IJERD – International Journal of Environmental and Rural Development (2020) 11-2

Research article
erdStudy on the Factors of Awareness for the Production andDevelopment of Vegetables by Farmers in Cambodia
SHINOBU YAMADA*
Research Center, Institute of Environmental Rehabilitation and Conservation, Tokyo, Japan
Email: sinobun4022@gmail.com

MACHITO MIHARA
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, JapanKUMIKO KAWABE
Extension Center, Institute of Environmental Rehabilitation and Conservation, Tokyo, Japan
Received 10 September 2019

Accepted 5 December 2020(*Corresponding Author)

Abstract Recently, vegetable has also been cultivated by many farmers in Cambodia due tothe increase of its demand in the local market. The main objective of this study was to clarify
the awareness levels of the local farmers with an emphasis on agriculture production
information for the sustainable development of vegetable production. Based on the multiple
correspondence analysis results, the information required by local farmers was shown to bedifferent depending on farm management scale. It can be concluded that it is important toprovide information and technology that is adapted to the development stage of agricultural
management of each local farmer. Moreover, a categorical principal component analysis isused to categorize and clarify the effectiveness of kind of agricultural production information
was needed to develop vegetable production.
Keywords categorical principal component analysis, Cambodia

INTRODUCTION
In Southeast Asia, the rapid increase of population and lifestyle transition results the increase of
demand in various agricultural crops particularly on vegetables. Although the main agricultural
product in Cambodia is rice, vegetable has also been cultivated recently by many farmers.
According to the Census of Agriculture of the Kingdom of Cambodia (National Institute ofStatistics, 2013), a wide variety of vegetables were cultivated across the country including leafy,
stem and fruit bearing, root, bulb and tuberous, leguminous green, and others. Leafy vegetables suchas Water Convolvulus (Ipomoea aquatica), Cabbage (Brassica oleracea), Lettuce (Lactuca sativa),
Green Garlic (Allium sativum), Spring Onion (Allium fistulosum), Celery (Apium graveolens),
Spinach (Spinacia oleracea), Watercress (Nasturtium officinale), Chinese Kale (Brassica oleracea),
Clover (Trifolium L.), Amaranth (Amaranthus L.) and Pigweed (Amaranthus) are amongst of thecommon vegetables produced in the country. Tomato (Solanum lycopersicum), Bitter Melon(Momordica charantia), Watermelon (Citrullus lanatus), Wing Bean (Psophocarpus
tetragonolobus), Bhendi (Abelmoschus esculentus), Snake Gourd (Trichosanthes cucumerina), Ivy
Gourd (Coccinia grandis), Winter Squash (Cucurbita moschata), Chili (Capsicum), Cucumber(Cucumis sativus), Muskmelon (Cucumis melo), Eggplant (Solanum melongena), Gourd (Lagenariasiceraria), Green Gourd (Cucurbitaceae) are also some of the commonly cultivated fruit bearing
vegetables. Fruit bearing vegetables were cultivated over 35,000 ha of which cucumber covers thelargest area accounting 7,000 ha followed by watermelon at more than 6,000 ha while chili andpumpkins are at 5,000 ha each. However, many of these areas are cultivated in small scale and forthe purpose of farmer’s household consumption. In addition, many local farmers cultivate vegetables
during the dry season that results lesser production than cultivated in the wet season. Thoseconditions contributed a limited supply of vegetable in the market. To boost up and sustain theⒸ ISERD116

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production of vegetables in Cambodia, it was determined that there is a need to support small scale
vegetable growers in technical, farm inputs and a win-win price control of vegetable in the market(Chen et al., 2018). However, there is also a need to find out the instincts of farmers on how they
will deal the possible supports they need. Therefore, this study has been proposed to clarify theawareness levels of the local vegetable growers with an emphasis on agriculture production
information.
METHODOLOGY
The research site was in Kampong Cham Province. It is located at the northeast of Phnom Penh, andsoutheast of Siem Reap. In Kampong Cham Province during the French colonial period, the hilly
terrain was developed as a rubber plantation zone. The population of Kampong Cham province isapproximately 1.75 million and much of the population is engaged in agriculture. Table 1 shows theacreage and domestic share of major vegetables in Kampong Cham province. According to table 1,Kampong Cham Province is a major producing center of cabbage, lettuce and muskmelon.
Table 1 Acreage of major vegetables produced in Kampong Cham Province
Kompong Cham
Cambodia
Planted Area
Planted Area
Composition ratio
Kind of Vegetables
(ha)
(ha)
(%)
Leafy or Stem Water Convolvulus
2306.539.9
1.7
Vegetables Cabbage
648.6
223.0
34.4
Lettuce
824.8
153.0
18.5
Chinese Kale
327.4
11.2
3.4
Fruit‐Bearing Chilli
4637.8618.4
13.3
Vegetables Cucumber
6894.5164.0
2.4
Muskmelon
1901.9847.4
44.6
Eggplant
2997.4
103.8
3.5
Gourd
1687.461.8
3.7
Pumpkin4624.922.7
0.5
Tomato1065.222.7
2.1
Watermelon
5911.871.8
1.2
Bhendi
406.2
302.0
74.3
Total
34234.4
2641.5
7.7
Source: Census of Agriculture of the Kingdam of Cambodia 2013

In this study, the following two multivariate analyses are applied. Firstly, Correspondence
analysis is an analysis method that visualizes the cross-tabulation and facilitates the interpretation of
the survey results. In this study, correspondence analysis is an appropriate method for understanding
the awareness of local farmers and the results of the response patterns of necessary agriculturalinformation. Secondly, Categorical principal component analysis is an analysis method forclassification by integrating strongly correlated variables and creating new synthetic variables.
Categorical principal component analysis is an appropriate method for creating new synthetic
variables from many variables such as local farmer attributes agricultural information for vegetableproduction, and information sources, which is one of the purposes of this study. In addition, aprevious study (Yamada et al., 2018) used categorical principal component analysis to classify farmmanagement characteristics for development assistance in rural Cambodia.
The research site was ten districts in Kampong Cham Province, Cambodia. The target area ofthe questionnaire survey consisted of the following ten districts: Batheay district: 45 respondents(10.3% of the total respondents), Chamkar Leu district: 50 (11.4%), Chueng Prey district: 36 (8.2%),
Kaoh Sotin district: 46 (10.5%), Kampong Siem district: 38 (8.7%), Krong Kampong Cham district:Ⓒ ISERD

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48 (11.0%), Kang Meas district: 44 (10.1%), Prey Chhor district: 36 (8.2%), Srei Santhor district: 50(11.4%) and Stueng Trang district: 44 (10.1%).

Fig. 1 Location of the study site in Kampong Cham Province
RESULTS AND DISCUSSION
Table 2 shows the index of the questionnaire carried out, which contains categories for gender, ageof the respondent, educational background, farm acreage, cropping pattern, sales destination andfarmers' revenues.
Table 2 Demographic information of respondents
IndexGender

1. Male2. FemaleAge
1.Less than 29 years old
2.30-39years
3.40-49years
4.50-59years
5.More than 60 years old
Educational background 1. Never had been to school
2. Primary
3. Secondary
4. High school
5.College/University
Farm acreage
1.Less than 0.5 ha
2.0.6-1ha
3.1.1-1.5ha
4.1.6-2ha
5.More than 2.1ha
Source ： Survey Date

Number of response
(n)
(％)
282
64.5
155
35.5
17
3.9
113
25.9
140
32.0
116
26.5
51
11.7
42
9.6
228
52.2
133
30.4
33
7.6
1
0.2
91
20.8
116
26.5
117
26.8
40
9.2
73
16.7

Index
Cropping pattern 1.Vagetables
2.Rice3.Rice,Vagetables
4.Rice,Vagetables,fruit
Sales destination 1.Middleman only
2.Middleman and Market in the village
3.Market in the village only
4.Other market only
5.No answer
farmers' revenues 1.Less than 1,000,000 Riel
2.1,000,000-2,999,999 Riel
3.3,000,000-4,999,999 Riel
4.5,000,000-6,999,999Riel
5.7,000,000-8,999,999 Riel
6.More than 9,000,000 Riel

Number of response
(n)
(％)
43
9.8
48
11.0
98
22.4
248
56.8
302
69.1
53
12.1
57
13.0
25
5.7
7
1.6
30
6.9
169
38.7
122
27.9
57
13.0
32
7.3
27
6.2

The attributes of the respondents are as follows. Concerning the “Gender” of the respondents,
64.5% were “male” and 35.5% were “female.” Concerning the “Age” of the respondents, the majorityof respondents were between the ages of “40-49” years (32.0%), followed by “50-59” years (26.5%)
and “30-39” years (25.9%). The lowest percentage was aged “Less than 20 years” old. Concerning
the “Educational background”, the majority of respondents had “Primary” (52.2%), followed by“Secondary” (30.4%) and lastly “Have never been to school” (9.63%). Concerning the “Farm acreage”
of the respondents, the majority of respondents were between a farm acreage of “1.1-1.5” ha (26.8%),
followed by “0.6-1.0” ha (26.5%) and “Less than 0.5” ha (20.8%). Concerning the “Cropping pattern”
of the respondents, the majority of respondents were “Rice, Vegetables, Fruit” (56.8%) followed by“Rice, Vegetables” (22.4%). Concerning the “Sales destination” of the respondents, about 70% of
the local farmers answered “Middleman only”. Concerning the “farmers' revenues” of therespondents, the majority of respondents were between the farmers' revenues of “1,000,0002,999,999” Riel (38.7), followed by “3,000,000-4,999,999” Riel (27.9%) and “5,000,000-6,999,999”
Riel (13.0%).
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As a result, vegetable production is carried out by local farmers with a management area of
1.5ha or less, and most of the produced vegetables are shipped to Middlemen. In addition, womenare often involved in vegetable production.
Characteristic of Respondents and Data of the Information for Sustainable developmentof Vegetable Production by Multiple Correspondence Analysis
This part clarifies characteristics of respondents and the information for sustainable development ofvegetable production by Multiple Correspondence Analysis. The indices used for the analysis are
X1: Farm acreage, X2: Farmers' revenues, X3: Information about seeds, X4: Information aboutmachinery, X5: New technology information, X6: Information about chemical fertilizer, X7:
Information about organic fertilizer, X8: Market information, X9: Information about training, X10:
Price information, X11: Information about harvest and X12: Planting information. Figure 1 shows
the results of the answer pattern for Characteristic of respondents and information with an emphasis
on sustainable development of vegetable production by Multiple Correspondence Analysis.
According to the results of Multiple Correspondence Analysis, respondents with small farm acreageand low income are X3: Information about seeds, X6: Information about chemical fertilizer and X12:
Planting information. Furthermore, the local famer who answered the farm acreage as 1.1-1.5 ha wasfound to be related to X5: New technology information and X9: Information about training.
Additionally, the local famer who answered the farm acreage as 1.6-1.2 ha and more than 2 ha werefound to be related to X10: Price information and X11: Information about harvest. This clarified thecharacteristics of the local farmer consciousness and information of the emphasis on sustainable
development of vegetable production. According to the results of Multiple Correspondence Analysis,
it was clear that the preference divisions of the local farmers were difference of farm acreage asimportant characteristics for the classification.

Fig. 2 The result of answer pattern for the local farmers consciousness andrequired agricultural information (Correspondence Analysis)Grouping of Respondents by Category Principal Component Analysis
In this section, information on each variable, such as farmer attributes, cultivated land, theinformation for sustainable development of vegetable production and Information source aresummarized and a "total index" is created and grouped. We employed a categorical principalⒸ ISERD

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component analysis for this purpose. Index and answer patterns used for categorical principal
component analysis were as follows.
Table 3 Estimation results of categorical principal component analysis
Index
X1 Gender
X2 AgeX3 Educational background
X4 Cultivated land
X5 Average annual income from agricultural activity
X6 Cropping pattern
X7 Sales destination
X8 Information about seeds
X9 Information about machinery
X10 New technology information
X11 Information about chemical fertilizer
X12 Information about organic fertilizer
X13 Market information
X14 Information about training
X15 Price information
X16 Information about harvest
X17 Pranting information
X18 Government officer (DDAFF officer)
X19 Village leader
X20 Other farmers in the village
X21 Parents
X22 Scientist
X23 NGO officer
X24 Other (Community)
Sourse Sarvey date
Note Eigenvalue: Factor 1; 2.69, Factor 2; 2.44

FACTOR FACTOR
12-0.0720.176-0.1120.206
0.264-0.0370.294-0.0960.422-0.438-0.1760.303-0.402
0.361
0.223
0.502
0.348
0.538
0.208
0.078
0.216
0.518
0.251
0.337
0.342
0.149
0.570
-0.073
0.486
0.511
0.491
0.393
0.081
0.396
0.494
-0.3180.326-0.130-0.2940.053-0.375
0.368
0.152
0.122
0.538
-0.384-0.090-0.041

Table 3 shows the estimation results of the categorical principal component analysis. At thesame time, the eigenvalues of each factor were factor 1: 2.69 and factor 2: 2.44. In the following, it
is confirmed for each principal component what index feature an element constitutes. In the following,
it is confirmed for each principal component what index feature an element is constituted. Firstly,
the indices positively contributing to factor 1 were X3: Educational background (0.264), X4:
Cultivated land (0.294), X5: Average annual income from agricultural activity (0.422), X10: New
technology information (0.208), X14: Information about training (0.570), X18: Government officer(DDAFF officer) (0.494) and X23: NGO officer (0.538). From these indices, factor 1 can beinterpreted as "the factor representing the priority of information for the future of agriculturalmanagement". Secondly, the index positively contributing to factor 2 were X1: Gender (0.176), X2:
Age (0.206), X6: Cropping pattern (0.303), X7: Cultivated land (0.361), X8: Information about seeds(0.502), X9: Information about machinery (0.538), X11: Information about chemical fertilizer(0.518), X17: Planting information (0.396), and X21: Parents (0.368). From these indices, factor 2
can be interpreted as "Factors expressing intention to form production areas by collaboration".
For local farmer grouping, it can be classified into the following 4 groups from each positive
and negative combination of factor 1 and factor 2. Group 1 is local farmers that are positive for both"the factor representing the priority of information for the future of agricultural management" and"Factors expressing intention to form production areas by collaboration." This group is underⒸ ISERD

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situations that ensure stable income such as agricultural machinery is in place, has an interest inimproving the quality of agricultural crops and sales outlets and also shows an understanding ofcooperation with others. Therefore, it is suggested that local farmers classified as group 1 are suitable
as model farmers for projects for sustainable vegetable production. The local farmers of group 2 arepositive for "the factor representing the priority of information for the future of agriculturalmanagement" and negative for "Factors expressing intention to form production areas bycollaboration". This group is already aiming to achieve modern agricultural production and toadvance agricultural management on an individual level. This is considered to be highly adaptableto projects aimed at improving the agricultural techniques for vegetable production of local farmers,such as the start of new crops and Introduction of new customers. Group 3 are local farmers that arenegative for both "the factor representing the priority of information for the future of agriculturalmanagement" and "Factors expressing intention to form production areas by collaboration." It isspeculated that the local farmers in this group are in a situation where the infrastructure required foragricultural production is not well developed. Therefore, a project is needed to disseminate basic
agricultural production techniques. The local farmers of group 4 are negative for "the factor
representing the priority of information for the future of agricultural management" and "Factors
expressing intention to form production areas by collaboration" positive. Similar to Group 3 andGroup 4, the development of agricultural production infrastructure is not sufficient. However, thelocal farmers expect the development of regional agriculture by collaborating with others to offset
its weaknesses. In addition, local farmers classified as Group 4 and Group need to propose to expand
vegetable production from self-consumption by the houses to vegetable production for sale.
According to the results of categorical principal component analysis, local farmers who produce
vegetables were found to have disparities in the development stage of agricultural management. Inaddition, it became clear that local farmers have a different awareness of the priorities of information
for the future of agricultural management and their intentions for the formation of main producing
areas.
CONCLUSION
In this study, focusing on the vegetable production in Cambodia, the local farmers analyzed whatkind of agricultural production information was needed to develop vegetable production based ondata obtained from a questionnaire survey. Therefore, the main objective of this study was to clarify
the characteristics of awareness of the local farmers with an emphasis on information of agriculture
production.
The results of the analysis are summarized as follows. Based on the multiple correspondence
analysis results, the information required by local farmers was shown to be different depending onfarm management scale. Based on the categorical principal component analysis results, the relevanceof awareness for the production and development of vegetables and the information required by local
farmers became clear. In addition, local farmers with multiple sales destinations are highly consciousof the factor representing the priority of information for the future of agricultural management.
According to the results of the analysis, for the development of vegetable production inCambodia, it can be concluded that it is important to provide information and technology that isadapted to the development stage of agricultural management of each local farmer.
ACKNOWLEDGEMENTS
This work was supported by JSPS KAKENHI Grant Number JP18K05857.
REFERENCES
Sokhan, C., Thavrak, H. and Mardy, S. 2018. The effect of agricultural cooperative on income’s smallholdersin Svay Chrum commune, Svay Rieng province, Cambodia. Asian Journal of Agricultural andEnvironmental Safety, 1, 6-11.
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The National Institute of Statistics. 2013. Census of agriculture of the Kingdom of Cambodia 2013. National
Report on Final Census Result, The National Institute of Statistics (NIS), Cambodia.
Yamada, S, Mihara, M. and Kawabe, K. 2018. Classification by characteristics of farm management fordevelopment aid in rural area of Cambodia. International Journal of Environmental and Rural Development,
9 (1), 191-197.

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