waters, decomposing macrophytes and algae in the sediment werealso responsible for eutrophic condition. In addition, livestock and sewage from the agricultural run-off
increased the amount of waste nutrients and then finally released the nutrients indirectly into the lake.
Then more and more sedimentation set up and deposited especially in the north and south-edges of thelake which made the beds to rise up. Sediment caused water pollution, affected aquatic biota bysmothering fish nursery area and wildlife by narrowing water channels and reducing lake basin area
(Indawgyi Wildlife Sanctuary, 2018).
Among three seasons, the highest amount of total hardness 92 mg/L was observed at site 1(a) of
the hot season with the range 62-92 mg/L and it was due to the evaporation of water by hightemperature. All sampling points of three seasons showed the lower amounts of total hardness (Tables
1, 2 and 3) than the permissible limit, 500 mg/L prescribed by WHO for drinking water (WHO,2017).The fresh water of the lake had not been detected the salinity. For all seasons, most of thesampling sites could be observed the concentrations of Fe under the guideline value 1.0 mg/L set by
WHO for drinking water standard (WHO, 2017). The concentrations of Cu in all samples (Tables 1, 2
and 3) lie below the WHO guideline value 2.0 mg/L. The investigation of As pointed out as the lower
levels in µg/L for all sampling points (Tables 1, 2 and 3) than WHO guideline value of drinking waterⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2021) 12-1

standard 0.01 mg/L. By comparison with WHO standard of Cr 0.05 mg/L, all sampling points
contained the lesser amount (Tables 1, 2 and 3) than the standard values. Though all metal constituents
showed with lower concentrations than the WHO limits, three heavy metals such as Hg, Cr and As
could affect to the water quality with their impacts as possible as they contain. All EC values (Tables 1,
2 and 3) which lie under guideline limit 1.5 mmhos/cm set by WHO informed as the existence of low
dissolved salts in the lake. Lake water also showed the lower TDS levels of all sampling points forthree seasons (Tables 1, 2 and 3) than the WHO limit within 600-1000 mg/L. ORP indicates the stateof oxidation and reduction process going on in water: a low value of ORP indicates high reduction
while a high value for oxidation. The ranges were 153-293 mV in the cold season, 139-247 mV in the
hot season and 216-442 mV in the rainy season respectively. Turbidity is caused by particular matterpresent in water such as clay, silt, colloidal particles, plankton and other microscopic organisms. In thisresearch, higher amounts of turbidity units than the WHO standard limit 5 NTU were found in most of
the sampling points of the cold, hot and rainy seasons were with the ranges 1.19-50.32 NTU, 1.81-8.22
NTU and 7.91-96.9 NTU but the outstanding values such as 96.9 NTU in site 1 of the rainy season
maybe probably resulted by touching of detector to the bed. North-west corner of Indawgyi lake, site 1
and 1 (a) were especially suffering from the overloading of particulate matters because these two werevery closed to the village and annual inflowing streams. This problem maybe probably the serious
threat of pollution to the transparency of lake water in later. Escherichia coli was considered as the
biological threat throughout the lake because it was found that 4-5 CFU/100 mL of all sampling sites(Tables 1, 2 and 3) along the west-lakeside from the north to the south due to direct latrine into theopen water. E. coli in drinking water set by WHO is 0 CFU/100 mL (WHO, 2017). The result showedthat it is necessary to carry out a proper water treatment for daily use and drinking water. Among threeseasons, the rainy season does affect the most to the quality of lake water with the acidic pH, thehighest turbidity, COD and BOD. The least affected season is the hot season which possesses thealkaline pH, the lowest turbidity, COD and BOD, the lowest contaminated mercury and chromium
level than the other two seasons. Fish catches have decreased in the lake over the last five years offisheries and late-rain in the rainy season were the current impacts to the environmental (Indawgyi
Wildlife Sanctuary, 2018). It could be still noticed the relatively high transparency of the lake water
about into 3.5m. But serious turbidity unit could replace the transparency of the lake water if theappropriate management was not applied to control the possible pollution sources in time. Natural
aging process and filling to the lake with sediment slowly over time could support the eutrophication of
the lake. The breeding of invasive species, water hyacinth and plants nutrients dominated many areasof the surface water and it could produce the reduction of the lake area for the aquatic biota. The use ofdetergents and soap, agricultural run-off, organic matter and sewage could increase the amount of
nutrients and encourage the phenomenon of eutrophication. Starting point of plankton growth could befound on the surface water of the north-west part of the lake. In this area, reddish-yellow color water
could be observed the whole year as shown in the Fig. 2. In conclusion, it could be said thatphysicochemical and biological properties of lake water quality was significantly suffered from the impactsof surroundings and needed to sustain its quality for the health of human and environmental.
CONCLUSION
This research was conducted as the assessment of the water quality of Indawgyi lake, the biggest lake
in Myanmar and performed some physical, chemical and biological examinations of the surface anddepth-water. The results obtained of the present work showed their significant variation with seasonal
fluctuations. The current situation and challenges of Indawgyi lake water were increasing withsedimentation, potential eutrophication and pollution of water quality by chemical, physical andbiological contaminants. The main causes of these problems were probably excessive human activity
such as domestic uses, agricultural runoff, recreation, tourism, fishing, nutrient submerged and floatingⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2021) 12-1

plants from the surrounding. The lake is now needed to be control for water quality and long-live of
biodiversity especially for agricultural drainage, gold mining effluents, excess nutrients and sewage
from direct toilets to prevent sedimentation and eutrophication of the lake and to protect the hazard ofwater pollution for health of populations from the surrounding area.
ACKNOWLEDGEMENTS
We would like to thank to Department of Environmental Conservation, Myitkyina and Mohnyin and Wildlife
Sanctuary, Indawgyi Lake for warm explanation, transportation, sampling, testing and giving permission to apply
the laboratory.

REFERENCES
Akaishi, F., Satake, M., Otaki, M. and Tominaga, N. 2006. Surface water quality and information about theenvironment surrounding Inle Lake in Myanmar. The Japanese Society of Limnology, 7, 57-62.
Hawng, H. 2019. Determination of water quality of Indawgyi Late. MSc Thesis. Department of Chemistry,
Myitkina University, Myanmar.
Indawgyi Wildlife Sanctuary an Fauna and Flora International. 2018. Indawgyi Wildlife Sanctuary management plan
(Part-II). Appendices (2018-2019 to 2022-2023).
World Health Organization (WHO). 1992. Water quality assessment: A guide to use of biota, sediment and water inenvironmental monitoring. 2nd ed., Geneva, Switzerland.
World Health Organization (WHO). 2008. Guidelines for drinking-water quality. 3rd ed., Incorporating the Firstand Second Addend, Volume 1, Geneva, Switzerland.
World Health Organization (WHO). 2017. Guidelines for drinking-water quality. 4th ed., Incorporating the First
Addendum, Geneva, Switzerland.

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Research article
erdExperimental Study of Extension Impact on Farmers’ KAP
towards Sri Lankan Cassava Mosaic Disease Prevention in
Cambodia
SOVANNARA MOUN
Faculty of Agriculture and Food Processing, National University of Battambang, Battambang,
Cambodia

HISAKO NOMURA
Faculty of Agriculture, Kyushu University, Fukuoka, JapanTAMON BABA
College of Letters, Ritsumeikan University, Kyoto, Japan

THA THAN
Faculty of Agriculture and Food Processing, National University of Battambang, Battambang,
Cambodia

KASUMI ITO*
International Center for Research and Education in Agriculture, Nagoya University, Nagoya, Japan
Email: kasumito@agr.nagoya-u.ac.jp

PAO SREAN
Faculty of Agriculture and Food Processing, National University of Battambang, Battambang,
Cambodia
Received 12 January 2021

Accepted 23 June 2021(*Corresponding Author)

Abstract The Sri Lankan Cassava Mosaic Disease (SLCMD) poses a major threat to thecassava industry in Cambodia, as it can decrease cassava yield by up to 80 percent. As SLCMD
has no clear prescription, and currently, the only remedy is pulling out and incinerating theinfected stems. The government of Cambodia, through the General Directorate of Agriculture(GDA), has moved swiftly to sensitize farmers on the causes, effects, and prevention measuresof SLCMD. The GDA has used various media and methods to disseminate knowledge on
SLCMD. However, effective information dissemination methods for changing farmers’
behaviors in terms of the knowledge, attitudes, and practices (KAP) still remain to be identified.
In this study, we investigate the effectiveness of two knowledge dissemination methods, i.e.,“single intervention”—distribution of printed educational materials (PEMs) and “multifacetedintervention”—distribution of PEMs combined with workshop training. The study was
conducted in Battambang, north-west Cambodia over two periods, from June to October 2019,and from November 2019 to February 2020. In the first period of investigation, 468 farmers
were randomly selected to participate in the study. We formulate the contents of the poster andworkshop based on the “initial” KAP results where farmers had lower KAP. Then, all 468
farmers were divided randomly into three groups, namely “Control,” “Treatment1,” and“Treatment2.” farmers in “Treatment1” were subjected to “single intervention” and those in“Treatment2” were subjected to “multifaceted intervention,” while those in “Control” were
subjected to no intervention. In the second period of investigation, the “second” KAP wasanalyzed to estimate the effectiveness of interventions applied in the first period. The results
show that “multifaceted intervention” is the effective method to improve the KAP of farmers inⒸ ISERD

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Cambodia. Our experience of running such farmers’ workshops and the materials we developed
could be useful to governments, non-governmental organizations, and commercial associations
that are keen to mitigate the effects of SLCMD through appropriate interventions.
Keywords field experiment, extension, KAP, SLCMD, RCTINTRODUCTION
Cassava is the second major income source for Cambodian small-scale farmers after rice. In 2019, thenumber of small-scale cassava farmers in Cambodia was above 300,000 (Codes et al., 2019). From2018 to 2019, land area under cassava cultivation increased from 611 thousand hectares to more than
624 thousand hectares (MAFF, 2019). In 2019, Cambodian cassava yield was, on average,
approximately 27 tons per hectare. The total harvested area was approximately 504 thousand hectares(FAOSTAT, 2019). The corresponding benefit-cost ratio analysis shows that for every 1 riel invested
in cassava farming, a profit of 1.31 riel was realized (Thav, 2017).
However, the increasing number of pests and cassava diseases poses a major threat to the quantityof cassava produce. This may in turn harm local food industries and consequently the national
economies in cassava producing countries (Chanda et al., 2016). For example, cassava crops in Africa
are being attacked by pests (such as the cassava green mite (CGM), the cassava mealybug, and thevariegated grasshopper) and diseases (such as the cassava mosaic disease (CMD), the cassava brownstreak disease (CBSD), and the cassava bacterial blight diseases) (Vanessa et al., 2011). In Africa,
CMD is the most harmful of the cassava diseases. Data have shown that it can reduce cassava yield by
up to 90 percent (Hahn et al., 1980). In Cambodia, the Sri Lankan cassava mosaic disease (SLCMD) isthe most harmful. It was first discovered at a commercial farm in Ratanakiri province, Eastern
Cambodia in May 2015 (Wang et al., 2016). By 2019, approximately 12 provinces had an outbreak of
the SLCMD (MAFF, 2019).
Some of these outbreaks have been attributed to the whitefly and stem transmissions (Vanessa etal., 2011 & Minato et al., 2019). Currently, integrated insect pest and pathogen control methods are
hardly used, owing to little sensitization of farmers on their availability and the trivial effort put by
manufacturers into the manufacture of needed equipment. Biological methods seem to be the popular
insect pest and pathogen control choice among farmers.
Therefore, there is an urgent need for improved uninfected planting tools (Nsiah-Frimpong et al.,2020). To address this situation in Cambodia, many strategies have been employed, including
controlling the movement of planting materials, selecting healthy and uninfected seedlings,
encouraging farmers to regularly check their fields, mapping the infected and non-infected areas, andimproving communication between farmers and local agricultural extension agencies (MAFF, 2017,2019). In addition, the general directorate of agriculture (GDA) has adequately sensitized farmers on
SLCMD via field-day workshops, posters, and information leaflets. Further, the GDA has produced an
educational video clip titled “managing the cassava mosaic disease,” which can also be found on social
media.
To cure infected cassava stems, the National University of Battambang, supported by the Japaneseinternational cooperation agency and the Japan Science and technology agency, has propagated tissue
culture of cassava (Tokunaga et al., 2020). Existing studies have recommended workshop training to
improve the knowledge, attitudes, and practices (KAP) in managing the pests and diseases of cassava(Chikoti et al., 2016 and Nsiah-Frimpong et al., 2020). Disseminating the relevant knowledge tofarmers could be the most splendid strategy for managing SLCMD (Houngue et al., 2018). However,there is no study that can help identify the information that the farmers are lacking. Further, and it has
not been determined which of the information dissemination methods is more effective in changing the
KAP of farmers. Therefore, in this study, we endeavor to identify the knowledge gaps and investigate
the effectiveness of two knowledge dissemination methods, i.e., “single intervention”—distribution ofⒸ ISERD

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printed educational materials (PEMs) and “multifaceted intervention”—distribution of PEMs
combined with workshop training.
OBJECTIVE
The main objective of this study is to evaluate the effectiveness of the current information
dissemination methods compiled by the project in order to examine farmers’ behavioral change interms of the KAP towards pest and disease control. We will determine if the information dissemination
methods have been effective in discouraging the re-use of contaminated seedlings and suggest policychanges that will help mitigate further damage that may be caused by the SLCMD.
METHODOLOGY
Study Site
Battambang was selected as the study site. Battambang is the largest cassava producing province innorthwest Cambodia. The total area under cassava cultivation in Battambang is 112,543 hectares whichis ~ 18 % of the total area under cassava cultivation in Cambodia (MAFF, 2019).

Fig. 1 Study area in Battambang Province, Cambodia
Research Design and Data Collection
The study was conducted over 2 periods—from June to October 2019 and from November 2019 toFebruary 2020 (Fig. 1). In the first period of the investigation, between June and August 2019, 468
farmers were randomly selected to participate in the study. Their “initial” KAP was analyzed,
following which all the 468 farmers were divided randomly into 3 equal groups—“Control,”“Treatment1,” and “Treatment2,” using Stata version 16 (a statistic software). Randomization was
evenly stratified at the commune level by gender, age, education, and knowledge level. Farmers in“Treatment1” received printed educational materials (PEMs) in the form of posters. Those in“Treatment2” received PEMs in the form of posters and were sensitized in educational workshops.
Those in “Control” did not receive any sensitization material. In the second period of investigation, the“second” KAP was analyzed to estimate the effectiveness of interventions applied in the first period.

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For this investigation, we received responses from 310 of the 468 research participants. This is because
158 farmers did not cultivate cassava during the second period of investigation.
1st survey(Jun.- Oct., 2019)
Farmers in
Treatment1
group

Knowledge regarding pest anddisease management disseminatedIntervention
treated with poster
by intervention

2nd survey(Nov.2019Feb.2020)

Intervention treated with workshop andposterNo-intervention or treatment

Farmers inControl group
Knowledge,
Attitude, Practicelevel

Knowledge,
Attitude, Practicelevel (afterFig. 2 Randomized Control Trial (RCT) design for Knowledge treatment)
transfer
Analysis of changing

Table 1 Results of randomization at the commune6 level
GroupsTreatment1
Plov Meas
Treatment2
Ampil 5 Derm
Control
Khleang Meas
Note: Data summarized by the authors

Communes
Sdao
Kdol Tahen
Onderk Herb

Treng
Reaksmey Sangha

Observations
231
157
80

Percentage (%)
49.35
33.54
17.09

Intervention Design
Single intervention - Printed educational materials (PEMs)7:
The PEMs distributed in the form of posters contained text, photos, illustrations, and spaces adorned inbright colors to attract the attention of readers. The majority of the information contained in the posters
was about SLCMD. Specifically, the causes, effects, and prevention measures of SLCMD. Informationon the transmission mechanisms of the disease was put in a conspicuous section of the poster.
Information on the devastating effects of SLCMD in Cambodia and Africa was shown via images andtext. SLCMD prevention and control measures were illustrated by cartoons, which made the poster
more accessible. The designers of the posters took great care to make sure that information visualizedby photos and cartoons could not be misunderstood or misinterpreted. The posters had a horizontal
layout. The size of posters (A3) was carefully decided to enable farmers to easily hang them on walls
and/or fold them in half during sessions of group/community information sharing.
Multifaceted intervention:
The multifaceted intervention involved combined use of PEMs distributed in the form of posters (thesame as used in the single intervention—PEMs) and a workshop. The workshop was held to improve
the knowledge of farmers on pest and disease management. Information disseminated in the workshop
was compiled based on pre-identified gaps in the farmers’ understanding, awareness, and practices.
The gaps were identified in the first survey. Similar to the poster’s design process, the workshop
content was created by the experts of agronomics from the National University of Battambang. The
experts took into account the language, culture, and literacy of the targeted farmers. These farmers;social data were collected during the first period of our investigation. Moreover, the workshops
included a Q&A session where misunderstandings of the audience were clarified. The audience wasalso allowed to share their experiences with SLCMD. We also used the workshops to collect more data
on SLCMD by asking the farmers what they deal with in real life.

7 Printed educational materials in the form of poster will be included in the Annex.

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Questionnaire:
The field survey was conducted in Rattanak Mondol, Reaksmey Songha, Plov Meas, Sdao, Treng,
Onderk Herb, Ampil 5 Derm, Kdol Tahern, and Kleang Meas communes (Table 1), and 468 samples
were collected. There were 5 parts to the questionnaire: the first part was about the correspondents’socio-demographic status; we concentrated on the participants’ knowledge, awareness, and practices inthe second, third, and fourth parts, respectively; the final part focused on the farmers’ understanding ofwillingness to pay for healthy cassava seedlings.
Data analysis methods:
The data were analyzed using Stata version 16. From the ex-ante data (first survey) and the ex-post
data (second survey), we scored the answers as 1 for correct and 0 for incorrect. For questions withmultiple answers, if the respondents chose more than one answer, it would automatically be classed asincorrect and scored as 0. To estimate the effects of the two knowledge interventions (PEMs andmultifaceted intervention), we used the differences between the treatment groups (Treatment 1 andTreatment 2) in the first and second surveys to compare with the difference in “Control” via t-test.
RESULTS
The paired t-test was conducted to compare the differences between before and after treatments,
namely difference-in-difference (DiD) among “Control” to “Treatment 1” and “Treatment 2.” Thedifferences in the means were calculated. Both DiD of mean differences of “Treatment1” versus“Control” and that of “Treatment2” versus “Control” showed statistical significance at a p-value of0.01. Table 2 shows that improved knowledge of whitefly and SLCMD symptoms in “Treatment1” and“Treatment 2” versus “Control” was significantly different at a p-value of 0.01. Likewise, getting toknow the name “whitefly” was significantly different at a p-value of 0.01 in “Treatment 2” and a pvalue of 0.10 in “Treatment 1.” Farmers’ knowledge after receiving dissemination from both “single
intervention” and “multifaceted intervention” increased at a p-value of 0.01.
Table 2 T-test results of Farmers’ Knowledge
Definition of variable
Variable(Correct=1; incorrect=0)

Mean-Diff (2

nd

st

–1 )

Coefficient

Control
Treatment1
Treatment2group (n=51) group (n=165) group (n=94)

Control vs.
Treatment1

Control vs.
Treatment2

KN_whitefly

Whitefly is an insect
pest.

-0.039

0.151

0.351

0.143 ***

0.185 ***

KN_slcmd

Do you know SLCMD?

0.156

0.557

0.861

0.200 ***

0.363 ***

KN_whitename

Name of whitefly

0.140

0.234

0.554

0.068 *

0.226 ***

KN_whiteanswer

Whitefly causing thesymptoms

0.039

0.327

0.489

0.304 ***

0.488 ****** Significant at 0.01 ** Significant at 0.05 *Significant at 0.1- Non-significant

As can be seen from the above table, before the distribution of PEMs and/or workshops, themajority of the respondents were not aware of the precariousness of getting their cassava seedlings
from a neighbor or a middleman. Additionally, they were not aware of the benefits of getting theircassava seedlings from a certified healthy seedling distributor (Table 3). It means that farmers continue
to face the risk of getting infected stems. On the other hand, when farmers are asked “I can dosomething to prevent (cassava) plants, Treatment1 seems to have more positive more than the farmers
in the Control while farmers in Treatment 2 were not significantly different from that of Control.
Moreover, farmers of both Treatment1 and Treatment 2, felt they need more authority advice regarding
SLCMD and farmers’ attitudes of both groups improved at a p-value of 0.01. Treatment 2 felt theimportance of authority advice more than farmers in Treatment 1.
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Table 3 T-test results of Farmers’ AttitudendDefinition of variable
Variable

AT_neighbor’s stem
AT_middle man’sstem
AT_certified healthy(Strongly diagree=1 tostrongly agree=5)
Neighbor's stem can reduce
the risk of SLCMD.
Middle man's stem canreduce the risk of SLCMD.
Certified healthy's stem canreduce the risk of SLCMD.

Mean-Diff (2

st

–1 )

Coefficient

Controlgroup (n=51)

Treatment1
group (n=165)

Treatment2group (n=94)-0.0780.090-0.510-0.745-0.915-0.765-0.486-0.236-0.567

Control vs.
Treatment1

AT_I_can_do_s.th

I can do something toprevent plant.

0.529

0.446

0.557

0.503 ***

AT_authorityadvise

It is important to know
SLCMD.

1.000-0.496-0.5740.568 ****** Significant at 0.01 ** Significant at 0.05 *Significant at 0.1

Control vs.
Treatment2

0.891 ***- Non-significant

Table 4 indicates that the mean of purchasing seedlings for planting from a trusted source
increased for both groups. On the other hand, the mean of timely spraying of insecticide to whitefly inneither group was significantly different. Finally, the mean of removing all cassava debris suspected ofbeing SLCMD in Treatment1 versus Control was significantly different at a p-value of 0.1. Also, it was
not significant in Treatment 2 versus Control, the direction was not to remove the debris. This mightmean that farmers have gotten the wrong impression after receiving the contents in PEMs andworkshops and can be a scope for improvement of the contents in the future training.
Table 4 T-test results of Farmers’ Practice
Mean-Diff (2nd – 1st)

Definition of variable
Variable(Never=1,
Sometimes=2,
Always=3)

Control
Treatment1 group
group (n=51)(n=165)

Coefficient
Treatment2group (n=94)

Control vs.
Treatment1

Control vs.
Treatment2

PR_trusted sources

Purchasing cassava
stem from trusted
sources.

0.098

0.418

0.414

0.320 ***

0.317 ***

PR_insecticidewhiteflyTimely treatment ofwhitely.

-0.2940.000-0.3080.294-0.014

PR_removed_debris

Removing all cassava
debris suspected ofbeing SLCMD-0.254-0.727-0.276-0.472 *-0.022*** Significant at 0.01 ** Significant at 0.05 *Significant at 0.1 - Non-significant

DISCUSSION
Our results show that the dissemination of information via the multifaceted intervention is the more
effective method of increasing farmers’ KAP. Previous studies had indicated that the majority offarmers who participated in workshop training became more aware of the cassava virus diseases (Eni etal., 2019). Nishiah-Frimpong et. al. also emphases the importance of training farmers on integrated
methods of insect pest and disease control (2020). It is also possible that the workshops increased theiroutreach by extending the programs to the less literate farmers. In addition, other studies have positedthat farmers are more susceptible to consume knowledge when it is disseminated by experts (Houngue
et al., 2018).
However, we also found out that neither of the two methods sufficiently improved the attitudes of
farmers toward disease preventive measures. When we asked whether they believe “certified healthystem can reduce the risk of SLCMD,” in the attitude question (Table 3), farmers in all groups slightlyⒸ ISERD

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shifted from disagreeing to agree; however, it was not statistically significant. On the other hand, when
we asked their purchasing behavior in the practice question (Table 4), farmers indicated that theywould purchase items for the next propagation from trusted sources. It could suggest that the majorityof them did not believe that purchasing the seedlings from trusted sources can reduce the risk of
SLCMD, and they would still face potential infection after purchasing healthy stems. On the other
hand, farmers both in Treatment1 and Treatment2 would purchase stems for next propagation fromtrusted sources. It indicates that they are more aware of the potential risk of SLCMD even for healthy
seedlings, but still purchasing stems from a trusted source is important to reduce the risk of gettingstems infected from seedlings. Another reason could come from the fact that although multifacetedintervention enabled farmers to understand the importance of purchasing the seedlings from trusted
sources, the majority of them did not believe that purchasing the seedlings from trusted sources can
reduce the risk of SLCMD. noticeably, the choice of cassava seedlings for the majority of the farmers
depends on their agronomical traits, such as tuber yield, fast harvesting, resilience in the soil, andadaptability to drought (Bentley et al., 2017). Also, the reluctance of farmers to purchase seedlings
from trusted sources can be attributed to the farmers realizing that purchasing seedlings from a trusted
source cannot by itself reduce the risk of SLCMD transmission unless farmers implement regularmonitoring and pest treatment management.
Moreover, the workshop disseminated information on well-known SLCMD symptoms and how to
identify uninfected cassava seedlings for the next planting session. Farmers who could not comearound to purchasing seedlings from trusted sources were taught how to propagate their own stems as
an alternative mitigation measure of reducing the spread of SLCMD (Mulenga et al., 2016). The
workshop with the poster group did significantly improve farmers’ practices, for example, purchasing
cassava seedlings from trusted sources. Our findings are consistent with those of a study conducted in
Nigeria (Ebewore and Isiorhovoja, 2019), which acknowledged that training is a major source of
information to cassava producers, and without training, only approximately 17.0% of the farmers will
(N=569) practice disease management.
Further, the study revealed that although spraying insecticide the whitefly is crucial to curbing the
spread of SLCMD, farmers were not spraying the insecticide. The two possible explanations are: (1)farmers have unsuccessfully tried to spray the insect before and had eventually given up; (2) farmers
do not have sufficient finances to spray the insect. Previous studies showed that approximately 91.1%(N=90) of farmers were familiar with insect pests and diseases as causes of significant damage to thecassava crop, but they could not distinguish between the treatments of insect pests and diseases(Chikoti et al., 2016). From the results, it became clear the need to emphasize the importance of early
treatment of whitefly, as it is the vector of SLCMD spread.
CONCLUSION
The objective of this study was to estimate the effectiveness of knowledge dissemination methods inchanging farmers’ KAP towards pest and disease management. We sought to determine which method
is effective in discouraging the re-use of contaminated seedlings. We also suggest policy changes thatmight help mitigate further damage due to the SLCMD. In general, we found that dissemination of
information via workshop training, combined with the distribution of PEMs is an effective way of
improving farmers’ KAP and, consequently, prevent the spread of SLCMD. Nonetheless, as effective
as workshop training combined with the distribution of PEMs was, this method did not sway farmers’
KAP completely. In this regard, we suggest that for future training, trainers should emphasize theimportance of early mitigation practices in minimizing yield loss. The emphasis is likely to further
improve farmers’ KAP. To do so, trainers should inform farmers of the actual yield loss per hectare
they are likely to incur and consequent income loss if they neglect the early mitigation. Where possible
trainers should give real-life examples that the farmers can relate to. This way, the farmers will see theⒸ ISERD

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magnitude of the potential yield and income loss. Nevertheless, our experience of running such
farmers’ workshops and the materials we developed could be evolved and improved, and they could be
useful to governments, non-governmental organizations, and commercial associations that are keen to
mitigate the effects of SLCMD through appropriate interventions.
ACKNOWLEDGEMENTS
This research was supported by the Science and Technology Research Partnership for Sustainable
Development (SATREPS) in collaboration with the Japan Science and Technology Agency (JST,JPMJSA1508) and Japan International Cooperation Agency (JICA)
REFERENCES
Bentley, J., Olanrewaju, A., Madu, T., Olaosebikan, O., Abdoulaye, T., Assfaw, W.T., Manyong, V., Kulakow,
P., Ayedun, B., Ojide, M., Girma, T.G., Rabbi, I.Y., Asumugha, G. and Tokula, M. 2017. Cassava farmers’
preferences for varieties and seed dissemination system in Nigeria: Gender and regional perspectives.
International Institute of Tropical Agriculture, Ibadan.
Chanda, P.I.N. and Schiller, J.M. 2016. Farmers perceptions on the causes and effects of cassava witches’ broom(CWB) on cassava production in three provinces of Cambodia, 186-191.
Chikoti, P.C., Melis, R. and Shanahan, P. 2016. Farmer’s perception of cassava mosaic disease, preferences andconstraints in Lupaula Province of Zambia. American Journal of Plant Sciences, (07), 1129-1138. Retrieved
from https://doi.org/10.4236/ajps.2016.77108
Codes, P., contribution, F.A.O., Info, C. and Representation, F.A.O. 2019. Strengthening capacity in managing
the incursion of cassava mosaic virus infestation in Cambodia May 2019. October 2016, 0-8.
Ebewore, S.O. and Isiorhovoja, R.A. 2019. Knowledge status and disease control practices of cassava farmers in
Delta State, Nigeria: Implications for extension delivery. Open Agriculture, 4 (1), 173-186. Retrieved fromhttps://doi.org/10.1515/opag-2019-0017
Eni, A.O., Efekemo, O.P. and Onile-ere, O.A. 2019. Farmer knowledge of cassava mosaic disease andmanagement practices in Ogun State. Twenty Fourth Annual Conference of the Agricultural Extension
Society of Nigeria, December, 232-237. Retrieved from https://doi.org/10.4314/jae.v24i1.28S
FAOSTAT. 2019. Data cassava yield in Cambodia, Retrieved from http://www.fao.org/faostat/en/#data/QC
Hahn, S.K., Terry, E.R. and Leuschner, K. 1980. Breeding cassava for resistance to cassava mosaic disease.
Euphytica, 29 (3), 673-683. Retrieved from https://doi.org/10.1007/BF00023215
Houngue, J.A., Pita, J.S., Cacaï, G.H.T., Zandjanakou-Tachin, M., Abidjo, E.A.E. and Ahanhanzo, C. 2018.
Survey of farmers’ knowledge of cassava mosaic disease and their preferences for cassava cultivars in threeagro-ecological zones in Benin. Journal of Ethnobiology and Ethnomedicine, 14 (1), 29. Retrieved fromhttps://doi.org/10.1186/s13002-018-0228-5
Minato, N., Sok, S., Chen, S., Delaquis, E., Phirun, I., Le, V.X., Burra, D.D., Newby, J.C., Wyckhuys, K.A.G.
and de Haan, S. 2019. Surveillance for Sri Lankan cassava mosaic virus (SLCMV) in Cambodia and Vietnam
one year after its initial detection in a single plantation in 2015. PLoS ONE, 14 (2), 1-16. Retrieved fromhttps://doi.org/10.1371/journal.pone.0212780
Minsitry of Agriculture, Forestry and Fishery (MAFF). 2017. Monthy report on December 2017. Phnom Penh.
Ministry of Agriculture, Forestry and Fishery (MAFF). 2019a. Report on establishing sustainable solution tocassava disease in mainland Southeast Asia. Phnom Penh.
Minsitry of Agriculture, Forestry and Fishery (MAFF). 2019b. Monthy report on October 2019. Phnom Penh.
Mulenga, R.M., Legg, J.P., Ndunguru, J., Miano, D.W., Mutitu, E.W., Chikoti, P.C. and Alabi, O.J. 2016. Survey,
molecular detection, and characterization of geminiviruses associated with cassava mosaic disease in Zambia.
Plant Disease, 100 (7), 1379-1387. Retrieved from https://doi.org/10.1094/PDIS-10-15-1170-RE
Nsiah-Frimpong, B., Oppong, A., Prempeh, R., Appiah-Kubi, Z., Abrokwah, L.A., B. Mochiah, M., N. Lamptey,J., Manu-Aduening, J. and Pita, J. 2020. Farmers’ knowledge, attitudes and practices towards management of
cassava pests and diseases in forest transition and Guinea savannah agro-ecological zones of Ghana. Gates
Open Research, 4, 101, Retrieved from https://doi.org/10.12688/gatesopenres.13114.1Ⓒ ISERD

86

IJERD – International Journal of Environmental and Rural Development (2021) 12-1

Thav, S. 2017. Economic analysis of cassava production in Cambodia. International Journal of Science andResearch (IJSR), 6 (6), 2403-2406. Retrieved from https://doi.org/10.21275/ART20174781
Tokunaga, H., Anh, N.H., Dong, N.V., Ham, L.H., Hanh, N.T., Hung, N., Ishitani, M., Tuan, L.N., Utsumi, Y.,
Vu, N.A. and Seki, M. 2020. An efficient method of propagating cassava plants using aeroponic culture.
Journal of Crop Improvement, 34(1), 64-83. Retrieved from https://doi.org/10.1080/15427528.2019.
1673271
Vanessa, B., Campo, H. and Hyman, G. 2011. Threats to cassava production: Known and potential geographic
distribution of four key biotic constraints. Food Security, 3 (3), 329-345. Retrieved from https://doi.org/
10.1007/s12571-011-0141-4
Wang, H.L., Cui, X.Y., Wang, X.W., Liu, S.S., Zhang, Z.H. and Zhou, X.P. 2016. First report of Sri Lankan
cassava mosaic virus infecting cassava in Cambodia. Plant Disease, 100 (5), 1029-1029. Retrieved fromhttps://doi.org/10.1094/PDIS-10-15-1228-PDNⒸ ISERD

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Research article
erd

Evaluation and Preference Analysis of Improved Rice
Genotypes in TharGaYa Village, Tharsi Township, Myanmar
NYO MAR HTWE*
Yezin Agricultural University, Nay Pyi Taw, Myanmar
Email: dr.nyomarhtwe@yau.edu.mm

SU LATT PHYU
Yezin Agricultural University, Nay Pyi Taw, Myanmar

KHANT SANDA HTET
Yezin Agricultural University, Nay Pyi Taw, Myanmar
Received 22 December 2020

Accepted 24 June 2021(*Corresponding Author)

Abstract This study was conducted to identify high yielding improved rice genotypes which
are adapted to the target location and accepted by local farmers. The experiment was conductedwith a randomized complete block design with three replications, at TharGaYa village, Tharsi
township, Myanmar. Sixteen improved rice lines and two check varieties were examined.
Farmers voted for acceptance or rejection of individual varieties at the vegetative and preharvest stages and these votes were converted to a preference score. A significant difference
among the genotypes tested occurred in yield per plant and its related characters. From thepreference analysis, farmers and researchers showed highest preference for SM1/THY-DH-1-1
at the vegetative stage, followed by YAU-1214-183-3-1-2-1-1 and YAU-1201-90-2-4. At preharvest stage, farmers and researchers elected YAU-1201-90-2-4, followed by YAU-1214-18335-1-1-1 and YAU-1214-183-3-1-1-1-1. According to the sensory evaluation test, the best line
was YAU-1201-90-2-4, with second and third being YAU-1201-26-1-1 and YAU-1201-26-1-3.
There was very strong agreement in preferences for the best performing lines shown by maleand female farmers. However, a weak correlation between researchers and farmers preferences
was identified. This seemingly indicates that farmers and researchers have different criteria inselecting good performing lines. Farmers have prioritized their own way of variety selection for
their localities, using features such as uniformity, lodging, panicle length, effective tillering andearliness. Therefore, it is important to include farmers’ preferences in any selection process todetermine a suitable and acceptable variety.
Keywords correlation, preference score, sensory evaluation, variety selection

INTRODUCTION
Rice is one of the most important cereal crops around the world. Myanmar is an agricultural country,
and agriculture is the backbone of its economy. The agriculture sector contributed 27.5% of GDP;
13.7% of total export earnings; and employed 61.2% of the labor force in 2010 (MOALI, 2010). One
fourth of Myanmar’s total area is cultivated land. In Myanmar, rice production accounted for the
majority of cropping area and it is also the major food source. Rice ecosystems in Myanmar are
generally classified into seven categories: 1) Irrigated rice land, 2) Regular rain-fed lowland, 3)
Drought-prone rain-fed lowland, 4) Deep water rice 5) Submerged rice land, 6) Sea water affected riceland and 7) Upland rice (MOALI, 2010). Since it is a staple crop, having high yielding varieties thatare adaptable for different agro-ecosystems is crucial.
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Plant breeders develop varieties through conventional breeding and release varieties that are mostproductive under ideal conditions; often they are not suitable for marginal farm conditions (Singh et al.,2014). Therefore, participatory varietal selection, facilitates development of varieties optimal for
marginal soils and acceptable to farmers. Farmers’ preferences represent a part of the genotype
selection and testing process, in order to ensure the large-scale adoption of the selected genotype.
Participatory Variety Selection (PVS) can be used to identify farmer-acceptable varieties
effectively, and thereby overcome the constraints that cause farmers to grow old or obsolete varieties,
or late maturing varieties which are susceptible to drought (Joshi and Witcombe, 1996; Witcombe et al.,1996). PVS trials were conducted on farm under the supervision of farmers, and identification ofadvanced promising lines from the breeding program occurred. It is crucial for plant breeders to
identify which genotypes farmers prefer, and which they dislike, and the reasons for these opinions,adjusted to local conditions. PVS is a rapid and cost-effective way of identifying farmer-preferredcultivars and whether a suitable choice of cultivars exists (Witcombe et al., 2008). Hence, researchcosts can be reduced and adoption rates increased since farmers participate in variety testing andselection (Joshi et al., 1995).
OBJECTIVE
The experiment was conducted to identify high yielding and acceptable improved rice genotypes
adapted to TharGaYa village, Tharsi Township, Myanmar through farmers’ participation.
MATERIALS AND METHODSThe experiment was laid out in a Randomized Complete Block Design (RCBD) with three replications
in farmers’ fields in TharGaYa village, Tharsi Township, Myanmar. A total of 18 genotypes, including
check varieties (Appendix 1), were raised in seedbeds and transplanted at 21 days after sowing, with adistance of 20 cm between rows and 20 cm within rows. All necessary precautions were taken to
maintain uniform plant population in each treatment replication. The data recorded were 50 %flowering (DTF), plant height (PH), effective tiller per hill (ETPH), panicle length (PLen), spikelets
per panicle (SPP), filled grain percent (FGP), 1000 grain weight (TGW) and yield per plant (YPP).
These data were collected to know the farmer selected line(s) has short duration, medium or short plantheight and high yield. These data were subjected to statistical analysis by using STAR software (STAR,
2014). Preference analysis through casting votes was conducted when most genotypes reached theactive tillering and pre harvesting stages. In case of the selection of farmers, there were a lot of
discussions and meetings with local staff from Department of Agriculture, key farmers and villageauthorities. After that, all the rice farmers who have different land access to grow rice for year-roundcultivation were selected to participate. Additionally, voluntary farmers were asked to include to have avariety of preferences. In order to conduct the preference analysis, 61 farmers and 8 researchers wereparticipated. In Myanmar, most of the farmers were males. Rahman et al., (2015) pointed that female
farmer had different perceptions with males regards the selection process. In this case, the votes wereseparated from male and female farmers and identify whether their perceptions have somehow agreewith each other.
A preference score was determined by using positive votes, negative votes and total votes cast(Paris, 2011). Firstly, the experimental plots were prepared with each line allocated with a bag forcollection of ballots. Farmers were provided with four ballots to vote for two best and two worst lines.
There were either positive or negative sign on one surface of each ballot and the possible reasons fortheir choice on one another. All the votes rooted from the quality of above-mentioned characters since
the participants needed to mention the reason for their votes. To allow examination of farmer
population’ preferences, male farmers’ ballots were blue, females’ pink and researchers’ green. It wasⒸ ISERD

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necessary to prepare separate ballot bags to collect the votes for each genotype. All the participants
were asked to mention the reasons for their choices on the back of the ballots. Then preference scoresfor men and women were computed independently through counting positive and negative ballots
collected in each ballot bag. The preference score was calculated as follows.

Pearson’s correlation was used to test the agreement of preferences between male and female
farmers and between researchers and farmers.
RESULTS AND DISCUSSION
Yield and Yield Contributing Characters
Significant differences among the improved rice lines were observed in days to 50% flowering,effective tillers per hill, panicle length, spikelets per panicle, filled grain percent, 1000 grain weightand yield per plant (Table 1). Yield per plant of YAU-1215-183-3-4-1-1-1 line was highest among thetested lines, followed by YAU-1214-183-3-1-1-1-1 and YAU-1214-183-3-3-1-1-1. Yield per plant of
the 9 improved rice lines were higher than those for the two local check varieties. However, effective
tillers per hill was the highest in local check variety, ManawThuKha, followed by YAU-1215-183-3-41-1-1.
Table 1 Mean performance of improved rice lines in TharGaYa village, Tharsi Township
Improved Rice Lines
YAU-1211-195-1-1
YAU-1211-82-1-1
YAU-1211-14-1-1
YAU-1201-26-1-1
YAU-1201-26-1-3
YAU-1201-90-2-4
YAU-1211-118-1-1
YAU-1214-183-3-1-1-1-1
YAU-1214-183-35-1-1-1-1
YAU-1214-183- 3-1- 2-1-1
YAU-1215-183-3-4-1-1-1
YAU-1214-183-3-3-1-1-1
Y8/THY-DH-4-1
Y8/THY-DH-5-1
Y8/THY-DH-9-2
SM1/THY-DH-1-1
MNTK
Yet 90Mean
CV
Pr (>F)
LSD0.05

DTF(days)
99.33
91.00
104.33
94.00
99.33
91.67
94.00
90.00
91.67
92.67
95.00
89.00
91.33
84.67
88.33
84.67
98.67
67.67
91.52
3.01
**
8.477

PH
(cm)
107.27108.20118.80105.33107.87115.9394.20
102.47107.9396.53
96.33
105.40104.6
107.13104.00119.13101.0096.13
105.46
11.45
ns
-

ETPH
(no.)
14.27
14.93
13.13
12.00
14.20
16.20
15.47
15.07
16.07
16.93
18.60
13.93
13.40
14.40
14.20
17.07
19.13
17.67
15.37
15.13
*
7.150

PLen
(cm)
22.23
21.82
21.85
23.34
22.22
22.60
25.57
24.05
23.39
22.90
26.86
24.47
23.63
21.24
20.45
19.90
19.80
21.29
22.65
10.08
*
7.016

SPP
(no.)
149.62142.51147.4396.66
124.0883.49
116.71115.7298.00
90.54
120.01121.92100.95
72.18
68.04
86.53
102.0964.11
105.5917.80**
57.806

FGP
(%)
52.49
70.08
53.51
69.84
58.65
80.62
71.49
89.54
88.13
82.25
83.02
84.94
75.92
88.83
85.1
74.69
73.18
90.94
76.29
8.04
**
18.867

TSW
(g)
19.19
24.75
20.97
28.53
25.20
29.16
26.61
27.53
27.61
26.86
27.53
27.34
26.7
27.32
26.98
23.02
18.55
24.59
25.47
4.85
**
3.797

YPP
(g)
20.73
35.42
21.70
22.74
25.97
31.13
33.97
42.12
38.01
33.83
48.73
39.44
26.90
25.06
22.05
25.11
26.23
25.00
30.23
16.14
**
15.001

DTF = Days to 50% flowering, PH = Plant Height, ETPH = Effective tillers per hill, PLen = Panicle length, SPP = Spikelets
per panicle, FGP = Filled grain percent, TSW = 1000 grain weight, YPP = Yield per plantⒸ ISERD

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Preference Analysis
The preference score of different improved rice lines are shown in Fig. 1. At the vegetative stage, bothmale and female farmers show preference for SM1/THY-DH-1-1, followed by YAU-1214-183-3-1-21-1 and YAU-1201-90-2-4. However, researchers selected the most preferable three improved lines in
the following order: Y8/THY DH-5-1, YAU-1201-90-2-4 and SM1/THY-DH-1-1. The reason why
farmers and researchers selected these lines as the most preferable are based on the uniformity in plant
growth, the high to medium tiller numbers per hill and no incidence of pest infestation or diseases. The
least preferred genotypes selected by the farmers were ManawThuKha, Yet90 and YAU-1211-195-1-1.
In combining farmers’ and researchers’ preferences, SM1/THY-DH-1-1, YAU-1214-183-3-1-2-1-1
and YAU-1201-90-2-4 were the most preferable lines at the vegetative stage.

Fig. 1 Farmers’ and researchers’ preferences for improved rice lines at vegetative stagein TharGaYa village, Tharsi TownshipAt pre-harvest stage, the three most preferred lines selected by farmers were YAU-1201-90-2-4,
YAU-1214-183-35-1-1-1-1 and YAU-1214-183-3-1-1-1-1 (Fig. 2). Farmers like these lines because
they exhibit uniform growth, are lodging resistant, have long panicle length, have medium to highly
effective tillers and display earliness. However, researchers selected YAU-1214-183-3-1-1-1-1,
Y8/THY-DH-5-1 and YAU-1214-183-3-4-1-1-1 as the top three preferable lines. At the other extreme,
YAU-1201-26-1-3, Yet90 and Y8/THY-DH-9-2 were the farmers’ choice as the bottom threegenotypes at pre-harvest stage. In all voting, YAU-1201-90-2-4, YAU-1214-183-35-1-1-1-1 and YAU1214-183-3-1-1-1-1 were selected as the most preferable lines. Only one line; YAU-1201-90-2-4 wasfavored at the vegetative and pre-harvest stages. The other genotypes were preferred at only one of thestages considered in the selection process.

Fig. 2 Farmers’ and researchers’ preferences for improved rice lines at pre-harvest stagein TharGaYa village, Tharsi TownshipⒸ ISERD

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According to Pearson’s correlations, the results show significant and moderate positive correlation
between male and female farmers’ preference scores (Table 2). This means that, with r = 0.5861 (at 5%
level of significance), male and female farmers ‘somewhat agree’ in their preferences for the best
performing genotypes. When farmers’ preferences (both male and female) are compared with breeders’preferences, the correlation analysis shows a non-significant and slightly positive correlation (r =0.311). The results indicate that there is weak agreement between the farmers’ preferences and theresearchers’ in selecting good performing genotypes.
Table 2 Correlation analysis of preference scores between farmers (male and female)and researcher in TharGaYa village, Tharsi TownshipMale Farmer
Female Farmer
FarmerResearcher

Male farmer
1
0.586*
0.930**
0.293

Female farmer
0.586*
1
0.843**
0.256

Farmer
0.930**
0.843**
1
0.311

Researcher
0.293
0.256
0.311
1

*, ** = significant at 5% and 1% level, respectively

The farmers’ perception of good-performing varieties, as indicated by the preference score, isweakly associated with the researcher-calculated yields for the improved rice lines, with r = 0.345 andnon-significant (Table 3). This means that there is a somewhat weak agreement between the predicted
yields and the resulting choices of the farmers based on their own set of criteria.
Table 3 Correlation analysis of preference scores and yieldin TharGaYa village, Tharsi Township
Yield per plant
1
0.345

Yield per plant
FarmerFarmer0.345
1

Table 4 Sensory evaluation frequencies of rice varietal lines tested
in TharGaYa village, Tharsi Township
Improved Rice Lines
YAU-1211-195-1-1
YAU-1211-82-1-1
YAU-1211-14-1-1
YAU-1201-26-1-1
YAU-1201-26-1-3
YAU-1201-90-2-4
YAU-1211-118-1-1
YAU-1214-183-3-1-1-1-1
YAU-1214-183-35-1-1-1-1
YAU-1214-183- 3-1- 2-1-1
YAU-1215-183-3-4-1-1-1
YAU-1214-183-3-3-1-1-1
Y8/THY-DH-4-1
Y8/THY-DH-5-1
Y8/THY-DH-9-2
SM1/THY-DH-1-1
MNTK
Yet 90

Bad
14
20
27
12
10
7
20
21
26
21
27
19
35
35
31
18
26
28

Fair
30
27
26
31
26
19
32
24
29
29
27
26
19
21
25
28
23
25

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Good
16
13
7
17
24
34
8
15
5
10
6
15
6
4
4
14
11
7

Bad (%)
1.30
1.85
2.50
1.11
0.93
0.65
1.85
1.94
2.41
1.94
2.50
1.76
3.24
3.24
2.87
1.67
2.41
2.59

Fair (%)
2.78
2.50
2.41
2.87
2.41
1.76
2.96
2.22
2.69
2.69
2.50
2.41
1.76
1.94
2.31
2.59
2.13
2.31

Good (%)
1.48
1.20
0.65
1.57
2.22
3.15
0.74
1.39
0.46
0.93
0.56
1.39
0.56
0.37
0.37
1.30
1.02
0.65

IJERD – International Journal of Environmental and Rural Development (2021) 12-1

According to the result of sensory evaluation, the improved rice line with good eating quality was
YAU-1201-90-2-4 (Table 4). Lines with fair eating quality were YAU-1211-118-1-1, YAU-1201-261-1 and YAU-1211-195-1-1. Lines with bad eating quality were identified as Y8/THY-DH-4-1,
Y8/THY-DH-5-1 and Y8/THY-DH-9-2. Among the selected line at pre-harvest stage, only YAU1201-90-2-4 had a good eating quality. The YAU-1214-183-3-1-2-1-1 line selected at vegetative stage
and YAU-1214-183-35-1-1-1-1 line selected at pre-harvest stage both had a moderate eating quality.
Therefore, the YAU-1201-90-2-4 line stands as the farmers’ prefer improved rice line and needs toundergo further field testing.
CONCLUSION
Significant differences exist among the tested genotypes and were found in yield per plant and its
component characters. From the preference analysis, farmers and researchers selected SM1/THY-DH1-1 as the best line at the vegetative stage followed by YAU-1214-183-3-1-2-1-1 and YAU-1201-90-24. The reason of selection of these lines are the uniformity in plant growth, the high and medium tillers
number per hill and no incidence of pest and diseases. At the pre-harvest stage, farmers and researchers
selected YAU-1201-90-2-4 as the best followed by YAU-1214-183-35-1-1-1 and YAU-1214-183-3-11-1-1. Farmers like these lines because they have growth uniformity, are lodging resistant, have long
panicle length, have medium to highly effective tillers and display earliness. According to the sensory
evaluation test, the best line is YAU-1201-90-2-4. This line is selected as the preferred line of farmers
at the vegetative and pre-harvest stages. Therefore, YAU-1201-90-2-4 line ranks as the farmers’
preferred improved rice line and needs to be tested again in field trials. There was a very strong
agreement between male and female farmers’ in their preference for the best performing lines at preharvest stage. However, only a weak correlation between researchers’ and farmers’ preferences has
been identified. This indicates that farmers and researchers have different criteria in selecting what is agood performing line. Therefore, it is vitally important to include farmers’ preferences in any variety
selection process.
ACKNOWLEDGEMENTS
The authors would like to express their sincere thanks to Japan International Cooperation Agency(JICA), Project for Capacity Development of Yezin Agricultural University for financial and technicalsupport to conduct this study. Special thanks go to the farmers and staff from Department ofAgriculture, Tharsi Township who participated in this research activities.
REFERENCES
Joshi, A. and Witcombe, J.R. 1996. Farmer participatory crop improvement. II. Participatory varietal selection, acase study in India. Experimental Agriculture, 32, 461-477.
Joshi, K., Rana, R., Subedi, M., Kadayat, K. and Sthapit, B. 1995. Effectiveness of participatory variety testingand dissemination programme: A case study of Chaite rice in the Western Hills of Nepal. LARC Working
Paper (Nepal). 95/49.
MOALI. 2010. Agricultural extension division annual report. Ministry of Agriculture, Livestock and Irrigation.
Paris, T.R. 2011. Guide to participatory varietal selection for submergence-tolerant rice. IRRI.
Rahman, M.A., Thant, A.A., Win, M., Tun, M.S., Moet Moet, P., Thu, A.M., Win, K.T., Myint, T., Myint, O.,
Tuntun, Y. and Labios, R.V. 2015. Participatory varietal selection (PVS): A “bottom-up” breeding approachhelps rice farmers in the ayeyarwady delta, Myanmar. Sabrao Journal of Breeding and Genetics, 47 (3), 299314.

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Singh, Y., Nayak, A., Sharma, D., Gautam, R., Singh, R., Singh, R., Mishra, V., Paris, T. and Ismail, A. 2014.
Farmers’ participatory varietal selection: A sustainable crop improvement approach for the 21st century.
Agroecology and Sustainable Food Systems, 38, 427-444.
STAR, V. 2014. Statistical tools for agricultural reserach. Biometrics and Breeding Informatics, PBGB Division,
International Rice Research Institute, Los Baños, Laguna.
Witcombe, J., Hollington, P., Howarth, C., Reader, S. and Steele, K. 2008. Breeding for abiotic stresses forsustainable agriculture. Philosophical Transactions of the Royal Society B: Biological Sciences, 363, 703716.
Witcombe, J.R., Joshi, A., Joshi, K.D. and Sthapit, B. 1996. Farmer participatory crop improvement. I. Varietal
selection and breeding methods and their impact on biodiversity. Experimental Agriculture, 32, 445-460.

Appendix 1 Rice genotypes included in PVS
Pedigree source
YAU-1211-195-1-1
YAU-1211-82-1-1
YAU-1211-14-1-1
YAU-1201-26-1-1
YAU-1201-26-1-3
YAU-1201-90-2-4
YAU-1211-118-1-1
YAU-1214-183-3-1-1-1-1
YAU-1214-183-35-1-1-1-1
YAU-1214-183- 3-1- 2-1-1
YAU-1215-183-3-4-1-1-1
YAU-1214-183-3-3-1-1-1
Y8/THY-DH-4-1
Y8/THY-DH-5-1
Y8/THY-DH-9-2
SM1/THY-DH-1-1
MNTK
Yet 90Varietal code
G1
G2
G3
G4
G5
G6
G7
G8
G9
G10
G11
G12
G13
G14
G15
G16
G17
G18

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Remarks
Pre-released, Salt Tolerance
Pre-released, Salt Tolerance
Pre-released, Salt Tolerance
Pre-released, Salt Tolerance
Pre-released, Salt Tolerance
Pre-released, Salt Tolerance
Pre-released, Salt Tolerance
Pre-released, High yield
Pre-released, High yield
Pre-released, High yield
Pre-released, High yield
Pre-released, High yield
Pre-released, High yield
Pre-released, High yield
Pre-released, High yield
Pre-released, High yield
Local Check, High yield
Local Check, Short growth duration

IJERD – International Journal of Environmental and Rural Development (2021) 12-1

Research article
erd

Rainwater Harvesting as a Mean for Water Conservation in
Ovche Pole Region, Macedonia
OGNEN ONCHEVSKI
Graduate School of Agriculture, Tokyo University of Agriculture, Japan
Email: ognenoncevski@hotmail.com

MACHITO MIHARA*
Faculty of Regional Environment Science, Tokyo University of Agriculture, Japan
Email: m-mihara@nodai.ac.jp
Received 30 December 2020

Accepted 30 June 2021(*Corresponding Author)

Abstract Ovche Pole is an area in Macedonia located in the central part of the country. The
region is the second largest grain producing area and agriculture is the main economic activity
for most of the people there. From an agro-ecological perspective, larger part of the region ispart of the sub-humid agricultural zone; however, there is an area that is identified as semiarid
agricultural zone, too. In both agro-ecological zones, main problem and constraint for
agricultural production are the dryness and low precipitation in the growing period. This
problem is especially emphasized on the agricultural land without irrigation system. Therefore,
this study analyzes rainwater harvesting approach as a mean for water conservation andimprovement of agriculture productivity in the research area. The first objective of this study is
to determine the runoff potential in the research area and the second is to identify suitable areas
and measures for water harvesting. Remote sensing and geographic information systemtechniques were used to obtain, prepare and analyze input data. Because of simplicity and lackof hydrological data in the research area, SCS-Curve Number method was used for rainfallrunoff modelling. Hydrologic Soil Group (HSG) map was build using data obtained from theMacedonian Soil Information System (MASIS). Land use/land cover was prepared by
combining supervised classification of Sentinel-2 satellite image and visual interpretation andediting to improve the classification. Slope map was generated from a 20-meter resolution
DEM data obtained from the Agency for Real Estate Cadaster. Data of the annual average
precipitation for the period between 1981 and 2010 was obtained from the National
Hydrometeorological Service. Set of rainwater harvesting measures were selected as
appropriate for the context of the research area. The site suitability for each rainwater
harvesting intervention was determined by considering different varying parameters like runoff
potential, slope, soil texture, land use, stream order of proximity to users. The results indicatethat rainwater harvesting can be considered as a strategy for water conservation in the research
area.
Keywords water conservation, rainwater harvesting, surface runoff, SCS-SN method,
suitable site selection

INTRODUCTION
Having in mind the United Nation Convention for Combating Desertification (UNCCD) definition,
approximately 40% of the global land can be considered as a type of dryland (WRI, 2012). Although
there are many challenges, in these areas water is the key limiting constraint for agricultural andbiological productivity. Irrigated areas to some extent can avoid this constraint, however rainfed
agricultural land is dependent on water that comes as precipitation. To unlock the potential of smallⒸ ISERD95

IJERD – International Journal of Environmental and Rural Development (2021) 12-1

scale rained agriculture, investments in better water management need to be emphasized. In dry area
water harvesting coupled with in situ water management as well as improved soil, nutrient and crop
management have great potential. In humid areas, in situ water management technologies such asconservation agriculture based on no-till, mulching and crop rotation are generally more suitable andappropriate. Mekdaschi and Liniger (2013) define water harvesting (WH) as “The collection andmanagement of flood water or rainwater runoff to increase water availability for domestic andagricultural use as well as ecosystem sustenance”. Although there are multiple benefits, there are someconsiderations that must be taken before water harvesting systems can be successful implemented.
Therefore, objectives of this study is to determine the runoff potential in the research area and to identify
suitable areas and measures for water harvesting. In this line different methodologies and criteria for WH
site and method selection were developed, however two main groups are defined-biophysical andsocio-economic (Ammar et al., 2016). In his study Ammar (2016) identified three commonly most
followed set of criteria for selection of suitable WH. Those are the sets proposed by: Integrated
Mission for Sustainable Development (IMSD) in 1995, Dr. Theib Y. Owesi in 1998 and Food andAgriculture Organization of the United Nations (FAO) proposed in 2003. Since field surveying can beresource consuming, nowadays the assessment and selection of suitable WH sites and measures is
assisted by remote sensing and geographical information systems, because when coupled they are costefficient and provide reasonably good possibility for analyzation of spatial data (Wani et al., 2009)
METHODOLOGY
Description of the Study Area
Ovche Pole is a plain located in central
part of Macedonia that takes an area of
649 km2. It lies between latitude 41 59
30 N and 41 38 43 N and longitude 21
48 13 E and 22 01 28 E (Fig. 1).
Agriculture is dominant economic
activity in the region. Agricultural landtakes 40,183 ha (62% of the study area),
however much of the arable land is not
irrigated. Forests cover 14,619 ha(22.5%) of which 2/3 are degraded,
whereas 9,134 ha (14%) are pastures(CORINE LC/LU, 2012). The climate
is modified warm continental with
Mediterranean influence (Zikov, 1995;
Filipovski et al., 1996). Considering the
Fig. 1 Geographical position of the research area
data between 1981 and 2010 the annual
average precipitation in the region is
455 mm. From an agro-ecological perspective, larger area of the region is part of the sub-humid
agricultural zone; however, there is an area that is identified as semiarid agricultural zone, too. In bothagro-ecological zones, main problem and constraint for agriculture production are the dryness and low
precipitation in the growing period (Aksoy et al, 2020).
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Different data such as land use/cover data, Sentinel-2 satellite images, elevation model (DEM), climate
data and soil data were used in this study analysis. Land cover/land use (LCLU) analysis was done byusing CORINE Land Cover (CLC2018) data set from 2018, program coordinated by European
Environmental Agency (EEA) and Sentinel-2 satellite image. Soil data were obtained from The
Macedonian Soil Information System (MASIS). Watershed delineation, stream network, slope,
topographic maps were generated using 20-meter resolution DEM obtained from the Agency for Real
Estate Cadaster. Average year precipitation of 455 mm. was derived from climatic data for the period
between 1981 and 2010 provided by the National Hydrometeorological Service (NHS).
Methods
Assessment of the water harvesting potential and identification of suitable water harvesting sites wasdone by evaluation of parameters such as: surface runoff, land use/cover, slope, soil infiltration
characteristics, stream order and proximity to users. The complete quantitative and qualitative data
analysis in this research was done by remote sensing and Geographical Information System (GIS)
techniques. To compute hydrological elements more accurately generation of Land Cover/Land Use
map is required (Rana and Suryanarayana. 2020). In this study CORINE Land Cover 2018(CLC2018)
dataset for Macedonia together with Sentinel-2 satellite image were combined to create the LCLU map
for the research area. The Sentinel-2 image captured on 20 August 2019 was used as a backgroundinformation for additional interpretation and editing of existing CLC2018 vector layer. We only used
the bands with 10-meter resolution (blue B2:490 nm, green B3:560 nm, red B4:665 nm, NIR B8:842
nm). As a result of this hybrid process, a new more precise and detailed land cover map was created.
For the Hydrological Soil Group (HSG) analysis and map generation three raster files on sand, silt andclay content in soil were used. Each pixel is specified with a 100 meters resolution and a value that
represents the content of specific soil fraction expressed in unit percent. Using raster calculator andreclassification function in ArcGIS Pro, the three layers were used to create new raster layer. In thisraster each pixel was assigned a value that represented some soil texture class, following the USDA
textural soil classification. Considering the infiltration rate of various soil texture each pixel wasreclassified into A, B, C or D hydrologic soil group. Accordingly, the hydrologic soil group (HSG)
map was prepared for the entire research area. The DEM data was used to generate the slope map andstream network map for the research area. Surface runoff is the primary component of every water
harvesting system. In this study Conservation Service Curve Number (SCS-CN) methodology wasused to calculate the runoff depth and generate run-off potential map for the research area. The SCS
run-off equation is widely used in estimating direct run-off because of its simplicity, flexibility andversatility (Kumar and Jharya. 2016) and often used by researcher for watersheds that are ungauged, as
it is the study area in this research. Although the method is designed for a single storm, it can be scaled
to calculate the annual values for run-off of an area. Basically, the SCS-CN method depends on therelationship between the rainfall parameter (P) and the run-off depth (Q), simplified into the CN
concept. The CN is a dimensionless run-off index determined based on Hydrological Soil Group(HSG), land use/land cover and Antecedent Moisture Conditions (AMC). The CN method can also
reflect the effect of changes in land use on run-off. The CN values range between 1 and 100. Higher
values of CN indicate higher run-off. In this study the runoff derived by SCS-CN method was
expressed as runoff coefficient which represent the ratio between the runoff and rainfall. Based on therunoff coefficient a runoff-potential map was created in which the run-off coefficient is reclassified infour classes: low run-off potential, moderate run-off potential, high run-off potential and very high runoff potential.
Rainwater Harvesting Measures Selection and Site Suitability AnalysisⒸ ISERD

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The rainfall, geology, physiographic, land use, climatic conditions and social set-up are the basic
factors to be understood by the rainwater harvesters to choose the proper technique of rainwater
management in a particular set-up identified by them. As first step in process, a set of RWH measures
were identified and selected; in the second step, for each measure a set of site selection suitability
criteria were compiled; and as last step, RWH site suitability analysis was performed. Selection ofsuitable RWH measures was done by revieing studies, manuals and other on-topic literature. We
identified to groups of measures. First group are measures that can be implemented in valley
formations such as impermanent water streams and gullies, such as: ponds, gabion checks, boulder
checks and brushwood checks. Second group of measures are those that can be implemented on theridge formations. Here we identified revegetation, contour ditches and contour bunds as possible
appropriate measure. In the second step, we complied criteria for suitable site selection for each of theidentified measure in the first step. The criteria for water harvesting site selection related to each water
harvesting group of measure are presented in Table 1 and Table 2. According to the review study doneby Ammar et al. (2016) on identification of suitable sites for water storage in arid and semi-arid
regions, it was found out that the most common biophysical layers or criteria applied were slope
followed by land use/land cover and soil type. In this study we selected runoff, land use/land cover,
HSG, slope, stream network and distance to users play a critical role in rainwater harvesting site
selection. These thematic layers were used as input data for a series of operations done in ArcGIS Pro
that enabled identification of appropriate sites for each identified rainwater harvesting measure.

On-ridge
interventions

Table 1 Criteria for selection of on-ridge rainwater harvesting measures
Water harvesting
intervention

Runoffcoefficient

Slope (%)

Revegetation

>0.5

>25

Contour trenching>0.5

10 - 25

Contour bunds

>0.5

0-10

Hydrology Soil
Group (HSG)

Land use

A, B, C, D

Land principally occupied byagriculture with significant areas ofnatural vegetation; Pastures,
Grasslands; Sparsely vegetated areas;
Transitional woodland-shrubs

A, B, C, D
A, B, C, D

On-stream interventions

Table 2 Criteria for selection of on-stream rainwater harvesting measures
Water harvesting
intervention

Runoffcoefficient

Slope
(%)

StreamOrder

Hydrology Soil
Group (HSG)

Distance to users

Irrigation ponds

>0.5

0-5

1-2

C, D

< 250 meters fromagricultural land

Livestock ponds

>0.5

0-5

1-2

C, D

< 1000 meters from livestockbarns

Gabion checks

>0.5

0-5

1-2
(gullies)*

A, B, C, D

n/a

Boulder Checks>0.5

5 - 201-2

A, B, C, D

n/aBrushwood checks

>0.5

> 20

1-2

A, B, C, D

n/a

RESULTS AND DISSCUSIONRunoff PotentialⒸ ISERD

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The run-off potential of the area affects the recharge and movement of surface water and is one of theimportant parameters for rainwater harvesting. (Kumar and Jhariya, 2016). The run-off potential map(Fig. 2) was built based the SCS-CN method by integrating average annual rainfall for the period
between 1981 and 2010, land use/land cover information, and distribution hydraulic soil groups in the
research area. The runoff derived by SCS-CN method is a function of runoff potential which can be
expressed in as runoff coefficient - ratio between the runoff and rainfall. To perform RWH site
suitability analysis the run-off potential was reclassified as very high run-off potential, high run-off
potential, moderate run-off potential and low run-off potential. Category description and distribution in
the research area is presented in Table 3. Results show that 26.8% of the total research area has very
high runoff potential and 66.8% has high runoff potential.
Table 3 Runoff coefficient category description and distribution
Runoff coefficient

Description

Surface area (km2)

Percent of total area (%)≤ 0.5 (0.00 – 0.5)

Low runoff potential

23.25

3.6

3.3

0.5

14.6

2.3

115.3

17.9

314.1

48.9

172.2

26.8

≤ 0.6 (0.51 – 0.6)
≤ 0.7 (0.61 – 0.7)

Moderate runoff potential≤ 0.8 (0.71 – 0.8)
≤ 0.9 (0.81 – 0.9)
≤ 1.0 (0.91 – 1.0)

High runoff potential
Very high runoff potential

Fig. 2 Runoff coefficient map of the research area
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Rainwater Harvesting Site Suitability Analysis
Rainwater harvesting site suitability analyses, based on the criteria given in Table 1 and Table 2
showed that identified measures can be applied on significant part of the research area. On-ridge
measures can be implemented on 59% of the total area. Detailed information for each on-ridge measure
is presented in Table 4, the distribution map is shown in Figure 4. Regarding the on-stream measuresdetailed analysis is presented in Table 5. and the distribution map in Figure 4. As an implementation
unit for ponds and gabion checks is the number of pond-structures that can be build, whereas forbrushwood and boulder check implementation unit is the length of stream sections appropriate for
implementation.
Table 4 On-ridge measure distribution analysis
On-ridge measures
Water harvesting intervention

Implementation area (km2)

Percent of total area (%)

Contour bunds

193

30

Contour trenches

109

17

Re-vegetation

77

12

Fig. 3 On-ridge measures distribution mapⒸ ISERD

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Table 5 On-stream measure distribution analysis
On-stream measures
Water harvesting intervention

Implementation units

Irrigation ponds

43 ponds

Livestock ponds

11 ponds

Gabion checks

183 checks

Brushwood checks

91 km (stream length)

Boulder checks

297 km (stream length)

Fig. 4 On-stream measures distribution map
CONCLUTION
Ovche Pole region is part of the sub-humid and semiarid agricultural zone in Macedonia. Main
constraint for agriculture production is dryness and low precipitation amounts during the growing
season. This study explores the potential for implementation of rainwater harvesting measures as
strategy for water conservation. The results show that 93.6% of the total area has potential to generatehigh and very high runoff. Rainwater harvesting site suitability analysis done in the study implies thatⒸ ISERD

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59% of the research area satisfy the criteria for implementation of the selected on-ridge RWH measures.
Analysis shows there are significant number of potential sites suitable for implementation of on-stream
RWH measures that can also significantly contribute to conservation of water resources in the research
area.
REFERENCES
Aksoy, E., Arsov, S., Mincev, I. and Fang, C. 2020. Agro-ecological atlas of the Republic of North Macedonia.
Rome, FAO.
Ammar, A., Riksen, M., Ouessar, M. and Ritsema, C. 2016. Identification of suitable sites for rainwater
harvesting structures in arid and semi-arid regions: A review. International Soil and Water Conservation
Research, 4 (2), 108-120.
European Union, Copernicus Land Monitoring Service. 2018. European Environment Agency (EEA).
Kumar, T. and Jhariya, D. 2016. Identification of rainwater harvesting sites using SCS-CN methodology, remotesensing and geographical information system techniques. Geocarto International, 32 (12), 1367-1388.
Mekdaschi S.R. and Liniger, H. 2013. Water harvesting: Guidelines to good practice. Centre for Development
and Environment (CDE), Bern; Rainwater Harvesting implementation Network (RAIN), Amsterdam;
MetaMeta, Wageningen; The International Fund for Agricultural Development (IFAD), Rome.
Ramakrishnan, D., Bandyopadhyay, A. and Kusuma, K.N. 2009. SCS-CN and GIS-based approach for
identifying potential water harvesting sites in the Kali Watershed, Mahi River Basin, India. J. Earth Syst. Sci.,
118, 355-368.
Rana, V. and Suryanarayana, T. 2020. GIS-based multi criteria decision making method to identify potential
runoff storage zones within watershed. Annals of GIS, 26 (2), 149-168.
State Statistical Office. 2002. Census of population, households and dwellings in the Republic of Macedonia,
2002 - Book XIII.
Wani, S.P., Rockstrom, J. and Oweis, T. (Eds.) 2009. Rainfed agriculture: Unlocking the potential. Wallingford,
UK: CABI; Patancheru, Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid
Tropics (ICRISAT); Colombo, Sri Lanka: International Water Management Institute (IWMI), 310.
(Comprehensive Assessment of Water Management in Agriculture Series 7).
World Resources Institute (WRI). 2012. Where are the world’s drylands? Retrieved from https://pdf.wri.org/drylands.pdf
Zikov, M. 1995. Climate and climate regionalization in Republic of Macedonia. Geographical consideration,
Skopje, 30, 13-21.

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Research article
erdMarketing Strategies and Grape Farmers’ Welfare
Improvement: Evidence from Afghanistan
SAYED JALIL HASHIMI*Southwest University, Chongqing, China
Email: jalilhashimi802@gmail.com

DAN YANGSouthwest University, Chongqing, ChinaZAINULLABUDDIN HAKIMI
Bamyan University, Bamyan, Afghanistan

ZAIGHAM KAZIMITokyo University of Agriculture, Tokyo, Japan
Received 7 March 2021

Accepted 30 June 2021(*Corresponding Author)

Abstract The role of marketing channels in agribusiness has been extensively studied in the last
decade. While most studies have focused on the general impact of marketing channels on sales,
the impacts of different choices of marketing channels on net returns has not been widely
studied. Focusing on Afghan grape farmers, this paper studied the determinants of marketing
channels, as well as the impact of marketing channel choices on net returns. The marketing
channels considered in this paper were channels through farmer’s organizations, local tradersand farmers who do on spot selling. Using survey data from 150 grape farmers in Kabul,
Parwan, and Kapisa provinces in Afghanistan, the results showed that grape farming experience,
selling price, province, distance to markets and internet use were some of the factors that
affected the choice of marketing channels. In addition, marketing channels through local traders
had a positive impact on returns as compared to channels through farmer’s organizations and on
spot selling. The selectivity correction terms for all marketing channels were significant
indicating the existence of selectivity bias from unobserved factors.
Keywords marketing channels, Afghan grapes, MNL model, BFG method,
selectivity correction

INTRODUCTION
Marketing channels act as a bridge between producers and consumers. According to Bowersox et al.
(1986), marketing channels are mediators that are responsible for taking products through themarketing system. Marketing channels are purposed to reduce transaction costs and enhancecompetitiveness on the market. Therefore, marketing channels will ensure that products are at the rightplace, at the right time, hence poor choices in marketing channels would in turn result in unwanted
costs.
It has been shown in literature that economic factors, social factors, and demographic factors aresome of the determinants of marketing channels (Jari, 2009; Safi et al., 2019). Specifically, factors likeaccess to information, the distance the nearest trading center, age, the level of education, income,experience and land holding have shown statistical significance in determining the choice of marketingⒸ ISERD

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contracts (Jari, 2009; Safi et al., 2019). Furthermore, Chiang (2016) and Uematsu and Mishran (2011)
reported that the choice of different marketing channels affects the net returns from farm produce.
Afghanistan, being one of the low income countries whose economy relies mainly on agriculture,
still faces financial and agricultural related challenges in the grape industry (USAID, 2016). In addition,
there is no proper marketing system for fruits, especially grapes in Afghanistan with less than 3 percent
of grape farmers having access to formal marketing systems Afif (2015). This has led to 30-40% of thegrapes getting destroyed due to poor marketing techniques with a lot of farmers opting for poppy
farming. This research therefore aims at finding the determinants of marketing channels and the impact
of marketing channels on net returns. Focusing on three major grape producing provinces, this researchaims at drawing a light on the marketing channels farmers can opt for to maximize their returns.
DATA AND METHODS
The study aims at finding factors that influence the choice of marketing channels by grape farmers in
Afghanistan. Three marketing channels considered were marketing channels through contracts with
farmer’s organizations, contracts with local traders and on spot selling (farmers with no marketing
contracts). The study focused on farmers cultivating grapes for commercial purposes. From the 7 maincommercial grape growing provinces, a random sample of three provinces was drawn, leading to thestudy provinces of this research which are Kabul, Kapisa and Parwan. Using the adjusted Cochran’s
formula, a sample size of 164 was drawn and randomly sampled from a list of commercial grape
farmers from the agricultural departments of Kabul, Kapisa and Parwan provinces. The study used aquestionnaire which consisted of closed-ended questions and it captured information on farmer's
demographic information, agricultural status and information on the marketing strategies, marketing
channels and net returns. All data analysis was done in excel, SPSS and STATA.
Model Description
Data was analyzed using a two stage model proposed by Bourguignon, Fournier, and Gurgand (2007)(hereinafter BFG), which controls for selectivity biasness. The first stage of the BFG method employsa multinomial logistic regression (MNL), which analyses the determinants of marketing channels. The
second stage BFG uses the Linear Logit model to assess the impact of marketing channels on net
returns while controlling for selectivity bias.
RESULTS AND DISCUSSION
Data from 164 households was collected in which 14 households were dropped because ofincompleteness, leaving the total number of households at 150. From the 150 farmer, 40 (26.67%) had
contracts with farmer's organizations, 68 (45.33%) had contracts with local traders and 42(28.00%) did
on spot selling. The mean age of farmers was 30.9 years and the average number of grape farming
experience in years was 10.4.
Table 1 shows that internet use and farm distance have a significant positive impact on the choiceof local traders. This means that farmers with access to intern and long farm distances respectively, aremore likely to choose local traders. However, both internet use and farm distance do not have any
impact on the choice of farmer’s organizations. In addition, experience in grape farming contribute tochoosing local traders, unlike farmer’s organizations, which are negatively affected by grape farming
experience. Furthermore, the negative marginal values of selling price and province indicate that anincrease in selling price reduces the probability of farmers choosing local traders and farmers from
Kabul are less likely to choose local traders unlike farmers from Parwan and Kapisa.

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Table 1 Determinants of marketing channels (First stage BFG)
Variable

On spot selling
Marginal
effects

Z-value

Channel with traders

Channel with farmers
organization

Marginal
effects

Marginal
effects

Z-value

Z-value

Experience

0.008195

0.254

0.014967

1.75*-0.02316-3.69***

Sales volume-7.00E-06-0.71

9.67E-06

1.02-2.67E-06-0.57

Selling price0.905188

0.147-0.61295-3.04***

0.707764

2.71***

Province

0.043652

0.46-0.2764-2.38**

0.232751

2.85***

Loans

0.03850.78

0.089493

1.5-0.12799-3.04***

Distance-0.00532-3.8***

0.004321

3.02***

0.001002

0.146

Internet-0.26078-2.87***

0.318437

3.25***-0.05765

0.336

Training-0.1369-1.51-0.00068-0.01

0.137588

2.16**

Area

0.008411

0.25-0.05485-1.39

0.046441

1.87*

vehicle-0.03844-0.72

0.03992

0.69-0.00148

0.963

Age

0.00373

0.97-0.00321-0.69-0.00052

0.844

Education-0.01832-0.47

0.018808

0.44-0.00048-0.02

Source: own survey data. *,**,*** represent significance at 0.1, 0.05 and 0.01 significant levels, respectively.
The base group is on spot selling.

Every increase in selling price and planting area increases the probability of farmers choosing
farmer’s organizations. On top of that, farmers from Kabul have a higher probability of choosing
farmer’s organizations unlike farmers from Parwan and Kapisa. Farmers who undergo training also
have a higher probability of choosing farmer’s organizations as compared to farmers without training.
On the other hand, the significant negative value of access to loans indicates that farmers who takeloans from farmer’s organization are less likely to choose farmer’s organizations as their marketing
channels, as compared to the farmers with loans from friends, family and banks.
The results presented in Table 2 show that the selectivity terms are significant in the choice ofchannels through contracts farmer’s organization, channels through contracts with traders and no spot
selling. The selectivity coefficient of channels through contracts with farmer’s organizations ascompared to channels through contracts with traders is positive, meaning that the expected revenue fora farmer with a contract with farmer’s organization will increase if they switched from farmer’sorganizations to local traders.
Likewise, the significant negative coefficient for on spot selling relative to contracts with farmer'sorganization indicate that the expected revenue for a farmer who does on spot selling will decrease if a
farmer switched from on spot selling to having a contract with farmer's organization.
The results for the OLS indicate that farming vehicle has a positive significant impact on the net
returns of farmers who choose farmers organizations. In addition, sales volume, internet use, planting
area, loans and education have a positive impact on the net returns of farmers who do on spot selling.
However, age has a negative impact on the net returns of farmers who do on spot selling. Furthermore,sales volume, training participation and education have a positive impact on the net returns of farmers
with contracts with traders while planting area has a negative impact.
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Table 2 Impacts of marketing channels on net returns (second stage BFG)
Variable

Channel with farmer'sorganization

On spot selling

channel with traders

coefficient

z-valuecoefficient

z-value

Coefficient

z-value

Experience-397.92

-0.11

-0.14

-0.25

-2731.29-2.09

Volume

1.98

1.71

0.99

1328***

8.28

10.56***

Price-138042.50-0.27-219.38-0.94138721.10

0.34

Province-38662.15

-1.09

-1.23

-0.14

19460.29

0.83

Loans

2421.51

0.13

13.07

3.15***-5447.20-0.59

Distance

265.52

1.45

0.62

1.66

-263.05-0.56

Internet

22279.50

0.97

42.17

1.90*

5689.33

0.17

Training

21013.61

1.05

7.86

1.00

35033.67

2.0*

Area

-559.30

-0.06

4.68

2.19**-17112.90-3.87***

Vehicle3929.92

0.69*

4.99

0.89

-6764.24-0.81

Education

7398.88

1.87

2.85

0.86**

16860.50

2.86***

Age-659.71

-1.25

-0.50

-2.19*

971.281.28_m123899.37

0.63-104.36-1.78*

166337.40

1.45

_m2

28801.98

0.39-91.87-2.5140147.60

0.96

_m3

153988.40

2.12**-19.51-0.33

95157.10

1.85*

_cons

125075.60

0.61

43.42

0.48

-37630.50-0.46

Source: own survey data. *,**,*** represent significance at 0.1, 0.05 and 0.01 significant levels, respectively.

CONCLUSION
The study used the BFG methods to analyze the factors that influence the choice of marketing channels
and the impact of marketing channels on net returns of grape farmers in Afghanistan. Focusing on
three Afghan provinces, the results of the study showed that selling price, province, participation intraining and planting area are some of the factors that influence the choice of marketing channels. In
addition, the results showed that marketing channels through local traders would improve the overall
net returns of farmers. However, future studies would incorporate management structures of farms, andfocus on all commercial grape growing provinces in Afghanistan to give a clearer picture.
REFERENCES
Afif, A.K. 2015. Grapes production in Afghanistan. Discourse Afghanistan, Retrieved from http://discourse
afghanistan.com/grapes-production-in-afghanistan/
Bourguignon, F., Fournier, M. and Gurgand, M. 2007. Selection bias corrections based on the multinomial logit
model: Monte Carlo comparisons. Journal of Economic Surveys, Wiley Blackwell, 21 (1), 174-205.
Brady, P.L., Thomsen, M. and Morris, J.R. 2010. Marketing options for grapes and grape products. Arkansas
Agricultural Experiment Station, Retrieved from http://arkansasagnews.uark.edu/1353.htm
CHAMP. 2009. Best practices for grape production and marketing in Afghanistan. The CHAMP Farm to Market
Guide, Retrieved from www.CHAMP.afⒸ ISERD

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Chiang, W.Y. 2016. Discovering customer value for marketing systems: An empirical case study. International
Journal of Production Research, 55, 5157-5167.
Davis, N.M. 2015. An assessment of mango farmers’ choice of marketing channels in Makueni, Kenya.
University of Nairobi, Kenya.
Emana, B. and Gebremedhin, H. 2007. Constraints and opportunities of horticultural production and marketing inEastern Ethiopia. Dry Lands Coordination Group Report, No. 46.
Jari, B. and Fraser, G.C.G. 2009. An analysis of institutional and technical factors influencing agricultural
marketing amongst smallholder farmers in the Rift Valley, Eastern Cape Province, South Africa. African
Journal of Agricultural Research, 4, 1129-1137.
Safi, M.A., Amekawa, Y., Isoda, H. and Hassanzoy, N. 2018. Cost - benefit efficiency and factors influencing
farmers, Choice of marketing channel in grape value chain: Evidence from Kabul, Afghanistan. Kyushu
University, 63 (1), 159-168.
Uematsu, H. and Mishra, A. 2011. Use of direct marketing strategies by farmers and their impact on farmbusiness income. Agricultural and Resource Economics Review, 40, (1), 19.
USAID. 2016. Market brief: Grapes an overview of export growth. Ministry of Agriculture, Irrigation andLinestock, Afghanistan.

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Research article
erd

Effect of Temperature on the Cohort Life Table of Brown
Planthopper (Nilaparvata lugens Stål) (Homoptera:
Delphacidae)
MAY THU HTET*
Yezin Agricultural University, Myanmar
Email:maythuyau91@gmail.com

MOE HNIN PHYU
Yezin Agricultural University, Myanmar

THI TAR OO
Yezin Agricultural University, Myanmar
Received 30 December 2020

Accepted 30 June 2021(*Corresponding Author)

Abstract We investigated the effect of constant temperatures (24, 28, 32, and 36 °C) and room
temperature on brown planthopper (BPH), Nilaparvata lugens, population dynamics; these data
are useful for forecasting BPH outbreaks. Eggs laid by gravid females were incubated in test
tubes at the treatment temperatures and observed for egg hatching. After hatching, first instar
nymphs were collected using a camel-hair brush and incubated individually in new test tubes,
each with a rice plant. The number of hatched and unhatched eggs were recorded. Nymphs
were monitored daily for life table parameters until adult emergence; the longevity of adults
were also recorded. From life table analysis, the greatest mortality occurred in the first nymphal
instar, which resulted in type III survivorship curves. Moreover, at 36 °C, 100% mortality
occurred at the egg stage. Brachypterous and Macropterous forms achieved high net
reproductive rates at lower temperature regimes. The growth parameters of BPH decreased at
36 °C; temperatures above 32 °C were detrimental to the development of BPH. Results indicatethat egg and nymphal stages were significantly affected by temperature. Egg hatch also
decreased drastically with increased temperatures, especially at 36 °C.
Keywords constant temperature, Nilaparvata lugens, life table parameters, rice

INTRODUCTION
Rice is an essential food crop and more than 90% of global production occurs in tropical and semitropical Asia (Food and Agriculture Organization, 2012). Each year, an additional 50 million riceconsumers will be added to the world population, meaning that rice production must increase markedly(Heong and Hardy, 2009). In Myanmar, rice is the single most important crop, both as a staple and as aforeign exchange earner (Ministry of Agriculture and Irrigation, 2003).
Rice is susceptible to pests and diseases. From sowing to harvest, all parts of the plant arevulnerable to insect-feeding (Grist and Lever, 1969). Eight hundred species of insects attack rice crops,
either in the field or during storage. Amongst these, one of the most economically important is thebrown planthopper (BPH), Nilaparvata lugens (Stal.) (Homoptera: Delphacidae). BPH has become
more problematic, posing a threat to rice production throughout South and South East Asia (Rao,1950). In Myanmar, the first outbreak of BPH happened in 1970 at the Kyaukse Central Farm
(Mandalay Division) and in Upper Myanmar (Myint, 1975). The BPH has since become a major pestof rice in Myanmar (Win et al., 2011).
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BPH is present on rice during both dry and wet seasons. Biotic and abiotic factors affect survival,
growth, development and multiplication of BPH; amongst these, temperature plays a significant role(abiotic factor). As a result of climate change, global mean temperatures are predicted to increase by
1.5 - 6.0 ºC by the end of the 21st century (IPCC, 2001). This significant temperature increase willaffect insect physiology, behavior and development as well as species distribution and abundance;
there is already evidence for this in the increase in number of generations a year, increasing survival
rates in winter, and the earlier appearance of some insects (Huang, et al., 2010).
OBJECTIVE
This study aimed to investigate the effect of temperature on BPH life table parameters to provide thedata needed for forecasting BPH outbreaks.
METHODOLOGY
Study site:
The experiments have been done at the JICA – ELB 1 Laboratory, Department of Entomology andZoology, Yezin Agricultural University, Myanmar between August 2018 and January 2020.
Brown planthopper (BPH) rearing:
The initial population of BPH was collected from rice fields at Yezin Agricultural University. To
establish a colony adult was confined on 30-day old potted rice plants (susceptible variety Manaw
thukha) in aluminum rearing cages in a screen house.
Life table study:
There were four constant temperature treatments (24, 28, 32 and 36 °C), and a control maintained atroom temperature (26.8-30.7 °C) which was recorded daily. Individual rice plants (tillering stage) wereplaced in test tubes with four gravid female BPH for the collection of eggs and sealed with a cottonwool plug. Replicate test tubes (n=5) were placed at each of the treatment, control temperatures andoviposition allowed to proceed for 24 h. After this time adult females were removed and the groups of
eggs incubated at their allocated temperatures. Eggs were observed daily and, when they hatched, acamel-hair brush was used to transfer each first instar nymph individually into new test tubes, eachwith a new rice plant. Eggs continued to be observed and hatching first instars transferred into new test
tubes until hatching stopped; the number of hatched and unhatched eggs were recorded at this point.
The later instars were collected and transferred as the same way. Nymphs were monitored daily and thetime to moult to each life stage recorded; they were provided with fresh plant materials as required. We
recorded mortality (dx) daily until their death. As adults emerged, one male and one female were paired
and moved to a new rice plant. The total numbers of laid eggs were recorded daily until females died.
Age specific life table construction:
The life table was built by partitioning the life-cycle into distinct development stages (i.e. eggs,
nymphs and adults) and recording the developmental time and survival or mortality of each stage. We
calculated the pivotal age for the age class in units of time (days) (X), number of alive at each life stage(nx), proportion of individuals surviving to the start of age interval x (l x), mean number of individuals
alive during the age interval x to x+1 (Lx), number dying during the age interval x to x+1 (dx), percent
apparent mortality (100 qx), total number of age X units beyond the age (Tx ), mean expectation of life
for individuals alive at the start of age x (ex), age-specific fertility, the number of living females born
per female in each interval class (mx), net reproductive rate (Ro), the innate capacity for an increase in
numbers (rm), and the mean length of a generation (G) from these data.

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Data analysis:
ANOVA and comparison of means was done using LSD in the software program (SAS 9.1).
RESULTS AND DISCUSSIONLife Table
Life table analysis showed that a mean of 26.4% of 139.4 BPH eggs survived to adulthood whenincubated at room temperature (control); this was 45.2% (of 143.0 eggs) at 24 °C, and 49.2% (of 113.8
eggs) at 28 °C. Win et al. (2011) found that 37.26% of 365 BPH eggs successfully emerged as adults ina field experiment where the temperature ranged between 23 °C and 33 °C.
Table 1 Mean number and percentage of BPH alive at each life stage when reared at different
constant temperatures
Mean ± SE

Control

24 °C

28 °C

32 °C

36 °C

Stages
nx

%

nx

%

nx

%

nx

%

nx

%

Egg

139.4 ± 3.9

100.0

143.0 ± 3.2

100.0

113.8 ±1.1100.0132.4 ±1.6

100.0135.8 ± 2.7

100.0

First
instarnymph

102.4 ± 6.2

73.5

137.0 ± 3.2

95.8

96.8 ± 1.5

85.1

107.4 ± 2.5

81.1

0.0 ± 0.0

0.0

Secondinstarnymph

77.6 ± 4.8

55.7

112.2 ± 3.1

78.5

77.4 ± 1.1

68.0

76.6 ± 1.857.9

0.0 ± 0.0

0.0

Third
instarnymph

67.4 ± 3.8

48.4

99.0 ± 2.6

69.2

72.8 ± 0.9

64.056.4 ± 1.842.6

0.0 ± 0.0

0.0

Fourthinstarnymph

57.2 ± 3.1

41.0

90.4 ± 2.6

63.2

67.2 ± 0.9

59.1

44.8 ± 1.333.8

0.0 ± 0.0

0.0

Fifth
instarnymph

48.0 ± 2.7

34.4

80.0 ± 2.4

55.9

64.0 ± 0.8

56.2

22.6 ± 1.1

17.1

0.0 ± 0.0

0.0

Adult

36.8 ± 2.4

26.4

64.6 ± 2.2

45.2

56.0 ± 0.849.2

0.0 ± 0.0

0.0

0.0 ± 0.0

0.0

In our study high mortality occurred during the early immature stages and at 32 °C and 36 °C
there was no survival to adulthood (Table 1). Begon and Mortimer (1981) reported that this low
survivorship at higher temperatures is common in most insect species.
The highest mortality of any life stage (135.8±2.7) was observed at 36 °C in the egg stage.
Greatest mortality in first, second, third, fourth and fifth instar nymph were found at 32°C. At 36 °C,
none of the nymphal instars survived (Table 2). Krishnaiah et al. (2005) reported that temperatures
ranging between 25 and 30 °C were most favorable for BPH multiplication, and that the insect could
not tolerate constant temperatures of 35 °C and higher. Rout and Jena (2012) found that BPH thrived
and multiplied well at a temperature of 30 ± 3 ºC, and that 33ºC and 35 ºC were unfavorable for insect
survival.

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Table 2 Mean number of BPH dying between age interval x and age interval x+1 (dx) whenreared at different constant temperatures
Mean ± SE
StagesControl

24°C

28°C

32°C

36°C

dx

dx

dx

dx

dx

Egg

37.0±2.4

6.0±0.7

17.0±0.9

25.0±1.7

135.8±2.7First instar nymph24.8±1.524.8±0.8

19.4±0.730.8±0.8

0.0±0.0

Second instar nymph10.2±1.1

13.2±0.5

4.6±0.3

20.2±0.6

0.0±0.0

Third instar nymph10.2±0.8

8.6±0.3

5.6±0.2

11.6±0.6

0.0±0.0

Fourth instar nymph

9.2±0.511.2±0.4

10.4±0.515.4±0.2

3.2±0.2
8.0±0.1

22.2±0.522.6±1.1

0.0±0.0
0.0±0.0

Fifth instar nymph

The highest net reproductive rate (175.6) of the brachypterous form was observed at room
temperature (control), followed by (168.2) at 24 °C, (166.8) at 28 °C and (74.6) at 32 °C, respectively.
The lowest net reproductive rate (32.6) was found at 36 °C. The intrinsic rate of natural increase (rm)
was 0.6, 0.5, 0.7, 0.9 and 0.8 for control, 24 °C, 28 °C, 32 °C and 36°C, respectively (Table 3). The net
reproductive rate of BPH was higher at lower temperature regimes. Manikandan et al. (2015) foundthat the net reproductive rate of BPH was greater at 30 °C (39.95) than at 36 °C (8.84).
Table 3 Life table parameters of brachypterous forms at different temperature
Brachypterous forms
Parameter

Mean length ofa generation (G)Instantaneousrate
Net reproductive
rate
Gross reproductive
rate

FormulaControl

24 °C

28 °C

32 °C

36 °C

𝑙𝑥 𝑚𝑥

8.5

9.8

7.4

4.7

4.4

𝑟𝑚 = log 𝑒 (𝑅𝑜 ) /𝐺

0.6

0.5

0.7

0.9

0.8

175.6

168.2

166.8

74.6

32.6

175.6

168.2

166.8

74.6

32.6

𝐺=

𝑙𝑥 𝑚𝑥 𝑋/

𝑅𝑜 =
𝑚𝑥

𝑙𝑥 𝑚𝑥

The highest net reproductive rate (204.0) of macropterous forms was found at room temperature(control), followed by 142.6 at 28 °C, 102.4 at 24 °C and 93.6 at 32 °C. The lowest net reproductive
rate (29.2) was found at 36°C. The intrinsic rate of natural increase (rm) was 0.6 in both the control andat 24 °C, 0.6 at 28 °C, 0.8 at 32 °C and 0.9 at 36 °C (Table 4). Similar results were reported for BPH
by Manikandan et al. (2015) who reported that the net reproductive rate was greater at lower
temperature regimes (30.0 °C) than at higher temperature regimes (36.0 °C).
Developmental stages of BPH varied in length and time at different temperatures. The mean
number of first instar nymph was highest at 24 °C with the value of (137.0 ± 3.2) and lowest at 28 °C(96.8 ± 1.5). The second instar nymph was highest at 24 °C (112.2 ± 3.1) and lowest at 32 °C (76.6 ±1.8). The third instar nymph was highest at 24 °C (99.0 ± 2.6) and lowest at 32 °C (56.4 ± 1.8). The
fourth instar nymph was highest at 24 °C (90.4 ± 2.6) and lowest at 32 °C (44.8 ± 1.3). The fifth instar
nymph was highest at 24 °C (80.0 ± 2.4) and lowest at 32 °C (22.6 ± 1.1). Adults had the highest at
24 °C (64.6 ± 2.2) and no adults emerged adult at 32 °C. All eggs died at 36 °C (Table 5). This showsthat, overall, 24 °C is the most favorable temperature for development of each life stage. Bae andⒸ ISERD

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Pathak (1970) reported that there was no apparent difference in rate of growth of nymphs when rearedat 25 °C or 29 °C but at 33 °C no nymphs survived beyond second instar.
Table 4 Life table parameters of macropterous forms at different temperature
Macropterous forms
Parameter
Mean length ofa generation (G)Instantaneousrate
Net reproductive
rate
Gross reproductive
rate

Formula𝐺=𝑙𝑥 𝑚𝑥 𝑋/

𝑙𝑥 𝑚𝑥

𝑟𝑚 = log 𝑒 (𝑅𝑜 ) /𝐺
𝑅𝑜 =

𝑙𝑥 𝑚𝑥

𝑚𝑥

Control

24 °C

28 °C

32 °C

36 °C

8.8

7.5

8.9

6.0

3.8

0.6

0.6

0.6

0.8

0.9

204.0

102.4

142.6

93.6

29.2

204.0

102.4

142.6

93.6

29.2

Table 5 Life table of developmental stages of BPH at different temperature
Mean ± SE
Stages

P- value
control

24 °C

28 °C

32 °C

36 °C

Egg

139.4 ± 3.9

143.0 ± 3.2

113.8 ± 1.1

132.4 ± 1.6

135.8 ± 2.7<.0001First instar nymph102.4 ± 6.2

137.0 ± 3.2

96.8 ± 1.5

107.4 ± 2.5

0.0 ± 0.00<.0001

Second instar nymph

77.6 ± 4.8

112.2 ± 3.1

77.4 ± 1.1

76.6 ± 1.8

0.0 ± 0.00<.0001

Third instar nymph

67.4 ± 3.8

99.0 ± 2.6

72.8 ± 0.9

56.4 ± 1.8

0.0 ± 0.00<.0001

Fourth instar nymph

57.2 ± 3.1

90.4 ± 2.6

67.2 ± 0.9

44.8± 1.3

0.0 ± 0.00<.0001Fifth instar nymph

48.0 ± 2.7

80.0 ± 2.4

64.0 ± 0.8

22.6 ± 1.1

0.0 ± 0.00<.0001Adult

36.8 ± 2.4

64.6 ± 2.2

56.0 ± 0.8

0.0 ± 0.0

0.0 ± 0.00<.0001

CONCLUSION
This study showed that population growth parameters were positively correlated with temperature up
to 28 °C but negatively correlated at 32 °C and above. At 36 °C, survival of all growth stages
decreased. Thus, temperatures above 32°C are detrimental to the development of BPH and no adults
emerged at 32 °C and 36 °C. The most favorable temperature for BPH development was 24 °C for
every life stage. According to results, BPH's net reproductive rate was greater at lower temperature
regimes (control, 24 and 28 °C) than at higher temperature regimes (32 °C and 36 °C). No eggs
hatched into nymph at 36 °C. BPH development was good at control, 24 °C and 28 °C although fitness
was reduced at 32 and 36 °C. In the life table analysis, the highest mortality occurred in the firstnymphal instar. The survivorship curve indicated a modest rate of mortality during the early life stages
that gradually decreased as individuals reached adulthood; this followed a type III survivorship curve.
Temperature had a direct effect on development, survival and reproduction of BPH.
Brown planthopper have high reproductive rate, so critical monitoring and forecasting of thebrown planthopper is very necessary for timely control management. The research finding will provide
valuable information to take monitoring program for control of BPH population outbreak according to
the climate change.
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ACKNOWLEDGEMENTS
The authors would like to thank the generous financial and technical training support provided by theJapan International Cooperation Agency (JICA) Project for Capacity Development of Yezin
Agricultural University (YAU-JICA TCP).
REFERENCES
Bae, S.H. and Pathak, M.D. 1970. Life history of Nilaparvata lugens (Homoptera: Delphacidae) andsusceptibility of rice varieties to its attacks. Annals of the Entomological Society of America, 63 (1), 149-153.
Begon, M. and Mortimer, M. 1981. Population ecology: A unified study of animals and plants. Massachusetts,
USA: Sunderland Sinauer Associated Inc.
Food and Agriculture Organization. 2012. Food and agriculture organization statistical year book. Retrieved fromwww.fao.org/statistics
Grist, D.H. and Lever, R.J.A.W. 1969. Pests of rice. Pests of rice. Longmans, Green and Co. London. 520.
Heong, K.L. and Hardy, B. (Eds.) 2009. Planthoppers: New threats to the sustainability of intensive riceproduction systems in Asia. Int. Rice Res, Inst.
Huang, S.H., Cheng, C.H. and Wu, W.J. 2010. Possible impacts of climate change on rice insect pests andmanagement tactics in Taiwan. Crop Environ. Bioinfo. 7, 269-279.
IPCC. 2001. The scientific basis. Cambridge Univ. Press, Cambridge.
Krishnaiah, N.V., Prasad, A.R., Rao, C.R., Pasalu, I.C., Lakshmi, V.J., Narayana, V.L. and Lingaiah, T. 2005.
Effect of constant and variable temperatures on biological parameters of rice brown planthopper, Nilaparvata
lugens (Stal). Indian Journal of Plant Protection, 33 (2), 181-187.
Manikandan, N., Kennedy, J.S. and Geethalakshmi, V. 2015. Effect of temperature on life history parameters ofbrown planthopper (Nilaparvata lugens Stal). African Journal of Agricultural Research, 10 (38), 3678-3685.
Ministry of Agriculture and Irrigation. 2003. Myanmar agriculture in brief. Yangon Ministry of Agriculture andIrrigation, Myanmar.
Myint, K.T. 1975. Studies on biology and control of brown plant hopper Nilaparvata lugens (Stal). MSc diss.,
University of Rangoon.
Rao, V.T. 1950. Nilaparvata lugens (Stal) as a pest of paddy cultivation in North Madras and its control. Indian
Journal of Entomology, 12, 241-246.
Rout, S. and Jena, M. 2012. Effect of high temperature on the multiplication of brown planthopper Nilaparvata
lugens (Stal.). ORYZA-An International Journal on Rice, 49 (4), 288-291.
Win, S.S., Rita, M., Ahmad, Z.A.M. and Adam, N.A. 2011. Life table and population parameters of Nilaparvata
lugens Stal. (Homoptera: Delphacidae) on rice. Tropical Life Sciences Research, 22 (1), 25-35.

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Research article
erd

Effects of adding Rice Husk Biochar on GHG Emission andCompost Quality during Cow Manure Composting
SARVESH 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 31 December 2020

Accepted 30 June 2021(*Corresponding Author)

Abstract Agriculture is a major anthropogenic source of Green House Gases (GHG) andcontributes 24% of total GHG emitted (IPCC, 2014). Livestock sector forms a significant partof agriculture and is responsible for major GHG emission. According to FAO, 2016, livestock
manure contributes 23% to total GHG emitted in agricultural sector. N2O, CO2 and CH4 areimportant GHG emitted during management of livestock manure. Against the background of
global warming, there is increased motivation in reducing GHG emission. Therefore, this study
deals to see the effects in N2O, CO2 and CH4 emission by adding 5%, 10% and 15% of rice husk
biochar during cow manure composting. In addition, degradation rate and C/N ratio of manuresamples were analyzed for determining the final compost quality. Subsequently, a composting
experiment was conducted using rice husk biochar and gases emitted were measured
periodically with static gas chamber method. The experimental results indicated that biochar
was effective in reducing N2O and CO2 emissions. The cumulative emission of N2O was
reduced by 40% in 5% treatment, 46% in 10% treatment and 60% in 15% treatment. Similarly,
there was decrease of CO2 by 69% in 5% treatment, 68% in 10% treatment and 48% in 15%
treatment. Biochar’s well developed pore structure and adsorption capacity reduced the gas
emission. Furthermore, addition of biochar enhanced degradation rate and better C/N ratio.
Biochar addition provided necessary nutrients and habitat, facilitating growth of various
microorganisms. The results of CH4 emission did not show any significant difference between
the treatments. The obtained results indicate that rice husk biochar addition is beneficial in
reducing GHG emission and improving compost quality with faster degradation and better C/Nratio.
Keywords GHG emission, cow manure composting, rice husk biochar, compost quality

INTRODUCTION AND BACKGROUND
As the human civilization has evolved, the global climate change has been continuously changing. In
recent years, global climate change issues and its relation to human activities has gathered widespreadattention. IPCC, 2007, IPCC, 2013, states that global warming is significantly affected by humanactivities. Furthermore, these reports states that fossil fuel use and land use change are the major
contributor of GHGs. AFLOU, which represents agriculture, forestry and other land uses is a majoranthropogenic source of GHGs emission and contributes 24% of total GHG emitted (IPCC, 2014).
GHGs are mainly composed of CO2 (76%), CH4 (16%), N2O (6%) (IPCC, 2013). Agricultural activities
contributes 20% of CO2, 70% CH4 and 90% of N2O (Cole et al., 1997; Yousefi, Damghani andKhoramivafa, 2016). Livestock sector forms a significant part of agriculture and is responsible formajor GHGs emission. FAO, 2016, reports that management of livestock manure contributes 23% toⒸ ISERD

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total GHGs emitted in agricultural sector. Generally, livestock manure has been used as a soil
amendment in the form of compost. Composting is an efficient way of manure management, but also
releases GHGs. N2O, CO2 and CH4 are important GHGs emitted during composting of livestock
manure. In recent years, biochar has gained significant interest in various environmental applications.
Biochar is a product of pyrolysis characterized with microscopic structure having large surface areaand has affinity to absorb nutrients, ions etc. (Dünisch et al., 2007; Major et al., 2009), and has been
applied in soil to mainatain soil fertility and reducing GHG emission (Van Zwieten et al., 2015). Dueto its unique properties, it is an efficeint, cost effective and environmentally friendly material fordiverse purposes. Against the background of global warming, there is increased motivation in reducing
GHG emissions. Therefore, this study focus to see the effects of adding rice husk biochar during cow
manure composting in reducing GHG gases emission and better compost quality.

Fig. 1 GHGs contributor in agricultural sector (FAO, 2016)
METHODOLOGY
Compost Box and Gas Chamber
A glass container of dimension 30 cm3 was used as a compost box. The box was covered with styrofoam sheets of thickness 2 cm on five sides for minimizing the heat loss (Fig. 2) during compostingprocess. A static gas chamber made up of acrylic sheet was used to measure the gas flux. Acrylic sheet
was used as it is inert to the target gases. A digital thermometer and a fan were attached inside thechamber for measuring temperature and uniform circulation of air during measurement of gas
respectively (Fig. 2 and 3). The volume of air inside the chamber was calculated as 0.017705 m3.

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

Fig. 3 Apparatus used during gas flux
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Conditions of the Experiment
The composting experiment was performed 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 period. Cow manure, rice straw and litter was used as main components
for composting. 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). These variations were made to see the most effective composition. The initialwater content of the composting material was set at 70±2% for all the treatments.
Table 1 Composition of treatments
Composition

Control

5% Treatment

10% Treatment

15% Treatment

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

Gas Flux Measurement
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).
(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 the chamber (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)Determination of Degradation Rate
Loss of organic content during a composting process is a measure of biological decomposition and isexpressed in the form of degradation rate. The degradation of composting substrates as a function oftime follows first order kinectics (Haug 1993, Kulcu and Yaldiz, 2004) and can be expressed asfollowing,= - kt(2)

Where,OM is mass of biodegradable organic matter at any time of composting,
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t is time in day,kt is reaction rate constant in days-1
Compost Sampling and Analysis
Compost was sampled at 5, 10, 20, 30, 40 and 60 days to analyze its quality. Temperature of thecompost pile was measured using Custom CT-0580 data logger and digital thermometer on daily basis.
Organic content was analyzed using Ignition loss method. Carbon content was determined by using theformula 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 DISCUSSIONSBiochar 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. 4 shows the periodic change
in emission between the treatments. The cumulative gas emission (Fig. 5) was lowered by 40%, 46.4%
and 60.4% in 5%, 10% and 15% biochar added treatments compared to control. The result observed is
supported by that of Jeffery et al., 2015, which states that addition of biochar can decrease the emissionof nitrous oxide. Although the mechanisms of N2O formation during composting process of animal
manure is not studied in details, N2O emission is mainly associated with nitrification and denitrification
process (Sanchez-Monedero et al., 2010). These microbial process is regulated by the quantity ofmineral nitrogen, presence of carbon sources and oxygen concentration in composting maerial. Wanget al., 2013, showed 31% lower emissions in biochar treated piles and linked this result in reduction tochange in the abundance and composition of denitryfying bacteria. It has been well reported that
addition of biochar increases pile porosity, thus increasing oxygen content, weaking denitrification
enzyme activity and inhibiting denitrification reaction under anaerobic conditions (Singh et al., 2010).

Fig. 4 Periodic change in N2O flux

Fig. 5 Cumulative gas flux of N2O

Biochar Effect on Carbondioxide Emission
The result of periodic changes in CO2 flux is shown in fig 6. The concentration of CO2 was highest incontrol amongst the treatments which conincides to the studies from Awasthi et al., 2017 and Jiang etal., 2016, according to which biochar reduces CO2 emission. The periodic change in CO2 flux showed
an initial rise and fall at the later stage of the composting process, which was consistent with change intemperature.
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Fig. 6 Periodic change in CO2 flux

Fig.7 Cumulative gas flux of CO2

The cumulative gas flux (Fig.7) showed that there was decrease of CO2 by 69% in 5% treatment,
68% in 10% treatment and 48% in 15% treatment. According to the obtained results, biochar added
treatments could reduce the emission of CO2. This result coincides to the ones reported by Ngo et al.,2013, which states that biochar could increase carbon sequestration of exogenous organic matter whenapplied in compost mixtures.
Biochar Effect on Methane Emission
Fig. 8 shows the results of periodic change of CH4 flux and fig. 9 shows the cumulative gas flux
emission. CH4 emission was observed in early stage of the composting period for all the treatmentswith biochar added treatments having higher emission compared to control. The emission was seen
during first week of the experiment with no emission observed after a week for all the treatments.
Although, studies have shown that biochar addition enabled to inhibit CH4 emission in cow manure(Sonoki et al., 2013) and muncipal solid waste composting (Vandecasteele et al., 2013), similar results
could not be observed in our study. CH4 production during composting is mainly associated toanaerobic processes.

Fig. 8 Periodic change in CH4 flux

Fig. 9 Cumulative gas flux of CH4

According to Sanchez et al., 2015, favourable conditions for CH4 emission can be found in theearly stages of composting process, where large amount of nutrients and organic matter are available
which enhances microbial growth, depleting the oxygen levels in the pile. Refering to the results oftemperature, (fig. 11) where biochar added treatments showed higher temperature gain, showes
increased microbial activity, creating anerobic environment. To support this, general bacteria count in
the early stage of composting showed higher microbial presence in biochar added treatments.
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Biochar Effects on Quality of Compost
C/N ratio and temperature profile are oftenly used as a criteria for determining the quality of compost.
C/N ratio is used as an indicator of compost stability and nitrogen availability. Wheareas, increased
temperature shows higher microbial activity and faster decompostion of organic substrates. The
periodic change of C/N ratio showed better values in biochar added treatments compared to control(Fig. 10).

Fig. 10 Periodic change in C/N ratio

Fig. 11 Periodic change in temperature

Better C/N ratio amongst the biochar added treatments compared to control may be due
increased surface area and moisture content favouring microbial activity for degradation andhumification of organic material. In addition, biochar increased sorption of nitrogeneouscompounds in its microspores, providing microorganism with sufficient nitrogen for their
metabolism.Temperature results (Fig. 11) showed rise in temperature in early stages of composting,followed by fall after one week and stabilising around 30 ºC at later stage of composting. Biochar
added treatments had higher temperature gain during early stages of composting compared to control
due to faster degradation due to reduced heat loss as addition of biochar filled the air pores (Zhang
and Sun, 2014). Khodadad et al., reports that adding of biochar increases the relative abundance ofActinobacteria, which are generally able to degrade more organic material, resulting in heatgeneration.
Biochar Effect on Degradation Rate
The results of degradation rate of organic substrates is shown in table 2. The highest biodegradability
after 5 days of composting was seen in 15% treatment with rate constant of 0.034 dˉ1, followed by 5%treatment with rate constant of 0.032 dˉ1, followed by 10% treatment with reaction rate of 0.031 dˉ1,
with the least in control having 0.013 dˉ1 of reaction rate. For 60 days of composting, the degradability
was in order of 10% treatment with 0.0056 dˉ1, 15% treatment with 0.0054 dˉ1, 5% treatment with
0.0052 dˉ1, followed by control with 0.0045 dˉ1. The obtained results shows that adding of biocharenhances decomposition of organic matter and degradation rate of the composting substrates. The
degradation rate was higher during early and decreased with time. The results of increased degradationin biochar added treatments also agree to that of Zhang and Sun, 2014, which states that biochar
amendment increased composting rate, increasing temperatures due to denser substrates with filledpore spaces reducing the heat loss that occurs because of greater air space. Moreover, adding of
biochar increased the oxygen uptake, moisture retention and increased the microbial activity resulting
in better degradation. Steiner et al., 2015, biochar is a highly porous material having high capacity toⒸ ISERD

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retain excess water, improve aeration conditions and provide suitable conditions for microorganisms,
promoting microbial activity and increasing organic matter degradation.
Table 2 Degradation rate at 5 and 60 days passed
Treatments

5 days kt (d-1)

60 days kt (d-1)

Control

0.013 a0.0045 a

5%
10%
15%

**

0.032 b0.031 c**0.034 c**0.0052 a
0.0056 a
0.0054 aNote: Significance difference at *:p<0.05, **:p<0.01

CONCLUSIONSThis study was conducted to see the effects of adding rice husk biochar in reducing GHG emission andenhancing final compost quality during cow manure composting. According to the results of this study,
addition or rice husk biochar was efficient in reducing GHG emission and significantly enhancing
degradation and mineralization of organic substrates during cow manure composting. In the given
conditions, addition of biochar was effective in decreased N2O and CO2 emission. However, nosignificance difference was seen in CH4 emission. On the other hand, compost quality with better
degradation and nitrogen mineralization was observed. Although, 15% treatment (750 g in 5 kg) had
highest reduction of GHG emission and enhanced compost quality, relatively small amount of 5% (250g in 5 kg) treatment was also effective. Considering these findings, rice husk biochar could be aneffective and beneficial practice for management of cow manure for reducing GHG emission and better
quality of cow manure compost.
REFRENCES
Awasthi, M.K., Wang, M., Chen, H., Wang, Q., Zhao, J., Ren, X., Li, D., Awasthi, S.K., Shen, F., Li, R. andZhang, Z. 2017. Heterogeneity of biochar amendment to improve the carbon and nitrogen sequestrtation
through reduce the greenhouse gas emission during sewage sludge composting. Bioresour. Technol, 221,428-438.
Cole, C.V., Duxbury, J., Freney, J., Heinomeyer, O., Minami, K., Moiser, A., Paustian, K., Rosenberg, N.,
Sampson, N., Sauerbeck, D. and Zhao, Q. 1997. Global estimates of potential green house gas by agriculture.
Nutrient Cycling in Agroecosystems, 49, 221-228.
Dünisch, O., Lima, V.C., Seehann, G., Donath, J., Montoia, V.R. and Schwarz, T. 2007. Retention properties ofwood residues and their potential for soil amelioration. Wood Sci. Technol. 41, 169-189.
FAO. 2016. The state of food and agriculture. FAO, Rome.
Haug, R.T. 1993. The practical handbook of compost engineering. LEWIS, Boca Raton, FL, USA.
IPCC. 2007. Climate change 2007. The Physical Science Basis. Contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA.
IPCC. 2013. Climate change 2013. The physical science basis. Working group I. Contribution to the fifth
assessment report of the IPCC. Cambridge University Press.
IPCC. 2014. Climate change 2014. Synthesis Report. Contribution of Working Groups I, II and III to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Chang IPCC, Geneva, Switzerland, 151.
Jiang, T., Ma, X., Tang, Q., Yang, J., Schuchardt, F. and Li, G. 2016. Combined use of nitrification inhibitor andstruvite crystallization to reduce NH3 and N2O emissions during composting. Bioresour. Technol, 217, 210218.

Ⓒ ISERD

120

IJERD – International Journal of Environmental and Rural Development (2021) 12-1

Kulcu, R. and Yaldiz, O. 2004. Determination of aeration rate and kinetics of composting some agricultural
wastes. Bioresour. Technol, 93 (1), 49-57.
Major, J., Steiner, C., Downie, A. and Lehmann, J. 2009. Biochar eﬀects on nutrient leaching. 271-288. In
Lehmann, J. and Joseph, S. (eds.), Biochar for Environmental Management, Science and Technology,
Earthscan, London.
Mihara, M. and Ueno, T. 2000. Nitrogen and phosphorus transfer in soil erosion process, IAHS Publication, No.
263.
Minamikawa, K., Tokida, T., Sudo, S., Pare, A. and Yagi, K. 2015. Guidelines for measuring CH 4 and N2Oemission from rice paddies by a manually operated closed chamber method. National Institute for AgroEnvironmental Sciences, Tsukuba, Japan.
Ngo, P.T., Rumpei, C., Ngo, Q.A., Alexis, M., Velasquez Vargas, G., Mora Gil Mde, L., Dang, D.K. and Jouquet,
P. 2013. Biological and chemical reactivity and phosphorus forms of buffalo manure compost, vermicompost
and their mixture with biochar. Bioresour. Technol, 148, 401-407.
Sanchez-Monedero, M.A., Serramia, N., Civantos, C., Fernandez-Hernandez, A. and Roig, A. 2010. Greenhousegas emissions during composting of two-phase olive millwastes with different agroindustrial by-products.
Chemosphere, 81 (1), 18-25.
Singh, B.P., Hatton, B.J., Balwant, S., Cowie, A.L. and Kathuria, A. 2010. Influence of biochar on nitrous oxide
emission and nitrogen leaching from two constrasting soils. J. Environ. Qual., 39 (4), 1224-1235.
Sonoki, T., Furukawa, T., Jindo, K. and Suto, K. 2013. Influence of biochar addition on methame metabolism
during thermophilic phase of composting. Journal of Basic Microbiology, 53, 617-621.
Steiner, C., Sanchez-Monedero, M.A. and Kammann, C. 2015. Biochar as an additive to compost and growing
media. In Lehmann, J. and Joseph, S. (Eds.), Biochar for Environmental Management: Science, Technology
and Implementation EarthScan, 2, 715-733.
Van Zwieten, L., Kammann, C., Cayuela, M.L., Pal Singh, B., Joseph, S., Kimber, K., Donne, S., Clough, T. andSpokas, K. 2015. Biochar effects on nitrous oxide and methane emissions from soil. In Lehmann, J. andJoseph, S. (Eds.), Biochar for Environmental Management: Science, Technology and Implementation
EarthScan, 2, 487-518.
Vandescateele, B., Mondini, C., D’Hose, T., Russo, S., Sinicco, T. and Quero Alba, A. 2013. Effect of biochar
amendment during composting and compost storage on greenhouse gas semissions, N losses and P
availability. 15th RAMIRAN International Conference. Recycling of Organic Residues for Agriculture: From
Waste Mangement to Ecosystem Services, Universite de Versailles St-Quentien-Yvellines, Versailles.
Yousefi, M., Mahdavi, A. and Khoramivafa, M. 2016. Comparison greenhouse gas (GHG) emissions and global
warming potential (GWP), Effect of energy use in different wheat agroecosystems in Iran. Environmental
Science and Pollution Research, 23. 10.1007/s11356-015-5964-7.
Zhang, L. and Sun, X. 2014. Changes in physical, chemical, and microbiologicalproperties during the two-stage
co-composting of green waste with spentmushroom compost and biochar. Bioresour. Technol, 171 (1), 274284.

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Research article
erdConservation and Ecological Threats of Agarwood (Aquilariasp.) on Leyte Island, Philippines
LORRAINE CRISTY E. CENIZA
Visayas State University, Baybay City, Philippines
Email: lorrainecristy.ceniza@vsu.edu.ph

JIMMY O. POGOSA
Visayas State University, Baybay City, Philippines

SUZETTE O. LINA
Visayas State University, Baybay City, Philippines

MARLITO M. BANDE*
Visayas State University, Baybay City, Philippines
Received 30 December 2020

Accepted 30 June 2021(*Corresponding Author)

Abstract Aquilaria is a genus of about 20 species distributed mainly in the Indo-Malesian
region. The genus Aquilaria belongs to the Thymelacaceae family of Indo-Malayan trees
known for producing the world's most expensive oils, which are naturally occurring throughout
the Philippines. Aquilaria is well known for the production of agarwood which is a highly
wanted forest product of substantial economic value. In the Philippines, there is limited
published information on the physical condition, habitat structure, and ecological threats of
Aquilaria, which is a crucial factor to determine the optimum requirements to develop
Aquilaria production system. Hence, this study was conceptualized to assess the habitat
structure of Aquilaria as well as its conservation and ecological threats in the wild. Assessment,
surveys and interview were conducted to gather on-site atmospheric data as well as ecologicalthreats of Aquilaria in the wild. Soil collection was also gathered to analyzed soil properties.
The results revealed that there were two species of Aquilaria documented in the study site,namely: A. malaccensis and A. cumingiana. Moreover, the soil chemical properties are notsignificant between different topographic expositions, but it is acidic and generally have low
nutrient status. Furthermore, the conservation and ecological threats documented in the studysite are unsustainable harvesting, a massive collection of regenerant, and inflecting damage of
Aquilaria by punching nails or drilling holes in the trunk of standing mature tree. Therefore, it
is highly recommended that Aquilaria should be protected, particularly A. malaccensis, since itis rare and considered a new record of occurrence to Leyte Island as one of the major findingsof the study. The establishment of Aquilaria based production system is necessary as an option
to reduce the rampant illegal poaching of agarwood in the wild but should be established on
private land and registered with the Department of Environment and Natural Resources(DENR).
Keywords Aquilaria, topographic expositions, ecological threats, habitat structure

INTRODUCTION
Aquilaria is a genus of about 20 species (International Plant Names Index, 2015), distributed mainly in
the Indo-Malesian region. Agarwood also known as gaharu in the South East Asia, oud in the MiddleⒸ ISERD

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East, chen xiang in China, jinkoh in Japan and agar in India is a highly valuable aromatic dark resinousheartwood of Aquilaria species (Liu et al., 2017). The genus Aquilaria belongs to the Thymelacaceae
family of Indo-Malayan trees known for producing the world's most expensive oils (Lee & Mohamed,2016). Fernando et al. (2018) reported that Aquilaria apiculata, A. filarial, A. malaccensis, A.
brachyantha, A. cumingiana, A. urdanetensis, A. citrinaecarpa, and A. parvifolia are just eight out of
the 24 species of agarwood oil-producing trees in the world that are naturally occurring in thePhilippines. In Leyte, Biliran, Samar, and Negros, Aquilaria sp. is locally known as lapnisan and laneti.
Agarwood is traded in several raw forms, ranging from large sections of the trunk to finishedproducts such as incense and perfumes (Barden et al., 2000). They reported that natural stocks arelargely depleted by overexploitation, and the demand for agarwood is higher. Foreign nationals arenow searching agarwood in the Philippines, specifically Leyte, Biliran, Samar, and Negros. There havebeen reports in the field that sometimes these nationals are impatient to find Aquilaria since it wasreported by Abdin (2014) that global agarwood prices can be ranging from US$ 20 – 6,000 per
kilogram for the wood chips depending on its quality or US$ 10,000 per kilogram for the wood itself.
There was a destructive exploitation of agarwood that has badly affected the wild population of
all Aquilaria species due to widely used as therapeutic perfumes, traditional medicine, religious
purposes and aromatic food ingredient (Tan et al., 2019).
There is a scarcity of information on the physical condition, habitat structure and ecologicalthreats of Aquilaria which is crucial factor to determine the optimum requirements to develop
Aquilaria based production system. This study was conceptualized to assess the habitat structure of
Aquilaria and its conservation and ecological threats in the wild.
OBJECTIVES
1. To characterize the habitat condition and structure of Aquilaria on Leyte Island; and2. To determine the ecological threats of Aquilaria in the natural habitat.
METHODOLOGY
Selection of the Study and Sampling Site
The study was conducted at the selected site of Leyte Island. The major criteria in selecting thesampling site were the presence of Aquilaria in the area which was selected through a preliminary
survey by directly collecting information from reliable individuals (i.e., poachers, a buyer of agarwood)
further identify the exact location of Aquilaria trees in the wild. Furthermore, the study site wasselected based on the incidence of rapid and over-harvesting of the Aquilaria matured trees andregenerant, respectively, during the pre-assessment.
Data Collection
The atmospheric data, i.e., light intensity (lux), temperature (°C), and relative humidity (%) werecollected on-site to determine the climatic factor that may affects the habitat structure of Aquilaria
under natural condition. The data was collected in each exposition at an interval of two hours, startingfrom seven o’clock in the morning until five o’clock in the afternoon.
Soil samples were collected in the study site. The soil samples were then brought to the laboratory
to analyze soil chemical properties such as soil pH, soil organic matter, total nitrogen, and available
phosphorus.

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To determine ecological threats, an actual field survey and photo documentation of poached
Aquilaria trees were done. Likewise, an interview was conducted to collect information from seedlinggrowers, poachers, and agarwood buyers.
Data Encoding and Statistical Analysis
All data gathered were collated, encoded, and summarized using an electronic spreadsheet editor,
Microsoft Excel 2013. The data were analyzed using the Statistical Package for Social Science (SPSS
version 20).
The atmospheric data were analyzed descriptively. The variability of the mean of soil chemical
properties was analyzed using the one-way analysis of variance (ANOVA). Moreover, in a case where
the significant variations at p≤0.05 was identified, Tukey and Least Squares Differences (LSD) was
carried out to compare means.
RESULTS AND DISCUSSION
Microclimatic Conditions in the Study Site
The data on atmospheric parameters such as light intensity, air temperature, relative humidity, andrainfall are presented in the figure below to describe the on-site condition of the study area.

A

B

C
Fig. 1 Microclimatic conditions between different expositions of the study site
at different time intervals
Results show that west-oriented topographic exposition had higher light intensity compared toother topographic expositions (Fig. 1A). Meanwhile, the average temperature at all periods of datacollection had an increasing trend from seven o’clock in the morning until three o’clock in theⒸ ISERD

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afternoon but had a slight drop at five o’clock in the afternoon (Fig. 2B). On the other hand, the resultof the relative humidity had a slight variation (Fig.1C).
Soil Chemical Properties
Table 1 shows the chemical properties of the study site. Statistical analysis showed no significant
difference between soil chemical properties of the study site between different topographic expositions.
Soil pH was very strongly acidic, and soil organic matter content was moderate. The total nitrogen in
the study site is medium, as interpreted by Landon (1991). According to Krofranek et al. (2007), total
N is deficient mostly in the tropics since it undergoes various losses such as leaching and volatilization.
The results on total N can therefore, be attributed to the lower soil organic matter in the study site.
Table 1 Soil chemical properties within study site between different topographic expositions
Expositions

pH

North-oriented
East-oriented
South-oriented
West-oriented

4.90 ± 0.18
4.85 ± 0.18
4.90 ± 0.18
4.74 ± 0.18

Soil Organic Matter(%)
4.27 ± 0.63
4.11 ± 0.63
4.46 ± 0.63
4.01 ± 0.63

Total Nitrogen(g/kg)
0.21 ± .03
0.21 ± .03
0.22 ± .03
0.20 ± .03

Available Phosphorus(mg/kg)0.96 ± 0.16
0.59 ± 0.16
0.92 ± 0.16
0.63 ± 0.16

On the other hand, according to the sufficiency ranges of soil of Landon (1991) the available P in
the study site is very low.
Therefore, the result of this study supports the findings of Piamonte et al. (2014), where theyfound out that the soil in Mt. Nacolod Mountain Range (Abuyog-Silago-Hinunangan-St. BernardLibagon-Mahaplag) is classified as Ultisols, which is characterized as acidic and generally have low
nutrients status.
Conservation and Ecological Threats
Unsustainable harvesting:In the study site, five illegally-cut matured Aquilaria were documented and recorded. Meanwhile, acommunity resident began harvesting Aquilaria in the forest in June 2019. According to the respondent,
since it was difficult for them to identify the exact Aquilaria trees, they would randomly cut trees
without properly distinguishing them and chopping them into pieces (Fig. 2A). Until eventually,
traders from other countries (China and Taiwan) and other places within the Philippines (Mindanao
and Samar) came and trained them to identify and distinguish the Aquilaria tree.

A

B
Fig. 2 Local practice in harvesting Aquilaria in the study siteA

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The traders instructed them that a matured Aquilaria tree that looks rotten or unhealthy has most
likely agar in it, whereas those that look firm and healthy are not good. This information was
confirmed by the study of Rasool and Mohamed (2016) that agarwood formation is often linked to thephysical wounding or damage of Aquilaria trees caused by thunder strike, animal grazing, and pest anddisease infestations.
According to the respondents, Aquilaria is cut into pieces from trees to reveal the resinous product(Fig. 2B). Apparently, poachers are chopping down trees faster than they can grow, including forest
across neighboring towns. According to Ng et al. (1997), not all Aquilaria trees produce agarwood,
estimating that only approximately 10% of wild Aquilaria spp. produce resin.
Furthermore, respondents stated that they are more eager to search for agarwood than work as ahired labor or harvest copra because of its immense value. They further explained that they earn higher
income from agarwood than farming and working as laborers. Harvesting Aquilaria and sellingagarwood enable them to triple their income, which consequently improved their living condition. For
instance, most of the respondents could buy new motorbikes, repair or enhance their houses and buy
property (such as land).
Massive collection of regenerant:
Based on the interview results, in 2019, when Aquilaria became widely known, the value of seedlingrose to 500 to 1000 Pesos. The seedling, particularly from Aquilaria malaccensis (Fig. 3A), generates
higher income since wildlings are abundant in the forest. According to Chua (2008), Aquilaria
malaccensis produces seeds after 7-9 years, while some other species produce seeds only once in theirlife cycle.

A

B

Fig. 3 Aquialaria malaccensis seedlings (A) and Aquilaria cumingiana seeds (B)owned by one of the respondents
As for Aquilaria cumingiana regenerant (Fig. 3B), only few are being propagated after collecting
seeds. According to the seedling growers, its value is worth 40,000 Pesos per kilo. The seedling grower
clarified that none of the seeds survived due to insufficient knowledge on how to properly propagate
Aquilaria seeds and the appropriate time of collecting the matured fruits.
Inflecting damage to the matured Aquilaria trees:
In the study site, landowners and poachers have created an illegal method of inflecting matured
Aquilaria. This is by punching nails or drilling holes on the trunk of standing matured Aquilaria (Fig.
4). They believe that by damaging the trunk using nails, it will gradually trigger infection andeventually develop agar inside. This method is widely visible on the study site.
Moreover, according to Ng et al. (1997), the age of the tree, differences in the tree caused byseasonal variation, environmental variation and genetic variation of Aquilaria spp. may also play anessential role in agarwood formation.

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Fig. 4 Locally practiced method of inflecting (by punching nails and drilling holes
at the trunk) matured Aquilaria species in the community
CONCLUSION
The results show that west-oriented topographic exposition had higher light intensity, while theaverage temperature during the conduct of the study had an increasing trend from seven o’clock in themorning until three o’clock in the afternoon but had a slight drop at five o’clock in the afternoon.
The result of the relative humidity showed that the east-oriented and north-oriented topographic
location got the highest and lowest average of 86.29% and 84.26%, respectively.
The soil chemical properties are not significant between different topographic expositions.
However, the soil on the study site is acidic and has a generally low nutrient status. The ecologicalthreats recorded in the study site were unsustainable harvesting, a massive collection of regenerants,
and inflecting damage of Aquilaria by punching nails or drilling holes in the trunk of a mature standing
tree.
Therefore, it is highly recommended that Aquilaria should be protected particularly Aquilaria
malaccensis since it is rare and is considered a new record of occurrence to Leyte Island. The
establishment and development of an Aquilaria-based production system are necessary as analternative option to reduce the rampant illegal poaching of agarwood in the wild. Furthermore, theAquilaria-based production system should be established in private land and registered with the
Department of Environment and Natural Resources.
ACKNOWLEDGEMENTS
We extend our gratitude to the Institute of Tropical Ecology and Environmental Management, Visayas
State University, Baybay City, Leyte and to the Municipality of Abuyog, Leyte, for the great support
for this study.
REFERENCES
Abdin, M.J. 2014. The agar wood industry: Yet to utilize in Bangladesh. Int. J. Econ. Manag. Sci, 3, 163-166.
10.2139/ssrn.2430055.
Barden, A., Anak, N.A., Mulliken, T. and Song, M. 2000. Heart of the matter: Agarwood use and trade and
CITES implementation of Aquilaria malaccensis. Cambridge, TRAFFIC International, 1-60. United
Kingdom.
Chua LSL. 2008. Agarwood (Aquilaria malaccensis) in Malaysia. NDF Workshop Case Stud: WG 1 - Trees:
Case Stud., 3, 1-17.
Fernando, E.S., Marciano, M.R., Galang, A.P., Mangara, G., Berroya, L.G. and Sarmiento I.P. (Eds) 2018.
Philippine Naïve Trees 303. Up Close and Personal. 520p. Green Convergence, Quezon City, Philippines.
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IJERD – International Journal of Environmental and Rural Development (2021) 12-1

International Plant Names Index (IPNI). 2015. Retrieved from http://www.ipni.org
Kofranek, A.M., Mcmahon, M.J. and Rubatsky, V.E. 2007. Plant Science, Pearson Education, Inc. Upper Saddle
River, New Jersey, 68-69.
Landon, J.R. 1991. Booker tropical soil manual. Longman Scientific and Technical, England, 474.
Lee, S.Y. and Mohamed, R. 2016. The origin and domestication of Aquilaria, An important agarwood-producing
genus, in Agarwood. Science Behind the Fragrance, Springer Singapore, 1-20. 10.1007/978-981-10-08337_1.
Liu, Y.Y., Wei, J.H., Gao, Z.H., Zhang, Z. and Lyu, J.C. 2017. A review of quality assessment and grading foragarwood. Chin. Herb. Med, 9, 22-30. 10.1016/S1674-6384(17)60072-8.
Ng, L.T., Chang, Y.S. and Kadir, A.A. 1997. A review on agar (gaharu) producing Aquilaria species. Journal of
Tropical Forest Products, 2 (2), 272-285.
Piamonte, M.K., Asio, V.B. and Lina, S.B. 2014. Morpho-physical and chemical characteristics of strongly
weathered soils in Silago, Southern Leyte, Philippines. Annals of Tropical Research, VSU, Leyte, Philippines,
36 (2), 115-147.
Rasool, S. and Mohamed, R. 2016. Understanding agarwood formation and its challenges in Agarwood: Sciencebehind the fragrance. Springer, 39-56. doi:10.1007/978-981-10-0833-7_3.
Tan, C.S., Isa, N.M., Ismail, I. and Zainal, Z. 2019. Agarwood induction: Current developments and future
perspectives. Front. Plant Sci, 10, 122. doi: 10.3389/fpls.2019.00122.

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Research article
erd

Effects of Air Injection and Iron Oxide Pellet Addition on
I
Hydrogen Sulfide Removal
and Biogas Production
7de
LYTOUR LOR*n
Biogas Technology and Information Centerand Faculty of Agricultural Engineering,t

Royal University of Agriculture, Phnom Penh, Cambodia
Email: lor_lytour@yahoo.com
4.
7
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, Japan
2

MACHITO MIHARABUNTHAN NGO

Royal University of Agriculture, PhnomwPenh, Cambodiaor
LYHOUR HIN
dBiogas Technology and Information Centerand Faculty of Agricultural Engineering,
Royal University of Agriculture, Phnoms Penh, Cambodia

DYNA THENG
Biogas Technology and Information Center and Faculty of Agricultural Engineering,
Royal University of Agriculture, Phnom Penh, Cambodia

CHAN MAKARA MEAN
Biogas Technology and Information Center, Royal University of Agriculture, Phnom Penh,
Cambodia

CHHORTH CHHIENGPUTHEAVY
Faculty of Agricultural Engineering, Royal University of Agriculture, Phnom Penh, Cambodia

BART FREDERIKS
Independent Bioenergy Consultant and Biogas Technology and Information Center,
Royal University of Agriculture, Phnom Penh, Cambodia
Received 22 January 2021

Accepted 30 June 2021(*Corresponding Author)

Abstract Hydrogen sulfide (H2S) in biogas resulting from anaerobic digestion process isunwanted trace compound, because it is toxic and can corrode biogas engine. This study aimed
to compare the 2 desulfurization methods, air injection and iron oxide pellets addition, on H2S
removal and quantity and quality of biogas. The experiment was carried out with two floating
drum digesters (1 m3 each) constructed at Royal University of Agriculture, Phnom Penh, fromJanuary to August 2020. Three levels of air injection at 2%, 4%, and 6% of O2 regarding thedaily biogas production and iron oxide pellets application at 1 kg, 2 kg, and 4 kg per m3 of
biogas were applied to remove H2S in biogas from different raw materials of pig manure andfood waste. The amount of daily biogas production was quantified by gas flow meter, also gas
quality was measured using a GEM5000 gas analyzer. The experimental results indicated thatfood waste had higher daily biogas production comparing to pig manure in both desulfurization
methods. Biogas from food waste increased from 544 L d-1 without iron oxide pellets addition(0 kg) to 657 L d-1 with 4 kg iron oxide pellets addition; and to 566.5 L d-1 with 2% of injected
O2. To the contrary, desulfurization for pig manure with 2% of O2 and 1 kg of iron oxide
showed high daily biogas yield of 348 L d-1 and 340 L d-1, respectively. For raw materials ofⒸ ISERD

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pig manure, in both desulfurization methods, had higher CH4 content than food waste. Air
injection was more effective in H2S removal than iron oxide for both substrates, but higher levelof H2S reduction was observed with pig manure. Accordingly, it was concluded thatdesulfurization methods with air injection and iron oxide pellets addition were effective inbiogas production as well as H2S removal, but a clear trend appeared in the raw material of foodwaste.
Keywords floating drum digester, anaerobic digestion, food waste, pig manure, H2S removal

INTRODUCTION
Biogas is a product generated from the anaerobic digestion of organic substances by appropriate
microorganisms through four metabolic stages; namely hydrolysis, acidogenesis, acetogenesis, andmethanogenesis (Dumont, 2015). It is considered one of the renewable energy sources that can provide
both heat and electricity for use in households, in farms, or in industries. Gas compositions containedin biogas include methane (CH4), carbon dioxide (CO2), and other trace elements such as ammonia(NH3), water vapor (H2O), and hydrogen sulfide (H2S). The majority of them are CH4 (60-70%) andCO2 (30-40%) (Okoro and Sun, 2019), but the only energy source is CH4, which has both benefits anddrawbacks. If released into the atmosphere unused, CH4 is a greenhouse gas which has 28 times more
powerful than CO2, which actually accelerates global warming (National Geographic, 2019). Whenburned or used for internal combustion engine, its harmful effects are reduced and at the same time, its
beneficial energy can be harnessed. Nevertheless, the presence of H2S in biogas may causes problems
in terms of health hazard and economic aspects (Pinate et al., 2017).
H2S is an unwanted gas mixed in biogas because it is toxic to humans at low concentrations andcorrosive to engines. High concentrations of H2S can corrode engines or metal parts, and lead to faster
degradation of engine lubricant oil. A maximum recommended level for generator operation ranges
from 200 to 500 ppm (Rodrighez et al., 2014). If it is used without treatment, oil lubricants must bechanged more often, or the lifespan of a generator is reduced, resulting in high investment andoperating costs. Furthermore, H2S limits for gas heating boilers, combined heat and power (CHP), fuelcells, and national gas upgrade are 1,000, 1,000, 1, and 4 ppm, respectively (Choudhury et al., 2019).
However, different substrates used for biogas production produce different concentrations of H2S.
Substrates that contain high-level protein produce higher H2S content than those contain carbohydrate
and lipid due to the presence of more sulfurous elements in it. Biogas produced from organic waste
may contain H2S in the range of 10-20,000 ppm, but about 10-40 ppm is found in biogas produced
from sewage and 50-300 ppm from landfill (Dumont, 2015). Moreover, Huertas et al. (2020) statedthat H2S produced from organic waste such as pig manure can be as high as 30,000 ppm.
To reduce its concentration to an acceptable level, desulfurization techniques can be applied
physically, biologically, and chemically. Biologically removal is done by air injection into digesterwith regulated amounts of oxygen (O2) between 0.3 and 3% of produced biogas; however, this reaction
may result in sulfur build-ups in the digester space (Hines et al., 2019). Many kinds of chemical
methods have been studied. Pinate et al. (2017) proposed a small batch test for H 2S removal byabsorbent granules soaked in ferric chloride (FeCl3) and in sodium hydroxide solution (NaOH), theremoval efficiency was around 90%. Zulkeflia et al. (2016) stated that H2S removal could be done byactivated carbon soaked in NaOH, potassium hydroxide (KOH), and potassium carbonate (K2CO3), but
added that the last chemical element was the most effective. Biological H2S removal methods are also
increasing popular and considered highly effective. Those biological methods include biofilters,
biotrickling filters, bioscrubbers, and activated sludge (Barbusiński and Kalemba, 2016). However,
H2S removal techniques are limited, or have not been well documented in Cambodia, which makes it
difficult for promotion of biogas use inside the country. Therefore, proposing an applicable
desulfurizing technique is highly valuable for economic reasons.
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Pig manure is a major waste from animal productions in Cambodia. According to MAFF (2020),
the number of pigs was around 2.18 million heads in 2019 of which more than half raised incommercial farms. On the other hand, kitchen waste amounted from 63.30% to 80.46% of all solid
wastes from households (Sour, 2017). Proper management of these two wastes by converting them tobiogas will provide both environmental and economic benefits.
OBJECTIVE
The objectives of this study were (1) to compare the effects of air injection and iron oxide pellets on
H2S removal and quantity and quality of biogas produced from two different substrates, food waste andpig manure; and (2) to identify relations between biogas production with CH4 content in the two
substrates.
METHODOLOGYThis research was conducted at the pilot biodigesters belonging to the Biogas Technology andInformation Center (BTIC), at the Royal University of Agriculture, Cambodia, starting from January toAugust 2020. Two floating drum biodigesters (1 m3 each) were used for this experiment. Two different
substrates, food waste and pig manure, were daily fed into the systems. At its full production, biogas
was then treated by two desulfurizing methods: air injection and iron oxide pellets. Air injection wasapplied directly into the gas holders (the floating drum) on top of the digesters at O2 levels of 2%, 4%,
and 6% of daily produced biogas. Meanwhile, iron oxide pellets were placed in a gas treatment
container through which raw biogas was passed through and treated before being quantified. The levelsof iron oxide pellets were applied at 1, 2, and 4 kg m-3 of daily produced biogas. Effectiveness of thedesulfurizing methods was also determined by comparing the results with raw untreated biogas.
Materials
Two floating drum biodigesters used in this experiment had concrete bases and walls constructed under
the ground. The whole volume was 1 m3, one third of which was for gas storage. The gas holders werehigh-grade polyethylene plastic water tanks that were cut in half and used as covers for the digesters.
They can move up and down, depending on the biogas pressure produced inside.

Fig. 1 Technical drawing of a floating drum biodigester used in this experiment
Substrates used in this experiment were food waste and pig manure. Their physical and chemical
properties were analyzed in advance. Still, there might be some variations due to everyday freshcollection of the substrates. To reduce errors, substrate collection was done at the same placethroughout the experimental period.
Food waste was daily collected from the university canteen. It contained 21.9% dry matter, 1.4%
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ash, 5.6% protein, 3.2% lipid, and a C/N ratio of 12.6:1. For high efficiency, bones and other organic
materials that were slowly biodegradable were removed. Before being fed into the digester, food waste
was thoroughly mixed with water at a ratio of 1:1, or 5 kg each. Pig manure used in this experiment
was collected from two small-scale pig farms nearby the university. It contained 25% dry matter, 8.1%
ash, 10.4% protein, 0.5% lipid, and a C/N ratio of 6:1. It was freshly collected, mixed with water, andfed into the system on daily basis at a ratio of 1:1, or 5 kg each.
Sampling Methods
Feeding time was done in the morning and repeated regularly every day for 30 days, which was
assumed as a minimum hydraulic retention time (HRT) for the digesters. Then, volume of untreatedbiogas was measured for 7 days to determine average daily biogas production, followed by air injection,
which lasted 7 days for each O2 level. In total, the whole process of air injection lasted one month.
After this process was completed, the untreated biogas was measured another 7 days before applicationof iron oxide pellets in filter for H2S removal. Experiments with each level of iron oxide lasted 7 days,
which took 1 month in total. The iron oxide pellets used in the experiment were proprietary ones which
had been imported from China.
To measure the quality of biogas produced from the two substrates, a GEM5000 gas analyser was
utilized. It was a product supplied by Geotech, UK, and was capable of measuring biogas quality suchas CH4, CO2, and O2, all in percentage; and H2S in ppm with the maximum concentration of 5,000 ppm.
Biogas quality was measured three times a day for each treatment after it was treated with air injection
a day before, or went through the desulfurizing container on the day of the measurement.
To quantify daily biogas production, a gas flow meter was used. When the biogas was used forboiling water, the flow meter started to record the consumption at the average outdoor temperature of
28-33 ℃.
Data Analysis
Analysis of Variance (ANOVA) were used for the data analysis, and when significant differences wereobserved, a post-hoc LSD-test was then used to show significant differences among the treatments at α
= 5%. Besides that, the data for air injection and iron oxide pellets were presented separately because
the experiments were done separately.
RESULTS AND DISCUSSION
Daily Biogas Production
Daily biogas production was compared among the two substrates, when treated with air injection at
different levels of O2 (Fig. 2a). Significant differences were detected among the treatments, and it wasobserved that biogas produced from food waste was higher than from pig manure. Daily biogas
quantity was similar for both substrates, when measured without application of air injection. It was
about 484 L d-1. For food waste, daily biogas production increased slightly with 2% of injected O2 toalmost 500 L d-1, but decreased continuously at O2 levels of 4% and 6%. Similar results were obtainedfor pig manure. Daily biogas quantity decreased more and more, as levels of air injection increased.
Daily biogas production from pig manure was 384, 260, 227 L d-1 at injected O2 levels of 2%, 4%, and
6%, respectively. This finding indicates that putting more air into the digester may affect biogas
production because its ideal condition is anaerobic. Hines et al. (2019) proposed a maximum air
injection level of 3%.

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Fig. 2 Comparison of biogas production with air injection (a) and with iron oxide pellet (b)Daily produced biogas quantity was compared among the two substrates when using iron oxidepellets (Fig. 2b). It was not affected by different levels of pellet, but by different substrates. Higher
daily biogas production was observed with food waste at all levels of applied iron oxide pellets. Its
average value was 585 L d-1. In contrast, daily biogas production from pig manure was quite low,
averaging 320 L d-1. Moreover, it was surprising to see the lowest daily biogas production with 4 kg ofiron oxide pellets. The reason why biogas quantity was higher for food waste used as a substratebecause it contained more lipid. Lipid has more carbon atoms than protein, thus producing more biogas(Morales-Polo et al., 2018).
Methane Content
CH4 content contained in biogas was compared among the two substrates with air injection at different
levels (Fig. 3a). It was found that there were significant differences among the treatments and CH4content was higher for pig manure, when compared with food waste. CH4 fluctuated for pig manure atdifferent levels of inject O2; however, the average value ranged from 57 to 61%. Meanwhile, CH4content for food waste increased, as the levels of O2 increased. The highest value was only around 56%.
Similar results were found for food waste when iron oxide pellets were applied at different levels (Fig.
3b). CH4 increased, as the amounts of pellets increased for both food waste and pig manure. Still, theaverage value was 60% for pig waste and 56% for food waste.

46Fig. 3 Comparison of CH4 content with air injection (a) and with iron oxide pellet (b)Hydrogen Sulfide Content
H2S cotent was compared between the two substrates under different levels of injected O2 (Fig. 4a).
Significant differences in H2S was observed among the treatment, and the higher level of O2 wasinjected, the more H2S was reduced. Greater H2S reduction was observed with the use of pig manure.
Without air injection, the level of H2S was 840 ppm and 1,000 ppm for pig manure and food waste,
respectively. this level decreased sharply for pig manure, as levels of injected O2 increased. H2SⒸ ISERD

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content was 522 ppm at O2 level of 2%, and decreased to 351 ppm at 4% and then to 271 ppm at 6%.
For food waste, H2S content decreased from 923 ppm at O2 level of 2% to 626 ppm at 4%, and thenslighly increased to 674 ppm at 6%. Higher levels of injected O2 reduced H2S, but also affected overal
biogas production (Fig. 2a). Thus, it should be applied at recommended levels only. Nevertheless,different levels of pellets did not affect H2S content, but different substrates did (Fig. 4b). It wasobserved that H2S content for pig manure was higher than for food waste, and varied greatly fromabout 800 to over 3,000 ppm, though treated with high levels of iron oxide pellets. Such unsual
increase was due to using pig manure from a different, as the first one emptied the barns. However, atendency for H2S removal was observed at 4 kg of pellets, as H2S decreased to above 2,500 ppm. For
food waste, iron oxide did not affect H2S content, and its average ranged from 1,000 to 1,100 ppm.

Fig. 4 Comparison of H2S reduction by air injection (a) and by iron oxide pellets (b)CONCLUSION
Pig manure and food waste were fed into two floating drum digesters for biogas production andinspection of biogas quality, along with H2S removal techniques by air injection and ferrous oxidepellets. It was observed that food waste produced more biogas than pig manure, but less CH4 content.
H2S contents were similar for both substrates, and the use of air injection tends to be more effective
than ferrous oxide in this study.
ACKNOWLEDGEMENTS
The study was made possible thanks to the project “Reduction of Greenhouse Gas Emission throughPromotion of Commercial Biogas Plant in Cambodia” implemented by United Nations Industrial
Development Organization (UNIDO) that not only provided funding, but also continuously assisted instrengthening the research team.
REFERENCES
Barbusiński, K. and Kalemba, K. 2016. Use of biological methods for removal of H2S from biogas in wastewater
treatment plants, A Review. Architecture Civil Engineering Environment, 103-112, Retrieved from https://www.exeley.com/exeley/journals/architecture_civil_engineering_environment/9/1/pdf/10.21307_acee-2016011.pdf
Choudhury, A., Shelford, T., Gooch, C. and Lansing, S. 2019. Evaluation of hydrogen sulfide scrubbing systems
for anaerobic digesters on two U.S. dairy farms. Energies, 12, 4605, Retrieved from doi:10.3390/en
12244605.
Dumont, E. 2015. H2S removal from biogas using bioreactors: A review. International Journal of Energy andEnvironment, 6 (5), 479-498. Retrieved from https://hal.archives-ouvertes.fr/hal-01945143
Hines, M.A., Choudhury, A., Felton, G. and Lansing, S.A. 2019. Micro-aeration for hydrogen sulfide removal inbiogas. College Park, MD: University of Maryland Extension.
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Huertas, J.K., Quipuzco, L., Hassanein, A. and Lansing, S. 2020. Comparing hydrogen sulfide removal efficiency
in a field-scale digester using microaeration and iron filters. Energies, 3, 4793, Retrieved from doi:10.3390/en13184793.
Ministry of Agriculture, Forestry and Fisheries (MAFF). 2020. Report on achievements of agriculture, forestry
and fisheries in 2019-2020 and future plan for 2020-2021. Agriculture, Forestry and Fisheries Conference.
Phnom Penh, Cambodia.
Morales-Polo, C., Cledera-Castro, M.M. and Soria, B.Y.M. 2018. Reviewing the anaerobic digestion of foodwaste: From waste generation and anaerobic process to its perspectives. Applied Sciences, 8, 1804,
Retrieved from doi:10.3390/app8101804.
National Geographic. 2019. Environment referece: Methane, explained, Retrieved from https://www.
nationalgeographic.com/environment/global-warming/methane/#:~:text=Each%20of%20those%20puffs%
20coming,more%20powerful%20over%2020%20years
Okoro, O.V. and Sun, Z. 2019. Desulphurisation of biogas: A systematic qualitative and economic-based
quantitative review of alternative strategies. Chemengineering, 3, 76, Retrieved from doi:10.3390/
chemengineering3030076.
Pinate, W., Dangphonthong, D., Sirirach, S. and Sukkhon, S. 2017. Removal of hydrogen sulfide (H2S) frombiogas for the community in the province of Maha Sarakham. Journal of Physics, Retrieved from doi :10.1088/1742-6596/901/1/012049.
Rodriguez, G., Dorado, N.D., Fortuny, M., Gabriel, D. and Gamisans, X. 2014. Biotrickling filters for biogas
sweetening: Oxygen transfer improvement for a reliable operation. Process Safety and Environmental
Protection, 92 (3), 261-268.
Sour, S. 2017. Country chapter state of the 3Rs in Asia and the Pacific: The Kingdom of Cambodia. The 8th 3R
Forum in Asia and the Pacific. United Nations Centre for Regional Development (UNCRD), Retrieved fromhttp://www.uncrd.or.jp/content/documents/5686[Nov%202017]%20Cambodia.pdf
Zulkeflia, N.N., Masdar, M.S., Jahim, J. and Majlan, E.H. 2016. Overview of H2S removal technologies frombiogas production. International Journal of Applied Engineering Research, 11 (20), 10060-10066, ISSN
0973-4562.

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Research article
erdChanging Spent Mushroom Substrate into a Quality
Vermicompost
SUPAWADEE RUANGJANDA
Department of Soil Science and Environment, Faculty of Agriculture, Khon Kaen University,
Khon Kaen, Thailand

CHULEEMAS BOONTHAI IWAI*
Integrated Land and Water Resource Management Research and Development Center in Northeast
Thailand, Khon Kaen University, Khon Kaen, Thailand
Email: chuleemas1@gmail.com
Received 3 March 2021

Accepted 30 June 2021(*Corresponding Author)

Abstract Safety food and healthy food gain widespread popularity nowadays. In Thailand,
mushroom consumption is increasing because of its high protein content and role as an effective
stimulant of various immune system functions. As mushroom farmers’ respond to this demand,
the need for sustainable waste management of mushroom cultivation has become important.
Therefore, the study examined the growth and reproduction of earthworm and chemical
properties of Eudrilus eugeniae vermicompost cultured under different ratio of spent mushroom
substrate. The study found that application of bedding with spent mushroom substrate at theratio 60:40 gave the highest growth rate of earthworms (2.5%) and had the highest increase innutrient content (Total N, P, K, Mg, Ca) and had the highest decrease in the value of pH,
electrical conductivity, organic carbon and C/N ratio significantly. Based on these results,
application of bedding with spent mushroom substrate at the ratio of 60:40 seemed to be themost suitable for vermicompost production. In general, recycling of spent mushroom substrate
through vermicomposting may reduce the environmental stress and can produce organic
fertilizers with better chemical and biological properties.
Keywords spent mushroom substrate, mushroom, earthworm, vermicompost, vermicompost
quality

INTRODUCTION
Safety food and healthy food gain widespread popularity nowadays. The effect to mushrooms
consumption is increasing because of mushroom are a healthy food. The world production of Pleurotus
ostreatus was about 4.1 million tons in the year 2019 (FAO, 2020). In Thailand has the potential toproduce mushrooms in the industrial sector. The export data of mushroom products in 2011 showedthat the total export volume was 939 tons, increasing from 2008 with 781 tons of export volume. The
total mushroom production is 120,000 tons per year (Chanyuth, 2018).
The production 1 kg of mushrooms generates about 5 kg of spent mushroom substrate(Hrebeckova et al., 2020) .The spent mushroom substrate (SMS) is a by-product generated frommushroom production, which contains a large amount of fungal mycelium and extra-cellular
lignocellulosic enzymes along with various organic substances (carbohydrates, proteins, and fats), as
well as a considerable quantity of inorganic nutrients such as ammonium nitrate, superphosphate, andpotassium salts, (Lou et al., 2017; Gong et al., 2019). In the year 2017 there was about 20.5 million
tons of SMS generated from Pleurotus ostreatus cultivation around the world. (Huan-Na et al., 2017;
Wang et al., 2020). In Thailand found about 1.1 million tons of SMS in the year 2020. These SMS areⒸ ISERD

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commonly sent to landfill or openly burnt at farms and could not be reused in the next cultivation due
to the possibility of contamination which will consequently affect the mushroom production. As
mushroom farmers’ respond to this demand, the need for sustainable waste management of mushroom
cultivation has become important.
Vermicomposting is a sustainable technique to convert organic wastes into a nutrient-rich humuslike product, important substances that are beneficial to plants such as plant growth promoters. throughthe joint action of earthworms and microorganisms under aerobic conditions (Edwards and Burrows,
1988). In addition, it can be easily done with low investment and high benefits. Can do both at thefamily and farm system. Therefore, this method is interesting for reduce SMS or organic waste andleading to produce organic fertilizers.
OBJECTIVE
This research aims to study the growth and reproduction of earthworm and chemical properties ofEudrilus eugeniae vermicompost cultured under different ratio of spent mushroom substrate.
METHODOLOGY
Materials for Vermicomposting
The SMS was obtained from the Sufficiency Mushroom Learning Center, Tha Phra, Mueang, Khon
Kaen, Thailand. Its is waste from the cultivation of Pleurotus ostreatus. The SMS containing sawdust(80-90%), rice bran (5-10%), rice broken (1%), lime (2%), gypsum (0.5%), sodium sulfate (0.2%) andpumice (2%), at 65-70% humidity. All samples are dried in the shade for 7 days to feed earthworms
and bedding was obtained from faculty of Agriculture, Khon Kaen University, Khon Kaen, Thailand
including soil, cow dung, rice husk ash and vegetable waste at ratio 4: 3: 2: 1 dry weight respectively(Iwai et al., 2011).
Treatments and Experimental Design
In this study, the earthworm Eisenia eugeniae (density about 20 pcs/kg) was selected forvermicomposting because it tolerates a wide range of pH, temperature and moisture content. Plastic
buckets of the diameter size 50 cm × height 18 cm were used to accommodate 4 kg of feed materials.
Each container had a 0.2 m2 of exposed surface covered with mosquito net to prevent hindrance fromsmall insects and other microorganisms which could hamper the findings of this study. Punch holes inthe bottom of the tank for good drainage and air. The materials used cultivating earthworms were
cultured under bedding (B) with SMS in 6 different ratios were 100: 0, 80:20, 60:40, 50:50, 40:60 and
20:80 respectively. The study planned a Completely Randomized Design (CRD) there were 6
treatments and 3 replications with 45 days period.
Measurements
The number of earthworms were monitored at the beginning and the end of process following themethods of Gong et al. (2019). The electrical conductivity (EC) and pH were determined in an aqueous
solution (sample: water, 1:10) using a conductivity meter and a pH meter (Unuofin and Mnkeni, 2014).
The wet oxidation method of Walkley (1934) was used for determination of organic carbon (OC).
Total nitrogen was analyzed following a modified semi-micro Kjeldahl method. For the determinationof total phosphorus, potassium, magnesium and calcium 0.2 g of dry sample was digested with 70%(v/v) sulfuric acid and 65% (v/v) hydrogen peroxide. Total phosphorus was measured byⒸ ISERD

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Spectrophotometric molybdovanadophosphate method. Total potassium was determined using a flame
photometer.Total Calcium and Magnesium measured with atomic absorption spectrophotometer.
(AOAC, 2000). C/N ratio values were calculated and analysed.
Statistical Method Analysis
The reported results are the mean of three replicates with standard error (SE). Chemical properties
were analyzed by repeated measures analysis of variance (ANOVA). Mean values were compared by
Tukey's HSD method. Differences were considered significant, only when P values were lower than
0.05.
RESULTS AND DISCUSSION
Earthworm Growth and Reproduction
During the 45 day of vermicomposting, The number of juvenile earthworm significantly increased in
all treatments (p≤ 0.05) and adult earthworm increased in B+SMS 50:50 and B+SMS 60:40 (Table 1).
Table 1 The growth and reproduction of earthworm at the beginning and the end of thevermicomposting process by using Eudrilus eugeniae in different ratio for 45 days
Parameter
B + SMS 100:0

Initial80 ± 0.00

B + SMS 80:20
B + SMS 60:40
B + SMS 50:50
B + SMS 40:60
B + SMS 20:80
F -test80 ± 0.00
80 ± 0.00
80 ± 0.00
80 ± 0.00
80 ± 0.00
ns

The number of adult earthworm
Final
Percent change (%)
77 ± 1.11-3.75 ± 1.38
77 ± 2.00
82 ± 0.89
83 ± 1.56
76 ± 4.22
80 ± 0.00
ns-3.75 ± 0.63
2.50 ± 1.11
3.75 ± 0.63
-5.00 ± 1.88
0.00 ± 0.00
ns

Juvenile
earthworm
4 ± 1.56b
49 ± 11.78ab
101 ± 7.00a
49 ± 14.00ab
69 ± 11.78ab
62 ± 2.44ab*

ns = non significantly different *, ** = significantly different at p≤ 0.05 and p≤ 0.01, respectively. Similar letter within thesame column indicates no significant difference by Tukey's HSD test. The numerical value of ± shows standard error. B =Bedding (soil: cow dung : rice husk ash: vegetable waste; 4:3:2:1 ), SMS = spent mushroom substrate.

These changes in amount of earthworm may have reflected the greater availability of food at thestart of the incubation and the reduction of food over time. The substrate ratios added significantly
altered Eisenia eugeniae number during the vermicomposting. The maximum individual number ofearthworm and maximum individual growth rate significantly higher in all bedding with SMS addition
treatments than in the control. Among all substrates tested, the growth rate of earthworm had thehighest increase in the B+SMS 60:40 and B+SMS 50:50. However, low quality organic waste may
cause earthworms to lose weight and increase mortality during vermicomposting. (Gong et al., 2019).
Physiochemical Change during Vermicomposting Process
The pH, EC, organic carbon and C/N ratio value at the end of the process significantly decreased in alltreatments (p≤ 0.05). (Table 2) The decreasing of pH in the early stage can be attributed to thedegradation of the organic compounds, which leads to the generation of ammonia, and the subsequent
decrease in pH might be due to the production of intermediate organic acids (Hanc and Chadimova,2014). The B+SMS 60:40 treatment had the highest decrease in the value of EC was 1.67 dS m -1
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(43.85%). The decreasing of EC during vermicomposting confirms by Lou et al. (2017) which spentmushroom compost for 12 weeks using earthworms Eisenia andrei and Eisenia fetida. It had (8580 μS
cm-1) decreased about 40% when compared to week zero (14,650 μS cm-1).
Table 2 Chemical characterization of materials at the beginning and at the end of thevermicomposting process using Eisenia eugeniae
B + SMS
100:0

ParameterpH

B + SMS
80:20

B + SMS
60:40

B + SMS
50:50

B + SMS
40:60

B + SMS
20:80

Ftest

Initial7.69 ± 0.11b

8.06 ± 0.07a

7.80 ± 0.08ab

7.53 ± 0.10bc

7.51 ± 0.01bc

7.36 ± 0.05c

**

Final

7.64 ± 0.02a

7.56 ± 0.03ab

7.35 ± 0.06ab

7.30 ± 0.18ab

7.30 ± 0.04ab

7.24 ± 0.11b*(%)- 0.65 ± 1.22b- 6.20 ± 0.79a- 5.77 ± 0.56a-3.05 ± 1.20ab-2.80 ± 0.70ab-1.63 ± 1.00b**

Initial2.55 ± 0.05b

2.48 ± 0.11b

2.96 ± 0.16ab

2.57 ± 0.11b

3.14 ± 0.17a

3.09 ± 0.13a

**

Final

2.27 ± 0.15ab

1.97 ± 0.13ab

1.67 ± 0.09b

1.57 ± 0.23b

1.83 ± 0.08b

2.84 ± 0.52a**

Change(%)-11.03 ± 1.13c-20.30±3.54bc-43.85± 3.93a-39.17±6.36ab-41.59± 3.43a-23.69±1.13bc**

Organic carbon(%)

Initial3.41 ± 0.19d

5.12 ± 0.04d

11.74 ± 1.24b

8.46 ± 0.64c

17.28 ± 0.18a

18.27 ± 1.29a

**

Final

2.69 ± 0.33d

3.49 ± 0.27d

4.62 ± 0.16c

5.85 ± 0.16b6.95 ± 0.22b

18.16 ± 0.52a**

Change(%)-20.63 ± 3.71b-31.76 ± 5.75b-60.14 ± 3.07a-30.31 ± 6.25b-59.77 ± 1.37a- 0.24 ± 4.76c**

Total nitrogen(g kg-1)

Initial1.17 ±0.02

1.28 ± 0.02

1.17 ±0.02

1.40 ±0.00

1.52 ±0.02

1.41 ±0.02

nsChange-1

EC (dS m )Final

1.87 ± 0.02b2.45 ± 0.02ab

3.03 ± 0.02a

2.10 ± 0.02b

2.33 ± 0.02ab

1.75 ± 0.00b

**

(%)

61.11 ± 7.41ab

97.22±12.50ab

166.67 ± 2.38a

50.00± 12.50b

55.00±13.33b

**

Total
phosphorus(g kg-1)

Initial0.36 ± 0.00a

0.34 ± 0.00ab

0.30 ± 0.00c

0.30 ± 0.00c

0.32 ± 0.00bc

30.56 ±
12.50c0.25 ± 0.00d

Final

0.79 ± 0.00a

0.64 ± 0.00b0.64 ± 0.00b

0.69 ± 0.00b

0.65 ± 0.00b

0.54 ± 0.00c**

Change(%)

119.63±5.63ab

83.56 ± 3.74c

113.63±3.05ab124.85±6.79a

100.09±7.78bc

113.09±3.12ab**

Total potassium(g kg-1)

Initial2.19 ± 0.01ab

2.10 ± 0.01ab

1.83 ± 0.01b

2.05 ± 0.01ab

2.28 ± 0.01a

2.02 ± 0.01ab

ns

Final

2.68 ± 0.01

2.97 ± 0.02

2.69 ± 0.02

2.46 ± 0.01

2.89 ± 0.01

2.44 ± 0.01

ns

Change(%)

22.81 ± 8.81

42.11 ±12.74

48.70 ± 12.58

20.35 ± 2.74

27.10 ± 3.04

20.78 ± 0.57

ns

Magnesium(g kg-1)

Initial1.02 ± 0.00d

1.35± 0.01cd

1.52 ± 0.01cd

1.65 ± 0.02bc

2.18 ± 0.02b

3.26 ± 0.02a

**

Final

1.21 ± 0.01d

1.49 ± 0.00cd

1.67 ± 0.02cd

1.97 ± 0.02c

2.52 ± 0.01b3.68 ± 0.02a**

Change(%)

18.96 ±0.15

11.07 ±2.26

10.26 ±3.85

21.07 ±1.79

16.54 ±5.74

13.84 ±1.03ns

Calcium(g kg-1)

Initial0.80 ± 0.01d

1.00 ± 0.00cd

1.06 ± 0.01cd

1.28 ± 0.00c

1.79 ± 0.01b2.80 ± 0.02a

**

Final

0.88 ± 0.01d

1.09 ± 0.00cd

1.41±0.01bcd

1.53 ± 0.01bc

1.91 ± 0.01b

3.96 ± 0.02a**

Change(%)

9.51 ± 4.69c

9.30 ± 3.13c

34.23 ± 4.38ab

19.40 ± 3.55b7.05 ± 3.05c

42.40 ± 3.36a*

C/N ratio

Initial29.92 ± 4.75c

40.61 ± 5.23c

102.45±5.04ab

60.45 ± 4.59bc

115.04 ± 9.83a

135.68 ± 9.07a**

ChangeChange**Final

14.63 ± 2.41c

14.32 ± 0.76c

15.46 ± 1.95c

28.49 ± 4.19b

30.02 ± 3.11b

103.77 ± 2.94a

**

(%)

-50.01 ± 5.83b-63.86±5.95ab-84.50 ± 2.96a-52.60 ± 8.07b-73.80±2.54ab- 20.00 ±
6.47c

**

ns = non significantly different *, ** = significantly different at p ≤ 0.05 and p≤ 0.01, respectively. Similar letter within thesame column indicates no significant difference by Tukey's HSD test. The numerical value of ± shows standard error. B =Bedding (soil: cow dung : rice husk ash: vegetable waste; 4:3:2:1 ), SMS = spent mushroom substrate. (-) = The percent
change of nutrient decreased.

The SMS is comprised of easily degradable carbon substrates which tend to benefit the microbial
growth, resulting in a greater organic matter decomposition rate. From the result, had the highest
decrease in the percent change of organic carbon (60.14%) and C/N ratio (84.50%) at the ratio 60: 40
of bedding with SMS. The organic carbon loss is likely due to the mineralization of the organic matterⒸ ISERD

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through time (Gong et al., 2019). Elvira et al. (1998) reported that during composting there was a lossof 20-43% of organic carbon in the form of carbon dioxide. Due to microbial respiration and from theingestion of organic matter. The longer duration either for vermicomposting or composting indirectly
enhanced the quality of final end product as a result of the loss of carbon and increased nitrogencontent (Kaviraj and Sharma, 2003).
The nitrogen, phosphorus, magnesium and calcium content at the end of the process significantly
increased in all treatments (p≤ 0.05). (Table 2) While, the percent change of potassium had the highest
increased at the ratio 60:40 of bedding with SMS (48.70%). But The potassium content non
significantly increased at the end of the process due to microorganisms present in vermicompost utilize
bound nutrients in vermicompost for their survival. This results in increased microbial activity, whichleads to quicker immobilization of nutrients, causing decreases potassium content in vermicomposting.
The percent change of nitrogen had the highest increased at the ratio 60:40 of bedding with SMS(166.67%) due to the high growth rate of earthworm. Causing bacteria to play a role in themineralization process. Moreover, the excretion of mucus, fluids, enzymes and dead tissue during
digestion processes containing nitrogen effect the total nitrogen content increased (Suthar, 2007; Lou etal., 2017). Application of bedding with SMS at the ratio 50:50 gave the highest percent change ofphosphorus (124.85%) and magnesium (21.07%) because of earthworm like a media to increased
phosphatase enzyme content, which increased content by waste from the gut becomes earthworm effectto increased phosphorus content in vermicompost (Gong et al., 2019). The calcium content had thehighest increase in vermicompost from bedding and SMS at the ratio 20:80 was 3.96 g kg-1. Increasing
SMS rate will be higher of calcium content. This is because in the process of mushroom production areadded lime, gypsum, salt and pumite to be used as food and adjusted the acidity and alkalinity for goodmushroom growth. This results cause a large amount of calcium in waste from mushroom cultivation.
CONCLUSION
Waste from mushroom cultivation could improve vermicompost quality. It is a biological method that
relies on earthworms farming to convert organic matter into high quality organic fertilizer. It was foundthat the spent mushroom substrate contained high protein and carbon content as a food source forearthworms. It can increase the amount of nutrients and microorganisms that are beneficial to plants.
Application of bedding with spent mushroom substrate at the ratio of 60:40 seemed to be the mostsuitable for vermicompost production in term of plant nutrition and earthworm production. In general,
recycling of spent mushroom substrate through vermicomposting may reduce the environmental stressand can produce organic fertilizers with better chemical and biological properties.
ACKNOWLEDGEMENTS
We would like to acknowledge the scholarship from Research Fund for Supporting Lecturer to Admit
High Potential Student to Study and Research on His Expert Program Year 2019, Integrated Land andWater Resource Management Research and Development Center in Northeast Thailand and JapaneseChamber of Commerce, Bangkok Scholarship 2019 and Department of Soil Science and Environment,
Faculty of Agriculture, Khon Kaen university, Khon Kaen, Thailand for facility supported thisresearch.
REFERENCES
Edwards, C.A. and Burrows, I. 1988. The potential of earthworms composts as plant growth media. 211-219. In
Edwards, C.A. and Newrhouser, E.F. (Eds.), Earthworms in Waste and Environmental Management, SPB
Academic Publishing, The Hague.
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Elvira, C., Sampedro, L., Benftez, E. and Nogales, R. 1998. Vermicomposting of sludges from paper mill anddairy industries with Eisena andrei: A pilot-scale study. Bioresource Technology, 63, 205-211.
Gong, X., Li, S., Carson, M.A., Chang, S.X., Wu, Q., Wang, L., An, Z. and Sun, X. 2019. Spent mushroom
substrate and cattle manure amendments enhance the transformation of garden waste into vermicomposts
using the earthworm Eisenia fetida. Environmental Management, 248, 1-12.
Hřebečková, T., Wiesnerová, L. and Hanč, A. 2020. Change in agrochemical and biochemical parameters during
the laboratory vermicomposting of spent mushroom substrate after cultivation of Pleurotus ostreatus.
Science of the Total Environment, 739.
Huan-Na, L., Yu Tao, W. and Ming-Jun, Z. 2017. Evaluation of spent mushroom compost as a lignocellulosic
substrate for hydrogen production by Clostridium thermocellum. International Journal of Hydrogen Energy,
42, 26687-26694.
Iwai, C.B., Ta-oun, M., Seripong, S. and Champar-ngam, N. 2011. Vermicomposting: Good management
practice for waste, soil and yield safety. Department of Plant Sciences and Agricultural Resources, Land
Resources and Environment Section, Faculty of Agriculture, Khon Kean University, Thailand, 171.
Kaviraj and Sharma, S. 2003. Municipal solid waste management through vermicomposting employing exotic
and local species of earthworms. Bioresource Technology, 90, 169-173.
Lou, Z., Sun, Y., Zhou, X., Baig, S.A., Hu, B. and Xu, X. 2017. Composition variability of spent mushroom
substrates during continuous cultivation, composting process and their effects on mineral nitrogentransformation in soil. Geoderma, 307, 30-37.
Ndegwa, P.M. and Thompson, S.A. 2000. Effects of C-to-N ratio on vermicomposting of biosolids. Bioresource
Technology, 75, 7-12.
Suthar, S. 2007. Nutrient changes and biodynamics of epigenic earthworm Perionyx excavatus (Perrier) during
recycling of some agriculture waste. Bioresource Technology, 98 (8), 1608-1614.

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Research article
erdStudy on Use of Plant Based Material as a Fertilizer
PA PA SAN*
Yadanabon University, Mandalay, Myanmar
Email: papasan1771@gmail.com

MYA YADANAR AUNG
Yadanabon Universtiy, Mandalay, Myanmar
Received 23 November 2020

Accepted 30 June 2021(*Corresponding Author)

Abstract Soil is the mixture of mineral, organic material, living organisms, air and water thattogether support the growth of plant life. Fruit peels contain potassium, vitamins, minerals and someessential elements which enhance the growth of plant. Generally, fruit peels are thrown in garbage
and it goes to solid waste dumping site. That causes odour problem due to degradation of peelcontent in dumping site. The present study deals with the utilization of different fruit peels such asorange peels and banana peels as fertilizer added to soil. The soil sample was collected from SharTaw-Lay village, Amarapura Township, Mandalay, Myanmar. Three soil samples were investigatedby addition of organic waste fruit peel powder of orange and banana. Soil sample, S1 (2000 g soil) as
control, soil sample, S2 (2000 g soil + 500 g orange peel powder) and soil sample, S3 2000 g soil +500 g banana peel powder) and soil sample, S3 (2000 g soil + 500 g banana peel powder) werefertilized soil. The physicochemical properties of three soil samples such as pH, electricalconductivity, organic matter and texture were determined. The elemental composition of three soil
samples was measured by applying EDXRF. The content of N, P, K nutrients of three soil samples
was determined by chemical instrumental method. Different fruit peel powders add soils to compare
the plant growth were investigated. The chemical fertilizers can be replaced by the fruit peel powder
to protect the soil from the infertility. This fruit peel materials have not cost bearing and thus aids inconverting this waste into a usable resource.
Keywords fertilizer, fruit peel powder, nutrient, soil

INTRODUCTION
Soil is important because it provides the nutrients that plant need to grow. Without soil, there would be
no plants, without plants, there would be no foods, without food, animals could not survive. Soil isrelating with fertilizer. Soil is needed the fertilizer that is one of the sources for plant growth. There are
two types of fertilizer used in agriculture, organic fertilizer and inorganic fertilizers. Organic fertilizers
are fertilizers derived from animal or vegetable matter (eg. compost, manure). Inorganic fertilizers areproduced or artificially in a chemical refinery. Fertilizers typically provide in varying proportions.
Good fertility is fundamental to success full plant growth, and the application of fertilizers andmanures is an essential graining act activity. The maintenance of adequate levels of nutrients in soil isessential for healthy plant growth; cheapest and harmless materials can be used for the plant growth.
Fruits contain a high amount of antioxidants that are beneficial to our health in many ways. The fruit
peels have nutrients like potash, iron, zinc, calcium, citrate content etc. In addition, among the many
fruit sample, orange and banana are traditional food, main consumed for Myanmar people and wellcultivated for everywhere in Myanmar. Therefore, fruit sample orange and banana were chosen in thisresearch.
OBJECTIVEⒸ ISERD

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The present work was to investigate how the changes in physical and chemical properties by adding banana
peel powder and orange peel powder to the soil from Shar-Taw Lay village, Amarapura Township,
Mandalay, Myanmar. Discard substance of orange peel powder and banana peel power into usable productas fertilizer that cheapest and harmless material can be used for the plant growth.

Fig. 1 Map of study area
METHODOLOGY
The soil samples were collected from Shar-Taw Lay village, Amarapura Township, Mandalay,
Myanmar. These soil was chosen for this study because these soil was supplied by natural fertilizer
such as cowdung, dry peat leaves before harvested. These selected soil sown so many crops and paddy.
The soil samples were dug the depth 20 cm from the earth surface in the study area and were put inthick plastic bags. The soil normally contain clay, pebbles, root, rock pieces, these soil is sieved byusing 2 mm sieve to remove the pebbles, root, rock pieces. The soil samples were air-dried and werecrushed into fine powder by using grinder. This dried soil was packed in sterile polythene bags andsent to soil quality center, Department of Agricultural Research, Yezin, Nay-Pyi Taw, Myanmar.
Collection and processing of fruit peel:
Generally fruit peels are collected from fruit juice vendors or thrown in garbage by peel household.
Discard fruit peels (orange peel and banana) are cut into small pieces and dried in air. After drying ofthese peels into powder by using blander.

Orange peel

Orange peel powder

Banana peel

Banana peel powder

Fig. 2 Different fruit peel and fruit peel powder
Preparation of fruit peel powder in soil:
Orange peel powder (500 g) and soil (2000 g) mixed with 300 mL of distilled water as soil sample, S2
were added to the pot. The amount of water to add was calculated by field capacity method.

The fertilized materials were weighed every 2 weeks and the loss of weight was re-filled withwater. Banana peel powders were prepared for the above as soil sample S3 and without peel powder assoil sample S1. After composting 45 days, these two soil samples sent to soil quality center,
Department of Agricultural Research, Yezin, Nay-Pyi-Taw, Myanmar.

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Control soil sample S1(2500 g)Soil sample- S2
Soil 2000 g + 500 g orange peel powder

Soil sample-S3
Soil 2000 g + 500 g banana peel powder(1) pH, 1 : 2.5 soil: Water suspension
method, pH meter (F-51 HORIBA)(2) Electrical conductivity (EC), Saturation extract
method, conductivity meter (DS-51HORIBA)(3) Organic matter, Tyurin's method,
Analytical balance(4) Soil texture, pipette method, Analytical balance(5) Moisture, Gravimetric method,
Temperature controlled oven(6) Total N, Kjaldehl distillation method,
Gerhardt Vapodest20s(7) Total P, Molybivanado phosphoric acid
method, UV-visible Spectrophotometer
Jenway 6305

(8) Total K, Wet digestion with HNO3 : HClO4 (4 :1),
Atomic Absorption Spectrophotometer,
NOV AA-400(9) Elemental percent composition, powder-pellet, EDXRF

Fig. 3 Control soil and fruit peel powder in soil analytical item, analytical method,
apparatus used
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In this study, the pH, electrical conductivity (EC), organic matter, texture, moisture, total N, P, K
and Elemental percent composition of control soil sample S1, orange peel powder soil sample S2,banana peel powder soil sample S3 were measured at Department of Agricultural Research, Yezin, Naypyi-daw, Myanmar.
The pH of three soil samples were measured by water suspension method using pH meter.
Electrical conductivity (EC) was also measured by saturation extract method using conductivity meter.
Organic matter of soil samples S1, S2 and S3 were determined Tyurin's method using Analytical balance
and also measured soil texture by pipette method. Moisture for three soil samples were known by
Gravimetric method using temperature controlled oven. Three soil samples S1, S2 and S3 were
measured by Kjaldehl distillation method for Total N, Molybivanado phosphoric acid method for Total
P and wet digestion with HNO3 : HClO4 (4 : 1) for Total K by respective apparatus and also
determined elemental percent composition by EDXRF.
Pot experiment of chili plants and pea plants:
Effects of prepared fertilized soil sample with chili and pea plants were investigated by pot experiment.
Pot experiment of chili plants:
Duration from time of emergence of chili plant to the end of time period was 45 days work. According
to result, increase the time period with increasing of plant growth of fertilized soil sample S2 and S3.
RESULTS AND DISCUSSION
Table 1 Result of growth length of chili plant on control soil and prepared fertilized soilTime frame
day

Plant height (cm)Soil sample S1

Fertilized soil sample S2

Fertilized soil sample S3

12

8.6

11.7

12.5

24

13.5

20.6

21.4

36

22.7

37.5

38.5

45

33.6

54.5

55.6

Fig. 4 The plot of growth length of chili plant cultivated on prepared fertilized soil sample
versus time frame dayⒸ ISERD

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Table 2 Physicochemical properties of soil sample S1, S2 and S3
No
1
2
3

Soil sample
Control soil sample, S1
Soil + Orange peel powder
soil sample, S2
Soil + Banana peel powder
soil sample, S3pH

Organic matter (%)

EC (dS/m)

TextureMoisture

8.1

2.0 %

0.18

sandy loam

1.4 %

7.2

11.6 %

0.39

sandy loam

2.4 %

7.8

13.2 %

0.38

sandy loam

2.8 %From the above Table, the pH of control soil sample was slightly higher than soil sample S2 and S3.
Soil organic matter decline in many agroecosystems occur because losses of carbon through oxidationand erosion by intensive cropping are not compensated by carbon inputs through the return of plantbiomass. Organic matter reduction is in turn, association with the soil structure degradation. Organic
matter of soil sample S2 and S3 were higher than control soil sample S1. Neither strong acidic nor strong
basic of three soil samples were found. Organic matter plays a major role in moisture retention, helping
crops withstand drought contributes to the chemical and biological properties of the soil and also a source ofand exchange for nutrients. The soil sample consists of 61% of sand, 26% of silt and 13% of clay. The
texture class of soil sample was sandy loam. Electrical conductivity (EC) is a measure of the salt
concentration in the soil solution. Increasing electrical conductivity of control soil sample, S1 than soil
sample S2 and S3. The moisture of control soil sample S1 (1.4%), soil sample S2 (2.4%), soil sample S3(2.8%) were observed.
Table 3 pH of orange peel powder and banana peel powder
No
1

Fruit peel powder
Orange peel powder

pH
5.6

N
7.2

P
2.3

K
1.2

2

Banana peel powder

7.7

4.6

3.7

2.8

By the experiment, it can quantify how much amount of different peel powder required for theparticular soil. The orange peel powder was used to decrease the pH of the soil and the banana peel
powder was used to increases the pH of the soil.
Citrate peel powder like, orange peel powder, reduces salinity of soil and alkaline peel powder
like, banana peel powder reducing acidity content present in soil. According to result, orange peel
powder has higher nitrogen content (7.2 mg/g), whereas banana peel powder has higher quantity ofphosphorous (3.7 mg/g) and potassium (2.8 mg/g).
Table 4 Major nutritive value of soil sample S1, S2 and S3
No

Soil sample

Total N

Total P

Total K

1

Control soil sample, S1

0.09%

0.11%

0.31%

2

Soil + Orange peel powder soil sample, S2

0.18%

0.40%

1.01%

3

Soil + Banana peel powder soil sample, S3

0.20%

0.35%

1.03%

The major naturitive value of prepared fertilized soil sample S1, S2, S3 were determined. The
primary nutrient (N, P, K) are mainly necessary for plants. The total nitrogen value of three soil
samples ranged from 0.09%, 0.18% to 0.21%. It was found that nitrogen content of three soil samples
were increased and that causes soil fertility.
The total phosphorus values ranged 0.11%, 0.40%, 0.35%. Phosphorus concentration may
increases as composting. Thotal potassium content ranged 0.3.1%, 1.01%, 1.03%. According to thisⒸ ISERD

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results, potassium content of prepared fertilized soil sample S2 and S3 are higher. Therefore it issuitable for crops and vegetable growing. By comparing these results, we can conclude that increased
N, P, K value with increasing fertility of soil, soil microorganism, plant growth and maximum nutrient
content.
The element composition of prepared fertilized soil sample were determined by EDXRF at
Department of Agricultural Research, Yezin, Nay Pyi Taw, Myanmar. According to table, allelemental composition of soil sample S2 and soil sample S3 are higher than control soil sample S1. In
this elemental composition, S2 and S3 fertilized soil sample contained highest amount of Si, Al, Fe andhigh amount of Ca, K. And then, meidum for Ti, Mn and low mineral levels are observed in Zn, Pb, Cr.
The high elemental content of S2, and S3 fertilized soil sample show increasing pest and pathoger
resistance, drought and heavy metal tolerance, and stability and yield of agricultural corps.
Table 5 Determination of elemental composition of soil sample S1, S2 and S3
Soil
Si
Al
Fe
Ca
K
Ti
Mn
Zn
Pb
Cr
sampleControl soil
30.726% 5.528% 2.059% 2.359% 1.243% 0.456% 0.084% 0.028% 0.018% 0.006%
sample S1Orange soil
47.100% 15.200% 18.300% 6.470% 8.630% 1.450% 0.217% 0.153% 0.186% 0.0889%
sample, S2
Banana soil
46.500% 15.000% 17.000% 7.190% 8.730% 1.580% 0. 276% 0.148% 0.180 0.0663%
sample, S3

Application of fruit peel powder in soil:
Soil samples S1, S2, S3 were filled with each pot. Pea seeds and chili seeds were sown in various pots
and water was poured every day. Control and two different peel powder was used to compare the plant
growth.
After 45 days, check the growth of pea plant and chili plant compare with the plant in control soil.
Length of the plant was very less in control soil than the orange peel powder applied soil. Size of theleaves was also increased in the plants which were grown on banana peel powder with soil. Length ofpeas and chili plant were also bigger than control.

(c) Soil +(b) Soil +(a) Control

orange peel

Fig. 5 Different in growth of chili planta) control soil and b) soil + banana
peelbanana peel

SoilFig. 6 Different ingrowth of pea plant
Powder soil
soila) control soil and powderb) soil+ banana
peelc) soil + orange peel
CONCLUSIONc) soil + orange peel

In this study, the plant base materials such as orange peel and banana peel were used as natural
fertilizer. The physicochemical changes during the combination of orange peel powder or banana peel
powder to soil were determined at enhancing properties of soil. The orange peel powder was used toⒸ ISERD

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decrease the pH of the soil and the banana peel powder was used to increase the pH of the soil. The pHof control soil samples is slightly higher than soil sample S2 and S3. It was found that, organic matter,electrical conductivity (EC), of orange peel powder soil sample S2 and banana peel powder soil sample
S3 were higher than control soil sample S1. Furthermore, total N, P, K value of soil sample S2 and S3
were also found higher than control soil sample S1 as well as elemental composition. So, the peel
powder can be feasibly used as a soil fertilizer, improving soil morphology, as micronutrient
supplement (Zn, Fe, Ca) and also for horticultural purposes. The plant growths of different fruit peel
powder add soil were more than control soil. The organic waste such as orange and banana fruit peel
powder were used as natural fertilizer. By using this orange peel powder and banana peel powder as
fertilizer, it can be reduced to fruit peel waste. Soil application of compost from organic residues, suchas animal manures, sewage sludge, household wastes, represents a management strategy that could
counteract depletion of organic matter in soils. Besides, organic residues recycling and further use insoils represents an attempt to alleviate the serious environmental problems caused by residue
accumulation. The use of compost in soils requires that it achieves and adequate degree of maturity,
which implies a stable organic matter content and the absence of phytotoxic compounds and plant or
animal pathogens. Mineral fertilization provides readily available nutrients for plant growth; however,it does not contribute to improve soil physical condition. For the enhancement of the growth of plantand vegetables in home gardens, banana peel power and orange peel powder were applied as soil
conditioner and its response was measured by using some parameters.
ACKNOWLEDGEMENTS
I express my gratitude to Rector Dr. Aung Aung Min and Pro-rectors Dr. Si Si Khin and Dr. Tint Moe
Thu Zar, Yadanabon University, for their permission to do this research. I also wish to express manythanks to Professor Dr. Hlaing Hlaing Myat, Head of Chemistry Department, Yadanabon University
for her kind permission to carry out this work and her interest, constructive comment and suggestions
and we also thank the ethical research committee of department of chemistry, Yadanabon University. I
am also thanks to Dr. Ni Ni Tint, Department of Agricultural Research, Yezin, Nay Pyi Taw,
Myanmar.
REFERENCES
Alan, W. 1993. Soils and the environment, An introduction. Cambridge University Press.
Introduction to soil and soil Resources. 2001. Retrieved from http://webcache.googleusercontent.com/search?
q=cache:dvxvRGnTyZkj and http://www.environemntal.ualberla.ca/soils ERM/Lecturer 2/ lecture 2.ppt
Jenny, H. 2005. Factors of soil formation. Dover Publications, Inc., New York.
Mercy, S., Muchira, B.S. and Jenifer, I. 2019. Application of different fruit peel formulation as a natural fertilizer
for plant growth. IJSTR Journal, 3 (1), 301-309, ISSN 2277-8616.
Sparks, D.L. 2002. Environmental soil chemistry. Academic Press, San Fransico, USA.
Tewari, H.K., Marwaha, S.S. and Rupal. 1986. Ethanol from banana peels. Agric Wastes 16, 135-146.

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Research article
erd

Effect of Chicken Manure and Chemical Fertilizer
Applications on Growth and Yield of Rice (Oryza sativa L.)
KYI KYI SHWE*
Yezin Agricultural University, Myanmar
Email: kyikyishwe@yau.edu.mm

SWE SWE MAR
Yezin Agricultural University, Myanmar

THEINGI WIN
Yezin Agricultural University, Myanmar

WIN YU HLAING
Yezin Agricultural University, Myanmar

HSU MYAT THWIN
Yezin Agricultural University, Myanmar

KYAW NGWE
Yezin Agricultural University, Myanmar

TORU SAKAI
Japan International Center for Agricultural Sciences, Japan
Received 30 December 2020

Accepted 30 June 2021(*Corresponding Author)

Abstract The field experiment was conducted at Yezin Agricultural University Farm, Yezin,
Nay Pyi Taw, during July to November wet season, 2020, to investigate the effects of chicken
manure and chemical fertilizer applications on growth, yield and yield components of rice. The
experimental design was a randomized complete block (RCB) design with four replications.
The four treatments were T1 (control, no application), T2 (80 N, 20 P, 32 K) kg ha-1
(recommended rate), T3 (5 ton ha-1of chicken manure), and T4 (2.5 ton ha-1 of chicken manure+ recommended rate of fertilizer). Urea, triple superphosphate and muriate of potash were usedas the N, P, and K sources and chicken manure was applied basally. The Sinthukha rice variety
was tested. The plant growth parameters were recorded at biweekly intervals and the yield andyield components data were also collected at harvest time. The results show that the combined
application of organic manure and recommended rate of fertilizer (T4 treatment) increased thenumber of panicles hill-1, number of spikelets panicle-1, filled grain percentage, and harvest
index in comparison to other treatments. Moreover, the highest grain yield (6.87 ton ha-1) was
observed in T4 treatment and the minimum grain yield (6.09 ton ha-1) was found in T1. Amongthe four treatments, T2 showed the second highest yield of rice in this study. The combined
application of chicken manure and chemical fertilizer (T4) increased grain yield up to 12% overcontrol. The application of chemical fertilizer only (T2) increased yields 7% more than thecontrol. The application of chicken manure (T3) resulted in a 6% yield increase compared to thecontrol. The application of chicken manure and chemical fertilizer (T4) increased yield per
hectare by up to 6-12% more than the control. Therefore, the combined application of chicken
manure and chemical fertilizer had the greatest effect on improving the yield of Sinthukha rice
variety and maintaining soil sustainability.
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Keywords chicken manure, chemical fertilizer, rice yield

INTRODUCTION
Rice (Oryza sativa L.) is the staple food of about 3 billion people, nearly half the world’s population,
who depend on rice for survival. In many countries, rice accounts for more than 70% of human caloric
intake and represents the main source of protein for poor people in all developing countries (Sharma,2014). Rice cultivation is the principal activity and source of income for millions of households aroundthe world, and several countries in Asia and Africa are highly dependent on rice as a source of foreignexchange earnings and government revenue (FAO, 2014).
Myanmar is the world’s sixth-largest rice producing country (Maclean et al., 2013). Rice is grown
during the monsoon and summer seasons in Myanmar’s four growing zones designated as delta, dryzone, coastal zone, and mountainous areas (Linn and Maenhout, 2019). The total area sown to rice in
Myanmar was 7.22 million hectare (ha) with an annual production of 28.01 million metric ton and an
average yield of 3.92 MT ha -1 in 2018-2019 (MOALI, 2019).
Due to the increasing cost of chemical fertilizers, depletion of soil micronutrients, environmental
and health hazards, the use of organic manure in farming has attracted much attention (Ramesh et al.,2005). The adequate fertilizers in rice are fundamental to achieve great productivity. On the other hand,
animal manures and green manures are little used as a source of nutrients in rice crops (Schmidt andKnoblauch, 2019). Organic manures are excellent fertilizers, containing nitrogen, phosphorus,
potassium and micronutrients essential for the healthy growth of plants. Furthermore, it has numerous
beneﬁts due to the balanced supply of nutrients including micronutrients. In agricultural ﬁelds, organic
manure that is manufactured from animal byproducts has been utilized to overcome environmental
pollution and plant productivity reductions that result from the constant utilization of chemicalfertilizers (Han et al., 2016).
Organic manure such as poultry manure increases the organic matter (OM) content of soil and inturn releases the plant nutrients in a form available for use (Magkos et al., 2003). It contains essential
nutrient elements associated with high photosynthetic activity and thus promotes root and vegetable
growth (John et al., 2004). Previous studies have shown that the integration of inorganic fertilizer withorganic manure has been more beneficial than the use of either mineral fertilizer or organic manurealone, especially in intensive agricultural production. Therefore, the integrated use of both organic
manure and chemical fertilizers can provide the best approach in providing greater stability inproduction and improving soil fertility status (Islam et al., 2011).
OBJECTIVES
The present study was conducted to investigate the effect of applications of different combinations ofchicken manure and chemical fertilizer on growth, yield and yield components of rice and to ascertain
the most favorable application rate to improve yield of rice.
MATERIALS AND METHODSThe experiment was conducted in the Yezin Agricultural University Farm Yezin, Nay Pyi Taw, during2020 wet season, from July to November. Yezin is located at 19º 52' N and 96º 37' E with an altitude of
103 meters above sea level. The experiment was laid out in a Randomized Complete Block Design(RCBD) with four treatments and four replications. The individual plot sizes were (5 x 5) m2. Double
bands provided separation between plots. Treated plots were separated about 1m from surrounding
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fertilizers during irrigation. The treatments were T1 (control, no fertilizer application), T2 (80 N, 20 P,
32 K) kg ha-1 (recommended rate), T3 (5 ton ha-1of chicken manure), T4 (2.5 ton ha-1 of chicken
manure + fertilizer at the recommended rate). Urea, triple superphosphate and muriate of potash wereused as N, P and K sources. Chicken manure and triple superphosphate and potash fertilizers wereapplied basally. Urea fertilizer was applied equally at three different stages (basal, active tillering andpanicle initiation). The soil texture of the experimental field was sandy loam, had a moderately acid(pH 5.8), was low in organic matter (1.09 %), had medium levels of available nitrogen (87 ppm), wasslightly low in available phosphorus (3.5 ppm), low in available potassium (104 ppm) and low in
available sulphur (36 ppm).
Sinthukha rice was the variety tested. The twenty day old seedlings were transplanted and spaced
at 20 cm × 20 cm. Irrigation was provided during the growing season as necessary. Weed control andother management procedures were regularly undertaken, especially at the early stages of growth.
Harvesting was at 98 days after transplanting (DAT).
Plant growth parameters such as plant height and number of tillers per hill from five randomly
selected sample hills in each plot were collected from 14 DAT to 98 DAT at biweekly intervals. The
yield and yield components were recorded at harvesting stage and grain yield was measured from a 1
m2 centrally located area in each plot.
The collected data were analyzed statistically using Analysis of Variance (ANOVA) and meanvalues were compared by least significant difference (LSD) at 5% probability level. All statisticalanalyses used statistix 8.0 software.
RESULTS AND DISCUSSION
Plant height was measured starting from 14 DAT to 98 DAT at biweekly intervals (Fig.1). Plant
heights were increased significantly under all treatments, from 14 DAT to 42 DAT. There was no
significant difference in plant heights in all treatments at 28 DAT, 56 DAT, 70 DAT, 84 DAT and 98
DAT. The tallest plants (116.91 cm) grew subject to T4 treatment, followed by T2 treatment (114.55cm), and T3 treatment (111.59 cm) at 98 DAT. The shortest plant was found in T1 treatment (110.53
cm). These differences may be due to the effects of the combined applications of organic manure andchemical fertilizer, releasing macronutrients and micronutrients to the plants.
The number of tillers per hill was recorded from 14 DAT to 98 DAT at biweekly intervals (Fig. 2).
The number of tillers per hill did not increase significantly with the application of chicken manure andchemical fertilizer application at the different growth stages of rice. The highest number of tillers perhill occurred in T4 (16.15) followed by T2 (14.25), T3 (13.75) and the lowest value was in T1(13.40)
at 98 DAT. It was similarly reported by Nayak et al. (2007), that a significant increase in effective
tillers hill -1 was obtained with the applications of organic manure and chemical fertilizer.
The effect of chicken manure and chemical fertilizer application on yield and yield components
are described in Table. 1. There was no significant variation in number of panicles per hill for eachtreatment. The number of panicles per hill ranged from 10.80 to 12.55. The maximum number ofpanicles per hill occurred in T4 (12.55) followed by T2 (12.50) and T3 (11.35) and the minimum
number of panicles per hill was in T1 (10.80). The greatest increase in the number of panicles per hill
occurred with the combined application of chicken manure and chemical fertilizer.
There was not a significant variation in panicle length among the different treatments. The panicle
length ranged (ranges) from 22.78 to 23.92 cm. The highest panicle length (23.92cm) was observed in
the T4 and the lowest panicle length (22.78 cm) was in T1. The increase in panicle length in response
to combined application of organic and inorganic fertilizers (T4 treatment) may be due to the greater
availability of macronutrients as well as micronutrients (Awan et al., 2011).

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Fig. 1 Influence of chicken manure and chemical fertilizer applications
on mean values of plant height of riceFig. 2 Influence of chicken manure and chemical fertilizer applications
on mean values of number of tillers hill-1 of rice
There was no significant difference in number of spikelets panicle-1 among the different fertilizer
treatment methods. However, the number of spikelets panicle-1 ranged from 145.60 to 158.40, with thehighest number of spikelets panicle-1 occurring in T4 (158.40), followed by T2 (152. 40), T3 (146.15),
and the lowest number of spikelets panicle-1 was in T1 (145.60) where there was no fertilizer applied.
Increases in spikelet panicles-1 may be due to the application of chicken manure and chemical fertilizer.
The maximum spikelets panicle-1 occurred with the addition of organic matter to the soil and this might
be due to the availability of macro as well as micro plant nutrients (Siavoshi et al., 2011).
The filled grain percent ranged from 65.61% to 80.10%. The maximum filled grain percentage
was shown in T4 (80.10%), followed by T2 (78.10%), T3 (76.48%) and the minimum value occurredin T1 (65.61%). The previous study reported that the combined application of manure and fertilizer
significantly increased the number of filled grains panicle−1 (Satyanarayana et al., 2002).
The 1000-grain weight ranged from 20.48 g to 22.49 g. The highest 1000-grain weight was in T4(22.49 g), followed by T2 (21.88 g) and T3 (21.45 g). The lowest 1000-grain weight was in T1 (20.48
g). This result was in conformity to the findings of Yang et al. (2004) who recorded that 1000-grain
weight was increased by the application of chemical fertilizer and organic manure.
The highest grain yield (6.87 ton ha-1) was in T4, followed by T2 (6.53 ton ha-1), T3 (6.49 ton ha1), with the lowest grain yield (6.09 ton ha-1) in T1 treatment where no fertilizer was applied (Fig.3).
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yields by up to 12%, over those in the control. The chemical fertilizer application in the T2 treatment
increased yields 7% more than in the control. The application of just chicken manure increased yield
by 6% compared to the control. To sum up, the application of chicken manure and chemical fertilizer
increased the grain yield 6-12% over the control, and also increased the straw yields of rice. It is clearthat organic manure in combination with inorganic fertilizers increased the vegetative growth of plants
and thereby increased the straw yield of rice (Rahman et al., 2009). The chemical fertilizer offers
nutrients which are readily soluble in soil solution and thereby instantly available to plants. Nutrient
availability from organic sources is due to microbial action and improved physical condition of soil
(Sarker et al., 2004). Several authors have shown that organic amendments play a crucial role in both
short-term nutrient supply and long-term build-up of soil quality in flood-irrigated rice crops(Nishikawa et al., 2014). Moreover, organic manures increased soil nutrient availability, improved soil
structure and root development and increased soil water availability (Han et al., 2016).

Fig. 3 Influence of chicken manure and chemical fertilizer applications
on mean values of grain yield of rice
Table 1 Effect of chicken manure and chemical fertilizer applications
on mean values of yield and yield components of ricens = not significant

There was no significant variation in harvest index with different fertilizers application (Table 1).
The harvest index ranged from 0.40-0.43. The maximum harvest index occurred in T4 (0.43), followedby T2 (0.42), T3 (0.41) and the minimum value was in T1 (0.40).
CONCLUSION
The application of chicken manure and chemical fertilizer influences the growth, yield and yieldcomponents of rice. The largest differences that occur in growth parameters are in plant height, andnumber of tillers and these occur where chicken manure and chemical fertilizer (T4) are used.
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Moreover, the T4 treatment results in the highest yield and yield components. The chicken manure
released enough nutrients that resulted in increase in growth and yield of rice and it also improved thesoil properties, which in turn resulted in better growth and yield. Additionally, the organic fertilizer can
be a better supplement of inorganic fertilizer to produce higher growth and yield. The lowest values forplant growth and yield parameters are recorded in T1 where no fertilizer was applied. Only use ofchemical fertilizer is not judicious for producing any crop in agriculture. Therefore, it can be concluded
that combined application of organic manure and chemical fertilizer is necessary for crop production as
well as maintaining soil fertility in study area.
ACKNOWLEDGEMENTS
The authors acknowledge the Japan International Center for Agricultural Sciences (JIRCAS), for thefinancial support allowing the completion of this research.
REFERENCES
Awan, T.H., Ali, Z., Manzoor, R., Ahmad, M. and Akhtar, M. 2011. Effect of different nitrogen levels and row
spacing on the performance of newly evolved medium grain rice variety, KSK-133. The Journal of Animal
and Plant Sciences, 21 (2), 231-34.
Food and Agricultural Organization. 2014. Promotion of underutilized indigenous food resources for foodsecurity and nutrition in Asia and Paciﬁc. Durst, P. and Bayasgalanbat, N. (Eds.), FAO Nations Regional
Ofﬁce for Asia and The Paciﬁc.
Han, S.H., An, Y., Hwang, J., Kim, S.B. and Park, B.B. 2016. The effects of organic manure and chemicalfertilizer on growth and nutrient concentrations of yelloe poplar (Liriodendron tulipifera Lin.) in a nurserysystem. Forest Science and Technology, 12 (3).
Islam, M.M., Karim, A.J.M.S., Jahiruddin, M., Majid, N.M., Miah, M.G., Ahmed, M.M. and Hakim, M.A. 2011.
Effects of organic manure and chemical fertilizers on crops in the radish stem amaranth-Indian spinachcropping pattern in homestead area. Australian Journal of Crop Science, 5, 1370-1378.
John, L.W., Jamer, D.B., Samuel, L.T. and Wanner, L.W. 2004. Soil fertility and fertilizers: An introduction to
nutrient management. Pearson education, India, 106-153.
Linn, T. and Maenhout, B. 2019. Analysis of the operational constraints of the rice value chain in Ayeyarwady
Region, Myanmar. Asian Journal of Agriculture and Development, 16, 53-76.
Maclean, J., Hardy, B. and Hettel, G. 2013. Rice almanac. Source Book for One of the Most Important Economic
Activities on Earth, IRRI, Philippines.
Magkos, F. 2003. Organic food: Nutritious food or food for thought? A review of the evidence. International
Journal of Food Science and Nutrition, 54, 357-371.
Ministry of Agriculture, Livestock and Irrigation. 2019. Myanmar agriculture sector in brief. Department ofPlanning, Ministry of Agriculture, Livestock and Irrigation, Myanmar.
Nayak, D.R., Babu, X. and Adhya, T.K. 2007. Long-term application of compost influences mineral biomass andenzyme activities in a tropical Aeric Endoaquept planted to rice under flooded condition. Soil Biology andBiochemistry, 39 (8), 1897-1906.
Nishikawa, T., Li, K. and Inamura, T. 2014. Nitrogen uptake by the rice plant and changes in the soil chemical
properties in the paddy rice field during yearly application of anaerobically-digested manure for seven years.
Plant Production Science, 17, 237-244.
Rahman, M.S., Islam, M.R., Rahman, M.M. and Hossain, M.I. 2009. Effect of cowdung, poultry manure andurea-N on the yield and nutrient uptake of BRRI dhan 29. Bangladesh Research Publications Journal, 2, 552558.
Ramesh, P., Mohan, S. and Rao, S. 2005. Organic farming: Its relevance to the Indian context. Current Science,
88 (4), 561-568.
Sarker, M.A.R., Pramanik, M.Y.A., Faruk, G.M. and Ali, M.Y. 2004. Effect of green manures and levels of
nitrogen on some growth attributes of transplant aman rice. Pakistan J. Biol. Sci., 7, 739-742.

Ⓒ ISERD

154

IJERD – International Journal of Environmental and Rural Development (2021) 12-1

Satyanarayana, V., Prasead, P.V.V., Murthy, V.R.K. and Rodty, K.J. 2002. Influence of combined application offarm yard manure and inorganic fertilizers on yield components of irrigated lowland rice. Journal of Plant
Nutrition, 25, 2081-2090.
Schmidt, F. and Knoblauch, R. 2019. Extended use of poultry manure as a nutrient source for flood-irrigated ricecrop. Pesquisa Agropecuária Brasileira.
Sharma, M.K. 2014. Characterization and evaluation of rice germplasm for yield and quality traits. Doctoral
Dissertation, Jawaharlal Nehru Agricultural University.
Siavoshi, M., Nasiri, A. and Laware, S.L. 2011. Effect of organic fertilizer on growth and yield components inrice (Oryza sativa L.). Journal of Agricultural Science, 3 (3), 217-24.
Yang, C.M., Yang, L., Yang, Y. and Ouyang, Z. 2004. Rice root growth and nutrient uptake as influenced byorganic manure in continuously and alternately flooded paddy soils. Agricultural Water Management, 70 (1),67-81.

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Research article
erdCharacterization and Diversity of Selected Maize (Zea mays
L.) Genotypes Using Qualitative Traits
SAN KYI*
Yezin Agricultural University, Nay Pyi Taw, Myanmar
Email: sankyi747@gmail.com

KYAW KYAW WIN
Pro-Rector’s Office of Administration, Yezin Agricultural University, Nay Pyi Taw, Myanmar

HLA THAN
Pro-Rector’s Office of Academic, Yezin Agricultural University, Nay Pyi Taw, Myanmar

SOE WIN
Department of Plant Breeding, Physiology and Ecology, Yezin Agricultural University,
Nay Pyi Taw, Myanmar

NYO MAR HTWE
Advanced Center for Agricultural Research and Education, Yezin Agricultural University,
Nay Pyi Taw, Myanmar
Received 22 December 2020

Accepted 30 June 2021(*Corresponding Author)

Abstract Phenotypic traits qualitatively and quantitatively are helpful as a preliminary
evaluation of maize genetic diversity and provided practical and critical information required
characterizing genetic resources. To find out the varietal characterization and grouping withsimilarity, fifty maize genotypes including hybrids and inbreds were examined in thisexperiment. This experiment was carried out in a randomized complete block design with three
replications during wet season from June to October, 2019, at Maize and other Cereal Crops
Research Section, Department of Agricultural Research (DAR), Yezin, Nay Pyi Taw, Myanmar.
The qualitative characters were recorded at different growing stages according to theInternational Union for the Protection of New Varieties of Plants (UPOV) Test Guide line (TG).
The agglomerative cluster analysis was computed using Ward’s hierarchical algorithm based ofqualitative characters. According to the results, diverse qualitative traits were observed among
the tested genotypes and genotypes varied different qualitative traits with different frequencies.
Thus, comparisons of qualitative traits were made to know the extent of variation among maize
varieties under investigation to estimate the genetic diversity. The tested maize genotypes could
be grouped five clusters based on qualitative traits. Although some maize genotypes collectedin an area are included in different groups because of the different characteristics they pose.
Since qualitative characters have less environmental influences, these traits could be used forDistinctness, Uniformity and Stability (DUS) test and Value for Cultivation and Use (VCU)
testing of plant varieties. Thus, the application of morphological markers according to UPOV
descriptor could contribute to more efficient selection of parental pairs in the early generationsof testing.
Keywords characters, cluster, maize genotypes, variationⒸ ISERD

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INTRODUCTION
Maize (Zea mays L.) is the world’s third most important cereal, widely used in poultry and foodindustries next to wheat and rice. As it has higher yield potential than any other cereals, it is referred toas “miracle crop” or the “queen” of cereals. Maize is grown both as food for human beings and fodder
for animals (Malhotra, 2017). In Myanmar, maize is the second most important cereal after rice. In
2017-2018, maize growing area is 504,000 ha and production is 1940,000 MT. As demand for maize
has increased annually since 2009, the maize growing area has expanded year by year (DoP, 2018).
Most of cultivars grown in Myanmar are introduced hybrid varieties and these varieties are registeredaccording to the Seed Law and Regulation. Although the registered varieties are increasing year by
year, the procedure on clarification as a new variety is weak. Awareness of genetic diversity among
elite breeding materials or adapted cultivars has an important role in the improvement of crop plants.
Many tools are now available to study the relationships among the cultivars, including various
types of molecular markers; however, morphological characterization is the first step in the description
and classification of germplasm (Smith and Smith, 1989). There is an important role of morphological
data in the management of genetic resources that are conserved in ex-situ gene-banks. The
characterization and grouping of lines helps the breeders to avoid duplication in sampling populations
and to aid in the identification of varieties and hybrids (Madhukeshwara and Sajjan, 2015). Although
there are the ways to collect the morphological character by using International Board for Plant Genetic
Resources (IBPGR) descriptor and International Union for the Protection of New Varieties of Plants(UPOV) Test Guide line (TG), collection of the data according to UPOV TG is better forcharacterization of the genotypes which are used to set up reference varieties in TG preparation, andDistinctness, Uniformity and Stability (DUS) testing. The inventory and agro-morphologicalcharacterization of these genetic resources (improved varieties and local’s accessions) are essential toprovide a sound database on the characteristics of the maize (Salami et al., 2015). Therefore, thisexperiment was conducted to characterize qualitative characters of maize and to identify similarity
among maize genotypes.
METHODOLOGYThis experiment was carried out during the wet season from June to October, 2019, at Maize and other
Cereal Crops Research Section, Department of Agricultural Research (DAR), Yezin Nay Pyi Taw,
Myanmar which was located at 19° 49' 33˝ N; 96° 16 44˝ E; 102 m above sea level. In the present
investigation, 38 hybrids (developed from DAR and imported by private companies) and 12 inbredlines and open pollinated varieties (OPVs) (developed from DAR, Yezin) were utilized. These
genotypes are selected based on the most recommended for importation in 2018-19, as new varieties
approved by 10th-14th National Seed Committee (NSC) and currently grown in Regions and Divisions.
All tested genotypes were evaluated in Randomized Completely Block Design with three
replications. Each entry (genotype) was grown at a spacing of 75 cm × 25 cm, in a plot size of two
rows with 4 m length. Two seeds were sown in each hill and thinning was done 14 days after sowing
and single plant per hill was left. For fertilizer application, Urea, Triple Super Phosphate and Muriateof Potash were applied as basal at the rate of 123.5 kg ha-1, 123.5 kg ha-1 and 61.75 kg ha-1,
respectively. Then, the first and second sides dressing of 61.75 kg ha -1 Urea, 30.86 kg ha-1 Muriate ofPotash were applied three weeks after sowing and five weeks after sowing, respectively. Intercultivation was done two times; just before the fertilizer applications which control weeds and also
improve soil aeration. The qualitative parameters were recorded on each tested genotype in each
replication on five representative plants according to UPOV test guidelines. The qualitative characters
were recorded at different growing stages following UPOV TG. The agglomerative cluster analysisⒸ ISERD

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was computed based on qualitative and morphological characters using Ward’s hierarchical algorithm
according to the procedure as described by Singh and Chaudhary (1977).
RESULTS AND DISCUSSION
Characterization of Maize Genotypes
The qualitative traits of tested maize genotypes were classified according to UPOV test guidelines.
Variations of 18 qualitative characters in 50 maize genotypes are shown in Fig. 1. Some distinctive
characteristics of tested maize genotypes are presented in Plate 1. The anthocyanin coloration of firstleaf sheet possessed five groups such as absence, weak, medium, strong and very strong color
anthocyanin. There were 29 genotypes (58%) having strong anthocyanin, 18 genotypes (36%) having
medium and 3 genotypes (6%) having weak anthocyanin. There was no genotype with absence ofanthocyanin and very strong anthocyanin coloration. In shape of first leaf apex, the maximum
percentage of genotypes exhibited rounded to speculate type (78.00%) and the minimum accessions
showed round apex type (22%) of leaf apex shape. The intensity of green color in foliage wasclassified into three groups such as light, medium and dark green for tested maize genotypes. Of thetotal, 28 genotypes showed medium green color foliage (56%), 14 genotypes (28%) had dark green
foliage and other 8 genotypes were observed (16%) light green foliage. The undulation of margin in
first leaf blade in tested genotypes was classified as absence, intermediate and strong. Among the
genotypes, 25 genotypes possessed intermediate undulation of margin of leaf blade, 15 genotypes wereabsent and the rest 10 genotypes had strong undulation leaf blade margin.
Five characters regarding with tassel were examined in this study: tassel anthocyanin coloration at
the base of glume, anthocyanin coloration of glume excluding base, anther anthocyanin coloration, theangle between main axis and lateral branches and curvature of lateral branches of tassel. In this result,different genotypes possessed different tassel characters. The types of spikelets density in tassel wasclassified into three types. Among the 50 genotypes, most were medium density of spikelets (66%) andthe rest was moderately lax (34%), and there was no moderately dense density of spikelets. In,
anthocyanin coloration of silk of ear, no anthocyanin (19 genotypes = 38%), weak anthocyanin (23genotypes = 46%), medium anthocyanin (8%) and strong anthocyanin (8%) colors were found among
the 50 genotypes. Among the maize genotypes studied, 30 genotypes (60 %) were found no purple
anthocyanin in brace root, 14 genotypes (28%) had weak anthocyanin, 4 genotypes (8%) had medium
color and 2 genotypes (4%) were noticed as strong anthocyanin.
Leaf sheath and stem (internode) anthocyanin color were classified five groups as absent or very
weak anthocyanin, weak, medium, strong and very strong. Leaf sheath anthocyanin was dispersed at allgroups as very strong anthocyanin color (16%), strong anthocyanin color (32%), medium anthocyanin
color (30%), weak anthocyanin color (14%) and very weak or absence of anthocyanin color (8%). In
stem color, absence of anthocyanin (68%), weak anthocyanin (24%), medium (4%) and light strong
anthocyanin (4%) were observed among the genotypes. There was no genotype with strong
anthocyanin color in internode color. The shape of maize ear was characterized as conical, conicalcylindrical and cylindrical. Most of the tested genotypes had conical-cylindrical type of ear (62%),
followed by cylindrical type of ear (34%) and the rest (4%) had conical type of ear.
In types of grain, UPOC TG showed nine types: flint, flint like, intermediate, dent-like, dent,
sweet, pop, waxy and flour. In this study, four types of grain were observed among the genotypes.
Most of the genotypes possessed flint like grain showing about 44% of total. Nineteen out of 50 (38%)
showed intermediate type of grain, 16% had flint of grain, and the rest genotype about 2% had dent
type of grain. There were 10 types of color of grain top such as white, yellowish white, yellow,
yellowish orange, orange, red orange, red, purple, brownish and blue black color. In this study, only
four types of color of grain top were observed among the tested genotypes with the frequencies of 2%Ⓒ ISERD

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in yellowish white, 26% in yellow, 56% in yellowish yellow and 16% in orange. No more color ofgrain top was examined. Although five types of anthocyanin coloration of glumes of cob wereclassified, only two distinct colors with distinct frequencies like absent anthocyanin (96%) and medium
anthocyanin (4%) were observed among the tested maize genotypes. According to the results,
comparisons of qualitative traits were made to know the extent of variation among maize varieties
under investigation to estimate the genetic diversity.

Fig. 1 Variation of different qualitative characters in maize genotypes
Note: 1-anthocyanin coloration of first leaf sheet, 2-apex shape of first leaf, 3-intensity of green infoliage, 4-undulation of leaf blade margin, 5-anthocyanin coloration at base of tassel glume. 6anthocyanin coloration of tassel glumes, 7-anthocyanin coloration of anthers, 8-tassel angle between
main axis and lateral branches, 9-curvature of lateral branches, 10-anthocyanin coloration of silks.
11-anthocyanin coloration of brace roots, 12-density of spikelets, 13-anthocyanin coloration of leaf
sheath, 14-anthocyanin coloration of internodes, 15-ear shape, 16-type of grain, 17-grain color, and18-anthocyanin coloration of glumes of cobⒸ ISERD

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Cluster analysis of maize genotypes
The cluster analysis, using WARD method based on the squared Euclidean distance criteria, wasconducted for measuring genetic diversity and relatedness among the studied genotypes by using
qualitative characters (Fig. 2). The studied maize hybrids were grouped into five clusters, showing theexistence of considerable genetic diversity among 50 maize genotypes. Some of the hybrids of thesame geographical region (source) were observed in different groups. Maximum number of genotypes
were grouped in Cluster III (16 genotypes), followed by cluster IV (14 genotypes) and Cluster I (12genotypes) whereas cluster V contained the least number of genotypes (only three genotypes) followed
by cluster II (five genotypes) (Table 1). It can be concluded that the genotypes present in the same
region were genetically distinct from each other. These results are in accordance with previous findingsof Babic et al. (2016) who concluded that the grouping of maize lines based on the scale-measured
characteristics was not in accordance with the information of their origin and even related line pairs
were not grouped in the same cluster.

Fig. 2 Dendrogram of maize genotypes based on morphological distances by a visual assessmentof the group of plants and single measurement of individual plants or part of plants
Table 1 Cluster based on qualitative characters
Cluster

No.

I

12

II

5

III

16

IV

14

V

3

Name of genotypes
03P13, AA-757, Armo 265, Armo 9034, Asia Seed A44, Asia Seed A55, CP 809, DK
9955, Hawaii Norma l8, KICF12003, Yezin Hybrid, YZCI 14-060
AA 747, AA737, Asia Seed A11, Asia Seed A88, Asia Seed A99
Armo 528, Armo 9698, GoldenTiger 029, GT 709, GT 722, Hanumar SP333,Hanumar
SP888, KMHE 3550, KMHE 422, LG 772, NK 625, Premier 515, Shwe Mye 10, TSF
1707, TSF 1898, YZI10-095
Armo 139, C7, CP 111, CP 808, NK 621, PAC 999, Premier 518, SD 5059, TSF 1633,
TSF 1818, TSF 555, Yezin Hybrid 10, YZCI 16-019, YZCI 16-049
YZCI16-053, YZI10-054, YZI16-045

Cophenetic correlation coefficient = 0.577Ⓒ ISERD

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CONCLUSION
The total 50 maize genotypes (including hybrids and inbreds) were examined their qualitative
characters. The results showed that genotypes varied different qualitative traits with different
frequencies. The fifty maize genotypes, with the help of cluster analysis, were successfully
characterized and accurately grouped into five clusters with distinct promising features. It should be
noted that some maize genotypes collected in an area are included in different groups because of thedifferent characteristics they pose. Thus, the application of morphological markers according to UPOV
descriptor could contribute to more efficient selection of parental pairs in the early generations oftesting.
APPENDIX
Some distinct qualitative traits of maize from this study were summarized below.

ACKNOWLEDGEMENTS
The authors are grateful to the responsible persons from DOA, DAR and YAU for their kindpermission and collaborating to conduct this study. We acknowledge all the staff of Maize and other
Cereal Crops Research Section, DAR for helping us.
REFERENCES
Babic, V., Nikolic, A., Andjelkovic, V., Kovacevic, D., Filipovic, M., Vasic, V. and Mladenovic-Drinic, S. 2016.
AUPOV morphological versus molecular markers for maize inbred lines variability determination. Chilean
Journal of Agricultural Research, 76 (4), 417-426.
DoP (Department of Planning). 2018. Myanmar agriculture sector in brief. Department of Planning, Ministry ofAgriculture, Livestock and Irrigation, Myanmar.
Madhukeshwara, B. and Sajjan, A. 2015. Morphometric characterization of maize hybrids and their parents using
DUS guidelines. Advance Research Journal of Crop Improvement, 6 (2), 178-182.
Malhotra, S.K. 2017. Diversification in utilization of maize and production. Perspective of Maize Production andValue Chain. A Compendium, 5, 49-57.
Salami, H.A., Adjanohoun, A., Padonou, W., Yacoubou, A.M., Aly, D., Yallou, C., Sina, H. and Baba-Moussa, L.
2015. Morphological diversity of corn’s (Zea mays L.) local cultivar and improved varieties in central andnorth of Benin. American Journal of Plant Sciences, 6 (18), 2867-2877.
Singh, R.K. and Chaudhary, B.D. 1977. Biometrical methods in quantitative genetic analysis. Kalyani Publishers,
New Delhi, 266.
Smith, J.C. and Smith, O.S. 1989. The description and assessment of distances between inbred lines of maize, II
The utility of morphological biochemical, and genetic descriptors and a scheme for the testing ofdistinctiveness between inbred lines. Maydica, 34 (2), 151-161.
Ⓒ ISERD

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Research article
erd

Biofunctool® Approach Assessing Soil Quality underConservation Agriculture and Conventional Tillage for Rainfed Lowland Rice Systems in Cambodia
VENG SAR*
Provincial Department of Agriculture, Forestry and Fisheries, Pailin province, Cambodia
Conservation Agriculture Service Center, Department of Agricultural Land Resources Management,
General Directorate of Agriculture, Cambodia
Faculty of Agriculture, Kasetsart University, Bangkok, Thailand
Email: sarveng@hotmail.com

KANNIKA SAJJAPHAN
Department of Soil Science and Center for Advanced Studies in Agriculture and Food,
Kasetsart University, Bangkok, Thailand

WUTTHIDA RATTANAPICHAI
Department of Soil Science and Center for Advanced Studies in Agriculture and Food,
Kasetsart University, Bangkok, Thailand

ALEXIS THOUMAZEAUCIRAD, Systèmes de pérennes, University of Montpellier, Montpellier, France

VANG SENG
Department of Agricultural Land Resources Management, General Directorate of Agriculture,
Phnom Penh, Cambodia

FLORENT TIVETCIRAD, AIDA, University of Montpellier, Montpellier, France
Received 30 December 2020

Accepted 30 June 2021(*Corresponding Author)

Abstract Rice productivity is often limited by soil fertility depletion, water availability andaccess. Conservation Agriculture (CA) cropping systems have been designed and tested withthe main objectives of restoring soil fertility, increasing productivity and profitability. This
study assessed changes in soil health under rain-fed lowland rice under (i) conventional tillage(CT), (ii) CA (CA7: 7 years under CA) and (iii) green manure management for one year
(CGM1) and for two years CGM2). Biofunctool®, a multi-functional soil assessment approachbased on a set of seven soil indicators, was used to evaluate changes in three main soil functions(C transformation, nutrient cycling, and soil structure). In addition, soil chemical analyses wereconducted in the 0-5, 5-10, 10-20 and 20-40 cm soil layers to assess changes in nutrient
contents. Our results emphasized positive impacts of CA on C transformation, soil structure andnutrient contents. Soil organic carbon and total N were significantly higher (p < 0.05) under
CA7 in the 0-5 cm layer with up to +7.5 g C kg-1 and +0.74 g N kg-1, respectively. Higher
values of labile C and soil respiration (p < 0.05) were observed under CA in the 0-5 and 5-10cm layers. More stable soil aggregates and improved VESS values (p < 0.05) were also
observed under CA. CA and CGM had 2 to 3 times more available phosphorus than CT in the
0-5 cm layer, and higher values were observed under CA from a depth of 0-20 cm. Higher Ca,
Mg and K contents were recorded under CA and CGM in the 0 to 40-cm soil layer. A SOC
stabilization trend was observed in soils under CA (0-5 and 5-10 cm layers) while a SOCⒸ ISERD

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mineralization trend was observed under CT and CGM. These results emphasize the positive
impacts of CA on maintaining and/or enhancing soil health and in contributing to SOC
accumulation. A diachronic analysis is now needed to assess the long-term on-farm impacts of
CA on soil health and crop performances.
Keywords soil organic carbon dynamic, sustainable intensification, climate change adaptation

INTRODUCTION
Cambodia produced over eight million tons of paddy for a total cultivated area of ~ 3.3 million ha(MAFF, 2018). Rice exports jumped from 100,000 tons in 2010 to ~ 540,000 tons in 2016. Based on
topographic position, rice agroecosystems range from upland rice, rainfed lowland or upper sandy
terraces, flooded rice in the plains, receding rice, and deep-water rice. Soil fertility across the rice
agroecosystems ranges from medium to low (Biswas et al., 2017). Yields are often limited by low
levels of soil nutrients, fluctuating water levels in the paddy field and related impacts on the form andavailability of nutrients in the soil (Pheav et al., 2005). Despite the increase in rice yields over the lasttwo decades, rice yield and profitability of farming systems around the Tonle Sap lake are still low. In
addition, increasing use of chemical fertilizers and pesticides are recorded both in flooded and irrigatedconditions raising concerns about environmental and health issues, and food safety. The low level ofdiversification, the increasing use of pesticides (Flor et al., 2018) and the combination of practices that
deplete soil fertility (i.e., continuous ploughing, use of rotary tillers, low inputs of organic compounds,
burning or removal of crop residue to feed cattle) call for the design and assessment of alternative ricebased cropping systems. There is an urgent need to promote diversified rice cropping systems tomaintain and improve soil quality, increase farmers’ incomes while simultaneously contributing tosafer food production. Several studies have demonstrated the positive impacts of diversified
conservation agriculture (CA) cropping systems which promote the accumulation of soil organiccarbon (SOC) and improve soil fertility (Boddey et al., 2010). Conservation agriculture (CA) is basedon three technical principles with (i) minimum soil disturbance (i.e., no tillage), (ii) a permanent soil
cover, and (iii) diversified cropping systems (FAO, 2014). Diversified CA cropping systems with highbiomass-C inputs (Séguy et al., 2006) insure a continuous supply of fresh organic compounds, thereby
improving soil aggregation (Tivet et al., 2013), increasing soil biodiversity (Lienhard et al., 2013), andSOC content (de Moraes Sá et al., 2015) while enhancing production and ecosystem services(Pittelkow et al., 2015). Multifunctional soil assessments are needed to better understand therelationships between cropping system management and soil health. Thoumazeau et al. (2019 a, b)
proposed an integrative, multifunctional approach, named Biofunctool®, that makes it possible toassess three main soil functions (i) carbon transformation, (ii) nutrient cycling, and (iii) soil structure,with a core set of in-field and low-tech indicators. Three indicators were used to assess the changes of
the carbon transformation including the labile soil organic C fraction (permanganate oxidizable carbon:
POXC) (Weil et al., 2003), the basal soil respiration (SituResp®) (Thoumazeau et al., 2017), and thesoil biological activity using the bait lamina test (van Gestel et al., 2003). Then, three indicators wereused for the soil structure maintenance function by assessing soil aggregate water stability (AggSoil) at
0-5 and 5-10 cm depths (Herrick et al., 2001), water infiltration (Beerkan) (Thoumazeau et al., 2019b),
and visual evaluation of soil structure (VESS) at 0-30 cm depth (Guimarães et al., 2011). Finally, thenutrient cycling function was assessed by quantifying available N, P, Ca, Mg and K.
OBJECTIVE
We hypothesised that rice-based CA cropping systems have direct and positive effects on soil health,increase the main soil functions through C transformation, soil structure and nutrient contents. TheⒸ ISERD

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overall objective of the study was to conduct an integrative and quantified assessment of therelationships between contrasted rice cropping systems (i.e., conventional plough-based tillage (CT),CA and green manure management) and soil health on the flood plains of Lake Tonle Sap using the
Biofunctool® approach.
METHODOLOGY
Study site:
In 2011, an on-farm experimental design was implemented in the hydromorphic plains in Kropeur Kert
village, Banan district, Battambang province (latitude 13°00′32.37″ N, longitude 103°04′27.31″E, 18 m
elevation, no slope). The soil in the 0-20 cm layer comprised 511 g kg-1 clay, 339 g kg-1 silt, 150 g kg-1
sand and 5.19 pH (H2O). The soil is classified as a Vertisol by the FAO and as clayey soil according to
the USDA soil classification. Mean annual precipitation was 1,306 mm and the mean temperature was
27.5 °C.
Cropping systems:
Soil functions and soil physical-chemical characteristics were assessed for four main cropping systems(CA7, CGM1, CGM2 and CT). It should be noted that the on-farm assessment was based on an
unequal number of fields under the same management, with two fields under CA management for 7
years (CA7), two fields under green manure management for one year (CGM1), three fields undergreen manure management for two years (CGM2), and seven fields under conventional plough-based
management (CT). CA was based on no-tillage with a cover crop following wet season rice. Two main
species were used, Stylosanthes guianensis (cv. Ubon Nina) and Centrosema pascuorum (cv.
Cavalcade). Green manure management comprised ploughing the cover crop biomass into the soil a
few weeks before rice was sown. Conventional tillage (CT) included ploughing (6-disc plough) andharrowing, and the residues of the previous crop were incorporated into the soil by ploughing or rotary
tiller.
Soil quality assessment:
According to the integrative view of the soil quality, the indicators used to assess changes in soil
quality should be the result of soil biota-physical-chemical property interactions (Thoumazeau et al.,2019). The Biofunctool® approach was chosen to provide this integrative view and to describe theimpacts of contrasted practices on soil quality. This approach is based on three main soil functions (i)soil carbon transformation, (ii) nutrient cycling and (iii) soil structure.
Soil sampling and soil physical-chemical analysis:
Soil samples were collected on January 25th, 2018 in four soil layers (0-5, 5-10, 10-20, and 20-40 cm)
with three replicates per field. The Biofunctool® approach was applied to the 0-5 and 5-10 cm layers.
In addition, the same two soil layers (0-5 and 5-10 cm) were sampled per subplot to assess water-stable
aggregates. Total C and N concentrations were analysed using a dry combustion method with anelemental CHN analyser (Wright et al., 2008), available P (Bray II method) (Bray and Kurtz, 1945),
and available K, Ca, Mg (AAS method) (Pyle et al., 1995). Available N was quantified on samples
sieved at 2-mm using the Kjeldahl method (Craft et al., 1991).
Data analysis:
Statistical analysis was performed using R software (Dessau and Pipper, 2008). Each Biofunctool®
indicator was first studied separately using a linear-mixed effects model (lme4 package, (Bates et al.,2015). Treatment was defined as the fixed factor and replicates (plots and inner-replicates) as random
factors. After checking the normality of the model residuals and the homoscedasticity of residual
variance, ANOVAs were run using the car package (Fox and Weisberg, 2013). This was followed bypost-hoc mean comparisons, using the Shapiro-Wilk test with Bonferroni adjustment (Hothorn et al.,Ⓒ ISERD

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2008). After studying each indicator separately, the indicators were subjected to Principal Component
Analysis (PCA) (FactoMineR package) (Lê et al., 2008).
RESULTS
Soil Chemical Analysis
Under CA7, higher SOC and N contents (p < 0.05) were recorded in the 0-5 cm soil layer, representing
an increase of up to +7.5 g C kg-1 and +0.74 g N kg-1, respectively (Table 2). Higher N contents wereobserved in the 0-5 and 5-10 cm soil layers under CA7 than under CT and significant differences
between treatments were observed in all soil layers. In addition, higher stratification of SOC and N
contents were observed under CA7 and CGM2 compared with CGM1 and CT in the top soil layer (010 cm). SOC and N contents were more uniformly distributed under CT mainly due to the successive
effect of ploughing which mixed the topsoil layers.
Table 1 Total soil organic carbon and nitrogen contents
Fields
CA7
CGM2
CGM1
CT

0-5
17.75 b11.52 a10.22 a10.75 aTotal OC (g kg-1)
5-10
10-20
9.97 ns
7.19 ab9.90 ns
7.35 b
9.56 ns
5.57 a9.22 ns
5.74 a20-40
4.86 ns
5.05 ns
4.11 ns
4.51 ns

0-5
1.76 b
1.15 a1.13 a1.02 aTotal N (g kg-1)
5-10
10-20
1.09 b0.81 ab1.07 ab
0.86 b
1.05 ab0.78 ab0.91 a0.66 a20-40
0.63 ab
0.67 b
0.66 b0.53 abNote: CA7: 7 years CA (experiment, 2 fields, n = 6), CGM2: 2 years of cover crops as green manure (3 fields, n = 9),
CGM1: 1 year of cover crops as green manure (2 fields, n = 6), CT: Conventional tillage as the control (7 fields, n = 21).
Different letters indicate significant differences according to Tukey’s test.

Soil Quality: C Transformation, Soil Structure and Nutrient Cycling
Carbon transformation:
Higher values of POXC and SituResp were observed in the 0-5 cm soil layer (p < 0.05) under CA7
than under CGM2, CGM1 and CT, representing higher labile-C inputs and soil biological activity ofthe mesofauna and microflora. No significant difference was observed for the bait lamina, however
higher substrate degradation was observed under CA7.
Table 2 Soil carbon transformation
Fields
CA7
CGM2
CGM1
CT

POXC
(mg C. kg-1)
0-5
5-10
877.3 b404.0 ns443.7 a324.7 ns429.7 a320.2 ns428.0 a290.2 ns

SituResp®
(Absorbance difference)
0-5
5-10
0.71 b0.25 b0.33 ab0.12 ab0.29 a0.13 ab0.26 a0.11 aLamina baits(Substrate degraded)
0-5
5-10
0.53 ns
0.36 ns
0.38 ns0.29 ns0.46 ns
0.40 ns
0.40 ns
0.33 ns

Note: CA7: 7 years CA (experiment, 2 fields, n = 6), CGM2: 2 years of cover crops as green manure (3 fields, n = 9),
CGM1: 1 year of cover crops as green manure (2 fields, n = 6), CT: Conventional tillage as the control (7 fields, n = 21).
Different letters indicate significant differences according to Tukey’s test. POXC: permanganate oxidizable carbon (POXC);
Situ Resp: Soil respiration

Soil structure stability:
A significant difference was observed in the 5-10 cm soil layer with higher values under CA7
emphasizing the higher soil aggregate stability (Table 4). A low water infiltration rate was observed inⒸ ISERD

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all fields with no significant differences between fields. CA7 exhibited the better visual soil structurewith a lower score.
Table 3 Soil structure stability
Aggregate (score)
0-5
5-10
5.20 ns
5.36 ns
5.59 ns
4.89 ns
5.69 ns
4.81 ns5.41 ns4.61 ns

Fields
CA7
CGM2
CGM1
CT

Beerkan(ml.mn-1)
4.22 ns
3.78 ns
4.17 ns
3.34 ns

0-5
2.00 a2.78 c2.10 a2.29 b

VESS (score)
5-10
2.00 a2.78 a3.00 b
3.75 c10-20
2.50 a2.83 a3.25 b
3.50 bNote: CA7: 7 years CA (experiment, 2 fields, n = 6), CGM2: 2 years of cover crops as green manure (3 fields, n = 9),
CGM1: 1 year of cover crops as green manure (2 fields, n = 6), CT: Conventional tillage as the control (7 fields, n = 21).
Different letters indicate significant differences according to Tukey’s test.

Nutrient contents:
Under CA7, higher concentrations of available nitrogen were recorded in the 0-5 cm soil layer (p <0.05) and in the other soil layers except in the 20-40 cm layer (Table 5). Two to three times higher
available phosphorus was measured in the 0-5 cm layer under CA7 and CGM1 than under CT andhigher values were observed under CA7 in the three top soil layers. Higher Ca, Mg and K contents
were recorded under CA7 and CGM2 in all soil layers (Table 5).
Table 4 Available phosphorus and nitrogen
Fields
CA7
CGM2
CGM1
CT

0-5
12.34 c5.75 a9.51 b
4.51 aP (mg.kg-1)
5-10
10-20
6.52 b
2.50 b
4.03 a1.85 ab5.66 b
1.73 ab3.60 a1.45 a20-40
1.53 ns
1.51 ns
1.50 ns
1.25 ns

0-51.28 b
0.78 a0.64 a0.81 aN (g.kg-1)
5-10
10-20
0.74 ns
0.55 b0.70 ns
0.58 b
0.59 ns0.26 a0.59 ns
0.36 a20-40
0.28 a0.44 b
0.23 a0.32 aNote: CA7: 7 years CA (experiment, 2 fields, n = 6), CGM2: 2 years of cover crops as green manure (3 fields, n = 9), CGM1:
1 year of cover crops as green manure (2 fields, n = 6), CT: Conventional tillage as the control (7 fields, n = 21). Different
letters indicate significant differences according to Tukey’s test.

Table 5 Available potassium, calcium and magnesium
K (mg.kg-1)
Ca (mg.kg-1)
Mg (mg.kg-1)
0-5
5-10
10-20 20-40
0-5
5-10
10-20 20-40
0-5
5-10
10-20
20-40
27.27
11.70
10.77
926.6
840.7
812.8
747.5
106.2
98.7
CA7
9.97 b118.8 c103.3 b
c
ns
ns
c
b
b
c
b
ab
22.93
9.47
10.37
10.93
804.0
794.6
809.0
714.4
105.6
106.9
132.9
124.3
CGM2
bc
ns
b
ns
bc
b
b
bc
bc
b
c
b
17.33
8.60
6.46
8.94
648.1
632.3
524.9
548.9
78.5
75.4
77.1
CGM1
79.8 ab
bc
ns
a
ns
ab
a
a
a
a
a
a
15.63
10.24
8.60
10.41
687.1
692.5
609.0
630.8
79.9
84.6
84.3
93.1
CT
a
ns
ab
ns
a
a
a
ab
a
a
a
a
Note: CA7: 7 years CA (experiment, 2 fields, n = 6), CGM2: 2 years of cover crops as green manure (3 fields, n = 9), CGM1:
1 year of cover crops as green manure (2 fields, n = 6), CT: Conventional tillage as the control (7 fields, n = 21). Different
letters indicate significant differences according to Tukey’s test.
FieldsSOC Stabilization vs. SOC Mineralization
Due to the unbalanced design, the trends and hypothesis presented hereafter need to be confirmed in a
medium to long-term study. We nevertheless present our results here as the relationship between
POXC and SituResp is a promising decision-making tool to assess SOC dynamics. A SOC stabilizationⒸ ISERD

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trend was only observed under CA7 (0-5 and 5-10 cm soil layers) (Fig. 1 a and b). For CT and CGM, atrend of SOC mineralization was observed with negative values of the residues of the linear
relationship between POXC and SituResp. The SOC mineralization trend under CT could result fromsuccessive ploughing and lower inputs of biomass compared with CA. In addition, disruption of soil
aggregates due to ploughing exposed SOC to microbial communities and to mineralization processes(Chen et al., 2009).
(a)

(b)

Note: CA7: 7 years CA (experiment), CGM2: 2 years of cover crops as green manure incorporating into the soil by tillage,
CGM1: 1 year of cover crops as green manure incorporated into the soil by tillage, CT: Conventional tillage as the control.
Regression was made between values of POXC and SituResp® (see Appendix 6). Residual mean values below zero represent
a trend of mineralizable soil organic C, above zero values reflect a trend of short-term SOC stabilization. The vertical lines
represent the standard error per treatment.

Fig. 1 Mean values of regression residuals per treatmentin the 0-5 cm (a) and 5-10 cm (b) soil layers
Principal Component Analysis
A principal component analysis (PCA) was conducted. Three main clusters were identified (1) CT and
CGM1, (2) CGM2 and (3) CA7 (Fig. 2). The first axis separates CA7 from CGM1 and CT, while thesecond axis separates CA7 from CGM2. It should be noted that the main indicators of the
Biofunctool® such as POXC, SituResp and lamina baits are correlated with long-term CA
management (CA7). Other Biofunctool indicators of soil structure stability (i.e., VESS and soil
aggregates) contributed less to both axes. As observed previously, CT and CGM1 are characterised bylower TOC, TN, Ca, Mg contents and SituResp compared with CA7 and CGM2.

Note: CA7: 7 years CA (experiment), CGM2: 2 years of cover crops as green manure, CGM1: 1 year of cover crops as green
manure, CT: Conventional tillage

Fig. 2 Principal component analysis of the impacts of cropping systems on soil functions andsoil chemical properties in the 0-10 cm soil layerⒸ ISERD

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DISCUSSION
This study assessed changes in soil quality under conventional plough-based and conservation
agriculture management, using a multi-functional approach integrating a set of seven soil quality
indicators related to three main soil functions (C transformation, nutrient cycling, and soil structure)(Thoumazeau et al., 2019). Soil physical-chemical analyses were also performed. Including all theindicators in a multivariate analysis made it possible to understand how soil functions in a clayey
Vertisol in the flood plains of Tonle Sap lake are affected by contrasted agricultural practices under atropical climate.
Impacts of Contrasted Cropping Systems on Soil Chemical Properties
The main soil indicators were positively affected by long-term CA practices (CA7) with an increase insoil organic C and N contents, an increase in labile-C (POXC), an improvement of soil aggregation thatcan protect SOC from microbial oxidation compared with under CT. However, a SOC accumulation
trend under CA cropping systems was only detected in the surface soil layer confirming the results ofthe study conducted by Hok et al. (2015). Soil organic C and N contents were stratified with depth
under CA compared with CT as a consequence of crop residues being left on the surface of the soil,
thereby regulating fluctuations in soil temperature and moisture, and limiting the SOC decomposition
rate (Franzluebbers, 2008).
In accordance with the results of Hok et al. (2015) and Pheap, Lefevre et al. (2019), under CA,
SOC and TN contents were higher in the topsoil layer. Zhang and He (2004) reported a gradualincrease in SOC contents in the 0-15 cm layer in the first 30 years of rice cropping in South-East Asia.
The highest accumulation of SOC and TN were associated with the higher biomass input and lessdisturbance (CA7) compared with other practices (CGM1, CGM2 and CT).
Impact of Contrasted Cropping Systems on Three Soil Functions
Carbon transformation:
Part of the carbon transformation function, POXC, defined as a “soil fraction that is sensitive tocropping system management” (Pheap et al., 2019) was indeed affected by the different practices withhigher carbon contents measured in the 0-5 cm layer under CA7. Hok et al. (2018) reported an increase
in POXC in the 0-10 cm layer under CA systems. In a similar experiment in Brazil, Sá et al. (2015)
observed twice higher POXC under the no-till system down to a depth of 20 cm compared with underplough-based management. The increase in the top soil layer could be attributed to the difference indistribution of crop and cover crop residues between CA7 and other practices (Chatterjee and Lal,2009) and due to a smaller amount of crop residues returned to the soil under CT than under CA7.
Concerning soil respiration, our results are in accordance with those of Pheap, Lefèvre et al.
(2019), who reported that SituResp® values tended to be, or were significantly higher under CA, due
to increased soil microbial activity linked to improved microorganism habitats and an increase in soil
labile carbon. Used in combination, POXC and SituResp® play complementary roles by providing aframework for evaluating the relative dynamics of soil organic C stabilisation and mineralisation
processes in agroecosystems (Hurisso et al., 2016). As observed by Pheap, Lefevre et al. (2019), CT
tends to mineralise fresh organic carbon, while CA7 tends to stabilise C inputs.
Soil structure:
The absence of tillage combined with higher biomass inputs resulted in significant changes in soil
physical properties, specifically improved soil aggregation (Lal and research, 1997). Soil physicalproperties improved rapidly under CA compared with under tillage-based systems. More stable
aggregates in the top soil layers and better soil porosity are generally observed under CA (Busari et al.,Ⓒ ISERD

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2015). As observed in our study, VESS was less sensitive to changes in the soil structure comparedwith water aggregate stability. This result is also in agreement with the results of other studies
including those by Castioni et al. (2018) on a Brazilian Oxisol (55% clay) and by Thoumazeau et al.
(2019b) for different land uses in Thailand.
Nutrient cycling and contents:
Ca, Mg and K contents were higher in the 0-10 cm sol layer under CA7 and CGM2 than under otherpractices It is widely reported in the literature that CA with long-term use of cover crops recycle
significant amounts of nutrients through the diversity of roots systems and aboveground biomass
deposition. Continuous decomposition of plant residues enriches the main nutrient contents of the soil.
Busari et al. (2015) reported that exchangeable Ca, Mg, and K were significantly higher in the surface
soil under CA than under CT. Lower P, K, Ca and Mg contents were recorded in CT soils possibly due
to the inversion of the top soil due to ploughing, which shifts less fertile subsoil to the surface with, in
addition, possible leaching (Ali et al., 2006).
CONCLUSION
A multifunctional soil assessment was conducted using the Biofunctool® approach to compare theimpacts of contrasted rice cropping systems on soil health. Long-term CA cropping systems (CA7) andearly green manure management for two consecutive years (CGM2) had positive impacts on the mainsoil functions compared with short-term green manure management (CGM1) and CT. These results
showed that POXC, SituResp®, and VESS are sensitive in-field indicators of the impacts of annual
rice cropping systems on soil health. By contrast, soil aggregate stability, beerkan, and lamina baits areless sensitive to changes. The relationship between POXC and SituResp® revealed a SOC
accumulation trend under long-term CA management and is thus a promising in-field and low-cost
indicator to assess the dynamics of soil organic C (stabilisation vs. mineralisation). The comparison
conducted here, with an unbalanced number of fields among the different cropping systems, only
highlights potential trends in changes in soil fertility. Additional studies are thus needed along with adiachronic analysis to assess the long-term on-farm impacts of CA on soil health, crop performancesand on the provision of ecosystem services.
ACKNOWLEDGEMENTS
This research was made possible through the support from the Department of Agricultural Land
Resources Management (DALRM) from the General Directorate of Agriculture (GDA) that conducted
field activities through the involvement of the team of the Conservation Agriculture Service Center(CASC) of Battambang province. We express our sincere gratitude to the Southeast Asian Regional
Center for Graduate Study and Research in Agriculture (SEARCA), for funding the research activities.
The field activities were made possible through the project Women in Agriculture Network Cambodia(WAgN, Royal University of Agriculture, University of Battambang and Pennsylvania State
University) through support by the United States Agency for International Development Feed the
Future Innovation Labs for Collaborative Research on Sustainable Intensification (Cooperative
Agreement No. AID-OAA-L-14-00006, Kansas State University) and the Conservation Agriculture
Network in South-East Asia (CANSEA). We also thank the Land Development Department (LDD) and
Kasetsart University laboratory for handling and providing assistance for the soil analysis. We arethankful to Mr. Sambo Pheap, lecturer at the Faculty of Agronomy of the Royal University ofAgriculture and Mr. Rotha Chork, Mr. Hang Socheat, Mr. Pengly Koun undergraduate students fromRUA and the lab team for their assistance in collecting soil samples and conduct soil analysis.

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REFERENCES
Ali, A., Ayuba, S.A. and Ojeniyi, S.O. 2006. Effect of tillage and fertilizer on soil chemical properties, leaf
nutrient content and yield of soyabean in the guinea savanna zone of nigeria. Nigerian Journal of Soil
Science. 16, 126-130.
Bates, D., Mächler, M., Bolker, B. and Walker, S. 2015. Fitting linear mixed-effects models using lme4. arXiv
preprint arXiv, 1406, 5823.
Biswas, S., Hazra, G.C., Purakayastha, T.J., Saha, N., Mitran, T., Roy, S.S., Basak, N. and Mandal, B. 2017.
Establishment of critical limits of indicators and indices of soil quality in rice-rice cropping systems under
different soil orders. Geoderma, 292, 34-48.
Boddey, R.M., Jantalia, C.P., Conceicao, P.C., Zanatta, J.A., Bayer, C., Mielniczuk, J., Dieckow, J., Dos Santos,
H.P., Denardin, J.E. and Aita, C. 2010. Carbon accumulation at depth in ferralsols under zero‐till subtropicalagriculture. Global Change Biology, 16, 784-795.
Bray, R.H. and Kurtz, L.T. 1945. Determination of total, organic, and available forms of phosphorus in soils. Soilscience, 59, 39-46.
Busari, M.A., Kukal, S.S., Kaur, A., Bhatt, R. and Dulazi, A.A. 2015. Conservation tillage impacts on soil, cropand the environment. International Soil and Water Conservation Research, 3, 119-129.
Castioni, G.A., Cherubin, M.R., Menandro, L.M.S., Sanches, G.M., de Oliveira Bordonal, R., Barbosa, L.C.,
Franco, H.C.J. and Carvalho, J.L.N. 2018. Soil physical quality response to sugarcane straw removal inbrazil: A multi-approach assessment. Soil and Tillage Research, 184, 301-309.
Chatterjee, A. and Lal, R. 2009. On farm assessment of tillage impact on soil carbon and associated soil quality
parameters. Soil and Tillage Research, 104, 270-277.
Chen, H., Hou, R., Gong, Y., Li, H., Fan, M. and Kuzyakov, Y. 2009. Effects of 11 years of conservation tillage
on soil organic matter fractions in wheat monoculture in loess plateau of china. Soil and Tillage Research,
106, 85-94.
Craft, C.B., Seneca, E.D. and Broome, S.W. 1991. Loss on ignition and kjeldahl digestion for estimating organiccarbon and total nitrogen in estuarine marsh soils: Calibration with dry combustion. Estuaries, 14, 175-179.
de Moraes Sá, J.C., Séguy, L., Tivet, F., Lal, R., Bouzinac, S., Borszowskei, P.R., Briedis, C., dos Santos, J.B.,
da Cruz Hartman, D. and Bertoloni, C.G. 2015. Carbon depletion by plowing and its restoration by no‐till
cropping systems in oxisols of subtropical and tropical agro‐ecoregions in Brazil. Land Degradation &Development, 26, 531-543.
Dessau, R.B. and Pipper, C.B. 2008. ''R"--project for statistical computing. Ugeskrift for laeger, 170, 328-330.
FAO. 2014. Conservation agriculture. Retrieved from http://www.fao.org/conservation-agriculture/en/
Flor, R.J., Chhay, K., Sorn, V., Maat, H. and Hadi, B.A.R. 2018. The technological trajectory of integrated pest
management for rice in Cambodia. Sustainability, 10, 1732.
Fox, J., Friendly, M. and Weisberg, S. 2013. Hypothesis tests for multivariate linear models using the car
package. R J, 5, 39.
Franzluebbers, A.J. 2008. Linking soil and water quality in conservation agricultural systems. J. Integr. Biosci, 6(1), 15-29.
Guimarães, R.M.L., Ball, B.C. and Tormena, C.A. 2011. Improvements in the visual evaluation of soil structure.
Soil Use and Management, 27 (3), 395-403.
Herrick, J.E., Whitford, W.G., de Soyza, A.G., Van Zee, J.W., Havstad, K.M., Seybold, C.A. and Walton, M.
2001. Field soil aggregate stability kit for soil quality and rangeland health evaluations. CATENA 44, 27-35.
Retrieved from https://doi.org/10.1016/S0341-8162(00)00173-9
Hok, L., de Moraes Sá, J.C., Boulakia, S., Reyes, M., Leng, V., Kong, R., Tivet, F.E., Briedis, C., Hartman, D.
and Ferreira, L.A. 2015. Short-term conservation agriculture and biomass-C input impacts on soil C
dynamics in a savanna ecosystem in Cambodia. Agriculture, Ecosystems & Environment, 214, 54-67.
Hok, L., de Moraes Sá, J.C., Reyes, M., Boulakia, S., Tivet, F., Leng, V., Kong, R., Briedis, C., da Cruz Hartman,
D. and Ferreira, L.A. 2018. Enzymes and C pools as indicators of C build up in short-term conservation
agriculture in a savanna ecosystem in Cambodia. Soil and Tillage Research, 177, 125-133.
Hothorn, T., Bretz, F. and Westfall, P. 2008. Simultaneous inference in general parametric models. Biometrical
Journal, Journal of Mathematical Methods in Biosciences, 50, 346-363.
Hurisso, T.T., Culman, S.W., Horwath, W.R., Wade, J., Cass, D., Beniston, J.W., Bowles, T.M., Grandy, A.S.,
Franzluebbers, A.J. and Schipanski, M.E. 2016. Comparison of permanganate‐oxidizable carbon andmineralizable carbon for assessment of organic matter stabilization and mineralization. Soil Science SocietyⒸ ISERD

170

IJERD – International Journal of Environmental and Rural Development (2021) 12-1

of America Journal, 80, 1352-1364.
Lal, R. 1997. Long-term tillage and maize monoculture effects on a tropical alfisol in Western Nigeria. I. Crop
yield and soil physical properties. Soil and Tillage Research, 42, 145-160.
Lê, S., Josse, J. and Husson, F. 2008. Factominer: An r package for multivariate analysis. Journal of Statistical
Software, 25, 1-18.
Lienhard, P., Tivet, F., Chabanne, A., Dequiedt, S., Lelièvre, M., Sayphoummie, S., Leudphanane, B., PrévostBouré, N.C., Séguy, L. and Maron, P.A. 2013. No-till and cover crops shift soil microbial abundance anddiversity in Laos tropical grasslands. Agronomy for Sustainable Development, 33, 375-384.
Ministry of Agriculture, Forestry and Fisheries (MAFF). 2018. Annual report 2018-2019. Phnom Penh,
Cambodia.
Pheap, S., Lefevre, C., Thoumazeau, A., Leng, V., Boulakia, S., Koy, R., Hok, L., Lienhard, P., Brauman, A. andTivet, F. 2019. Multi-functional assessment of soil health under conservation agriculture in Cambodia. Soiland Tillage Research, 194, 104349.
Pheav, S., Bell, R.W., Kirk, G.J.D. and White, P.F. 2005. Phosphorus cycling in rainfed lowland rice ecosystems
on sandy soils. Plant and Soil, 269, 89-98.
Pittelkow, C.M., Liang, X., Linquist, B.A., Van Groenigen, K.J., Lee, J., Lundy, M.E., Van Gestel, N., Six, J.,
Venterea, R.T. and Van Kessel, C. 2015. Productivity limits and potentials of the principles of conservation
agriculture. Nature, 517 (7534), 365-368.
Pyle, S.M., Nocerino, J.M., Deming, S.N., Palasota, J.A., Palasota, J.M., Miller, E.L., Hillman, D.C., Kuharic,
C.A., Cole, W.H. and Fitzpatrick, P.M. 1995. Comparison of aas, icp-aes, psa, and xrf in determining leadand cadmium in soil. Environmental Science & Technology, 30, 204-213.
Séguy, L., Bouzinac, S. and Husson, O. 2006. Direct-seeded tropical soil systems with permanent soil cover:Learning from Brazilian experience.
Thoumazeau, A., Bessou, C., Renevier, M.S., Panklang, P., Puttaso, P., Peerawat, M., Heepngoen, P., Polwong,P., Koonklang, N. and Sdoodee, S. 2019. Biofunctool®: A new framework to assess the impact of land
management on soil quality. Part b: Investigating the impact of land management of rubber plantations on
soil quality with the Biofunctool® index. Ecological Indicators, 97, 429-437.
Thoumazeau, A., Bessou, C., Renevier, M.S., Trap, J., Marichal, R., Mareschal, L., Decaëns, T., Bottinelli, N.,
Jaillard, B. and Chevallier, T. 2019. Biofunctool®: A new framework to assess the impact of land
management on soil quality. Part a: Concept and validation of the set of indicators. Ecological Indicators, 97,
100-110.
Thoumazeau, A., Gay, F., Alonso, P., Suvannang, N., Phongjinda, A., Panklang, P., Chevallier, T., Bessou, C.
and Brauman, A. 2017. SituResp®: A time- and cost-effective method to assess basal soil respiration in thefield. Appl. Soil Ecol. 121, 223-230. Retrieved from https://doi.org/10.1016/j.apsoil.2017.10.006
Tivet, F., de Moraes Sa, J.C., Lal, R., Briedis, C., Borszowskei, P.R., dos Santos, J.B., Farias, A., Eurich, G., da
Cruz Hartman, D. and Junior, M.N. 2013. Aggregate C depletion by plowing and its restoration by diverse
biomass-C inputs under no-till in sub-tropical and tropical regions of Brazil. Soil and Tillage Research, 126:203-218.
van Gestel., C.A.M., Kruidenier, M. and Berg, M.P. 2003. Suitability of wheat straw decomposition, cotton strip
degradation and bait-lamina feeding tests to determine soil invertebrate activity. Biol. Fertil. Soils, 37, 115123. Retrieved from https://doi.org/10.1007/s00374-002-0575-0
Weil, R.R., Islam, K.R., Stine, M.A., Gruver, J.B. and Samson-Liebig, S.E. 2003. Estimating active carbon forsoil quality assessment: A simplified method for laboratory and field use. Am. J. Altern. Agric, 18, 3-17,
Retrieved from https://doi.org/10.1079/AJAA2003003
Wright, A.L., Wang, Y. and Reddy, K.R. 2008. Loss‐on‐ignition method to assess soil organic carbon in
calcareous everglades wetlands. Communications in Soil Science and Plant Analysis, 39, 3074-3083.
Zhang, H., Zhang, G.L., Zhao, Y.G., Zhao, W.J. and Qi, Z.P. 2007. Chemical degradation of a ferralsol (oxisol)under intensive rubber (hevea brasiliensis) farming in tropical China. Soil and Tillage Research, 93, 109-116.

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Research article
erd

Assessment of Biogas Production Potential from Commercial
Pig Farms in Cambodia I7de
LYHOUR HIN*nBiogas Technology and Information Centerand Faculty of Agricultural Engineering,t

Royal University of Agriculture, Phnom Penh, Cambodia
Email: hlyhour@rua.edu.kh
4.
7
Royal University of Agriculture, Phnom Penh, Cambodia
2

BUNTHAN NGO
LYTOUR LORw
Biogas Technology and Information Centerand Faculty of Agricultural Engineering,
Royal University of Agriculture, Phnomo Penh, Cambodiard
SUYHEANG SORNs
Biogas Technology and Information Centerand Faculty of Animal Science,

Royal University of Agriculture, Phnom Penh, Cambodia

DYNA THENG
Biogas Technology and Information Center and Faculty of Agricultural Engineering,
Royal University of Agriculture, Phnom Penh, Cambodia

CHANPHAKDEY DOK
Biogas Technology and Information Center, Royal University of Agriculture, Phnom Penh,
Cambodia

SOKHOM MECH
Biogas Technology and Information Center, Royal University of Agriculture, Phnom Penh,
Cambodia

CHAN MAKARA MEAN
Biogas Technology and Information Center, Royal University of Agriculture, Phnom Penh,
Cambodia

SOVANNDY YUT
Biogas Technology and Information Center, Royal University of Agriculture, Phnom Penh,
Cambodia

MAKARA LAY
Biogas Technology and Information Center, Royal University of Agriculture, Phnom Penh,
Cambodia; Laboratory of Organic Electronic, Department of Science and Technology,
Linköping University, Norrköping, Sweden

BART FREDERIKS
Independent Bioenergy Consultant and Biogas Technology and Information Center,
Royal University of Agriculture, Phnom Penh 12401, Cambodia
Received 28 December 2020

Accepted 30 June 2021

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IJERD – International Journal of Environmental and Rural Development (2021) 12-1

Abstract Commercial pig farms in Cambodia are increasing, representing 30% of the overall
pig production in 2018. To run the farms, huge quantity of water is used on daily basis, and its
large proportion ends up being wastewater that can cause air and water pollution, fly-related
illnesses, and methane emissions. In response, anaerobic digestion (AD) is applied to convert
waste into energy. Covered lagoons are an anaerobic biodigester that has long been used forcommercial biogas plants in Cambodia due to acceptable investment cost and favorable climaticconditions for biogas production. However, lack of local technical data and technicalassessment is a barrier to wider implementation of biogas systems in the country. Therefore, thestudy was conducted to explore the characteristics of commercial pig farms and wastewater use;
to analyze the quality of biogas compositions from different covered lagoons; and to estimate
biogas production, electricity generation, and CO2 reduction equivalent from the collected data.
The study was started from January to October 2020, selecting 9 farms with evaporative
cooling systems for in-depth interviews, along with direct observations, biogas analysis, andpower analysis. The findings indicate that all the studied farms were fattening farms operated
under purchase contract with private companies. Pig production varied from 2,800 to 7,200
head per cycle, with two cycles per year. Moreover, daily wastewater was 0.033 m3 d-1 head-1,
with dry matter (DM) accounting for 0.9%. Annual biogas production and electricity generation
were 32.7 m3 y-1 head-1 and 42.5 kWh y-1 head-1, respectively. Biogas quality was 59.5% CH4,
31.5% CO2, 1.3% O2, and 2,256 ppm H2S. With biogas systems, individual farms could reduce
CO2 emission by 0.676 tCO2eq y-1 head-1, which is economically and environmentally
beneficial. However, a business model should be taken into account for successful
implementation.
Keywords CO2 reduction, covered lagoon, methane emission, pig manure

INTRODUCTION
Pork is considered an important protein source for daily Cambodian diets. In this country, an average
person eats 17.6 kg of meat per year, of which 9.29 kg is pork. Likewise, annual domestic demands formeat in 2018 was estimated at 285 thousand tons, and pork alone accounted for 52.8%. This highdemand led to a 126% increase in local pig production between 2014 and 2017, from 2.44 million toapproximately 3.18 million heads. A tendency toward large-scale production has emerged, as
commercial pig farms rose by 30% in 2018 (MAFF, 2019). Commercial pig farms in Cambodia arefarms with more than 100 fattening pigs, or more than 50 sows. There are around 500 farms inoperation across 10 provinces, with 30-40% concentrated in Kampong Speu Province (NBP, 2019).
Commercial pig farms in Cambodia are operated similarly with Thailand because they use evaporative
cooling systems to maintain optimal temperatures inside the barns from 25 to 27 0C (Thanapongtharm,2018). Such operation is vital for pig growth and disease prevention. For secure market and prices,
farms turn to contract farming with private companies, such as C.P. Cambodia Co., Ltd., that provide
both technical and financial support. Farm operation differs by pig type. In Cambodia, pig farms areclassified into breeding farm and fattening farm. Among them, fattening farms are more popular.
Fattening farms have three categories: small-sized (100 - 1,000 heads), medium-sized (1,001 - 5,000
heads), and large-sized (>5,000 heads) (MAFF, 2018).
Pig farms normally use large amounts of water for pig drinking, pig bathing, and barn cleaning.
Daily water use rates vary by production type and pig weight. It is reported that average daily required
water rates for breeding farms, fattening farming, and nursery farms are 92, 48, and 32 L d -1 head-1,
respectively. However, a large proportion (50-70%) ends up being wastewater (Nokyoo, 2016).
Improper treatment of wastewater is associated with odor, flies, water pollution, and greenhouse gas
emission. Some key elements used as pollutant indicators include chemical oxygen demand (COD),
biological oxygen demand (BOD), total Kjedahl nitrogen (TKN), and total suspended solid (TSS). A
study by Tokhun (2010) indicates that untreated wastewater from large-scale pig farms in ThailandⒸ ISERD

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contains 4,889 mg L-1 COD, 3,555 mg L-1 BOD, 481 mg L-1 TKN, and 2,317 mg L-1 TSS. These
parameters are too high to be directly discharged into natural lakes. In the Cambodian wastewater
standards for public water areas and sewers, COD, BOD, TSS, and nitrate (NO3) must be no more than100, 80, 80, and 20 mg L-1, respectively (Council of Ministers, 1999). Thus, sound waste management
is strongly required, as it is important for the sustainable operation of pig farms. One of the mosteffective wastewater treatment methods is the adoption of anaerobic digestion (AD). AD is known as aprocess under which organic matters, mainly in the form of fine particles, are fermented with theabsence of air. The process consists of four stages: hydrolysis, acidogenesis, acetogenesis , andmethanogenesis, with biogas produced as a final product and convertible into energy (Deublein andSteinhauser, 2011). Biogas is a gas mixture that contains 50-70% methane (CH4), 30-40% carbon
dioxide (CO2), and other trace elements (EESI, 2017). CH4 contained in biogas is the only source ofenergy such as heat and electricity. Nevertheless, it is harmful to the environment, if released into theatmosphere, because it is 28 times more powerful than CO2 in terms of global temperature potential(GTP) for 100 years (IPCC, 2014). In contrast, converting biogas into electricity or upgrading it into
bio-methane can reduce its harmfulness.
To promote manure management, the National Biodigester Program (NBP) was established in
Cambodia in 2006 to turn cow manure into biogas for cooking and lighting. The program has built
nearly 30 thousand biodigesters for smallholder farmers nationwide. In recent years, attention has been
turned to large-scale biogas systems, which are covered lagoons. Covered lagoons are an AD
technology, commonly used in commercial pig farms in Cambodia. It is reported that there are 44
covered lagoons in operation across the country (NBP, 2019). This number is still considered low incomparison to the potential pig farms and other biogas resources. This is due to lack of necessary
documents and knowledge, or biogas skills. Therefore, in-depth studies on commercial pig farms are
deemed vital for solving problems with wastewater and for economic profitability through energy
generation.
OBJECTIVE
The objectives of this study were (1) to explore the characteristics of commercial pig farms andwastewater use; (2) to analyze the quality of biogas from covered lagoons; and (3) to estimate methane
production, electricity production, and CO2 reduction equivalent.
METHODOLOGY
The selection criteria for pig farms were farms that had thousands of pigs, used evaporative coolingsystems, and were interested in setting up biogas systems. However, the scope of this research was
based on a one-year study period only, from January to October 2020. To represent diverse pig farmcharacteristics, 9 commercial farms were selected from 6 different provinces: 5 from Kampong Speu
and 1 each from Kampong Chhnang, Kampong Cham, Kampong Thom, Siem Reap, and Kratie. Morefarms were selected from Kampong Speu, as this province had the greatest farm number in the country.
The study procedure was arranged by face-to-face interviews with farm owners on the site, direct
observation, biogas analysis, and power analysis.
Materials
Biogas quality is an important indicator to determine generator efficiency. A 5000 gas analyser,
supplied by Geotech, UK, was used to analyze biogas quality based on the percentage of CH4, CO2, O2,
and H2S in ppm with the maximum of 5,000 ppm.
Peak load is considered a vital indicator to determine the generator size for the farm. A HiokiⒸ ISERD

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PW3365-20-01/5000 power logger was used to measure daily peak loads. It is capable of measuring
three-phase voltage with high electrical current of up to 5,000 A.
Sampling Methods and Data Analysis
In the study, a set of questionnaire was used to collect information on pig type, business type, pig
number, barn number, cycle number, and duration within a cycle, as well as plans to increase farm size
within the next few years. Moreover, information on sources of farm power supplies, existing biogas
systems, biogas system type, generator type, and operation was also gathered. Annual electricity
demand was estimated based on collection of electricity bills from the farms. All the information wasverified by direct observation.
In the sampling process, biogas was directly measured before and after desulfurization—a process
for removing hydrogen sulfide from the biogas systems. As desulfurizing systems did not function,
biogas quality was only measured in the outlet pipe from covered lagoons. The measurement lasted 2min, and average values were plotted from at least 3 different measurements. Before measuring new
samples, the biogas analyzer was flushed. In the sampling process, peak loads were measured byattaching the power logger to the electricity panels of the farms for 1 hour. Night load was not recorded
due to low electricity demand. Nevertheless, peak load recording was possible for a few farms only due
to blackouts.
Total daily wastewater production from pig farms was estimated by combining daily water use forindividual pigs, daily manure, and urine rate. In Thailand, daily wastewater left from cleaning, bathing,and drinking is estimated at 24 L d-1 head-1 for fattening farms (Kulpredarat, 2016). Because Thai pig
farms share similarity with Cambodia, wastewater rate used in this study was assumed to be 30 L dhead-1, about 20% more for conservative estimation. In this study, manure production used forindividual pigs was 1.5 kg d-1 head-1 (Mek et al., 2018), and urine rate was assumed to be 2.5 L d-1
head-1. These estimated values were roughly similar to the data in Thailand, where daily manure andurine production for typical fattening farms are 1.2-1.4 kg d-1 head-1 and 3.0-3.2 L d-1 head-1,
respectively (Nokyoo, 2016). Besides that, the evaporation rate was also included and assumed to be
0.5 m3 d-1 barn-1.

Fig. 1 Diagram of full biogas systems in a typical pig farm, but in Cambodia,
desulfurizing systems, gas meter, and flare are missing
Daily biogas production was estimated by multiplying manure dry matter (DM) with biogas yield.
It is reported that average pig manure contains 20% DM (DEFRA, 2011), and that biogas yield is 0.33
Nm3 kg-1 DM based on the experimental result by the Biogas Technology and Information Center(BTIC). Electricity production potential was calculated by multiplying biogas production with theconversion factor that varies from 1 to 1.7 depending on scale and average loading rate of the generator.
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From biogas systems, CO2 emission can be reduced in two ways: reduction by not emitting CH4
directly into the atmosphere and reduction by using it to run biogas generators instead of gridelectricity. In this regard, CO2 reduction by avoidance of CH4 release is equal to CH4-to-CO2 equivalentx CH4 density x Annual CH4 production. Meanwhile, CO2 reduction by avoidance of grid electricity isequal to electricity-to-CO2 equivalent x annual electricity demand met by methane production.
Descriptive statistics and frequency distribution were applied to analyze the data using MS Excel.
To determine relations between CH4 and O2, or CH4 and H2S, a simple linear regression was used at α
= 0.05 probability level using R program 4.0.4, available for free online.
RESULTS AND DISCUSSION
Table 1 Characteristics of commercial pig farms in the studied areas (N = 9)
Variable description
Fattening farm
Yes
No
Contract farming
Yes
No
Production cycle (cycle/year)
Barn Number<5≥5
Production size (head/cycle)< 10001000 – 3000
3001 – 4999
≥ 5000Plan to increase production
Yes
No
Sources of Energy supply
Electricity grid
Other Energy sources
Existing covered lagoons (m3)YesNo
Biogas systems in operation
Yes
No
Generator range (kVA)< 100
100 – 200
> 200
Generator typeNew
Modified
Pig number per barn (head/barn)
working days (d/y)
Daytime peak load (kW)Max peak load (kW/barn)

Frequency(N = 9)Percentage
(%)

9
0

100%
-

9
0

100%
-

3
6

33%
67%

0
2
5
2

22%
56%
22%

4
5

44%
56%

5
4

56%
44%

4
5

44%
56%

3
6

33%
67%

0
2
1

67%
33%

0
3

100%

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Mean ± SE

Min

Max

2
5.6 ± 0.6

4

94,194 ± 470.4

2,800

7,200

4,085 ± 564

2,965

4,761

186 ± 49.1

100

375

749 ± 11.8
330
59.8 ± 9.4
9.1 ± 0.4

740

800

32
7.8

97
10

IJERD – International Journal of Environmental and Rural Development (2021) 12-1

Pig Farm Characteristics and Wastewater Use
The studied farms were all fattening farms operated under official contract with private companies(Table 1). The farms had two cycles per year, 5.5 months each. In early production, the farms were
given piglets and had to sell back pigs to the contracting companies at the end of the cycle, also known
as all-in/all-out batch operations. The number of barns varied from 4 to 9, with an average of 5.6 barns
per farm. Average production size was 4,194 heads per cycle, but varied from 2,800 to 7,200 heads per
cycle. No farms had pigs less than 1,000 heads per cycle. Of all the farms, 7 were medium-sized farms(1,000 to 5,000 heads); 2 were large-sized farms (>5,000 heads).
In Table 1, when asked about plans to increase production within the next few years, about 44%of the farm owners said yes. The main electricity supplies came from the local electricity grid. Of all
the interviewees, 5 depended solely on EDC, while 4 also used biogas as an alternative. The biogas
systems in the farms were simple covered lagoons that had different sizes, but the average value was
4,085 m3. Only 3 farms had the systems in operation, while another one just stopped due to problems
with the biogas generator. In addition, the farms that had biogas systems preferred to second-hand,modified diesel generators for economic reasons.
Farm electricity was mainly consumed by evaporative cooling systems and to some extent bypumping and lighting (Table 1). Average maximum daytime peak load the farms was 59.8 kW, butvaried from 32 to 97 kW according to the production size. Maximum peak load per barn was alsomeasured with full operation of evaporative cooling systems. It was 9.1 kW and varied from 7.8 to 10
kW. However, peak loads were low at night, in cold seasons, and in early production.
Table 2 Estimated total wastewater production in the fattening farms (N = 9)
SourceWater
Dung (fresh)
Urine
Evaporation
Total waste water
DM content
Total DM
BiogasUnit
m3 d-1
t d-1
m3 d-1m3 d-1m3 d-13 -1
m d head-1%
t d-1
Nm3 d-1
Nm3 y-1
Nm3 y-1 head-1

Average ± SE
125.8 ± 14.1
6.3 ± 0.7
10.5 ± 1.2
2.8 ± 0.3
139.8 ± 15.7
0.033 ± 0.004
0.9%
1.3
415
137,033
32.7

Wastewater rate produced from pig bathing and barn cleaning was 125.8 m3 day-1 in each farm
(Table 2). Fresh manure and urine production was 6.3 t d-1 and 10.5 m3 d-1, respectively. Evaporation
rate was estimated at 2.8 m3 d-1, so total wastewater produced by individual farms was 139.8 m3 d-1, or
0.033 m3 d-1 head-1. DM accounted for 0.9%, so average daily biogas production in each farm wasestimated to be 415 Nm3 d-1, or equivalent to 137,033 Nm3 y-1. Thus, biogas production rate per pig
was 32.7 m3 y-1 head-1. However, this calculated value was just an average because swine growth stage
affects biogas production yield (Amara et al., 2015).
Biogas Quality
In Table 3, CH4, CO2, O2, and H2S were 59.5%, 31.5%, 1.3%, and 2256 ppm, respectively. O2
measured in this study might come from air that had entered the biogas system. Its presence suggeststhat there is also N2 in the biogas, in a ratio of approximately 4:1. So 1.3% O2 will mean 5.2% N2,
possibly more. Biogas also contains water vapor, which may then lower CH4 content. So, less air needsⒸ ISERD

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to be mixed for the generator. High H2S content may reduce the generator lifespan through corrosion.
Thus, desulfurizing systems are needed to lower its content before running any biogas generator.
Nevertheless, the measured biogas quality was similar to a study by Sweeten, Fulhage, and Humenik(1981), who reported 50-60% methane, 40-50% CO2, and H2S <10,000 ppm. Dumont (2015) reported
higher methane content (60-70%), lower CO2 content (30-40%), and H2S (10-20,000 ppm) for biogas
produced from organic waste.
Table 3 Biogas quality measured on the pig farms that had biogas systems (N = 9)
Biogas quality
CH4
CO2
O2
H2S

Unit
%
%
%
ppm

Mean ± SE
59.5 ± 3.0
31.5 ± 2.8
1.3 ± 0.7
2,256 ± 504

Min
52.0
26.9
0.1
818

Max
64.5
40.0
3.0
3,295

Fig. 2 Relationships between CH4 and H2S (left); CH4 and O2 (right); sample size ( N = 9)
In Fig. 2, CH4 had a negative relationship with both H2S and O2, meaning that increased
concentrations of H2S or O2 may lower CH4 content. H2S reduction is made possible by desulfurization
through the use of desulfurizing systems, and O2 reduction by preventing air from entering the systemthrough proper sealing. However, Siripat (2019) and Deublein and Steinhauser (2011) reported that
increased H2S content reduces biogas quantity, not CH4 directly.
Methane Production, Electricity Generation and CO2 Reduction
In table 4, annual electricity production in each farm was estimated to be 178,142 kWh y-1, but varied
by farm size. Pig weight varies by month, or by age, so does daily manure production. Daily biogas
production also varies accordingly, but tends to increase constantly from early production to the end of
the cycle. At the same time, electricity demands at the farms also showed variations, as the need forbarn cooling varied with pig age and daily outside temperature. Thus, not all biogas could be used to
produce electricity. In the study, the estimated average annual electricity demand for each farm was
166,667 kWh y-1, and electricity produced from biogas could meet only an average of 75%, or 127,700
kWh y-1. This estimation may vary according to farm production size.
Biogas systems also contribute to reduction in greenhouse gas emissions. In this study, the annual
CO2 reduction equivalent was estimated to be 2,832 tCO2eq y-1 in each farm. This amount was huge,and further reduction could be achieved when more and more farms turn to using biogas systems. In
short, about 0.676 tCO2eq y-1 head-1 could be reduced per pig. This CO2 reduction equivalent was
higher, when compared to Peerapong and Limmeechokchai (2017), whose estimatin was 0.469 tCO2eqⒸ ISERD

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y-1 head-1 for a typical pig farm in Thailand. Thus, a business model should be considered for wider
adoption of biogas systems for farm benefits and for the environment.
Table 4 Estimation of electricity demand potential and CO2 reduction (N = 9)
Description
Potential electricity production
Estimated electricity for farm coverage
Estimated farm demand
Percentage of farm coverage from biogas
CO2 reduction
From CH4 reduction equivalent
From grid electricity equivalent
Total
CO2 reduction per head/year

Unit
kWh y-1
kWh y-1
kWh y-1%
Unit
tCO2eq y-1
tCO2eq y-1
tCO2eq y-1 head-1

Value
178,142
124,700
166,667
75
2,751
82
2,832
0.676

CONCLUSION
Commercial pig farms in Cambodia were studied in terms of pig production, wastewater use for biogas
systems, electricity demands and contribution to CO2 reduction. It can be seen that these farms are
typical, and some already used covered lagoons to produce biogas. The farm size is huge, and there is ahigh potential for further development of biogas systems that may benefits socially, economically, andenvironmentally. However, further studies shall be made on profitability of actual biogas investment asa model for farm investment and policy making.
ACKNOWLEDGEMENTS
The study was made possible thanks to the project “Reduction of Greenhouse Gas Emission throughPromotion of Commercial Biogas Plant in Cambodia” implemented by United Nations Industrial
Development Organization (UNIDO) that not only provided funding, but also continuously assisted instrengthening the research team. Many thanks may also go to the farm owners that allowed for theinterviews and site visits and to local authorities that helped facilitate the connections among us.
REFERENCES
Agriculture and Horticulture Development Board. 2019. How much water do your pigs need? AHDB Quarter
report.
Amaral, A.C., Kunz, A., Steinmetz, R.L.R., Scussiato, L.A. and Tapparo, D. 2015. Anaerobic digestion of swine
manure: Stratified production units and its biogas potential. IV Symposium on Agricultural andAgroindustrial Waste Management.
Council of Ministers. 1999. Sub-degree on water pollution control. Phnom Penh, Cambodia, Retrieved fromhttp://www.cambodiainvestment.gov.kh/wp-content/uploads/2011/09/Sub-Degree-27-on-Water-PollutionControl_990406.pdf
Department for Environment Food and Rural Affairs. 2011. Fertilizer manual. Agricultural Document Library,
University of Hertfordshire, Hertfordshire, UK, Retrieved from http://adlib.everysite.co.uk/adlib/defra/
content.aspx?id=2RRVTHNXTS.88UF9X3S7QMDH:
Deublein, D. and Steinhauser, A. 2011. Biogas from waste and renewable resources (2nd ed.). Weinheim,
WILEY_VCH Verlag GmbH & Co. KGaA.
Dumont, A. 2015. H2S removal from biogas using bioreactors: A review. International Journal of Energy andEnvironnement, International Energy & Environment Fondation, 6 (5), 479-498. hal-01945143, Retrieved
from https://hal.archives-ouvertes.fr/hal-01945143/documentⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2021) 12-1

EESI. 2017. Fact sheet - biogas: Converting waste into energy. Environmental and Energy Study Instute,
Washington DC.
IPCC. 2014. Anthropogenic and natural radiative forcing. Intergovernmental Panel on Climate Change, Retrieved
from https://www.ipcc.ch/site/assets/uploads /2018/02/WG1AR5_ Chapter08_FINAL.pdf
Kulpredarat, N. 2016. The impact of pig wastewater to water environment in Thailand. Department of Livestock
Development, Bankork, Thailand.
MAFF. 2018. Prakas 549. Ministry of Agriculture, Foresty, Fisheries, Phnom Penh.
MAFF. 2019. MAFF Annual report 2018-2019. Ministry of Agriculture, Forestry, and Fisheries, Phnom Penh.
Mek, S., Vong, P., Khoem, N. and Mech, S. 2018. Assessment the potential of pig manure in electricity
generation for pig commercial farms in Kean Svay District, Kandal Province and Oddong District, Kompong
Speu Province. Bachelor’s thesis from the Royal University of Agriculture, Phnom Penh, Cambodia.
NBP. 2019. Market study on medium-scale and large-scale biodigesters. National Biodiester Programme, General
Department of Animal Production and Health.
Nokyoo, C. 2016. Swine waste management in Thailand. The 11th Annual Meeting of Water Environment
Partnership in Asia (WEPA), Workshop and Field Visit in Vientiane, Lao P.D.R.
Peerapong, P. and Limmeechokchai, B. 2017. Biogas-based electricity generation in swine farm in Thailand:
Economic and CO2 reduction aspects. Energy Procedia. Elsavier, 657-661.
Siripat. 2019. Fundamental of biogas. Energy Research and Development Institute, Chiang Mai, Thailand.
Sweeten, J.M., Fulhage, C. and Humenik, F.J. 1981. Methane gas from swine manure. Michigan State University,
Retrieved from https://archive.lib.msu.edu/DMC/Ag.%20Ext.%202007-Chelsie/PDF/e1532-1981.pdf
Thanapongtharm, W., Linard, C., Chinson, P., Kasemsuwan, S., Visser, M., Gaughan, A.E., Epprech, M.,
Robinson, T.P. and Gilbert, M. 2018. Spatial analysis and characteristics of pig farming in Thailand. BMC
Veterinary Research, 12, 218.
Tokhun, N. 2010. Piggery farm wastewater: Alternative solution for agriculture and soil fertility. International
Journal of Environmental and Rural Development, 1 (2), 55-61.

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Research article
erd

Evaluation of Some Nutritional Values and Antioxidant
Activities of Dried Tea Leaves in Seasonally
CHAW SU HLAING*
Department of Chemistry, Taunggyi University, Taunggyi, Myanmar
Email: chaw88@gmail.com

A MAR WINT
Department of Chemistry, Taunggyi University, Myanmar

OHN MAR AYE
Department of Chemistry, Taunggyi University, Myanmar

MYO THIDA HTEIK
Department of Chemistry, Taunggyi University, Myanmar
Received 30 December 2020

Accepted 30 July 2021(*Corresponding Author)

Abstract Tea Leaf is one of the traditional food in Myanmar. It is eaten as well as drunk withtwo main forms which are wet tea leaf (Myanmar name, Lahpet so) and dried tea leaf
(Myanmar name, Lahpet chauk). Tea leaves can be picked starting from April until October. At
Namhsan area, Northern Shan State in Myanmar, tea leaves are generally divided into six kinds
depending on the plucked season. There are “Shwephi-Oo”, “Shwephi-Hnaung”, “Khakan-Oo”,“Khakan-Hnaung”, “Kha-Naing”, and “Kha-Hawt” or “Hnin Tet”. In this study, somenutritional value and antioxidant activities of six kinds of dried tea leaves in Namhsan region
were evaluated by the aiming of which kind of dried tea leaves have the best quality for
consumers. The analyses included examination of the dried tea leaves were their nutritional
value (carbohydrate, protein, and amino acids), assessment of selected minerals, caffeine,
catechin mixture, dietary fiber and total ash. Nutritional contents of tea leaves were examinedby preliminary phytochemical screening method and the selected mineral contents (Ca, Zn, Fe,Mn) were analyzed by atomic absorption spectrophotometry (AAS). Total ash contents were
determined by ignition method. Antioxidant activities of dried tea leaves were evaluated by the
DPPH free radical scavenging assay. Caffeine and catechin mixture (flavonoid) were extracted
by using chemical reagents such as dichloromethane and chloroform. From the observed results,
dried tea leaves in Namhsan region were rich in mineral contents (2.33-5.48 mg/kg of Ca, 1.401.94 mg/kg of Zn, 1.06-1.77 mg/kg of Fe and 8.06-21.06 mg/kg of Mn), 0.525-3.413% protein,
8-11.5% crude fiber, 4.20-7.39% total ash, 0.075-0.200% caffeine, 0.4-2.0% catechin mixture
and high antioxidant activities (2.3-5.9 g/L, IC50 value of DPPH scavenging assay) were found.
Based on the studied nutritional values and antioxidant activity results, Shwephi-Oo dried tea
leave was confirmed that the best quality.
Keywords dried tea leave, nutritional value, antioxidant activity, phytochemical screening,
free radical scavenging, AASINTRODUCTION
Tea is grown in different states of Myanmar, mainly in Shan state. The best quality and major
cultivation are in Namhsan region, Northern Shan State. The total tea plantation area is around 70,000
hectares with an annual production of 78 million Kg green leaves. Namhsan area is a mountainousⒸ ISERD

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region with deep slope that forms a natural drainage system, vital for tea plantation (website 1). There
are generally six kinds of tea seasonal names in use for the tea leaf. These are "Shwephi-Oo" if the tea
leaf is picked 3rd week of March to 2nd week of April,"Shwephi-Hnaung" if it is picked 3rd week ofApril to 2nd week of the May, "Khakan-Oo" if it is picked 3th week of May to 2nd week of July,"Khakan-Hnaung" if it is picked mid-July to 2nd week of August, "Kha-Naing" if it is picked 3rd weekof August to end-September, and,"Kha-Hawt" or “Hnin-Tet” if it is picked 1st October to endNovember. The period of these seasons may be differed in locally.
Tea leaves contain thousands of the chemical compounds. When tea leaves are processed, thechemical compounds within them break down, form complexes with one another and form newcompounds. The most important compounds in fresh tea leaves are polyphenols, amino acids, enzymes,
pigments, carbohydrates, methylxanthines, minerals and many volatile flavor and aroma compounds.
These components are responsible for producing teas with desirable appearance, aroma, and taste(Tony, 2019). All types of tea leaves (green tea, black tea, white tea, oolong tea, and pu-erh tea)
contain unique antioxidants called flavonoids. The most potent of these, known as Epigallocatechingallate (EGCG), may help against free radicals that can contribute to cancer, heart disease, and clogged
arteries. All these teas also have caffeine and theanine, which affect the brain and seem to heighten
mental alertness.
The more processed the tea leaves, usually the less polyphenol content. Polyphenols include
flavonoids. When the tea leaves are oxidized or fermented, so they have lower concentrations ofpolyphenols than fresh green tea leaves; but their antioxidizing power is still high (Julie, 2009). Tasteand aroma, as well as price and brand are the main factors impacting consumers’ preferences withregard to tea of their choice; on the other hand consumers less frequently pay attention to the chemicalcomposition and nutritional value of tea (Maria, 2017). Therefore, in this study, some nutritional values
and antioxidant activity of dried tea leaves were examined by dividing the six kinds based on theirplucked periods.
OBJECTIVE
This study assessed the nutritional values and antioxidant activities of dried tea leaves in seasonallyand confirmed the best quality dried tea leave based on the studied results.
MATERIALS AND METHODS
Sample Collection
Tea leaves were plucked in one of the tea farms from Zayangyi village in Namhsan region, Nothern
Shan State, Myanmar. “Shwephi-Oo” kind of tea leaves were plucked in 2nd week of April 2018,“Shwephi-Hnaung” was plucked in 4th week of April 2018, “Khakan-Oo” was plucked in 4th week of
May 2018, “Khakan-Hnaung” was plucked in 3rd week of July 2018, “Kha-Naing” was plucked in 3rd
week of August 2018 and, “Kha-Hawt” was plucked in 1st week of October 2018. After plucking, allkinds of tea leaves were cleaned and dried at room temperature for two weeks. The dried tea leaves
were stored in the air tight plastic bags before the analyses were performed.
Sample Preparation
Aqueous extract of all kinds of dried tea leaves were prepared for the drinking purpose of dried tea
leave infusion with boiling water.
Preliminary Phytochemical Analysis of Dried Tea LeavesⒸ ISERD

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Preliminary phytochemical investigations of aqueous extract of dried tea leaves were carried out toknow what chemical constituents were involved in the dried tea leaves according to the standard
procedure of qualitative test for preliminary phytochemical screening. The procedure and results arepresented in Table 1 and 2, respectively.
Table 1 Procedure for preliminary phytochemical screening
Constituents

Procedure

Observation

Alkaloid

Dragendroff’s test, Few mL of sample filtrate + 1-2mL of Dragendroff’s reagent

Reddish brown
precipitate

Carbohydrate

Barfoed’s test, 1 mL of sample filtrate + 1 mL of Barfoed’s reagent (heated for2min)

Red precipitate(monosaccharides)

Glycoside

Borntrager’s test, 2 mL of sample filtrate hydrolysate + 3 mL chloroform (shakenwell) + chloroform layer separate + 10 % ammonia solution

Pink colored solution

Protein andamino acids

Ninhydrin test, 2 mL of sample filtrate + 2 drops of Ninhydrin solution (10 mg
Ninhydrin + 200 mL acetone)

Purple colored solution(Amino acids)

Flavonoid

Conc. H2SO4 test, plant extract + conc. H2SO4

Orange color

Phenolic
compounds

Ferric chloride test, Extract aqueous solution + few drops of 5 % ferric chloride
solution

Dark green/bluish blackcolor

Tannins

Braymer’s test, 1 mL of sample filtrate + 3 mL distilled water + 3 drops of 10 %ferric chloride sol:

Blue-green color

Determination of Some Nutritional and Mineral Contents of Dried Tea Leaves
Some nutritional (protein, total ash and crude fiber) contents of dried tea leaves were examined. The
crude protein contents of the samples were determined by Macro-kjeldahl method (AOAC, 1990).
Total ash was obtained by igniting tea sample in muffle furnace until it is free from carbon (FoodChemistry, 2012). The 5 g of each dried tea leave sample was taken in a tarred silica dish/crucible. It
was heated at 100°C in an oven until moisture is expelled and incinerated at as low a temperature as
possible. Then the dish was placed in furnace at 525  20°C for about 1 hour and leave until white ash
is obtained. The dish was transferred in a desiccator for cooling and weighted the dish. The results
were expressed as % total ash.
The crude fiber contents in the dried tea leave samples were examined by the method given in“The chemical analysis of Foods” (Joslyn, 1970).
The selected mineral contents (Ca, Zn, Fe, and Mn) were analyzed by atomic absorption
spectrophotometer. 1 g of each dried tea sample was dissolved in 10 mL of 1 M nitric acid and boiled
to complete the dissolution and filtrated. The precipitate was washed with 1 M nitric acid and filtered.
The obtained filtrate was transferred to 25 mL volumetric flask and fill up to the level with double
distilled water. Then the digested solution was analyzed with AAS spectrophotometer (SHIMADZU
AA-7000). The results are described in Table 2.
Determination of Caffeine, Catechin Mixture and Antioxidant Activities of Dried Tea Leaves
Caffeine and catechin mixture (flavonoid) were extracted by using chemical reagents such asdichloromethane or chloroform and ethyl acetate. 10 g of ground tea leaves were extracted with 300
mL of pure water at 80 C for 40 min. Leaves were removed by filtration using a filter paper(Whatmann, No. 1). Aqueous tea infusion was initially partitioned with chloroform then secondⒸ ISERD

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partition was carried out with ethyl acetate (Row and Jin, 2006). Caffeine and catechin mixture were
calculated by the equation 1 and 2 respectively (Ezgi, 2015).
% Caffeine (w/w) = (mass of caffeine extract / mass of tea sample) x 100% Catechin (w/w) = (mass of catechin extract / mass of tea sample) x 100

(1)
(2)

Antioxidant activities of dried tea leaves were evaluated by the DPPH free radical scavenging
assay according to Lee et al, 2004. The results are described in Table 3. All experiments were
performed in the laboratory of Department of Chemistry, Taunggyi University, Southern Shan State,
Myanmar.
RESULTS AND DISCUSSION
Fig. 1 shows the collected six kinds of air dried tea leaves from one of the tea farm in Zayangyi Village,
Namhsan Region, Nothern Shan State, Myanmar.

Shwephi-Oo

Shwephi-Hnaung

Khakan-Oo

Khakan-Hnaung

Kha-Naing

Kha-Hawt

Fig. 1 The six kinds of dried tea leaves
Table 2 Preliminary phytochemical screening of six kinds of dried tea leaves
Sample(H2O extract)

Alkaloid

Carbohydrate

Glycoside

Protein andamino acids

Flavonoid

Phenolic
compounds

Tannins

Shwephi-Oo

+

-

+

+

+

+

+

ShwephiHnaung

+

-

+

+

+

+

+

Khakan-Oo

+

-

−

+

+

+

+

KhakanHnaung

+

-

_

+

+

+

+

Kha-Naing

+

_

+

+

+

+

Kha-hawt

+

_

+

+

+

+

-

*(+) = Present, (-) = absentⒸ ISERD

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The qualitative tests of preliminary phytochemical screening for aqueous extract of six kinds ofdried tea leaves showed the results which described in Table 2. According to these results, alkaloid,
protein and amino acids, flavonoid, phenolic compounds and tannins were observed in all kinds ofdried tea leaves. Glycoside was found in the two kinds, Shwephi-Oo and Shwephi-Hnaung.
Carbohydrate was found to be absent in all kinds of dried tea leaves. By viewing these results, it isrecommended that all kinds of dried tea leaves infusion with boiling water could significantly
contribute to the health management and our daily need of secondary metabolites.
Nutrients can be divided into two categories: macronutrients, and micronutrients. Macronutrientsare those nutrients that the body needs in large amounts. These provide the body with energy (calories).
There are seven main classes of nutrients that the body needs. These are carbohydrates, proteins, fats,vitamins, minerals, fibre and water. It is important that everyone consumes these seven nutrients on adaily basis to help them build their bodies and maintain their health. Micronutrients are those nutrientsthat the body needs in smaller amounts. There are 7 essential plant nutrient elements defined as
micronutrients [boron (B), zinc (Zn), manganese (Mn), iron (Fe), copper (Cu), molybdenum (Mo),chlorine (Cl)]. They constitute in total less than 1% of the dry weight of most plants (website 2). Table
3 shows the observed results of some nutritional and mineral contents of six kinds of dried tea leaves.
From these results, it is found that the nutritional values are more or less differ from one anotheraccording to their plucked seasons. The protein (3.413%), crude fiber (11.5%) and zinc (1.9409 mg/kg)
contents are the richest in “Kha-Naing” kind. The richest iron content (1.7661 mg/kg) is found in“Khakan-Oo” kind. The other parameters, total ash (7.39%), calcium (5.4764 mg/kg) and manganese(21.0559 mg/kg) are observed as higher significance in “Shwephi-Oo” kind. Despite the little
difference in contents, on the basis of these observation, all kinds of tea leaves appeared that thepresence of significance sources of nutritional and minerals for daily necessary human diet.
Table 3 Some nutritional and mineral contents of six kinds of dried tea leaves
Amount of parameters
SampleShwephi-Oo
ShwephiHnaung
Khakan-Oo
KhakanHnaung
Kha-Naing
Kha-hawt

Protein (%)
0.525

Crude fiber(%)
8

Total ash
(%)
7.39

Ca
(mg/kg)5.4764Zn(mg/kg)1.5873Mn(mg/kg)21.0559Fe(mg/kg)1.3953

0.525

8

6.60

5.44131.507915.19111.3514

2.625

10.5

5.79

3.00481.886510.8227

1.7661

1.138

10

5.39

4.42261.40708.0566

1.0664

3.413
2.144

11.5
8

6.60
4.20

3.30612.33821.94091.7858

11.6994
11.1704

1.75691.5577

Table 4 shows the amount of caffeine, catechin mixture and antioxidant activities of six kinds ofdried tea leaves. The caffeine and catechin mixture contents were significantly higher in “Shwephi-Oo”
kind. Numerous studies have indicated that catechins and other polyphenols in tea exhibit powerfulantioxidant activities. They function as antioxidants in vitro by scavenging nitrogen species andreactive oxygen generated due to a variety of oxidative stress and by sequestering metal ions. The
stable organic free radical, DPPH, has been considered as a useful reagent for determining free radical
scavenging capacity of antioxidant materials (Tao, et al, 2019). The observed antioxidant results werecompared with that of ascorbic acid as standard materials. The IC50 values (the effective concentrationof 50% inhibition) were showed that all kinds of tea leaves have significance antioxidant activities.
Among them, “Shwephi-Oo” has highest caffeine, catechin and antioxidant activity. It is considerable
that “Shwephi-Oo” is the foremost plucked season kind at the beginning of summer. It was free fromrain and the leaves were fresh and best strength. Therefore, it is confirmed that the quality of tea leaves
depend upon the plucked season as well as other geographical conditions.
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Table 4 The amounts of caffeine, catechin mixture and antioxidant activities
of six kinds of dried tea leaves
Amount of parameters
SampleShwephi-Oo
Shwephi-Hnaung
Khakan-Oo
Khakan-Hnaung
Kha-Naing
Kha-hawt

Caffeine (%)

Catechinmixture (%)

0.200
0.075
0.100
0.100
0.125
0.075

2.0
1.3
0.4
0.6
0.8
0.5

Antioxidant activity(DPPH scavenging assay, IC50 value), (g/L)
ascorbic acid (Standard)
2.3
2.7
4.8
1.2
4.1
4.7
5.9

CONCLUSION
From the experimental results, six kinds of dried tea leaves in Namhsan region have valuable
nutritional values and high antioxidant activity. Among them, the selected mineral contents, total ash,catechin mixture, caffeine content and antioxidant activity were found higher rich in “Shwephi-Oo”
than another five kinds of dried tea leave apart from protein, crude fiber, Zn and Fe contents. Therefore,“Shwephi-Oo” kind of dried tea leave is generally regarded as the best quality for consumers.
ACKNOWLEDGEMENTS
The authors would like to express special gratitude to Pro-rector Dr Win Win Ei, Taunggyi University
and our Ministry of Education for giving us the opportunity to present this research paper. I also thanks
to Professor and Head, Dr Ah Mar Yi, Department of Chemistry, Taunggyi University, our co-workers
and friends for their guidance, helpful and encouragements during this field. We also thank thereviewers of IJERD for their help in improving the quality of our research paper and their criticalreviews.
REFERENCES
AOAC. 1990. Official methods of analysis of the AOAC international. 15th edition, Association of Official
Analytical Chemists, Arlington, VA.
Chinnawat, S. 2016. The study of the amount of heavy metals in green teas determine by using atomic absorption
spectrophotometer. International Journal of System Application, Engineering & Development, 10, 20-24.
Ezgi, D., Gonul, S. and Munevver, S. 2015, Comparison of some extraction methods for isolation of catechinsand caffeine from Turkish green tea. International Journal of Secondary Metabolite, 2 (2), 16-25.
Food Chemistry. 2012. Determination of total ash, alkalinity of water soluble ash and water extractives in tea.
Lesson 30, Module 13.
Joslyn, M.A. 1970. Methods in food analysis, physical, chemical and instrumental methods of analysis. 2ndedition, Academic Press, New York, 109-140.
Julie, E. 2009. Types of tea and their health benefits. Nourish by WebMD.
Lee, S., Son, D., Ryu, J., Lee, Y.S., Jung, S.H., Lee, S.Y. and Shin, K.H. 2004. Antioxidant activities ofacanthopananax senticosus stems and their lignan components. Archives of Pharmacal Research, 27, 106-110.
María, P.T., Rita, Y.C., María, I.C. and José, L.V. 2019. A simple and a reliable method to quantify antioxidant
activity. In Vivo, Antioxidants MPDI Journal, 8, 1-11.
Row, K. and Jin, Y. 2006. Recovery of catechin compounds from Korean tea by solvent extraction, Bioresour.
Technol, 97, 790-793.

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Tao, T., Ya-Juan, L., Jinhong, K., Cheng-Mei, Z. and Seong-Gook, K. 2019. Antioxidant activity and mainchemical components of a novel fermented tea, Retrieved from www.mdpi.com
Tony, G. 2019. Chemical compounds in tea. Tea Epicure Blog, Retrieved from https://www.nagarpyan.com/
plantation.htm; received date 11.12.2020. and https://mynutrition.wsu.edu>Nutrition Basicsat WSUⒸ ISERD

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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
single organization can achieve sufficiently the aspirations of sustainable rural development. Collaboration
among international, governmental and non-governmental organizations, together with the academe andscientific sector, is indispensable.
The knowledge and intelligence accumulated in universities and research institutions are also expected
to make the programs facilitated by the international, governmental and non-governmental organizations
more adequately implemented and meaningful to societal development. However, these cases especiallythose implemented locally have been scattered without having been summarized well or recorded in annals
academic or scientific societies.
So, the International Society of Environmental and Rural Development founded in 2010, aims to discussand develop suitable and effective processes or strategies on sustainable rural development focusing on
agricultural and environmental aspects in developing countries. The ultimate goals of the society are tocontribute to sustainable rural development through social and economic development in harmony with thenatural environment, and to support the potential or capacity building of local institutions and stakeholders in
the rural area with academic background.
Purposes of ISERD:
The primary purposes of ISERD are to contribute to sustainable rural development through social andeconomic development in harmony with the natural environment and to support the potential or capacity
building of local institutions and stakeholders in the rural area with academic background.
In order to enhance the realization of the primary purposes of ISERD, the secondary purposes are;- to facilitate interaction among international, governmental, non-governmental organizations and local
communities,- to hold conferences or symposia on environmental and rural development,- to edit the International Journal of Environmental and Rural Development,- to confer some awards based on scientific achievement, research paper or poster presentation, and- to encourage and develop local awareness concerning sustainable rural development.
Membership:
There shall be two categories of membership.
(a) Individual(b) Organizational
An application for membership of ISERD shall be submitted to the secretariat of ISERD, where is located in
the Research Center at Institute of Environmental Rehabilitation and Conservation (Japan) by writing or byother appropriate means.
Also, every presenter who attends the International Conference on Environmental and Rural
Development (ICERD) is registered as an Individual Member of ISERD.

IJERD – International Journal of Environmental and Rural Development

Council of ISERD:
The affairs of ISERD shall be governed and managed by the ISERD Council. The councilors are as follows.
President
Prof. Dr. Mario T. Tabucanon, United Nations University, Institute for the Advanced Study of
Sustainability, Japan
Deputy Presidents
Prof. Dr. Bunthan Ngo, Royal University of Agriculture, Cambodia
Prof. Dr. Anan Polthanee, Khon Kaen University, Thailand
Prof. Dr. Eiji Yamaji, The University of Tokyo, Japan /Institute of Environmental Rehabilitation and Conservation, Japan
Executive Secretary / Editor-in-Chief of IJERD
Prof. Dr. Machito Mihara, Tokyo University of Agriculture, Japan /Institute of Environmental Rehabilitation and Conservation, Japan
Managing Editors
Dr. Narong Touch, Tokyo University of Agriculture, Japan
Dr. Shinobu Terauchi, Institute of Environmental Rehabilitation and Conservation, Japan
Dr. Jeeranuch Sakkhamduang, Institute of Environmental Rehabilitation and Conservation /Association of Environmental and Rural Development, Thailand
Treasurer
Dr. Lalita Siriwattananon, Rajamangala University of Technology Thanyaburi, Thailand
Regional Vice Presidents
Dr. Shafiqullah Rahmani, Ghanzni University, Ministry of Higher Education, Afghanistan
Prof. Dr. Barry N. Noller, The University of Queensland, Australia
Prof. Dr. Sinisa Berjan, Embassy of Bosnia and Herzegovina in Japan /University of East Sarajevo, Bosnia and Herzegovina
Dr. Buntong Borarin, Royal University of Agriculture, Cambodia
Dr. Ping Li, Institute of Grassland Research, China
Prof. Dr. Dieter Trautz, University of Applied Sciences, Germany
Dr. Sri Wahyuni, Islamic University of Riau, Indonesia
Prof. Dr. Hiromu Okazawa, Tokyo University of Agriculture, Japan
Prof. Dr. Nang Hseng Hom, Yezin Agricultural University, Myanmar
Prof. Dr. Junaid Alam Memon, Institute of Business Administration Karachi, Pakistan
Prof. Dr. Regucivilla A. Pobar, Bohol Island State University, Philippines
Dr. Chuleemas Boonthai Iwai, Khon Kaen University, Thailand
Dr. David R. Ader, University of Tennessee, USA
Dr. Nguyen Khoi Nghia, Cantho University, Vietnam
ISERD Secretariat:
Research Center, Institute of Environmental Rehabilitation and Conservation (ERECON)
2987-1 Onoji Machida-Shi, Tokyo 195-0064, Japan
Tel/Fax: +81-42736-8972
E-mail: iserd.secretariat@gmail.com
Webpage: www.iserd.net
Collaborated with

Association of Environmental and Rural Development (AERD)
93/64 Moo.3, Sinsab Village 2, Bungyeetho Sub-district, Thanyaburi District,Pathum Thani 12130, Thailand
Tel/Fax: +66-2957-8064
E-mail: iserd.secretariat@gmail.com
Webpage: www.iserd.net

Institute for the Advanced Studyof Sustainability
All articles and reports published in this journal were accepted through a peer-review process. However,most articles and reports published in this journal were presented at the International Conference on
Environmental and Rural Development that was co-organized by United Nations University, Institute for
the Advanced Study of Sustainability.

IJERD – Environmental and Rural Development

Vol. 12 No. 1

June 2021