ISSN 2185-159X

erd

International Journal ofEnvironmental and Rural Development

Volume 14 Number 2

December 2023

International Society of Environmental and Rural Development

Institute for the Advanced Studyof Sustainability
All articles and reports published in this journal were accepted through a peer-review process. However,every article or report published in this journal was presented at the International Conference on
Environmental and Rural Development (ICERD) in collaboration with the United Nations University Institutefor the Advanced Study of Sustainability.

IJERD – Environmental and Rural Development

Vol. 14 No. 2

December 2023

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.)

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The International Journal of Environmental and Rural Development, IJERD, is an interdisciplinary journal
dedicated to environmental and rural development research related to the education for sustainable
development, ESD. The IJERD is devoted to publishing peer-reviewed research articles, review articles andtechnical reports in the fields of environmental and rural development, education for sustainable ruraldevelopment, environmental management, and 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 developments and achievements, to strengthen
education systems, to promote cooperation in environmental and rural development and to contribute toprogress in sustainable development. The editorial board of this journal includes a variety of scientificdisciplines.

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The following areas are included in the scope of IJERD.
Education for Sustainable Rural Development:
Environmental Education, Food and Agricultural Education, Participatory Approach, Capacity Building,Community Empowerment, Agricultural Extension, etc.
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Marketing, Partnership, Value Added Product, Community Development, Access to Technology,
Cultural Preservation, etc.
Environmental Management:
Bio-Diversity, Soil Degradation and Land Conservation, Water Quality Conservation, Deforestation andSustainable Forest Management, Environmental Management, etc.
Agricultural Systems:
Organic Farming, Conservation Tillage, Mechanization, Irrigation and Drainage, Nutrient and Pest
Management, Cattle Breeding, Agro-Forestry, Indigenous Technology, etc.
Infrastructural Systems:
Water Resource Development, Land Reclamation, Road Construction, etc.

Editorial Board:
Editor in Chief:
Prof. Dr. Machito Mihara, Tokyo University of Agriculture, Japan /Institute of Environmental Rehabilitation and Conservation, Japan
Managing Editors:
Dr. Narong Touch, Tokyo University of Agriculture, Japan
Dr. Shinobu Terauchi, Institute of Environmental Rehabilitation and Conservation, Japan
Dr. Santa Pandit, Institute of Environmental Rehabilitation and Conservation, Japan
Editorial Advisory Board:
Prof. Dr. Mom Seng, Royal University of Agriculture, Cambodia
Dr. Insa Kuhling, University of Applied Sciences, Germany
Dr. Carlos Onan Tovar Mendoza, Honduras Embassy in Japan, HondurasProf. Dr. Noureddin Driouech, Mediterranean Agronomic Institute of Bari, Italy
Prof. Dr. Kiichiro Hayashi, Nagoya University, Japan
Prof. Dr. Kingshuk Roy, Nihon University, Japan
Dr. Yuri Yamazaki, Tokyo University of Agriculture, Japan
Prof. Dr. Merites M. Buot, University of the Philippines Los Baños, Laguna, Philippines /UP Open University, Philippines
Prof. Dr. Salvador Catello, University of the Philippines, Philippines
Prof. Tomas Reyes, Jr. Bohol Island State University, Philippines

IJERD – International Journal of Environmental and Rural Development

Dr. Julian Torillo, Jr. Bohol Island State University, Philippines
Dr. Venkatachalam Anbumozhi, International Centre for Trade and Sustainable Development,
Switzerland
Dr. Anchasa Pramuanjaroenkij, Kasetsart University, Thailand
Prof. Dr. Rajendra P. Shrestha, Asian Institute of Technology, Thailand
Dr. Somsak Srisontisuk, Khon Kaen University, Thailand
Dr. Vidhaya Trelo-Ges, Khon Kaen University, Thailand
Prof. Dr. Vo Quang Minh, Cantho University, Vietnam

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Webpage: www.erecon.jp

IJERD – International Journal of Environmental and Rural Development (2023) 14-2

Contents
Review on Causes, Effects, and Management of Soil Salinity on Irrigated Rice Fields in Tanzania
Jimmy Felix Macha and Machito Mihara

1

Evaluation of the Effect of Lactic Acid Bacteria on Histamine-producing Bacteria Isolated from
Cambodian PrahokSamoeun Dana, Top Sothalong, Buntong Borarin, Chhe
Chinda and Chrun Rithy

7

Influence of Native Trees on Soil Fertility at a Rainforestation Site in Mailhi, Baybay City, Leyte,
Philippines
Lorraine Cristy E. Ceniza, Jimmy O. Pogosa,
Marlito M. Bande and Victor B. Asio

14

Evaluation of the Effect of Alternate Wetting and Drying Irrigation on the Growth of Paddy Rice
Plant Height Using a Logistic Model
Kiseki Kurashina, Sarvesh Maskey, Dickson M. Mazibuko
and Hiromu Okazawa

20

Key Factors Influencing Education Disparity in the Low-Grown Tea Cultivation Community of Sri
Lanka
Yuko Fukuda

27

Growth and Mineral Uptake of Moringa oleifera Lam. in Low-Permeability Soils at Different
Salinity Levels
Takashi Kume, Chuleemas Boonthai Iwai,
Tetsuya Shimamura and Shinsuke Haruta

35

Environmental Impact Assessment of Air Emission from Fertilizer Utilization and Rice Straw
Burning from Rice Production in Cambodia
Leakhena Hang, Sreyneang They, Sela Kong, Chenda Lai,
Dalin Um and Raksmey Yim

42

Seasonal Changes of Water Quality in Cheung Ek Lake, Cambodia
Oum Somara and Machito Mihara

49

Consumer Behavior Towards Ethical Bananas in Japan
Mei Matsumoto and Nina N. Shimoguchi

55

Ecosystem Carbon Stock Assessment in Upland Forest: A Case Study in Koh Kong, Mondulkiri,Preah Vihear, and Siem Reap Provinces
Soben Kim, Sarun Horn, Pheak Sok, Teamhy Sien and
Chomroeun Yorn

61

Application of Probabilistic Risk Assessment to the Chalky Rice Grain Issue in Japan
Hiroki Oda, Momoka Masano, Takashi Nobuoka andKoshi Yoshida

68

Agricultural Support for New Farmers in H City, Tokyo, Japan
Rika Terano, Nina N. Shimoguchi, Ramadhona Saville,
Hettige Samitha Lakshan Gunasekara and Katsumori
Hatanaka

74

Technical Efficiency of Potato Producers in Benguet Province, Philippines
Asres Elias, Cheryll Casiwan Launio, Kumi Yasunobu,
Yuki Toyama, Birara Endalew and Maryan J. Altaki

80

Effect of Dry Brewery Residue on the Growth of Local Chicken (Gallus domesticus)
Bunhoa Uk, Sopheak Houn, Vathana Thun and Tory Chhun

87i

IJERD – International Journal of Environmental and Rural Development (2023) 14-2

Development of Indexes to Evaluate the Effectiveness of Low Water Level Control in Irrigation
Ponds – A Case Study of Irrigation Ponds in Tottori, Japan
Ryo Harasawa, Katsuyuki Shimizu and Yuri Yamazaki

93

Impact Evaluation of Climate Change on Disaster Risk of Forested Watershed Rivers in Snowy
Regions using SWAT+
Shotaro Kikuchi, Sarvesh Maskey, Hiromu Okazawa,
Sergio Azael May Cuevas, Makoto Oba, Shogo Nakamura
and Seiji Hayashi

100

Challenges and Prospects for Increasing Vegetable Production in Cambodia
Kasumi Ito, Tatsuya Yoshida, Boratana Ung and
Mitsuru Hamano

107

Antimicrobial Resistance Profiles Found in a Case Study of Escherichia coli from Cohabitant Petsand Environmental Surfaces at Animal Clinics, Phnom Penh City
Ban Naiheak, Chea Bunna, Venn Vutey and Kang Kroesna

117

Assessing the Acceptability and the Feasibility of an Agricultural Package of Technologies for
Risk Management in Southern Haiti
Rival Valcin, Tomohiro Uchiyama, Katsumori Hatanaka,
Yasuo Ohe and Benedique Paul122

Changes in Soil Physical Properties Owing to Soil Reduction Treated with Electrokinetic Treatment
Takahiko Nakamura and Narong Touch132

On the Number of Seedlings for the System of Rice Intensification: Experiment Results from
Lombok Island, Indonesia
Yamaji Eiji and Sato Shuichi

138

Experiment on Smart Mushroom Cultivation Using an Environmental Control System
Chanreng Sey Nhim, Nita Chek, Chanthan Hel andRothna Pec

144

ii

IJERD – International Journal of Environmental and Rural Development (2023) 14-2

Research article
erd

Review on Causes, Effects, and Management of Soil Salinity
on Irrigated Rice Fields in Tanzania
JIMMY FELIX MACHA
Graduate School of Agro-Environmental Science, Tokyo University of Agriculture, Japan

MACHITO MIHARA*
Faculty of Regional Environment Science, Tokyo University of Agriculture, Japan
Email: m-mihara@nodai.ac.jp
Received 22 May 2023

Accepted 2 August 2023(*Corresponding Author)

Abstract Salinization is a process of increasing concentrations of salts in soil and water. Soilsalinity is the most serious agricultural problem in irrigation agriculture in Tanzania. Severe
salinity can cause significantly lower yields, food insecurity, and environmental degradation.
If not properly addressed, soil salinization can accelerate and potentially expand to currently
unaffected irrigated agricultural land. Despite the efforts made by the government and otherstakeholders, rice production in irrigated rice fields in Tanzania is still experiencing low
yields. Current rice yields are between 2.5 and 4 tons/ha, which is low compared to theaverage rice yields reported in other countries. Salinity problems are caused by the prevailingclimate characterized by high evapotranspiration, geological and geographical characteristicsof the area, quality of irrigation water, and inadequate land use practices. This paper aims toreview available information on the causes and effects of soil salinity on irrigated rice fields
in Tanzania and to provide information on management strategies to adjust soil salinity andimprove soil fertility. References from books, papers, journals, online readings, anddissertation papers from different universities and institutions were used to get information
for this paper review. In this review, the various reasons that cause soil salinity in irrigatedfields in Tanzania have been identified, which are the nature of rocks, poor irrigation
management, and non-compliance with established irrigation regulations. Also, the effects ofsoil salinity in Tanzania have resulted in a decrease in yields, an increase in costs ofproduction, poor quality of products, and water shortages for irrigation. Lastly, somerecommendations identified to solve the challenges of soil salinity include setting up a local
drainage system on affected land, renovating some irrigation facilities, reclaiming theaffected soil with gypsum and manure, and flushing all salt-affected water with non-salt water.
Keywords review, soil salinity, causes and effects, irrigated rice field, Tanzania,

INTRODUCTION
Globally, the challenge of soil salinity in areas that practice irrigation agriculture has been increasing
day after day, resulting in crop production decreasing while also causing land environmental
problems such as degradation, erosion, and siltation. Salinity affects about 800 million ha of arable
lands worldwide and approximately 33 % of irrigated areas (about 74.25 million ha) are currently
considered threatened by soil salinization by various degrees (Shrivastava and Kumar, 2015).
According to Jamil et al. (2011), it’s projected that by the year 2050, there will be more than 50 %of the farmland worldwide, which would become salt affected. Rice is the most sensitive cereal crop
while barley is the most tolerant cereal crop (Munns and Tester, 2008; Karan et al., 2012). Salinity
has serious effects on the percentage of filled spikelet, and grain weight, and can also hinder the
uptake of essential nutrients in rice (Clermont-Dauphin et al., 2010). Most irrigation schemes, which
are especially within arid and semiarid environments, are already experiencing increasing levels ofsalt-affected soil, solely due to the mismanagement of the soils, the use of poor-quality irrigationwater, poor drainage systems, poorly designed and managed irrigation infrastructures, excessive useⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

of irrigation water and climate change (Kashenge-Killenga, 2010). Most irrigation water qualities
have adverse effects on the physical properties of soil because it is mostly connected with the buildup
of sodium ions on the soil exchange complex. The quality of water can impact the volatility of thesoil aggregates which eventually leads to the dispersion of the clay particles and the clogging of soil
pores. When underground water is made to move to the soil surface by evapotranspiration, the soluble
salts condense on the soil particles on the surface and form a white crust.
Irrigation practices affect land by increasing the rates of leakage and groundwater recharge
which results in the rise in the water table. The water tables when it rises bring salts into the plantroot zone which affects both plant growth and soil structure; then the salt remains behind in the soil
surface after the water has been taken up by plants or lost due to evaporation (Popay et al., 2009).
Soil salinity is generally measured by electrical conductivity (EC) (Allison and Richards, 1954;
Sonmez et al., 2008; Scudiero et al., 2017). A soil is considered saline if the EC of a saturation extract
exceeds 4 dS m-1 at 25°C (Sonmez et al., 2008). Soil salinity or EC may be measured on the bulk soil(ECa), in the saturation paste extract (ECe), in soil: water ratio suspensions of 1:1 to 1:5 such as 1:1,
1:2, 1:2.5 and 1:5 or directly on soil water extracted from the soil in the field (ECw) (Allison andRichards, 1954; Sonmez et al., 2008; Kargas et al., 2018). Like other countries in Africa, soil salinity
in Tanzania contributes to one of the most serious ecological and environmental problems in most of
the irrigation schemes, so understanding the causes and effects of soil salinity in various irrigation
schemes in Tanzania, will provide an opportunity for the government to come up with enabling
policies to solve this challenge help farmers to practice irrigation agriculture that will specificallyeliminate and reduce the problem of soil salinity in their respective areas. This is because among thefactors that contribute to the increase in soil salinity are methods that are used by farmers in
irrigation agriculture, like the use of fertilizers application and improper uses of irrigation facilities.
The objective of this paper is to review the information on the causes and effects of soil salinity
on irrigated fields and to come up with information on management strategies to control the
problem.
OBJECTIVE
This review study tries to sum up and review available information on the causes and effects of soil
salinity on irrigated rice fields in Tanzania. And it provides information on management strategies
to adjust to deal with this challenge of soil salinity on rice irrigated fields in Tanzania to strengthen
Irrigation Agriculture so that it can bring productivity and income to farmers in Tanzania.
METHODOLOGY
The methodology used to get information for this paper are references from papers, journals, online,
and dissertation papers from different Universities and Institutions.
Causes of Soil Salinity on Rice Irrigated Fields in Tanzania
The main source of salt in arid and semi-arid areas includes rainfall (Rengasamy and Olsson,1991), mineral weathering (Gunn and Richardson, 1979), irrigation and various surface water(Mehanni and Chalmers, 1986; Rengasamy and Olsson, 1993), groundwater which redistributes
accumulated salts during evaporation, chemical applications (Rengasamy and Olsson, 1993) and man
activities (Dregne, 1976). These sources, coupled with environmental modifications, lead to threedifferent classes of salinization and sodicification that are grouped so for management purposes.
These classes include Saline soils that have ECe > 4 dS m−1 at 25° C and ESP < 15 (high soluble salts
and low exchangeable Na+). Sodic soils have ECe < 4 dS m−1 and ESP > 15 (low soluble salts andhigh exchangeable Na+). Saline-sodic soils are characterized by ECe > 4 dS m−1 and ESP > 15 (bothsalts and exchangeable sodium are high), pH15%, and pH>8.5) (Allison and Richards, 1954). In
Tanzania, some studies have been done to determine the sources of soil salinity in irrigation schemes,Ⓒ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

those studies identify various reasons from different areas that do irrigation farming, and some ofthose reasons are as follows.
Table 1 Case papers used for gathering information used in this study
Researcher
and year of
publication

Research title

Journal name andpublished dateCauses of soil
salinity

Effects of soil
salinity

Management ofsoil salinity

Meliyo etal. (2017)

Evaluation of Salt
Affected Soils for Rice(Oryza Sativa)
Production in Ndungu
Irrigation Scheme Same
District, Tanzania

Sustainable
Agriculture
Research, 6 (5262017-2656)

Naturelocation of the
Irrigation area

Decrease inyields.

Manure andlimestone
application

Reclamation of sodic
soils in northern
Tanzania, using locallyavailable organic andinorganic resources

African Journal of
Biotechnology 6,
no. 16.

Soil characterization forsalt problems inselected rice irrigation
schemes

Journal ofAdvances in
Developmental
Research, 3(1).

Poor
Irrigation
Management

Exploring farmers’
perception, knowledge,
and management
techniques of saltaffected soils to enhancerice production on small
land holdings.in
Tanzania
Extent of Salt-Affected
Soils and Their Effects
in Irrigated and
Lowland Rain-Fed Rice
Growing Areas. ofSouthwestern Tanzania

Cogent Food &Agriculture, 8(1),2140470

Fertilizer
application

Makoi et al.
(2007)

Kashenge etal. (2012)

Omar et al.
(2022)

KashengeKillenga etal. (2016)

Poor Irrigation
Management
Noncompliance

Decrease incultivationarea.
Increase incosts ofproductions

The local
drainage system
infarms
The local
drainage systemin farms

Water
Shortages

Extension tofarmers on
irrigation
management
Application ofdrainagesystem

High costsof
production
Decrease inyields.

Renovation
facilities
Flashing salt
water

Poor
Irrigation
Management

Climate Change
and Sustainability
in Agriculture,
97-126

Poor drainage
systemDecrease incultivation
Area.

Poor
Irrigation
management

Increase incostsof production.

Extension
services tofarmers
Proper Irrigation
management

Firstly, the cause of soil salinity in irrigated fields in Tanzania is the natural location of an area,
most schemes which are experienced soil salinity due to the nature of rocks in their respective areas,
and this normally happens to the water source which is the source of water is from Spring or aquifer.
Irrigation salinity occurs due to increased rates of leakage and groundwater recharge causing thewater table to rise. Rising water tables can bring salts into the plant root zone which affects bothplant growth and soil structure. This is common in an area surrounded by a mountain like in the
Kilimanjaro region irrigation scheme as the Ndungu source of its irrigation water has salinity due tothe nature of the rock (sedimentary rock) which causes the soil salinity.
According to Makoi, J. H. (2007), the strong reason for the soil salinity in areas where they are
doing Irrigation Agriculture in Tanzania is poor irrigation management, which is often contributed
to the farmers themselves not knowing the methods and principles of Irrigation Agriculture. This ismuchly related to knowing when and how to irrigate (irrigation schedule), application of fertilizer,
and poor drainage system which causes accumulation of water which in the end results in soil salinity
irrigating with saline water adds salt to the soil and increases the need for applying more irrigationwater to leach salts past the plant root zone.
Another source of soil salinity is non-compliance with various laws and guidelines set under theirrigation laws in the country, which states that before starting an irrigation scheme, the source ofⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

water must be measured and tested to see if its water complies with the required quality for irrigation,
including measuring the level of salt and consider the installation of salt control infrastructure as adrainage system in the scheme. Some people do not follow the guidelines set by the Irrigation
Commission responsible for Irrigation Agriculture in the country.

Source: NIRC

Fig. 1 Soil affected by salt (A); yields of paddy affected by salt ( B ) at irrigation schemesin Tanzania due to poor irrigation management
Effects of Soil Salinity on Rice Irrigated Fields in Tanzania
Soil salinity is an environmental stress affecting agriculture globally, substantially reducing
cultivated land area, crop productivity, and quality. Farmers can keep salinity problems in check byusing fertilizer. It is widely recognized by soil scientists that soil salinity reduces crop yield.
Salinity not only decreases the agricultural production of most crops, but also, affects soil
physicochemical properties, and the ecological balance of the area. The impacts of salinity include—
low agricultural productivity, low economic returns, and soil erosion (Hu and Schmidhalter, 2005).
In Tanzania, soil salinity has caused various challenges, but the biggest one is the decrease in yields
production, as statistics show that almost five percent (5%) of the area suitable for agriculture inmany irrigation schemes in Tanzania is not cultivated due to soil salinity problem also there are somecases in some Irrigation scheme where they usually produce 3-4 t/ha now they are producing 1-1.5
t/ha due to problem of soil salinity (Luzi-Kihupi et al., 2015). Being not cultivated and abandoned
automatically leads to a decrease in Production yields in a particular area and affects farmers' income
incomes. Also, soil salinity, causes production costs to increase for farmers in Tanzania. as many ofthem use various methods to reduce salt, including using a lot of fertilizer so that their plants arehealthy remaining, others also use gypsum to improve the soil structure damaged by salts. By doing
all of this it leads to an increase in the cost of production (Kashenge, 2016). Another effect of soil
salinity in Tanzania is irrigation water shortages, this is because in Tanzania most of the farmers
reduce the salinity in their fields by applying much water to fields to flush out the water affected bysalts and also to balance salt-affected soil, so a lot of water is used to flush out and by doing that it
leads to shortages of water for irrigation on downstream. Water shortages downstream lead toconflicts among the farmers in most of the irrigation schemes in Tanzania (NIRC).
DISCUSSIONManagement to Control Soil Salinity
There have been various efforts taken by farmers to control soil salinity in their irrigated fields toeliminate or reduce the problem, also governments, stakeholders, and research institutions arecontinuing to come up with a different approach to controlling soil salinity.
Provision of drainage system to the area affected, this method is mostly used by most farmers,
they create local drainage systems on farms to remove water with salinity from the soil, and therefore
the amount of soil salinity automatically is reduced and the drained water is removed through theⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

outlet. The other suggested ways of removing soil salinity recommended by researchers are such torenovate and close supervision of water inlet and outlet canals to prevent leakage of water between
blocks, affected fields require reclamation by the use of gypsum, farm-yard manure, good drainage
and flushing using good quality water, It is essential that drainage water, with its salt load from theaffected soil, be removed from the scheme, the use of farmyard manure at a rate of 3 to 8 tons/ha isrecommended to boost soil organic matter and improved soil infiltration to facilitate washing of salts
to minimize the effect of salts (Meliyo, 2016). The farmyard manure will also supply some nutrients(N, P, and K) that were limiting in some soils and therefore need to be added to improve riceproductivity andSoil reclamation will probably need to be a continuing practice in the most affectedareas. The most economical solution to maintain productivity will probably be a combination of salttolerant variety selection and soil amelioration practices (Mdemu, M.L., 2020).
CONCLUSION
Controlling soil salinity, sustainable soil, and irrigation water management practices have a vitalrole in the production of rice at irrigation schemes in Tanzania country. Many approaches areavailable to rehabilitate salt-affected soils. These include physical methods (irrigation anddrainage management); chemical amendments (gypsum, H2SO4, combined application gypsum
with other organic amendments); and biological approaches (screening salt-tolerant genotype,growing ameliorating crop species, crop species in rotation, etc.). The agro-ecological conditionsin Tanzania are comparable to many other countries where these approaches have successfully
been tested. Therefore, there is a good potential for adopting these practices that will increase
economic benefits to farmers.
ACKNOWLEDGEMENTSFirst, I thank God for his blessing to me, special thanks to research fellows in the laboratory ofTokyo University of Agriculture for his supervision, mentorship, guidance, and advice. Lastly
many thanks to JICA for the financial support of my research.
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Rengasamy, P. and Olsson, K. A. 1991. Sodicity and soil structure. Soil Research, 29 (6), 935-952.
Rengasamy, P. and Olsson, K. A. 1993. Irrigation and sodicity. Soil Research, 31 (6), 821-837.
Scudiero, E., Corwin, D.L., Anderson, R.G., Yemoto, K., Clary, W., Wang, Z. and Skaggs, T.H. 2017. Remotesensing is a viable tool for mapping soil salinity in agricultural lands. California Agriculture, 71 (4).
Shrivastava, P. and Kumar, R. 2015. Soil salinity, A serious environmental issue and plant growth promoting
bacteria as one of the tools for its alleviation. Saudi Journal of Biological Sciences, 22 (2), 123-131.
Sonmez, S., Buyuktas, D., Okturen, F. and Citak, S. 2008. Assessment of different soil to water ratios (1: 1, 1:
2.5, 1: 5) in soil salinity studies. Geoderma, 144 (1-2), 361-369.
Zaman, M., Shahid, S.A. and Heng, L. 2018. Guideline for salinity assessment, mitigation, and adaptation
using nuclear and related techniques. Springer Nature, 164.

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Research article

Evaluation of the Effect of Lactic Acid Bacteria on
Histamine-producing Bacteria Isolated from Cambodian
Prahok
SAMOEUN DANA*
Faculty of Agro-Industry, Royal University of Agriculture, Phnom Penh, Cambodia
Email: samoeundana99@gmail.com/sdana@rua.edu.kh

TOP SOTHALONG
Faculty of Agro-Industry, Royal University of Agriculture, Phnom Penh, Cambodia

BUNTONG BORARINDivision of Research and Extension, Royal University of Agriculture, Phnom Penh, Cambodia

CHHE CHINDA
National Institute of Science, Technology, and Innovation, Phnom Penh, Cambodia

CHRUN RITHY
Faculty of Agro-Industry, Royal University of Agriculture, Phnom Penh, Cambodia
Received 2 June 2023

Accepted 5 August 2023(*Corresponding Author)

Abstract Prahok (fermented fish paste) is commonly used as a side dish or condiment in
Cambodian cuisine. Because of the condition of the raw material and inadequate hygiene
during processing, this type of fermented fish is frequently loaded with histamine toxicity.
The purpose of this study was to examine the potential histamine-producing bacteria (HPB)
isolated from prahok collected from three provinces in Cambodia and to evaluate the efficacy
of local lactic acid bacteria (LAB) in controlling those HPB. For HPB isolation, a modified
Niven's agar was used and the bacteria species were identified using the Biolog GEN III
Semi-automatic system, while histamine concentration was quantified using a colorimetricenzyme assay. Ten LAB strains from the stock collection of Laboratory Food Biotechnology,
Faculty of Agro-Industry, were used to evaluate their antibacterial activities against HPB
using the well diffusion method and the co-culture method, and compared to the control
which involved growing HPB without LAB. As a result, five identified HPBs isolated fromprahok, including Enterobacter aerogenes, Klebsiella oxytoca, Morganella morganii,
Proteus penneri, and Staphylococcus xylosus, were found to produce histamine at levels of
more than 200 ppm in vitro. A well diffusion and co-culture examination demonstrated thatthe growth of all potential HPB cultured with three LABs (Lactobacillus plantarum,
Lactobacillus gasseri, and Lactococcus lactis) of a total of 10 LAB strains was inhibitedcompared to the control. In conclusion, because the three LABs listed above cannot create
histamine, they are promising candidate LABs to apply in Prahok processing to control HPB
as well as histamine levels in the final product.
Keywords Prahok, histamine producing bacteria, histamine, lactic acid bacteria,
antimicrobial activity

INTRODUCTION
Fish is a vital source of food for millions of Cambodians, who live in a country where bodies of waterabound. The Mekong River, Sap River, Bassac River, and Tonle Sap Lake, Southeast Asia's largest
freshwater lake, are home to hundreds of thousands of fish species, which locals harvest and process
into a variety of fermented dishes such as Prahok, Pa ork, and many others, moreover, a techniquethat has been around since ancient times and continues to this day (Chuon et al., 2014). However,Ⓒ ISERD

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the majority of the production of these fermented fish is still traditionally produced by local
families, where knowledge of hygiene is limited (Ly et al., 2018). Otherwise, the outbreak offoodborne disease caused by bacteria in Cambodia is not publicly reported or taken seriously by theauthority, which ultimately becomes normal in people's livelihoods, obviously, this can cause serious
harm to human health (Chrun et al., 2017).
One of the key chemicals that contributes to food-borne intoxication is histamine (C5H9N3), a
biogenic amine that will cause significant health effects such as diarrhea, rash, nausea, vomiting,fever, abdominal pain, and a variety of other symptoms similar to allergic reactions when consumed
at high levels by humans (Shimoji et al., 2019; Surya et al., 2019). Foods with high histamine content
can lead to histamine intoxication, also known as scombroid fish poisoning. Histidine isdecarboxylated into this biogenic amine by bacterial decarboxylases (Madejska et al., 2022). The
maximum level of histamine content should be 100 mg/L and not exceed 200 mg/L, according totrustworthy food regulators like the European Commission (regulation No. 2073/2005/EC),
Australia-New Zealand Food Standards (ANZFS), the Food Safety and Standards Authority (FSSAI),
and CODEX (Surya et al., 2019; CODEX.,2015). Histamine has been found in raw fish, salted or
fermented fish and smoked fish, and other fish products (Kuda et.al., 2007; Madejska et.al., 2022;
Moon et.al., 2013; Pawul-Gruba and Osek, 2021; Sokvibol et.al., Tao et.al., 2022).
A popular strategy to combat these bacteria recently is known as biocontrol, in which lactic acidbacteria (LAB) are used to produce amine oxidase enzymes, which are used to de-structure thebiogenic amine. LAB has been widely accepted as a safe (GARS) biocontrol in food processing by
many food producers due to its potency as an anti-pathogen (Lim, 2016). The main purpose of thisstudy was to examine the potential histamine-producing bacteria (HPB) isolated from Prahok
collected from three provinces in Cambodia and to evaluate the efficacy of local lactic acid bacteria(LAB) in controlling those HPB.
OBJECTIVE
The objectives of this study are to examine the potential histamine-producing bacteria isolated from
Prahoks collected from three provinces of Cambodia and to evaluate the efficacy of local lactic acidbacteria (LAB) in controlling those histamine-producing bacteria (HPB).
METHODOLOGY
Sample Selection
The sampling for the analysis is collected randomly from three separate provinces' local markets suchas Kampong Thom, Kampong Cham, and Siem Reap. A total of 15 samples were gathered, whichwere then sealed in polyethylene bags in an ice box and delivered right away to the microbiology
Laboratory, Faculty of Agro-Industry, Royal University of Agriculture, Phnom Penh.
Isolation and Identification of HPB
The samples were accurately weighed out at 1.0 g, added to 10 mL of sterile TSB, supplemented
with TSB (3.0%), histidine monohydrochloride (0.1%), and pyridoxal hydrochloride (0.5%),
cultured for 24 h at 30 °C (Lim, 2016). The presence of histamine was demonstrated by streaking on
Niven's agar (Niven et al., 1981), which was incubated at 35 °C for 24 hrs. By monitoring how well
the isolates grew in Niven's medium, supplemented with Tryptone (0.5%), Yeast extract (0.5%), LHistidine 2HCl (2.0%), NaCl (0.5%), CaCO3 (0.1%), Agar (3.0%), and Bromecresol purple (0.006%),
it was determined that the isolates produced histamine. A doubt colony with a purple halosurrounding it and a yellowish backdrop were found (Joosten and Northolt, 1989; Mavromatis
and Quantick, 2002; Refai et al., 2020). The HPB species have been identified by using the Biolog
Semi-automatic MicroStation with Microlog Software System (Model: MicroStation Brand:
BIOLOG) (Al-Dhabaan and Bakhali, 2017).
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Colorimetric Enzyme Assay
To measure the level of PHB, a single colony was chosen and grown in 5 ml of TSB-Histidine brothfor 24h at 35°C and then the result was read by Spectrophotometer (Model: DNM-9602G, Brand:
Nanjing Perlove Medical) using the formula of Histamine (Karami et al., 2021) (Eq. 1).
𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 (𝑝𝑝𝑚) = [(𝐸𝑠 − 𝐸𝑏) ÷ (𝐸𝑑 − 𝐸𝑐) × 40

Eq. 1

Antimicrobial Activity Testing by Applying Well Diffusion and Co-Culture Method
Well diffusion method: Ten LAB kept in the microbiology laboratory at the Faculty of AgroIndustry, Royal University of Agriculture were cultured in 2 ml of De Man, Rogosa, and Sharpe
broth (MRS broth) for 24 hrs. at 30 °C, and then separated using a centrifuge engine (Model: Z 326,
Brand: HERMLE - Germany) running at 6,000 rpm for 15min. HPB is disseminated on MuellerHinton agar (MHA), measured at 90% equal to 10-8 (% transmittance) using a Biolog Turbidimeter(model: MicroStation, brand: BIOLOG), and then holes with a 6 mm diameter are made. The
perforated hole was filled with 100 microliters of LAB, which was incubated for 24 h at 35±2 °C(Cotton et al., 2019; Jahangirian et al., 2013).
Co-culture method: 10% of LABs suspension 9 Log CFU/ml were inoculated with 6 Log CFU/mlof HPB in mixed broth (MRS broth + TSB) in each tube. This experiment was performed underaerobic conditions at 30 °C in the water bath for 72 hrs. Every 24 h, the mixed broths have beenstreaked on selective agar to observe the presence of HPB (Mellefont et al., 2008).
RESULTS AND DISCUSSION
Isolation and Identification of HPB
HPBs were investigated in Prahok from 3 provinces, Kampong Thom, Kampong Cham, and Siem
Reap. After incubating at 30°C for 24 hrs., 15 homogenate Prahok samples were determined to behistamine positive. In the overall 15 Prahok samples, 8 (53%) had histamine levels from 0<100 ppm
which is the level allowed by USFDA (2001), and 6 (40%) were detected with histamine 100<200
ppm had histamine levels beyond Cambodian regulation standards (CS, 2015), and 1 (7%) was thehighest amount of histamine found in Prahok Trey Sandaiy purchased from Kampong Cham province
which was contained 228 ppm of histamine concentration over Cambodian regulation standards >200
ppm (Table 1). Similar to Sokvibol (2022) research on the assessment of biogenic amine levels from
Cambodia fermented fish products showed in the three samples of Prahok products brought from
Kampot, Phnom Penh, and Kampong Chhnang had histamine levels were over the Cambodian
guideline of 200 ppm, while those in 11 samples (Prahok and Teuk Trey) met the FDA's
recommended level of 50 ppm (Sokvibol et al., 2022).
The identification of HPB by the Biolog GEN III Semi-automatic system analysis detected 7
species. 5 of 7 species that can produce histamine over 1000ppm were chosen to evaluate the effectof LAB on HPB (Table 2) including Enterobacter aerogenes (1610 ppm), Proteus penneri (1576ppm), Staphylococcus xylosus (1420 ppm), Morganella moganii (1507 ppm), and Klebsiella oxytoca(1520 ppm). In contrast, 2 more species, Enterococcus faecalis (67 ppm) and Citrobacter youngae(153ppm) also generate histamine, although at levels that were beyond 200 ppm (Table 1). As
research of Moon (2013) investigated the Isolation and Characterization of Histamine- Producing
Bacteria in Fermented Products and found bacteria that could produce histamine such as Morganella
moganii and Enterobacter aerogenes by rRNA testing. (Moon et al., 2013). According to researcher
Tao (2022) studied on Prevalence of Histamine-Forming Bacteria in Two Kinds of Salted Fish at
Town Markets of Guangdong Province of South China was identified six bacteria species in thesamples of salted fish pickled overnight, there are Vibrio alginolyticus, Vibrio rumoiensis (360 to
363 mg/kg), Staphylococcus saprophyticus (95 to 113 mg/kg), Staphylococcus xylosus (65 to 96
mg/kg), Lactococcus lactis (3 mg/kg), and Morganella morganii (2,000 mg/kg) and 1 specie fromⒸ ISERD

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dry salted fish, Enterobacter aerogenes (351 to 352 mg/kg). (Tao et al., 2022).
The presence of histamine found in Prahok products may be caused by several variables,
including the type of fish used, the species of fish used, the location of the fish, and producer
processing techniques. However, histamine is heat stable, and if it was there before automatedprocessing began, it can be detected in the final products (Madejska et al., 2022). As a result, it is not
surprising that foodborne outbreaks are common in Cambodia because in Cambodian food, people
use Prahok as an ingredient in many dishes, and it can be eaten raw by mixing it with lemon and chili.
(Ly et al., 2020; Norng et al., 2011).
Table 1 Histamine level and HPB identification from prahok productsin the three different provinces
Province
Kampong
Thom

Type of Prahok
Trey
Prahok Trey
ChomrusPrahok Trey
ChomrusPrahok Trey
KampleanhPrahok Trey Chdor

Kampong
Cham

Siem Reap

Prahok Trey
SandayPrahok Trey Riel
Prahok Trey
Chongva
Prahok Trey Lait
Prahok Trey Riel
Prahok Trey
KampleanhPrahok Trey
KampleanhHistaminelevel*(ppm)

Scientific nameIdentification bacteria
species

-

144

ND

-

87

Klebsiella oxytoca

Osphronemidae

157

ND

Channa micropeltes

1Enterococcus faecalis
Citrobacter youngae

Histaminelevel**
(ppm)
1520
67
153
1610
-

Wallagonia attu

228

Cyprinidae

13Enterobacteraerogenes
ND

Rasbora tornieri

157

ND

-

Cyprinidae

52
157

ND
ND

-

Osphronemidae

47

ND

-

Osphronemidae

124

Staphylococcus
xylosusProteus penneri
Morganella moganii

Prahok Trey
Osphronemidae
18
KampleanhPrahok Trey Ros
Channa striata
118
Prahok Trey Ros
Channa striata
53
Prahok Trey Pra
Pangasiidae
36
Histamine level*= Histamine level in Prahok samples Histamine,
Histamine level**= Histamine level produced by HPB

1422
1576
1507

ND

-

ND
ND
ND

-

Antimicrobial Activity Testing by Applying Well Diffusion and Co-culture Method
Table 2 displays the results of the inhibition zone measurements for 10 LAB against Enterobacteraerogenes, Proteus penneri, Staphylococcus xylosus, Morganella moganii, and Klebsiella oxytoca.
These substances have significant antibacterial activity against both Gram-positive (Staphylococcus
xylosus) and Gram-negative (Enterobacter aerogenes, Proteus penneri, Morganella moganii, andKlebsiella oxytoca) bacteria. According to Table 2, all 10 LAB showed increased antibacterial
activity when compared to the five HPB, however, Lactobacillus plantarum, Lactococcus lactis, andLactobacillus gasseri had greater zone values. According to the five LAB strains found in Myeolchijeot, the research of the Inhibitory Effect of bacteriocin-producing lactic acid bacteria against
histamine-forming bacteria isolated from Myeolchi-jeot shared 98.3–100% sequence identity withthe following bacteria: Pediococcus acidilactici, Leuconostoc mesenteroides, Enterococcus faecium,
Lactobacillus sakei, and Lactobacillus acidophilus produced an antibiotic substance that inhibitedthe growth of HPB like B. licheniformis, S. marcescens, S. xylosus, A. hydrophila, or M. morganii(Lim, 2016).
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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

Table 2 Growth inhibition of HPB by 10 LABs using well diffusion method
Time(hrs.)

LAB
Lactobacillus mali

24
48
72
24
48
72
24
48
72
24
48
72
24
48
72
24
48
72
24
48
72
24
48
72
24
48
72
24
48
72

Lactococcus
garvieae
Lactobacillus
plantarum
Lactococcus lactis

Lactobacillus
gasseri
Leuconostoc
gelidum
Tetragenococcus
solitarius
Pediococcus
parvulus
Lactobacillus
curvatus
Leuconotoc
citrerum

Staphylococ
cus xylosus
0
0
0
0
0
0
0
0
0
0
0
0
0
8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

HPB [Zone value (mm)]Enterobacter
ProteusMorganella
aerogenes
pennerimorganii
0.0
0.0
0.0
0.0
0.0
0.0
0.0
10.0
10.5
0.0
0.0
0.0
0.0
0.0
12.0
12.4
10.5
14.0
0.0
0.0
0.0
10.5
8.6
12.0
10.5
10.5
13.6
0.0
0.0
0.0
10.6
0.0
12.4
13.9
11.0
15.8
0.0
0.0
0.0
0.0
0.0
12.0
13.9
11.0
17.0
0.0
0.0
0.0
0.0
0.0
11.5
0.0
0.0
12.4
0.0
0.0
0.0
12.4
0.0
12.0
0.0
0.0
13.0
0.0
0.0
0.0
0.0
0.0
9.6
00.0
0.0
10.5
00.0
0.0
0.0
00.0
0.0
10.0
00.0
9.0
10.5
00.0
0.0
0.0
00.0
0.0
10.0
00.0
10.5
10.5

Klebsiella
oxytoca
0.0
0.0
0.0
0.0
0.0
8.6
0.0
0.0
11.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
11.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

Table 3 Three effective LABs performed co-culture analysis to find the presence of HPB
LAB

N

Lactobacillus
plantarum3

Lactococcus
lactis3Lactobacillus
gasseri3

Mixed LAB

3

Time
(hrs.)
24
48
72
24
48
72
24
48
72
24
48
72

Staphylococ
cus xylosus
(-)
(-)
(-)
(-)
(-)
(-)
(+)
(+)
(-)
(-)
(-)
(-)

Enterobacteraerogenes
(-)
(-)
(-)
(-)
(-)
(-)
(+)
(-)
(-)
(+)
(+)
(-)

HPB
Proteuspenneri
(-)
(-)
(-)
(-)
(-)
(-)
(+)
(-)
(-)
(+)
(-)
(-)

Morganella
morganii
(-)
(-)
(-)
(-)
(-)
(-)
(+)
(-)
(-)
(+)
(-)
(-)

Klebsiella
oxytoca
(-)
(-)
(-)
(-)
(-)
(-)
(+)
(+)
(-)
(+)
(-)
(-)

Since they produced better results in good diffusion, Lactobacillus plantarum, Lactococcus
lactis, and Lactobacillus gasseri were chosen to examine the co-culture strategy to find HPB.
According to Table 3, 2 LAB, which included Lactobacillus plantarum and Lactococcus lactis, could
not be detected when co-cultured for 24 hours at 30 °C with the five HPBs. In contrast, Lactobacillus
gasseri proved effective against Enterobacter aerogenes, Proteus penneri, and Morganella moganii
for 48 hours at 30 °C, whereas Proteus penneri, Morganella moganii, and Klebsiella oxytoca wereⒸ ISERD

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examined for after 48 hrs and Staphylococcus xylosus after 24 hrs in mixed LABs.
In research of the control of tyramine and histamine accumulation by lactic acid bacteria using
bacteriocin-forming lactococci showed that L. lactis subsp. lactis produced nisin Z induced the deathof the histamine-producing strain S. thermophilus. However, L. lactis subsp. lactis EG46- produced
lacticin 481 was able to reduce its growth extent and histamine accumulation (Tabanelli et al., 2014)
CONCLUSION
This study showed that the seven different bacterial species, including Enterobacter aerogenes,
Proteus penneri, Staphylococcus xylosus, Morganella moganii, Klebsiella oxytoca, Citrobacter
freundii, and Citrobacter youngae, were found in Prahok products, according to this study. Five of
these seven are known histamine makers since samples show levels of histamine that are higher thanthe 200ppm threshold set by Cambodian regulations. Therefore, Prahok producers must follow theregulatory limitations on histamine that have been defined by international and national standards tomaintain adequate hygiene procedures to prevent cross-contamination and proper handling methods
to control the bacterial development dependent on the fermented fish products. Antibacterial activityof Lactobacillus plantarum, Lactococcus lactis, and Lactobacillus gasseri was stronger than other 7
LAB by using well diffusion method to evaluate the effeteness of grate zone values, whereas inco-culture the antibacterial activity of Lactobacillus plantarum and Lactococcus lactis werestronger than Lactobacillus gasseri in inhibited Enterobacter aerogenes, Proteus penneri,
Staphylococcus xylosus, Morganella moganii, Klebsiella oxytoca, Citrobacter freundii, andCitrobacter youngae. Therefore, Lactobacillus plantarum and Lactococcus lactis can be used as
potential antibacterial agents in Prahok products. In a study performed in Sweden, bacteria on
samples were reduced by cleaning with water (Uhlig et al., 2017). Therefore, to rid the Prahok
product of bacteria and histamine, the prahok manufacturer should clean the fish multiple times withwater.
ACKNOWLEDGEMENTS
This work was funded by Cambodia Higher Education Improvement Project (Credit No. 6221-KH).
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Refai, M.A.E., El-Hariri, M., Ahmed, Z.A.M. and Jakee, J.El. 2020. Histamine producing bacteria in fish.
Egyptian Journal of Aquatic Biology and Fisheries, 24 (7-Special issue), 1-11, Retrieved from DOI https://doi.org/10.21608/ejabf.2020.117856
Shimoji, K., Bakke, M., Hungerford, J.M., Mireles Dewitt, C.A. and Köse, S. 2019. Validation study ofhistamine test for the determination of histamine in selected fish products. Journal of AOAC International,
102 (1), 164-180, Retrieved from DOI https://doi.org/10.5740/jaoacint.18-0177
Sokvibol, C., Arunya, P., Chuleeporn, C., Wanticha, S. and Kriangkrai, P. 2022. Assessment of biogenic
amine level from Cambodia fermented fish products. Food Research, 6 (2), 294-302, Retrieved from DOI
https://doi.org/10.26656/fr.2017.6(2).298
Surya, T., Sivaraman, B., Alamelu, V., Priyatharshini, A., Arisekar, U. and Sundhar, S. 2019. Rapid methods
for histamine detection in fishery products. International Journal of Current Microbiology and Applied
Sciences, 8 (03), 2035-2046, Retrieved from DOI https://doi.org/10.20546/ijcmas.2019.803.242
Tabanelli, G., Montanari, C., Bargossi, E., Lanciotti, R., Gatto, V., Felis, G., Torriani, S. and Gardini, F. 2014.
Control of tyramine and histamine accumulation by lactic acid bacteria using bacteriocin forming
lactococci. International Journal of Food Microbiology, 190, 14-23, Retrieved from DOI https://doi.org/
10.1016/j.ijfoodmicro.2014.08.023
Tao, Z., Wu, X., Liu, W., Takahashi, H., Xie, S., Ohshima, C. and He, Q. 2022. Prevalence of histamineforming bacteria in two kinds of salted fish at town markets of Guangdong Province of South China.
Journal of Food Protection, 85 (6), 956-960, Retrieved from DOI https://doi.org/10.4315/JFP-21-215
Uhlig, E., Olsson, C., He, J., Stark, T., Sadowska, Z., Molin, G., Ahrné, S., Alsanius, B. and Håkansson, Å.
2017. Effects of household washing on bacterial load and removal of Escherichia coli from lettuce and“ready-to-eat” salads. Food Science and Nutrition, 5 (6), 1215-1220, Retrieved from DOI https://doi.org/10.1002/fsn3.514Ⓒ ISERD

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Research article
erdInfluence of Native Trees on Soil Fertility at a
Rainforestation Site in Mailhi, Baybay City, Leyte,
Philippines
LORRAINE CRISTY E. CENIZA
Visayas State University, Baybay City, Philippines

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

MARLITO M. BANDE
Visayas State University, Baybay City, Philippines

VICTOR B. ASIO*
Visayas State University, Baybay City, Philippines
Email: vbasio@vsu.edu.phReceived 23 May 2023

Accepted 20 August 2023(*Corresponding Author)

Abstract The Philippines is one of the few countries in the world which was originally andthoroughly covered by rain forest. The conversion of natural forests to agricultural land uses
has led to land degradation. Soil is a vital resource for human survival in that it is the medium
in which most plants grow, it cleans and stores water, detoxifies pollutants, and plays a keyrole in the regulation of the Earth’s temperature. One of the intentions of rainforestation
farming (planting of native tree species) is to regenerate soil fertility. However, the effect ofnative forest trees on soil fertility is still poorly understood. Hence, this study was conducted
to evaluate the influence of native trees on the fertility status of the soil in Mailhi, Baybay
City, Leyte, Philippines. Two adjacent sites were evaluated and sampled. These were the 22year-old rainforestation farm and the nearby coconut plantation. At each site, a 20 m x 20 m
plot for sampling purposes was established which was divided into four parts. In each part,four (4) composite soil surface samples were collected from a soil depth of 0-20 cm using asoil auger. Each of the four composite samples came from three (3) subsamples. The
subsamples were mixed, and one-half kilogram was placed in properly labelled plastic bags
and brought for processing and laboratory analysis. Results revealed that the rainforestation
farm did not have an effect on water holding capacity and soil pH but significantly increased
the organic matter and total N contents of the soil, when compared with the nearby coconut
plantation. On the other hand, available P and exchangeable K were lower in the soil undernative tree species compared to the coconut plantation. The results indicate that indeed, thenative trees in the rainforestation site have caused important changes to the fertility status of
the soils.
Keywords rainforestation, native trees, soil fertility

INTRODUCTION
The Philippines is one of the few countries in the world that was originally and thoroughly coveredby rainforests (Schulte, 2002). In the past decades, forests were considered one of the most importantresources of Leyte Island. But after the conversion of part of Leyte's original forest vegetation tosecondary forest due to logging operations, kaingin or slash-and-burn cultivation, forest fire, andother natural phenomena such as pest diseases, and natural calamities, large parts of the upland areas
were colonized by farmers (Contreras-Hermosilla, 2000). The conversion of natural forests toagricultural land uses has led to land degradation.
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Asio et al. (2009) explained that soil is a vital resource for human survival in that it is themedium in which most plants grow, it cleans and stores water, detoxifies pollutants, and plays a keyrole in the regulation of the Earth’s temperature. Soil is also the habitat of a multitude of soilorganisms necessary for the cycling of elements and for keeping a healthy environment for humanbeings (Blum, 2007). Worldwide, soil resources are degraded at an unprecedented rate due to various
human activities. Soil degradation means the deterioration of soil properties to the extent that the soil
is no longer productive (Fullen and Catt, 2004).
The Philippines as one of the most severely deforested countries worldwide (Kummer andTurner, 994 as cited by Marohn, 2007), has officially adopted Rainforest Farming for its natural
governing program. As defined in the Memorandum Circular No. 2004-06 issued by the Departmentof Environmental and Natural Resources (DENR), rainforestation farming is a concept in forest
restoration, wherein only indigenous and endemic tree species are used as planting materials which
include but are not limited to dipterocarp species, premium tree species, etc. It is a kind ofreforestation whose aim is to preserve biodiversity expand Philippine forests and simultaneously
sustain human food production. One of the intentions of rainforestation farming is to regenerate soil
fertility. Asio and Milan (2002) reported that rainforestation farming is a helpful strategy to improvesoil quality.
Therefore, this study was conducted to focus on the effects of native trees as rainforestation
species on the nutrient status of the soil. Likewise, the study validated the hypothesis thatrainforestation farming improves the soil nutrient status.
OBJECTIVE
The objective of this study is to evaluate the influence of native trees as rainforestation species on
the fertility status of the soil and to determine the soil properties affected by planting native trees in
the marginal upland site in Mailhi, Baybay City, Leyte, Philippines.
METHODOLOGY
Sampling Site Selection
The field study was conducted in 2016 in the rainforestation site in Mailhi, Baybay City, Leyte (Fig.
1) about 23 km southwest of Baybay at 351 m asl elevation. A preliminary field survey was done toassess and select the sampling area. The rainforestation site was established twenty-two (22) years
ago in 1995.

Fig. 1 Location of the sampling site in Mailhi, Baybay City, Leyte, Philippines
Note: the location was a marginal upland under the coconut plantation before the rainforestation site was established.

Soil Collection and PreparationⒸ ISERD

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Two adjacent sites were selected. One is inside the rainforestation farm, and the other is under thecoconut plantation which is typical of the area. A 20 m x 20 m plot was measured and then divided
into four parts. In each part, four (4) composite soil surface samples were collected from the 0-20 cm
soil depth using a soil auger. Each of the four composite samples came from three (3) subsamples.
The subsamples were mixed, and one-half kilogram was placed in properly labelled plastic bags andbrought for processing and laboratory analysis.
Laboratory analysis includes soil physical properties such as particle size distribution using thehydrometer method (ISRIC, 1995), water holding capacity (Alef and Nannipieri, 1995), and soil
chemical properties such as soil pH analyzed potentiometrically using the soil-to-water ratio of 1:2.5(ISRIC, 1995), soil organic matter obtained using Loss of Weight on Ignition (Schlichting et. al,(1995), total nitrogen using Micro-Kjeldahl method (ISRIC, 1995), available phosphorus analyzed
using Bray 2 method (Bray and Kurtz, 1945) and exchangeable potassium using Metson method(Metson, 1956).
Data Encoding and Statistical Analysis
All data gathered were collated, encoded, and summarized using an electronic spreadsheet editor,
Microsoft Excel 2013. The data were analysed using the Statistical Package for Social Science (SPSS
version 20). The variability of the mean of soil properties was analysed using the one-way analysis
of variance (ANOVA). Moreover, in a case where significant variations at p≤0.05 were identified,
Tukey and Least Squares Differences (LSD) were carried out to compare means.
RESULTS AND DISCUSSION
Soil Physical Properties
Figure 2 shows the particle size distribution of the two sampling sites. Results revealed that in termsof particle size distribution, the soil in the rainforestation had clay and sand contents of 48.3 ±1.98 %and 35.2 ±1.40 %, respectively, while the soil in the coconut plantation had 31.61 ±1.98 % and 50.78±1.40 %, respectively (Fig. 2). The soil textural class was clay and sandy clay loam, respectively.
Based on the results of the statistical analysis, there was a significant difference (p≤0.05) between
the soils in the rainforestation and coconut plantation in terms of the sand and clay contents but nosignificant difference in the silt.

Note: Values with different superscript letters(a-b)
of particle size within the site are significantly
different at p≤0.05; N=32

Note: Values with a different superscript letter (a-b) of
WHC within the site are significantly different at
p≤0.05; N=32

Fig. 2 Particle size distribution of the soil
in rainforestation farm and coconut
plantation

Fig. 3 Water holding capacity in the soil in
rainforestation farm and coconut
plantation

Clays have a large specific surface, often predominantly negatively charged, that retains waterand nutrients. The clay itself may be a source of plant nutrients when it degrades. On the results,
rainforestation soil had higher clay content which indicates good water and nutrient retention.
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On the other hand, Fig. 3 reveals that the water holding capacity in the coconut plantation andthe rainforestation farm had average values of 93.19 ±3.02 % and 90.21 ±3.02 %, respectively. This
result suggests that the kind of land use has not yet influenced the water-holding capacity of the soil.
Soil Chemical Properties
Figure 4 shows slight variations in the pH of the soil. Results showed that the soils in therainforestation site and the coconut plantation had comparable pH values. This indicates that the kindof land use has not yet caused a significant change in the soil reaction.
Soil properties are usually improved when trees are grown due to the increase in surface soilorganic matter (Huxley, 1999). Organic matter refers to all decomposed, partly decomposed, andundecomposed materials of plant and animal origin (FAO, 2006).
Figure 5 shows that the soil in the rainforestation farm had higher organic matter content than
the soil in the coconut plantation (p≤0.05). The higher amount of OM of the rainforestation farm
could be due to its higher organic material addition in the form of litterfall than in the coconut
plantation. The amount of OM in the soil depends on the litter fall (leaves, twigs, bark, etc.)
contributed by trees as the primary vegetation in the area and the consequent losses through thedecomposition of these materials.

Note: Values with different superscript letters (a-b)
of soil pH within the site are significantly differentat p≤0.05; N=32

Note: Values with a different superscript letters (a-b)
OM within the site are significantly different at p≤0.05;
N=32

Fig. 4 Soil pH values of rainforestation
farm and coconut plantation

Fig. 5 Soil organic matter contents ofthe soils under rainforestation
farm and coconut plantation

Soil Nutrient Status
Figure 6 shows higher soil nitrogen content in the rainforestation farm than in the coconut plantation(p≤0.05). It agrees with the study of Atup (2016) where total N contents of the soils under coconut
plantation were low. Bande (2004) reported that during his study the soil in the rainforestation had
0.17% nitrogen content according to Landon (1991) the sufficiency ranges in soil, soils with values
within the range between 0.1-.20 classified as low, while the nitrogen content in the current study
had 0.24 ±.01 % classified as medium in sufficiency ranges in soil. The nitrogen content in the soilof rainforestation significantly improves. Apart from the application of N fertilizers, the main sourceof N in soils is the breakdown and humification of organic matter. The results on total N can therefore
be attributed to the higher OM content of the rainforestation farm compared to the coconut plantation.
The soil in the coconut plantation had a higher available P level compared to the soil in therainforestation farm (Fig. 7). The difference is significant at p≤0.05. This level of available P cannot
easily be explained since P fertilizer application is not being practiced by the coconut owner (Fig. 7).
This may be explained by the periodic burning that occurs in the coconut plantation. It is also possiblethat the cycling of P is faster in the coconut plantation. As coconut is burned, it produces ash whichⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

is a residual material produced when it is burned for energy production. According to Erich (1991),
ashes were found to be more similar to conventional P fertilizer materials and the addition of coconut
ashes to the surface soil in the coconut can increase the availability of P.
As can be seen in Figure 7, available phosphorus in rainforestation farms is 6.97 ±1.08 mg kg-1
while, in coconut plantations, it is 11.66 ±1.08 mg kg-1. According to Landon (1991), the sufficiency
ranges in soil, soils with values within the range between 5-9 mg kg-1 are classified as low. However,the presently available phosphorus in the soil of the rainforestation is higher than the previous result
reported by Bande (2004) 3.4 mg kg-1 values range <5 classified as very low. This indicates animprovement in the availability of P due to the native trees.

Note: Values with different superscript letters (a-b)
of N within the site are significantly different at
p≤0.05; N=32

Note: Values with a different superscript letter (ab) of P within the site are significantly different at
p≤0.05; N=32

Fig. 6 Total N (%) of soils in therainforestation farm andunder coconut plantation

Fig. 7 Available P of the soil in therainforestation farm and undercoconut plantation

Note: Values with the different superscript letter a of K
within the site are significantly different at p≤0.05; N=32

Fig. 8 Exchangeable K of soils in the rainforestation farm and under coconut plantation
Figure 8 shows the average value of exchangeable potassium in the soils in the rainforestation
farm which is 30.57 ±36.74 mg kg-1. It is classified as very low available potassium in the soil basedon Landon (1991). On the other hand, the average value of exchangeable K in the soil under coconut
which is 104.16 ±36.74 mg kg-1 classified as medium in the sufficiency ranges in soil according toLandon (1991). In addition, Bande (2004) reported that the soil in the rainforestation during his study
had 130.5 mg kg-1 which is higher than the current available potassium. This may be explained thatduring the current study, some potassium is still in the leaves and the uptake of the potassium is slow,
while in the soil in the coconut plantation, the possible reason could be the periodic burning of thecoconut husk. Coconut husk ash will never totally replace K fertilizer; however, it can recycle a
substantial proportion of nutrients in a coconut plantation.
CONCLUSIONⒸ ISERD

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Planting native trees as rainforestation species has changed the fertility status of the soil after twentytwo (22) years from its establishment.
The results also revealed that the rainforestation farm when compared with the nearby coconut
plantation did not have an effect on water holding capacity and soil pH but significantly increased
the organic matter and total N contents of the soil. On the other hand, available P and exchangeable
K were lower in the soil under native tree species compared to the coconut plantation. However,based on the previous results of Bande (2004) rainforestation increased total nitrogen and available
P but decreased the exchangeable K of the soil.
ACKNOWLEDGEMENTS
We extend our gratitude to the Institute of Tropical Ecology and Environmental Management andthe Department of Soil Science, Visayas State University, Baybay City, Leyte, for the support forthis study. The authors would also express their sincere thanks to Professor Dr. Barry Noller and theInstitute of Environmental Rehabilitation and Conservation (ERECON) for financial support to thisstudy.
REFERENCES
Asio, V.B., Jahn, R., Perez F.O., Navarette, I.A. and Abit, S.M. 2009. A review of soil degradation in thePhilippines. Annals of Tropical Research, 31 (2), 69-94, Retrieved from DOI https://doi.org/10.32945/
atr3124.2009
Asio, V.B. and Milan, P.P. 2002. Improvement of soil quality in degraded lands through rainforestation farming.
In Proceedings of the International Symposium on Sustaining Food Security and Managing Natural
Resources in Southeast Asia: Challenges for 21st Century, 8-11 January 2002, Chiang Mai, Thailand.
Atup, C.M.C. 2016. Properties and fertility status of soils under coconut in Mt. Pangasugan. Undergraduate
Thesis, Visayas State University, Philippines.
Bande, M.M. 2004. Development of sustainable abaca (Musa textiles Nee) production in a diversified multistrata agroecosystem in Leyte, the Philippines. MS Thesis, University of Hohenheim, Stuttgart, Germany.
Blum, W.E.H. 2007. Bodenkunde in Stichworten (6th ed.). Borntraeger, Stuttgart, Germany. (in German)
Contreras-Hermosilla, A. 2000. The underlying causes of forest decline. CIFOR Occasional Paper No. 30,
Center for International Forestry Research, Retrieved from URL https://www.cifor.org/publications/pdf_files/ OccPapers/OP-030.pdf
Department of Environmental and Natural Resources. 2004. Guidelines in the integration of rainforestation
farming strategy in the development of open and denuded areas within protected areas and other appropriate
forest lands. DENR Memorandum Circular No. 2004-06, Department of Environmental and Natural
Resources, Republic of the Philippines, Retrieved from URL https://faolex.fao.org/docs/pdf/phi178883.pdf
Erich, M.S. 1991. Agronomic effectiveness of wood ash as a source of phosphorus and potassium. Journal ofEnvironmental Quality, 20 (3), 576-581, Retrieved from DOI https://doi.org/10.2134/jeq1991.0047242500
2000030012x
Food and Agriculture Organization of the United Nations (FAO). 2006. Guidelines for soil description (4 th ed.).
Vialedelle Terme di Caracalla, Rome, Italy, Retrieved from URL https://www.fao.org/3/a0541e/a0541e.
pdf
Fullen, M.A. and Catt, J.A. 2004. Soils management problems and solution. Oxford University Press, New
York, USA.
Huxley, P.A. 1999. Tropical agroforestry. Blackwell Science Ltd., USA.
Kummer, D.M. and Turner, B.L. 1994. The human causes of deforestation in Southeast Asia. BioScience, 44(5), 323-328, Retrieved from DOI https://doi.org/10.2307/1312382
Landon, J.R. 1991. Booker tropical soil manual. John Wiley and Sons Inc., New York, USA.
Marohn, C. 2007. Farming on Leyte Island, Philippines, Aspects of soil fertility and carbon sequestration
potential. Ph.D. Thesis, Faculty Agricultural Sciences, University of Hohenheim, Stuttgart, Germany.
Schulte, A. 2002. Rainforestation farming, Option for rural development and biodiversity conservation in thehumid tropics of South East Asia. Shaker Verlag GmbH, Germany.

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Research article
erd

Evaluation of the Effect of Alternate Wetting and Drying
Irrigation on the Growth of Paddy Rice Plant Height Usinga Logistic ModelKISEKI KURASHINA
Graduate School of Agro-Environmental Science, Tokyo University of Agriculture, JapanSARVESH MASKEY
Faculty of Regional Environment Science, Tokyo University of Agriculture, Japan

DICKSON M. MAZIBUKO
Graduate School of Agro-Environmental Science, Tokyo University of Agriculture, Japan

HIROMU OKAZAWA*
Faculty of Regional Environment Science, Tokyo University of Agriculture, Japan
Email: h1okazaw@nodai.ac.jp
Received 21 June 2023

Accepted 10 September 2023(*Corresponding Author)

Abstract Climate change poses a challenge to irrigation water supply, especially in the caseof the continuous flood irrigation method (CF). Alternate wetting and drying irrigation(AWD) is a method of rice cultivation widely used as a mitigation measure for water
consumption, without reducing yields. To better promote AWD in areas where water scarcity
occurs in the future, it is necessary to quantify the impact of AWD on growth. The objectiveof this study was to evaluate the impact of AWD on rice growth under AWD irrigation using
logistic curves. Subsequent analysis demonstrated that by using AWD, irrigation capacity
was reduced by approximately 19%. Additionally, multiple regression analysis indicated thattemperature, sunshine hours, and relative humidity had significant effects on plant growth. A
growth model (RMSE = 2.98 cm to 3.82 cm) was generated by applying a logistic curve
where the daily mean integrated values of meteorological data as explanatory variables andplant growth as the objective variable. Using the model, it was found that AWD increased
the growth rate of the rice plant; however, rice plant growth in the initial period was lower
compared to CF.
Keywords alternate wetting and drying, logistic model, plant growth model,
climatic parameter, water management

INTRODUCTION
Rice is a staple food for more than half of the world's population. According to FAOSTAT (2021),
rice is produced in more than 100 countries, mostly in Asia. Rice is usually grown in flooded fields
until 7-10 days before harvest, which requires large amounts of water. Irrigation water for growing
rice accounts for 34-43% of the world's total irrigation water (IRRI, 2014). Currently, climate change
is increasing the intensity and frequency of extreme weather events. This is expected to change
rainfall patterns, significantly affecting the hydrological cycle, and increasing the number of ricegrowing regions that suffer from water scarcity (IPCC, 2021). In addition, rapid population growthand competing demands for water in Asian developing countries are causing irrigation water
shortages. To ensure stable rice yields in water-scarce regions, more accurate and efficient water use
in all areas of agriculture is required. More efficient water management methods, or water-saving
technologies, are needed to meet the food demands of a rapidly growing population in an increasingly
water-scarce world. One of the water-saving technologies developed for rice cultivation in Asia isalternating wet-dry (AWD) irrigation. This method was developed by the International Rice ResearchⒸ ISERD

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Institute (IRRI) to reduce irrigation water use in paddy fields without reducing yield compared tocontinuous flooding (CF) irrigation. AWD, as recommended by IRRI, is an irrigation method inwhich the water level is lowered to 15 cm below the ground surface, then irrigated to the normal
water level, and then the irrigation is stopped, and the water level is lowered to below the groundsurface repeatedly. AWD is recommended to start a few weeks after transplanting rice plants and tokeep them flooded from one week before to one week after flowering, but to continue until maturity.
In various environments, AWD irrigation has been shown to save water by reducing evaporation due
to soil drying, reducing reactive discharge due to pouring, and reducing subsurface infiltration
without significantly affecting yield (Pandey et al., 2010; Taminato et al., 2016). Many studies havecompared growth under AWD with that of CF, but the effects of AWD irrigation on the growth of
paddy rice grasses have been reported to be different for different nitrogen application rates andgrowing environments (Yakubu et al., 2019; Zheng et al., 2020). Therefore, it is important toquantitatively evaluate the effect of AWD irrigation on the growth of rice plants to assess its impact
more accurately on the growth of rice crops.
OBJECTIVE
The objective of this study is to quantitatively evaluate the impact of climatic factors on rice growth
in AWD irrigation by using a logistic curves equation.
MATERIALS AND METHODS
Conditions of the Plant Growth Experiment
The experiments in this study were conducted from June to September 2022 in an experimental net
room of the Setagaya Campus, Tokyo University of Agriculture in Japan, using 1/1400 pots (Fig. 1).
The soil used for the experiment was loam soil, which was sampled at paddy fields in Nagano
Prefecture, Japan. “Koshihikari” a cultivar of Japonica was used as a rice variety for the experiments.
Rice was grown in two test plots: a CF plot irrigated with continuous waterlogging, which is aconventional irrigation method, and an AWD plot irrigated using the AWD method. Base fertilizer
was applied at 4 kg/10a, 8 kg/10a, and 8 kg/10a of nitrogen (N), phosphorus (P), and potassium (K),respectively (the fertilizer standard for the “Koshihikari” variety), and additional fertilizer wasapplied at 3 kg/10a of both N and K, except P. The base fertilizer was applied after the first day ofplanting (June 1st), and the additional fertilizer was applied 15 to 20 days before the first day of ear
emergence (July 23rd). Fertilizer was applied to the test soil, and three seedlings were transplanted
into each pot. The pots were transplanted on 1st June 2022, and the transplanted pots were placed
randomly in an experimental net room and rearranged every week to avoid differences in growthamong locations. Drying was completed when cracks appeared on the soil surface and a gap between
the pot and the soil could be seen.

Fig. 1 Schematic diagram of experimental pot used in this study (A); rice plant pot (B)Ⓒ ISERD

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Water Management During the Experiment
A 50 cm height polyvinyl chloride pipe having many holes on all sides was inserted into the soil tocheck the water level in the pot. In CF, the water level was checked twice daily, in the morning andevening, and irrigated when the water level reached 15 cm below the soil surface to maintain awaterlogging depth of 3 to 5 cm. In AWD, irrigation was continued from 2 weeks after transplanting
until maturity, except during the mid-drying period and around the flowering period.
Collection of Growth and Climate Data
Plant growth data were collected weekly from 1st June to 15th September (106 days after
transplanting). The growth-related parameters measured were rice plant height. Plant height wasmeasured from the base of the stem at the soil surface to the tip of the extended leaves using ameasuring tape. Meteorological data were measured using a weather station (U30-NRC Logger,
Onset) in the net room for temperature, solar radiation, relative humidity, rainfall, and wind speed.
The measurement period was 122 days from 1st June to 30th September after transplanting. The
weather station was designed to record an average value for every hour.
Fitting a Logistic Curve
The logistic curve is an idealized curve that describes the pattern of population growth or growth ofan organism with an upper bound such as resources (food) and is expressed by Eq. (1).
𝐾

𝑦 = 1+𝑏∙𝑒𝑥𝑝−𝑎𝑥(1)where y is the objective variable, x is the explanatory variable, K is the coefficient (Limit values ofplant height), and a and b are coefficients of the logistic curve.
For each combination of explanatory variable (x) and objective variable (y), the coefficients aand b and the marginal value K were estimated. Fig. 2 shows a graph in which the marginal values
are set to K = 1 and a and b are varied.
In a logistic curve, a is a constant that expresses the rate of increase of the curve on the rise withrespect to the explanatory variables, and b is a constant that varies the period until the curve rises.
The larger the value of a in the growth curve, the faster the growth rate increases, and the smaller thevalue of b, the earlier the growth starts. The combination of coefficients a and b makes it possible toestimate and evaluate the growth pattern.

Fig. 2 Graph of logistic curves
Selection of Explanatory Variables
In this study, plant height growth (cm) in each test plot was used as the objective variable (y), andthe daily mean integrated values in temperature (°C), sunshine hours (hr), relative humidity (%), andwind speed (m/s), which are generally considered to influence plant growth, were used as explanatoryⒸ ISERD

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variables (x). Sunshine hours were calculated from the time when the direct solar radiation was 0.12
kW/m² based on the definition by Japan Meteorological Agency (2014). Multiple regression analysis
was performed to select the explanatory variable (x) by normalizing the integrated values fortemperature, sunshine duration, relative humidity, and wind speed, and standard partial regression
coefficients were calculated. To evaluate the effect of AWD irrigation on the growth of plants, amodel with a single explanatory variable was created to avoid complicating the equation, and thevalues of the coefficients a and b were compared. The evaluation was conducted by comparing thevalues of the coefficients a and b. To evaluate the accuracy of the logistic model, the Root Mean
Square Error (RMSE) between the estimated and measured rice height.
RESULTS AND DISCUSSION
Comparison of Water Usage in CF and AWD
Figure 3 shows the average daily temperature and solar radiation in the net room during the growing
period. The temperatures measured in the net room were higher than normal during the growing
period, except in early June. Solar radiation was below the annual average value during theexperiment (Fig. 3). The daily average wind speed was less than 1.0 m/s on most of the days, althougha maximum instantaneous wind speed of 8 m/s was recorded during the typhoon.

Fig. 3 Periodic change in average daily temperature and solar radiation
During the growing period, 116.5 L of water was used in CF and 94.2 L was used in AWD. The
amount of water saving rate (%) using AWD was calculated such as [(116.5 L - 94.2 L) ×100] /116.5
L. This gives 19% water saved when the AWD method was used. The amount of irrigation and water
applied to the pots from rainfall and irrigation during the trial is shown in Fig. 4. Comparing the
AWD and CF treatments, the number of irrigations was reduced by half, and the irrigation rate was
reduced by about 19%. There were large differences in irrigation rates during the vegetative growthstage (2nd June to 8th July) and ripening stage (2nd to 14th in September), but little difference wasobserved during the reproductive growth stage (21st July to 15th August).

Fig. 4 Irrigation water and rainfall during the growing trial periodⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

Plant Height Growth and Yield
The result of plant height and yield for CF and AWD irrigation methods is shown in Table 1. It wasobserved that the height of the plant at various stages of growth and yield was almost same for theboth methods. However, from the obtained results it can be said that AWD is an efficient method of
irrigation as it can produce the same results with respect to CF with less irrigation water.
Table 1 Plant height (cm) and yield (g/plant) as influenced by water management
Experimental plot
CF
AWD
CF
AWD

14
27.3 ±1.227.6 ±0.6
79104.3 ±0.4*
101.0 ±0.8

Days after transplanting
22
46
47.5 ±1.6
91.0 ±4.444.9 ±2.992.7 ±3.3
92AH105.6 ±2.1*105.7 ±2.0*101.7 ±1.1101.9 ±1.1

5895.6 ±2.8
94.6 ±3.0
Yield (g/plant)
17.9 ±0.5
17.5 ±1.8

The data are presented as mean ± standard deviation., AH: at harvest* Indicates significant difference (p>0.05, Mann-Whitney's U test)

Selection of Explanatory Climatic Parameter Variables for the Logistic Model
The results of the multiple regression analysis are shown in Tables 2 and 3. The absolute values ofthe standard partial regression coefficients were large for integrated temperature, integrated sunshine
duration, and integrated relative humidity, while the value of integrated wind speed was small
compared to other weather factors. Therefore, integrated wind speed was excluded, and integrated
temperature, integrated sunshine duration, and integrated relative humidity were used as explanatory
variables.
Table 2 Results of multiple regression analysis (I)Based on cumulative temperature, cumulative sunshine hours, cumulative relative humidity,
and cumulative wind speed as explanatory variables for grass growth in CF

Explanatory variable
Cumulative
temperature
Cumulative
sunshine hours
Cumulative relative
humidity
Cumulative wind
speed

Partial regression
coefficientStandard partial
regression coefficientt-valueSignificance-0.3441-9.7759-5.7916**0.69154.76674.2914**0.05434.51512.8732**9.2612

1.40982.4472** Indicates significant difference (p<0.05) ** (p<0.01)

Table 3 Results of multiple regression analysis (II)Based on cumulative temperature, sunshine hours, humidity, and wind speed as explanatory variables
for plant height in AWD

Explanatory variable
Cumulative
temperature
Cumulative
sunshine hours
Cumulative
relative humidity
Cumulative wind
speed

Partial regression
coefficientStandard partial
regression coefficientt-valueSignificance-0.3682-10.6270-5.6352**

0.59084.13723.3338**0.06675.63103.2073**

11.4116

1.76452.7414*** Indicates significant difference (p<0.05) ** (p<0.01)Ⓒ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

Assessment of Plant Growth Patterns by Fitting Climatic Parameters in the Logistic Model
The values of each coefficient and its logistic curves are shown in Fig. 5. The logistic model withrice plant height as the objective variable and integrated temperature, integrated sunshine hours, andintegrated relative humidity as explanatory variables had an accuracy of 2.98-3.82 cm RMSE.
Comparing the value of a with that of CF, the growth rate is marginally better for AWD. However,the values of K and b indicate a smaller growth maximum and a longer period before growth begins
compared to CF. Temperature, sunshine hours, and relative humidity were also almost the same. In
other words, higher values of temperature and sunshine hours after transplanting would lead to faster
growth and a longer growth initiation period, leading to differences in final plant height under AWD
irrigation. In the study by Zheng et al. where final plant height was reported as similar results of thisstudy, the temperature and sunlight hours after transplanting were probably low. On the other hand,
Yakubu et al. reported that the final plant height was significantly higher than that of CF may be due
to higher temperatures and sunshine hours after transplanting.

Fig. 5 Fitting logistic curves (left: CF, right: AWD)
(A) cumulative temperature, (B)cumulative sunshine hours, and (C) cumulative relative humidity

CONCLUSION
This study was conducted to clarify the effects of climatic parameters on rice plant growth and yieldusing the AWD irrigation method. Based on the results of this study, the plant height and yield weresimilar to CF, however, AWD saved approximately 19% water compared to CF. A study of weather
factors affecting paddy rice growth revealed that cumulative temperature, cumulative sunshine hours,
and cumulative relative humidity have a significant impact on paddy rice growth. A logistic model
was fitted with the objective variable as paddy plant height and the explanatory variables asⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

integrated temperature, integrated sunshine hours, and integrated relative humidity. A logistic model
created was accurate from 2.98 cm to 3.82 cm by RMSE. The results showed that AWD had a slightly
better growth rate than CF, however, the initial growth of the rice plant was slower compared to CF.
ACKNOWLEDGEMENTS
We would like to thank Ms. Hiyori ISEKI and other undergraduate students who belong to the
Department of Bio-production and Environment Engineering at the Tokyo University of Agriculture
for their cooperation in the research. We also thank Dr. Eiji Matsubara of JIRCAS for introducing
and explaining AWD irrigation in detail.
REFERENCES
Belder, P., Bouman, B.A.M., Cabangon, R., Guoan, L., Quilang, E.J.P., Yuanhua, L., Spiertz, J.H.J. and Tuong,
T.P. 2004. Effect of water-saving irrigation on rice yield and water use in typical lowland conditions in Asia.
Agricultural Water Management, 65 (3), 193-210, Retrieved from DOI https://doi.org/10.1016/j.agwat.
2003.09.002
Elliott, R.D. and Saito, K. 2021. Impact of management practices on weed infestation, water productivity, rice
yield, and grain quality in irrigated systems in Côte d'Ivoire. Field Crops Research, 210, 108209, Retrieved
from DOI https://doi.org/10.1016/j.fcr.2021.108209
Food and Agriculture Organization (FAO). 2021. FAOSTAT, World, Retrieved from URL https://www.fao.
org/faostat/en
Intergovernmental Panel on Climate Change (IPCC). 2022. Climate change 2022, Impacts, adaptation, andvulnerability. IPCC, Retrieved from URL https://www.ipcc.ch/report/ar6/wg2/
International Rice Research Institute (IRRI). 2014. Water management, IRRI rice knowledge bank. Retrieved
from URL http://www.knowledgebank.irri.org/step-by-step-production/growth/water-management
Mboyerwa, P.A., Kibret, K., Mtakwa, P.W. and Aschalew, A. 2021. Evaluation of growth, yield, and waterproductivity of paddy rice with water-saving irrigation and optimization of nitrogen fertilization.
Agronomy 2021, 11 (8), 1629, Retrieved from DOI https://doi.org/10.3390/agronomy11081629
Pandey, N., Verma, A.K and Tripathi, R.S. 2010. Response of hybrid rice to the scheduling of nitrogen andirrigation during the dry season. ORYZA-An International Journal on Rice, 47 (1), 34-37, Retrieved from
URL
https://www.indianjournals.com/ijor.aspx?target=ijor:oryza&volume=47&issue=1&article=006&
type=pdf
Taminato, A. and Matsubara, E. 2016. Impacts of two types of water-saving irrigation system on greenhousegas emission reduction and rice yield in paddy fields in the Mekong delta. Transactions of The JapaneseSociety of Irrigation, Drainage, and Rural Engineering, Retrieved from DOI https://doi.org/10.11408/jsidre.84.I_195
Yakubu, A., Ofori, J., Amoatey, C. and Kadyampakeni, D.M. 2019. Agronomic, water productivity andeconomic analysis of irrigated rice under different nitrogen and water management methods. Agriculture
Sciences, 10, 92-109, Retrieved from DOI https://doi.org/10.4236/as.2019.101008
Zheng, C., Zhang, Z., Hao, S., Chen, W., Pan, Y. and Wang, Z. 2020. Agronomic growth performance of super
rice under water-saving irrigation methods with different water-controlled thresholds in different growthstages. Agronomy 2020, 10 (2), 239, Retrieved from DOI https://doi.org/10.3390/agronomy10020239Ⓒ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

Research article

Key Factors Influencing Education Disparity in the LowGrown Tea Cultivation Community of Sri Lanka
YUKO FUKUDA*
Organization for the Strategic Coordination of Research and Intellectual Properties,
Meiji University, Ikuta, Japan
Email: yukotsu70@yahoo.co.jp
Received 12 July 2023

Accepted 20 September 2023(*Corresponding Author)

Abstract Education indicators in Sri Lanka compare well with other South Asian countries
relative to economic standards. However, educational disparities between living sectors(urban, rural, individual farming, and estate) remain, and children in the estate sector face thegreatest challenges in improving educational attainment. Tea is a prominent industry in theestate sector, with several common types of tea cultivation management, and the educational
attainments of children whose parents work in this sector have been previously found to varydepending on the cultivation management type in which their parents are involved. This study
aimed to investigate the factors that contribute to children's educational disparities,
specifically by focusing on the different management types within the low-grown tea
cultivation community. This study adopted a two-step mixed-method analysis. The first step
examined children’s educational disparity among four living sectors across 9 tea-growing
districts, using data from the Child Activity Survey 2008/09. The second step investigatededucational disparities within three management types (plantation companies, private estates,
and individual farming) of the tea cultivation community. Quantitative analysis employedcross-tabulation, while qualitative analysis relied on questionnaires and interviews. Six
indicators of the children's environment were considered key factors influencing educational
disparities, with school enrollment status as an indicator of educational attainment. The
quantitative analysis revealed that children in the estate sector had the lowest school
enrollment rates, particularly those belonging to private estate communities. Contrary to thewidely-held belief that household income is a significant factor in educational disparities, it
was not found to be statistically significant within either the estate sector or the tea cultivation
community. The qualitative analysis indicated that relocation is one of the factors contributing
to the low enrollment status of private estate children. It also suggested that other indicators,
such as household income, are interconnected factors contributing to educational disparities.
Keywords Sri Lanka, children, educational disparity, tea cultivation community

INTRODUCTION
Tea cultivation was introduced as an alternative to coffee during the colonial period in the 19th
century. The laborer houses, known as "line houses," were originally built for seasonal workers
migrating from South India for coffee harvesting. However, these houses continue to be used evenafter transitioning to tea cultivation, lacking adequate facilities and sanitation. This has raised
concerns regarding children's development (SOMO 2006). In the 1970s, significant changes in the
management structure of the tea cultivation community (referred to as “tea community”) occurred
due to land reforms, leading to a diversification of management types. Furthermore, economic
reforms during the same period brought about overall economic and social development, which
impacted the living conditions of those involved in tea cultivation1.
Previous studies have focused on examining the impact of living environments on educational
1 In the 1970s, the existing large-scale estates were nationalized and later bundled into 22 Regional Plantation Companies(RPC). In 1995, management was handed over to the private sector. Concurrently, Private Estates (PE) expanded the tea
cultivation area and the number of Individual Farmers (IF) increased.

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attainment in various fields, with household income considered a crucial factor influencing
educational disparities. However, an interesting finding has been reported: despite a higher
prevalence of low-income households among tea farmers, all children achieve good educational
attainment within the tea community (Fukuda, 2023). This suggests that there may be additional
factors beyond household income that are associated with educational disparities. Thus, based on the
management types in the tea community, this study attempted to identify factors that affect education
disparities among children and explored relations between “the living environments surrounding
children” and “their educational attainment” by using quantitative and qualitative methods.
MATERIALS AND METHODS
Methods
This study employed a two-step mixed-method analysis to investigate factors contributing to
educational disparities. In the first step, disparities among children in four living sectors across 9 teagrowing districts2 were examined. The second step focused on disparities among three management
types within the tea community. Quantitative and qualitative methods were employed. The
quantitative analysis examined educational attainment differences among the living sectors and thetea community. The qualitative analysis was conducted to identify key factors contributing to
education disparities and explore the relations between these factors, based on the different
management types of tea cultivation in the low-grown tea areas. Six indicators of the living
environment are “household income, house-head’s education level, housing condition, the safety ofliving space, and child economic and housework”, with “school enrollment” as an indicator of
educational attainment.
Data Collection
In the first step, descriptive analysis was conducted using data from the Child Activity Survey
2008/09 (CAS 08/09), which represented all of Sri Lanka except the Northern Districts. For thisstudy, 3,683 households and 6,119 children were extracted from the 9 districts where tea estates areconcentrated. Three living sectors were originally defined: urban, rural, and estate. However, due tothe significant increase in individual farming, the classification scheme was expanded to include
urban, rural, individual farming, and estate. In the second step, questionnaires based on CAS 08/09
were distributed, and interviews were conducted between 2013 and 2015 in the Kotapola division of
the Matara district. This area is known for tea cultivation conducted by regional plantation companies(referred to as "plantation companies" or "RPC"), private estates (referred to as "PE"), and individual
farming (referred to as "farming" or "IF"). The sample included 302 housing units, with 103 from
RPCs, 100 from PEs, and 99 from IFs across 11 villages. Additionally, interviews were conductedwith school teachers, estate owners and managers, and local government officers.
RESULT AND DISCUSSION
Educational Disparity Among Living Sectors in Tea-Growing Districts
Despite Sri Lanka’s focus on social policies, the estate sector in the country struggles with low
educational attainment and quality of education (UNICEF 2014). Sri Lanka, formerly Ceylon, is ageographically diverse and naturally abundant island. Within the nine targeted districts of the CAS
08/09, the sample distribution was as follows: 473 children (7.9%) in urban areas, 3,352 children(54.8%) in rural areas,1,289 children (21.1%) in farming communities, and 1,005 children (16.4%)
in the estate sector.
Table 1 presents the enrollment status and grade repetition/dropout experience of children. Out
2 9 districts include Kalutara, Kandy, Matale, Nuwara eliya, Galle, Matara, Badulla, Ratnapula and Kegalle.

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of 6,119 children, 5,147 were attending school, 633 were not attending, and the status of 339 children
was unknown. Notably, the estate sector had the highest percentage (15.5%) of children not attending
school, compared to approximately 10% in other residential sectors.
Upon completing primary education, some students face challenges in continuing theireducation. When examining enrollment status based on the level of education, there were 4,562
children belonging to the compulsory education age group, 97% of whom attended school overall,while only 93% were in the estate sector. For children aged 15-17, 66% overall progressed to seniorsecondary education, but the rate dropped significantly to 27% in the estate sector.
Table 1 Enrollment status and grade repetition/dropout experience
Livingsectors
Urban
Rural
Farming
EstateTotal

Enrollment status

Grade repetition / Dropout experience

AttendNon-Attend

Total

401 (89.3%)
2,855 (90.0%)788 (89.9%)
1,103 (84.5%)
5,147 (89.0%)

48 (10.7%)317 (10.0%)
144 (10.1%)
124 (15.5%)633 (11.0%)

499
3,172
932
1,227
5,780

Nonexperience
408 (88.9%)
2,823 (87.4%)
692 (89.9%)113 (71.3%)
5,056 (85.4%)

ExperienceTotal

51 (11.1%)
407 (12.6%)
692 (10.2%)
1,133 (28.7%)
866 (14.6%)

459
3,230
971
1,262
5,922

Furthermore, 5,056 children had no experience of grade repetition or dropping out, while 866
children had such experiences. The proportion of children with these experiences varied acrosssectors, with 11% in urban, 13% in rural, 10% in farming, and a significantly higher 29% in the estate
sector. These findings highlight significant differences in the educational attainments of children
between the estate sector and other sectors.
Impact of Household Income on Educational Attainment
Past studies have often mentioned that out-of-school attendance is deeply rooted in structuralinequalities and disparities. Extensive research has been conducted in various fields to investigate
the impact of the family environment on educational attainment, with household income identified
as one of the key factors affecting educational disparity (Harsh and Paul 2007).
The average monthly income for all 3,682 households is approximately Rs. 16,000, with urban
Rs. 25,595, rural Rs. 17,075, farming Rs. 20,765, and estate Rs. 10,585. In terms of income groups,
approximately 50% of households in urban areas belonged to the high-income group, while in ruraland farming areas, it ranged from 30% to 40%. However, in the estate, only 4% of households
belonged to the high-income group, and a significant 59% lived under 10K, indicating that estatechildren are much more likely to face poverty than those who live in other sectors.

Fig. 1 Educational attainment within living sectors by income group
Figure 1 shows the relationship between household income and educational attainment. In nonestate sectors, higher household income is associated with improved enrollment and reduced grade
repetition/dropout rates. However, this pattern is not observed in the estate sector. Further analysisⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

reveals that while enrollment status and grade repetition/dropout status are statistically significant
overall, they are not significant within the estate sector. This suggests that factors other than income
are related to the educational disparities within estates.
Educational Disparity within Tea Cultivation Communities
Sri Lanka is divided into 25 districts and 9 provinces, with 331 divisions and 14,021 GN divisions.
The study focuses on the Kotapola division in the Matara District, which consists of 37 GN divisions.
Data collection and interviews were conducted in four selected divisions characterized by low living
conditions. This area is known for its low-grown tea, with diverse management types due to historicalbackgrounds. In contrast to the upcountry, where large tea plantations were established on hillsides
during the colonial era, tea cultivation in the low-grown tea areas has evolved on land adjacent to thevillages (Wenzlhuemer, 2008).
Table 2 provides an overview of households and children categorized by management types,
including the enrollment status of children. The cross-tabulation analysis revealed a statisticallysignificant difference among the three types of management communities. It is noteworthy thatchildren living on private estates had lower enrollment rates compared to the other management types,
with 22.4% of children in the private estate community not attending school. In contrast, only 6.9%of children in the RPC community and none in the IF community were not attending. (Fukuda, 2023)
Table 2 Family overview and enrollment status by management types
Management
types
Plantationcompany (RPC)
Private estate(PE)
Individual
farming (IF)
Total

Enrollment
status

Boy

Girl

Total

Enrollment status
AttendNon-attend

103

106

90

196

175 (93.1%)

13 (6.9%)

100

117

88

205

149 (77.6%)

43 (22.4%)

99

72

61

133

131 (100.0%)

302

295

239

534

455 (89.0%)

56 (11.0%)

Living Environments and Their Impact on Educational Attainment
Figure 2 illustrates the relationship between six living environment indicators and children's
educational attainment. The data indicates an overall trend where lower attendance is linked to factors
such as low household income, lower education levels of the household head, lack of ownership orresiding in a line-house, reduced security of living space, and extended hours of activities.

Fig. 2 Impact of living environments on educational attainmentⒸ ISERD

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Table 3 presents the statistical results of the cross-tabulation analysis, investigating therelationship between the living environments and children's enrollment status within the tea
community. The findings indicate that within tea communities, household income alone does not
significantly impact educational disparities. Instead, factors such as the education level of thehousehold head, housing condition, safety of the living space, and children's economic activities
show a significant association with educational disparities. These findings suggest that some of thesefactors may play more influential roles in shaping educational disparities.
Table 3 Relationship between living environments and enrollment status
Family environments
Household
Head Education
IncomeNon-significant
Significant

Living conditions
Housing

SafetySignificant

Significant

Child activities
EconomicHousework
work
Non-significant

χ-square test (p=0.05): Significant = Different, No significant =No different*Economic work: Due to the sample size, statical analysis could not be conducted.

Factors Behind Educational Disparity
Table 4 provides the comparisons of the six environmental factors. It indicates that children living in
the IF community engaged in economic and housework to some extent, but they had favorable
housing conditions and living conditions. Children living in the RPC community had a lower
household income and housing conditions, but they fared relatively better in the other four
environmental factors. Children living in the PE community had moderate household income andhousing conditions, but they experienced poorer levels in the remaining four environmental factors.
This discrepancy within the tea community can be attributed to the social structure of eachmanagement type.
Table 4 Quality of living environments within tea cultivation communities
Management type
Plantationcompany (RPC)
Private estate(PE)
Individual
farming (IF)Family environments
HouseholdHeadincomeeducation

Living conditions
Housing

SafetyChild activities
EconomicHousework
work

Low

MiddleLow

MiddleNo

Low

MiddleLow

Middle

Low

High

High

High

High

High

High

MiddleMiddleThe social structure varies according to management type. Individual farmers (IFs) are selfemployed and rely on income from tea harvesting and side jobs. They have higher average incomes,
but a significant number of them fall below the poverty line. In contrast, wages for workers in RPCs
are determined through wage board discussions involving the RPCs, trade unions, and thegovernment. In contrast, PE workers' salaries are decided individually by each estate, potentiallyresulting in lower wages and fewer benefits, particularly in medium and small PEs. The higher
average household income on PEs compared to RPCs can be attributed to multiple-income earners
Housing types also differ. IF households typically own their housing, while most children in theestate community live in line houses provided by the estate. A notable difference between the RPC
and PE households is their right to residency. Those in the RPCs can stay in line houses after
retirement, even if their family members are not working. In contrast, PE residents can only stay ifthey or their family members are employed, except for some large estates. The better housing
arrangement in PEs compared to RPCs can be partly attributed to some PE workers commuting fromoutside the estate and owning their own homes. Additionally, the education environments also vary.
Children from the IF community have more opportunities to access education and attend relatively
large schools in town. Conversely, children in the RPC and PE communities have fewer opportunitiesⒸ ISERD

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to access education. Children in the RPC community primarily attend estate schools (with schoolbuses for distant homes). Similarly, children in the PE community typically attend schools locatednearby (Fukuda, 2023).
Reasons for School Non-attendance
As shown in Table 2, all of the IF children were attending school, while 13 RPC and 43 PE children
were not attending. Table 5 provides the main reasons explained by the families of 39 non-attending
children. The most common reasons were 'financial difficulty' (10 children), 'relocation between
estates' (9), and 'child dislikes school/study' (7). Other reasons included 'child labor' (5), 'inadequate
documents and procedures' (4), and 'disability' (4).
Table 5 Reasons for school non-attendance
Financialdifficulty

Relocation

Adjustment
to school

Economic
activities

Inadequate
paperwork

DisableOthersNo answer

3
7

2
7

3
4

2
3

4

3
1

4

1
16

No
relocation

RPC
90(100.0%)1(1.1%)
89(98.9%)
9(100.0%)2(22.2%)
7(77.8%)

PE
84(100.0%)42(50.0%)
42(50.0%)
24(100.0%)18(75.0%)
6(25.0%)

Total
174(100.0%)43(24.7%)
158(75.3%)
33(100.0%)20(60.0%)
13(39.4%)

Total

99

108

207

RPC
PE

Table 6 Enrollment status: impact of housing ownership and relocation
AttendDoesn’t own house
Owns house
Non-Attend
Doesn’t own house
Owns house
Total

RPC
174
(100.0%)154(85.5%)
20(11.5%)
13(100.0%)9(69.2%)
4(30.8%)

PE
148(100.0%)110(74.3%)
38(25.7%)
43(100.0%)40(93.0%)
3(7.0%)

Total
453(100.0%)264(58.3%)
189(41.7%)
56(100.0%)49(87.5%)
7(12.5%)

187

191

509

AttendRelocatedNo
relocation
NonAttend
Relocated

Furthermore, six estates managers, five school teachers, and five GN officers described thechallenges faced by the children. They mentioned that children growing up on the estates faceeconomic disadvantages, as their families have limited awareness of education due to their educationlevel. Consequently, workers, particularly those employed in PEs, often move from one estate toanother in search of a better salary, which is an obstacle to children's education. In some cases,
children stop attending school due to various factors, such as incomplete procedures by parents,
financial constraints within their families, or difficulties in adapting to new schools. These findings
suggest that the family's economic status and migration between estates impact children's enrollment
status. As shown in Table 2, there was no significant difference between household income andschool attendance status, but there was a significant relationship between housing conditions andschool attendance status. Table 6 presents the relationship between housing ownership andenrollment status, as well as the relationship between relocation and enrollment status. The crosstabulation analysis revealed statistically significant findings within the tea community overall. It isnoteworthy that there is an observed educational gap based on residence rights and relocation
experiences. Specifically, 58.3% of the school-attending children lived in line houses, while 87.5%of the non-attending children did. Among non-attending children on PEs, only 7.0% belonged tofamilies who owned their housing, while 93.0% lived in line houses. As for relocation, overall, 24.7%
of school-attending children had experienced relocation, while 60.6% among non-attending children.
Among non-attending children in PEs, 25.0% had not experienced relocation, but 75.0% had.

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Exploring the Process of Educational Disparity by Interrelated Living Environments
The cross-tabulation analysis conducted in this study revealed that household income was not directly
associated with educational disparity in the estate sector or the tea community. However, insightsfrom interviews highlighted the link between household income and residential mobility.
Figure 3 illustrates the process of educational disparities by the living environments surrounding
children, with a focus on residency rights, which were found to be statistically significant andemphasized in the interviews. Families without residency right tend to move from one estate toanother, influenced by their lower income. That is families with lower incomes frequently relocate
between estates in search of better employment conditions. These relocations may require children
to change schools, which negatively impacts their educational attainments because of inadequate
paperwork, additional cost, or difficulty adjusting to a new school environment.
No Right of Residency

Low Income Level
Relocation

Safety of Living SpaceChild Activities
・Paperwork
・Additional Costs
・Adequate school

Perception ofHouse Head

School AttendanceFig. 3 Process of educational disparities in private estate communities
Furthermore, families without a residential right may be less willing to invest in housing andnecessary facilities, which is potentially leading to unsafe living spaces and an increase in the time
the children spend helping with housework, in turn negatively affecting their education attainment.
Moreover, children often play an important role in low-income families, not only as income providers
but also as chore providers. Long hours of engagement in these activities can affect their learning
time and is one of the factors that widen the educational gap. families with low educational
backgrounds often lack a concrete understanding of the importance of education or a knowledge ofproper sanitation or are unaware of the adverse consequences of child labor, which give a negative
impact on children’s education attainment. By exploring the influence of these interrelated living
environments on educational disparity, we gain valuable insights into the challenges that hinder
equitable access to education.
CONCLUSION
This study employed a mixed-method analysis, combining quantitative and qualitative approaches,
to examine the factors contributing to educational disparities. The quantitative analysis revealed thatchildren in the estate sector, particularly those on private estates, had the lowest school enrollment
rates.
Contrary to the prevailing belief that household income is a significant factor in educational
disparities, the findings indicated no statistically significant relationship between household income
and educational disparities within the estate sector or the tea community. However, the qualitative
analysis suggested that relocation is one of the factors influencing the low enrollment status amongchildren in private estates. Furthermore, it revealed the presence of other interrelated factors,
including household income, which collectively contribute to the differences in enrollment statusⒸ ISERD

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among the three management types.
One limitation of this study was the absence of questions about relocation in the initialquestionnaires. Further research could focus on comparing the factors that contribute to education
disparities between the high-grown and low-grown tea areas of Sri Lanka.
ACKNOWLEDGEMENTS
I would like to express my heartfelt gratitude to all the interviewees who generously dedicated theirvaluable time to participate in this study. I am deeply grateful for the support, assistance, and valuableadvice provided by the individuals who contributed to this research.
REFERENCES
Aturupane, H., Glewwe, P. and Wisniewski, S. 2007. The impact of school quality, socio-economic factors
and child health on students’ academic performance, Evidence from Sri Lankan primary schools. World
Bank, D.C, USA, Retrieved from URL https://openknowledge.worldbank.org
Department of Census and Statistics, Government of Sri Lanka (DCS). 2011. The child activity survey 2008/09.
DCS, ISBN 978-955-577-748-3, Sri Lanka, Retrieved from URL https://www.ilo.org/ipecinfo/product/
download.do?type=document&id=18735
Department of Census and Statistics, Government of Sri Lanka (DCS). 2013. Classification of administrative
divisions by quality of housing. DCS, ISBN 978-955-577-823-7, Sri Lanka.
Fukuda, Y. 2023. Disparity in quality of life and education attainment of children within the tea sector, Casestudy in low-country in Sri Lanka. IJERD, 13 (2), 119-126, Retrieved from URL https://iserd.net/
ijerd81/Volume8-1-27.pdf
Herath, D. and Weersink, A. 2009. From plantations to smallholder production, The role of policy in the
reorganization of the Sri Lankan tea sector. World Development, 37 (11), 1759-1772, Retrieved from DOI
https://doi.org/10.1016/j.worlddev.2008.08.028
SOMO. 2006. Sustainabilitea, The Dutch tea market and Corporate Social Responsibility. SOMO, ProFound& India Committee of the Netherlands, Retrieved from URL https://www.somo.nl/sustainabilitea/
UNICEF. 2014. All children in school by 2015, Global initiative on out-of-school children, South Asia regional
study covering Bangladesh, India, Pakistan and Sri Lanka. UNICEF Regional Office for South Asia,
Kathmandu, Nepal, Retrieved from URL https://unesdoc.unesco.org/ark:/48223/pf0000226221
Wenzlhuemer, R. 2008. From coffee to tea cultivation in Ceylon, 1880-1900, An economic and social history.
Brill’s Indological Library, BRILL, The Netherlands.

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Research article
erd

Growth and Mineral Uptake of Moringa oleifera Lam. in
Low-Permeability Soils at Different Salinity Levels
TAKASHI KUME*
Graduate School of Agriculture, Ehime University, Matsuyama, Japan
Email: kume@ehime-u.ac.jp

CHULEEMAS BOONTHAI IWAI
Department of Agriculture, Khon Kaen University, Khon Kaen, Thailand

TETSUYA SHIMAMURA
Graduate School of Agriculture, Ehime University, Matsuyama, JapanSHINSUKE HARUTA
Graduate School of Agriculture, Ehime University, Matsuyama, Japan
Received 24 July 2023

Accepted 1 October 2023(*Corresponding Author)

Abstract This study investigated the uptake of minerals by moringa (Moringa oleifera Lam.)and examined the salt tolerance of moringa under different salinity treatments (0, 4, 8, and16 dS/m). The effect of root growth on soil permeability at different salinity levels was also
examined. Moringa showed significant negative effects of salinity on growth parameters at 8
dS/m. Significant growth inhibition was observed for moringa at 16 dS/m. The higher C/F
ratios, calculated as (assimilated organ mass)/ (non-assimilated organ mass), in moringa in
the 16 dS/m treatment may be due to the inhibition of nitrogen uptake by the roots, causing
photosynthesis in the leaves to produce assimilates to sustain the body of the tree. In eachmoringa organ, the Na concentration increased as salinity increased. Significant differencesin concentration were observed for Ca and K in leaves and Fe in stems at different salinity
levels. The correlation analysis showed that only Mg and P in the branches and Fe and Mg
in the stems were significantly negatively correlated with Na concentration, suggesting that
increases in Na concentration cause limited inhibition of mineral uptake by moringa. A
significant positive correlation was found between Na and P in the roots. It was suggested
that moringa roots may have promoted growth by increasing P uptake in response to increased
Na. There was a positive correlation between the length of moringa main roots and saturated
hydraulic conductivity. The saturated hydraulic conductivity of the soil without moringa
cultivation was 1.1 × 10-6 cm/s, and moringa root growth increased the saturated hydraulicconductivity by two orders of magnitude (10-4 cm/s) at 0 and 4 dS/m and by one order ofmagnitude (10-5 cm/s) at 8 and 16 dS/m.
Keywords moringa, soil salinity, root growth, mineral uptake, soil permeability

INTRODUCTION
Soil salinity is a major threat to land globally, and 833 million ha of soil is affected by salt (FAO,
2021). Coastal paddy fields in the Tohoku region were salinized by the tsunami caused by the Great
East Japan Earthquake. In addition to natural disasters, coastal areas are also said to be widely
affected by salt damage due to sea level rise caused by climate change. In addition, it has been
calculated that the saline front will move up to 20 km upstream of the Mekong Delta in the 2030s
due to climate change (Khang et al., 2008).
Saline soils need to be amended by improving soil permeability to discharge salts from soil
layers. It is known that tree roots, such as Alnus Mill. roots, enhance soil permeability (Vergani andGraf, 2015). However, other studies suggest that the effect of trees and roots on soil permeabilityⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

depends on soil type, soil and land use history, and vegetation cover type (Bonell et al., 2010).
Therefore, the effect of each plant on the improvement of soil permeability should be investigated.
Moringa (Moringa oleifera Lam.) is highly salt tolerant, and all parts of moringa can be used
for multiple purposes (Horn et al., 2022). Moringa seedlings survived up to 8 dS/m with a slight
reduction in biomass chlorophyll a, crude protein, and mineral concentrations with increasing salinity(Nouman et al., 2012). Two-month-old moringa transplants might be NaCl tolerant up to 8 dS/m(Elhag and Abdalla, 2014). Farooq et al. (2022) investigated the impact of various salinity levels on
the root attributes of moringa and revealed higher salinity levels (7.5 dS/m and 11.5 dS/m)significantly minimized the root surface area compared to 1.5 dS/m and 3.5 dS/m. These results showthat salt tolerance has been studied, but a comprehensive assessment of mineral concentrations ofmoringa under saline conditions has not been completed. Research on root growth parameters has
been conducted; however, the effect of moringa root growth on the improvement of soils has notbeen examined.
OBJECTIVE
This study investigates the growth and mineral uptake of moringa and the salt tolerance of moringa
at different salinity levels. Root growth and its effect on soil permeability in soils with different
salinity levels were also examined.
METHODOLOGY
A cultivation experiment was conducted in a greenhouse for two months. Root boxes (width: 60 cm,
height: 40 cm, depth: 10 cm) made of acrylic plates were used for the cultivation experiments. The
root boxes were filled with a 20-cm-deep plow layer and a 10-cm-deep plow layer, as shown in Table
1. The soil texture of paddy soil was CL (Clay Loam) and the soil hydraulic conductivity of twolayers of soil as shown in Table 1 was 1.1 × 10-6 cm/s.
Table 1 Physical properties of soils in the cultivation experiment

Plow layer
Plow sole layer

Dry bulk
density(Mg/m3)
1.2
1.7

Particle density(Mg/m3)A mixing ratio of experimental soil

2.53
2.59

Paddy soil (CL)Gravel

100%
75%

0%
25%

The soils were subjected to four salinity treatments: one without NaCl (0 dS/m) and three with
NaCl added to the soil to adjust the soil salinity to ECe = 4, 8, and 16 dS/m. Indian moringa seeds
were used, and a total of 24 sample trees were grown, with six moringa tree replications in eachsalinity treatment. 10 mm irrigation water was supplied when the soil surface was dried, and no
fertilizer was used.
Growth parameters (tree height and dry matter (DM) weight of leaves, branches, stems, androots) were measured. Dry matter weights were determined after samples were oven-dried at 80 °C
for 48 hours. The C/F ratio was calculated as the (assimilated organ mass) ÷ (non-assimilated organmass), and the aerial C/F ratio was calculated as (assimilated organ mass) ÷ (aboveground nonassimilated organ mass); these ratios were used as indices to describe the structure of material
production systems (Nomoto and Yokoi, 1981).
The mineral concentrations in each organ of the harvested moringa were determined by ICP
optical emission spectrometry (Varian, Vista-MPX). A soil permeability experiment was performed
after harvesting the aboveground portions of the moringa plants, with the roots remaining in the rootbox. The root box was placed in a bucket filled with water and it was capillary saturated, then water
was ponded to a depth of 10 cm, and changes in water level over time were recorded. The measured
data were substituted into Darcy's equation to obtain the saturated soil hydraulic conductivity.
ANOVA was performed at the 5% significance level for the height, dry matter, and mineralⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

concentration of each part of moringa grown under each salinity condition. When the ANOVA
showed significant differences, multiple comparisons (Tukey test) were used to test between samples.
RESULTS AND DISCUSSION
Effect of Salinity Treatments on Growth Parameters
Significant differences were found in tree height, root dry matter weight, total dry matter weight, andthe C/F ratio under different salinity levels (Table 2). There were no significant differences between
0 dS/m and 4 dS/m in any of the parameters. All the parameters except the CF/ratio at 8 dS/m hadsignificantly smaller values than those at 0 and 4 dS/m. All the parameters except the C/F ratio at 16
dS/m had significantly smaller values than those at the other salinity levels. The C/F ratio at 16 dS/m
(0.346) was significantly larger than that at 4 dS/m (0.164). Noumen et al. (2012) showed thatmoringa was able to survive under 8 dS/m conditions with little reduction in nutritional value. Elhag
and Abdalla (2014) showed that moringa is salt tolerant up to 8 dS/m, with significantly lower values
for tree height and leaf dry weight at 16 dS/m. Farooq et al. (2022) also concluded from theirexperiment results that germination rate, root surface area, and aboveground growth are largely
unaffected at salinity levels up to 3.5 dS/m. Our results were similar to those of previous studies.
Table 2 Effects of salinity treatments on growth parameters
Different letters indicate significant difference at p < 0.05 (DM: dry matter)
Salinity Tree height
treatment(cm)

Leaf DM(g)

Branch DM(g)Stem DM(g)

Root DM(g)

Total DM(g)

0 dS/m

53.5±7.9ab 2.020±0.35 0.977±0.20 2.470±0.62

9.45±2.20ab

14.9±2.6ab

0.164±0.02a 0.711±0.10

4 dS/m

63.5±7.1a 2.390±0.49 1.110±0.28 2.960±0.73

10.20±1.40a

16.7±2.1b

0.164±0.01a 0.650±0.10

8 dS/m

52.5±8.5ab 1.890±0.70 1.030±0.40 2.760±1.20

4.15±0.88b

9.78±2.5a

0.199±0.04ab 0.555±0.04

16 dS/m27.5±3.9b 0.812±0.20 0.307±0.12 0.650±0.24

1.42±0.40c

3.18±0.1c

0.346±0.09b 0.925±0.20

C/F ratio

AerialC/F ratio

Mean ± standard deviation (Data adopted from Kume et al., 2023)

Fig. 1 Composition of dry matter weight by the organ of moringa grownat different salinity levels
The compositions of moringa organs at 0 dS/m and 4 dS/m were similar, while the percentagesof moringa organ dry matter weights at 8 dS/m were approximately the same as those at 0 dS/m, with
a 7% increase in stem dry matter weight and a 10% decrease in root dry matter weight (Fig. 1).
Moringa in the 16 dS/m treatment had 10% greater leaf dry matter weight, 2% greater branch dry
matter weight, 5% greater stem dry matter weight, and 17% lower root dry matter weight thanmoringa in the 0 dS/m treatment, with a significant difference in the C/F ratio (0.164 at 0 and 4 dS/m
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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

compared to 0.346 at 16 dS/m). The aerial C/F ratios were not significantly different among thesalinity levels. Carbon is acquired by aboveground plant parts, and nitrogen is acquired by
belowground plant parts (Hirose, 1988). Individuals showing abnormally high C/F ratios are thought
to die due to an imbalance between assimilated and non-assimilated organs (Higo, 1987). Moringain the 16 dS/m soil, where growth inhibition was observed, showed a significantly higher C/F ratio
than moringa in the other treatments, and that photosynthesis in the leaves produces assimilates tosustain the body of the tree.
Mineral Uptake by Moringa
The Na concentrations in the organs were significantly different among the different salinity levels(Table 3). In each moringa organ, the Na concentration increased as salinity increased. Tukey’s test
showed no significant differences in Na concentration between the 0 dS/m treatment and 4 dS/mtreatment for all organs. In leaves, branches, and stems, the Na concentrations at 8 dS/m and 16 dS/m
were significantly different from those at 0 dS/m. For roots, the Na concentration at 8 dS/m wassignificantly different from that at 0 dS/m and 4 dS/m, and the Na concentration at 16 dS/m wassignificantly different from those at the other salinity levels.
Table 3 Mean values of mineral concentrations in each organ of moringa grown
at various salinity levels
Different letters indicate significant difference at p < 0.05 (DM: dry matter)

LeavesSalinityNa
treatment mg/g DM
0 dS/m 1.110±0.21a

K
Ca
Fe
P
Mg
Cu
Zn
mg/g DM mg/g DM
mg/g DM mg/g DM mg/g DM μg/g DM μg/g DM
24.9±1.4a 8.69±0.89ab 0.166±0.05 7.72±0.86 3.18±0.25 11.3±2.7 50.1±17.0

4 dS/m 0.958±0.13a

21.7±1.9a 10.10±1.20a 0.122±0.09 6.24±0.70 2.58±0.35 10.3±0.9 44.8±8.8

8 dS/m 2.410±0.30b 18.5±0.7ab 7.90±0.36ab 0.183±0.05 5.79±0.91 2.66±0.40 13.8±4.2 82.7±25.0
16 dS/m 1.890±0.35ab 14.2±2.1b 6.10±0.50b 0.120±0.02 5.42±0.87 2.61±0.36 8.03±0.7 34.9±4.3
Stems

BranchesRoots

0 dS/m 0.574±0.08a

24.2±2.3

11.10±1.4 0.538±0.08a 7.21±0.86 3.21±0.50 9.82±0.9 55.8±6.6

4 dS/m 1.570±0.16a

18.9±1.3

9.95±1.10 0.479±0.01a 6.58±0.60 3.12±0.31 10.3±1.4 57.8±13.0

8 dS/m 3.930±0.68b

19.5±2.8

10.90±1.10 0.115±0.05b 5.02±0.10 2.14±0.21 11.3±1.7 69.3±20.0

16 dS/m 4.670±0.62b

25.5±7.0

10.10±0.67 0.142±0.02b 6.38±1.20 1.94±0.21 14.1±2.2 73.3±13.0

0 dS/m 0.499±0.10a

30.2±1.8

10.50±1.20 0.171±0.04 9.13±0.35 1.73±0.28 13.2±1.7 80.0±18.0

4 dS/m 1.240±0.15a

25.9±1.3

11.80±1.20 0.109±0.01 8.66±0.43 1.68±0.21 9.11±2.5 56.4±21.0

8 dS/m 3.870±0.80b

31.6±3.78.33±0.52

16 dS/m 4.680±0.62b

24.5±3.0

10.60±2.60 0.172±0.08 8.04±1.30 1.23±0.12 8.83±2.5 48.6±8.9

0 dS/m 0.768±0.11a

13.7±1.1

6.23±0.95

0.716±0.03 4.38±0.32 3.25±0.24 14.7±1.2 91.3±17.0

4 dS/m 1.550±0.12a

12.4±0.75.30±0.59

0.560±0.01 5.00±0.27 2.31±0.15 12.6±1.6 63.2±14.0

8 dS/m 5.650±0.75b

13.8±1.3

5.36±0.74

0.714±0.07 5.02±0.47 2.38±0.13 12.4±0.1 75.9±11.0

16 dS/m 9.070±0.84c

13.3±2.0

5.58±0.48

1.040±0.20 6.68±1.00 2.89±0.33 15.5±1.8 78.9±15.00.114±0.04 6.66±0.80 1.07±0.10 6.02±0.7 41.0±4.6

Apart from Na, significant differences in concentration were observed for Ca and K in leaves
and Fe in stems. For these values, the concentration decreased with increasing salinity. Cu, Mg, P,
and Zn did not show significant differences among organs or salinity levels. In leaves, the Na
concentration increased and the K concentration decreased with increasing soil salinity. Mg and P in
the branches and Fe and Mg in the stems showed a decreasing trend in response to increasing Na(Table 4). The P concentration increased with increasing Na concentration in the roots. There was nocorrelation between Na and other minerals. The P concentration was significantly correlated withvarious ion concentrations in all organs. The P concentration was significantly positively correlatedwith Ca, Cu, Fe, and K in leaves; with Ca, Cu, Fe, Na, and Zn in branches; with Fe, K, and Mg inⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

stems; and with K, Mg, and Na in roots. In branches, the Ca concentration was significantly positively
correlated with Cu, Fe, Mg, P, and Zn, and the Cu concentration was significantly positively
correlated with Fe, Mg, P, and Zn.
Table 4 Correlation matrix of mineral concentrations in each organ

LeavesBranches

Stems

Ca
Cu
Fe
K
Mg
Na
P
Zn
Ca
Cu
Fe
K
Mg
Na
P
Zn
Ca
Cu
Fe
K
Mg
Na
P
Zn

Ca
-

Cu
0.28
*

Fe
0.19
0.85
-

K
0.57
0.10
0.10
-

*
*
Fe
0.78
0.75
-

*

Ca
*
*

*
*
Cu
0.71
*

*

*

*

*
*
Ca
-

*
*
Cu
-0.31
-

*
*
Fe
0.24
-0.22
-

*

*

*
*

*
*
*
*
Cu
0.13
-

Ca
Fe
Ca
-0.07
Cu
0.37
Fe
Roots
K
*
*
Mg
*
Na
P
Zn
*
* Indicates a significant correlation coefficient at p < 0.05.

Mg
0.15
0.15
0.19
0.33
-

Na
-0.20
0.05
0.05
-0.06
0.06
-

P
0.46
0.44
0.67
0.67
0.23
-0.02
-

Na
-0.11
-0.35
-0.05
-0.21
-0.47
*

*

Mg
0.66
0.80
0.59
-0.10
*
*
*
Mg
0.67
-0.22
0.58
-0.01
*
*

P
0.54
0.55
0.42
0.00
0.48
-0.45
*
P
0.19
0.27
0.42
0.68
0.56
-0.25
-

K
0.42
0.07
-0.12
*

Mg
0.50
0.28
0.12
0.58
-

*

*

Na
-0.11
0.17
0.32
0.14
0.08
*

K
-0.36
-0.08
-0.18
-

K
-0.17
0.56
0.05
-

Na
0.11
0.31
-0.57
-0.05
-0.42
-

P
0.20
0.09
-0.02
0.65
0.45
0.63
-

Zn
0.16
0.96
0.83
0.05
0.10
0.22
0.35
Zn
0.57
0.89
0.52
0.06
0.78
-0.30
0.43
Zn
-0.32
0.91
-0.21
0.31
-0.15
0.19
0.09
Zn
0.06
0.74
0.40
-0.13
0.16
-0.01
-0.25
-

In the study by Elhag and Abdalla (2014), leaf Ca and K in moringa also decreased withincreasing Na concentration. Increases in Na have been found to decrease K in triticale, barley, andrice (Flowers and Hjibagheri, 2001; Naidoo, 2007). There were no significant differences in theconcentrations of P, Cu, Mg, and Zn. Matsumaru (1990) found no clear relationship between Fe, Mn,
and Zn concentrations and increasing salinity in a cucumber salt tolerance experiment. The resultsof the correlation analysis in this study also showed that only Mg and P in the branches and Fe andMg in the stems were significantly negatively correlated with Na concentration, suggesting that
increases in Na concentration cause limited inhabitation of mineral uptake by moringa. A significant
positive correlation was found between Na and P in the roots. The same results were obtained by
Elhag and Abdalla (2014) and Nouman (2012). It was suggested that moringa roots may havefunctioned to promote growth by increasing P uptake in response to increased Na.
Root Growth and Soil Permeability
There was a positive correlation between the length of moringa main roots and saturated hydraulicconductivity in the salinity-treated soils (Fig. 2). The relationship between the circumference of themain root and saturated hydraulic conductivity was highly positive (Fig. 3). The saturated hydraulicⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

conductivity of the soil without moringa was 1.1 × 10-6 cm/s, and moringa root growth increased thesaturated hydraulic conductivity by two orders of magnitude (10-4 cm/s) at 0 and 4 dS/m and by one
order of magnitude (10-5 cm/s) at 8 and 16 dS/m. Sophora japonica has tap roots, which growrelatively deep and vertically; these roots loosen the soil more deeply and greatly increase soil
infiltration rates (Zhang et al., 2019). Alunus incana roots were shown to improve soil permeability(Vergani and Graf, 2015). The length and circumference of the main root were greatest at 0 dS/m
and 4 dS/m. The circumference was 13.1 cm and 13.9 cm at 0 and 4 dS/m, respectively, and 6.8 cm
at 16 dS/m, approximately half of that at 4 dS/m. There was a strong positive correlation between
root dry weight and the length and circumference of the main root in each treatment (length: r = 0.99,
p = 0.0006; circumference: r = 0.98, p = 0.001).

Fig. 2 Relationship between root length and saturated hydraulic conductivity

Fig. 3 Relationship between root circumference and saturated hydraulic conductivity
CONCLUSION
Moringa has high salt tolerance and can be grown in paddy soils up to 8 dS/m with almost no growth
inhibition. As soil salinity increased, Na concentrations in each moringa organ also increased. The
results of the correlation analysis in this study also showed that only Mg and P in the branches andFe and Mg in the stems were significantly negatively correlated with Na concentration, suggestingthat increases in Na concentration cause limited inhabitation of mineral uptake by moringa. There
was a strong positive correlation between the length of moringa main roots and saturated hydraulicconductivity, the results showed that moringa root growth increased the soil permeability.
ACKNOWLEDGEMENTSⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

This research was financially supported by The Mitsui & Co. Environment Fund and JSPS
KAKENHI 21H03692.
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Odyssea paucinervis (Staph) (Poaceae), to salinity. FLORA, 437-447, Retrieved from DOI https://doi.org/
10.1016/j.ﬂora.2007.08.003
Nomoto, N. and Yokoi, Y. 1981. Material production in plants. Tokai University Press. (in Japanese)
Nouman, W. M.T., Siddiqui, S.M.A., Basra R.A., Khan T., Gull M.E., Olson and H. Munir. 2012. Responseof Moringa oleifera to saline conditions. International Journal of Agriculture and Biology, 14, 757-762.
Vergani, C. and Graf, F. 2015. Soil permeability, aggregate stability, and root growth, A pot experiment froma soil bioengineering perspective. Ecohydrology, 9 (5), 830-842, Retrieved from DOI https://doi.org/10.
1002/eco.1686
Zhang, D., Wang, Z., Guo, Q., Lian, J. and Chen, L. 2019. Increase and spatial variation in soil infiltration
rates associated with fibrous and tap tree roots. Water, 11, 1700, Retrieved from DOI https://doi.org/10.
3390/w11081700Ⓒ ISERD

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Research article
erd

Environmental Impact Assessment of Air Emission from
Fertilizer Utilization and Rice Straw Burning from Rice
Production in Cambodia
LEAKHENA HANG*
Faculty of Hydrology and Water Resources Engineering, Institute of Technology of Cambodia,
Phnom Penh, Cambodia
Email: leakhena.hang@itc.edu.kh

SREYNEANG THEY
Faculty of Chemical and Food Engineering, Institute of Technology of Cambodia,
Phnom Penh, Cambodia

SELA KONG
Faculty of Chemical and Food Engineering, Institute of Technology of Cambodia,
Phnom Penh, Cambodia

CHENDA LAIFaculty of Hydrology and Water Resources Engineering, Institute of Technology of Cambodia,
Phnom Penh, Cambodia

DALIN UM
Research and Innovation Center, Institute of Technology of Cambodia, Phnom Penh, Cambodia

RAKSMEY YIM
Department of Air Quality, Noise, and Vibration Management, Ministry of Environment,
Phnom Penh, Cambodia
Received 1 August 2023

Accepted 10 November 2023(*Corresponding Author)

Abstract Rice is the most important staple food for feeding nearly half of the world’s
population, and almost the entirety of the population of Asia. An increase in food demandleads to an increase in agricultural residues, resulting in impacts on human health andenvironmental consequences. The current study aimed to estimate the emission of primaryand secondary fine particulate matter attributable to fertilizer from the open burning or ricestraw in Cambodia in terms of country-specific characterization factors (CFs), and to estimate
the human health and ecosystem impact of particulate matter formation and terrestrial
acidification. Additionally, the study aimed to propose an alternative scenario to reduce thenegative impact on human health and the ecosystem. Three scenarios were set to conduct the
study’s assessment, including a baseline scenario representing current farmer practices,
including typical fertilizer application rates and open burning of rice straw after harvest (S0);a scenario reducing fertilizer use to 60% while still allowing open rice straw burning (S1);and a scenario with no open rice straw burning and a 60% reduction in fertilizer use (S2).
Human health damage was calculated in units of Disability Adjusted Life Year (DALY), andecosystem impact was expressed in the units of Potentially Disappeared Fraction of species(PDF/m2/yr) presented under the scenarios. The total human health impact of S0 was
5.35E+01 DALY, S1 was 5.27E+01 DALY, and S2 was 3.75E-01 DALY, while the total
ecosystem impact of S0 was 4.38E-02 (PDF/m2/yr), S1 was 3.60E-02 (PDF/m2/yr), and S2
was 4.85E-03 (PDF/m2/yr). The results of this study indicated that minimizing the use ofchemical fertilizer and zero open burning of agricultural waste can reduce the number ofpollutants that affects human health and ecosystem soil acidification. It showed that reducing
burning straw waste can reduce the toxins that affect human health by 99% and reduce theincrease of soil acidity by 94%.
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Keywords air pollution, DALY, LCA, particulate matter (PM2.5), rice production

INTRODUCTION
Rice has now become a foreign exchange earner for several countries and plays an important role intheir economies (Kumar et al., 2017). In the case of Cambodia, rice production has increased
significantly in the last decades, particularly since the major economic reforms in 1989. The plantedareas of rice increased from 1.9 million hectares (ha) in 1990–1991 to about 2.6 million hectares in
2009–2010 (ASEAN Development Bank, 2012). Favorable weather conditions, an increase in theavailability of rural credit and private investment, technology improvement, new high-yield rice
varieties, application of chemical fertilizer and other inputs are the factors of the increasing rice yield(The World Bank, 2015). Excessive fertilization and mindless use, obviously caused soil salinity,
heavy metal accumulation, water eutrophication, and accumulation of nitrate, considered in terms of
air pollution in the air of gases containing nitrogen and sulfur, which can lead to problems such as
the greenhouse effect (Savci, 2012; Chandini et al., 2019). In the United States, uncontrolled
agricultural emissions will influence states' ability to satisfy global environmental impacts. Despite
recent progress to reduce sulfur oxide (SOx) and nitrogen oxide (NOx) emissions, NH3 plays asubstantial role in PM2.5 formation, and increasing ammonia may increase PM2.5 (aerosols withaerodynamic diameters less than or equal to 2.5 µm) concentrations (Clappier et al., 2021). The
compound of these pollutants such as criteria pollutants of known tropospheric O3, SO2, and PM2.5
affects human health and the environment. Animal production, chemical fertilizer application, land
use land changes, biomass burning, and other agricultural practice is a primary producer of increased
gases and particulate matter (Clappier et al., 2021). Particulate Matter of air pollution is an airsuspended mixture of solid and liquid particles that vary in number, size, shape, surface area,
chemical composition, solubility, and origin. The size distribution of total suspended particles (TSPs)
in the ambient air is tri-modal, including coarse particles, fine particles, and ultrafine particles (Chow,
1995). Agricultural residue open burning in Southeast Asia accounts is up to 43% of the total open
biomass burning (BB) which contributes significantly to air pollution. BB is a source of global air
pollution that typically contains PM2.5 sulfur dioxide (SO2), nitrogen oxides (NOx), carbon monoxide(CO), carbon dioxide (CO2), ammonia (NH3), methane (CH4), and other air pollutants (Le et al.,2020). It is estimated that in developing countries approximately 300,000 to 700,000 people can besaved from premature death if aerosol levels are reduced to a safe level (an Air Quality Index (AQI)
under 100 signifies good or acceptable air quality) (Seinfeld and Pandis, 2016). Thus, this study aims
to investigate the impact assessment of air pollutants emitted from chemical fertilizer and rice straw
open burning from rice production in Cambodia and to analyze human health impact assessment andecosystem impact terrestrial acidification using an analytical tool called Life Cycle Assessment(LCA). Also, to provide a recommendation on negative impact reduction under three scenarios are
set to conduct the study’s assessment.
METHODOLOGY
Data Collection
The chemical fertilizer application data was collected from the survey questionnaires of threeprovinces known as Prey Veng, Svay Reing, and Kampong Thom province. The practical applicationof chemical fertilizer (Urea; DPA; KCl) from the farmers is 27 kg/1ton of rice production; it wasconsidered as scenario S0 and from a lab-scale experiment at Cambodia Agriculture Research
Development Institute (CARDI) which was carried out by the plantation in the column-based
experiment of a known diameter by Lai et al. (2022) based on the soil type used in the experimentwith the application of 11kg chemical fertilizer/1ton of rice production (Chemical fertilizer
application was recommended by CARDI); represent the scenario S1, and the scenario S2 is thecondition of combination chemical fertilizer application recommendation by CARDI with ZeroⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

burning after harvesting.
Emission Inventory Assessment
Emission of NH3 and NOx from chemical fertilizer and PM2.5, NOX, NH3, and SO2 from rice straw
open burning were estimated according to the methodology of the EMEP guidelines 2016 (EEA
(European Environment Agency) 2016) and Tier1 of the EMEP/EEA guidebook 2019, respectively.
Emission from fertilizer:
𝑀

NH3 = 17/14 ∑𝑚−1(EF𝑎𝑚 × 𝑃 + 𝐸𝐹𝑏𝑚 × (1 − 𝑃))𝑁𝑚𝑖𝑛,𝑚(1)

where NH3 is ammonia emission after mineral fertilizer application [kg NH3]; m is fertilizer type; M
is the number of fertilizer types; EFam is the emission factor on soil with pH ≤ 7 [kg NH3-N/kg N];
EFbm is the emission factor on soils with pH > 7 [kg NH3-N/kg N]; P is the fraction of soils with pH≤ 7 [%/100]; Nmin is mineral fertilizer application [kg N]; 17/14 is the conversion factor from N to
NH3 (NH3 = 17 g/mol and N = 14 g/mol).
Emission from rice straw open burning:
E = Activity data × Emission factor (EF)(2)

where E is the emission of the pollutant; EF is the emission factor obtained from EMEP/EEA tier 1
The EF of PM2.5 140 g/GJ, NOx 91 kg/GJ, SO2 11 g/GJ table 3.5 page 17 in 1.A.2 EMEP/EEA 2019;
Activity data is the amount of burnt source categories.
Where the total amount of rice straw from rice production was calculated following the formuladeveloped by (Shrestha et al., 2013).
Life Cycle Impact Assessment
The human health impact assessment was estimated from particulate matter formation (PM2.5, NOx,
NH3, and SO2), while the Ecosystem impact assessment was estimated from the terrestrial
acidification (NH3, SO2, and NOx).
Human Health Impact Assessment
The human health impact is expressed as DALYs (disability-adjusted life years), the years of life lost
due to death + years of lived with disability, was calculated by applying the ReCiPe 2016 v1.1 (Aharmonized life cycle impact assessment method at midpoint and endpoint level by (Huijbregts et al.,2017).
HI = E × CFs(3)

where HI is human health impact expressed in DALYs; E is the emission of pollutants; CFs is
endpoint characterization factors of human health (yr. kton-1) level in Cambodia.
Ecosystem Impact Assessment (Terrestrial Acidification)
Ecosystem impact = E × CF

(4)

where E is the emission of the pollutant; Ecosystem impact is the potentially disappeared fraction of
species (PDF/m2/yr); CF is the endpoint characterization factors of an ecosystem (species/kg) levelin Cambodia.
To propose a scenario to reduce air pollution that affects human health and ecosystem impact,
the estimation was carried out under three scenarios. First scenario S0; the baseline is the conditionof using chemical fertilizer (Urea, DAP, KCl) and rice straw open burning by farmers practiced fromthe survey. The second scenario (S1), is the application of chemical fertilizer (Urea, DAP, KCl)Ⓒ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

recommended by CARDI with the practice of rice straw open burning by column based-experimental(Lai et al., 2022). The third scenario (S2); the optimization is the application of chemical fertilizer
NPK recommendation by CARDI with zero burning of rice straw.
RESULTS AND DISCUSSION
Emission Inventory
The estimation of air pollutants NH3, NOx, PM2.5, NOx, and SO2 from chemical fertilizer and ricestraw open burning is expressed as kg/1ton of rice production. Figure 1 reveals the concentration ofemissions released from chemical fertilizer and rice straw open burning under the three studied
scenarios. It is noticed that the pollutants released from the application of fertilizer by farmers
practiced (S0) are higher than the pollutants emitted from the reducing fertilizer application
recommended (S1) by 76%. In addition, the result showed that rice straw open burning is the majorityof source of PM2.5 and SO2 (Fig. 1) of scenario S2. The study by (Lorn et al., 2022) investigatingPM2.5 from fertilizer usage, fuel combustion, and straw residue burning revealed that 51.56% of thetotal emission in the study is generated by rice straw open burning and 24.10% generated fromfertilizer.

Fig. 1 Emission of pollutants of the studied scenarios from 1 ton of rice production
Life Cycle Impact Assessment
Life cycle impact assessment from two endpoint categories, human health impact (the particulatematter formation) and ecosystem impact (terrestrial acidification) are estimated from the emission ofchemical fertilizer and rice straw open burning, is represented as kg/ton of rice production, areexpressed as DALYs and PDF/m2/yr, respectively. The human health impact estimation revealed thehighest value in scenario S0 (5.35E+01 DALYs) if compared to scenario S1 (5.27E+01DALYs) andS2 (3.75E-01DALYs).
Figure 2 showed that the human health impact is contributed majority from PM2.5 and SO2 which
contributed from rice straw open burning, following by NOx, and NH3. It is also noticed that thehighest value of ecosystem impact estimation is found in scenario S0 (4.38E-02 PDF/m2/yr), while
S1 and S2 are 3.60E-02 PDF/m2/yr and 4.85E-03 PDF/m2/yr, respectively (Fig. 3). Based on thefinding result, SO2 is the most contributor to ecosystem impact following by NH3, and NOx,
respectively. As can be seen in the result, a great negative impact on both human health impact andthe ecosystem impact happened under scenario S0. The particulate matter formation from bothprimary and secondary sources of rice straw open burning practiced and excessive application ofchemical fertilizer are seriously affecting both human health and the ecosystem. In the case of easternand north-central China, regions with large population densities and high levels of PM2.5,
approximately 4% of all-cause mortality in the country can be avoided (95% confidence interval: 17%) by reducing emissions of primary particulate matter and gaseous particulate matter precursors,
and thus lower ambient concentrations of PM2.5 (Zhao et al., 2011). Mahmood and Gheewala, (2020)Ⓒ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

stated that rice straw open burning which emitted the majority of PM2.5, exhibited significant impacts
on the environment, terrestrial acidification, freshwater eutrophication, and human damage to ozone
formation. To reduce the amount of particulate matter formation and terrestrial acidification emitted
to the environment which affects greatly human health and the ecosystem, it is suggested to stop theactivity of open burning and excessive use of chemical fertilizer so it can prevent damage to humanhealth by 99% and to the ecosystem by 94%.
In Asia, managing rice straw remains a challenging matter. The available information andknowledge are scattered and either cannot reach the target practiced (farmers, and rural people) (Van
Hung et al., 2020). The common management practice of rice straw is to leave straw to integrate into
the soil known as rice straw incorporation. The incorporation practice can improve soil fertility yetadequate time must be allowed to ensure the effectiveness of the decomposition, thus it may not be
considered by a short cycle of crop growing practice (Zhang et al., 2021). Cattle feed and mushroom
cultivating are other alternatives to rice straw management (Gummert et al., 2020), andrecommended rice straw used in biochar studies is suggested (Ly et al., 2015; Chandra andBhattacharya, 2019). However, positive and effective results can be obtained unless all parties must
involve (farmers and government) and recognize the serious effect of the uncertain method ofmanaging rice straw.

Fig. 2 Human health impact from particulate matter formation

Fig. 3 Ecosystem impact from terrestrial acidification
CONCLUSION
Chemical fertilizer and rice straw open-burning activity emitted pollutants that are harmful to bothhuman health and the ecosystem. Rice straw open burning released the majority of PM 2.5 and SO2
while the most pollutant emitted from chemical fertilizer is NOx. Emissions released from rice straw
open burning alone cause great effects on both human health and the ecosystem. It is suggested tostop the activity of open burning and the excessive practice of applying chemical fertilizer, therefore
it can reduce 99% of human health impact and 94% of ecosystem impact.
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To tackle this issue, educating people about the harmful effects of burning rice straw and otheragricultural waste is crucial. Additionally, implementing improvements in straw utilization rates andminimizing open-field burning are vital steps towards establishing a circular bio-economy thatutilizes agricultural straw as a valuable resource. Moreover, it is important to invest in improving
technology for mushroom and ruminant farming. Lastly, promoting the elimination of agricultural
waste and open burning is crucial for a sustainable future.
ACKNOWLEDGEMENTS
This paper was funded by Cambodia Higher Educational Improvement Project (credit No.6221-KH).
The author would like to thank the Institute of Technology of Cambodia for all its facilities.
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Lai, C., Ly, N., Touch, V., Hin, S., Podwojewski, P., Ket, P., Jouquet, P., Degré, A. and Ann, V. 2022.
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Le, H.A., Phuong, D.M. and Linh, L.T. 2020. Emission inventories of rice straw open burning in the Red River
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Lorn, S., Ket, P., Or, C., Kong, S., Um, D., Aun, S., Taing, C. and Hang, L. 2022. Health impact assessment
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Mahmood, A. and Gheewala, S. 2020. A comparative assessment of rice straw management alternatives inPakistan in a life cycle perspective. Journal of Sustainable Energy and Environment, 11, 21-29, Retrieved
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Savci, S. 2012. Investigation of effect of chemical fertilizers on environment. APCBEE Procedia, 1, 287-292,
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C. and Lovley, D.R. 2013. Syntrophic growth with direct interspecies electron transfer as the primarymechanism for energy exchange. Environmental Microbiology Reports, 5 (6), 904-910, Retrieved from
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M. 2020. Rice straw overview, Availability, properties, and management practices. In Gummert, M., Hung,
N.V., Chivenge, P. and Douthwaite, B. (Eds.), Sustainable Rice Straw Management, 1-13. Springer
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Research article
erd

Seasonal Changes of Water Quality in Cheung Ek Lake,
Cambodia
OUM SOMARA
Graduate School of Agro-Environmental Science, Tokyo University of Agriculture, Japan
Email: oum.somara128@gmail.com

MACHITO MIHARA*
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, Japan
Email: m-mihara@nodai.ac.jp
Received 15 September 2023

Accepted 15 November 2023(*Corresponding Author)

Abstract Cheung Ek Lake is the biggest wastewater lake inside Phnom Penh City and has
massive water and nutrient inflow. Around 70% of rain runoff and wastewater in Phnom Penhflows into Cheung Ek Lake through three main canals: Trabek Canal, Stung Meanchey Canal,and Lou Pram Canal, before discharging into the Bassac River. Since 2008, the rapid
urbanization of the satellite city in Phnom Penh has decreased the area of the lake andcontributed to the degradation of the lake's water quality. Cheung Ek Lake performs many
functions such as flood control, natural wastewater treatment, and provision of water forvegetable production. Previous studies identified the positive and negative impact of lakewater on the local ecosystem and human livelihoods. The aim of the current study was tomonitor seasonal changes in water quality in Cheung Ek Lake, Cambodia. Additionally, theobjectives of this study were to (i) analyze the changes in water quality parameters in therainy season and the dry season and (ii) describe daily and hourly changes in phosphate(PO43-) concentration in the lake. For that, selected chemical parameters such as pH,
electroconductivity (EC), phosphate, nitrate (NO3-), iron (Fe2+), and dissolved oxygen (DO)were measured in the rainy and dry seasons. In addition, phosphate (PO43-) was analyzed
hourly for a week in the dry season. The water samples were collected at 3 points: inlet,
middle, and outlet of the lake, at a depth of 0.5 m. Water samples were analyzed in situ using
a spectrophotometer. The decreased values of EC, NO3-, PO43-, and Fe2+ from inlet to outletshow that the lake has the potential to reduce the nutrient level in both seasons. Phosphateincreases at 10 am, 1 pm, and 7 pm indicated that water quality was remarkably affected bydaily rush hours of household activities. This further suggested that household activities could
be the main sources of the presence of a high concentration of PO43- in the lake.
Keywords Cheung Ek Lake, water quality, seasonal change

INTRODUCTION
Scarce freshwater resources are increasingly being used to satisfy the demands of a rapidly increasing
population and a growing global economy. The increasing rate of uncontrolled and unplanned
urbanization in developing countries in Asia, Africa, and South America is likely one of the mostimportant factors in the decline of the quality of urban water bodies and the increasing health andother associated risks for urban residents (Pankaj et al., 2019).
The major cities in Southeast Asia have millions of inhabitants and a great number of smalland large-scale industries. Often these cities have no formal wastewater treatment facility, and thewastewater is discharged into a drainage system whose main purpose is to prevent flooding of thecity. This is also the case for Phnom Penh city. The city of Phnom Penh had a population of 2.3
million people in 2019 (NIS, 2019) and a demographic growth of 3.2% in the period 2008-2019. In
2014, Phnom Penh boasted a total of 97,200 established enterprises, reflecting a 1.4 percent increase
compared to 2011 (NIS, 2015). Moreover, only 9 percent of discharged water in Cambodia isproperly treated before being released into the main water bodies, making Cambodia the secondⒸ ISERD49

IJERD – International Journal of Environmental and Rural Development (2023) 14-2

lowest capacity after Lao PDR in Southeast Asia (Buth et al., 2019). Until now, there have been no
public sewage treatment plants in Phnom Penh and the majority of the industries have been
constructed without wastewater treatment facilities. Domestic and industrial wastewater as well as
storm water is drained out of the city into Cheung Ek Lake (Takahashi et al., 2002).
Cheung Ek Lake is essential in the treatment of Phnom Penh’s sewage and the prevention ofdevastating floods both in the city center and the southern border town of the city. Acting as a large
natural wastewater treatment area, this lake has been found to be semi-effective through the use ofplants such as water morning glory and water mimosa, both of which are planted by local aquatic
farmers, which assist in cleansing the water of harmful pollutants (Irvine et al., 2015). As the city’s
wastewater, including sewage, drains directly into the lake through three major canals, the presenceof this lake is a natural barrier protecting pollutants from entering the Bassac River downstream.
However, the uncontrolled human occupation on the lake’s bank has led to encroachment of thelake's surface area every year, especially since 2013 when the satellite cities project started(Sahmakum Teang Tnaut, 2019). As the lake's surface area gets smaller, its potential for wastewater
treatment reduces as well. Even though several studies have examined the water quality in Cheung
Ek Lake (Irvine et al., 2008; Nara et al., 2015), the study on the changes in the water quality related
to seasons is still not being examined yet. Thus, this study aims to address the gaps seen.
OBJECTIVE
The purpose of this study is to monitor the seasonal changes of water quality in Cheung Ek Lake,
Cambodia. Accordingly, the objectives of this study are to (i) analyze the changes in water quality
in the rainy season and the dry season and (ii) describe daily and hourly changes in phosphate (PO43-)
concentration in the lake.
METHODOLOGY
The study was conducted on Cheung Ek Lake located south of Phnom Penh city (104°90’–104°94’E
and 11°46’-11°53’N). It is the catchment area where 80% of the total wastewater from this catchment
is drained into the lake (Van der Hoek et al., 2005). The lake’s surface area changes from 1,300 to
3,000 ha approximately from dry to rainy seasons, with an average depth of 0.5-1.5 m in the dry
season and 7-9 m in the rainy season. The average annual rainfall is 1,440 mm and the elevation isaround 10 m above sea level (Nara et al., 2015). The rainy season is from May to September and the
dry season starts from October to May.

Fig. 1 Map of Cambodia and sampling point in Cheung Ek Lake (Google map)
Water Sampling and AnalysisⒸ ISERD

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To analyze the changes in water quality parameters, water sampling was conducted for the rainy season
in 2019 and 2020 for dry seasons. Water samples were taken at 0.5 m depth by using Heyroth Water
Sampler. Sampled water was kept in the plastic containers at 5°C and transported to the laboratory
within 3 h of collection for analysis.
Water samples were analyzed for pH, electrical conductivity (EC), iron (Fe), dissolved oxygen(DO), phosphate (PO43-), and nitrate (NO3-) using a pH meter, EC meter, and DR 900 portable datalogging colorimeter instrument. The water samples were collected at the inlet, middle, and outlet for
3 days in the rainy season (9th - 11th Sep 2019) and 7 days in the dry season (19th - 25th Dec 2020).
To analyze the daily changes of PO43- concentration in the lake, the water sample was taken
hourly in the lake from 5 am until 7 pm for 7 days in the dry season (19 th - 25th Dec 2020). During
this date range, no rainfall was recorded, and the average daily air temperature ranged from 23-30°C.
Totally 107 water samples were analyzed for PO43- in the inlet point of the lake to determine its
possible sources.
RESULTS AND DISCUSSION
The results from Table 1 and Table 2 for pH in both seasons range from 7.06 to 7.7 indicating it basestate of the lake’s water. The electrical conductivity (EC) decreased from the rainy to the dry season,and one of the primary factors influencing this change in EC levels in the water body could be
rainwater. Iron (Fe2+) concentrations ranged from 0.03 to 0.08 mg L-1, which, based on the waterpollution control standard in Cambodia for public water areas (20 mg L-1), indicated no sign of Fe
contamination (RGC, 2009).
Table 1 Chemical water parameters of the inlet, middle, and outlet of the lake
in rainy season
Sampling
location
Inlet
MiddleOutletpH
7.06 ± 0.30
6.98 ± 0.27
7.15 ± 0.36

EC
(mS/cm)0.75 ± 0.04
0.72 ± 0.01
0.66 ± 0.02

NO3(mg/L)
0.66 ± 0.29
0.11 ± 0.16
0.55 ± 0.18

PO43(mg/L)
5.29 ± 2.26 a*
1.42 ± 0.59 b*
1.90 ± 0.50 b*

Fe2+(mg/L)
0.03 ± 0.03
0.05 ± 0.05
0.06 ± 0.03

DO
(mg/L)
5.14 ± 2.24
6.06 ± 1.99
3.55 ± 0.66

Note: Values are mean ± SD (n=3), * p< 0.1, Source: Data from analysis in Sep 2019

Table 2 Chemical water parameters of the inlet, middle, and outlet of the lakein the dry season
Sampling
locationpH

Inlet
MiddleOutlet7.7±0.18
7.7±0.28
7.6±0.23

EC

NO3-

PO43-

Fe2+

(mS/cm)0.37±0.04 a*
0.36±0.01 a*
0.33±0.01 b*(mg/L)
0.4±0.05
0.3±0.26
0.1±0.33(mg/L)
4.64±0.91 a*
3.66±0.25 a*
2.56±0.54 b*(mg/L)
0.08±0.04
0.06±0.18
0.03±0.09

DO(mg/L)
3.25±0.96 a*
6.40±1.02 b*
6.75±0.97 b*

Note: Values are mean ± SD (n=7), * p< 0.1, Source: Data from analysis in Dec 2020

Phosphate (PO43-) varied drastically in the rainy season, decreasing from 5.29 mg L-1 in the inlet to1.42 mg L-1in the outlet. In both seasons PO43- decreases from inlet to outlet point. The drop of PO43concentration in both middle and outlet points may be attributed to agricultural activities occurringon the lake's surface, which absorb the available PO43- in the water.
Similar to PO43-, nitrate (NO3-) concentrations were observed to be decreasing in the dry season
suggesting urban run-off. In the outlet point in the rainy season, NO3- increase again may be causedby the construction activities nearby. These activities in proximity to the lake have induced water
turbulence, leading to the release of sediment from the lake's bottom causing the elevation of NO 3level in the outlet point.
Dissolved oxygen (DO) in the lake ranged from 3.55 to 6.06 mg L-1 in the rainy season and 3.25
to 6.75 mg L-1 in the dry season. The elevated levels of DO observed at the inlet point during therainy season can be attributed to temperature fluctuations. The wastewater discharged into CheungⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

Ek Lake flows from the city through an open channel. In the rainy season, this water becomes diluted
with rainwater, leading to a reduction in water temperature. This drop in temperature is responsible
for the rise in DO levels. Additionally, the runoff during the rainy season can raise the PO43- levels,
potentially promoting increased algae growth along the open channel in the city. Algae, in turn,contributes to dissolved oxygen (DO) production in the daytime.
The low and high precipitation during dry and rainy seasons in tropical countries can greatly change
the water quality of the lake (Ling et al., 2017). The high precipitation during the wet season can either
decrease the pollutant concentration by dilution or deteriorate the lake water quality due to increased
surface runoff from anthropogenic activities. Seasonal variations in the water quality of the Cheung Ek
Lake have a relatively minor impact on the pH parameter, whereas EC increases because of dilution withrainwater. However, in terms of nutrients such as NO3-, PO43-, and DO, the water quality exhibits
improvement during the dry season.
The concentration indicated in Table 3, Figs. 2 and 3 show the load of four parameters in Cheung
Ek Lake between the inlet and outlet site. The result for PO43-, NO3- showed a decreasing trend. Fe
increased in the rainy season and decreased in the dry season, which contrasts with DO in both sites.
As the outlet's load was reduced compared to the inlet’s load it can be said that the lake can remove
inflow pollutants in both seasons.
Table 3 Nutrient loads of the inlet and outlet in the rainy and dry season
ParameterNO3- (g/s)
PO43- (g/s)
Fe2+ (g/s)
DO (g/s)

Inlet
28.08
222.81
1.26
216.50Outlet
18.65
64.42
2.03
120.38Source: Data from analysis in Sept 2019

Inlet

Cheung Ek Lake
NO3PO43Fe2+
DO

>
>
<
>

ParameterNO3- (g/s)
PO43- (g/s)
Fe2+ (g/s)
DO (g/s)

Inlet
0.24
2.74
0.05
1.92

Outlet
0.04
1.07
0.01
2.84

Source: Data from analysis in Dec 2020

Outlet

Inlet

Cheung Ek Lake
NO3PO43Fe2+
DO

NO3PO43Fe2+
DO

>
>
>
<

OutletNO3PO43Fe2+
DO

Fig. 2 Function of Cheung Ek Lake inthe rainy season

Fig. 3 Function of Cheung Ek Lake inthe dry season

Fig. 4 Average
PO43- in weekend
and weekday
Fig. 4 Average
concentrationof PO43-

3Fig. 45 Daily
HourlyChanges in PO
34
Fig.
concentrationof PO
4

Figures 4 and 5 indicated the hourly changes in phosphate concentration between weekdays andweekends. Changes in the concentration of PO43- can be seen and fluctuate starting from 6 a.m. until 6 p.m.
At 5 a.m., the concentration ranges from 1.2 to 3 mg L, while the data at 7 p.m. show a concentration
range between 3.5 to 5 mg L. These observations suggest that during the nighttime, particularly on
weekdays, concentration experienced a significant decrease. A significant difference was found at 10 am,
1 pm, and 7 pm with confidence levels at 95% and 99%, respectively. The lake’s hours represent the busy
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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

hours of household activities, which can indicate that the main contribution of PO43- level in the lake’s
water came from the household.

CONCLUSION
According to the findings of this study, the current situation pH and EC of the lake’s water did notchange much in both seasons, whereas an increase in nutrient levels was seen by comparing the water
quality of the dry season to the rainy season, especially in the inlet point. This result indicates theurban run-off of nutrients and suggests the importance of implementing effective management for
the inlet point to prevent any further deterioration in the lake’s water quality. Furthermore, the result
from nutrient loads indicates that the lake's water quality has improved from the input to the output,
demonstrating the possibility for potential pollution removal in the lake for both seasons.
Additionally, from the results of hourly analysis of PO43-, water quality monitoring should focus
more on inlet sites, which are one of the primary sources of pollution. Further research is required togain a better understanding of PO43- and how to address it effectively. Also, when it comes to regulatingwater discharge from cities, particularly from households, stricter measures should be implemented
to control nutrient content.
REFERENCES
Buth, S.N., So, S. and Jan, T. 2019. Overview of wastewater management in Phnom Penh city. Briefing Note,
Regional Fellowship Program, Parliamentary Institute of Cambodia, Phnom Penh, Cambodia.
Irvine, K., Sampson, M., Visoth, Y., Yim, M., Veasna, K., Koottatep, T. and Rupp, J. 2008. Spatial patterns ofE. coli and detergents in the Boeng Cheung Ek treatment wetland, Phnom Penh, Cambodia. In Proceedingsof the 6th International Symposium on Southeast Asian Water Environment, 78-81, University of Tokyo,
Tokyo.
Irvine, K., Sovann, C., Suthipong, S., Kok, S. and Chea, E. 2015. Application of PCSWMM to assess
wastewater treatment and urban flooding scenarios in Phnom Penh, Cambodia, A tool to support eco-cityplanning. Journal of Water Management Modelling, 23, C389, Retrieved from DOI https://doi.org/10.
14796/JWMM.C389
Ling, T.Y., Gerunsin, N., Soo, C.L., Nyanti, L., Sim, S.F. and Grinang, J. 2017. Seasonal changes and spatial
variation in water quality of a large young tropical reservoir and its downstream river. Journal of Chemistry,
2017, ID 8153246, Retrieved from DOI https://doi.org/10.1155/2017/8153246
Nara, I., Kensuke, K., Endan, S. and Yuji S. 2015. Monitoring land use and land cover effects on water quality in
Cheung Ek Lake using ASTER Images. American Journal of Environmental Sciences, 11 (1), 1-12, Retrieved
from DOI https://doi.org/10.3844/ajessp.2015.1.12
National Institute of Statistics (NIS). 2019. General Population Census of Cambodia 2019, National report on
census results. National Institute of Statistics, Ministry of Planning, Phnom Penh, Cambodia, Retrieved
from URL https://nis.gov.kh/nis/Census2019/Final0General%20Population%20Census%202019-English.
pdf
National Institute of Statistics. 2015. Cambodia inter-censal economic survey 2014. National Institute of
Statistics, Ministry of Planning, Phnom Penh, Cambodia, Retrieved from URL https://www.stat.go.jp/info/
meetings/cambodia/pdf/c14ana01.pdf
Pankaj, K., Masago, Y., Binaya, K.M., Shokhrukh-Mirzo, J., Mohamed, K., Misato, D. and Kensuke, F. 2019.
Future outlook of urban water environment in Asian cities. A Water and Urban Initiative Report, United
Nations University, Institute for the Advanced Study of Sustainability, Tokyo, Retrieved from URL https://collections.unu.edu/eserv/UNU:6454/UNU_IAS_WUI_2018_0302_02_2.pdf
Royal Government of Cambodia. 2009. Sub decree on water pollution control. Retrieved fromhttps://www.vertic.org/media/National%20Legislation/Cambodia/KH_Subdecree%20No%2027%20on%
20the%20Control%20of%20Water%20Pollution.pdf
Sahmakum Teang Tnaut. 2019. The last lake, Facts and figures. Sahmakum Teang Tnaut, 40, 3-4, Retrieved
from URL https://teangtnaut.org/wp-content/uploads/2019/12/STT-Facts-and-Figures-40-Last-lakes-_
ENG_Final.pdf
Takahashi, Y., Doi, R., Endomoto, H. 2002. Section 7, The environment. In The Kingdom of Cambodia —From Reconstruction to Sustainable Development, Country Study for Japan’s ODA, 278-315, Institute forInternational Cooperation, Japan International Cooperation Agency (JICA), Tokyo, Japan.
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van der Hoek, W., Anh, V.T., Cam, P.D., Vicheth, C. and Dalsgaard, A. 2005. Skin diseases among peopleusing urban wastewater in Phnom Penh. Urban Agriculture Magazine, 4, 30-31.

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Research article
erdConsumer Behavior Towards Ethical Bananas in Japan
MEI MATSUMOTO*
Graduate School of International Food and Agricultural Science,
Tokyo University of Agriculture, Japan
Email: 13722006@nodai.ac.jp

NINA N. SHIMOGUCHI
Faculty of International Agriculture and Food Studies, Tokyo University of Agriculture, Japan
Received 1 September 2023

Accepted 24 November 2023(*Corresponding Author)

Abstract Japan imported over 1 million tons of bananas in 2021, 76% of which were fromthe Philippines. Meanwhile, in banana production fields including in the Philippines, there
are problems such as unfair contracts between banana producers and companies and badworking conditions. Although consumers' interest in ethical consumption is growing in Japan,
the market of ethical bananas, including bananas certified under the Fair Trade (FT),
Rainforest Alliance (RA), and organic1 standards that are the focus of the current study, is
still small. Therefore, this study aimed to identify consumer behavior toward ethical bananas
and the possibility of expanding the market for ethical bananas in Japan. An online
questionnaire survey using Google Forms was conducted throughout August 2021, and 279
valid answers were obtained. Factor Analysis and Cluster Analysis revealed that the
respondents were segmented into three clusters, which are "quality-conscious", "ethicalpossible," and "unconcerned". In addition, Conjoint Analysis clarified that when the
respondents purchased bananas, consumers attached the highest importance to the “price”
attribute, followed by the "appearance," "ethical", and "cultivation method" attributes.
Furthermore, the perceived utility for consumers of RA and organic bananas was as high asthat of non-ethical bananas, while that of FT bananas was low. As such, the results suggested
that there is significant potential for an increase in the demand for organic and RA bananas,
while the potential for an increase in demand for FT bananas is low. Meanwhile, the "ethicalpossible" cluster, which accounted for over 50% of the respondents, displayed the highest
utility scores towards FT bananas among the three clusters. To expand the market for ethicalbananas in Japan, the authors recommend increasing consumer recognition andcomprehension of FT and issues in the banana production field and promoting the sale of RA
and organic bananas in supermarkets.
Keywords consumer segmentation, ethical consumption, fair trade, rainforest alliance,
organic

INTRODUCTION
Bananas are the most consumed fruits in Japan (Statistics Bureau of Japan, 2022). Japan imported
over 1 million tons of bananas in 2021, 76% of which were from the Philippines (Ministry ofAgriculture, Forestry and Fisheries Japan, 2021). In the production field, there are problems caused
by multinational companies. One of the problems is the unfair contract between banana producers
and the company. Long-term agreements and low purchase prices are regulated in the contract.
Besides, several expenses are deducted automatically from the producers' sales amount. Thus, theproducers usually resort to borrowing some money from the company to continue producing bananas.
This situation strengthens the company's domination. The other problem is bad working conditions.
The company forces the workers to work long hours and does not make suitable payments to them.
1Fair Trade products are produced and exported in a way that seeks greater equity in international trade. Products withRainforest Alliance certification is produced in an environmentally-friendly way that mainly aims to conserve forest.
Lastly, organic agriculture is a production system that sustains the health of soils, ecosystems, and people (IFOAM, 2008).

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

In addition, pesticide spraying causes health damage to the workers and residents around the
production area. It also causes environmental damage (Ishii et al., 2020).
Meanwhile, consumers’ interest in ethical consumption is growing according to the survey by
Watanabe in 2020. This survey also clarified that while “organic” is a well-known term, the degrees
of recognition of labels of Fair Trade (FT) and Rainforest Alliance (RA) are increasing in Japan.
On the other hand, the market for ethical bananas is still small in Japan. For this study, ethicalbananas refer to FT, RA, and organic bananas. The market share of multinational companies’ bananas
exceeds 90% in the current situation (Ishii et al., 2020). One company imports FT bananas with an
FT label, but these bananas are from Ecuador and Colombia. Thus, there seems to be no bananas
with an FT label from the Philippines in Japan. A Japan-based trading company has been importing
barangon bananas. Grown naturally in the Philippines, these bananas are cultivated without
agrochemicals and are imported as non-labeled FT bananas to Japan. However, the share of thesebananas accounts for only 0.1% of total imported bananas (Alter Trade Japan, Inc., 2021). For the
improvement of the harsh situation in the production field, there is a need to expand the ethicalbanana market in Japan.
For market expansion, grasping consumer perception for the purchasing of ethical bananas
seemed to be vital. However, past studies discussing consumer behavior towards ethical bananas arelimited in Japan. Kishimoto et al. (2021) studied consumer preferences for FT bananas. Their study
focuses on discussing the relationship between providing information and willingness to pay.
Consumers' utility and importance of each factor when they purchase FT bananas are not clarified.
Besides, this study has discussed organic and FT bananas. As of now, RA bananas are sold morethan FT bananas in the Japanese banana market. Considering the market situation, consumer behavior
towards RA bananas is also better to be clarified. Therefore, this study includes RA in addition to FT
and organic.
OBJECTIVE
To identify the possibility of expanding the market for ethical bananas in Japan, this study aims toidentify consumer behavior towards ethical bananas. Specifically, this study aims to identify thecharacteristics of the respondents using Factor Analysis and Cluster Analysis and to analyze
consumer behavior towards ethical bananas using Conjoint Analysis.
METHODOLOGYThis study is based on primary data obtained by an online questionnaire survey using Google
Forms, throughout August 2021, targeting people who live in Japan. A total of 279 valid answers outof 314 respondents were obtained. Factor Analysis and Cluster Analysis were utilized to segment
respondents into clusters based on their purchase behavior of agricultural products. The data utilized
for the segmentation was obtained by asking them about the important points when purchasing
agricultural products. The respondents chose their answer from five choices which are “important”,“slightly important”, “neutral”, “slightly not important”, and “not important”. Besides, a Conjoint
Analysis was conducted to clarify consumers' utility and importance of each attribute regarding thepurchase of ethical bananas.
Table 1 Attributes and levels for conjoint analysis
Attribute
Appearance
Cultivation Method
EthicalPrice (yen)*

Level
Yellow, Brown
Custom (no indication), Organic
Custom (no indication), FT (label), FT (no label), RA
100, 200, 300, 400

Notes: Standard: 4-5 banana (700-800g). JPY100=USD0.87 (as of March 2022)Ⓒ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

The data used was obtained from 234 respondents' answers to eight questions for the analysis, except
for 45 respondents who did not purchase bananas by themselves. The respondents chose their answer
from three choices which are “buy”, “do not know”, and “do not buy”. Table 1 shows attributes andlevels used in Conjoint Analysis. The data obtained were analyzed using R for Factor Analysis andCluster Analysis and Microsoft Office Excel for Conjoint Analysis.
RESULTS AND DISCUSSION
Segmentation of the Consumer-respondents
Table 2 shows three factors obtained from Factor Analysis. Factor 1 is composed of "producer
information", "cultivation method", "effect on the environment", and "effect on society". Since these
items are related to ethical consumption, this factor is regarded as an "ethical" factor. Factor 2 iscomposed of "place of production" and "brand/company", which are related to the person who
produces the product. Therefore, factor 2 is regarded as a "producer" factor. Factor 3, which iscomposed of "freshness" and "taste" is regarded as a "quality" factor.
Cluster Analysis was applied to the data of factor scores obtained via Factor Analysis. Table 3
shows the result of the segmentation. The respondents were segmented into three clusters. Regarding
the first cluster "quality-conscious", the value of the "ethical" factor was very low, and the value ofthe "quality" factor was high. It indicates that the respondents belonging to this cluster do not caremuch about the ethical aspect, but quality is their top priority. Over 50% of the respondents belong
to the second cluster "ethical-possible". All values were positive for this cluster, especially the"ethical" factor. It implies most consumers have the potential for ethical consumption of agricultural
products. As for the last cluster "unconcerned", all values were relatively low, especially the "quality"
factor.
Table 2 Results of factor analysis1
Items
Producer information
Cultivation method
Effect on the environment
Effect on society(producers, workers, subcontracts, etc.)
Place of production
Brand / Company
Freshness
Taste
Price
SafetyEthical
0.501
0.591
0.957

Extracted factors2
Producer
0.471
0.334
-0.114

Quality-0.130-0.038
0.061

0.971

-0.114

0.028

0.183

-0.195
0.041
0.009
0.053
-0.015
0.036

0.883
0.749
-0.0400.120-0.1080.462

0.036-0.101
0.788
0.605
0.165
0.292

0.394
0.454
0.402
0.525
0.976
0.551

UniquenessNotes 1) Method: Maximum Likelihood Method. Rotation: Promax.
2) The numbers are factor loadings. Those in bold represent that the absolute value is over 0.5.

Table 3 Results of cluster analysis1

EthicalFactors
Producer
Quality
Cluster size

Clusters2
Ethical-possible
0.521
0.272
0.305
153

Quality-conscious-1.248-0.2790.414
64Notes: 1) Cluster method: ward.D. Distance: Euclidean.
2) The values represent the average of each factor of the cluster.
Those in bold represent the absolute value is over 0.3.

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Unconcerned
0.004-0.384-1.181
62

0.275
0.289
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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

Attribute Preferred by Consumer-respondents
As a result of Conjoint Analysis, the importance of "price" was the highest followed by "appearance"and "ethical" (Fig. 1). The "cultivation method" was the lowest. These results fit with the past survey
by the Japan Banana Importers Association (2021), which reported that consumers care about theprice the most when purchasing bananas, and the Kishimoto et al. (2021) study reported that “FT”
has the highest willingness to pay (WTP) followed by “appearance” and “organic”. The “qualityconscious” cluster was sensitive to “price” the most among the three clusters. According to the resultof segmentation, this cluster concerns quality the most. However, the importance of “appearance”,
which is related to the quality of bananas, was relatively low. The “ethical-possible” cluster concerns
ethical aspects more than quality. However, the importance of “appearance” was higher than“ethical”. Figure 2 shows the result of the part-worth utility. The “non-labeled FT” was negative forall clusters. Although labeled FT was positive for "ethical-possible" and "unconcerned" clusters, theutility was close to zero. On the other hand, RA was positive for all clusters. This finding seems toconvey that consumers are more familiar with environmental issues such as global warming thansocial issues in the production field and are more interested in the improvement of environmental
issues. However, it must be mentioned that, even though there is an explanation of RA and FT in thequestionnaire, it is unknown how much the respondents understand them. As for the cultivation
method, the utility of organic was positive for only the "ethical-possible" cluster.
50
40
30
20
10
0
Price (yen)Whole

Ethical

Cultivation
MethodAppearance

Quality-conscious

Ethical-possible

Unconcerned

Fig. 1 Relative importance (%) of each attribute

JPY100=USD0.87 (as of March 2022)

Fig. 2 Part-worth utility of each levelⒸ ISERD

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Utility of Ethical Bananas
In the current Japanese market, FT bananas including organic and conventional are usually soldonline, and the price is over 400 yen per 500g. Organic and RA bananas are sold not only online but
also in supermarkets. The prices of these bananas range from 200 yen to less than 400 yen. Organic
bananas are sometimes sold at 200 yen and below.
Figure 3 shows the utility of each banana by each cluster. The utility of RA and organic bananas
were almost the same as non-ethical bananas which are not organic and have no ethical label. On theother hand, they had a low utility towards FT bananas, especially non-labeled bananas. Compared toeach cluster, the "quality-conscious" cluster had a negative utility to FT bananas including labeled
and non-labeled with a price of over 300 yen. Similarly, the "unconcerned" cluster had a negative
utility to those FT bananas with the price except FT-labeled organic bananas. The "ethical-possible"
cluster had a positive utility to FT-labeled bananas at 300 yen but negative to non-labeled FT bananasat the same price. Concerning organic and RA bananas, "ethical-possible" had a positive utility for
those bananas at 400 yen, while the other two clusters had positive utility at 300 yen and below. The
bananas that consumers have the highest utility are organic bananas. Therefore, it is considered thatorganic and RA bananas have a possibility that the demand for those bananas increases but nopossibility for FT bananas in the current situation.
A premium price is more effective than a reduced price for the sales promotion of ethicalproducts (Onishi, 2021). In addition, the decrease in the current volume of FT products is a challenge
to accomplish FT purposes. Therefore, to increase FT banana purchases, there is a need to increase
consumers’ recognition and comprehension of FT and issues in the banana production field while
keeping the current retail price. Consumers mainly purchase fresh products in supermarkets becauseof their accessibility (National Supermarket Association of Japan, 2015). Therefore, for RA andorganic bananas, it is considered that promoting the sales of these bananas in a supermarket leads tothe expansion of the market.

*JPY100=USD0.87 (as of March 2022)

Fig. 3 Utility of each banana by each cluster
CONCLUSION
From the results of Factor, Cluster, and Conjoint Analyses, the respondents have the utility towards
RA and organic bananas as high as non-ethical bananas and have low utility towards FT bananas.
Therefore, it is considered that organic and RA bananas have a possibility to increase demand whileⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

there seems to be no possibility of an increase in demand for FT bananas in the current situation.
Meanwhile, the "ethical-possible" cluster which accounts for over 50% of the respondents has thehighest utility towards FT bananas among the three clusters. Hence, to expand the market for ethicalbananas in Japan, there is a need to increase the recognition and comprehension of FT and issues inthe banana production field and promote the sales of RA and organic bananas in a supermarket.
ACKNOWLEDGEMENTS
This study was conducted as a part of the undergraduate thesis accepted in January 2022. I would
like to extend my appreciation to all the people who supported this research with their kindcooperation in answering my questionnaire.
REFERENCES
Alter Trade Japan, Inc. 2022. Annual report 2021. Retrieved from URL https://altertrade.jp/pdf/annual report_
2021. pdf
Ifoam Organics International. n.d. Definition of organic agriculture. Retrieved from URL https://www.ifoam.
bio/why-organic/organiclandmarks/definition-organic
Ishii, M. (Ed.), Alyssa, P. Ichihashi, H. Nogawa, M. Sekine, K. Tanaka, S. and Tasaka, K. 2020. The bitter
reality of sweet bananas. Commons Press, Tokyo.
Japan Banana Importers Association. 2022. The 18th Trend survey of consumption of bananas and fruits.
Retrieved from URL https://www.banana.co.jp/database/trend-survey/docs/trend18.pdf
Kishimoto, H., Sato, T. and Suzuki, N. 2021. An analysis of consumer preferences for Fair Trade bananas byconjoint analysis, The relationship between providing information and willingness to pay. Food System
Research, 27, 201-207, Retrieved from DOI https://doi.org/10.5874/jfsr.27.4_201
Ministry of Agriculture, Forestry and Fisheries Japan (MAFF Japan). 2021. Import and export statistics ofagricultural, forestry, and fishery products. Retrieved from URL https://www.e-stat.go.jp/stat-search/files?
page=1&layout=datalist&toukei=00500100&tstat=000001018079&cycle=7&year=20210&month=0&tcl
ass1=000001164306
National Supermarket Association of Japan. 2015. White paper on supermarket chapter 4 and 5. Retrieved from
URL http://www.super.or.jp/wp-content/uploads/2015/02/supermarket-hakusho2015-3-0924.pdf
Onishi, S. 2021. Empirical research on promotion of ethical consumption. Ph.D. Dissertation, Shiga University,
Japan. (in Japanese)
Statistics Bureau of Japan. 2022. Japan Statistical Yearbook 2022, 514-515.
Watanabe, T. 2021. Fair trade and ethical consumption in Japan, Trends observed in a recent national survey(II). Tokyo Keizai Law Review, 40, 95-144.

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Research article
erd

Ecosystem Carbon Stock Assessment in Upland Forest:
A Case Study in Koh Kong, Mondulkiri, Preah Vihear, andSiem Reap Provinces
SOBEN KIM*
Faculty of Forestry Science, Royal University of Agriculture, Cambodia
Email: kimsoben@gmail.com

SARUN HORN
Faculty of Forestry Science, Royal University of Agriculture, Cambodia

PHEAK SOK
Office of Research and Community Development, University of Heng Samrin Thbongkhmum,
Cambodia

TEAMHY SIEN
Faculty of Forestry Science, Royal University of Agriculture, Cambodia

CHOMROEUN YORN
Faculty of Forestry Science, Royal University of Agriculture, Cambodia
Received 11 September 2023

Accepted 25 November 2023(*Corresponding Author)

Abstract The climate change problem is caused by human-induced increases in the stock ofgreenhouse gases (GHGs) in the atmosphere. In Cambodia, forests provide many important
ecosystem services to local people such as food production, natural medicine, water supply,and wind/storm protection. Additionally, Cambodian forests sequester a considerable amountof carbon, contributing to the mitigation of greenhouse gas-induced climate change on a
global level. However, the amount of carbon stored in forests differs according to spatial andtemporal factors such as forest type, size, age, stand structure, associated vegetation, andecological zonation, among other things. The current research aimed to i) conduct forest
inventory of upland forest area in the Koh Kong, Mondulkiri, Preah Viher, and Siem Reap
provinces of Cambodia, and ii) assess carbon stock at the target sites across different
provinces. The study applied the carbon stock assessment methodology as outlined in the
National Forest Inventory and the Field Manual for the National Forestry Inventory of
Cambodia issued by the Food and Agriculture Organization (FAO) in 2018, applied acrossdifferent types of forest at several pilot project sites. In addition, the study conducted anassessment of carbon stock in soil and ground litter carbon pools. The research studied fivecarbon pools: aboveground biomass pool (AGB), belowground biomass pool (BGB), litterbiomass pool, dead wood biomass pool, and soil organic carbon pool (SOC). The results
indicated total carbon stock in Koh Kong Province at 200.04 tonnes C/ha for evergreen forest,in Mondulkiri Province at 246.18 tonnes C/ha for deciduous forest, in Preah Vihear Province
at 185.06 tonnes C/ha for deciduous forest, and in Siem Reap Province at 207.67 tones C/hafor deciduous forest and 414.13 tones C/ha for evergreen forest
Keywords aboveground biomass, belowground biomass, carbon stock, dead wood, litter,soil organic carbon

INTRODUCTION
Many plant species, especially the native species are essential to understand the plant communities
in Cambodia. The country is predominantly rich in biodiversity and other natural resources for socioeconomic development, food, livelihoods, and well-being. Most of them are threatened withⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

extinction through human activity. Most of the flora species, especially the native species are key tothe plant communities in the country. The forest area of Cambodia is managed by three government
institutions: Forestry Administration (FA) of the Ministry of Agriculture, Forestry and Fisheries(MAFF), the General Directorate of Administration for Nature Conservation and Protection(GDANCP) of the Ministry of Environment (MoE). FA is the government agency under MAFF, andits mandate is to manage forest and forest resources of the Permanent Forest Estate (PFE), including
naturally growing and planted state forest resources and is subdivided into the Permanent Forest
Reserve (PFR) and Private Forest. The PFR consists of production forest, protection forest, andconversion forestland. According to the Forestry Law (2002), Private Forests shall be maintained byowners with interesting rights to manage, develop harvest, use, sell, and distribute the product bythemselves. Reducing Emission from Deforestation and Forest Degradation (REDD); and the role ofconservation, sustainable management of forests, and enhancement of forest carbon stocks indeveloping countries (REDD+) is a mechanism to use financial incentives to reduce greenhousegases emitted by deforestation and forest degradation.
OBJECTIVE
The research aims i) to conduct forest inventory of upland forest area in the four provinces, and ii)
to assess carbon stock in different provinces of the target sites.
METHODOLOGY
The study was conducted in four provinces such as Koh Kong (KK), Mondulkiri (MDK), Preah Viher(PVH), and Siem Reap (SR) province. There were 8 clusters or 24 permanent plots conducted forest
inventory survey, and the case study was implemented from September 2021 until June 2022.
The overall procedure of this survey followed the National Forest Inventory (NFI) sampling
design and Field Manual for the National Forestry Inventory of Cambodia issued by the Food andAgriculture Organization (FAO, 2018). The tool is used for producing information on the state offorests and forest resources for land use policy at the national and regional levels.
Carbon Stock Estimation
Aboveground biomass was estimated using different allometric equations for different types of forest.
The allometric equation developed by Chave et al. (2014) was applied to evergreen forests and theequation developed by Kim et al. (2019) was used for semi-evergreen and deciduous forests.
Table 1 Allometric equations used to determine tree mass and each carbon pool of
the forest types encountered in the study areas
Forest types / Pools
Evergreen forest
Semi-evergreen forest
Deciduous forest
Standing Dead trees
Stump

Equation

References

AGB = 0.0673* (DBH^2* H* WD) ^0.976
AGB = 0.0607* DBH^2.2692* H^0.5122 WD^0.3183
AGB = 0.0607* DBH^2.2692* H^0.5122 WD^0.3183
Mass = V * WD decomposition class
V = A * L * 100
Mass = V * WD decomposition class
V = A * L * 100
A = (d1/2) * (d2/2) * π
Mass = V * WD decomposition class

Chave et al. 2014
Kim, et al. 2019
Kim, et al. 2019

Fallen Dead Wood

Chao et al. 2008
Chao et al. 2008

Chao et al. 2008

Note: AGB = Aboveground biomass (kg); BGB = Belowground biomass (kg);DBH = Diameter at breast height (cm); WD= wood density (g/cm3)Ⓒ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

Trees aboveground biomass was summed to plot level and converted to ton per hectare. A 95 %confidence interval was calculated with the forest types in average aboveground biomass. The carbon
stocks were finally calculated as the sum of aboveground and belowground biomass multiplied byconversion factors see Eq. 1.
Equation Conversion from AGB to Carbon Stock:
C stock = AGB ∗ (1+RS) ∗ CF ∗ 44/12(1)

Where RS is the Root-to-shoot ratio. Different root-to-shoot ratios were applied to the different
forest types: 0.37 for evergreen forest (IPCC 2006) and 0.2 for all other types (IPCC 2006). CF is
carbon fraction, using the carbon fraction value 0.47 (IPCC 2006), and 44/12 is atomic massconversion from carbon to CO2.
Table 2 Main species in each forest type and province
Species Name
Shorea obtusa
Terminalia alata
Xylia xylocarpa
Terminalia
mucronate
Careya arborea
Shorea siamensis
Syzygium cumini
Irvingia
malayana
Knema corticosa
Catunaregam
tomentosa
Dipterocarpus
tuberculatus
Melodorum
fruticosum
Aporosa ficifolia
Cratoxylum
cochinchinense
Shorearoxburgshii
Haldina
cordifolia
Mitragyna sp.
Spondias pinnata
Syzygium
lineatum
Antidesma
ghaesembilla
Cinnamomum
litseifolium
Grewia eriocarpa
Vatica odorata
Microcos
paniculata

KK1

MDK1

MDK2

MDK3

Semievergreen

MDK4

PVH1

SR1

Deciduous

SR2
Evergreen

Total

FRE

%

0
0
0

0
8
9

1
4
1

23
9
4

4
4
0

1
0
0

18
2
2

0
0
0

47
27
16

5
5
4

62.5
62.5
50.0

0

0

4

1

7

0

1

0

13

4

50.0

0
0
0

2
4
0

2
85
0

2
0
0

0
0
0

0
0
4

2
11
1

0
0
2

8
100
7

4
3
3

50.0
37.5
37.5

0

0

0

0

0

2

1

2

5

3

37.5

0

0

1

1

0

3

0

0

5

3

37.5

0

0

4

0

0

0

9

0

13

2

25.0

0

0

0

12

1

0

0

0

13

2

25.0

0

0

0

0

0

1

0

11

12

2

25.0

0

1

0

0

0

0

9

0

10

2

25.0

0

0

0

0

4

0

0

2

6

2

25.0

0

0

2

0

4

0

0

0

6

2

25.0

0

0

1

3

0

0

0

0

4

2

25.0

0
0

0
1

0
0

1
3

0
0

0
0

3
0

0
0

4
4

2
2

25.0
25.0

0

3

0

0

1

0

0

0

4

2

25.0

0

0

2

1

0

0

0

0

3

2

25.0

1

0

0

0

0

2

0

0

3

2

25.0

1
0

0
0

0
0

0
0

0
0

0
2

0
0

2
1

3
3

2
2

25.0
25.0

1

0

0

0

0

1

0

0

2

2

25.0

Wood density was added to the tree-level data based on species and genus averages from the
Global Wood Density (GDW) database (Jerome Chave et al., 2009; Zanne et al., 2009). The datafrom Southeast Asia and Southeast Asia Tropical were selected, and averages were calculated foreach species and genus. Wood density for each tree was based on species if available in the GWD,
genus if species were not available, or a default value of 0.57 g/cm 3 if both species and genus were
unknown, not recorded, or not in the data. The default value was based on a wood density average
for Tropical Asia by Reyes et al. (1992). To estimate dead wood mass, it was initially calculated thevolume of each dead wood piece. Then, converted the volume to dry mass using wood densities
based on decomposition class, the wood density of class 1 was 0.55, class 2 was 0.41, and class 3
was 0.23. Some literature values are useful to adopt estimation of dead wood mass. For example,Ⓒ ISERD

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wood densities in three decomposition classes are estimated in Amazonian forests, in Peru (Chao etal., 2008).
Calculation of Soil and Litter Carbon Pools
Soil Sample was taken from a field survey at the General Directorate of Agriculture (GDA) laboratory.
Soil samples were dried in the oven and recorded timely. The laboratory processing for soil samples
was to explore the following parameters: Organic matter, Nitrogen, pH, total carbon, and Hydrogen.
The liter sample was taken and put in a plastic bag and then dried in the dry oven or solar dryer.
Follow-up and weigh the samples every day until constant drying.
RESULTS AND DISCUSSION
Main Tree Species for Each Forest TypeAccording to the field inventory survey in the study site, the tree species were found 98 species,
including unknown Species 4 species in 8 clusters, 24 plots. It also described on number of species
in each forest type and cluster ID in each province. The main tree species in the study site such as
Shorea obtusa, Terminalia alata, Xylia xylocarpa, Terminalia mucronate, and Careya arborea.
Comparison of Height and Diameter of Height Breast by Each Cluster ID (Provinces)
Depending on the study on comparison of height, the maximum tree height in the research site isabout 36 meters, where the highest Cluster ID is in SR2, located in Kulen mountain, Siem Reap
province. DBH maximum cluster is MDK1 which is rich in Terminalia alata and Xylia xylocarpa.

Fig. 1 Comparisons of height by cluster ID (a) and of DBH by cluster ID (b)Carbon Pool (AGB, BGB, and Deadwood)
As a result, the aboveground tree (AGB) biomass in each plot was different depending on the numberof trees present in the plot and forest types, too. The total aboveground biomass in this study
excluded bamboo. Therefore, the estimated value of total aboveground biomass was higher than the
Cambodia FRL, which excluded bamboo in aboveground biomass. The total aboveground biomassin the deciduous forest was 98.24 t/ha or 46.17 Ct/ha, semi-evergreen forest was 183.20 t/ha or 86.11
Ct/ha, while evergreen forest was 336.07 t/ha or 157.95 Ct/ha, and other was 75.92 t/ha or 35.68
Ct/ha.
Deadwood was defined as one of the carbon pools in terms of the calculation of carbon stock.
In this study, the average biomass from deadwood in deciduous forests, semi-evergreen forests, andevergreen forests was a bit different (11.58 t/ha, 10.74 t/ha, and 6.79 t/ha, respectively). The result
was similar to (USAID, 2014) which estimated the biomass from deadwood in Cambodia. ByⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

including the biomass from deadwood in the estimation, the total carbon stock in the research
location will be increased.
Table 3 Aboveground biomass, belowground biomass, and dead wood biomass
ClusterID
KK1
MDK1-4
PVH1
SR1
SR2

Plots

Forest Type

AGB

BGB

SD

CI95%

2
12
3
3
2

Semi-evergreen
Deciduous
Deciduous
Deciduous
Evergreen

96.76
119.0761.98
114.90222.57

19.35
23.81
12.40
22.98
82.35

11.11
58.79
48.33
35.52
161.45

15.40
33.26
54.69
40.20
223.75

Dead
Wood
3.33
3.30
0.55
2.54
4.30

SD

CI95%

2.59
2.14
0.96
2.22
2.04

3.58
1.21
1.08
2.51
2.83

Soil Organic Carbon and Litter Carbon Pools
According to the manual of the National Forest inventory, the size of take soil sample was 40 x 40
cm, with three levels of soil layer (SL) (sampling position deep was 5 cm, 15 cm, 25 cm). The study
showed that soil carbon pool as well as the C stocks in each of the three layers. Soil depths were 40
cm respectively. The mean of carbon in the soil was 80.06 C t/ha for the semi-evergreen forest in
Koh Kong, the deciduous forest in Mondulkiri was 99.35 C t/ha, deciduous forest in Preah Vihear
was 109.37 C t/ha, and deciduous forest in Siem Reap was 65.95 C t/ha and evergreen forest in Siem
Reap was 104.09 C t/ha.
Litter was defined as one of the carbon pools in terms of the calculation of ecosystem carbon
stock. Based on forest type, the semi-evergreen forest in Koh Kong was 0.54 C t/ha, the deciduous
forest in Mondulkiri was 0.65 C t/ha, the deciduous forest in Preah Vihear was 0.76 C t/ha, thedeciduous forest in Siem Reap was 1.3 C t/ha and evergreen forest in Siem Reap was 0.82 C t/ha.
The result was similar to (RUA, 2020) which estimated the biomass from turnover leaves in
Cambodia.
Table 4 Carbon pool in soil organic matter and litter by different forest types and provinces
Cluster ID

Forest type

KK1
MDK1-4PV1
SR1
SR2

Semi-evergreen
Deciduous
Deciduous
Deciduous
Evergreen

SL1

Soil C (t/ha)
SL2

SL3

Total C(t/ha)

CO2

Litter C(t/ha)

CO2

33.42
48.12
36.79
18.03
38.04

24.22
25.95
36.45
24.11
37.98

22.43
25.28
36.13
23.80
28.07

80.06
99.35
109.3765.95
104.09293.57364.29401.01241.82381.66

0.54
0.65
0.76
1.30
0.82

1.96
2.39
2.77
4.76
3.02

Table 5 Total carbon stock in each province by forest types
Provinces
Forest Types
ABG
BGB
Deadwood
LitterSoil
Total C stock

Koh Kong

Mondulkiri

Preah Vihear

Siem Reap

Siem Reap

Semi-evergreen(C t/ha)
96.76
19.35
6.31
0.54
80.06
200.04

Deciduous(C t/ha)119.07
23.81
5.97
0.65
99.35
246.18

Deciduous(C t/ha)
61.98
12.40
1.18
0.76
109.37185.06

Deciduous(C t/ha)114.90
22.98
5.41
1.30
65.95
207.67

Evergreen(C t/ha)222.57
82.35
8.87
0.82
104.09414.13

Total Carbon Stock in Four Provinces Based on Different Forest Types
Forest ecosystem carbon stock is the amount of carbon that has been sequestered from the atmosphereand is stored within the forest ecosystem, mainly within living biomass and soil, and dead wood andlitter. Results of forest carbon stock in the study areas shown in Table 5, semi-evergreen in Koh Kongwas 200.04 Ct/ha, deciduous forest in Mondulkiri was 246.18 Ct/ha, deciduous forest in Preah Vihear
was 185.06 Ct/ha, deciduous forest in Siem Reap was 207.67 Ct/ha, and evergreen forest in Siem
Reap was 414.13 Ct/ha.
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CONCLUSION
This case study provides a current estimation of forest biomass and carbon stock in the different
forest types by four provinces of Cambodia, which are important biophysical outcomes of the forest
landscape. A total of 8 clusters or 24 permanent sample plots within different forest types in naturalforests were assessed from September 2021 until June 2022. Vegetation parameters, along with thetotal carbon stock were calculated separately for different forest carbon pools. It can be concluded
that the forest, on the third forest type, can sequester more carbon in the future as the trees haveenough DBH values, which means a greater tendency to build biomass, and therefore carbon content.
There are 98 species in the tree layer were recorded, and Shorea obtusa, Terminalia alata, Xylia
xylocarpa, Terminalia mucronate, Careya arborea were the dominant tree species in the third forest
type based on density. The carbon stock of semi-evergreen forest in Koh Kong (200.04 C tone/ha)
was lower than that of deciduous forest in Mondulkiri (246.18 C tone/ha), deciduous forest in Preah
Vihear (185.06 C tone/ha) was lower than Siem Reap (207.67 C tone/ha) and evergreen forest in
Siem Reap (414.13 C tone/ha) was highest carbon stock, if compare with each province in thisresearch. Finally, the result indicates that different carbon stocks in different provinces and forest
types contribute to improving environmental quality, reducing greenhouse gas emissions, andsupporting the government strategy in terms of sustainable management of the forestry sector in
Cambodia.
ACKNOWLEDGEMENTS
The study was made possible thanks to the project “Digitizing Herbarium Collection andConservation of Native Species in Cambodia” implemented by the faculty of forestry science of theRoyal University of Agriculture, funded by the HEIP Project of the Ministry of Education, Youthand Sports.
REFERENCES
Chao, K.J., Phillips, O.L. and Baker, T.R. 2008. Wood density and stocks of coarse woody debris in anorthwestern Amazonian landscape. Canadian Journal of Forest Research, 38 (4), 795-805, Retrieved from
DOI https://doi.org/10.1139/X07-163
Chave, J., Coomes, D., Jansen, S., Lewis, S.L., Swenson, N.G. and Zanne, A.E. 2009. Towards a worldwide
wood economics spectrum. Ecology Letters, 12 (4), 351-366, Retrieved from DOI https://doi.org/10.1111/j.1461-0248.2009.01285.x
Chave, J., Réjou-Méchain, M., Búrquez, A., Chidumayo, E., Colgan, M.S., Delitti, W.B.C., Duque, A., Eid, T.,
Fearnside, P.M., Goodman, R.C., Henry, M., Martínez-Yrízar, A., Mugasha, W.A., Muller-Landau, H.C.,
Mencuccini, M., Nelson, B.W., Ngomanda, A., Nogueira, E.M., Ortiz-Malavassi, E. and Vieilledent, G.
2014. Improved allometric models to estimate the aboveground biomass of tropical trees. Global Change
Biology, 20 (10), 3177-3190, Retrieved from DOI https://doi.org/10.1111/gcb.12629
FA-MAFF. 2018. Field manual for the national forestry inventory of Cambodia. Forestry Administration of
the Ministry of Agriculture, Forestry and Fisheries under the Support from FAO.
JICA-Asia Air Survey Co., Ltd. 2017. Project for facilitating the implementation of REDD+ strategy and policy
for Cambodia. General Directorate of Administration for Nature Conservation of Ministry of Environment
and Protection and Forestry, Administration of Ministry of Agriculture, Forestry and Fisheries, Cambodia.
Kim, S., Horn, S., So, T., Sola, G. and Rijn, M.V. 2019. Development of emission and removal factors for
multi-species and belowground biomass, upland forest, Cambodia. Technical Report. FCPF Project, Phnom
Penh, Cambodia, Retrieved from URL http://wangyee.net/ photo/Cambodia/Misc.htm
Muchiri, M.N. and Muga, M.O. 2013. Preliminary yield model for natural yashinaalpina bamboo in Kenya.
Journal of Natural Sciences Research, 3 (10), 77-84, Retrieved from URL https://core.ac.uk/download/pdf/234654235.pdf
Reyes, G., Brown, S., Chapman, J. and Lugo, A.E. 1992. Wood densities of tropical tree species. U.S.
Department of Agriculture, Forest Service, Southern Forest Experiment Station, Retrieved from DOI
https://doi.org/10.2737%2Fso-gtr-88
RGC. 2022. Second forest reference level for Cambodia under the UNFCCC Framework. Royal Governmentof Cambodia, Retrieved from URL https://redd.unfccc.int/media/cam_2nd_ frl_jan_8_2021.pdfⒸ ISERD

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Royal University of Agriculture. 2020. Litterfall product and species composition of difference forest types in
Cambodia. Frawasa Project Report, Phnom Penh.
So, T. 2015. The allometric equations (AE), Protocols for deciduous forest in Cambodia. UN-REDD
Programme.
Sohel, M.S.I., Alamgir, M., Akhter, S. and Rahman, M. 2015. Carbon storage in a bamboo (Bambusa vulgaris)
plantation in the degraded tropical forests, Implications for policy development. Land Use Policy, 49, 142151, Retrieved from DOI https://doi.org/10.1016/j.landusepol. 2015.07.011
Sola, G., So, T. and Van Rijn, M. 2019. Technical report, Updated forest carbon stocks for REDD+ emission
and removal factors in Cambodia. FCPF Project, Phnom Penh, Cambodia, Retrieved from URL
https://cambodia-redd.org/wp-content/uploads/2021/01/Annex3-Updated_REDD_Emission_Factors_
Cambodia.pdf
USAID 2014. Technical report of project on carbon stock assessments in Cambodian mangrove forest. USAID.

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Research article
erdApplication of Probabilistic Risk Assessment to the Chalky
Rice Grain Issue in JapanHIROKI ODA*
Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa-city, Chiba, Japan
Email: 2215920513@edu.k.u-tokyo.ac.jp

MOMOKA MASANO
Shizuoka Prefectural Office, Shizuoka-city, Shizuoka, Japan

TAKASHI NOBUOKATokyo Electric Power Service Corporation, Koutou-ku, Tokyo, JapanKOSHI YOSHIDAThe University of Tokyo, Kashiwa-city, Chiba, Japan
Received 19 November 2023

Accepted 26 November 2023(*Corresponding Author)

Abstract In recent years, extreme weather events have become more frequent, and there areconcerns about the increase of chalky rice grains. Therefore, it is important to understand thefrequency and severity of high-temperature injuries to rice grains occurring nationwide inJapan. Thus, the objective of this research was to analyze the hazard of high temperature
through a probabilistic method using big weather data and to assess the risk of chalky ricegrain. In this research, we analyzed air temperature data from approximately 800 stations of
the AMeDAS (Automated Meteorological Data Acquisition System) in Japan and checked
the chronological changes. To assess the risk of chalky rice grain, the Probabilistic Risk
Assessment (PRA) method was applied and the risk for each of the 20 years from 1980 to1999 and from 2000 to 2019 was compared. Chalky rice grains, which reduce rice quality,
increase when the average temperature exceeds 26℃ during the first 20 days after the heading
date of rice. A comparison of the areas with air temperatures exceeding 26℃ at least once
per three years (33.3%) between the past and the recent 20 years shows that the risk of hightemperatures has increased in the inland areas of the southern Tohoku region and the southernpart of Ibaraki Prefecture. Subsequently, a multiple regression model was applied to identify
the factors affecting high temperature risk. The 20-day averaged daily mean temperature,
which has a probability of 33.3%, was set as the dependent variable, and longitude, altitude,
and urban area ratio were set as explanatory variables. As a result, regions located further
north experience greater temperature increases due to climate change, and regions with higher
urban rates also experience greater temperature increases due to anthropogenic effects.
Keywords climate change, climate hazard, agricultural damage, paddy rice quality

INTRODUCTION
In recent years, there have been many reports of high-temperature injury in rice (Terashima et al.,2001). The main symptoms are a decrease in grain filling rate and a decrease in grain weight per
grain (MAFF, 2006), which can reduce the inspection grade and yield, respectively, thereby
decreasing farmer income (Morita, 2008). The inspection grade is determined by the minimum grain
filling rate and the maximum proportion of rice classified as a chalky rice grain or other non-grain
filling categories (National Food Safety and Quality Association, 2002).
The reason CRGs appear "chalky" is due to empty spaces between starch granules in theendosperm, which cause light to scatter randomly (Tashiro and Ebata, 1975). In fact, in "chalky"areas caused by high temperatures, there have been observations of empty spaces betweenⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2amyloplasts, normally polyhedral amyloplasts that are round, and small amyloplasts that only containtwo or a few single-grain starches (Tashiro and Wardlaw, 1991; Zakaria et al., 2002).
Masutomi (2019) revealed that CRG begins to occur when the average temperature exceeds
26°C during the 20 days after rice heading. The study also presented the predicted spatial distributionof this damage under future climate conditions. However, in analyzing the risk of agricultural damage,it is important to consider not only the severity of the damage but also the probability (frequency) of
its occurrence.
OBJECTIVE
This study aims to use a probabilistic method and weather big data to analyze the risk of hightemperatures and assess the risk of CRGs in Japan.
METHODOLOGY
Probabilistic Risk Assessment
Probabilistic Risk Assessment (PRA) is a method of quantitatively evaluating risk using probability
theory and is commonly used in the safety assessment of structures. For example, the International
Atomic Energy Agency (IAEA) uses probabilistic risk assessment to evaluate the safety of nuclear
facilities. PRA involves integrating probability fields that represent the predicted seismic forces atthe construction site of the facility, represented as a hazard curve, with the load represented as a
fragility curve when specific seismic forces are applied, and evaluating the risk in terms of annual
damage probability (IAEA, 2010). In addition to structures, some studies quantitatively evaluate therisk that herbicides for rice fields pose to the surrounding ecosystem (Nagai et al., 2008). To assess
the risk of CRGs, we applied the PRA method and compared the risk for two 20-year periods: 19801999 and 2000-2019. CRGs, which can negatively impact the quality of rice, are more likely to occur
when the average temperature exceeds 26℃ during the first 20 days after the heading date of rice.
We utilized daily mean temperature data from Japan's Automated Meteorological Data
Acquisition System (AMeDAS), which consists of over 1600 observatories. However, due to theinclusion of recently established stations and the presence of missing values, we did not computedata for any station that had less than 90% data availability during the analysis period. After applying
this criterion, the number of stations eligible for our calculations was approximately 830. The dataof 20 days mean air temperature were extracted in descending order and the top five values per year
were selected for further analysis. Using Eq. (1), the annual exceedance probability was calculated
based on these values.
𝑃(𝑥) = 1 − 𝑒

𝑛
𝑇

(− )

(1)

where, x: 20 days mean air temperature (℃), T: observation period, n: The number of occurrencesof mean temperatures above x℃.
Multiple Regression Analysis
To identify the factors that have the most significant impact on the increase in temperature resulting
from global warming and anthropogenic effects, multiple regression analysis was conducted. The
dependent variable was the 20-day averaged daily mean temperature with an annual exceedance
probability of 33.3% (i.e., once in the 3-year probability of occurrence), while latitude and elevation
were chosen as explanatory variables due to their well-established negative correlation withtemperature. The reason for adopting a threshold of 33.3% can be exemplified by Sompo Japan
Thailand. This company has set the frequency of early drought coverage in its weather index
insurance for rice drought damage in Thailand to about once every three years. Additionally, we
included urban land use coverage as an explanatory variable due to its known role in raisingⒸ ISERD

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temperatures. For latitude and elevation, we used the coordinates and elevation data of AMeDAS
stations published by the Japan Meteorological Agency. For urban land use coverage, we utilized
land use data in 2010 from JAXA, calculating urban land use coverage within a buffer of 10 km indiameter centered on the AMeDAS station being examined following Fujibe (2011).
RESULTS AND DISCUSSION
To analyze the risk of CRGs, we calculated the annual probability of 20-day averaged daily mean
temperature exceeding 26°C for the recent 20 years (2000-2019) in Fig 1. The probability ofexceeding 26°C is low in Hokkaido or the northern part of Japan, where temperatures are relatively
low throughout the year, and in high-elevation areas (mountainous regions), indicating a low risk ofhigh-temperature injury. In the areas south and western parts of Japan, the annual probability ofexceeding 26°C frequently occurs, except in high-elevation areas.
Eligible stations: 830

Fig. 1 Probability of 20-day averaged daily mean temperature
exceedance by 26°C for the last 20 years (2000-2019)
To examine the changes in the probability of annual exceedance of 26°C for the 20-day averaged
daily mean temperature over the past 20 years and the recent 20 years, we calculated the differenceof temperature with a probability of 33.3% between the two periods. Yoshida et al. (2019) indicatedthat the most frequent climate hazard in Northeast Thailand occurs once every four years. The
concern of this research lies in high-frequency climate hazards that are not covered by insurance,
hence setting a threshold of 33.3%, which means a frequency of once every three years. To identify
locations with significant differences, we used color-coded observation points based on thesignificance of the difference. As shown in Fig. 2, locations with insufficient data are represented ingray, those with negative change are depicted in blue, locations with no change are represented ingreen, locations with a change greater than 0°C and less than 0.3 are depicted in yellow, and locations
with a change greater than 0.3°C are depicted in red. Notably, Fig. 2 has more 'No Data' points thanFig. 1. This is because each station needed to have over 90% data availability in both time periods.
This stricter rule reduced the number of sites with sufficient data, resulting in an increased numberof locations categorized as 'No Data'. With a few exceptions, most of the sites depicted in red arelocated slightly inland from the coast. This is likely due to the high heat capacity of the oceans incoastal areas, which makes it difficult for temperature changes to occur. There are many locations inthe southern part of the Ibaraki prefecture where the risk of high-temperature injury has increased,Ⓒ ISERD

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consistent with the findings of Masutomi et al. (2019), who observed a decreasing trend in the ratioof first-class rice in future climates.
Eligible stations: 799

Fig. 2 Difference in temperature for two periods with a yearlyexceedance probability of 33.3%
The data were organized for multiple regression analysis with the dependent variable being thedifference of past and recent 20-day averaged daily mean temperature which occurs in 33.3%
probability of the year and the explanatory variables being latitude, elevation, and urban land use
ratio within a 10 km diameter buffer centered on the AMeDAS observatory. In the analysis, points
with more than 90% of the observation period for substantial explanatory variables were extracted
and examined. Applying this rule reduced the sample size to 799. The explanatory variables werestandardized using the following Eq. (2), and multiple regression analysis was conducted using thestandardized variables.
𝑥′ =

𝑥−𝑥

(2)

𝑠

where, 𝑥: mean value in sample, s: standard deviation in sample.
The difference in temperature between two periods with a yearly exceedance probability of
33.3% was used as the dependent variable, and latitude and urbanization rate were used as
explanatory variables in a multiple regression analysis. Table 1 shows the coefficients and p-values
for the results. The coverage of urban land use within a 10 km buffer is represented by the percentage
of JAXA land use maps classified as "urban," so it is referred to as the "urban rate" in the table. From
this multiple regression analysis, it was found that there were statistically significant results forlatitude and urban land use coverage at the 1% level. The partial regression coefficients for the
explanatory variables were 0.124 for latitude and 0.279 for urban land use coverage. This indicatesthat regions located further north experience greater temperature increases due to climate change andthat regions with higher levels of urbanization also experience greater temperature increases due toanthropogenic effects. Among these, it was also shown that urbanization has a greater contribution
to temperature increases. Also, the analysis suggested that the impact of urbanization on temperaturerise is more pronounced in northern regions compared to southern regions. In Fig. 2, most points nearcities in northeastern regions are shown in red, while there are fewer red points near cities in southern
regions. This means that cities in different places are affected differently by urbanization when it
comes to temperature changes.
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Table 1 Results of the multiple regression analysis
Intercept
Elevation
Latitude
Urban rate

coefficients
0.000-0.0290.124
0.279

P-value
1.000
0.401
0.000
0.000

CONCLUSION
In this study, hazard curves were probabilistically created from nationwide AMeDAS data mainly
using temperature as a meteorological factor, and based on the results, the risk of chalky rice grain
was examined using a probabilistic risk assessment method. The 26°C yearly exceedance probabilityof the 20-day averaged daily mean temperature was calculated for the past 20 years (1980-1999) andthe recent 20 years (2000-2019) at all AMeDAS observatories in Japan, and the change in the two
periods was calculated to analyze the change of high-temperature risk. When the acceptablefrequency of damage for farmers was set to 33.3%, it was shown that the risk of high-temperaturedamage in inland areas of the southern Tohoku region and the southern area of Ibaraki Prefecture has
increased in the recent 20 years. The characteristics of points where the risk of high-temperaturedamage has greatly increased were inland areas slightly away from the coast. On the other hand, therisk of high-temperature damage decreased in the northern part of the Tohoku region, especiallysurrounding areas near the ocean. To quantitatively measure regional differences in temperature
increases due to climate change, multiple regression analysis was conducted with the change intemperature with a yearly exceedance probability of 33.3% as the dependent variable and latitude,
elevation, and urbanization rate as dependent variables. It was found that there were statisticallysignificant results for latitude and urban land use coverage at the 1% level. The partial regression
coefficients for the explanatory variables were 0.124 for latitude and 0.279 for urban land use
coverage. This shows that climate change affects the northern part more and might become
significant in the agricultural land located near the urban city area. In this study, we just used 26°C
Celsius as a threshold of CRG and 33.3% probability as a farmer's acceptable frequency level,
however further research is needed about the rice varieties or farmer's perception.
To tackle the issue of Chalky Rice Grains (CRGs), various strategies have been suggested.
Morita et al. (2015) recommended applying enough nitrogen during or after the panicle initiation
stage. Similarly, Chiba et al. (2013) suggested using deep-flood irrigation to control the temperature
at the surface of rice paddies. In addition to these immediate solutions, Masutomi (2019) proposed
the development and use of rice varieties that can better withstand high temperatures, as a long-term
approach. Although these methods have been effective in reducing CRGs, they often involve
significant costs. Therefore, it is important for future research to include detailed cost-benefit
analyses to identify the most cost-effective adaptation strategies.
ACKNOWLEDGEMENTS
This research was supported by the MEXT/JSPS Grant-in-Aid for Scientific Research (KAKENHI)
no.19H03069 and no.20KK0346.
REFERENCES
Chiba, M., Matsumura, O., Watanabe, H., Takahashi, Y. and Terao, T. 2013. Deep-flood irrigation suppresses
the occurrence of chalky grains through high source ability, despite the high percentage of secondary rachis
grains. Japanese Journal of Crop Science, 82 (3), 223-232, Retrieved from DOI https://doi.org/10.1626/jcs.82.223
Fujibe, F. 2011. Long-term climate changes in Japan and urbanization. Tenki, 58, 5-18. (in Japanese)
International Atomic Energy Agency. 2010. Development and application of level 1 probabilistic safetyassessment for nuclear power plants. IAEA Safety Standards Series No. SSG-3, IAEA, Vienna, RetrievedⒸ ISERD

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from URL https://www-pub.iaea.org/MTCD/Publications/PDF/Pub1430_web.pdf
Masutomi, Y., Takimoto, T., Shimamura, M., Manabe, T., Arakawa, M., Shibota, N., Ooto, A., Azuma, S.,
Imai, Y. and Tamura, M. 2019. Rice grain quality degradation and economic loss due to global warming inJapan. Environmental Research Communications, 1 (12), 121003, Retrieved from DOI https://doi.org/10.
1088/2515-7620/ab52e7
Ministry of Agriculture, Forestry and Fishery, 2006. Overcoming high temperature distress in paddy rice,
Report on measures to prevent high temperature distress. Retrieved from URL https://www.maff.go.jp/j/kanbo/kihyo03/gityo/g_kiko_hendo/suito_kouon/pdf/report.pdf (in Japanese)
Morita, S. 2008. Prospect for developing measures to prevent high-temperature damage to rice grain ripening.
Japanese Journal of Crop Science, 77 (1), 1-12, Retrieved from DOI https://doi.org/10.1626/jcs.77.1 (inJapanese)
Morita, S., Wada, H. and Matsue, Y. 2016. Countermeasures for heat damage in rice grain quality under climatechange. Plant Production Science, 19 (1), 1-11, Retrieved from DOI http://dx.doi.org/10.1080/1343943
X.2015.1128114
National Food Safety and Quality Association. 2002. Handbook of agricultural product inspection, Rice. 157201.
Schroer, S. and Modarres, M. 2013. An event classification schema for evaluating site risk in multi-unit nuclear
power plant probabilistic risk assessment. Reliability Engineering and System Safety, 117, 40-51, Retrieved
from DOI https://doi.org/10.1016/j.ress.2013.03.005
Tashiro, T. and Ebata, M. 1975. Studies on belly white rice-4-morphological characteristics of endosperm cells
in white opaque parts. Japanese Journal of Crop Science, 44 (2), 205-214, Retrieved from DOI https://doi.
org/10.1016/j.ress.2013.03.005
Tashiro, T. and Wardlaw, I.F. 1991. The effect of high temperature on kernel dimensions and the type andoccurrence of kernel damage in rice. Australian Journal of Agricultural Research, 42 (3), 485-496,
Retrieved from DOI https://doi.org/10.1071/ar9910485
Terashima, K., Saito, Y., Sakai, N., Watanabe, T., Ogata, T. and Akita, S. 2001. Effects of high air temperaturein summer of 1999 on ripening and grain quality of rice. Japanese Journal of Crop Science, 70 (3), 449458, Retrieved from DOI https://doi.org/10.1626/jcs.70.449
Yoshida, K., Surisutham, M., Sritumboon, S., Suanburi, D., Janjirauttikul N. and Suanpaga W. 2019.
Evaluation of economic damages on rice production under extreme climate and agricultural insurance foradaptation measures in Northeast Thailand. Engineering Journal, 23 (6), 451-469, Retrieved from DOI
https://doi.org/10.4186/ej.2019.23.6.451
Zakaria, S., Matsuda, T., Tajima S. and Nitta Y. 2002. Effect of high temperature at ripening stage on thereserve accumulation in seed in some rice cultivars. Plant Production Science, 5 (2), 160-168, Retrieved
from DOI https://doi.org/10.1626/pps.5.160Ⓒ ISERD73

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Research article
erd

Agricultural Support for New Farmers in H City, Tokyo,
Japan
RIKA TERANO
Faculty of International Agriculture and Food Studies, Tokyo University of Agriculture, Japan

NINA N. SHIMOGUCHI*
Faculty of International Agriculture and Food Studies, Tokyo University of Agriculture, Japan
Email: n3nocon@nodai.ac.jp

RAMADHONA SAVILLE
Faculty of International Agriculture and Food Studies, Tokyo University of Agriculture, JapanHETTIGE SAMITHA LAKSHAN GUNASEKARA
Graduate School of International Food and Agricultural Science,
Tokyo University of Agriculture, JapanKATSUMORI HATANAKA
Faculty of International Agriculture and Food Studies, Tokyo University of Agriculture, Japan
Received 6 September 2023

Accepted 26 November 2023(*Corresponding Author)

Abstract In Japan, engaging in agriculture as a new farmer comes with various challenges
and uncertainties. To sustain and secure the agriculture sector’s future, national and local
governments, farmers, private entities, and other stakeholders provide a wide range of
support. Seeking best practices is a sound basis for increasing the number of new farmers andimproving their resilience. Through a case study approach, this study aimed to clarify thecurrent condition and issues of agricultural support for new farmers in H City, Tokyo, anddetermine the initiatives and agricultural support that assisted a young female farmer inestablishing an urban farm. This study conducted interviews with a young female farmer andcity officers. The in-depth key informant interviews revealed that training, subsidies, securing
land services, and referral to farmer’s organizations are some of the available forms of
support. The interviewed farmer considered the recent revision of the Law on Productive
Green Areas a significant opportunity for her to rent farmland, which is a challenge for most
new farmers. Despite the fact that a 5-year rental period is commonly practiced, she could
avail of a 30-year lease, conveying the importance for new farmers to also develop trust,confidence, and a good relationship with the farmland owner. Although the interviewed
farmer has successfully established a farm, there is a need for further investigation to clarify
consumer preferences, buying behavior, and their degree of familiarity with her farm andproducts to improve her farm business and resilience.
Keywords new farmers, law on productive green areas, farm resilience, female farmer

INTRODUCTION
According to the Ministry of Agriculture, Forestry and Fisheries Japan (MAFF Japan, 2020), thecountry’s self-sufficiency level was 73% in 1965 and decreased severely to 38% based on caloriebased calculation in 2021. Therefore, the primary source of food for human consumption and feed
for livestock mainly depends on imports from abroad. Although the country must increase its selfsufficiency level, decreasing trends in farmland area and population are apparent challenges in the
agricultural sector of Japan. According to MAFF Japan (2021a), the total cultivated area was 6.0
million ha at a peak in 1961 and gradually decreased to 4.3 million ha in 2021. For the past fourⒸ ISERD

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decades, the number of individuals employed in the agricultural sector declined by more than 60%
from 5.77 million in 1980 to 2.13 million in 2020 (Statistics Bureau Japan, 2022).
Under such circumstances, urban agriculture supplies agricultural products and provides amulti-functional environment in the cities. According to MAFF Japan (2022), 13.0% of the total
agricultural management entities are in urbanization-designated area (shigaika-kuiki in Japanese),commonly defined by the urban municipalities. These 140,000 agricultural management entities
contribute about 7.0% of gross domestic agricultural production, only cover 62,000 ha of farmland
limitedly 1.4% of the total domestic farmland. Looking at those farmers who work mainly inagriculture, 70% of them are over 65 years old and only 11% of them are younger than 49 years old(MAFF Japan, 2022). In this apparent situation of aging farmers, identifying ways to increase thenumber of new farmers is crucially important. What kind of support new farmers need remains aquestion. In this study, we organize policies of the government and attempt to elucidate how new
farmers are supported by introducing a case of a new tomato farmer in H city.
METHODOLOGY
We described policies and trends of agriculture in the H city based on key informant interview of H
City Agricultural Affairs Division on 20 June 2022 at the H city hall. Through a case study approach,we collected reference materials related to the sequence of events when starting farming andestablishing agricultural cooperation, and conducted an interview and observation survey regarding
the current business of Ms. K.
RESULTS AND DISCUSSION
Policy for New Farmers in Japan
According to Bureau of Urban Development (2022), the promotion of productive green areas wasproceeded as the Productive Green Area Act among large cities for city planning development, toavoid farmland conversion to housing land area and maintain the agricultural environment in cities.
If landowners keep the land as the green area which are not only farmland but also related businessincluding farm restaurant and direct selling shop for agricultural products; benefits include paymentof fixed asset tax of the green area under farmland category, and eligibility for inheritance tax
payment grace system. It is expected that this kind of promotion of agriculture leads to land retention.
The Productive Green Area Act and at same time Urban Farmland Leasing Facilitation Act
assist new farmers in accessing farmland for the long-term. The latter act secures the landowner’s
right over the farmland for a long-term lease contract. It protects them not to lose the land rights after
leasing out the land to non-family members. Historically, Japan had a landlord system between
landowners and tenants before land reforms were implemented twice in the 1940s. With this act,
tenants were entitled to hold landownership of the land as actual cultivators. This may be a reason
why the landowner avoids long-term leases to others, especially non-families. When new farmers’
parents are not from a farming family without succession, they experience difficulties in starting
farming. Hashimoto and Mitsuhashi (2017) studied the case of Utsunomiya City’s new farmers.
When they start farming, they need access to land, financial support, and training opportunities.
Osawa (2014) also pointed out that access to farmland become a barrier for new farmers from nonfarming families. His research illustrates a way to create places for new farmers to sell vegetables at
direct selling markets and let them gain more profit even though they produce small volumes at atime. In addition to the land access, a new farmer needs support. Tokyo Metropolitan Government(Bureau of Industrial and Labor Affairs, n.d.) designs supporting systems for new farmers: A farming
preparation fund (supports financially new farmers during the training period before they start
farming on their own within 2 years) and Business start-up capital (supports new farmers
immediately after they start farming when management is unstable within 3 years). In the new farmer
settlement support project as a farming preparation fund, the Tokyo Chamber of Agriculture supports
newly certified farmers. Municipalities subsidize 3/4 of the operating costs for cultivation-relatedⒸ ISERD

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facilities and agricultural machinery (Tokyo Chamber of Agriculture, 2021). The new farmers gained
experience and reached an annual income of 3 million yen.
Background of H City, Tokyo
With regards to agriculture, blueberry, pear, apple, persimmon strawberry, radish, and tomato are
well-known agricultural products of H City. There are many picking farms of blueberries and/or
apples as popular agritourism destinations. The high season of picking farms is in summer, between
July and August. Radish is Tokoji (Toko temple) in a line of Nerima radish as one of the Edovegetables.

Source: RESAS, 2020

Fig. 1 Management entities by marketing channels for agricultural products (%)
According to the H City based on an interview, agriculture in the H City was once used to be
active in rice farming, but from the mid-1950s onwards there was a shift to upland farming. The
conversion was to produce crops with a higher selling price than rice. Tomatoes were one of them.
Cultivation in greenhouses was introduced in the 1960s, and it became important. Since 2009,
tomatoes have been produced using a barrel cultivation system. Tomatoes have become a specialty
product. Currently, there are 8 tomato farmers in the H City. Based on the key informant interview
of H City agricultural affairs division staff, the total number of farm households decreased from 371
in 2005 to 273 in 2020. Moreover, there are only 57 farmers who are younger than 50 years old, 238
farmers are older than 50 years old including 76 farmers above 75 years old. In H City, more than
100 cases convert farmland of paddy fields and fields into housing areas every year. There is a totalof 57 ha of farmland under cultivation of H City, comprising of paddy field 4 ha, a vegetable field
37 ha, and a fruit orchard 16 ha. The ratio of farmland compared to the whole land of H city decreases
from 4.1% in 2005 and 2.1%. Out of 129 farm households in 2020, 123 households averagely
cultivate less than 1 ha, 5 households cultivate in the range from 1.0 to 1.5 ha and only one household
cultivate larger than 1.5 ha in 2020. Notedly, as shown in Fig. 1, 67% of the agricultural products
harvested in H city are sold directly to the citizens in H city through farmer’s market or roadsidestand locally, indicating that producers and consumers seems to have close distance in H City. It mayⒸ ISERD

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be predictable that people in H City are used to purchasing local products and being familiar withthe local brands or producers of fruits and vegetables.
A Case of the New Farmer as a Tomato Producer
There are three main attempts by the municipal administration based on the Tokyo metropolitan
government’s policy: (1) implementing the New Farmer Settlement Support Project with theadditional subsidy by H City, (2) increasing the motivation of citizens to support farm work for local
farmers, and (3) producing and consuming locally through the direct farm shop and school lunch
catering. As of 20 June 2022, funds of new farmers come from the Tokyo metropolitan government
subsidy (equivalent to 75% of the total expense) and the remaining 25% is equally sourced from H
city subsidy and new farmer’s own capital.
There are three types of new farmer support in the H City: (1) new farmer as the successor ofan existing farmer, (2) newly employed farmer at agricultural cooperation, and (3) new entrants fromnon-farm households. Among these new farmer types, there is the “authorized new farmer (nintei
shinki shunousha)” who submits own management plan to the municipal office. New entrants offarming in this system are limited to youth in the age range from 18 to 44 years old. H City prepares
not only subsidies but also support to rent in farmland and introduces agricultural organization underthe municipal administration.
In the H City for the past 22 years from the year 2000 to 2021, there have been 29 new farmers
as heirs of the existing farmers who start working at home. On the other hand, there are only 2 newfarmers as authorized new farmers, and Ms. K is the first case of new farmer under the new farmer
settlement support project in 2020 to develop one tomato cultivation facility. According to Ministryof Land, Infrastructure, Transport and Tourism (n.d.), the Law on Productive Green Area was enacted
first in 1974. In 1992, it was revised to add tax reductions on inheritance tax and property tax withthe condition of area larger than 500 sq. m. In 2017, it was again partially revised to allow thefollowing: (1) municipalities can reduce the area from 500 sq. m. to 300 sq. m. or more by ordinance,(2) to set up restaurants, processing facilities, and farmer’s markets in the area, and (3) If 30 years
have passed after the municipality register the land as a productive green area, the purchase offer
period can be extended to 10 years. Landowners can receive tax break for property tax as farmlandtax not as housing and deferring inheritance tax payment. In September 2018, after the Urban
Farmland Leasing Facilitation Act was enacted, tenants could rent the land directly from the ownerof the land, and those owners still could maintain the grace period for paying inheritance tax after
they rent out in the urban area (MAFF, 2021b). This act is limited to the land that is designated as a
Productive Green Area. Advantages of the act area; for tenant it is easier to rent-in directory, and forowner after the contract period usually long term, the farmland will be returned, therefore thelandowner can rent it with confidence.
After graduating from university, Ms. K started working for a large-scale tomato greenhousecompany. In 2017, she became a farm trainee in Tokyo and established the Farm in H City in 2018.
When she was looking for farmland to start farming, there was a revision of the law on productive
green areas in 2018. It triggered for Ms. K to rent farmland from a local landowner. The law allows
landowners to receive tax breaks even if they rent own farmland out to others. Ms. K became thevery first new farmer who could have access to the land lease in Japan after the law on productive
green area was revised. She also gained support from Tokyo Chamber of Agriculture member (Tokyo
Chamber of Agriculture, 2021) and the municipal officer as third parties. When someone is new tofarming, it is quite hard to gain trust from landowners. With the support of the Tokyo Chamber ofAgriculture and the H City, she was assisted in having a 30-year rental contract for the land andinstalled facilities to produce tomatoes and other crops. For the land rental, she seemed to havebenefitted from Urban Farmland Leasing Facilitation Act and the revision of Productive Green Act.
Moreover, she purchased three greenhouses with the subsidies under new farmer settlement support
project. Since seven-eighth was paid by subsidies, her expense became only one-eighth of the total
expense. As Ms. K explains, the landlord understands her desire to make a large investment and build
greenhouses, and in anticipation of the payback period of the investment, she was able to rent theland for an extended period.
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She produces vegetables in the cultivating facilities of the plastic greenhouse as shown in Fig.
2a. Her products are certified as Tokyo GAP (Good Agriculture Practice) and eco-farm products ofTokyo (Fig. 2b). In addition to medium-sized, large-sized, and mini-sized tomatoes, the farm also
produces turnips, blueberries, and other vegetables. Most vegetables are sold fresh, except for sometomatoes that are processed tomato puree with yellow, green, and red colored tomatoes and bottled
separately. These colorful puree products are sold online. She sells fresh tomatoes and others throughher online shop in a box. The marketing channels of the fresh tomatoes include the unmanned store(mujin hanbai) in front of the farm (Fig. 2c), the farmer’s market of Japan Agricultural Cooperative(JA), a shop supported by the municipality, local fruits and vegetable shops, and chain supermarkets
in Tokyo. Moreover, she proactively participates in a local young farmer’s club since 2020 torevitalize the agriculture of H City. She also sells her own products at those coordinated events. She
also opens events at her farm for local kids with parents to and feel the importance of food throughvarious farming activities such as harvesting fruits and vegetables.
To promote sustainable agriculture in Tokyo, issuance of Tokyo GAP started in April 2018. Asof 2 March 2022, 130 farmers were certified (Bureau of Industrial and Labor Affairs, n.d. on
Promotion of GAP in Tokyo). Farmers who apply for Tokyo GAP are required to fill out the checklist
and comply with water quality, recordings of management, soil cultivation, IPM (Integrated Pest
Management) to reduce chemical input, and other recommended farming practices. On the other
hand, the certification of eco-farm products started in 2021. It intends to reduce the usage of synthetic
pesticides and chemical fertilizers. There are three categories of certification: 25% or more reduction,
50% or more reduction, and no usage at all. When farmers do not apply any of the inputs, theirproducts are considered “Tokyo Eco-100” (Tokyo Metropolitan Government, 2017). In the case of
tomatoes, these are commonly certified as “Tokyo Eco-25” with a 25% reduction of synthetic
pesticides and chemical fertilizers through agriculture. These two certifications ensure her concerns
on human health and the environment load reduction by applying sustainable farming practices.

(a) Production facility(b) Certification sign boards(c) Unmanned store (mujin hanbai)

Source: Farm visit, 2022

Fig. 2 Pictures taken at Ms. K’s farm
CONCLUSION
This paper presents the national and municipal policies to support new farmers. After the overview
of agriculture in the H City was introduced, the case of new farmers was described. In the H City,
the number of farmers has been on the decline, and increasing the number of farmers is an importantmission. For this reason, the city actively implements support for new farmers. When new farmers
start farming, they need access to land, financial support, and training opportunities. Thus, the city
supports not only those who become farmers as successors but also non-farmers who are new tofarming as one of the important policies of the city. This article took up the case of Ms. K, a tomato
producer who also produces blueberries and vegetables. To start production in the H city in 2019,
access to land was a challenge. But after the revision of the Productive Green Area Act, as the firstⒸ ISERD

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new farmer (non-farmer who is from a non-farming household), she has been producing tomatoes.
Now, she is one of the popular local farmers featured in various media, so people often recognize her
face and name. A specific element of the successful case in the H City is still not commonly seen in
Japan. Municipal officers and the Tokyo Chamber of Agriculture supported Ms. K in gaining accessto a 30-year land lease, which is a very rare case. However, this implies that without third party
support, including with regard to land access, new farmers from non-farming families find it very
difficult to start farming.
ACKNOWLEDGEMENTS
The authors wish to extend appreciation to the H City Officers and Ms. K for sharing time andvaluable information.
REFERENCES
Bureau of Industrial and Labor Affairs. n.d. Comprehensive measures for training new farmers. Tokyo
Metropolitan Government, Tokyo, Japan, Retrieved from URL https://www.sangyo-rodo.metro.tokyo.lg.
jp/nourin/nougyou/shuunou/jisedai/ (in Japanese)
Bureau of Industrial and Labor Affairs. n.d. Promotion of GAP in Tokyo. Tokyo Metropolitan Government,
Tokyo, Japan, Retrieved from URL https://www.sangyo-rodo.metro.tokyo.lg.jp/nourin/shoku/anzen/gap/(in Japanese)
Bureau of Urban Development. 2022. Productive green area. Tokyo Metropolitan Government, Tokyo, Japan,
Retrieved from URL https://www.toshiseibi.metro.tokyo.lg.jp/seisaku/midori_kakuho/seisanryokuchi.html(in Japanese)
Hashimoto, M. and Mitsuhashi, N. 2017. Efforts towards farming issues of new farmers and heirs in the suburbsof the metropolitan area, Case study of Utsunomiya City. Journal of Rural Planning, 36, 264-270, Retrieved
from URL https://www.jstage.jst.go.jp/article/arp/36/Special_Issue /36_264/_pdf (in Japanese)
Ministry of Agriculture, Fisheries and Forestry (MAFF Japan). 2020. Self-sufficiency level in Japan. Tokyo,
Japan, Retrieved from URL https://www.maff.go.jp/j/zyukyu/zikyu_ritu/012.html (in Japanese)
Ministry of Agriculture, Fisheries and Forestry (MAFF Japan). 2021a. Statistical information 2021 on arable
land area. Tokyo, Japan, Retrieved from URL https://www.maff.go.jp/j/tokei/kekka_gaiyou/sakumotu/
menseki/r3/kouti/index.html (in Japanese)
Ministry of Agriculture, Fisheries and Forestry (MAFF Japan). 2021b. Summary of the annual report on food,agriculture and rural areas in Japan. Retrieved from URL https://www.maff.go.jp/e/data/publish/attach/pdf/index-69.pdf
Ministry of Agriculture, Fisheries and Forestry (MAFF Japan). 2022. The annual report on food, agriculture,and rural areas in Japan. Tokyo, Japan, Retrieved from URL https://www.maff.go.jp/j/wpaper/w_maff/r3/pdf/zentaiban.pdf (in Japanese)
Ministry of Land, Infrastructure, Transport and Tourism (MILT Japan). n.d. Productive green areas. Retrieved
from URL https://www.mlit.go.jp/toshi/park/toshi_city_plan_tk_000041.html (in Japanese)
Osawa, M. 2014. New farmers from non-farming families, Five reasons to become a farmer. Japan Studies:
The Frontier, 9, 73-86, Retrieved from URL https://researchmap.jp/makotootokam/published_papers/
32461859/attachment_file.pdf
Regional Economy Society Analyzing System (RESAS) on Industrial Characteristics in Hino City. Retrieved
from URL https://summary.resas.go.jp/summary.html (in Japanese)
Statistics Bureau Japan. 2022a. Statistical Handbook of Japan 2022. Retrieved from URL https://www.stat.go.
jp/data/handbook/index.html
Statistics Bureau Japan. 2022b. Census of Agriculture and Forestry. Retrieved from URL https://www.estat.go.jp/en/stat-search/files?page=1&toukei=00500209&tstat=000001032920
Tokyo Chamber of Agriculture. 2021. New farmer settlement support project. Retrieved from URL https://www.
tokaigishinki.com/syunoshien/gyoseishien/teichakushien/ (in Japanese)
Tokyo Metropolitan Government 2017. Tokyo Agriculture Promotion Plan. Retrieved from URL https://www.
sangyo-rodo.metro.tokyo.lg.jp/plan/nourin/295/Ⓒ ISERD

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Research article
erdTechnical Efficiency of Potato Producers in Benguet
Province, Philippines
ASRES ELIAS*
Faculty of Agriculture, Tottori University, Tottori, Japan
Email: asres@tottori-u.ac.jp

CHERYLL CASIWAN LAUNIO
Research and Extension Publication Office, Benguet State University, Philippines

KUMI YASUNOBUFaculty of Agriculture, Tottori University, Tottori, Japan

YUKI TOYAMA
United Graduate School of Agricultural Science, Tottori University, Tottori, Japan

BIRARA ENDALEW
Graduate School of Sustainability Science, Tottori University, Tottori, JapanMARYAN J. ALTAKI
Philippine Rice Research Institute, Philippines
Received 25 September 2023

Accepted 28 November 2023(*Corresponding Author)

Abstract The Benguet Province of the Philippines is endowed with distinct agroecologicalzones favorable for the production of high-value vegetables. Potato is one of the top ten
vegetable crops in Benguet Province in terms of production area, volume, and value.
However, potato production in the Philippines in general is characterized by low yield, witha yield per hectare of 15.5 tons compared to the world average yield of 20.9 tons in 2018.
This study examined the technical efficiency (TE) and inefficiency determinants of potato
producers in Benguet Province, Philippines. Data from 101 farmers from the major potatoproducing municipalities in Benguet Province was collected in 2019. We employed the
Cobb-Douglas stochastic production frontier after the null hypothesis for the translog
function was rejected. The production function result revealed that land, hired labor, organic
fertilizer (chicken dung) and fungicide had significant effects on potato yield. We also foundthat the mean technical efficiency of potato farmers is about 0.89, implying that farmers canincrease their production by about 11%, with an average yield increase from 15.8 to 17.6
t/ha, using existing resources. The yield gap result further revealed that farmers are producing
below the production possibility frontier. However, the observed average yield is about 50%
lower than the potential yield (31 t/ha) found by Benguet State University. The inefficiency
model result depicted that a larger household size, higher level of education, using the
Granola potato variety, receiving training related to vegetable production, and partial
irrigation significantly improve technical efficiency, while hand tractor ownership andrainfed cultivation were the major sources of technical inefficiency in potato production. In
conclusion, while comprehensive plot-level studies including various parameters remain
important to understanding why potato producers’ yields are significantly lower than thepotential yields, we recommend promoting the Granola variety along with the use of organic
fertilizers and the provision of training to enhance the efficiency of producers.
Keywords potato production, technical efficiency, stochastic production function,
Philippines

INTRODUCTIONⒸ ISERD

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Vegetable production in the Philippines shows an increasing trend in terms of total value of
production compared to rice production which was the leading crop until 2020 (Department ofAgriculture, 2021). The vegetable production is divided into two main classifications based on
location and climatic conditions i.e., highland and lowland. Benguet province in the Cordillera
Administrative Region (CAR) is the leading highland vegetable producer area endowed with distinct
agroecological zones favorable to producing high-value vegetables. Potato is one of the top ten
vegetables in terms of production area, volume, and high value in Benguet. Among the six regionsthat grow potatoes in the Philippines, CAR contributes 73% and 83% of production area and volume
respectively (PSA, 2020). However, potato production is characterized by low yield in thePhilippines in general is among the lowest yield per hectare (15.5 tons) compared to the world
average yield (20.9 tons) in 2018. According to the JICA 2019 report, there is a reduction in the
production of cabbage, cauliflower, carrots, and potatoes in Benguet province. The decrease in thearea planted, extreme weather events, pest and disease problems, and low adoption of high-yielding
varieties were among the main causes. In this context, only a few studies exist including the statusof potato varieties (Kiswa et al., 2020); identification of potato varieties, agronomic characteristics,
and potential yields (Gonzales et al., 2016). As far as our knowledge, no studies have been conducted
in Benguet to investigate whether the declining production trend is attributed to the technicalefficiency of producers. Therefore, this study aims to understand the extent to which potato producers
can attain the utmost output from the available combination of inputs.
OBJECTIVES
The objectives of this research are as follows;
1) to estimate the technical efficiency of potato producers, and2) to identify major sources of technical inefficiency in potato production.
METHODOLOGY
The data used in this study was collected by Benguet State University (BSU) with funding from the
Department of Agriculture, Bureau of Agricultural Research (DA-BAR) in 2019, in reference to 2018
production season. Farm-level information including farm profile, facilities, input-output
information, perception of recommended practices, and other variables were collected from 700
farmers (7 crops) in eight selected municipalities of Benguet Province, Cordillera Administrative
Region, Luzon Island. This study used the potato data collected from 101 farmers in the Atok, Bakun,
Buguias, Kibungan, and Mankayan municipalities. Multistage sampling was employed first by
selecting the top five municipalities of Benguet with the largest area planted to potatoes. All thevillages in the sample municipalities with areas that planted potatoes were then shortlisted from
where three villages were randomly drawn.
Empirical Model
Following the neo-classical definition of technical efficiency, a production process is considered
technically efficient when it attains the highest achievable output from a given combination of inputs.
This study utilized the Stochastic Frontier (SF) approach, which is a parametric method forevaluating farm-level efficiency. The SF model dissects the error term into a two-sided random errorthat accounts for uncontrollable random effects beyond the firm's control (the decision-making unit),
and a one-sided efficiency component. In this research, we adopted the model outlined by Battese
and Coelli (1995), and the model specification is provided as follows:(1)

𝑌𝑖 = 𝑓(𝑥𝑖 ; 𝛽) exp(𝑣𝑖 − 𝑢𝑖 )where Y is the quantity of output (potato yield) on the 𝑖th firm, x is a vector of inputs (production
inputs shown in Table 1) used, 𝛽 is a vector of parameters, 𝑓(𝑥𝑖 ; 𝛽) is a suitable production function,v is a random error term assumed to be independently and identically distributed as 𝑁(0, 𝜎𝑢2 ) ,Ⓒ ISERD

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independent of u, which represents technical inefficiency and is identically and independently
distributed as truncated normal, with truncation at zero of the normal distribution (Battese and Coelli,1995). The maximum likelihood estimation of Eq. 1 yields an estimator for 𝛽 and 𝛾, where γ = 𝜎𝑢2 /𝜎 2and 𝜎 2 = 𝜎𝑢2 + 𝜎𝑣2 . The total variation of output from the frontier, which is attributed to technicalinefficiency, is given by  and has a value between zero and one. Battese and Coelli (1995) proposeda model in which the technical inefficiency effects in a stochastic production frontier are a functionof other explanatory variables. In their model, the technical inefficiency effects, u are obtained bytruncation (at zero) of the normal distribution with mean, 𝑧𝑖 𝛿 and variance 𝜎𝑢2 , such that:(2)𝑢𝑖 = 𝑧𝑖 𝛿

where 𝑧𝑖 is a vector of farm-specific explanatory variables (Table 1) and 𝛿 is a vector of unknown
coefficients of the farm-specific inefficiency variables.
The technical inefficiencies in Eq. 2 can only be estimated if the technical inefficiency effects,
ui are stochastic and have distributional properties (Coelli and Battese, 1995). These conditions leadto conducting different hypothesis tests using the generalized likelihood-ratio statistic, λ, given by
Eq. 3.
(3)𝜆 = −2[ln{𝐿(𝐻0 )} − ln{𝐿(𝐻1 )}]where L ( 𝐻0 ) and L ( 𝐻1 ) denote the values of the likelihood function under the null ( 𝐻0 ) andalternative (𝐻1 ) hypotheses, respectively. In the end, predictions of technical efficiency scores weredone as follows in Eq. 4.
(4)

𝑇𝐸𝑖𝑗 = exp(−𝑢𝑖𝑗 )Summary statistics of output, input, and farm-specific explanatory variables are given in Table
1.
RESULTS AND DISCUSSION
Technical Efficiency of Potato Production
To select the accurate functional form for potato producers’ technical efficiency estimation, aloglikelihood ratio test was done. The test result revealed that the Cobb-Douglas production
functional form is appropriate to estimate potato technical efficiency. Moreover, gamma wascomputed following the Cobb-Douglas production function to confirm the presence of technicalinefficiency in the stochastic frontier model. The result shows that about 25.5% of the total variationin potato production is explained by technical inefficiency. Following the lower value of the gamma,
both the unrestricted (stochastic frontier model) and restricted (ordinary least square) estimations
were undertaken to confirm whether the presence of technical inefficiency is significant or not. For
this purpose, the loglikelihood ratio (LR) test result was generated by applying LR = -2[L(H0)-L(H1)4.
The LR test result (42.68) was statistically significant at p < 0.01, affirming the significance oftechnical inefficiency in potato production. This substantiates the appropriateness of employing thestochastic frontier model to estimate potato producers’ technical efficiency. Additionally, the loglikelihood of the stochastic frontier model was also statistically significant at p < 0.01, indicating theoverall fitness of the model is robust. This suggests that the model's parameters for the variables
differ significantly from zero.
The model result revealed potato productivity and the area allocated for potato production had
a negative and statistically significant relationship. This implies that an increase in area allocated topotato production results in a decrease in potato productivity. This implies that a large farm size isless productive than a small farm size. On the other hand, hired person day per hectare and potato
productivity had a positive and statistically significant relationship. For instance, a 1% increase in
hired person day per hectare will increase potato productivity by 0.041%. Similarly, chicken dung
4 L(H ) and L(H ) are the loglikelihoods of the unrestricted and restricted models, respectively.
0
1Ⓒ ISERD

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per hectare had positively and significantly affected potato productivity. This means that potato
productivity will be increased by 0.334% for a 1% increase in chicken dung per hectare. Moreover,the number of fungicides had a positive and statistically significant effect on potato productivity at
p<0.05. This implies that potato producers were effectively using fungicides to protect against
harmful fungal diseases that severely affect potato growth and productivity.
Table 1 Descriptive statistics and variables included in the stochastic frontier analysis
Variables

Description

MeanStandard
deviation
12.52

Potato yield
Potato yield ton/ha
15.78
Production inputs
Land
Potato planted area in hectare (ha)
0.48
0.37
Family labor
Total family and exchange labor in person day/ha
4108.00
8108.00
Hired labor
Total hired labor person day/ha
323.88617.49Chemical fertilizer
Total chemical fertilizer in kg/ha
543.18483.88Organic fertilizer
Total chicken dung fertilizer in kg/ha
9522.817904.91
Insecticides
Total insecticides kg a.i.* /ha
2.33
3.44
Herbicides
Total herbicides kg a.i.* /ha
2.97
3.20
Fungicides
Total fungicides a.i.* /ha
41.84
38.13
Technical inefficiency variables
Household size
Number of family members
4.68
2.19
Education
Number of years of schooling
9.52
3.36
Training
Dummy (1= if farmers have veg. production training, 0= No)
0.24
0.43
Potato seed variety
Dummy (1= Granola, 0= Igorota (PO3))
0.60
0.49
Land topography
Dummy (1= hilly/undulating terraced, 0= river/flood plain)
0.82
0.38
Hand tractor ownership
Dummy (1= yes, 0= No)
0.28
0.45
Partially irrigated
Dummy (1= if farmers use partial irrigation, 0= otherwise)
0.38
0.49
RainfedDummy (1= if farmers use fully rainfed, 0= otherwise)
0.63
0.48
Atok Municipality
Dummy (1= if Atok Municipality, 0= otherwise)
0.03
0.17
Bakun Municipality
Dummy (1= if Bakun Municipality, 0= otherwise)
0.10
0.30
Buguias Municipality
Dummy (1= if Bugaias Municipality, 0= otherwise)
0.39
0.49
Kibungan Municipality
Dummy (1= if Kibungan Municipality, 0= otherwise)
0.29
0.45
Mankayan Municipality Dummy (1= if Mankayan Municipality, 0= otherwise)
0.20
0.40
Number of observations
101.00Source: Benguate State University (BSU) in collaboration with the Department of Agriculture (DA), 2019
Note: Actual use in liters and kg/ha were converted to kg active ingredient (ai)using ai equivalent from FPA, 2021

On the other hand, herbicide and potato productivity had a negative and statistically significant
relationship. This shows that more herbicide users are less likely to be efficient in potato production
than their counterparts. The model result further reveals that potato productivity will be decreased
by 0.076% for a 1% increase in herbicide. The technical efficiency scores result shows that the score
ranges from 0.19 to 0.99 and the average score was 0.89. This shows that potato producers were
producing about 11% below the average production capacity. Moreover, the distribution of technicalefficiency was categorized into six groups considering statistical procedures. The result shows thatabout 78.2% of the technical efficiency level was 0.9 and above. Whereas, about 7.9% of thetechnical efficiency level was 0.8.
Factors Affecting Technical Inefficiency in Potato Production
The results in Table 2 demonstrated that various factors influenced the technical efficiency of potato
production. Household size had a negative effect on the technical inefficiency of potato production,
indicating that an increase in household size is associated with reduced technical inefficiency.
Agricultural production, especially potato farming, is known for its labor-intensive nature. Therefore,a larger household size facilitates the demanding tasks of farm plot preparation, sowing, follow-up,
and harvest. Furthermore, a larger household size plays a crucial role in lowering the costs of hiring
labor and reducing the time required to find labor during peak production seasons.
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Table 2 Parameter estimates of the stochastic frontier and inefficiency model
Variables
Coefficient
Std. Error
T-valueP-valueLog (Planted area)-0.275***0.091-3.0400.002
Log (Family labor)-0.0450.035-1.2700.205
Log (Hired labor)
0.042*
0.022
1.920
0.055
Log (Chemical fertilizer)-0.0180.030-0.6000.549
Log (Organic fertilizer)
0.334***
0.066
5.070
0.000
Log (Insecticide)
0.023
0.033
0.690
0.493
Log (Fungicide)0.135**
0.062
2.180
0.029
Log (Herbicide)-0.076***0.034-2.2700.023
Constant
6.092
0.562
10.8400.000
Technical inefficiency
Household size
-0.739**0.333-2.2200.026
Educational level-0.429**0.175-2.4500.014
Training-1.648**0.828-1.9900.047
Potato seed variety-1.078*0.604-1.7800.074
Land topography-1.230**0.607-2.0300.043
Hand tractor ownership3.117**
1.259
2.480
0.013
Partially irrigated-1.274**0.553-2.3000.021
Rainfed2.845**
1.355
2.100
0.036
Bakun
0.755
4.156
0.180
0.856
Buguias
0.559
4.099
0.140
0.892
Kibungan-2.1984.308-0.5100.610
Mankayan
3.226
4.006
0.810
0.421
Constant
3.942
4.581
0.860
0.389
σu
0.268**
0.120
2.230
0.026
σv
0.458***
0.035
13.1800.000
λ0.584***
0.126
4.640
0.000
𝛾
0.255
𝜎 2 = 𝜎𝜇2 + 𝜎𝜈2
0.281
Mean technical efficiency
0.898
Loglikelihood-64.59***-2[L(H0)-L(H1)42.68***
Number of observations
101
Source: Benguate State University (BSU) in collaboration with the Department of Agriculture (DA), 2019. *, **,*** indicate significant at 10%, 5% and 1% level respectively.

Similarly, there is a negative and statistically significant relationship between educational level
and technical inefficiency. As the educational level of the household head increases, the technicalinefficiency of potato production decreases. This implies that less educated farmers exhibit greater
inefficiency compared to those with higher educational attainment. This might be due to the
proficiency of literate household heads in crop production and management, understanding the
dynamics of production trends, and implementing yield-enhancing strategies that surpass that of lesseducated farmers. Likewise, a negative and statistically significant relationship occurred between
training access and technical inefficiency.
Granola potato seed had a negative and statistically significant effect on the technicalinefficiency of potato production. This shows that Igortoa seed users are more inefficient than
Granola seed users. Moreover, the mean comparison result confirms that the mean productivity ofGranola seed (16.8 tons/ha) is higher than Igorota seed (14.2 tons/ha). The cross-tabulation result
depicted that about 53.16% and 46.84% of Granola and Igorota seed users’ technical efficiency
distribution is 0.9 and above, respectively. This shows that Granola seed users are more efficient than
Igorota seed users. Therefore, the model result is congruent with the cross-tabulation and mean
comparison results. Additionally, land topography had negative and statistically significant effects
on the technical inefficiency of potato production. This implies that river/floodplain land topography
increases potato production inefficiency more than hilly/undulating terraced land topography. Thismight be due to river/floodplain land topography being more susceptible to soil erosion that resultsin poor soil fertility. Then, poor soil fertility directly affects potato growth and productivity.
Conversely, hand tractor ownership had a positive and statistically significant effect on thetechnical inefficiency of potato production. This shows that hand tractor users are more inefficientⒸ ISERD

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than non-users. The mean potato productivity comparison result also reveals that the mean potato
productivity of hand tractor owners and non-owners was 14.3 tons/ha and 16.4 tons/ha, respectively.
This result is unexpected and might be hand tractor owners renting out their tractors for better returns
instead of using them for potato cultivation, or the tractor type may not be suitable for potato
production. However, this requires further validation.
Whereas potato production through partial irrigation had a negative and statistically significant
relationship with technical inefficiency. In contrast, rainfed production and technical inefficiency had
a positive and statistically significant association. This illustrates that rainfed producers are more
inefficient than fully irrigated production users. This further shows that rainfed potato production
might be prone to water shortage due to drought and erratic rainfall as potato production needs morewater compared to other cereal crops.
CONCLUSION
This study found that the observed average potato yield (15.8 t/ha) in Benguet province is about 50%
below the potential yield found by the BSU-Northern Philippine Root Crops Research and Training
Center. About 60% of producers used the Granola variety obtaining 16.8 t/ha on average and theremaining used the Igorota variety (14.2 t/ha). Besides, the estimated mean technical efficiency ofpotato producers was 0.89. The model result depicted that yield and technical inefficiency of potato
production are determined by land size, hired labor, amount of organic fertilizer (chicken dung),
agro-chemicals, household size, education level, training access, Granola variety, topography,
irrigation use, and hand tractor ownership respectively. Therefore, we conclude that concerned
stakeholders such as the Department of Agriculture, universities, and research institutes, among others
should conduct a measured plot level study including various parameters to understand why potato
producers' yield is much lower than the potential yields of the two dominantly grown varieties (Igorota
variety (25-35 t/ha) and Granola (21-30 t/ha)) despite farmers’ relatively efficient potato production.
Furthermore, the promotion of the Granola variety, coupled with the application of organic fertilizers
and the provision of training related to potato production, is essential for enhancing the efficiency ofpotato producers and, consequently, boosting productivity in Benguet province.
ACKNOWLEDGEMENTS
We acknowledge Benguet State University and the Department of Agriculture, Bureau of
Agricultural Research for allowing us to use part of the data on “Highland Vegetable Value Chain
Analysis for Use in Policy Formulation and Impact Evaluation of Agricultural Training Posts”.
REFERENCES
Battese, G.E. and Coelli, T. J. 1995. A model of technical inefﬁciency effects in a stochastic frontier production
function for panel data. Empirical Economics, 20, 325-332, Retrieved from DOI https://doi.org/10.1007/BF01205442
Department of Agriculture-Brue of Agricultural Research. 2021. Philippines vegetable industry roadmap(2021-2025). Diliman, Quezon City, Philippines, Retrieved from URL https://www.pcaf.da.gov.ph/wpcontent/uploads/2022/06/Philippine-Vegetable-Industry-Roadmap-2021-2025.pdf
Gonzales, I.C., Kiswa, C.G. and Bautista, A.B. 2016. Sustainable potato production in the Philippine Cordillera
Region. International Journal of Engineering and Applied Sciences (IJEAS), 3 (6), 29-37, Retrieved fromhttps://www.ijeas.org/download_data/IJEAS0306034.pdf
JICA. 2019. Survey on issue analysis of food value chain in the Philippines. Final Report, Retrieved from URL
https://openjicareport.jica.go.jp/pdf/1000041342.pdf
Kiswa, C.G., Dao-ines, G.K. and Gayao, B.T. 2020. Status of introduced potato varieties in Benguet andMountain Province, Philippines. Mountain Journal of Science and Interdisciplinary Research, 80 (1), 2027, Retrieved from URL http://journals.bsu.edu.ph/index.php/BRJ/article/view/261/334Ⓒ ISERD

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Philippine Statistics Authority (PSA). 2017. Consumption of Selected Agricultural Commodities in thePhilippines, Volume 2. Philippine Statistics Authority, Retrieved from URL https://library.psa.gov.ph/cgibin/koha/opac-retrieve-file.pl?id=8c52290a940f90af6a411407883d8f3cⒸ ISERD

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Research article
erd

Effect of Dry Brewery Residue on the Growth of Local
Chicken (Gallus domesticus)
BUNHOA UK
Chea Sim National University of Kamchaymear, Prey Veng, Cambodia

SOPHEAK HOUN
Prek Leap National Institute of Agriculture (NIA), Phnom Penh, Cambodia

VATHANA THUN
Prek Leap National Institute of Agriculture (NIA), Phnom Penh, Cambodia

TORY CHHUN*
Prek Leap National Institute of Agriculture (NIA), Phnom Penh, Cambodia
E-mail: chhuntory@maff.gov.kh
Received 20 October 2023

Accepted 2 December 2023(*Corresponding Author)

Abstract Residue from the food industry such as dry brewery residue (DBR) has thepotential to replace traditional ingredients used in chicken feed, the costs of which havebeen increasing greatly year by year. The current study is aimed at evaluating the effect ofdifferent levels of DBR in chicken feed on chicken growth. The basic diet was supplemented
with 0% (used as a control), 20%, 25%, or 30% DBR content. The broiler chickens wereraised in a Completely Randomized Design (CRD) with three replicates, and each replicate
contained ten six-week-old broilers. Experimental results showed that there was no
significant difference in the mean weight of chickens among the four treatments from week
1 to week 5. However, in weeks 6 and 7, the second treatment which applied 25% DBR,
resulted in greater weight gain than the control (0% DBR), the first treatment (20% DBR),
and the third treatment (30% DBR) at P<0.05. Additionally, the intake of feed by chickens
in the second treatment was significantly higher than for other treatments (P <0.05) during
weeks 6 and 7. Taken together, these results suggest that basic feed supplemented with 25%
DBR can promote chicken growth better than other levels of DBR, and this increased
growth is likely promoted by more feed intake.
Keywords dry brewery residue (DBR), local broiler chicken, intake, growth

INTRODUCTION
Animal husbandry plays a pivotal role in contributing to the growth of the agricultural sector.
According to the General Department of Animal Health and Animal Production, Cambodia achieved
USD 5.1 billion in 2021 in the animal husbandry sector, an increase of six percent compared to2020’s USD 4.8 billion (Khmer Times, 2022).
In rural Cambodia, the people’s livelihoods are dependent predominantly on agriculture. Morethan half of all rural Cambodian households keep poultry (IFAD, 2021). Still, Cambodian farmers
who raise chickens face a range of problems, such as slow-growing chickens, disease, markets, andexpensive feed. Feed is commonly imported from neighboring countries such as Vietnam andThailand, costing approximately USD 200-300 million each year (The Phnom Penh Post, 26 July2022).
Poultry production in Cambodia is still a small-scale industry compared with neighboring
countries and the production can be classified into traditional/backyard, semi-intensive small or
medium-scale, and intensive large-scale industrial (Birhanu et al., 2021). About 90% of poultry
production is under the traditional/backyard system that has small flock sizes usually less than fiftyⒸ ISERD

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birds per household (Birhanu et al., 2021). Demand for poultry products has significantly increasedin the past decades and this upward trend is projected to continue (ICEM, 2014; Sun, 2018). Local
bird breeds are the highest in demand in the local market, especially during festivals and celebrations(IFAD, 2021).
Animal feed is one of the most important factors for determining animal growth, quality, andprice in animal chain production. However, the main problem needed to be addressed is to lower thecost of animal feed through the efficient utilization of local input. The current study applied different
levels of Dry Brewery Residue (DBR) discarded by the brewery industry to the chickens’ basic feed
diet and we found that the application of 25% DBR promoted growth better than other DBR inputs.
OBJECTIVE
The main objective of the study is to find out the optimal level of DBR supplemented with the basic
diet that best promotes local chicken growth.
METHODOLOGY
Experimental Location
The experiment was conducted at the Department of Livestock Research Section, Kampong Speu
Institute of Technology (Figure 1) from June 1st to July 12th, 2022.

Fig. 1 Location of the experimental site in Angkom Village, Amlaing Commune,
Thpong District, Kampong Speu Province
The red point indicates the chicken farm (11°48'05.8"N 104°17'29.4" E).

Table 1 Experimental design with 4 treatments and 3 replicates
Experimental Design
Number of Chickens
T0 (control)
T0R1
10
T0 (control)
T0R2
10
T0 (control)
T0R3
10
T1
T1R1
10
T1
T1R2
10
T1
T1R3
10
T2
T2R1
10
T2
T2R2
10
T2
T2R3
10
T3
T3R1
10
T3
T3R2
10
T3
T3R3
10
Note: T stands for Treatment, whereas R for ReplicateⒸ ISERD

88

Level of DBR (%)
0%
0%
0%
20%
20%
20%
25%
25%
25%
30%
30%
30%

IJERD – International Journal of Environmental and Rural Development (2023) 14-2

Experimental Design
The experiment was designed as a Complete Randomized Design (CRD) including 4 treatments with
3 replicates with each replicate containing 10 chickens (Table 1). Each treatment (referred to as T)
was added with different levels of DBR as follows: T0 (0%), T1 (20%), T2 (25%), and T3 (30%).
The same amount of basic diet including rice bran 10%, broken rice 8%, dietary supplement 42%,
final feed product 18%, brewers Dried Grains 20%, and corn 9% was added to each treatment.
Feeding: First, brewery residue was received from the brewery factory (Khmer Beverages) and was
put in the dryer at 280°C for 45 seconds in order for it to become Dried Brewery Residue (DBR)(Kuleile et al., 2019). Different levels of DBR (as mentioned in Table 1) were supplemented with abasic diet. This feeding method is primarily based on the description by Leang, 2004 with a slight
modification. The feed was provided three times a day starting at 6:00 AM for the first time, 12:00
pm for the second time, and 17:00 pm for the third time (Leang, 2004).
Data collection and statistical analysis: The amount of feed intake (FI), initial weight of chicken,
and weekly chicken growth weight were recorded. All relevant data was analyzed using the Excel
program. One-way ANOVA was performed to compare the quantitative data among the treatments.
RESULTS AND DISCUSSION
Chicken Growth
The current study focused on the growth of local chickens (Gallus domesticus), through theapplication of different levels of DBR. The experimental results revealed that each treatment steadily
increased chicken weight growth from week 1 to week 7 (Table 2).
There was no significant difference in weight growth of chickens during weeks 1, 2, 3, 4, and 5
among the 4 treatments (P>0.05). However, the average chicken weight of treatment 2 (T2) using
25% DBR increased significantly as compared to the other treatment levels tested in weeks 6 and 7(P<0.05) (Table 2). Nevertheless, the application of 30% DBR (T3) to the basic diet showedsignificantly decreased chicken weight as compared with chicken feed with 25% DBR (Table 2).
Hence, these results indicate that the optimal level of DBR for chicken growth is 25%, and anincreased amount of DBR beyond that value led to the reduction of chicken growth weight. This
result is consistent with the previous study in poultry that found that the application of 10-20% of
DBR resulted in better growth of young birds and up to 30% in older poultry (Fasuyi et al., 2018).
Parpinelli et al. (2018) reported that a 10% inclusion rate of DBR in broiler chickens during thefinishing phase maintained a high production performance. In broilers between 12 and 33 days old,
the inclusion rate ranged between 10% and 20% of DBR, supporting acceptable growth and feed
utilization and seeming to favor the development of a well-functioning gizzard (Denstadli et al.,2010). According to the National Research Council (NRC 1994), standard brewers’ ingredientscontain 25.3% protein, 6.3% fat, 92% dry matter, and approximately 2080 kcal/kg of metabolizableenergy.
Table 2 Chicken weight growth during 7 weeks of feeding (g/chicken)
Weeks after
T0
T1
T2
T3
P-valuefeeding(DBR=0%)(DBR=20%)(DBR=25%)(DBR=30%)1702.58 ± 92.4
700.64 ± 47.4
672.25 ± 24.8
695.48 ± 97.8
0.833
2855.80 ± 61.0
860.00 ± 77.5
841.29 ± 48.3
829.33 ± 43.5
0.918
3991.61 ± 32.8
1024.51 ± 97.9
1027.74 ± 61.1
998.00 ± 78.8
0.883
41156.12 ± 97.5
1159.05 ± 43.2
1171.61 ± 73.9
1141.83 ± 28.6
0.970
51302.25 ± 34.9
1326.00 ± 96.2
1389.66 ± 89.3
1317.35 ± 93.3
0.597
61478.06 ± 21.1
1441.33 ± 25.2
*1560.66 ± 25.4
1496.89 ± 45.8
0.041
71532.74 ± 23.1
1554.00 ± 35.1**1680.66 ± 22.9
1518.10 ± 45.1
0.001
Notes: Values shown are each average of 30 chickens (±SD) with 3 replicates.
* and ** indicate significance at the 5% and 1% level, respectively, compared with other treatments at thesame period of feeding, as judged by One-way ANOVA analysis. T is referred to as Treatment.

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Feed Intake Index (FI)The feed intake index (FI) is crucial for chicken weight growth and ensuring economic benefit inpoultry production. To understand whether the chicken gains weight as related to FI, we observe theaverage amount of chicken of different DBR treatments for 7 weeks. Table 3 shows that in the first
week, the FI showed no significant difference among the 4 treatments. In the second week, control(T0), without the supplement of DBR, produced higher FI (P<0.05). However, there was no
significant difference at week 3 (P>0.05) among the 4 treatments. At week 4, the highest FI wasobserved in treatment 2 (P<0.05). From weeks 5, 6, and 7 treatment 2 (T2) (DBR=25%) produced
significantly higher weights than other treatments (P<0.05). These results suggest that younger
chickens showed no clear feed intake among different levels of DBR. However, it is probable thatwhen a basic diet is supplemented with 25% DBR, the chickens consume more feed, a trend that is
consistent with the increased weight of chickens among the group (Table 2).
Table 3 Feed intake index of chicken of different DBR treatments(% of the total amount of diets)
Weeks after
T0
T1
T2
T3
P-valuefeeding(DBR=0%)(DBR=20%)(DBR=25%)(DBR=30%)
1
56.49 ± 7.80
53.61 ± 9.84
43.73 ± 16.0
52.28 ± 8.60
0.250
2**76.73 ± 5.57
52.77 ± 8.22
59.60 ± 11.3
58.20 ± 7.72
0.001
353.68 ± 2.66
60.39 ±12.264.65 ± 9.96
65.27 ± 9.84
0.235
467.81 ± 2.31
81.78 ± 4.17
85.48 ± 4.87**88.08 ± 1.37
0.001
578.04 ± 1.47
81.92 ± 5.29
*85.20 ± 3.71
82.20 ± 4.24
0.011
684.39 ± 3.57
84.04 ± 5.60
*88.87 ± 4.36
80.96 ± 8.12
0.044
785.12 ± 5.82
83.15 ± 6.20**90.13 ± 6.54
78.85 ± 7.78
0.001
Notes: Values shown are each average of 30 chickens (±SD) with 3 replicates.
* and ** indicate significance at the 5% and 1% level, respectively, compared with other treatments at the same
period of feeding, as judged by One-way ANOVA analysis. T is referred to as Treatment.

Feed Conversion Ratio (FCR)
Feed conversion ratio (FCR) is the amount of feed eaten relative to the weight of one chicken. It
plays an important part in determining feed efficiency and production cost. To see whether the
chicken growth is relevant to FCR, we observe the feed conversion ratio of the 13-week-old chicken.
As shown in Table 4, the ratio of FCR seems slightly decreased when the basic diet supplemented
with T1 (DBR=20%), T2 (DBR=25%), and T3(DBR=30%) compared with control T0 (without DBR
supplement) but there is no significantly different among 4 treatments tested. These results suggestthat the optional DBR promoted chicken growth by 25% DBR is not relevant to the ability of feed
conversion but it is caused by the increased intake (Table 3).
The main factors affecting the conversion ratio in poultry include genetics, age, and feed quality.
In the current experiment, we used the same chicken variety and age. Another study reported thatfeed conversion was not significantly different when Ross 308 chickens fed cassava flour mixed with
universal concentration (UC+Cas) gained weight higher than chickens fed a commercial diet
(Jeremiah et al., 2015).
Table 4 The Feed conversion ratio (FCR) of 13 weeks-old chickens
Treatment

T0(control)

T1(DBR=20%)

T2(DBR=25%)

T3(DBR=30%)

P-value

FCR ratio

4.36 ± 0.64

3.71 ± 0.60

3.35 ± 0.59

3.37 ± 0.60

0.186

Notes: Values shown are each average of 30 chickens (±SD) with 3 replicates

CONCLUSIONⒸ ISERD

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This study finds that the optional inclusion of DBR in the basic diet for the better growth of local
chicken (Gallus domesticus) is 25%. This performance of the chicken growth is likely due to feedintake (FI). The result is similar to findings from Fasuyi et al., in which the application of 10-20% of
DBR resulted in better growth of young birds and up to 30% in older poultry (2018). Similarly,
another study demonstrated that the inclusion of 25% DBR only (without basic diets such as soybean,
sunflower, fish milk, and maize) caused a similar effect on broiler growth compared with control(basic diets) (Kuleile et al., 2019). This finding suggests that DBR can be used to substituteconventional protein sources and reduce production costs. Yet, in this present study, further
investigation is required to find different types of basic diets supplemented with DBR and the meat
quality of chicken after taking DBR.
ACKNOWLEDGEMENT
We thank Khmer Beverages for providing the brewery residue for the experiment. We also thank Ms.
Rachel Brown, a U.S. State Department Fulbright Student researcher affiliated with Prek Leap
National Institute of Agriculture for kindly review and English corrections.
REFERENCES
Birhanu, M.Y., Geremew, K., Workku, S., Kebede, F.G., Ty, C., Tum, S., Unger, F. and Dessie, T. 2021. Poultry
production, marketing, and consumption in Cambodia, A review of literature. ILRI Research Report 81,
Nairobi, Kenya, Retrieved from URL https://cgspace.cgiar.org/bitstream/handle/10568/116145/RR81.
pdf ?sequence=3&isAllowed=yDenstadli, V., Balance, S., Knutsen, S.H., Westereng, B. and Svihus, B. 2010. Influence of graded levels of
brewers dried grains on pellet quality and performance in broiler chickens. Poultry Science Journal, 89 (12),2640-2645, Retrieved from DOI https://doi.org/10.3382/ps.2010-00724
Fasuyi, A.O., Aturanmu, O.A. and Lawal, A.S. 2018. Part II, Economic analyses and the growth performanceof broiler finisher (29-58 days) birds on high fiber-low protein industrial plant by-products. Asian Journalof Advanced in Agricultural Research, 6 (3), 1-10, Retrieved from DOI https://doi.org/10.9734/AJAAR/
2018/40606
ICEM. 2014. USAID Mekong ARCC climate change and adaptation on livestock. Livestock Report Prepared
for the United States Agency for International Development by ICEM, Retrieved from URL
https://www.climatelinks.org/sites/default/files/asset/document/mekong_arcc_main_report_printed_final.
pdf
IFARD. 2021. Chicken raising to improve the livelihood and living conditions of poor, landless farmers in rural
Cambodia. Retrieved from URL https://www.ifad.org/en/web/knowledge/-/cambodia-chicken-raising
Jeremiah, A., Amben, S., Roberts, A., Besari, F., Janet, P., Kohun, P.J. and Glatz, P.C. 2015. Feed conversion
and growth of broiler chicken feed cassava blended with a universal concentration diet during the finishingphase: an on-farm study in Jiwaka province, Papua New Guinea. Journal of South Pacific Agriculture, 18(2), 19-26, Retrieved from URL https://journalofsouthpacificagriculture.com/index.php/JOSPA/issue/view
File/2/Jeremiah%20et%20al%202015%20JOSPA%2018%3A19-26
Khmer Times. 2022. Cambodia’s animal husbandry sector is valued at more than five billion dollars. The
Khmer Times, January 5, 2022, Retrieved from URL https://www.khmertimeskh.com/501001192/
cambodias-animal-husbandry-sector-valued-at-more-than-five-billion-dollars/
Kuleile, N., Adoko., G. and Nkheche, M. 2019. The Influence of dried brewery grain in broiler diets on
production performance. 9 (3), 134-138, Retrieved from URL https://www.ojafr.ir/main/attachments/
article/140/OJAFR%209(3)%20134-138,%202019.pdf
Leang, H. 2004. Nutrition and animal feed. Prek Leap National Institute of Agriculture.
National Research Council. 1994. Nutrient requirements of the poultry. 9 th Revised Edition, National Academic
Press, Washington DC, Retrieved from DOI https://doi.org/10.17226/2114
Parpinelli, W., Cella, P.S., Savaris, V.D.L., Broch, J. and Nunes, V. 2018. Dry brewery residue in broiler chicken
feed. Semina: Ciências Agrárias, 39 (4), 1707-1716, Retrieved from DOI https://doi.org/10.5433/16790359.2018v39n4p1707
Post Phnom Penh. 2022. ADB loan to lift animal feed trim raw material exports. The Phnom Penh Post News,
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Sun, H. 2018. The local chicken value chain in Cambodia, Constraints and challenges of local chicken
smallholder producers. Australasian Agribusiness Perspectives, 21 (3), 43-56, Retrieved from URL https://cpb-ap-se2.wpmucdn.com/blog.une.edu.au/dist/4/1340/files/2018/02/AAP-Vol-21-Paper-3-Heng-11e2c
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Research article
erd

Development of Indexes to Evaluate the Effectiveness of
Low Water Level Control in Irrigation Ponds – A Case
Study of Irrigation Ponds in Tottori, JapanRYO HARASAWA*
Graduate School of Sustainability Science, Tottori University, Tottori, Japan
Email: ryoharasawa2018@outlook.jp

KATSUYUKI SHIMIZU
Faculty of Agriculture, Tottori University, Tottori, JapanYURI YAMAZAKI
Faculty of Agriculture, Tottori University, Tottori, Japan
Received 27 September 2023

Accepted 3 December 2023(*Corresponding Author)

Abstract The spillways of most irrigation ponds in Japan need additional repairs to safely
pass the 200-year flood event, which is the design criteria set by the government. At the sametime, low water level control (LWLC) in irrigation ponds is currently recognized as one of
the countermeasures for mitigating floods and avoiding dam collapse. However, the pond
managers do not know to what extent the water level of the pond should be lowered. In thisstudy, the flood mitigation function of LWLC in each pond was quantitatively evaluated, andindexes were proposed to determine whether it is worth practicing LWLC in each pond orwhether the spillway needs to be repaired. Seventy-two irrigation ponds in Tottori prefecture
were selected for analysis and the water balance of each pond was calculated with 10-, 50-,
and 200-year rainfall events. The results showed that half of the ponds cannot safely copewith a 10-year rainfall event even if they are empty prior to the event. The ratio of thecatchment area to water surface area at full capacity in such ponds often exceeds 50 to 1. On
the other hand, ponds with a catchment to water surface area ratio of less than 50 show a highflood mitigation function due to LWLC. In addition to this, priority for repair work can beevaluated in terms of whether the spillway can pass peak runoff or not, because the size andthe type of spillway are different among ponds. The effectiveness of LWLC can be evaluatedin terms of the ability of the spillway to pass peak runoff from the catchment when the pond
is full. The three indicators, namely, the ratio of catchment area to water surface area at full
capacity, difference of peak flood reduction rate, and peak discharge ratio, are simple anduseful indexes for LWLC to prevent dam collapse.
Keywords flood mitigation, spillway, water management, water balance

INTRODUCTION
In recent years, unexpected, short, and heavy rain has been increasing in Japan. Especially in 2021
one of the strongest rainfall events occurred at many meteorological observing stations in Japan. Someirrigation ponds have been damaged and broken down due to heavy rainfall. For instance, Hutago
Pond in Tottori was broken down because of a heavy rainfall event in July 2021. An irrigation pondis generally constructed to irrigate agricultural water which is mainly used for the paddy rice fields
if there is no river near the irrigated district. The number of irrigation ponds in Japan is about 150
thousand and most of them are constructed over 100 years ago (MAFF). The spillways of someirrigation ponds in Japan are too small and require repairs to safely pass the 200-year flood event
which means the probability of flood occurrence, is once per 200 years, which is the design criteria
set by the Japanese government (MAFF, 2015). However, due to the large number of ponds and thehigh repair costs, extensive time will be required to repair the spillways of every irrigation pond.
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Therefore, the low water level control (LWLC) was suggested as one of the low-cost and nonstructural solutions to prevent dam collapse. However, the water control entirely depends on the
manager’s experience and the municipality doesn’t have clear guidelines or indicators on the amountof water that should be released in advance. Moreover, no assessment of the flooding mitigation
function of irrigation ponds when LWLC is practiced has been done.
OBJECTIVESIn this study, the flood mitigation function of LWLC in irrigation ponds was quantitatively evaluated,
and indexes were proposed to determine whether it is worth practicing LWLC in each pond orwhether the spillway needs to be repaired. This study was conducted on the irrigation ponds certified
as priority irrigation ponds for disaster prevention in Tottori Prefecture, Japan.
METHODOLOGY
Study Area
In this study, the water levels of the irrigation
ponds in Tottori were estimated using a waterbalance model under the different heavy
rainfall events at each rainfall station. The
number of irrigation ponds in Tottori is around
950 (Tottori Prefecture, 2022). Of these 72
irrigation ponds were selected for analysis.
Figure 1 shows selected 72 irrigation pondsthat are indicated as black spots. Most of themwere constructed in mountainous areas andstore water only from the runoff from thecatchment areas. Therefore, when LWLC isFig. 1 Selected irrigation ponds in Tottori
practiced, the farmers will take on the risk
Location source: Tottori web map
increase that the storage water would be dried
up. They are located near residences which could be damaged if the ponds are flooded. Besides, allof them cannot safely pass the peak runoff with a 200-year rainfall event, which is a design criterion
set by the government. Table 1 shows the features of irrigation ponds in the study area. The irrigation
ponds are classified by storage capacity.
The distribution of the storage capacities is biased to a small, less than 10,000 m 3. Of the 72
selected irrigation ponds, the number of the small-sized ones is 55. Those with a storage capacity ofless than 3,000 m3 account for 36. The number of beneficiary farmers in these irrigation ponds isquite small, and some of them are often not well maintained. The capacity of medium-sized irrigation
ponds ranges from 10,000 m3 to 100,000 m3. The number of medium irrigation ponds is 15. The
capacity of large-sized irrigation ponds is more than 100,000 m3 and there are two such irrigation
ponds in the study area, with capacities of 101,000 m3 and 120,000 m3. The number of beneficiary
farmers is large, and downstream damage due to a potential dam collapse on these ponds would beenormous.
Table 1 Features of irrigation ponds in the study area
Irrigation Ponds

Capacity (m3)

Numbers of ponds

Small<10,000 m3

55

Medium

10,000~100,000 m3

15

Large

> 100,000 m3

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Water Balance Analysis
Outline of Irrigation Pond Water Balance Model
The irrigation pond water balance model was developed by referring to the Guidance of flood
control function intensify of irrigation ponds (MAFF, 2018). The difference points are estimation
formulas of coefficient K for the storage function method, effective rainfall intensity, outflow water
estimation from the spillway, and height-volume curve of the irrigation pond (H-V curve).
To determine and compare the effectiveness of LWLC from the aspect of the return period, 10year, 50-year, and 200-year return period hourly and daily rainfall was estimated by Iwai formulaand Talbot formula. The hourly and daily return period was estimated at 9 stations such as Iwai,
Tottori, Shikano, Kurayoshi, Shiotsu, Daisen, Ebi, Yonago, and Chaya. The rainfall duration was
assumed to be 3 hours, and the rainfall distribution was assumed to have a rainfall intensity peak in
the latter half of the rainfall. The inflow of water to the ponds is estimated by the storage function
method. It needs to estimate coefficient 𝐾, 𝑝, and effective rainfall intensity. 𝐾 is referred from Nagai
et al. (1987). p is set to 0.6 as Sugiyama et al. (1988) suggested. Effective rainfall is estimated tomultiply rainfall intensity by the theoretical peak outflow rate referred from the internal survey
documents of Tottori Prefecture. Only the outflow from the pond is considered for the outflow fromthe spillway of the pond. In this study, the outflow from intake taps is not considered because it isnegligibly small compared to that from the discharge from the spillway. In addition to this, due to theassumption of short and heavy rainfall, evaporation and infiltration from the irrigation pond are also
ignored (Shimizu et al, 2016). To estimate the amount of released water, the relationship between thewater level and the storage capacity of a pond is required, but these data are not available. Therefore,
in this study, a simplified method is employed to make an H-V curve with the assumption that theshape of the irrigation pond is approximated as frustum (Tanakamaru et al., 2015).
Validation
To validate the results of the water balance model, the actual water level fluctuation was observed
at 3 irrigation ponds in Tottori using the heavy rainfall data at 2 rainfall stations from 7th July to 9th
July 2021.
Indexes Proposal and Classification
In the analysis, the extra height of each irrigation pond was defined as the height of the spillway incase of seepage collapse of the irrigation pond. There are three main reasons for dam collapse
following heavy rainfall, namely overflow erosion, sliding, and seepage collapse. In the case of
seepage collapse, internal erosion occurs from the upstream slope near the full water level (Hori etal., 2002). Moreover, the interval of intake taps is supposed to be 0.5 m and release water level 0.5
m each, and estimate released water previously that peak water level is less than the height of thespillway at each irrigation pond. To calculate how many meters of water level should be lowered,storage water was released corresponding to 0.5m each from full water level until the peak waterlevel in case the irrigation pond conducts LWLC would not over the height of the spillway. The waterlevel reduction rate (R) can be expressed as a ratio of released water level and water storage depth(the height difference between the bottom and full water level) shown in Eq. 1.
𝐷

(1)

𝑅=𝐻

Where R is the water level reduction rate, D is the depth after the water is released from the fullwater level, and H is the water storage depth.
As follows, we proposed 3 indicators that are useful to classify the irrigation ponds based on thecalculation from the data of irrigation ponds and rainfall data. Catchment to storage ratio (CSR) is
one of the indexes that can be calculated to divide the catchment area with the storage area (fullsurface water area) of each irrigation pond shown in Eq. 2.
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𝐴(2)

𝐴𝑟 = 𝑆

1

Where Ar is the catchment to storage ratio, A is the catchment area, and S1 is the full water
surface area.
Design discharge to peak discharge ratio (DPR) is the index that can be calculated to divide thedesign discharge of the spillway (Qout) by peak discharge (Qp) when the water level is full of eachirrigation pond shown as Eq. 3.
𝐷=

𝑄𝑜𝑢𝑡
𝑄𝑝

(3)

Where 𝐷 is the design discharge to peak discharge ratio, Qout is the design discharge of thespillway, and Qp is peak discharge when the water level is full.
In addition to this, to understand how much water discharge could be reduced by conducting
LWLC, the flood peak mitigation rate was calculated when the initial water level was 0.5m lower
than the full water level. The difference in flood peak reduction ratio (PRRd) is expressed as the ratioof the difference in peak discharges with and without LWLC implementation to the peak runoff fromthe catchment area shown in Eq. 4.
𝑄𝑑 =𝑄𝑝 −𝑄𝑝 ′(4)

𝑄𝑛

Where Qd (%) is a difference in flood peak reduction ratio. Qp is the peak water flow rate when
the irrigation pond is full, Qp’ is the peak water flow rate when the irrigation pond conducts LWLC,
and Qn is the peak inflow water flow rate from the catchment area.
RESULTS AND DISCUSSIONS
Validation
The difference between the estimated peak water levels and observed ones of the irrigation ponds arearound 10 cm, 1 cm, and 13 cm. Figure 2 shows observed and simulated water level data at the
Kanaguri irrigation pond. The irrigation pond is located in the middle of Tottori prefecture. The peakwater level difference is 13cm at Kanaguri irrigation pond.
The observed water level until the peak water level seems to be lower than the calculated peakwater level. It is considered that because of the degree of soil saturation. If the soil is dried, theoutflow rate is affected to be lower inevitably.

Fig. 2 Comparison with observed water level and calculated water level
Relationship between DPR, CSR, and WLRR

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Figure 3 shows the relationship between design discharge to peak discharge ratio (DPR), catchment
to storage ratio (CSR), and water level reduction rate (WLRR) in the case of 10-year rainfall. From
Fig. 3, half of the irrigation ponds need to release water level until empty because to spillway size iscomparatively small to consider their catchment area. Especially under the conditions that the DPR
is smaller than 1 and CSR is relatively higher (>50), WLRR gets almost 100 % which means if thewater level is released to empty, the theoretical peak water level would exceed the height of thespillway of each irrigation pond. It can be considered that the effectiveness of LWLC has a strong
relationship with the inflow of water from the catchment area while it has almost no relationship withthe size of the spillway, but the priority of LWLC for preventing dam collapse can be judged by DPR.

Fig. 3 Relationship between DPR, CSR, and WLRR in case of a 10-year return period
Relationship between CSR and PRRd
Figure 4 shows the relationship between CSR and the difference in flood peak reduction ratio (PRRd).
The figure shows the difference when the irrigation pond is full and, the irrigation pond has released
storage water corresponding to 0.5m from the full water level. If the CSR is under 50, PRRd tends tobe higher. However, in the case of heavy rainfall, PRRd becomes lower. Therefore, it needs toconsider the PRRd and rainfall intensity carefully when the CSR is under 50. Tanakamaru. et al (2019)selected 1,902 irrigation ponds with a large effect on flood mitigation by water release in advance at
Awaji district in Hyogo prefecture, Japan. The effectiveness of LWLC was calculated by theirrigation pond water balance model. After that, the outflow during the flood period was estimated ateach irrigation pond. As a result, the study showed that if normalized storage becomes higher, theeffectiveness of LWLC will be higher. However, it needs to be careful when calculating the flood
control function using normalized storage since the reliability of capacity is low.

Fig. 4 Relationship between CSR and PRRd in case of a 10-year return periodⒸ ISERD

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Classification of Irrigation Ponds
Based on the above results and discussion, studied irrigation ponds are classified. We proposed 3classifications as groups A, B, and C. Group A is characterized by lower CSR (<50) and lower DPR(<1). Irrigation ponds categorized in group A are recommended to implement LWLC or rehabilitate
the spillway immediately due to the high risk of dam collapse. Group B is characterized by lower
CSR (<50) and sufficient DPR (>1). It is recommended to implement LWLC to prevent dam collapse
and the overflow of the downstream canal even if the pond is safe. Group C is characterized by higher
CSR (>50). It is recommended to rehabilitate the spillway immediately due to the high risk of dam
collapse. Thus, it is of no use in implementing LWLC due to its quite small effect (PRR d≒0). The
number of irrigation ponds classified as groups A, B, and C is shown in Table 2.
Table 2 Classification of irrigation pondsGroup

10-year return period rainfall

50-year return period rainfall

200-year return period rainfall

A

20

28

31

B

17

9

6

C

35

35

35

CONCLUSION
There are a lot of irrigation ponds in Japan, and some of these do not have large enough spillways tosafely pass the 200-year return period. In this study, we proposed a method to classify irrigation
ponds concisely using relatively easy-access data. As a result of calculation, even if low water level
control is implemented, some of these have very little effectiveness against flood mitigation. In thecase of the ponds with high irrigation use frequency, i.e., high dependence, immediate rehabilitationof spillways should be considered, while in the case of the ponds with very low irrigation use
frequency, the abolition of irrigation ponds should be considered to prevent flood damage in case ofheavy rainfall events.
ACKNOWLEDGEMENTS
The authors would like to express their deepest gratitude to the Tottori Prefecture of Japan for thedata provision on irrigation ponds in Tottori Prefecture and the financial support for this study.
REFERENCES
Hori, T., Mohri, Y. and Aoyama, S. 2002. Causes and features of damage to small earth dams induced by heavy
rainfall. Transactions of the Japanese Society of Irrigation, Drainage and Reclamation Engineering, 2002
218, 253-263, Retrieved from DOI https://doi.org/10.11408/jsidre1965.2002.253
Iwai, S. and Ishiguro, M. 1970. Application of hydrological statistics. Morikita Publishing, ISBN
9784627491502.
Japan Meteorological Agency. Search past meteorological records. Retrieved from URL http://www.data.
jma.go.jp/obd/staats/etrn/index.php (in Japanese)
Ministry of Agriculture, Forestry and Fisheries (MAFF) Japan. 2018. Guidance of flood control function
intensify of irrigation ponds, Retrieved from URL https://www.maff.go.jp/j/nousin/bousai/bousai_saigai/
b_tameike/attach/pdf/index-47.pdf (in Japanese)
Nagai, A., Yomota, A. and Masahiko, O. 1987. Interrelations of parameters of flood runoff models, Runoff in
the grazing area. Drainage and Rural Engineering Journal, 69-76, Japan.
Shimizu, K., Fukuda, Y. and Yoshioka, Y. 2016. Development of an emergency discharging device and an early
warning system for floods at irrigation ponds. International Journal of Environmental and Rural
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Sugiyama, H. and Kadoya, M. 1988. Discussion on parameters of the storage function model. Drainage andRural Engineering Journal, 11-18, Japan.
Tanakamaru, H., Tatebayashi N. and Tada, A. 2019. Selection of irrigation ponds with large flood mitigation
effect by water release. Applied Hydrology, 31, 21-30, Retrieved from URL https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=201902267843509399 (in Japanese)
Tottori Prefecture Web Site. 2022. Disaster prevention and mitigation measures for reservoirs. Retrieved from
URL https://www.pref.tottori.lg.jp/item/1210634.htm#itemid1210634 (in Japanese)Ⓒ ISERD

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Research article
erd

Impact Evaluation of Climate Change on Disaster Risk ofForested Watershed Rivers in Snowy Regions using SWAT+SHOTARO KIKUCHI
Graduate School of Agro-Environmental Science, Tokyo University of Agriculture, JapanSARVESH MASKEY
Faculty of Regional Environment Science, Tokyo University of Agriculture, Japan

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

SERGIO AZAEL MAY CUEVAS
Faculty of Regional Environment Science, Tokyo University of Agriculture, Japan

MAKOTO OBA
Center for Climate Change Adaptation, National Institute for Environmental Studies, JapanSHOGO NAKAMURA
Fukushima Regional Collaborative Research Center, National Institute for Environmental
Studies, JapanSEIJI HAYASHI
Fukushima Regional Collaborative Research Center, National Institute for Environmental
Studies, Japan
Received 1 October 2023

Accepted 13 December 2023(*Corresponding Author)

Abstract In this study, we analyzed the reduction in snowfall and snowmelt under climatechange scenarios in a cold snowy region. Also, we clarified the causal relationship between
deforestation and disaster risk in watershed management in Mishima Town, Fukushima
Prefecture. We used SWAT+ to conduct simulations; the SWAT+ model of Mishima town
was built in a previous study, and its reproducibility was verified by NSE and RMSE bycorrecting parameters and using auto-calibration. The latest set of models from the CMIP6,
also used in the 6th Impact Assessment Report of the IPCC and statistically downscaled
scenarios in Japan developed by the Center for Climate Change Adaptation, National Institutefor Environmental Studies, was adapted to simulate climate change scenarios. The Emission
Scenarios are based on RCP8.5, which has been used in future climate change discussions(i.e., the assumption that greenhouse gases will continue to increase without any global
warming countermeasures). We simulated the end of the 21st century situation. As the resultof the simulation, the RCP8.5 scenario showed a significant decrease in snowfall andsnowmelt, with only one day of snowfall of RCP8.5 from January to March. The potential
for early spring snowmelt water availability of RCP8.5 at the end of the 21st century could
have been much higher. Additionally, we conducted a simulation of deforestation. We
assumed that 5% of the forested area became agricultural land due to deforestation.
Simulation results indicated that the surface flow under deforestation conditions was 145.1mm, a 7% increase over the surface flow of the original forest prior to deforestation (135.1mm). Heavy and long-lasting rainfall showed no difference in water storage function between
the two cases, but 5% deforestation was shown to increase river discharge at the beginningof a rainfall event.
Keywords SWAT+, climate change, snowmelt, runoff, predictionⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

INTRODUCTION
Climate change significantly impacts the increase in average annual temperatures and fluctuations inrainfall and snowfall, resulting in more frequent natural disasters worldwide. The IPCC Sixth
Assessment Report (2021) states that a 2°C rise in temperature would lead to a 14% intensificationof rainfall intensity. On the other hand, land use change due to deforestation is known to increase
runoff and affect flood risk, with 5.2 million hectares deforested each year (2000-2010), mostly indeveloping countries in Africa, Latin America, and Southeast Asia, where agricultural activities
account for about 80% of the loss (Synthesis Report for REDD+ Policymakers, 2012). The impactsof climate change are also significant in Japan, where mountain disasters and floods are becoming
more frequent and severe, and the importance of the water source recharge and flood mitigation
functions of forests is increasing (Gunma Prefecture Basic Plan for Forest and Forestry 2021- 2030).
Since an increase in average temperature due to climate change is expected to significantly impact
the amount of snowfall and snowmelt and affect the availability of water for agriculture in early
spring in snowy and cold regions, future predictions based on climate change scenarios are essential.
Still, the assessment of the impact of climate change on water resources at the small watershed levelhas not been sufficiently verified.
In addition, although previous studies have examined the relationship between land use change
due to urban development and other factors and disaster risk (Qingmu et al., 2017) and the flood
control effects of agricultural land (Yoshikawa et al., 2009), there are few studies on the relationshipbetween deforestation and additional agricultural land.
OBJECTIVE
In this study, we analyzed the effects of climate change on snowfall and snowmelt and the impact of
deforestation on making agricultural land in the Hashigo River in Mishima town, Fukushima
prefecture, a forested area with a cold and snowy climate.
We used SWAT+ to conduct simulations, the SWAT+ model of Mishima town was built in aprevious study (Kikuchi et al., 2022), and its reproducibility was verified by NSE and RMSE bycorrecting parameters and using auto-calibration.
METHODOLOGY
Study Site
The study site is the Hashigo River watershed in Mishima Town in Fukushima Prefecture (Fig.1).
The Hashigo River is a tributary of the Tadami River. The watershed is located at latitude 37.50 o37.47 o, longitude 139.62 o -139.63 o, and range of altitude 350-370 m. The watershed area is 2.85
km2, covered mainly by mountain forests (Table 1). 0.4 % of paddy fields are located along the mainriver. The annual mean daily temperature, the minimum, and maximum daily temperatures are
10.6°C and 6.9°C, snowfall is from December to February, and snowmelt is from March to April.
Rainfall is relatively heavy during the rainy season from June to July, and typhoons are more frequentfrom September to October. Annual precipitation was 1,340 mm in 2020.
Table 1 Land-use of the target watershed
Watershed area

Land-use (%)

River length(km2)

ForestGrassland

Paddy field

(km)

2.85

98.4

1.2

0.4

2.5

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FUKUSHIMAIJERD – International Journal of Environmental and Rural Development (2023) 14-2

Fig. 1 Location of target watershed (Hashigo River)
SWAT+
Utilizing a hydrological model for analyzing the hydrological cycle throughout a river basin is aneffective method to assess the impacts of climate change on water resource management, flood
control, and agriculture. SWAT (Soil and Water Assessment Tool) developed by USDA-ARS is ahydrological cycle model used worldwide for over 20 years (Williams et al., 2008). To face present
and future challenges in SWAT code has undergone significant modifications over the past few years,
resulting in SWAT+, a completely revised version of the model (Katrin et al., 2016). SWAT+ is amodel that analyzes water movement by subdividing the target watershed into subwatersheds called
HRUs (hydrologic runoff units) and can predict river discharge and water quality for an unspecified
watershed with few input parameters. The data collection is shown in Table 2, and data sets of
SWAT+ are shown in Table 3. The time interval for the calculations in this study was set to days. The
warm-up period was set from 1st February 2019 to 31st December 2019, and the simulation period
was set from 1st January 2020 to 31st December 2020. Some parameters were adjusted and enteredfrom actual measurements.
Table 2 Data collection
Data
Elevation
data

SourceGeospatial Information Authority of Japan
(GSI)

Note

Land use

The Japan Aerospace Exploration Agency(JAXA) and Earth Observation ResearchCenter (EORC)

10m mesh <Land use: 12 categories>.
Land use was confirmed by foot survey, andsome obviously different land uses werecorrected （Fig. 1 B）

Soil map

The National Institute of Agrobiological
Sciences (NIAS)’s basic land classification
survey data (Shapefile, 1:200,000)

Shapefile (polygon)

Soil
component

Field survey data (17/Dec/2021)

Soil moisture content, EC, pH, specific gravity,
permeability coefficient, particle size distribution

Weather data

National Institute for Environmental Studies ofJapan (NIES)

10 min Data: River discharge (m3/s)
Daily data: rainfall, temperature (max., min.,avg.), relative humidity, solar radiation, wind
speed

Raster data (10 m mesh)（Fig. 1 A）

Table 3 Data set of SWAT+ model
Item

Contents

Version- SWAT+ Editor 2.0.4

Period- Warm-up：2019/2/1-2019/12/31

Time interval- Daily data

Input parameters- The following parameters are adjusted and input from actual measured values
Permeability (soil k), maximum water capacity (awc), clay, silt, sand, rock, EC, clay, silt,
sand, rock.
- Snowfall temperature: 2.51°C, Melting snow temperature: 2.71°C- Evapotranspiration calculation: Penman-Monteith method- Calculation of surface flow: Curve Number method

Calculation
methodⒸ ISERD

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Simulation：2020/1/1-2020/12/31

IJERD – International Journal of Environmental and Rural Development (2023) 14-2

Climate Change Scenario
In this study, we generated a hypothetical dataset for the year 2090 by adjusting the data of 2020
using the variability parameters (e.g., temperature increases) from published climate change
scenarios. As the publish methods, We adopted the latest set of models from the CMIP6 (Coupled
Model Intercomparison Project 6th), which is also used in the 6th Impact Assessment Report of theIntergovernmental Panel on Climate Change (IPCC) and adapted statistically downscaled scenarios
NIES2020 in Japan developed by the center for climate change adaptation, National Institute forEnvironmental Studies (NIES)（Ishizaki, 2021）. The Emission scenarios use the Representative
Concentration Pathway (RCP) scenarios, introduced as part of the IPCC's Special Report on
Emissions Scenarios (SRES). There are several RCP scenarios, but in this study, RCP8.5, whichassumes no action on global warming and continued greenhouse gas emissions, was selected. The
hypothetical dataset as input for the SWAT+ model to simulate how climate change will impact
various factors: snowfall, snowmelt, and flow, comparing conditions between 2020 and 2090.
Table 4 Basic information on climate change scenario
Name of climate scenario

NIES2020

Climate model

MIROC6

Climate parameters

Max. and mini. temperature, precipitation, total solar radiation, relative humidity

Emission scenarios

RCP8.5

Forecast period

2090

Table 5 Data prediction of RCP 8.5 for 2090 and 2020 in Fukushima
SSP585-MIROC6-NIES2020
for Fukushima

2090
Jan.

Feb.

2020
Mar.

Jan.

Feb.

Mar.

Max temperature
Min temperature

6.2
-2.2

7.5
-2.1

11.4
1.2

3.0
-4.9

3.5
-5.1

8.1
-1.7

Relative humidity
Solar radiation

70.3
6.8

67.1
9.5

70.1
13.1

73.6
7.1

72.2
9.7

71.8
12.7

Precipitation

103.3

77.1

129.0

90.3

73.2

107.7

Deforestation Situation
In Mishima Town, a renewable energy project using woody biomass resources is being consideredwith the establishment of the Regional Recycling and Symbiosis Promotion Council. On the other
hand, flood risk from deforestation needs to be adequately assessed. According to studies by the
National Institute for Environmental (Ooba et al., 2017), the upper limit of forest use is estimated to
be 1/50 (2%) due to the limitation of age class composition. In this study, the upper limit of forest
use, 2%, was exceeded, and a case was assumed in which 5% of the forest area became agriculturalland due to logging. From the standpoint of convenience in transporting timber, the area of
deforestation was assumed to be two downstream watersheds (Fig. 1 Watershed 3 and 4).
RESULTS AND DISCUSSION
Climate Change Scenario
The simulation results based on the observed data for 2020 (Original) and the 2090 climate change
scenario (RCP8.5) are shown in Fig. 2. The river discharge of January is more significant for RCP8.5
because precipitation is more considerable for RCP8.5. On the other hand, after mid-February, theoriginal data tends to have more significant river discharge.
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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

1

400

Precipitation(mm)River Discharge(m3/s)

ORG RCP8.5

0.1

0.01
January 2020

500

February 2020

March 2020

Fig. 2 River discharge of original (2020) and RCP8.5 (2090)
The results of average river discharge, snowmelt, snowfall, evapotranspiration, days of snowfall,and days of snowmelt from January to March are shown in Table. 6. River discharges of RCP 8.5 arehigher than the originals, except in March. After mid-February, the river discharge of RCP8.5
temporarily decreased a little, but there is no significant difference between RCP8.5 and the originalin terms of monthly river discharge. On the other hand, the original from January to March had 78.8mm of snowfall and 93.4 mm of snowmelt, while RCP 8.5 had 5.7 mm of both snowfall and snowmelt.
In addition, RCP 8.5 has only 6-7% of the original snowfall and snowmelt, and RCP 8.5 has only
one snowfall and snowmelt day. The above results indicate a significant decrease in snowfall andsnowmelt in RCP 8.5 in 2090. The evapotranspiration of RCP 8.5 during January-March is 19.48mm, which is about 1.37 times higher than the original (14.19 mm). The impact of climate changeon the flow rate indicates that there is no significant difference between RCP8.5 and the original(2020) due to increased rainfall. The results indicate that while changes in river discharge may not
be immediately apparent due to increased rainfall in the RCP 8.5 scenario, the alarming decrease insnowfall and snowmelt could have far-reaching consequences for water resources management andthe ecosystem.
Table 6 Simulation results of the original (2020) and RCP8.5 (2090)
Original
Average River discharge (m3/s)
Snowmelt (mm)Snowfall (mm)Evapotranspiration (ET) (mm)Snowfall days
Snowmelt days

Jan.
0.086
52.80
38.30
0.65
7
10

Feb.
0.067
32.80
32.70
1.80
6
10

Mar.
0.050
7.80
7.80
11.74
5
4

Total
0.068
93.40
78.80
14.19
18
24

Jan.
0.098
0.00
0.00
1.13
0
0

RCP 8.5
Feb.
Mar.
0.069
0.049
5.70
0.00
5.70
0.00
3.17
15.18
1
0
1
0

Fig. 3 River discharge of the simulation of deforestationⒸ ISERD

104

Total
0.075
5.70
5.70
19.48
1
1

IJERD – International Journal of Environmental and Rural Development (2023) 14-2

Deforestation Situation
In the one-year river discharge data for 2020 (Fig. 3), days with particularly high daily river discharge(July 12th, July 16th, and August 1st) were extracted and compared between the original anddeforestation. there were no significant differences for July 16th and August 1st, but July 12 thincreased the daily river discharge by a factor of 1.66. Heavy and long-lasting rainfall showed nodifference in water storage function between the two cases, but 5% deforestation was shown toincrease river discharge at the beginning of a rainfall event. The result of the deforestation situationis shown in Table 7. Surface flow of deforestration was 145.1 mm, 7% increase over surface flow of
the t original (135.1 mm). The ET of deforestration was 395.9 mm, 0.3% increase over ET of theoriginal (394.7 mm). The result of surface flow also indicates that the water storage function of thewatershed is declining due to the decrease in forest area. This effect was particularly pronounced at
the beginning of precipitation events, indicating that the presence of forests role which moderate
early-stage discharge.
Table 7 Simulation results of the original (2020) and deforestation 5%
Surface flow

Evapotranspiration(ET)

Annual averageRiver discharge
July 12th
July 16th

Aug. 1st

Original

135.1 mm/y

394.7 mm/y

0.083 m3/s/y

0.50

1.13

2.24

Deforestation 5%

145.1 mm/y

395.9 mm/y

0.084 m3/s/y

0.83

1.09

2.24

CONCLUSION
Herein, we analyzed the effects of snowfall and snowmelt (from January to March) in a small
watershed in Mishima, Fukushima Prefecture. The results of the climate change simulations indicate
a significant reduction in snowfall and snowmelt in RCP8.5 (2090) within CMIP6 when comparedto the original simulation results for 2020. If climate change were the RCP8.5 scenario, agriculturaluse of spring snowmelt would be problematic by the end of the 21st century. However, there would
be no significant difference in river discharge because of increased precipitation. However, climatechange mitigation measures need to take into account that the water storage function of forests
through snowfall and snowmelt will disappear. In addition, it may not show significant changes inthe short term, but the loss of snowfall and snowmelt and the associated changes in forest water
storage functions by snow raise concerns about the long-term sustainability of water resources andecosystems.
The results of the deforestation simulation showed a 7% increase in annual surface runoff and areduction in water storage function during the first rainy season. It is crucial to consider the need forthe water storage function of forests in combination with the effects of climate change anddeforestation. Both climate change scenarios and land use change simulations are few in small
watershed studies. However, local governments need to update their water resource management
based on climate change scenarios, and in small watersheds, water balance simulations based on
climate change scenarios should be more necessary. Although only runoff analysis was conducted inthis study, inundation analysis based on calculated river flow data would provide a more accurate
assessment of disaster risk.
ACKNOWLEDGEMENTS
This research was supported by the Fukushima Regional Collaborative Research Center (FRECC) in
the National Institute of Environmental Studies (NIES) and by the Tokyo University of Agriculture
as the Doctoral Research Grant Program of the Tokyo NODAI Research Institute, and JSPS
KAKENHI (JP20K06351).

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REFERENCES
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IPCC. 2021. IPCC sixth assessment report, Climate Change 2021. Retrieved from URL https://www.ipcc.ch/report/ar6/wg1/about/how-to-cite-this-report/
Ishizaki, N. 2021. Bias corrected climate scenarios over Japan based on CDFDM method using CMIP6. National
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html
Katrin, B., Jeffrey, G.A., Hendrik, R., Michael, J.W., David, D.B., Peter, M.A., Martin, V. and Raghavan, S. 2016,
Introduction to SWAT+, A completely restructured version of the Soil and Water Assessment Tool. Journal of theAmerican Water Resources Association, 53, 115-130, Retrieved from URL https://api.semanticscholar.org/Corpus
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Kikuchi, S., Okazawa, H., Maskey, S., May, S., Oba, M., Nakamura, S. and Hayashi, S. 2021. Estimation of
long-term river discharge in the forested watershed in the snowy region by SWAT. International Journal ofEnvironmental and Rural Development, 13 (2), 105-112, Retrieved from URL https://iserd.net/ijerd132/
13-2-17.pdf
Kissinger, G.M., Herold, M. and De Sy, V. 2012. Drivers of deforestation and forest degradation, A synthesis
report for REDD+ policymakers. Lexeme Consulting, Retrieved from URL https://www.cifor.org/
knowledge/publication/5167/
Ooba, M., Togawa, T., Nakamura, S. and Nemoto, K. 2019. Toward regional circular economy with suitable forest
management and usage of biomass, A case study in Oku-Aizu region and Mishima Town. In Proceedings of theConference on Biomass Science, 15 (0), 13-14, Retrieved from URL https://www.jstage.jst.go.jp/article/jiebiomass
ronbun/15/0/15_13/_pdf/-char/ja
Qingmu, S., Kaida, C., and Lingyun L. 2021. The impact of land use change on disaster risk from the perspective ofefficiency. Sustainability 2021, 13, 3151, Retrieved from DOI https://doi.org/10.3390/su13063151
Williams, J.R., Arnold, J.G., Kiniry, J.R., Gassman, P.W. and Green, C.H. 2008. History of model development
at Temple, Texas. Hydrological Sciences Journal, 53 (5), 948-960, Retrieved from DOI https://doi.org/10.
1623/hysj.53.5.948
Yoshikawa, K., Nagao, N. and Misawa, S. 2009. Watershed scale evaluation of flood mitigation function of paddy fields
installed with runoff control devices. Transactions of the Japanese Society of Irrigation, Drainage, and RuralEngineering, 77 (3), 273-280, Retrieved from DOI https://doi.org/10.11408/jsidre.77.273Ⓒ ISERD

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Research article
erd

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

TATSUYA YOSHIDA
Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya, JapanBORATANA UNG
Cambodia Japan High-value Agricultural Product, Phnom Penh, Cambodia

MITSURU HAMANO
Faculty of Agriculture, Shinshu University, Nagano, Japan
Received 5 October 2023

Accepted 21 December 2023(*Corresponding Author)

Abstract Vegetable production in Cambodia is minimal; thus, 70% of the country’s internal
consumption is dependent on imported vegetables, primarily from Thailand and Vietnam.
Although the government is encouraging vegetable production, clarifying the status, issues,and challenges is crucial to establishing detailed strategies for vegetable production in the
country. This study aimed to identify the challenges and prospects of vegetable production in
Cambodia by examining the status of vegetable cultivation, including production volumes
and varieties, vegetable business conditions, and producers' socio-economic characteristics.
A commune in Takeo province, which has year-round access to agricultural water, wasselected as the study area. Semi-structured interviews based on a questionnaire wereconducted with both vegetable producers and non-producers for comparative analysis.
Producers were asked about their vegetable production and other income-generating activities
and expenditures. Non-producers were asked about their income-generating and expenditure
activities. Valid responses were obtained from 113 vegetable-producing households (HHs)
and 89 non-producing HHs. The survey results indicated that 97% of vegetable producers
had a surplus; however, the average net profit (35 USD) was minimal compared with otherincome sources, such as factory work. The net profit was strongly influenced by the
production volume derived from the size of owned farmland and the limited planted area due
to unsuitable production methods. Nevertheless, nearly 60% of non-producers had arable
upland, but they stopped production owing to a lack of sales channels. Thus, the low
production volume per producer and limited sales channels were identified as the key
challenges Cambodia faces in terms of increasing vegetable production volumes.
Keywords vegetable production, current status, issues, challenges, Cambodia

INTRODUCTION
Cambodia is located on the Indochina Peninsula and is surrounded by Thailand, Laos, and Vietnam(Fig. 1). Owing to rapid economic growth in recent years, Gross National Income (GNI) per capita
exceeded 1,000 USD in 2013. It reached 1,500 USD in 2018 (World Bank, 2023. However, as many
people live below the poverty line in rural areas, it is one of the poorest countries. Agriculture
accounts for 25% of the country's GDP. In addition, about 40% of the people in Cambodia areengaged in agriculture, which is one of the country's core industries. The main crops include rice,maize, and cassava. Although self-sufficiency in rice has been achieved, vegetable production could
have been more active, with approximately 70% of domestic consumption dependent on importsⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

from neighboring countries. In response to this situation, Cambodia's Rectangular Strategy (Royal
Government of Cambodia, 2013) indicated the necessity of research activities to develop a policy
framework for expanding vegetable production. During the expansion of vegetable production,anyone can purchase domestic vegetables, and increasing production and distribution volume isnecessary to realize the situation (Fig. 2). Existing research on vegetable production in the country
covers marketing channels, such as market status (Itagaki, 2010), marketing (Olney et al., 2009), andlogistics (Kawahara and Yoshida, 2006). However, research should focus more on the reality andchallenges of production volume, including the yield, acreage, and number of producers, which isessential for exploring the potential for expanding production.

Th
ail
an
d

Laos

Tonle sap lake

Road
#

5

m
na
et
i
V

Road # 2

Road # 3

Phnom Penh
Takeo province

Fig. 1 Map of Cambodia showing the study area
OBJECTIVE
This study aims to identify the challenges of vegetable production for increasing production volume
in Cambodia by clarifying the status of vegetable cultivation, including production amount andvarieties, vegetable production business conditions, and producers' socio-economic characteristics.
Expansion of national veg production★

≒ Anyone can buy Cambodian vegetables（Kouy et al. 2016、Olney et al. 2009）

Volume of production

Production
amount/ HH

Number of
producers

Production
areas

Volume of distribution★

market（Itagaki 2010）★
marketing★

logistics（Olney et al. 2009）（Kawahara & Yoshida 2006）★

Existing studies

Fig. 2 Concept map of the study
METHODOLOGY
The survey area is in Takeo Province, about 80 km south of the capital Phnom Penh. It is Cambodia's
main agricultural production area (Fig. 3). A commune comprising 12 villages near a huge reservoir
constructed during the Pol Pot regime was selected as the study area. This commune has year-round
access to agricultural water, such as reservoirs and irrigation, and has been confirmed as a vegetableproduction area. It is 20 minutes by car from one of the largest markets in the province along National
Road No. 3. This road connects to Sihanoukville Port, the only port in Cambodia, and Phnom Penh,the capital city (Fig. 3).
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IJERD – International Journal of Environmental and Rural Development (2023) 14-2

Non-producer
Producer

Irrigation

Road No.3

Irrigation

生産者
0.5-1.0 ha(0.5-1.0ha)
Distance
農業用水
from
water
からの距離

非生産者
0.5-1.0 ha(0.5-1.0ha)

農業用水
Distance
からの距離
from
water

生産者
>1.0 ha(1ha未満)Road No. 2

非生産者>1.0 ha(1ha未満)Local
MarketReservoir

Image of sampling
Map of survey area

to Vietnam

Map of Takeo province

Fig. 3 Study area and sampling
For comparative analysis, a semi-structured interview based on a questionnaire was conductedwith both vegetable producers and non-producers. All households in the area producing vegetables
for sale during the 2017 production season were selected as “vegetable producers.” All the otherhouseholds in this area were automatically identified as “non-producers.” To select non-producers
facing natural environments and economic conditions similar to those faced by producers wereselected through stratified sampling. Stratification was based on water access and agricultural landownership (Table 1). First, the neighbors of each "producer" who could access the same water source
were selected as candidate respondents. Then, we selected households in the same land size category
as neighboring “producers” among the candidates. Only households that met the criteria wereselected. Consequently, 122 vegetable-producing and 92 non-producer HHs were selected as subjects
for the survey. Producers were asked about their vegetable production activities from October 2016
to September 2017 and their other income-generating activities and expenditures. Non-producers
were asked about their income-generating activities and expenditures. The number of valid responses
included 113 (92.6%) producer HHs and 89 (96.7%) non-producer HHs.
Producers (n=113)

Non-producers (n=89)

1. Production & Business
1) Veg production
2) Business status
2. Socio-economics and future potential
3) Socio-economic status

3) Socio-economic status4) Livelihood activities

4) Livelihood activities

5) Awareness forvegetable production5) Awareness forvegetable production

Fig. 4 Data collection framework
Table 1 Results of stratified sampling
Area（ha）

Producers1

below 1.0
30
1.050
2.025
3.08
Total
113
*Number of households and ratio(%)

Non-producers1(26.5)(44.2)(22.1)(7.1)(100.0)

27
35
23
4
89

(%)
(30.3)(39.3)(25.8)(4.5)(100.0)This study first focuses on the respondents’ profile, followed by vegetable production status andbusiness conditions, including costs and profits. Then, we explore the possibility of increasingⒸ ISERD

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production and producers by conducting a comparative analysis of the socio-economic status,
including household composition, income, and awareness of both producers and non-producers (Fig.
4).
RESULTS AND DISCUSSION
Socio-economic Status of Respondents
Table 2 shows the profile and socioeconomic status of all respondents. Producer HHs had fewer
family members than non-producer HHs. Producer HHs had fewer female members, children under
15, and employed members. By contrast, the average age of each household and the primary
agriculture worker were significantly higher in producer HHs. The sizes of owned agricultural land
did not differ due to stratified sampling according to land size; both producer and non-producer HHs
tended to have an upland field suitable for vegetable production. No socio-economic or cultural
anthropological studies were found to provide background on the significant differences in householdcomposition. Household income data are analyzed in the later section.
Status of Vegetable Production
A total of 27 vegetables were produced by the respondents, including peanuts (30 HHs), pumpkins(23 HHs), melons (22 HHs), water spinach (21 HHs), and yams (21 HHs), with an average of 2.7
items per household (Fig. 5). Many of these were seen as native vegetables in the tropics, and direct
sowing was the mainstream. Therefore, it is assumed that the vegetable production in this area is notfor specific demands, such as exporting or sending to foreign residents in the country but for local
needs. Vegetable cultivation averaged 4.8 months per year and was concentrated in the dry season.
Substantially little cultivation occurs during the rainy season (Fig. 6). It is common to growvegetables using paddy fields after harvesting rice in Cambodia, according to existing studies(Hamano et al., 2013). In that case, cultivation in the rainy season is physically impossible. However,the producers in this region should be able to cultivate vegetables even in the rainy season, as morethan 70% (80 HHs) of them own upland fields (Table 2).
Table 2 Socio-economic profile of respondents
Respondents' attributes
Total number of HH2 members
FemaleMale

Producers

Non-producers

Total（N=113）
4.1
2.0
2.1

（N=89）
4.6
2.5
2.1（N=202）
4.3
2.2
2.1

1

P-value

0.049 *
0.003 **
0.957

Workforce (age 16-64)

2.9

3.1

3.0

0.546

Age 15 or more

1.0

1.3

1.1

0.028 *

Age 65 or more
Employed worker

0.3
0.4

0.2
0.7

0.3
0.5

0.212
0.009 **

Average age among all members

36.2

28.7

33.0

0.000 **

Main agriculture worker
Age

52.4

46.0

49.6

0.000 **

Years of schooling

5.1

5.0

5.1

0.995

1.5
1.2
0.5

0.567
0.266
0.315

3

4

1.49 (113)
1.43 (89)
Owned agricultural land area
paddy field
1.16 (113)
1.31 (89)upland field
0.45 (80)0.49 (21)
1 Mann-Whitney U test
P-value: *(P<0.05) **(P<0.01)
2 HH: Household
3 average area in hectar
4 Respondents who own each type of land

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40

30

# of HH
（世帯数）

20

10

0

Sugar cane

Soy beanRoot

Mung beanLong bean

RadishPeanutSweet Potato

Cassava

Fingering potato

Fruit

Egg plant

Sponge gourd

Wax gourd

Water melon

Corn

CucumberMelon

PumpkinGreen petiole

S. mustard

C. cabbage

W. convolvulus

Leafy

Bean

Native varieties by Nawata (1996)

Fig. 5 Vegetable items produced
As a result of asking about vegetable production, the most common answer was damage bypests and disease (63.0%), followed by lack of water (39.8%), growth failure (32.4%), and lack oflabor force (17.6%) (Table 3). Additionally, many producers needed help using pesticides, including
how to use them appropriately (Table 4). Unsuitable cultivation techniques were sometimes observed,
such as crawling cultivation that cultivates vine vegetables, including cucumber, horizontally without
using any poles. Those vegetables will be placed directly on the soil, and insects will quickly eat
them. Moreover, the fruits will be soaked in rainwater and quickly rot.

Fig. 6 Cultivation calendar and timing of seeding
Table 3 Issues of vegetable production

Table 4 Problems of using pesticides

Problems of veg. production# of HH (%)
Lack of water43(39.8)
Excess water by flooding
13(12.0)
Growth failure
35(32.4)
Damage by diseases & insects
68(63.0)
Lack of labor force
19(17.6)
Hight costs
13(12.0)
No special problem
4
(3.7)
Total
195 (180.6)
Multiple answers allowed (N=100)

Problems of using pesticide# of HH (%)

Selection

71(65.7)How to use

67(62.0)

Amount to use

55(50.9)

Pests & diseases control

55(50.9)

Others13(12.0)

Total
261
Multiple answers allowed (N=100)(241.7)

Although this area has a vast reservoir built during the Pol Pot regime in the 1970s, only the
primary channel works. Water is not well distributed far from the reservoir owing to the poor
irrigation system. These results imply that a probable reason for rare cultivation in the rainy season
is the difficulty of pests and disease management due to a lack of knowledge and appropriate
cultivation techniques. Another possible explanation is that producers may need more workforce togrow vegetables and rice simultaneously, even if there is land.
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Business Conditions of Vegetable Producers
Analysis of the vegetable producers’ business revealed that 100 out of 113 producer HHs (88%) soldvegetables. Still, three HHs (3%) were at a loss, or in the red, as shown in Fig. 7. The average monthly
net profit was 35 USD or 166 USD for the cultivation period. This is considerably small comparedto the study area's factory labor wage of 160–200 USD/month. Thirteen households did not sellvegetables for unknown reasons, even though they produced them with the intention of selling.
A comparative analysis was done by dividing producers into two groups based on the medianprofit to find the critical factors for lower or higher profits (Table 5). Producers above the mediantended to own more upland and planted areas, resulting in significant production and sales volumes
and larger profits. Their cost per hectare, unit yield, and unit selling price were lower than those of
producers below the median. Although this study did not include own labor costs, productivity was
nearly the same between the two groups. In this survey, no producers planted in the same farmland
twice; thus, the planted area’s size equals the owned farmland area. Therefore, the larger the farmland
area owned, the larger the net profit obtained.
Red

Black

（# of HH）

Median166USD

50

Labor work：
160-180USD/month

Average
290USD

Vegetable:
35 USD/month
166USD/ 4.8 months

40

40
30

21
20

16

10

5

3

6
3

1

3

2

0
-0

0-

125- 250- 375- 500- 625- 750- 875- 1000(USD)

7 Profitsfrom
from vegetableproduction(N=100)Fig. 7Fig.
Profits
vegetable
production(N=100)Pearson’s correlation coefficients show positive correlations between net profit and total saleamount, cost, planted area, production, sales volume, number of items, and upland field ownership.
Meanwhile, unit yield, which is also an essential component of production volume, was notcorrelated with net profit and costs per hectare (Table 5). Thus, profits from vegetables seem to be
influenced more by production scale than by productivity.
Table 5 further shows the material inputs households used. Among 100 producer HHs, 69
purchased seeds, 79 purchased fertilizers, 39 purchased pesticides, 4 hired workers, 21 rented
machinery, and 51 purchased fuels. However, these costs did not influence the net profits.
Table 6 shows the results of the analysis of the relationship between the unit yield of four kinds ofvegetables and six material input costs. The costs of seeds, fertilizer, and fuel had highly significant
positive correlations with leafy vegetables and beans yields. Labor and rental fees had a weakrelationship with root vegetable yields, whereas fruit vegetable profits were not correlated with any
input goods. Seeds and fertilizer costs had a highly significant correlation with profits. Some input
material costs, such as pesticides, had slightly negative relationships with the unit yield of somevegetables. Pesticides are generally effective at preventing crop loss to pests, diseases, and weeds;
however, producers may lack knowledge on how to use them appropriately. Thus, introducing inputs
could be more effective in increasing unit yields and profits in vegetable production.
The critical factor of net profits for vegetable producers in this area is production scale — notstrategic production, such as low cost, high selling price, high productivity, and high yield. The
World Bank (2015) has indicated that the farm size of small-scale farmers is decreasing while thatof large-scale farmers is expanding. Rural households with smaller agricultural land have fewerⒸ ISERD112

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opportunities to get more significant net profits from current vegetable production. Small landholders
need to increase productivity, selling price, or production frequency for more profits. Increasing
productivity needs technical improvement, including appropriate ways of using input materials, suchas fertilizers and pesticides.
Table 5 Status of vegetable production and the results of the analysis
Variables

All producers
N=100

Unit

Total sale amount

USD

Total cost
Net profit

1rabove 166USD
N=50

below 166USD
N=50

571

84

0.000 **

2

P-value328

0.995 **

USD

38

0.466 **

52

23

0.011 *

USD

290

1.000

520

61

0.000 **

Total sales amount/ha

USD/ha

1,989

0.064

2,047

1,931

3

USD/ha464 (100)-0.119261 (50)

666

(50)

0.461

seeds

USD/ha282 (69)-0.082171 (33)

383

(36)

0.862

fertilizers

USD/ha306 (76)-0.010263 (40)

354

(36)

0.397

pesticides

USD/ha63 (39)0.020

49 (21)labor fee

USD/ha396-0.022147

rental machines

USD/ha111 (21)

0.009

fuel

USD/ha533 (51)-0.108Net profit/ha

USD/haTotal cost per hectare

(4)

1,526

0.169

0.007 **

78

(18)

0.460

1,146

(1)

0.500

146 (10)

80

(11)

0.756

103 (30)

1,149

(21)

0.034 *

(3)

1,786

1,265

0.000 **

Total planted area

ha

0.31

0.510 **

0.43

0.19

0.000 **

Total production

kg

1,115

0.641 **

1,917

313

0.000 **
0.000 **

Total sale volume

kg

735

0.850 **

1,253

217

Unit yieldkg/ha

11,440-0.0099,979

12,902

0.058

Unit price

USD/kg

0.60

0.005

0.61

0.59

0.150

Number of items

number2.73

0.322 **

3.24

2.22

0.013 *
0.004 **

Self-consumption

kg

380

0.204 *

664

97

Main agriculture worker

age

52.02

0.019

53.68

50.36

0.155

Education

yrs

4.92

0.032

4.82

5.02

0.601

Years of schooling

yrs

20.08

0.018

22.32

17.84

0.084

Owned agricultural land area

ha

1.49

0.222 *

1.7

1.28

0.075

ha

1.15

0.036

1.25

1.05

0.420

paddy field

upland field
ha
0.34
0.375 **
0.45
0.23
1 Peason's correlation coefficient between net profit and other variables* significant at 5% level, ** significant at 1% level (two-sided)
2 Mann-Whitney U test * significant at 5% level, ** significant at 1% level (two-sided)
3 The number in parentheses ( ) indicates the number of households that have invested costs.

0.049 *

Table 6 Relationship between costs and unit yield and profits
Unit yield (kg/ha)
Costs (USD/ha)

Unit yield(kg/ha)Profits(USD/ha)

Leafy

Fruit

Root

Beans

(N=27)(N=61)(N=33)(N=59)

Seeds

0.509 **

0.101

-0.1270.456 **

0.847 **

0.857 **

Fertilizers

0.525 **

0.230

0.263

0.833 **

0.396 **

0.424 **

Pesticides-0.0220.230-0.0650.909 **

0.073

0.018

Labor fee--

0.392 *

-0.045-0.0160.000

Rental fee (machine)

0.320

0.005

0.407 *

-0.018

0.053

0.086

Fuel

0.538 **

0.159

0.334

0.829 **

0.304 **

0.088

* significant at 5% level, **significant at 1% level (two-sided) by peason's correlation analysisⒸ ISERD

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Characteristics of Producer Households
Table 7 shows the structure of household income for both vegetable producers and non-producers.
The average total household income was 2,309 USD, and there was no significant difference between
producers and non-producers. Non-agricultural income accounted for an extremely high proportionof total household income. This trend was common for both producers and non-producers. However,
the average agricultural income of vegetable producers was two times greater than that of nonproducers. Additionally, the composition of agricultural income, such as rice, natural resources, andprocessing, was significantly different between the two groups. The primary source of income foreach household shows that more than 50% (55.8%) of vegetable producers and 73.1% of nonproducers depend on non-agricultural income. Only 21 HHs (18.6%) of all producers answered thatvegetable production was their primary source of income. Although vegetable production was an
essential contributor to the agricultural income of producers, its contribution to total householdincome is still low.
Perceptions of Producer and Non-producer Households
This study asked producers and non-producers about their perception of vegetable production.
Among 100 producers, 74% of them answered positive intentions for future production, including
increasing production amount (6%), the number of product items (24%), and production area (7%),
as well as maintaining the current production (37%) (Table 8). Since current vegetable production
does not bring much economic benefit, and many producers mentioned production issues (Tables 3
and 4), producers may see some benefit or significance, such as self-consumption other than financial
benefit. Figure 8 shows the results of asking for future intentions for non-producers. Among 89 nonproducers, 63% (56 HHs) had experience in vegetable production for sale. According to the socioeconomic profile of all respondents of this study (Table 2), 23.6% (21 HHs) of 89 HHs had an uplandfield. Additionally, 78% (69HHs) answered that they want to produce vegetables for sale. The results
show that the number of vegetable producers in this region may be increased. However, clarifying
why they stopped vegetable production and eliminating their concerns is necessary.
Table 7 Structure of household income and primary sources of income
Income souces
Income sources

All respondents

Total household income

3

Producers

3

(a)

(b)

Main source of income

Non-producers

3

P-value

4

Producers (%)

4

Non-producers (%)

2,309

(202)

2,113

(113)

2,558

(89)

0.530

Agricultural income

412

(202)

570

(113)

211

(89)

0.000 **

vegetable

257

(113)

257

(113)

0

(0)

-

21

(18.6)

0

(0.0)

rice

-97

(202)

90

(113)

-335

(92)

0.000 **

13

(11.5)

12(13.5)

animals

177

(122)

205

(78)

127

(44)

0.094

7

(6.2)

3

(3.4)

N. resources

1

116

(106)

115

(68)

117

(38)

0.007 **

4

(3.5)

3

(3.4)

N. resources

2

467

(13)

462

(12)

525

(1)

0.615

3

(2.7)

0

(0.0)

processing

787

(17)

116

(8)

1,383

(9)

0.002 **

0

(0.0)

6

(6.7)

rice milling

-385

(14)

-513

(11)

85

(3)

0.456

2

(1.8)

0

(0.0)

Non-agricultural income

1,996

(192)

1,661

(105)

2,400

(87)

0.032 *

16

(14.2)

11(12.4)

off-farm business

2,594

(46)

2,493

(24)

2,704

(22)

0.800

34

(30.1)

43(48.3)

employed work

1,986

(107)

1,551

(52)

2,397

(55)

0.002 *

13

(11.5)

11(12.4)

355

(144)

378

(89)

319

(55)

0.510

113

(100.0)89(100.0)Remittence(a) Income in USD from each souce(b) the number of household according to the result of calculation of each household income
1 rsources: fruits, fishes, medicinal plants, mushrooms, etc. (backyard)
2 rsources: fruits, fishes, medicinal plants, mushrooms, etc. (forest etc.)
3 ( ) indicates the number of household
4 according to the result of calculation of each household income*significant at 5% level, **significant at 1% level (two-sided) by Mann-Whitney U testⒸ ISERD

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Table 8 Future intentions of producers
Future vegetable production
Increase production amount
Increase # of products
Increase production area
Keep current production
Decrease production
Stop production
Others
Total
Single answer (N=100)# of HHs (%)
6
(6.0)
24
(24.0)
7(7.0)
37(37.0)
6(6.0)
17(17.0)
3(3.0)
100(100.0)No experience
33 HHsExperienced（37%）
56 HHs（63%）

N=89

Not interesting
20 HHs（22%）
Interesting
69 HHs（78%）
N=89

Fig. 8 Future intentions of non-producers

CONCLUSION
This study attempted to identify the challenges to increasing the vegetable production volume in
Cambodia by clarifying the status of vegetable cultivation, including production amounts andvarieties, vegetable production business conditions, and producers' socio-economic characteristics.
The results implicated two major challenges for increasing vegetable production volume in
Cambodia and several means to achieve them.
The first challenge is to increase production volume per producer. This study showed thatvegetable production in rural Cambodia is profitable, but the profits need to be more significant tosustain the family as their primary income source. The critical factor for low net profits fromvegetable production was the production scale derived from the size of owned farmland — notstrategic production, such as low cost, increased selling price, increased productivity, or high yield.
Growing farmland area or productivity is essential to increase production volume per producer.
However, land size, including farmland, is reducing owing to a system of equal inheritance among
siblings in Cambodia (Acharya et al., 2003; World Bank, 2013; Diepart, 2015), and a growing
number of households (more than 25% in the low land) live with less than 0.5 ha of land, which is
not enough to sustain a family throughout the year (Taylor, 2011). Therefore, increasing the total
planting area will be more practical than expanding the farmland size. In the study area, mostproducers did not cultivate any vegetables during the rainy season, despite owning arable uplandareas, owing to the risk of disease due to unsuitable cultivation methods. The limited cultivation isthought to be owing to the avoidance of damage from pests and root rot that tend to occur during therainy season. The annual planted area is expected to increase through the appropriate use of pesticides
and the introduction of cultivation techniques, such as ridges and stanchions, to allow cultivation in
the rainy season and double-cropping. The development and introduction of a combination ofsuitable cultivation techniques, agricultural materials, and chemicals in Cambodia may positively
influence the production volume.
Increasing the number of producers will be another challenge to obtain more production volume
with the limited farmland size per household. This study identified that more than 60% of nonproducers would produce vegetables, with 23.6% having an upland field. The main reason for
stopping vegetable production was the limitation in sales channels and the difficulties of making thechannel by themselves. However, nearly 80% of non-producers were interested in starting vegetableproduction. To involve experienced households and new producers, vegetable production needs tobe more attractive as a profitable income-generating activity. Currently, rural households withsmaller agricultural land have few opportunities to attain reasonable net profits from vegetableproduction. As mentioned above, the net profit should be improved by increasing production volume.
Moreover, post-harvesting issues, such as quality management, the sales channel for rural smallscale producers, and value addition are as significant as those for other agricultural products in
Cambodia. Developing a one-stop service for agriculture-related issues, including technical andinformative dimensions, is urgent.
This study focused only on the status of vegetable production. Further studies to verify theabove-mentioned means to increase output per producer, such as developing a combination ofsuitable cultivation techniques, agricultural materials, and chemicals, are needed. In addition, theⒸ ISERD

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target of this study was limited to the active vegetable-producing area with enough agricultural water
throughout the year. Further studies need to cover potential production areas to discuss the possibilityof expanding the total production area in the country.
ACKNOWLEDGEMENTS
This work was supported by JSPS KAKENHI, Grant Number JP21KK0013—Fund for the Promotionof Joint International Research (Fostering Joint International Research [B]).

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of the literature and source book. Learning Institute and IDRC, Phnom Penh.
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Research article
erdAntimicrobial Resistance Profiles Found in a Case Study ofEscherichia coli from Cohabitant Pets and Environmental
Surfaces at Animal Clinics, Phnom Penh CityBAN NAIHEAK*
Faculty of Veterinary Medicine, Royal University of Agriculture, Cambodia
Email: naiheakban@gmail.com

CHEA BUNNA
Faculty of Veterinary Medicine, Royal University of Agriculture, Cambodia

VENN VUTEY
Faculty of Veterinary Medicine, Royal University of Agriculture, Cambodia

KANG KROESNA
Faculty of Veterinary Medicine, Royal University of Agriculture, Cambodia
Received 20 November 2023

Accepted 22 December 2023(*Corresponding Author)

Abstract Escherichia coli (E. coli) can be transmitted from dogs to surfaces through direct
exposure to dog faeces, pus, and urine; alternatively, it can live in the environment for a shortperiod. We aimed to determine the prevalence and antibiotic resistance profile of E. coli fromdogs and environmental surfaces of animal clinics in Phnom Penh, Cambodia. We swabbed
twenty-four samples from dogs (18 faeces and 6 pus) and twenty-three from environmental
surfaces (8 cage-floors, 7 cage-walls, 1 feeding-plate, and 7 treatment-tables) at animal clinics.
The bacterial culture method was used to isolate E. coli from both dog and environmental
surface samples. An antibiogram of the isolates was tested using the disk diffusion method with
six antibiotics (amoxicillin, ampicillin, ceftriaxone, gentamycin, levofloxacin, andtetracycline), following the standard of the Clinical and Laboratory Standard Institute. The
results showed that E. coli was present in approximately 54% of dog samples and 22% ofenvironmental surface samples. The E. coli isolates from dog samples showed high resistanceto amoxicillin, ampicillin, and tetracycline (54%) and lower resistance to ceftriaxone, gentamycin(27%), and levofloxacin (8%). E. coli isolates from environmental surfaces demonstrated highresistance to amoxicillin, ampicillin, ceftriaxone, and tetracycline (80%-100%), and lower
resistance to gentamicin and levofloxacin (60%). In conclusion, E. coli was found in both dogs
and veterinary clinic surfaces and exhibited resistance to numerous antibiotics. This study
suggests that further research is necessary to identify the specific genes responsible for theantibiotic resistance of E. coli found in dogs, on clinic surfaces, as well as in humans –including clinic technicians and dog owners – in order to better understand how this resistanceis affected by the transmission of E. coli between different carriers.
Keywords dogs, environmental surfaces, E. coli, antibiotic resistance, antibiogram
INTRODUCTION
The use of antimicrobial drugs does not only increase the antimicrobial resistance (AMR) of
pathogenic bacteria but also the endogenous commensal flora. (Berge et al., 2006; Dancer, 2004;
Goosens, 2009). It is reported that the potential problem of AMR among pet animals may cause
human health problems, due to the increasing utilization of the same antimicrobial substances inhuman medicine and to the close contact between pets and their human cohabitants (Guardabassi etal., 2004; Moyaert et al., 2006; Schwarz et al., 2001).

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The spread of antimicrobial-resistant bacteria can occur directly, by skin-to-skin contact andcontact with bacteria-containing material (e.g., saliva, feces, urine), or indirectly via the household
environment (Guardabassi et al., 2004; Schwarz et al., 2001). Leite et al. (2013) reported that direct,
close contact between all the cohabitants including pets and humans, and the touch of contaminatedhousehold surfaces and objects could lead to shared antimicrobial-resistant bacteria. In the new host,resistant bacteria can both colonize and infect and remain in that particular environment for a shorttime. During this period, the resistant bacteria are capable of either spreading their resistance genes
to other bacteria residing in the new host (endogenous or pathogen bacteria), but also accepting
resistance genes from other bacteria (Livermore, 2003; Schwarz et al., 2001). E. coli has possible
biology in spreading resistance not only to the acceptor and donor of the transmissible drug-resistantgene but also commonly found in the intestinal tract of both humans and animals (Costa et al., 2008).
E. coli can also be implicated in various intestinal and extra-intestinal diseases including urinary tract
infection (Johnson et al., 2008; Johnson et al., 2001) and Pyometra (Bassessar et al., 2013). Likewise,
Markey et al. (2013) showed that E. coli can cause diseases such as Colisepticaemia, Pyometra, andUrinary tract infections in dogs. No study has been conducted in Cambodia regarding the E. coli
resistance in pets and its associated environment.
OBJECTIVE
The present study aimed to determine the prevalence and resistance profiles among E. coli fromcohabitant pets and environmental surfaces at animal clinics in Phnom Penh City, Cambodia.
METHODOLOGY
Study Site and Sampling
The study was conducted in three animal clinics in Phnom Penh city, Cambodia from March to May
2019. In total, forty-seven samples were swabbed from dogs and environmental surfaces. The petsamples were selected based on the diagnosis of bloody diarrhea (3 to 8 months old), canine
parvovirus, and Pyometra disease. The environmental samples were selected based on the casereported animals above which animals were exposed to. Twenty-four samples (18 faeces and 6 pus)
were collected from dogs. Twenty-three samples (8 cage-floors, 7 cage-walls, 1 feeding plate, and 7
treatment-tables) were collected from the environmental surface. All samples were collected andtransported on the same day to the Veterinary Microbiology Laboratory of the Faculty of Veterinary
Medicine, Royal University of Agriculture, Cambodia.
E. coli Isolation and Identification
Samples were collected and the bacterial culture was followed by Markey et al. (2013). Briefly, theswap was subcultured into 5 mL of Buffered Peptone Water (BPW) and incubated at 370C for 24hours. Then the presumptive was streaked on MacConkey agar and incubated at 370C for 24 hours.
The suspected colonies with large/medium dark pink colonies were confirmed with biochemical tests
such as gram staining, Catala's test, Triple Sugar Iron (TSI), and Motility-Indole-Lysine (MIL) test.
Antimicrobial Susceptibility Testing
Antimicrobial susceptibility testing was performed using the disk diffusion method following
Clinical and Laboratory Standards Institute (CLSI, 2017) guidelines. Briefly, fresh E. coli isolates
were plated on Trypton-Soja-Agar (TSA) plates and incubated at 370C for 24 hours. Then 2-3
colonies on TSA were inoculated on NaCl suspension to a turbidity equivalent to 0.5 McFarland
standards. One hundred microliter suspension was plated on 15cm depth Mueller-Hinton agar
(MHA) and standard discs were applied using a dispenser and incubated for 16-18 hours at 370C. AⒸ ISERD

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total of six antimicrobial agents including Tetracycline (30 µg), Ampicillin (10 µg), Amoxicillin (10µg), Gentamycin (10 µg), Ceftriaxone (30 µg), and Levofloxacin (5 µg) were tested. All antibiotics
used for the study are based on the treatment protocol of each animal clinic.
RESULTS AND DISCUSSION
The present study found that a high prevalence of E. coli (54.16%) presented in dogs; whereas, a low
prevalence (21.74%) presented in environmental surfaces isolated from the animal clinics in Phnom
Penh city. E. coli presented in about 67% and 17% of dog faeces and pus, respectively. The result issimilar to the previous findings of Das et al. (2012) on the faeces of dogs. E. coli is the major
pathogen in the genital tract (Bassessar et al., 2013). Some studies have shown 50-85% prevalenceof E. coli in bitches suffering from pyometra disease (Bjurström et al., 1993; Sandholm et al., 1975;
and Trevena et al., 1996). Sharif et al. (2017) found that the prevalence of E. coli in the pus of bitcheswith pyometra was higher than the present study as the sample is much larger. Dog age is a factor
contributing to the prevalence of E. coli in faeces. Greene et al. (1998) have reported two strains ofE. coli were associated with gastrointestinal disease in young dogs. 77% of E. coli is present in young
dogs at age 1-3 months (Younis et al., 2015). However, Torkan et al. (2016) showed a significantly
lower prevalence of E. coli (47%) in the adult dog's faeces. The present study was on dogs aged 3-8months and showed a prevalence somewhere between the high and low values reported by the
aforementioned studies.
Table 1 The prevalence of E. coli distributed the animal clinics
Dogs (faeces and pus)
Number (n/N)
Percent (%)
01/50
20.00
05/10
50.00
07/90
77.78
13/24
54.16

Animal Clinics
A
B
C
Total

Environmental surface
Number (n/N)
Percent (%)
00/20
00.00
05/16
31.25
00/40
00.00
05/23
21.74

Table 2 The prevalence of E. coli distributed in animal and environmental surface samples
SampleDogs
Environmental
surfaces

FaecesPus
Cage-floor
Cage-wall
Feeding plate
Treatment table

Prevalence
Number (n/N)
12/18
01/60
3/8
1/7
0/1
1/7

Percent (%)
66.67
16.67
37.50
14.28
00.00
14.28

Total
Number (n/N)

Percent (%)

13/24

54.16

05/23

21.74

Table 3 The antimicrobial resistance profiles of E. coli in dogs and environmental surfaces
Antibiotics
Tetracycline
Ampicillin
Amoxicillin
Gentamycin
Ceftriaxone
Levofloxacin

Dogs
Number (n/N)
7/13
7/13
7/13
3/13
4/13
1/13

Percent (%)
53.85
53.85
53.85
23.08
30.77
07.69

Environmental surfaces
Number (n/N)
Percent (%)
5/5
100.004/5
080.005/5
100.003/5
060.004/5
080.003/5
060.00The prevalence of E. coli on the environmental surface varies based on the animals that havebeen exposed, according to Sidjabat et al. (2006). The highest prevalence of E. coli (37.50%) foundon environmental surfaces of the animal clinics in this study was isolated from the cage floor, whichis the most contaminated between faeces or pus of the dogs on the cage floor during hospitalization.
Instead, it might also be attributed to poor hygiene of the animal clinics and the cross-contamination
between dogs and the surfaces. Madubuike et al. (2016) reported a lower prevalence of E. coli
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bacteria on environmental surfaces (3.60%), especially the cage floor (0%) of the animal clinics inNigeria might be the biosecurity measures taken in the animal clinics were able to reduce thecontamination of E. coli.
E. coli showed high resistance to amoxicillin, ampicillin, and tetracycline (54%); while lessresistant to ceftriaxone (31%), gentamycin (23%), and levofloxacin (8%) in dogs. Similarly, these
isolates also had high resistance to amoxicillin, ampicillin, ceftriaxone, and tetracycline (80-100%);
while less resistance to gentamicin and levofloxacin (60%) on environmental surfaces. The highresistance to antibiotics on environmental surfaces was higher than the dog samples in the present
study. This might be fewer samples in the environment and the possibility of E. coli surviving on thesurfaces. Leite et al. (2013) and Carvalho et al. (2016) reported that pet dogs were shown to be apotential household source of multi-resistant E. coli strains to humans through the environment.
Resistance to antibiotics in dogs in the present study is consistent with the study of Nam et al. (2010).
CONCLUSION
In conclusion, both dogs and environmental surfaces showed identical prevalence of E. coli and its
resistance to several important antimicrobials. This is correlated with the spreading of antimicrobial
resistance into the environmental surfaces of the animal clinics from hospitalized dogs and could bespreading to owners and veterinarians, a cause of concern with regard to animal and public health.
ACKNOWLEDGEMENTS
The study is supported by the Center of Excellence on Sustainable Agricultural Intensification and
Nutrition (CE SAIN) of the Royal University of Agriculture (RUA) receives financial support fromthe U.S. Agency for International Development (USAID) through Feed the Future Innovation Lab
for Sustainable Intensification (SIIL).
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Research article
erd

Assessing the Acceptability and the Feasibility of an
Agricultural Package of Technologies for Risk Managementin Southern Haiti
RIVAL VALCIN*
Graduate School of International Food and Agricultural Studies,Tokyo University of Agriculture, Japan
Email: rivalcing7@gmail.com

TOMOHIRO UCHIYAMA
Faculty of International Agriculture and Food Studies, Tokyo University of Agriculture, JapanKATSUMORI HATANAKA
Faculty of International Agriculture and Food Studies, Tokyo University of Agriculture, JapanYASUO OHE
Faculty of International Agriculture and Food Studies, Tokyo University of Agriculture, Japan

BENEDIQUE PAUL
Faculty of Agriculture and Environmental de Sciences, Quisqueya University, Haiti
Received 22 November 2023

Accepted 23 December 2023(*Corresponding Author)

Abstract Haitian farming faces serious climate risks. Losses due to hurricanes, droughts,
floods, and diseases seriously threaten productivity. This paper aimed to study the feasibility
and acceptability of agricultural technology packages that could help manage risk while
improving the productivity of farming in Haiti. Significant risks faced by farmers include
uninsurable disasters due to their systemic and catastrophic nature. Therefore, a package oftechnologies including a paid-in-kind "stabilization account" component may be an
appropriate financial tool for risk management. Are Haitian farmers open to such innovation?
To answer this question, we collected data from 28 agricultural experts and 1,400 farmers,
including 234 maize farmers in southern Haiti, and adopted the new product development(NPD) process to test the feasibility of the concept. We used the Spearman correlation,
multinomial logistic regression, and linear regression to determine factors affecting theopenness of Haitian farmers to innovation. We also conducted the Cochran-Mantel-Haenszel
test to analyze the association between "financial inclusion" and "openness to innovation."
Based on the results, 70% of farmers expressed their willingness to pay 6% to more than 12%of their harvested crop as a stabilization account. Farmers with "financial inclusion" areexpected to be twice as likely to adopt innovative technologies, while farm size, gender,
household size, and revenue had a significant positive impact on openness to innovation.
However, contrary to the trends found in literature, we found that risk aversion may positively
affect the acceptance and adoption of some technologies. The results also suggest that, in
Haitian farming, risks linked to natural disasters are more strongly related to financial
incapacity than uncertainty. Therefore, access to proper just-in-time inputs complemented bya financial tool to overcome uncertainty will significantly boost the adaptability and resilienceof Haitian farmers toward climate risks.
Keywords climate risks, product development, stabilization account, technology adoption

INTRODUCTION
Natural hazards represent a severe issue disrupting farming activities in Haiti. Between 1990 and
2008, Haiti was the Caribbean country most affected by natural disasters (3 droughts, one epidemic,Ⓒ ISERD

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22 floods, 23 storms, and hurricanes) (Weissenberger, 2018). Regions with exceptionally highproduction in agriculture generally present higher vulnerability to natural risks and disasters(earthquakes, landslides, floods, hurricanes, and droughts) (UNDP, 2015). In the aftermath of
Hurricane Matthew in 2016, the agricultural sector was most affected; with losses and damages
estimated at 603.8 million dollars (around 4.3% of the country's GDP at the time), 80% of the
agricultural plantations were destroyed in its path (Icart, 2017). The southern peninsula of Haiti isparticularly exposed to tropical storms, hurricanes, floods, and landslides (IFAD, 2022). Haitian
agricultural producers, who already operate with inadequate working capital, additionally findthemselves confronted with various risks accelerating the process of decapitalization. Thus, anappropriate model of Haitian agriculture should put risk mitigation first (Bureau et al., 1994) because
exposure to uninsured risks is a proven major cause of low yields, slow growth, and persistent poverty(Carter et al., 2015). Over 20% of the country's GDP is from agriculture (Vansteenkiste, 2022);
therefore, the agricultural sector demands serious consideration despite its high-risk exposure.
Technology refers to tools and techniques or knowledge, ideas, and methods people use toachieve an activity (Dholey, 2019). In agriculture, climate-smart technology has been used for risk
mitigation, such as drought-resistant seed, rescheduling planting, and micro-irrigation (Tanti et al.,2022). Therefore, adopting technology (tools and techniques) for managing climate risk may be an
essential alternative for Haitian farmers. In addition, agribusiness firms supplying inputs to farmers
may need to provide new products and services. Thereby, this research provides insights relevant tothe acceptability and feasibility of integrating stabilization accounts in an agricultural package oftechnologies in Southern Haiti as tools and techniques for managing risks linked to climate hazards.
The southern peninsula of Haiti is particularly exposed to tropical storms, hurricanes, floods, andlandslides (IFAD, 2022). Previous works mention the risk aversion of Haitian farmers (Bureau et al.,1994) and its possible negative impact on technology adoption in general (Murray and Bannister,
2004; Macours et al., 2018). These works focused on lessons learned from agroforestry projects anda pilot phase of subsidizing technology transfer. For this reason, our study provides a specific viewof how adopting technology to manage hazards may be approached differently.
Armstrong et al. (2014) suggest eight (8) stages to develop a new product: idea generation, idea
screening, concept development and testing, marketing strategy development, business analysis,
product development, test marketing, and commercialization. As a feasibility study, this researchcovers the three initial phases of a new product development process (NPD), the purpose of which is
to find out whether Haitian farmers are willing to accept such innovation. Continuous anddiscontinuous innovations are inevitable for all business activities regarding productivity
improvement and risk management. The NPD process is suitable for continuous and discontinuous
innovation (Corso and Pellegrini, 2007); therefore, we focused on this process to guide ourmethodology.
In the remaining part of this work, after clarifying the content of the package of technologies,
we present a brief review of the "stabilization account," the study's objectives, the methodology used,
the results, the discussions, and the conclusions with some recommendations.
The package of technologies: It was demonstrated by Valcin and Uchiyama (2021) that an adequate
package of technologies has a significant positive impact on productivity in southern Haiti. This
package included plowing service, certified seed, fertilizers, irrigation, and pesticides. These inputs
would be provided to farmers in the contract framework as a loan. Besides, farmers mentioned thatclimate risk may prevent them from reimbursing their credit. This feedback is the basis for testing
the feasibility of including a financial tool, the “stabilization account,” as a risk management
component in the package.
A brief review of "stabilization accounts": Several approaches to agricultural insurance have been
implemented worldwide (Nair, 2010); each country finds a more suitable scheme based on its
situation (Kalfin et al., 2022). However, for countries or regions where catastrophic risks are frequent,risk management tools such as stabilization accounts for farmers are increasingly being considered(ČOP and Njavro, 2020).
According to Diaz-Caneja et al. (2009), "the stabilization accounts is a form of self-insurance.
They consist of individual accounts where farmers put in a certain amount of money every year.
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Which they can withdraw in a year of big losses. Stabilization accounts can be based on yield,
revenue, or other indices." The stabilization accounts will be managed as a mutual fund. It is basedon the accumulation of reserves from participants' contribution (in kind, in our scenario) from which
members will receive support in the event of a loss according to the arrangement between farmers
and the managing company (Meuwissen et al., 2013; Kislingerova and Spika, 2022). Risks such as
drought and hurricanes are examples of systemic and complex for insurers to diversify (Kislingerova
and Spicka, 2022); they are hard to insure. Having a disaster-based stabilization account paid in kindas part of a mutual fund can be an excellent financial tool for managing risk for Haitian farmers. The
stabilization account is the most innovative aspect of this research. Its acceptability by farmers is theultimate purpose of this feasibility study. This alternative has been chosen instead of the traditional
form of agricultural insurance because we assume that Haitian farmers' most significant risks are
insurable.
Conceptual framework: The concept consists of an arrangement between firms and farms, throughwhich farmers will receive a package of technologies on credit from the firm after a subscription fee
is paid in kind. Farmers will repay the firm at harvest time by returning the crop-equivalent value ofthe granted package. According to a prior agreement, the firm will also buy the farmer's harvested
crop. The subscription fee paid in kind will be held as a contribution to the stabilization account ofeach farmer and will vary according to the plot size. We are testing the following concept: anagreement between the firm and the farm. This institutional arrangement will help farmers accesshigh-quality inputs on time and credit. We aim to reduce the inability to meet the crop calendar while
offering specific coverage in case of significant loss. Consequently, it is advantageous for firms toprovide risk management tools to their clients (farmers), as it helps to protect, increase, and sustain
their investment (Maggio and Sitko, 2019).
OBJECTIVESThis study assesses the Acceptability and Feasibility of Integrating Stabilization Accounts in an
Agricultural Package of Technologies among farmers in Southern Haiti. Specifically, the study aims
include: i) discovering the perception of Haitian agricultural experts on introducing a climate-smart
package of technologies to Haitian farmers, ii) identifying the relationship between farms andfarmers’ characteristics and the propensity to adopt new technologies, and iii) evaluating the Haitian
farmer’s willingness to pay (in-kind) for the agricultural new technologies (Disaster-based
Stabilization Account (DSA)) through a contractual agreement.
METHODOLOGY
Two categories of data were used in this research:Primary data: Besides the variables from the primary data that we used to analyze the determinantsof farmers’ openness to innovation, some additional variables necessary for the idea screening andthe concept testing, such as the willingness to accept and to pay, were collected in the summer of2022 in the framework of this study. Two samples were surveyed for this purpose: a sample of 28
Haitian agricultural experts and a sample of 234 farmers. A Google Form was used for data collection
among experts; however, a direct paper survey was distributed to farmers. Experts were asked to givethe concept a score (from 0-100%) based on the following criteria: “How much will the product meeta need? Will it offer superior in-use value? Can it be distinctively advertised? Will the product deliver
the expected sales volume, sales growth, and profit?” For each question, 70% was considered as thetarget to conclude validity. We adopted this methodology from Toubia and Florès (2007). This
information was explicitly used to conduct a qualitative analysis. In addition, key informants wereinterviewed for a comprehensive understanding of the sector. This sub sample includes the Directorof the Organization for the Rehabilitation of the Environment (ORE), the Dean of the Faculty ofAgricultural Sciences and Environment at Quisqueya University (FSAE/UniQ), the Director of theAgro-socio-economy of FSAE/UniQ, a former employee of the Financing and Agricultural InsuranceⒸ ISERD

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System in Haiti (SYFAAH), an employee of the Industrial Development Fund (FDI), and an
employee of Haitian Foundation for Sustainable Agricultural Development (FONHDAD). They wereasked to give their expert opinion, on the importance, the advantages, inconveniences, and challengesof the proposed package concept. Primary data were collected from farmers in the Sud district ofHaiti; however, secondary data also cover an additional district called Artibonite (Fig. 1).

Fig. 1 Location of the study area
Secondary data: In the fall of 2022 and winter of 2021, a survey targeting more than 1,200 farmers
was conducted by Quisqueya University and the Ministry of Agriculture in Haiti, funded by theInteramerican Development Bank. Until our study, this database remained unused for similar
research purposes. Variables related to our objectives found in this database were used in this paper.
This information enabled us to make a quantitative analysis, including modeling and previsions.
For data analysis, multinomial logistic regression and the Cochran-Mantel-Haenszel test wereused through the Statistical Package for Social Science (SPSS). The techniques were used
respectively to analyze factors affecting openness to innovation and the association between financial
inclusion and openness to innovation. The New product development process is used as the guideline
for this research project. Three steps are taken in this paper: ideation, idea screening, and concept
testing.
RESULTS AND DISCUSSIONSThe General Situation of Haitian Farmers
The interview with key informants revealed that farmers usually begin in risky agricultural
timeframes because of their inability to start their cultural operations on time. The crop calendar isso tight in Haiti that even slight delays may lead the farmer into the beginning of spring's heavy rainor the cyclonic season (June to November). Our study defines uncertainty as a lack of knowledge ordata on future events based on Machiels' findings (Machiels, 2023). In Haitian farming, risks linked
to natural disasters seem more correlated with financial incapability to observe an ideal crop calendar
than general uncertainty.
Expert Perception of the Firm-Farm Concept
For the idea screening, each expert surveyed was an agronomist. Among the 28 experts, 16participants had 10 to 38 years of experience, and 9 participants had experience working in financial
institutions. They were asked to score the firm-farm concept (from 0 to 100%) based on the questions
from Table 1.

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Table 1 Screening result of the concept according to expert point of view
Score
Level
100%
92%
59%
60%
86%

Label / Indicator
Will the product meet a need?
Does the product offer superior in-use value?
Can the product be distinctively advertised?
Does the company have the necessary know-how and capital?
Will the product deliver the expected sales volume, sales growth, and profit?

Validity
High
High
Fair
Fair
High

Note: Number of respondents=28, Source: Survey by authors, 2022

When questioned about the possibility of success, in terms of acceptability, adoption, and profit,
their answers were as illustrated in Fig. 2.

7%
25%

Possibility of wide acceptation andprofit7%

Certitude of wide acceptation andprofit61%

Fairly accepted but not profitable
Possibility wide acceptation but notprofitable

Fig. 2 Experts’ points of view on the prospective success of the concept
Results shown in Table 1 and Fig. 2 suggest that experts highly score the concept's potential
ability to meet a need, its superior in-use value, and its expected financial success. However, it will
require more effort to build awareness and may require additional know-how and capital. These
efforts do not limit the concept quality as innovation is always costly, risky, and requires knowledge
capital (Laperche, 2013; Zambon and Monciardini, 2015).
Farms and Farmers' Characteristics and the Propensity to Adopt New Technologies
In this section, we highlight a key finding suggesting that aversion to risk linked to natural hazards
may boost the propensity to adopt new technologies to mitigate those risks. This could be considereda sort of risk preference influenced by loss aversion (Filiz et al., 2020). Many factors have alreadybeen identified for their impact on technology adoption. Some of them have been confirmed in the
Haitian farmer's framework; some factors have been expressed differently. Farmers from this area
are not significantly different in education level since most have only completed primary education.
Table 2 shows how the relevant factors affect their openness to innovation.
Table 2 Factors affecting the openness of farmers to technology for risk management
GenderAge
Revenue
Farm Size
Household Size
Org. Membership
Constant

B
-.463
-.004
.000
.985
.113
-.365
.322

S.E.
.172
.005
.000
.203
.041
.135
.330

Wald
7.200
.714
16.82423.491
7.441
7.300
.954

df
1
1
1
1
1
1
1

Sig.
.007
.398
.000
.000
.006
.007
.329

Exp(B)
.630
.996
1.000
2.677
1.119
.694
1.380

Note: Dependent variable: Openness to innovation; Number of respondents: 1078. The model was well fitted as shown by
the Omnibus Tests of Coefficients (Chi-square: 83.8; Sig.: .000) and the Hosmer and Lemeshow Test (Chi-square:
10.3; Sig.: 0.244). However, the Nagelkerke R Square was relatively low (0.10), therefore including more variablesin the model might increase the explanatory power.
Source: Computed from FSAE-UniQ data by the authorsⒸ ISERD

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Female Haitian farmers were more likely to accept new technologies, which is opposite to thefindings of Doss (2001) who found that female African farmers were less likely than their malecounterparts to adopt improved crop varieties and management systems. However, this result is inline with what was observed in Zimbabwe (Masuka et al., 2016), as well as a New Zealand case
(Brown and Roper, 2017).
Farmers with a higher revenue were more capable of affording new technologies; by acquiring
good quality or lasting tools, they enact changes less frequently than farmers with lower revenue. In
addition, low-revenue farmers have much to lose; adoption decisions appear rational. Therefore, if
there is no direct benefit, farmers will not adopt (Suri, 2011).
Household size could affect the decision to positively adopt new technology. Suppose the newtechnology will require additional routine maintenance and operation. In that case, it may be more
adapted to a bigger family (Uhunamure et al., 2019). However, if it is seen as an opportunity todiminish labor, it will be more attractive to smaller households.
Like the Haitian case, most studies agreed on a farm size’s positive effect on technology
adoption because, in many cases, technologies help farmers reduce the cost and difficulties of farmoperations (Gargiulo et al., 2018). However, Mwangi and Kariuki (2015) suggest a mixed effect.
Ultimately, having affiliation with some local organization or being part of a farmers' association
is known to influence the adoption of new technology (Ruzzante et al., 2021). However, in somecases, the effect may differ based on the type of technology (Buyinza and Wambede, 2008; Chuchird
et al., 2017); these groups will also influence individual decisions.
Association Between Openness to Innovation and Financial Inclusion“Having a bank account” is critical evidence of “financial inclusion” (Karlan and Morduch, 2009;
Brune et al., 2016; Grohmann et al., 2018). The Cochran-Mantel-Haenszel was used to analyze theassociation between financial inclusion and openness to innovation using a sample of 1,078 Haitian
farmers included in the original survey data. They were asked whether they had owned a savings
account within the past five years. Table 3 shows a significant association between “financial
inclusion” and the “Openness to Innovation.” According to the estimated Odds ratio, a farmer with“financial inclusion” tends to be 1.5 times more disposed to accept and use new technologies. A
farmer who is open to new technology and has financial inclusion will be a potential target for thedeveloping concept.
Table 3 Crosstabulation and tests of conditional independence

Oppenness
Total

No
Yes

Financialinclusion
No
Yes
367
159
333
219

Total
526
552

Cochran-MantelHaenszel test

702

1,078

Est. odds ratio

378

Chisquareddf

Asymp.
sig (2-sided)

10.55410.134

1
1

.001
.001

1.518

.001

Source: Computed from FSAE-UniQ data by the authors

With the integration of the financial component (disaster-based stabilization account) for risk
management, farmers must have a bank account to join the contract. Therefore, it is a good antecedent
for farmers to have financial inclusion history because it enhances access to financial services suchas credit and insurance (Peprah et al., 2020).
Farmer’s Willingness to Pay (In-kind) for Agricultural New Technologies (WSA)
At the beginning of all interviews, the concept features were explained in detail to farmers, as
described in the conceptual framework section. Then, we asked questions concerning theirunderstanding of the concept, willingness to accept the agreement, and willingness to pay in-kind as
an account deposit. Respondents were primarily male (91%) and between 22 and 82 years old,
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averaging 47.5 years old. They had anywhere from 0 to 17 years of schooling, with 32% having atleast seven years (up to the secondary level). As a result, seventy percent of farmers express apropensity to accept the arrangement by paying from approximately 6% to more than 12 % of theharvested crop as a stabilization account, as shown in Fig. 3. This percentage refers to thesubscription fee which will be held in the stabilization account. This contribution will return to thefarmer in the event of a qualifying natural hazard as stipulated in the agreement.

Fig. 3 The percentage that farmers are ready to pay from their harvest
CONCLUSIONS AND RECOMMENDATIONSThis study proposes a systemic approach by including risk management tools and techniques in a
package of technologies. The latter includes appropriate inputs, including credit and a stabilizationaccount as financial tools for risk management. The stabilization accounts were chosen instead of the
traditional form of agricultural insurance because major risks affecting Haitian farming are complex
to insure due to their systemic and catastrophic character. Interviews and surveys were conducted toassess the arrangement's acceptability and feasibility. The main results were as follows:i. Haitian agricultural experts and farmers favor the concept and predict its financial success.
ii. Farmers expressed a high propensity to pay up to 12% of their harvested crop as a stabilizationaccount. Therefore, the concept successfully passes the screening and testing phases of its
development. The results convey some evidence of its acceptability and feasibility in Haitian
farming.
iii. "Financial inclusion" plays an essential role in financial technology adoption; farmers withfinancial inclusion will be two times more open to innovative technologies. Thus, prioritizing
farmers with bank accounts may guarantee higher success rates during concept implementation.
iv. In Haitian farming, risks linked to natural disasters are more related to financial incapacity thanuncertainty. Therefore, if farmers could adhere to the crop calendar, that would be a significant
step towards risk mitigation in Haitian farming.
As per the above results, the following recommendations were given:
1. The implementation of this concept should be started with a limited group of farmers, preferably
young, literate, and financially included. This small group will be used as a model for potential
scaling.
2. A clear explanation or a demonstration of any concept to the potential buyers (farmers in theframework of this study) will enhance its probability of acceptance and adoption. That will bevery important in promoting new technologies to Haitian farmers.
3. A paid-in-kind stabilization account worth 7% to 10% of the farmer's harvested crop or about
180 kg of maize equivalent will be affordable for most farmers.
Limitations of the Study
The study could not provide more information regarding the viability aspect of the concept feasibility.
A subsequent study should focus on developing a cross-sector business model demonstrating theconcept's viability.

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ACKNOWLEDGMENTS
This research has been funded by followings.
The Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan, Tokyo
Nodai Research Institute (TNRI), The Research and Development Fund (FRD) of the Haitian Central
Bank (BRH), (BRH) PITAG/MARNDR through CHIBAS/UniQ/Partners.
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Research article
erdChanges in Soil Physical Properties Owing to Soil Reduction
Treated with Electrokinetic Treatment
TAKAHIKO NAKAMURA
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, Japan

NARONG TOUCH*
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, Japan
Email: nt207118@nodai.ac.jp
Received 17 September 2023

Accepted 26 December 2023(*Corresponding Author)

Abstract Increasing soil water content due to soil reduction is generally confirmed in rice
paddy soils after flood irrigation or in littoral sediments with high organic matter content.
This could be caused by soil aggregates during the reduction process through biological andelectrostatic phenomena. In the literature, changes in liquid and plastic limits owing to soil
reduction treated with electrokinetic treatment (ET) have been reported; however, there wasno report relating to changes in other soil physical properties, such as grain size distribution(GSD) and water-holding capacity (WHC). Thus, this study aimed to examine changes in soil
physical properties caused by soil reduction treated with ET. Changes in GSD, hydraulicconductivity, and WHC were examined in laboratory experiments to understand soil
aggregates due to soil reduction. During ET application, a decrease in electrical conductivity
was observed, indicating the cohesion of ions (soil aggregate). This resulted in increases in
the percentage of particles ranging from 0.075-0.212 mm, hydraulic conductivity, and WHC.
However, particle dispersion occurred when the electrical current was high (10 mA), resulting
in a significant decrease in hydraulic conductivity and WHC. Therefore, it can be said thatsoil aggregates can develop electrostatically. Thus, ET can be used for developing soil
aggregate.
Keywords soil reduction, electrokinetic treatment, soil aggregate, grain size distribution,
hydraulic conductivity, water-holding capacity

INTRODUCTION
In rice paddy fields, oxygen diffusion from air into the soil is suppressed after flood irrigation on thesoil surface, causing soil reduction. Touch et al. (2021) experimentally examined soil redox potential(ORP) after making a water layer on paddy soils. They observed that soil reduction occurred in 45
days. Generally, the water content of paddy soil increases temporally during soil reduction. Ozaki etal. (2013) observed an increase in water content from 30% to 50% after flooding irrigation in winter.
Moreover, an increase in water content due to soil reduction occurs in littoral sediments. Fukui et al.
(2012) reported that the water content of sediments with high organic matter content is higher thanthat of sediments with low organic matter content. Hattori et al. (2018) reported an increase in watercontent with an increase in total organic carbon. Therefore, it can be said that organic matter
influences the water content of sediments. However, the mechanisms behind the increasing watercontent owing to soil reduction or organic matter remain unclear.
It is believed that an increase in water content owing to soil reduction occurs through soil
aggregate by biochemical and electrostatic mechanisms. Generally, the released substances during
the decomposition of organic matter can act as binders in soil aggregate. With the proceeding oforganic matter decomposition, and fungal hyphae development, water-stable aggregates increased
and then decreased (Limura and Egawa, 1956). Many researchers have since focused on thecorrelation between soil organic matter and soil aggregate (Okolo et al., 2020; Mustafa et al., 2020).

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Soil aggregate may also occur through electrostatic bonding. Liaki et al. (2010) investigatedphysicochemical effects on clay after ET application. They observed changes in water content owing
to ET application and pointed out that ET application varied the zeta potential of clay particles,
causing changes in share strength, liquid limit, and plastic limit of clayey soils. However, the effectsof ET on other soil physical properties remain unknown.
OBJECTIVE
This study aims to examine changes in soil physical properties owing to soil reduction treated with
ET. This can be done by inducing reduction reactions in upland soils with a constant flow of electricalcurrent using a potentiostat (current fixation). Specifically, changes in grain size distribution (GSD),
hydraulic conductivity, and water-holding capacity (WHC) are examined. In addition, changes in ion
concentration present in soil pore water after the generation of electrical current are determined.
METHODOLOGY
Experimental Procedures and Operations
Figure 1 shows the experimental device, comprising a cylindrical bottle with an inner diameter anda height of 120 mm and 150 mm, respectively. First, the upland soil collected from the farmland wasplaced in the bottle until it reached a height of 20 mm from the bottom, and an electrode (anode) was
placed on the soil layer. 30 mm of the soil layer was then placed on the electrode (Fig. 1a). Finally,
tap water was poured over the soil layer. The bottle was then placed in a container (360 mm in width,
510 mm in length, and 300 mm in height) filled with tap water (Fig. 1b). In the container, an electrode(cathode) was submerged close to the water surface.
The electrode material was carbon cloth (News Company, PL200-E), which was heated at
500°C for 1 h before using it, as Nagatsu et al. (2014) suggested. The heated carbon cloth with awidth of 10 cm and height of 10 cm was separated into fibers to form a brush-type electrode (Fig.
1c) used in the experiments.
Potentiostat

(b)

RE

WE

CE

120 mm

100 mmTap water

Upland soil

Electrons

300 mm(a)

50 mm

Cathode20 mm

Tap water
510 mmCarbon fiber electrode(c)Fig. 1 Experimental devices and operations
To generate an electrical current, the anode, cathode, and reference electrode (Toyo Corp., WRE-7A) were connected to a potentiostat (Hokuto, HA-151B) using the circuit shown in Fig. 1b. A
potentiostat was used to maintain a constant electrical current of 4, 6, 8, and 10 mA. The currentⒸ ISERD

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generation lasted for 7 days. As the treatment duration was less than 7 days, the effects of organic
matter decomposition on soil aggregate are thought to be less significant.
Analyses
Soil sampling was conducted at the end of the experiments. Surface soil of 1 cm was removed, andthe residue was centrifuged at 6000 rpm for 5 min (As One, CN-2060) to extract pore water. The
electrical conductivity (EC) and calcium ion (Ca2+) of extracted pore water were measured using an
EC meter (Horiba, D-74) and a Ca2+ meter (Horiba, Ca-11), respectively.
Grain size distribution (GSD), hydraulic conductivity, and water holding capacity (WHC) were
measured to examine changes in soil physical properties owing to soil reduction. GSD, hydraulicconductivity, and WHC were measured using the wet sieving method, the falling head permeability
test, and the centrifugation method, respectively. In the falling head permeability test, the soil sample
was placed in a 100 cm3-sampler can with a soil surface area of approximately 20 cm 2 and a soilheight of 0.4-1.2 cm. In the centrifugation method, the soil sample was centrifuged at 500 rpm (pF =2) and 3900 rpm (pF = 3.8).
RESULTS AND DISCUSSION
Changes in Ion Conditions in Soil Pore Water Owing to Soil Reduction
Figure 2 shows changes in EC and Ca2+ concentration in soil pore water at different electrical currents.
EC is an index that indicates the total ion concentration. Generally, changes in ion concentration bycohesion or dispersion cause a variation in EC. For example, from Fig. 2a, EC decreased from 120
mS/m to 28 mS/m with an increase in electrical current from 0 mA to 6 mA and increased from 28
mS/m to 68 mS/m with an increase in electrical current from 6 mA to 10 mA.

(b) Ca2+ concentration(a) Electrical conductivity (EC)

Fig. 2 Electrical conductivity and Ca2+ concentration at different electrical currents
In other words, decreases in ion concentration occurred when the electrical current increased
until 6 mA, and the release of ions occurred when the current was higher than 6 mA. This can be
confirmed in Fig. 2b, wherein Ca2+ concentration decreased from 150 mg/L to 31 mg/L when theelectrical current increased from 0 mA to 6 mA. However, when the electrical current was higher
than 6 mA, Ca2+ concentration increased from 31 mg/L to 145 mg/L. These results suggest that ion
cohesion occurs in soils with an increase in electrical current from 0 mA to 6 mA. Interestingly, ion
dispersion occurs in soils with an increase in electrical current from 6 mA to 10 mA. Furthermore, it
was observed that ion cohesion or dispersion strongly correlates with electrical current, and thecorrelation coefficient was higher than 0.95 (Fig. 2). It is thought that this cohesion or dispersion
induces soil aggregates, resulting in changes in soil physical properties.
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Based on the results of the sieve analysis, the residual rates on the sieve range of 0.075-0.212 mm,
0.212-0.850 mm, and 0.850-2 mm were determined, and Fig. 3 shows their relationship with theelectrical current. In Fig. 3, the correlation coefficients were 0.22 and 0.41 for the particle size rangeof 0.85-2 mm and 0.212-0.850 mm, respectively. This suggests that soil reduction has less effect onparticles larger than 0.212 mm.
Interestingly, the residual rate of the particle size range of 0.075-0.212 mm increased with anincrease in electrical current, with a correlation coefficient of 0.61. Thus, it can be said that soil
reduction causes the cohesion of soil particles less than 0.075 mm, contributing to the increase inresidual rate. Liaki et al. (2010) and Wang et al. (2022) reported that generating electrical current in
the soil causes changes in the zeta potential of soil particles, leading to cohesion or dispersion of theparticles responding to an increase or decrease in zeta potential.
35

R = 0.22

Residual rate (%)

30
25

R = 0.41

0.85<D<2mm

200.212<D<0.85mm

15

0.075<D<0.212mm

10

R = 0.61

5
0
0

2

4
6
8
Electrical current (mA)

10

Fig. 3 Particle cohesion due to soil reduction
Changes in Hydraulic Conductivity Owing to Soil Reduction
Figure 4 shows the hydraulic conductivity at different electrical currents and its relationship with EC.
The hydraulic conductivity increased from 0.29 to 0.45 mm/day when the current increased from 0
mA to 8 mA (Fig. 4a). Based on Fig. 2, the cohesion of particles occurred in the electrical current
range of 0-6 mA. This cohesion induced soil aggregates, leading to an increase in hydraulicconductivity.

(a) Hydraulic conductivity(b) Correlation of hydraulic conductivity with EC

Fig. 4 Hydraulic conductivity at different electrical currents and its relationship with EC
Furthermore, the dispersion of particles occurred in the electrical current range of 6-10 mA.
However, there was no decrease in hydraulic conductivity when the current was 8 mA, while there
was a decrease when the current was 10 mA (Fig. 4a). This indicates that cohesion and dispersion ofparticles influence the hydraulic conductivity of soils, and a high rate of dispersion causes largeⒸ ISERD

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decreases in hydraulic conductivity. It was also observed that hydraulic conductivity had a strong
correlation (R = −0.84) with EC, and a larger hydraulic conductivity with a lower EC (cohesion state).
Changes in Water-Holding Capacity Owing to Soil Reduction
Generally, soil aggregates influence not only hydraulic conductivity but also the WHC of soils.
Figure 5 shows the water content at pF 2 and 3.8. A similar trend was observed with the variation in
hydraulic conductivity. The water content at pF 2 increased with an increase in electrical current
from 0 mA to 8 mA, and a strong correlation (R = 0.83) was observed (Fig. 5a). An increase in thewater content at pF 3.8 was also observed (Fig. 5b); however, the correlation was less significant (R= 0.54). In addition, a large decrease in water content was observed when the current was 10 mA(Fig. 5a). From Fig. 5a, it can also be said that the cohesion of soil particles induces soil aggregates,
which increases WHC; however, high rates of particle dispersion decrease WHC.
Commonly, there are three types of water in soil: adhesion (the water on the surface of soil
particles), cohesion (the water attached to adhesion water), and gravitational (the water that flows
with gravitational force). The water content at pF larger than 1.8 refers to the amounts of adhesion
and cohesion water. From these results, because a strong correlation between the electrical current
and the water content at pF 2 was observed, it is thought that only cohesion water is influenced byelectrical current generation (soil reduction).
33

Water content at pF = 3.8 (%)

Water content at pF = 2 (%)

53

52
51

R = 0.83
50

49

32

31

R = 0.54

30
29
28

48

0

2

4
6
Electrical current (mA)810(a) Water content at pF 2

0

2

4
6
Electrical current (mA)810(b) Water content at pF 3.8

Fig. 5 Hydraulic conductivity at different electrical currents and its relationship with EC
CONCLUSIONS
Laboratory experiments were conducted to examine the electrostatic effects of soil reduction on thephysical properties of soil. Specifically, we examined changes in GSD, hydraulic conductivity, and
WHC after introducing an electrical current into the soil (i.e., soil reduction caused by ET). Whenthe electrical current was increased from 0 mA to 10 mA, EC and Ca2+ concentrations decreased
when the current was less than 6 mA and increased when the current was higher than 6 mA. Thissuggests that the cohesion of particles occurs in soils due to soil reduction; however, a high current(10 mA) can lead to the dispersion of soil particles. From GSD, the residual rate of the particle size
range of 0.075-0.212 mm increased when the current increased, indicating that soil reduction causes
soil aggregates. Furthermore, the hydraulic conductivity had a strong correlation (R = −0.84) with
EC, indicating that cohesion or dispersion influences the hydraulic conductivity of soils. This study
also confirmed that hydraulic conductivity and WHC increased when the cohesion of soil particles
occurred. Therefore, it can be concluded that soil aggregates can form electrostatically during soil
reduction. In other words, ET can be used to develop soil aggregates and change the physicalproperties of soils.
ACKNOWLEDGMENTSⒸ ISERD

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The authors gratefully acknowledge partial funding from the Tokyo University of Agriculture:
FY2021 Grant-in-Aid for Sustainable Agriculture Research Projects. The authors would like to thankthe students of the Rural Environmental Engineering Laboratory, Tokyo University of Agriculture,
for their efforts in collecting data.
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Fukui, S., Lee, I.C., Saito, T. and Hibino, T. 2012. Design of a method for the restoration of sea bottom
environment in Yong-Won Bay (Korea). Journal of Japan Society of Civil Engineers, Ser. B2 (Coastal
Engineering), 68 (2), I_1446-I_1450, Retrieved from DOI https://doi.org/10.2208/kaigan.68.I_1446
Hattori, K., Nakamura, Y., Inoue, T., Higa, H., Naito, R. and Okada, T. 2018. Effects of organic content onwater content of bottom sediment in eutrophic coastal and brackish water region. Journal of Japan Societyof Civil Engineers, Ser. G (Environmental Research), 74(7), III_43-III_51, Retrieved from DOI https://doi.
org/10.2208/jscejer.74.III_43
Liaki, C., Rogers, C.D.F. and Boardman, D.I. 2010. Physico-chemical effects on clay due to electromigration
using stainless steel electrodes. Journal of Applied Electrochemistry, 40, 1225-1237, Retrieved from DOI
https://doi.org/10.1007/s10800-010-0096-8
Limura, K. and Egawa, T. 1956. A study on decomposition of organic matter and aggregate formation. Soil
Science and Plant Nutrition, 2, 83-88, Retrieved from DOI https://doi.org/10.1080/00380768.1956.
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Mustafa, A., Minggang, X., Shah, S.A.A., Abrar, M.M., Nan, S., Baoren, W., Zejiang, C., Saeed, Q., Naveed,M., Mehmood, K. and Núnez-Delgado, A. 2020. Soil aggregation and soil aggregate stability regulateorganic carbon and nitrogen storage in the red soil of southern China. Journal of Environmental
Management, 270, 110894, Retrieved from DOI https://doi.org/10.1016/j.jenvman.2020.110894
Nagatsu, Y., Tachiuchi, K., Touch, N. and Hibino, T. 2014. Factors for improving the performance of sedimentmicrobial fuel cell, Journal of Japan Society of Civil Engineers Ser B2 (Coastal Engineering), 70 (2), 10661070, Retrieved from DOI https://doi.org/10.2208/kaigan.70.I_1066 (in Japanese)
Okolo, C.C., Gebresamuel, G., Zenebe, A., Haile, M. and Eze, P.N. 2020. Accumulation of organic carbon invarious soil aggregate sizes under different land use systems in a semi-arid environment. Agriculture,
Ecosystems and Environment, 297, 106924, Retrieved from DOI https://doi.org/10.1016/j.agee.2020.
106924
Ozaki, H., Nakamura, K. and Kawashima, S. 2013. The effects of flooding during the non-irrigation period on
soil nitrogen transformations in a paddy plot on an alluvial fan. Journal of the Science of Soil and Manure,Japan, 84 (6), 447-454, Retrieved from DOI https://doi.org/10.20710/dojo.84.6447
Touch, N. and Nakamura, T. 2021. Potential measurement as a method for monitoring the soil chemical
environment. International Journal of Environmental and Rural Development, 12 (1), 66-71, Retrieved
from URL https://iserd.net/ijerd121/12-1-11.pdf
Wang, J, Wang, T., Li, Z., Fu, B., Zhai, Y., Wang, W., Zhai, M., Chovelon, J.M., Gong, Y. and Wang, H. 2022.
Enhancement of sludge dewaterability by electrolysis coupled with peroxymonosulfate oxidation process:
Performance, mechanisms, and implications. Chemosphere, 307, 13685, Retrieved from DOI https://doi.
org/10.1016/j.chemosphere.2022.135865Ⓒ ISERD

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Research article
erd

On the Number of Seedlings for the System of RiceIntensification: Experiment Results from Lombok Island,
Indonesia
YAMAJI EIJI
The University of Tokyo / Research Center, Institute of Environmental Rehabilitation andConservation, Tokyo, Japan
Email: yamaji@edu.k.u-tokyo.ac.jp

SATO SHUICHI
Consulting Engineering, Hayama, Japan
Received 4 October 2023

Accepted 28 December 2023(*Corresponding Author)

Abstract The System of Rice Intensification (SRI) is an environmentally friendly andsustainable rice farming practice developed in the 1980s in Madagascar. It has spread to many
countries in the early years of the 21st century, with Indonesia becoming one of the early
adopters of SRI practices. The authors conducted experiments on SRI plots on Lombok Island,
Indonesia, measuring water consumption, rice growth, GHG emissions and other variables.
The focus of the current study was on accessing the appropriate number of seedlings under
different irrigation regimes. This experiment prepared 30 small plots and set 2 types ofirrigation: intermittent and continuous; 3 types of seedlings by nursery age: 0, 7, and 21 days;and 3 types of seedling groups: 1, 2, and 4 seedlings. At the harvest stage, the number oftillers, number of panicles, grain weight, filled grain weight, and root weight of 5 samples
were measured from each plot. In terms of the irrigation method, intermittent irrigationachieved a higher yield than continuous irrigation. 2 seedlings achieved the best yield underintermittent irrigation, regardless of nursery age, while 4 seedlings achieved the best yieldunder continuous irrigation for both 7 day- and 21 day-old seedling types. In Indonesia, SRI
promoters including local government and NPO staff recommend farmers to transplant one
7-day seedling. However, the experiment conducted in this study demonstrated that two dayold seedlings achieved better results. The two-seedlings method costs twice as much as theone-seedling method, but is only about half the cost of the traditional method. Moreover, thetwo-seedlings method gives farmers peace of mind at the transplant stage. The two-seedling
method can thus be considered more reasonable than the one-seedling method.
Keywords system of rice intensification, number of seedlings, intermittent irrigation, yield

INTRODUCTION
The System of Rice Intensification (SRI) is an environmentally friendly and sustainable rice farming
practice developed in the 1980s in Madagascar. This practice can reduce resources, such as irrigationwater, chemical fertilizer, number of seedlings by keeping and sometimes increasing the yield of rice.
It has spread to many countries in the early years of the 21st century. Indonesia is one of the early
diffused countries and many farmers adopted this practice (Sato, 2006; Sato, 2007).
In Indonesia, SRI promoters of local government and NPO recommend farmers to transplant
One 7-day seedlings. However, farmers are very afraid of less growth, and they often transplant two
seedlings although they were taught to transplant one.
Previous research has shown that the adoption of intermittent irrigation reduced water use but
not yield (Shimizu et al., 2007). However, a deeper consideration of the appropriate number of
seedlings was needed. So, the authors made an SRI Experimental station at Puyung village, Lombok
Island of Indonesia, and executed an experiment to identify an appropriate number of seedlings,
measuring water consumption, rice growth, GHG emissions and other variables.
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OBJECTIVE
The authors set the objective to identify which number of seedlings would be appropriate inconventional rice growing and in the System of Rice Intensification.
METHODOLOGY
The experiment involved setting up 30 small plotsof size 5.0 m x 2.5 m (Fig. 1) and applying a
number of different experimental treatments,
including 2 types of irrigation: intermittent andcontinuous; 3 types of seedlings by nursery age: 0,7, and 21 days, and 3 types of seedling groups: 1,
2, and 4 seedlings. The most popular rice variety inthe area around the experiment station, Ciherang
rice, was planted. The spacing of each seedling was
25 cm by 25 cm and irrigated each plot as anirrigation schedule (Table 1). At the growing stage,
the number of tillers and height of 5 samples fromeach plot are precisely measured every week. At
the harvest time, the number of panicles, grain
Fig. 1 View of experimental plots
weight, filled grain weight, and root weight of 5
samples from each plot. Also, grain weight with moisture content is measured in each plot. Grain
weight was converted to yield (t/ha) at 14% moisture.
Table 1 Setup of the experiment
Treatment
Directseeding
Nursery
7 days

D1 D2 D4 S1
〇

〇

S2

S4

〇

〇

〇
〇

Number of
Seedlings〇〇

Intermittent

〇

1

〇

〇

〇

〇

〇

〇

〇
〇

〇

〇

〇
〇

〇

〇

〇

〇

〇

〇

〇

〇

〇
〇

〇
〇

〇

〇

〇
〇

〇

〇

〇

〇
〇

〇

〇
〇

2

2

〇

〇

〇

Continuous

4
Repetition〇〇

21 days
Irrigation

T1 T2 T4 N1 N2 N4 C1 C2 C4

〇

〇
〇

〇

〇

〇

〇
〇

〇

〇

Table 2 Variety characteristics of ChiherangGrowing period
Average 1000-grain weight
Average number of tillers
Average plant height
Average yield
Yield potential

116 ~ 125 days
28 g
14 ~ 17 tillers
107 ~ 115 cm
6.0 t / ha
8.5 t / haNote: Data from Suprihartno et al., 2006

Seedlings for all plots were transplanted on May 17th, 2007. Rice variety is a local high-yield
variety called Chiherang, which is widely cultivated on Lombok Island. Characteristics of ChiherangⒸ ISERD

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are summarized in Table 2.
RESULTS AND DISCUSSION
Weather and Irrigation
In this experiment, the sowing and harvesting dates were aligned. Sowing took place on 5 December
2007. Direct sowing was fielded on that day, 7-day seedlings were transplanted to the main field after
7 days in the nursery, as were 21-day seedlings. The harvest date was 8 April 2008 in every plot.
It was the rainy season and there was occasional precipitation. The total precipitation for the 118
days until harvest was 1,286 mm. During the experiment, the average temperature was 30.1 degrees,
and the relative humidity was 60.2%.
Intermittent irrigation is applied until the end of the vegetative growth stage, about 60 days after
transplanted, and after this water depth is also kept shallow (1-2 cm water depth) until stopping water
to harvest, while conventional irrigation uses continuous flooding with deep water (4-5 cm water
depth) (Fig. 2).
Treatments are two irrigation patterns, three seedlings, and three transplanting methods, a totalof 15 treatments l duplicated two times, are prepared. A control plot was also prepared.
For all plots, seedling age, spacing, weeding method, fertilizer amount, and variety of rice are
selected to match for local rice cultivation method. Fertilizers are chemical fertilizers standing on theposition SRI technology does not include the application of compost. Weeding for 15 treatment plots
was done 3 or 4 times depending on weed condition.
D : Direct Seeding and Intermittent Irrigation

HARVEST-7 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 115 120

S : 7 Day Seedling Transplant and Intermittent Irrigation-7 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 115 120

N : 7 Day Seedling Transplant and Continuous Irrigation-7 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 115 120

T : 21 Day Seedling Transplant and Intermittent Irrigation-7 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 115 120

C : 21 Day Seedling Transplant and Continuous Irrigation-7 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 115 120

Ponding Depth : Intermittent Irrigation just wet, maximum 2cm depth
Continuous Irrigation keep 4 cm depth

Fig. 2 Irrigation schedule (2007/2008 rainy season)
GrowthRice seedlings were planted in each plot at 200 rice plant hills. Measurements of height and stem
number were taken weekly on five plant hills in the middle of the plot. After the earing stage, thenumber of tillers is also counted. Changes in the number of tillers are shown in Fig. 3. One seedlingset (D1, S1, N1, T1, C1) starts with one seedling, two seedlings set (D2, S2, N2, T2, C2) with twoseedlings and four seedlings set (D4, S4, N4, T4, C4) with four seedlings. The seedlings then growand divide by earing. The number of tillers declines after the maximum tillering period. The numberof tillers at harvest time was almost all between 15 and 20.
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Fig. 3 Number of tillers
The plant height is shown in Fig. 4. Although plant height was low in the nursery due to dense
cover, growth was vigorous after transplanting, with almost all reaching 130-140 cm at harvest time.

Fig. 4 Height of rice plant
Yield and Components
At the harvest, the number of tillers, number of panicles, grain weight, filled grain weight, and root
weight of 5 plant hills from each plot are measured. One plant hill produces 43-73 grams of grains,meaning 1500-2600 grains. To identify 1000 grains' weight, sampled grains are well mixed, and 2030% of grains are used for counting. By this method, 1000 grains weight from each plot wascalculated.
From this measurement, the following was revealed. The intermitted irrigation group (D, S, T)
had a few fewer panicles but greater grain weight, which was attributed to the fact that each grain
was larger and heavier. It was revealed that the roots of the intermitted irrigation group were very
heavy. Especially, the root weight of treatment D4 were several times heavier than in continuous
irrigation. This suggests that many roots grow deeper under intermittent irrigation, which is presumed
to be the reason for the higher growth. The comparison between seedling numbers did not show aclear difference.
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Table 3 Details of rice plant at the harvest time
Number of Number ofTreatment
tillers
paniclesSample grains

Grainweight(gr)

Filledgrain
weight(gr)

Rootweight(gr)

Weight(gr)

Number

Weight ofMoisture
1000 grains
(%)
(gr)
23.4
29.2
22.9
30.5
22.8
30.3
23.4
25.4
23.3
33.1
23.3
28.1
23.6
25.3
23.5
27.6
23.1
28.6
22.7
24.2
23.5
24.2
21.5
24.0
23.2
24.4
22.5
23.9
21.7
23.4

D1
D2
D4
S1
S2
S4
T1
T2
T4
N1
N2
N4
C1
C2
C4

16.3
17.9
20.6
15.7
12.9
15.0
16.8
19.6
18.5
14.7
18.0
18.9
14.8
16.4
18.0

15.2
17.4
19.4
14.8
12.7
14.7
15.8
15.9
17.4
14.6
17.3
17.6
14.3
16.2
17.4

63.1
64.5
73.5
50.9
53.8
50.8
53.8
52.9
59.2
45.8
50.6
60.4
43.5
49.4
43.7

61.8
62.5
72.3
48.9
52.8
48.2
50.8
51.1
57.4
43.9
48.3
58.8
42.1
47.0
42.0

24.4
20.3
43.0
16.4
19.1
23.2
19.9
23.7
21.0
8.7
10.5
10.7
7.9
10.1
7.4

16.4
15.8
15.9
20.0
19.3
20.5
20.9
18.2
21.4
12.4
10.8
14.3
13.6
16.9
11.4

561.3
517.7
523.9
784.3
583.5
731.5
824.4
658.6
751.0
512.7
445.0
595.7
558.7
705.4
485.4

D, S, T
N, C

17.0
16.8

15.9
16.2

58.1
48.9

56.2
47.0

23.4
9.3

18.7
13.2

659.6
550.5

23.2
22.5

28.7
24.0

1
2
4

15.7
17.0
18.2

14.9
15.9
17.3

51.4
54.2
57.5

49.5
52.3
55.7

15.5
16.8
21.0

16.6
16.2
16.7

648.3
582.0
617.5

23.3
23.1
22.5

25.7
27.9
26.9

Yield
12
(t/ha)
10

Intermittent Irrigation
Direct a

7 days a

21 days aContinuous Irrigation
7 days b

21 days c

8
6
4
2
0
D1 D2 D4 S1 S2 S4 T1 T2 T4 N1 N2 N4 C1 C2 C4Treatment

Fig.5 Yield of each treatment
After measuring five plant hills in detail, the rest 195 plant hills were harvested from the entire
plot. There are 2 plots for each treatment and the average yield is shown in Fig. 5.
From these results, intermittent irrigation produces greater yields than continuous irrigation.
On the number of seedlings, 2 seedlings achieved the best yield in intermittent irrigation and 4
seedlings achieved the best yield in continuous irrigation. Comparison between the five groups showsthat 3 groups of intermittent irrigation achieved best, 7 days seedling in continuous irrigation wassecond-, and 21-days seedling in continuous irrigation was third. All comparisons are statisticallysignificant at the risk of 5%.
CONCLUSION
The results of this experiment showed that in continuous irrigation, two seedlings were better thanⒸ ISERD

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one, and four seedlings were better than two in terms of yield. In traditional farming, the number oftransplanted plants is 3-5. As a result, the correctness of the farmers’ choice of number of seedlingsin traditional farming methods was proved as rational.
In intermittent irrigation, the highest yield was obtained with two seedlings. The two-seedling
method needs two times the cost of seedlings than one seedling method. However, this method costs
about half the cost compared with the traditional method. Moreover, two seedlings method gives
farmers peace of mind or a sense of security during the transplant stage. So, this method is to be said
as more reasonable than one seedling method. We need to execute this experiment in every place
where the SRI method is introduced.
ACKNOWLEDGEMENTS
The authors are deeply grateful for the support of the DISIMP team, Mr. Bagus Santika, and teammembers.
REFERENCES
Sato, S. 2006. An evaluation of the System of Rice Intensification (SRI) in Eastern Indonesia for its potential
to save water while increasing productivity and profitability. International Dialogue on Rice and Water:
Exploring Options for Food Security and Sustainable Environments, IRRI, Los Baños, Philippines, March
7-8, 2006, Retrieved from URL http://sri.ciifad.cornell.edu/countries/indonesia/wwfindosato06.pdf
Sato, S. 2007. 4 Years experience with SRI practice under DISIMP in Eastern Indonesia. Decentralized
Irrigation System Improvement Project in Eastern Region of Indonesia Report.
Sato, S., Yamaji, E. and Kuroda, T. 2011. Strategies and engineering adaptions to disseminate SRI methods inlarge-scale irrigation systems in Eastern Indonesia. Paddy and Water Environment, 9, 79-88, Retrieved from
DOI https://doi.org/10.1007/s10333-010-0242-2
Shimizu, K., Yamaji, E., Sato, S., Budiharto, P.S. and Mizoguchi, M. 2007. Sustainability of system of riceintensification, Benefits of SRI focusing on effects of intermittent irrigation on yield increase and watersaving. Proceedings of the 6th International Conference on Sustainable Rural Development andManagement, 25-37.
Suprihartno, B., Dradjat, A.A., Satoto., Baehaki, S.E., Widiarta, N., Setyono, A., Indrasari, S.D., Lesmana, O.S.,
Sembiring, H. 2006. Deskripsi varietas. Balai Besar Penelitian Tanaman Padi, 15. (in Indonesian)Ⓒ ISERD

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Research article
erdExperiment on Smart Mushroom Cultivation Using an
Environmental Control System
CHANRENG SEY NHIM*
Institute of Technology of Cambodia, Phnom Penh, Cambodia
Email: nhimchanrengsey@gmail.com

NITA CHEK
Institute of Technology of Cambodia, Phnom Penh, Cambodia

CHANTHAN HEL
Research and Innovation Center, Institute of Technology of Cambodia, Phnom Penh, Cambodia

ROTHNA PEC
Research and Innovation Center, Institute of Technology of Cambodia, Phnom Penh, Cambodia
Received 12 September 2023

Accepted 29 December 2023(*Corresponding Author)

Abstract The purpose of this research was to implement smart mushroom cultivation by
applying a sensor network and control algorithm to test the performance of the cultivation
system and yield obtained compared to the conventional method. This research introduced
innovative automated methods to the cultivation of rice-straw mushrooms by utilizing sensor
technology and a controller with a control algorithm. The control algorithm automaticallycontrolled the environment in the mushroom house based on feedback from the sensors tomaintain the environment in an optimum condition for mushroom growth. The experimental
setup included two cycles for growing the rice-straw mushrooms in different control
environments, with three stages per cycle. The first stage was used during the vegetative
phase, the second stage was used to control mushroom growth during the spawn-run phase,
while the last stage was applied during the pinhead and cropping phases. The first cycle ofthe experiment was implemented to verify the circumstances surrounding mushroom growthunder automated control based on three parameters: ambient temperature, relative humidity,
and carbon dioxide (CO2). The experiment utilized one controller with four pieces ofequipment to automate control based on those parameters, which included the ventilation fan,exhaust fan, sprinkler, and motor pump for the cooling pad. During the first cycle, someproblems with the CO2 sensor and the exhaust fan were encountered, so it was necessary to
implement a combination of manual control and automated control in the controllingalgorithm for the second cycle. After completing these experiments, we determined thatutilizing a combination of automatic and manual control, mushroom farmers will be able tosave time, money, and labor while also increasing mushroom yield to satisfy market demand.
Keywords smart mushroom cultivation, rice-straw mushroom, automation control system,
automation technology, environmental control system.

INTRODUCTION
Agriculture is the most critical sector of the Cambodian economy in terms of its contribution to theGross Domestic Product (GDP) because most of the population are farmers (Encyclopedia Britannica,2021). Nowadays, most farmers in Cambodia are trying to evolve farming into modern agricultural
techniques (Fosbenner, 2018). With the help of various technologies, agriculture has gotten moreconvenient while also producing higher yields to meet the demand for supplies. Among otheragriculture sectors, the mushroom industry is still done in a very traditional way, which restricts
production capacity and prevents it from producing enough yield to support both the farmer and theⒸ ISERD

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country's economy. As a result of these factors, it is essential to modernize the facilities to maximize
production through the most efficient use of resources.
The main purpose of this research is to apply automation technology to the production of ricestraw mushrooms by utilizing sensor technology such as a Temperature sensor, Humidity sensor,
CO2 sensor, and a controller with a control algorithm that can control the devices in the mushroom
house and automatically based on feedback from the sensors to maintain the environment in an
optimum condition for mushroom growth. This experiment's goal is to test the performance of thesystem and the yields obtained compared to the conventional method.
MATERIALS AND METHODS
Experimental Setup
The smart mushroom house is constructed with dimensions of 5 meters by 6 meters (5 m × 6 m)
which is equal to 30 square meters (32 m2). The bamboo trees used to build the mushroom trays are
1.6 meters by 4 meters (1.6 m × 4 m) or 6.4 square meters (6.4 m2), and there are ten trays altogether
as shown in Fig. 1. One motor pump, one cooling pad, one exhaust fan, a shrinker system, a control
panel, and monitoring sensors such as a temperature and humidity sensor and a CO2 sensor were all
included in the smart mushroom house's control equipment. The surrounding area is constructed ofplastic. The ventilation fan is placed against the mushroom house wall above the door and faces theexhaust fan on the other side. The motor is mounted over the cooling pad that leans up against thewall, and the shrinker system is equipped around the mushroom house's corner.

Fig. 1 Smart mushroom house simulation in 3D viewEnvironmental Requirement
Normally, the Rice Straw Mushroom grows in warm weather, typically in the tropics and sub-tropics.
The optimal temperature for rice straw mushrooms to grow in mycelial is from 28°C to 30°C for its
fruiting body production. where the ideal temperature for growing mushrooms is between 25°C and
40°C (Fasidi, 1996). The ideal humidity range for rice straw mushroom growth is between 70% and
90% (Bawis, 2014). While other research claimed that the mushroom's humidity might reach 99%.
Additionally, the level of carbon dioxide ranges from 1000 to 2000 ppm.
Mushroom Substrate
In our case study, several materials, including rice straw, mung bean shells, rice bran, sugarcane(black sugar), limestone, and gypsum, have been employed to create the compost or substrate. The
process of making this compost will take around six to seven days. First, the rice straw and mungbean shell will need to be soaked for at least a night to make them soft, then drain before mixing withlimestone water. After combining these three ingredients, it must be fermented once more for a totalof six days, with two days of re-mixing. The remaining components must be combined after six days
and fermented for one night before use.

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Table 1 Ingredients for substrate
Ingredients
Rice Straw
Mung Bean Shell
Rice Bran/ Wheat
Sugarcane (Black Sugar)
Limestone
GypsumTotal

Amount (kg)
400.0
150.0
50.0
2.0
5.5
3.0
610.5

Table 2 Parameter requirements during each stage
Parameters
Temperature (°C)
Relative Humidity (%)
CO2 (ppm)

Stage I
29-36
100
>1500

Stage II

Stage III

32-36
92-96
<1500

26-33
86-92
<1500

System Overview
The Smart Mushroom House has been equipped with all components and sensors, including a solar
panel, a battery (Power Can), a circuit breaker, a transformer, a motor pump, a cooling pad, an
exhaust fan, a ventilation fan, a sprinkler, a Honeywell ambient temperature and humidity sensor, a
Honeywell carbon dioxide sensor, and a Honeywell controller (CIPer Model 50). To do theconfiguration, we must first ensure that the controller is powered with the appropriate current andvoltage. Since the controller and the sensors require 24 VDC or VAC to work, but the standard rangeof electrical power is 220 VAC, a transformer is required to convert 220 VAC to 24 VAC for thecontroller.
The rice-straw mushroom grows in four stages, including the vegetative phase, spawn run, pinhead initiation, and cropping mode. We have decided to divide the controlling stage into three stages.
The first stage is to manage the vegetative phase which the temperature will stay between 32°C and
36°C. The second stage is to manage the system during the spawn run which the temperature will
persist between 32°C and 36°C, the relative humidity stays between 92% and 96%, and the carbon
dioxide stays at about 1500 ppm. The last is to handle the system during the pin head initiation andcropping mode, in which the temperature will stay between 26 and 33°C, the relative humidity willremain between 85% and 92% RH, and carbon dioxide will remain at about 1000 ppm. Also, we
have decided to test in 2 cycles. The first cycle is in March, and the second cycle is in May because
both months have similar weather and it is easier for us to study since both cycles use different
configurations to test the differences between each condition.

Fig. 2 System architecture of smart mushroom houseⒸ ISERD

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Experiment for 1st Cycle
After setting up all the controllers and assigned points, it needs to configure each step of mushroom
cultivation to obtain and control the environment inside the mushroom home to its excellent
condition during its growth stage.
The mushroom must be in stage I (vegetative phase), where the temperature must range from
29°C to 36°C, the humidity must range from 92% to 96%, and the CO2 level must be less than 10000
ppm. In this case, the configuration follows the flowchart as shown in Fig. 3. below. It indicates that
the Exhaust Fan and Sprinkler will activate whenever the temperature rises above 36°C and remainon until the ambient temperature falls below 36°C. The time required for this stage is approximately
3-4 days before we can see the spawn running through the compost and appearing as a thin white
thread.

Fig. 3 Flowchart of stage I configuration
Under stage II (spawn run), the mushroom must grow in conditions with a CO2 level of less than
1500 ppm, a temperature range of 29°C to 36°C, and a relative humidity range of 92% to 96%. In
this case, the system configuration follows the flowchart as shown in Fig. 4. In this stage, thecontroller will read data from all three sensors, and based on that data, it will control the system. For
example, if the temperature rises above 36 °C, the sprinkler and exhaust fan will turn on until thetemperature falls below 36 °C. Additionally, if the relative humidity rises above 96% RH, the system
will automatically switch on the ventilation and exhaust fans until the humidity returns to its typicalrange of 92-96%. In this situation, if the relative humidity falls below 92%, the system willautomatically turn on the spray and motor for the mushroom house to hydrate the compost and moist
the air, as too much dryness will prevent the compost from producing much output. The controller
will also read information from the CO2 sensor when the CO2 level rises above 1500 ppm, the system
will activate the exhaust fan to draw in the fresh air and the ventilation fan to circulate the air inside
the mushroom house so that it is all the same. The time required for this stage is approximately 3-4
days before we can see the little thread that will eventually grow into a mushroom leg rising fromthe spawn that spread during the earlier stage.

Fig. 4 Flowchart of stage II configuration
Stage III includes the pin head initiation and cropping mode. The ambient temperature must be
between 29°C and 33°C, the relative humidity must range from 92% to 96%, and the CO2 level mustbe less than 1000 ppm for the mushroom to thrive. The flowchart illustrates the system configurationⒸ ISERD

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in this condition as shown in Fig. 5. The configuration for this stage is the same as the previous stage,
but there are differences between the fixed static variables that we input in the temperature, humidity,
and CO2 sensors. The controller will read the parameters from all three sensors and control the system
based on those data. For example, if the temperature rises above 33°C, the sprinkler and exhaust fan
will turn on until the temperature drops below 33°C. When relative humidity rises above 92%, thesystem automatically turns on the ventilation and exhaust fans until the humidity falls below thetypical range of 85% to 92%. In this condition, the system will automatically turn on the spray andmotor for the mushroom house if the relative humidity falls below 85% in exchange for hydrating
the compost and moistening the air, as too much dryness will prevent the compost from producing
much yield. The controller will also read data from the carbon dioxide sensor, and when the CO2level rises above 1000 ppm, the system activates the exhaust fan to filter out the fresh air and theventilation fan to circulate the air inside the mushroom house so that it is equally heated and cooled.
The time required for this stage is approximately six to seven days since the little mushroom mustfirst grow to the point where its head is an egg shape.

Fig. 5 Flowchart of stage III configuration
Experiment for 2nd Cycle
Based on the results from the first cycle of the experiment, we encountered some problems with thecarbon dioxide sensor and the exhaust fan, which caused the compost to become excessively dry andprovide less yield than expected. To find a solution, we come up with another configuration in which
we will use scheduling time to regulate the exhaust fan to turn on for five minutes and three times a
day during stage II (spawn run) because in stage II the mushroom does not require too much oxygenfrom outside and the air does not require to ventilation much. Additionally, because mushrooms needfrequent ventilation during stage III and require more fresh air to grow, the scheduling time for thesestages was adjusted to five minutes every hour or 24 times per day. In addition to fixing the problem,
we also have installed another two sensors, including an ambient temperature and relative humidity
sensor and a carbon dioxide sensor outside the mushroom house to compare the environment outside
and inside the mushroom house.
RESULTS AND DISCUSSION
Results of 1st Cycle
The stage I configuration has turned on and the system has been running based on the design
algorithm. During this process, the temperature has never risen above 36°C which is the set point toturn on the exhaust fan and spray. It means that the environment inside the mushroom house has
stayed under our control. When it reached the pin head stage, the stage II configuration system turned
on, and we can see that the temperature during this stage increased by 36°C, allowing our control
system to turn on the exhaust fan and spray. After both components were turned on, we observedthat the temperature decreased below 36°C. The ventilation fan and exhaust fan have been turningon when the CO2 level has risen above 1500 ppm and turning off when it has fallen below 1500 ppm.
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However, this procedure has made both components run inexorably because the CO2 level rarely
stays below 1500 ppm. The problem with our sensor's response time is that it responded too late,which causes problems for our system. During stage III of the control process, the set points fortemperature, CO2, and relative humidity are 33°C, 1000 ppm, and 92%, respectively. However, thereceived data have all exceeded the set points of all parameters, which makes all of the mushroom
house's components turn on unstoppable. With this issue, we have decided to turn off the automatic
system and use the manual system instead.
With the results from the first cycle, we harvested a total yield of 80 kg with a total substrate
weight of 610 kg, which represents a percent of 0.13%, or 0.13 kg of rice straw mushrooms per
kilogram of the substrate. According to the standard, 10 kg of dried straw yields 2 kg. Our yield isless than 0.7 kg compared to the standard because some errors also happened during the growing
process (Gummert, 2022). The CO2 sensor that we utilized has caused errors in the data flow, causing
the receiving data to be delayed and the ventilation and exhaust fans to operate automatically basedon configuration, drying up the compost and producing less yield.
Results of 2nd Cycle
The only difference between the received data and the first cycle of control from stage I to stage III
is that the relative humidity has reached 99%, causing our exhaust fan and ventilation fan to run
nonstop following the stage configuration and causing the compost to become too dry. Regarding
this issue, we have set the set point to 99% in stage II and stage III configurations in favor of
activating the exhaust fan and ventilation fan. As previously mentioned, the second cycle's stage IItemperature set point is the same as the first cycle, but the humidity only remains at 99%, which ishigher than the set point of the first cycle, so we changed the set point of the second cycle to 99%
instead to prevent the control the environment inside the mushroom better. The ventilation andexhaust fans were not directly controlled by the CO2 level during this stage because we already had
a schedule for when to turn them on and off. During stage III, the process is the same as the firstcycle but we have only added the scheduled time to turn on and off the exhaust fan and ventilation
fan every five minutes.
With the second cycle implementation, we harvested the rice straw mushroom with a total yieldof 85 kg and a total substrate of 610 kg, which equals a percent of 0.14%, which means that one kiloof the substrate produces 0.14 kg of rice straw mushroom. There were still a few errors that happened
between these stages where the humidity sensor reading was above 99% RH, causing the ventilation
fan and exhaust fan to run continuously in accordance with the stage setting, drying out the compost.
CONCLUSION
Based on the results of both cycles, we can compare the configuration and yield and find that thesecond cycle is a little better than the first cycle, which generated more than 5 kg of rice straw
mushrooms. With both results, we have discovered and faced some obstacles regarding the exhaustfan, ventilation fan, CO2 sensor, and humidity sensor. Even though there were a few issues with theexperiment, this is not the end of the research. We have recommended a different configuration toassist in resolving the issue. Since some sensors were not capable of the system, we will replace thoseand do another config algorithm to help upgrade the systems. This study is focused on figuring out
the best control algorithm for the mushroom house. It is also possible to estimate and analyze theaccuracy of real-time data using the data collected from all the sensors. The data achieved in thisstudy have to be regarded as a significant input for subsequent research on the issue that cropped up
during the testing phase to make the system more stable and dependable. Applying both automatedcontrol and manual control has helped local farmers save their labor, money, and time and create
products of the mushroom to meet the demand of the market.

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ACKNOWLEDGEMENTS
This work was funded by the Cambodia Higher Education Improvement Project (Credit No. 6221KH) and the Laboratory-Based Education Project.
REFERENCES
Biswas, M.K. and Layak, M. 2014. Techniques for increasing the biological efficiency of paddy straw
mushroom (Volvariella volvacea) in eastern India. Food Science and Technology, 2 (4), 52-57, Retrieved
from DOI https://doi.org/10.13189/fst.2014.020402
Encyclopedia Britannica, Inc. 2021. Cambodia, Agriculture, forestry, and fishing. Retrieved from URL https//www.britannica.com/place/Cambodia/Agriculture-forestry-and-fishing
Fasidi, I.O. 1996. Studies on Volvariella esculenta (Mass) Singer, Cultivation on agricultural wastes andproximate composition of stored mushrooms. Food Chemistry, 55 (2), 161-163, Retrieved from DOI
https://doi.org/10.1016/0308-8146(95)00082-8
Fosbenner, L., Predmore, T., Evers, S., Conway, S., Payne, T. and Mehta, K. 2018. Implementation of smallscale mushroom production systems in rural Cambodia. 2018 IEEE Global Humanitarian Technology
Conference (GHTC), 1-6, Retrieved from DOI https://doi.org/10.1109/GHTC.2018.8601535
Gummert, M., Hung, N.V., Chivenge, P.P. and Douthwaite, B. 2020. Sustainable rice straw management.
Springer Nature, Retrieved from DOI https://doi.org/10.1007/978-3-030-32373-8Ⓒ 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 amount of agricultural chemicals applied to the farmland is 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 theirimpact on 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
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Membership:
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Also, every presenter who attends the International Conference on Environmental and Rural
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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. Santa Pandit, Institute of Environmental Rehabilitation and Conservation, Japan
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
Prof. Dr. Ping Li, Institute of Grassland Research, China
Prof. Dr. Dieter Trautz, Osnabrück University of Applied Sciences, Germany
Dr. Sri Wahyuni, Islamic University of Riau, Indonesia
Dr. Kasumi Ito, Nagoya University, Japan
Prof. Dr. Nang Hseng Hom, Yezin Agricultural University, Myanmar
Prof. Dr. Junaid Alam Memon, Institute of Business Administration Karachi, Pakistan
Prof. Dr. Anthony Makinato Penaso, 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

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