Market Orientation and Market Participation of ...

International Journal of Social and Economic Research, 5(4): 132-149. DOI: 10.5958/2249-6270.2015.00062.8.

Market Orientation and Market Participation of Farmers in Awlegama, Wariyapola, Sri Lanka: Constrains and Potentials for Crop Diversification and Commercial Transformation G.M.P. Kumara1, H. Rathnasekara2, M.D.D. Perera1, M.I.M. Mowjood3, L.W. Galagedara4 1

Postgraduate Institute of Agriculture, University of Peradeniya, Kandy, Sri Lanka Department of Agricultural Economics and Business Management, Faculty of Agriculture, University of Peradeniya, Kandy, Sri Lanka 3 Department of Agricultural Engineering, Faculty of Agriculture, University of Peradeniya, Kandy, Sri Lanka 4 Grenfell Campus, Memorial University of Newfoundland, Corner Brook, NL, Canada [email protected], Tel: (+94718583705)

2

ABSTRACT Paddy cultivation has become uneconomical in marginal paddy lands, thus farmers have started crop diversification in marginal paddy lands. Commercial transformation of subsistence agriculture would result in sustainable food security and enhanced links between agricultural markets. Market Orientation (MO) and Market Participation (MP) are proxies for commercialization of agriculture through crop diversification. The objectives of this study were to find out the degree of crop diversification and commercial transformation in terms of MO and MP of farmers and to investigate the determinants of MO and MP by using socio-economic variables. Pre-structured questionnaire survey and focus group discussion were carried out to gather data from randomly selected 115 farmers in the study area. Data were analyzed using Software Package for Social Sciences and Microsoft Excel. Results revealed that, moderate MO and lower MP were recorded in the study area and determinants of MO and MP were varying for both cultivation seasons. Education, income and extension service are found as factors of utmost importance to enhance MO and MP. The significant effect of extension service on MP implied that a successful commercial transformation of farmers and need for strengthening of extension service to promote crop diversification and commercial transformation of paddy and other field crops among different farming communities. Proper policy planning, technological innovation, and organizational and institutional interventions aim at promoting commercial transformation of subsistence agriculture by improving MO at production level and facilitation of market entry and MP in output and input markets are needed. Keywords: Market orientation, Market participation, Multiple regression, Tobit model, Wealth index


INTRODUCTION The cost of paddy production has increased tremendously during the past few decades and consequently the paddy production has become uneconomical in marginal paddy lands, especially in the wet zone and under minor irrigation systems located in dry and intermediate zones (1). Therefore, farmers have started crop diversification in marginal paddy lands. Areas potential for crop diversification in Sri Lanka can be found in different ecological settings and primarily vary according to the agro-ecological conditions (1). According to (2), there are nearly 185,000 ha of irrigated command areas under minor tanks. Cultivation of paddy in above areas results in lower returns compared to other filed crops (OFC) per unit of water used (1). Accordingly, crop diversification or cultivation of OFC required lesser amount of water so that farmers can obtain relatively higher returns (1). Further, problems of poverty, unemployment, illiteracy, migration, and illegal activities are more acute in the paddy fallow systems (3). Promotion of OFC such as cereals and pulses in the existing fallows would also improve sustainability of the paddy production system besides enhancing production and income of the cropping system (3). Commercial transformation of subsistence agriculture is an indispensable path towards economic growth and development for many agriculture dependent developing countries (4). Commercial transformation of subsistence agriculture would results in sustainable food security in the households (5) while it enhances the links between the input and output sides of agricultural markets. Market Orientation (MO) and Market Participation (MP) can be considered as proxies for commercialization of agriculture through crop diversification (6, 7 and 8). However, analysis of determinants of MO and MP separately would be useful in guiding the type of interventions that has to be implemented in order to facilitate commercial transformation at both production and marketing levels. Commercial transformation of farmers entails production decisions (i.e. cultivation practices, crop choices considering market conditions, irrigation methods etc.) and marketing decisions. However, available literatures on commercialization of farmers rarely distinguish MO and MP. However, MO of small scale subsistence agriculture has been identified as an issue of production decision; the answer to the question what to produce since individual farm households are minor players in the market. Hence, MO of the farmers can be defined as the degree of allocation of resources (land, labor and capital) to the production of agricultural produce that are meant for exchange or sale (9). The MP refers to the extent by which a household participates in the market as a seller. The determinants of MO and MP may not be the same, because a household may produce marketable commodities, but use them for home consumption, if the household specific endogenous prices lie between the mark-up selling and buying prices. This situation is more common when there are high transaction costs and the price band is wide (10). In a contrary, a household could also have higher MP because of surplus production due to various reasons, including favorable weather conditions, although it may not be market oriented. According to a study by (11), numbers of variables influence on crop diversification and marketability of the outputs such as trade expenditure, social capital, farm size and extension service are significantly affect to MO and MP. On the other hand, education, age, income, market accessibility, irrigation method and number of family members have been identified as insignificant variables.


Additionally (12) have proved that family size, family labor, livestock activities, market accessibility and extension service significantly affect to MO and MP while education, age, sex, and land size are mentioned as insignificant variables. Therefore, those socio-economic variables are different according to the situation of MO and MP. Hence, this study used representative variables from all socio-economic backgrounds developed conceptual framework below (Figure 1) such as age, education, household size, family labor, livestock activities, social capital, extension service, land size, farmer wealth (household and farm equipment) income and market accessibility.

Household and household head characteristics Livestock ownership

Household endowment of factors of production

Market orientation index (MOI)

Crop output market participation index (COMP)

Farmer wealth

Market access

Institutional/organi zational services

Figure 1: Conceptual model the determinants of household level MO and MP The objectives of this study are to find out the degree of crop diversification and commercial transformation in terms of MO and MP of farmers in the Awlegama minor irrigation system and to investigate the determinants of MO and MP by using socioeconomic variables for paddy and OFC cultivation during both Yala and Maha seasons.


METHODOLOGY Study area The study was conducted in the Awlegama minor irrigation system (70 69’N; 800 20’E), Wariyapola, Kurunagala districts, Sri Lanka. Bayawa tank is the fourth largest tank in Awlegama agrarian service area (Fig. 2) which comprised 30 ha of cultivable lands in the command area with 128 farmers. More than 5 smaller minor tanks are located adjacent to the Bayawa tank and few numbers of farmers are cultivating crops using irrigation water from those small minor tanks.

Figure 2: Map of study area - Awlegama Pre-structured questionnaire survey and focus group discussion were carried out to gather data on socio-economic variables affect to MO and MP from 115 farmers selected randomly to represent the farming community in the Awlegama minor irrigation system. The questionnaire was formulated to gather information on household characteristics, farmer wealth, cultivations (Yala and Maha seasons), livestock ownership, social capital, extension service, market facilities and perceptions on cultivation. Collected data were analyzed using Software Package for Social Sciences (SPSS) and Microsoft Excel. Empirical Model Specification of empirical model is based on conceptual framework described in Figure 1. Two separate multiple regression models were tested to investigate the determinants of MO and MP and their operational definitions are explained below. Market Orientation Index (MOI) The MOI is calculated for each farmer based on the MO of cultivation choice. Farmers are considered market oriented if its production plan follows market signals and produce commodities that are more marketable. All crops produced by a household may


not be marketable in the same proportion. Consequently, a crop specific marketability index () is computed for each crop produced using the following formula (Eq. 1), where  represents the proportion of total amount sold in the market to the total production at household level (adapted from 12). N

k 

S i 1 N

ki

Q S and 0  1

Q

Eq: 1

ki

i 1

Where k is the proportion of crop k sold (Ski) to the total amount produced (Qki) aggregated over the total sample households. Alpha () values were calculated for the crops cultivated in both Maha and Yala seasons separately and the marketability of crops was computed. In the next stage, the MOI was calculated to each household, as a proportion of amount of land allocated to crop k to amount of total land owned by the household (Eq. 2). k

 L k

ik

L 0 and 0 MOI 1 Eq: 2 LiT Where MOIi is market orientation index of household i. Lik is amount of land allocated to crop k, and TiL is the total crop land operated by household i (adapted from 12). MOI i 

k 1

Crop Output Market Participation (COMP) Index The COMP index can be calculated as the proportion of the value of crop sales to the total value of crop production (Eq. 3). k

P S k

COMPi 

k 1 k

ik

P Q k

k 1

0 COMP 1

Eq: 3

ik

Where Sik is quantity of output k sold by household i evaluated at an average community level price (Pk), Qik is total quantity of output k produced by household i (adapted from 12). Wealth Index (WI) The WI was calculated for each household using data collected on endowment of household equipment and farm equipment. Weights were assigned to each and every equipment based on the market price as per July 2015 (adapted from 12).

WI 

{( X

P )  ( X b Pb )..........( X n Pn )}

a a

(X

P)

n n T

0 WI 1

Eq: 4


Where, Xa and Xb (1, 0) = Availability of a or b equipment, Pa and Pb = Price of a or b equipment, XnT = Availability of all equipment and PnT = Price of each and every equipment Modeling of MOI and COMP Index Household level MOI was modeled using multiple regression analysis (Eq. 5). In MOI, u is an error term assumed to be independently and identically distributed with zero mean and constant variance. Model was estimated using OLS (Ordinary Least Square) method. MOI = 0 + 1Y1 + 2Y2 + 3Y3+ 4 Y4+ 5 Y5+ 6 Y6+ 7Y7+ 8 Y8+ 9 Y9+ 10Y10+ 11 Y11+ 12 Y12 + u Eq: 5 0 =Constant or intercept term, 1 to 11 = Slope or Alpha coefficient for each and every variable, Y1 =Age, Y2 =Education (years), Y3 =Household size (no of persons), Y4 =Livestock ownership (yes=1, no=0), Y5 =Land size (ha), Y6 =Family labor (no of persons), Y7 =Income (monthly), Y8 =Farmer wealth (by WI), Y9 =Market distance (residence place to market, km), Y10 =Extension service (through government, private and non-government organizations), Y11 =Social capital (membership and participation) The COMP index was analyzed using a Tobit regression model (Eq: 6). In order to test whether MO translates into higher MP, the MOI also included as an independent variable in the equation. Model was estimated using MLE (Maximum Likelihood Estimates) methods. Yi* = Xiβ + εi

Eq: 6

Where: yi* is a latent variable (COMP) and β is the corresponding vector of explanatory variables. The model errors εi are assumed to be independent, N (0, σ2) distributed, conditional on the Xi. The observed yi is defined as 1 if yi* > 0 and 0 if yi* ≤ 0. The Tobit regression model used in the study is presented as follows (Eq: 7). COMP = β0 + β1 X1 + β2 X2 + β3 X3 + β4 X4 + β5 X5 + β6 X6 + β7 X7 + β8 X8 + β9 X9 + β10X10 + β11 X11 + β12 X12 + ε Eq: 7 β0 = Constant or Intercept term, β1 to β12 =Slop or Beta coefficient for each and every variable, X1 =MOI, X2 =Age (years), X3 =Education (years), X4 =Household size (no of persons), X5 =Livestock ownership (yes=1, no=0), X6 =Land size (ha), X7 =Family labor (no of persons), X8 =Income (monthly), X9 =Farmer wealth (by WI), X10 =Market distance (residence place to market, km), X11 =Extension service (through government, private and non-government organizations) and X12 =Social capital (number of membership and participation)


RESULT AND DISCUSSION Characteristics of households Results reveal that 88.7% of households are headed by males of 50 years old in average. Of the household heads, only 4.4% have education qualification above O/L and 84.4% have education up to O/L or below. The rest of them have not attended to school at all. Mean income level is LKR 21,227.00 and average land area of 1.43 ha is owned by a farm family (Table 1). Distances to credit facilities, markets of agricultural inputs and outputs are 4.19, 2.85 and 1.90 km, respectively. Table 1: Household characteristics Item Household size Household head age (year) Monthly Income (LKR) Land area (ha) Distance to credit (km) Distance to input (km) Distance to output (km) Family labor

Mean 4.47

Max 7.

50.5 80 21227.27 40000.00 1.43 5.0 4.19 12.0 2.85 12.0 1.90 6.0 1.40 3

Min 3.

SD 0.91

30 5000.00 0.25 0.25 0.05 0.0 0.

11.16 7749.50 1.1 4.44 3.42 1.61 0.84

According to Table 2, 64.4% of households practice farming as the primary occupation and 30.4% practice it as the secondary occupation. Only 31.3% of farmers engaged in livestock activities such as cattle and buffalo rearing. Of the sample, 90.4% of household heads are members of social organizations named; farmer organization, women’s society, dead benevolence society, youth society, rural development society etc. Accessibility to electricity is 96.5% and only 1.7% households have accessibility to pipe water and the rest of them totally depend on domestic dug wells. Household accessibility to credit facilities is 40.0% and other farmers are not interested in credit facilities for their cultivation. Farmer’s accessibility to information on cultivation practices is 61.7% from various institutions/persons such as government agricultural officers, development officers, chemical traders/dealers and farmer organization. Table 2: Summary of household characteristics in Awlegama agrarian service area Item Agriculture as main occupation Agriculture as secondary occupation Engaged in livestock activities Membership of social organizations Accessibility to electricity Accessibility to pipe water Accessibility to credit facilities Accessibility to information on cultivation practice

% 64.4 30.4 31.3 90.4 96.5 1.7 40.0 61.7


Cultivation practices Paddy, Okra, Groundnut, Mung bean, Beetle and Coconut are the major cash crops cultivated in the study area (Table 3). Out of the total cultivable lands, 81.0% is used for paddy cultivation. Farmer percentage of paddy cultivation is 94.0% and 89.0%, respectively in Maha and Yala seasons showing prominent mono crop cultivation in the study area rather than crop diversification. Coconut is grown as a commercial perennial crop in home gardens. According to the calculated  value, half of paddy harvest is used for household consumption and only the remaining is sold. Therefore, paddy is not a good MO crop, but more than 90.0% of cultivated OFC are sold and can be considered as MO than paddy. However, land area dedicated and number of farmers engaged in OFC cultivation is less compared to paddy cultivation (Table 3). Table 3: Crop cultivation in Yala and Maha seasons Maha Crop % of growers# % of land used  Paddy 0.54 93.9 81.26 Okra 0.92 8.7 1.89 Groundnut 0.97 1.7 0.79 Mung Beetle 1.00 11.3 4.09 Coconut 0.79 26.1 11.97 #: cultivate multiple crops including home gardens

 0.58 0.94 0.92 0.83 1.00 0.71

Yala % of growers# 88.7 4.3 1.7 2.6 9.6 26.1

% of land used 81.13 1.76 0.71 0.53 3.88 11.99

The derived indexes for the study area are given in Table 4. The MOI in Awlegama area is moderate in both seasons. The COMP is almost similar in both seasons, but lower values are recorded in Awlegama area according to MOI and COMP ranking of (12). Results reviled that framers are MO, but they do not actively participate to market because of 94.0% farmers in Maha season and 89.0% farmers in Yala season mainly cultivate paddy. According to calculated , paddy is not a well MO crop and farmer use half of the harvest for their own consumption and 26.0% of farmers cultivate paddy only for their household consumption. The WI in Awlegama area is varying between 0.62 and 0.02 with a mean value of 0.17 implying a very low WI in the study area. Table 4: Summary of derived indexes for Awlegama farming activities Item Mean Max Min SD MOI (Maha) MOI (Yala) COMP (Maha) COMP (Yala) Wealth Index

0.59 0.61 0.38 0.36 0.17

0.78 0.76 1.00 0.91 0.62

0.54 0.58 0.00 0.00 0.02

0.06 0.05 0.31 0.34 0.16


Result of regression analysis Results revealed that, education level above O/L of the household head, income and market distance in both Maha and Yala season are significantly affecting factors for MO in the study area (Table 6). Also, education level above O/L of the household head, income, land size and extension service significantly affect to MP in both Maha and Yala seasons (Table 7). In addition, market distance significantly affects to MO and land size significantly affects to MP in Awlegama area in both Yala and Maha seasons. The education level higher than the O/L of farmers is significant at 5% probability level and is positively correlated with both MO and MP. It implies that those farmers with higher level of education would increase crop diversification and commercial transformation. Farmers with higher level of education can easily learn and understand the importance of the MO and MP from different sources. They are more receptive to other things and accept changes towards new innovations. The finding in the case of education is in agreement with that of (13) as education of farmers were significantly correlated with adoption of improved animal husbandry practices in West Bengal. Annual farmer’s income has a highly significant and positive relationship with the MO and MP. Since majority of farmers in the study area are economically poor {mean monthly income of all farmers are 21,227 LKR, and it is for farmers whose main occupation as agriculture is 13,594 LKR (Table 5) and mean WI in the study area is 0.17} and they face hardship in procuring the recommended inputs as well as practices for crop diversification in their farming situations. Thus, commercial transformation is significantly influenced by farmers’ income generated from those cultivated crops. Extension service from government and private institutions is found to be positively significant to MO and MP in Awlegama area. With a higher level of extension service, farmers are exposed to more interactions with extension officers of the department of agriculture and private organization. According to advice from those officers, farmers make decisions to cultivate crops for a particular season. Farmers receive scientific guidance to access production, new agricultural knowledge and management practices from different sources, which help in higher level of crop diversification among the farming community. In addition, extension service introduces high yielding crop varieties (hybrid seeds), new irrigation methods etc. Therefore, farmers obtain surplus of their products and it encourages active MP of farmers. Awareness programs followed by location specific and enterprise based training programs on crop diversification and demonstrations are essential to enhance farmers’ knowledge and acceptance of crop diversification and commercial transformation of paddy and OFC. The variable of extension service had significant influence on adoption of rice-fish culture in north of Iran (14) and (15) proved that new technology transformation to farmer community encouraged MO and MP. In Awlegama, almost all farmers ( cultivate paddy as their major crop (Table 3) since climate conditions are favorable for paddy cultivation. Specifically, farmers who own higher extent of lands and labor tend to cultivate paddy. The alpha () value for paddy is very less (Table 3) compared to OFC and can consider paddy as not a well MO crop. Therefore, land area and family labor is not significantly involved to MO in both seasons. Distance to market from farm house is negatively significant to MO. This is due to long travelling distance, lack of transportation facility and low farmer wealth (mean


WI =0.17). Long distance to market discourages farmers and slow information transformation from market to farmer reduces awareness about MO. The role of marketing costs is completely hindering or limiting the level of farmer MO and MP (16). Nearness to markets and roads, and ownership of transport equine reduce marketing costs, thus encourage MO and MP. Size of cultivated land has a positively significant relationship with MP. Farmers who own higher operational farm size tend to go for paddy cultivation plus OFC. Additionally, surplus of production from the entire field encourage farmer participation to market. According to a study done by (17) also proved that higher land size promote MP of farmers. Age of farmers is not found to be significant on both MO and MP. This indicates that crop diversification and commercial transformation are not determined by the age of farmers. This is attributed to the fact that most farmers under study are in the category of old age ranging from 40 to 72 years and they are traditionally paddy farmers. According to data collected using focus group discussion from livestock owners, they are less aware of crop diversification. Because of OFC cultivation is time consuming, fields need to be always protected from farm animals and they already have an alternative source of income through livestock activities. However, livestock activities are very helpful to paddy cultivation and it helps to improve agronomic practices as well. In addition during fallow periods, animals can release to paddy fields for grazing purpose and vegetative parts of the paddy crop can be used as fodder for livestock animals. Consequently, cow dung and other dairy wastes could be used as manures and compost for paddy production. Hence, all livestock owners cultivate paddy and ultimate result is a lower MO. Household size detracts from household MO and MP due to its effect on increasing household domestic consumption needs. This result implies that interventions aimed at promoting family planning amongst farming communities in Awlegama can contribute to commercial transformation of subsistence agriculture. Higher farmer wealth encourages MO and MP by reducing market cost and cost of production. In this study, WI however is not affected to MO and MP significantly. Another interesting finding is that the effect of social capital is not significant, but famers’ membership in the study area with social organization is above 90%. These memberships are found to be adversely affecting the probability of crop diversification. The farmer being a member of a social organization like farmer organization, his probability of crop diversification is less than 3%. Farmers who are non-members or inactive members engage in crop diversification in Awlegama, but those farmers face lot of difficulties to supply water to their fields. Hence, some farers use individual agro wells as an alternative water source for cultivation rather than tank water. The result suggests that social organizations have their particular objectives (mono cropping) and focus on specific crops, which narrow the probability of crop diversification of farmers. Table 5: Income distribution among farmers in Awlegama Item Mean income Max Only Agriculture 13594.94 40000.00 Agriculture+ Other 22888.89 40000.00

Min 5000.00 5000.00

SD 8005.64 10172.17


The R2 values for Maha and Yala seasons are 0.810 and 0.832, respectively indicating selected models and variables significantly contribute to explain farmers MO in Awlegama area. Table 6: Results of multiple regression analysis for MOI MOI Explanatory variables

Maha season Coefficient Standard error 0.001 0.001 0.006 0.011 0.035 0.017* 0.007 0.017 -0.006 0.005 -0.007 0.005 0.001 0.000*** -0.043 0.033 -0.008 0.003* 0.001 0.002* 0.000 0.003 0.492 0.035

Yala season Coefficient Standard error 0.000 0.000 0.000 0.012 0.038 0.0161* 0.000 0.015 -0.001 0.005 0.000 0.004 0.001 0.000*** -0.003 0.031 -0.006 0.003** 0.003 0.003* 0.002 0.003 0.486 0.033

Age Education up to O/L Education after O/L Livestock ownership Land size Family labor Income Framer wealth Market distance Extension service Social capital Cons Test Statistics No of Observation 85 85 F(12,72) 25.61 29.75 Prob>F 0.000 0.000 R2 0.810 0.832 Adj R2 0.779 0.804 ***, ** and * are significant at 1%, 5% and 10% significant levels, respectively

The pseudo R2 values for Maha and Yala seasons of the model are 0.454 and 0.739, respectively. The selected models are highly effective for Yala season than the Maha season according to pseudo R2 values. Thus, the overall model is significant and the explanatory variables used in the model are collectively able to explain the farmers’ decisions regarding the MP.


Table 7: Results of Tobit regression analysis for COMP COMP Explanatory variables

Maha season Yala season Coefficient Standard error Coefficient Standard error 0.245 1.122 0.365 1.002 0.001 0.005 0.004 0.004 0.101 0.111 0.119 0.094 0.139 0.165* 0.189 0.142* 0.036 0.054 0.021 0.045 0.115 0.046* 0.086 0.039* -0.032 0.045 -0.045 0.037 0.001 0.001** 0.001 0.001** -0.037 0.318 -0.088 0.265 -0.037 0.025 -0.025 0.020 0.035 0.019* 0.041 0.017* 0.007 0.023 0.005 0.019 -0.149 0.644 -0.283 0.565

MOI Age Education up to O/L Education after O/L Livestock ownership Land size Family labor Income Framer wealth Market distance Extension service Social capital Cons Test Statistics No of Observation 85 85 LR chi2 (12) 47.75 47.06 Prob>chi2 0.000 0.000 Pseudo R2 0.454 0.739 ***, ** and * are significant at 1%, 5% and 10% significant levels respectively

CONCLUSION The degree of MO and MP is different in Yala and Maha seasons in the Awlegama minor tank system. Further, the determinants/variables of MO and MP are varying for both the major cropping seasons. Education, income and extension service are found as factors of utmost importance to enhance MO and MP in the study area. In addition, market distance is negatively significant to MO and land size is positively significant to MP. Agricultural extension services are not only instrumental in promoting improved technologies, but also in enhancing farmer’s skills and expected to facilitate market entry through facilitation of collective marketing, farmer linkages with buyers and the supply of market information. The significant effect of extension service on MP implied that a successful commercial transformation of farmers in Awlegama and need strengthening of extension service along with building social capital in order to achieve successful commercial transformation. However, effects of social capital in certain cases do not contribute to crop diversification in farming community due to mono-crop promotion through the social organizations. Therefore, the socio-economic variables are substantially influencing the crop diversification and commercial transformation of paddy. OFC in different farming communities must be taken into consideration while accelerating the pace of MO and MP under diversified farming systems. Finally, proper policy planning, technological innovation, and organizational and institutional


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