Estimating Marketing Efficiency of Organic Farmers: Evidence from Districts of West Bengal,India

Abstract

The growing organic food market offers considerable trading opportunities for organic farmers in India. In this context, this article intends to find out whether the organic farmers are exploring these marketing opportunities in the three districts (namely, Purulia, Purba Bardhaman and Nadia) of West Bengal. Farm-level marketing efficiency is measured in a framework of data envelopment analysis. Evidence reveals that farmers’ marketing activities determine the level of marketing efficiency. Results show that organic farmers in Nadia district are relatively marketing efficient in comparison to the farmers in Purulia and Purba Bardhaman districts. Moreover, an attempt has been made to identify the determinants of marketing efficiency. The result shows that marketing efficiency is affected by the economic status, level of education, years of farming experience, size of landholding of the farmer household and the type of marketing arrangement.

Keywords

Data envelopment analysis farm-level marketing efficiency India marketing channel organic farming truncated bootstrap regression

Introduction

In recent times, the demand for organic products has been witnessing rapid growth all over the world. In India, there is no such exception. The organic food sector in India has started booming mainly due to growing consumer awareness about the health and acceptability of organic products. Besides, there has been plenty of policy emphasis on organic farming. With the introduction of various schemes such as ‘Paramparagat Krishi Vikash Yojana’ (PKVY), ‘National Project on Organic Farming’ (NPOF) and ‘Mission Organic Value Chain Development for North-Eastern Region’ (MOVCDNER), the government of India is providing strong support for organic farming. In addition, key growth drivers such as increasing disposable incomes, growing awareness around health, increasing acceptability and a conducive policy environment suggest a strong potential for India’s organic food segment.

Organic farming offers considerable trading opportunities for farmers in developing as well as developed countries (Garibay and Jyoti 2003). In the Indian context, with the expansion of the market, it will be possible for the farmers to reap the benefits of these marketing opportunities with relatively high price premiums. In exploiting such opportunities, organic farmers need to enhance their marketing efficiency. In this context, the article intends to evaluate the marketing efficiency of organic farmers in the three districts of West Bengal.

Review of Literature

A considerable volume of work has been carried out to evaluate the efficiency of agricultural markets. These studies have assessed the efficiency of a marketing system by comparing the efficiency of alternate marketing channels (Anjugam and Alagumani 2019; Chand et al. 2020; Das et al. 2023; Dukpa and Ezung 2020; Gebre et al. 2021; Mandal et al. 2011; Mgale and Yunxian 2020; NCAP 2010; Sharma and Sharma 2019). However, the marketing process involves several functionaries with each performing different activities which influence the efficiency of the marketing system. Producers being one of the key functionaries of this process have a significant role in making the marketing system efficient.

In general terms, marketing efficiency can be defined as the achievement of maximum output from given resources (Yotopoulos and Nugent 1976). Therefore, the efficiency of a marketing system depends on marketing activities that can increase or maximise the ratio of marketing output to input (Mgale and Yunxian 2020). From the perspective of the producers, marketing efficiency constitutes the achievement of better marketing outputs that yield higher prices (Abbott and Markeham 1979). Incidentally, the realisation of higher prices depends on many factors such as quality of output, allocation of resources aimed at improving access to attractive markets and the effort to engage in negotiation for higher prices with the existing marketing channel or search for better marketing channels (Singbo et al. 2014). Keeping these factors in mind, the farmers set their marketing strategies which involve producing better-quality outputs as well as obtaining better prices through negotiating and putting time and effort into choosing better marketing arrangements. So, the higher price is often an outcome of the better marketing activities undertaken by the farmers (Singbo et al. 2014).

In the existing literature, a considerable volume of work has focused on production efficiency as a tool for improving performance in agriculture. However, the profitability of farms is not only dependent on the efficiency of the production process but also on the farmers’ marketing efficiencies (Mgale and Yunxian 2020; Singbo et al. 2014). However, the issue of farm-level marketing efficiency has received little attention in the existing literature. We have found only one study by Singbo et al. (2014) in which the authors have estimated the marketing efficiency of conventional vegetable farmers in Benin. However, to the best of our knowledge, there are no such studies on farm-level marketing efficiency of the organic produces.

The expansion of the organic market has resulted in a sizable increase in the number of marketing channels. The growing number of marketing channels can help organic farmers take advantage of the high price premium by increasing their selling avenues. Empirical evidence also suggests that with the expanding set of buyers, the producers’ profit share in a supply chain increases (Chambolle and Villas-Boas 2015). However, the organic price premium significantly varies across the distribution channels (Offermann and Nieberg 2000; Staudigel and Trubnikov 2022). Further, it has been seen that the price premium received by an organic producer depend on both the consumers’ willingness to pay a premium on organic product and the producers’ ability to access the marketing channels that capture the premium and meet the quality requirements of these channels (Offermann and Nieberg 2000). Therefore, there is a need to analyse the marketing channels of organic farming. In the context of West Bengal, we could find only one study¹ (Das et al. 2021), which identified the marketing channels used by the organic vegetable and paddy growers in the South 24 Pargana district. In this backdrop of analysis, our article has twofold objectives: first, is to explore the existing marketing channels for organic farming in the three districts of West Bengal, and second, is to measure the farm-level marketing efficiency and its determinants in the study region.

Data and Methodology

Data

The study is exclusively based on primary survey evidence in the state of West Bengal, India. For the empirical survey, a total of 230 farmer households were selected from three districts (namely, Purulia, Purba Bardhaman and Nadia) of West Bengal. Convenience sampling technique is used in the selection of surveyed districts. Out of the 230 farmer households selected in the empirical survey, 123 are organic farmer households² and the rest (i.e., 107) are conventional farmer households. To collect data, a face-to-face interview with sample farmers was carried out at the end of the Kharif season³ of 2017–18 (November 2017–June 2018). A structured questionnaire was used to collect data regarding agricultural production, quantity retained and sold, marketing channel used, farmgate price of crops, marketing costs incurred and demographic characteristics of the surveyed farmer households. The data relating to production, sale, retention, price and marketing costs cover only two⁴ Kharif seasons, that is, 2017–18 (the season concluded just before the primary survey) and 2016–17 (the earlier season). The study has considered ‘Aman rice’ (also known as Kharif rice) as the reference crop since it was found to be the only major crop grown throughout the study region.

Methodology

Estimation of Farm-level Marketing Efficiency: Farmers’ Marketing Efficiency Measure

Marketing efficiency can be expressed as a ratio of market output to market inputs (Jeyanthi 2018). The market input in this context indicates the resources utilised in the marketing process, which is measured in terms of the costs incurred while utilising these resources. The market outputs, on the other hand, represent the benefits resulting from marketing activities (Crawford 1997). However, it is very difficult to measure marketing output, as it is not directly observed (Singbo et al. 2014). There are three marketing efficiency indicators (i.e., conventional measure, Shepherd’s approach and Acharya’s modified measure of marketing efficiency) that are widely used to measure the efficiency of marketing channels (Acharya and Agarwal 2021). However, the objective of this article is to estimate the farm-level marketing efficiency in the study region. So, none of these approaches can be directly applied in our study. However, drawing lessons from these existing measures, we have developed an indicator that measures the marketing efficiency of farmers. The newly developed indicator is referred to as Farmers’ Marketing Efficiency Measure (FMEM), which is computed as a ratio of marketing output to marketing input. The marketing input in this context represents the total marketing costs incurred by the farmers in connection with the marketing activity. Since the study is based on farm-level data, the marketing output used in this indicator is measured by prices (following Singbo et al. 2014). Moreover, price is not only the output of the efforts put in by the farmers with their marketing activities⁵ but also the sole criterion of farm level marketing efficiency.

The Farmers’ Marketing Efficiency Measure is worked out as follows:

Farmers' Marketing Efficiency Measure (FMEM) = \frac{Average Farmgate Price}{Average Marketing Costs}

Marketing costs used in this measure include the costs incurred on physical inputs (for packing and storing), transportation charges (incurred in connection with storing the produces and bringing them into the market outlets), commission and market fees. Here, price indicates the average farmgate price received by the farmers. Both the input and output (i.e., average farmgate price and average marketing costs) used in this indicator are computed by taking the data for the last two Kharif seasons (i.e., 2016–17 and 2017–18). Both price and marketing costs⁶ are measured in Rs/Quintal.

The newly constructed farmers’ marketing efficiency measure reflects the efficiency of farmers in achieving better prices (output) for their crops as a function of marketing costs (input). The higher the ratio, the higher the efficiency, and vice versa. Based on this, a farmer is said to be marketing efficient, if he/she is getting equal or better prices (output) with minimum marketing cost (input). Conversely, if higher marketing costs do not correspond with higher price realisation, then the farmer is said to be marketing inefficient.

Estimation of Farm-level Marketing Efficiency: Data Envelopment Analysis

Data envelopment analysis (DEA) is used to estimate the marketing efficiency of each farmer (both organic and conventional) in the study region. We have applied the output-oriented⁷ DEA model under the assumption of Variable Returns to Scale (VRS).

For the estimation of the marketing efficiency of the farmers, the study has considered a single marketing output and a single input. The input used in the DEA model is the average marketing costs incurred by the farmer and the output is measured by average farmgate price.

To analyse marketing efficiency, we have categorised the surveyed farmers into three different classes (i.e., low, moderate and high) depending on their achievement of marketing efficiency scores. In order to classify farmer households, first mean (µ) and standard deviation (σ) of the efficiency scores are computed for the study region. Next, based on the computed values of µ and σ, the three classes are determined as follows: low-efficiency (having a marketing efficiency score below µ–σ), moderate-efficiency (efficiency score between µ–σ and µ+σ) and high-efficiency class (efficiency score above µ+σ).

Determinants of Farmers’ Marketing Efficiency: Truncated Bootstrap Regression

The second stage of the analysis deals with the identification of various determinants of farmers’ marketing efficiency. Truncated bootstrap regression model⁸ is used to find out the factors that influence the marketing efficiency scores. This model has been popularly used to study the determinants of farm-level technical (Pham et al. 2018; Singbo et al. 2010, 2014) and marketing (Singbo et al. 2014) efficiency. The specification of the empirical model is as follows:

ME = γ₀ + γ₁ ECON_STATUS + γ₂ EDUCATION + γ₃FARM_EXP + γ₄FARM_SIZE + γ₅DISTRICT 1 + γ₆DISTRICT 2

where ME represents the marketing efficiency score obtained in the first stage of the analysis

γ₀ indicates the coefficient of the intercept of the regression

γ_i (i = 1…6) are the coefficients of the efficiency determinants

A list of independent variables along with their expected effect⁹ on the efficiency scores is presented in Table 1.

Table 1.

A List of Determinants of Farmers’ Marketing Efficiency.

Determinants	Code	Measurement	Expected Sign in the Regression
Economic status of the farmer household	ECON_STATUS	If above poverty line (APL) then 1; if below poverty line (BPL) then 0	+/–
Formal education of the household head	EDUCATION	Number of years of schooling	–
Farming experience of the household head	FARM_EXP	Number of years engaged in farming	–
Landholding of the farmer household	FARM_SIZE	Cultivable landholding measured in hectares	+/–
District dummy 1	DISTRICT 1	if the farmer is from Purulia then 1, otherwise 0	+/–
District dummy 2	DISTRICT 2	if the farmer is from Purba Bardhaman then 1, otherwise 0	+/–

Results and Discussion

Alternative Marketing Channels in the Study Region

Survey evidence highlights the existence of two types of marketing channels (shown in Figures 1 and 2) in the study region as discussed below.

Figure 1.

Marketing Channel for Organic Farming in Nadia District (Channel 1: Contract Sale).

Figure 2.

Marketing Channel for Organic as Well as Conventional Farming in Purulia and Purba Bardhaman Districts (Channel 2: Direct Sale).

Channel 1 in Figure 1 (featuring contract sale) shows the marketing channel for organic farming in the Nadia district. It can be seen that there is only one intermediary (i.e., the contracting company) operating in between the organic producers and the final consumers. Primary survey evidence reveals that the organic farmers in Nadia sell their organic produce to the contracting company (i.e., ‘ONganic Foods Pvt Ltd’) at a pre-agreed price. After purchasing from the farmers, the contracting company makes further processing in its production facility and then sells it to exporters or domestic consumers through their retail outlets and online delivery platforms. The contracting company has also played a crucial role in helping the organic farmers in Nadia to get their products certified under the ‘third-party certification system’.¹⁰ This has helped the organic farmers in Nadia to enter into attractive markets and get higher prices for their organic produce.

Channel 2 in Figure 2 (depicting direct sale) indicates the marketing channel for conventional farming in all of the surveyed districts. Interestingly, the same marketing channel is observed for the organic farmers in Purulia and Purba Bardhaman districts (unlike Nadia district, where the marketing channels of organic farming were distinct from conventional farming). Due to the absence of separate marketing channels for selling organic crops, organic farmers in Purulia and Purba Bardhaman districts were compelled to use the traditional marketing channels (i.e., channels used by the conventional farmers). As a result, they did not receive a price premium for the organic produce. The conventional farmers mostly sold their agricultural produce to local middlemen at the farm gate. However, farmers who sold their crops to public procurement agencies (popularly known as APMC mandis) received higher prices (i.e., Minimum Support Prices declared by the government) for their produce, but only a handful of conventional farmers in Nadia district were able to access this channel. Interestingly, the study did not find such a government-supported procurement system for organic produce. A comparison between the two channels also revealed that in channel 2, more intermediaries (i.e., middlemen or commission agents, processors, wholesalers and retailers) are operating between the farmers and final consumers.

Estimates of Farm-level Marketing Efficiency: Farmers’ Marketing Efficiency Measure

This stage of the empirical analysis is carried out in two steps. In the first step, the existing marketing arrangements of the farmers have been identified. Survey evidence shows a noticeable variation in the marketing arrangements of the farmers in the study region. The heterogeneity in marketing arrangements can be attributed to the varying choices of farmers regarding their nature of sale (direct or contract sale), type of crop variety (ordinary or high value-rice variety) and type of certification system (third party or PGS).

The second step deals with a comparison of the marketing efficiency between the organic and conventional farmers in the study region with the help of FMEM. Table 2 presents the farm level marketing efficiency in the study region.

Table 2.

District-wise Variation in the Farmers’ Marketing Efficiency.

District	Purulia		Purba Bardhaman		Nadia
District	Organic	Conventional	Organic	Conventional	Organic	Conventional
Average production (Qt/Ha) [P]	26.6	36.03	25.29	34.32	26.01	45.62
Average retention (Qt/Ha) [C]	1.5	1.27	0	0	0	1.15
Average marketed surplus (Qt/Ha) [MS = P-C]	25.1	34.76	25.29	34.32	26.01	44.47
Average marketing costs (Rs/Ha) [MC]	4,410	5,631	7,589	7,845	8,103	7,065
Average marketing costs (Rs/Qt) [I = MC ÷ MS]	175.70	162.00	300.08	228.58	311.53	158.87
Average farmgate price (Rs/Qt) [O]	1,298	1,375	2,875	2,925	3,154	1,483
Farmers’ marketing efficiency measure [FMEM = O ÷ I]	7.39	8.49	9.58	12.80	10.12	9.33

Source: Field Survey, 2017-18.

Table 2 shows a considerable variation in the average farmgate price and average marketing costs (per quintal) depending on the method of cultivation (organic or conventional). It is observed that average marketing costs (per quintal) are considerably higher for organic farmers as compared to the conventional farmers in the study region. The lower average production in organic farming resulted in lower marketed surplus and thereby increased the marketing costs.¹¹ Among the organic farmers, marketing costs (per quintal) are highest in Nadia, followed by Purba Bardhaman and Purulia districts. The other element (besides production) responsible for variation in marketed surplus is retention (i.e., quantity retained for home consumption and seeds). There is a noticeable inter-district variation in the average retention of the farmers. The average retention is higher for the farmers (both organic and conventional) in Purulia. Since the majority of the farmers in this district are poor and subsistence farmers, a larger portion of the produced crop is retained for home consumption and seeds. This, in turn, reduces the marketed surplus of the farmers in Purulia district. On the other hand, the farmers in Purba Bardhaman did not retain any of their produced crops. The farmers (both organic and conventional) in Purba Bardhaman mainly grow ‘aromatic rice’ (a high value-cash crop). Hence, the entire quantity of production is sold in the market. The requirement for home consumption in this district is fulfilled by another rice variety (grown in the Boro season). In the case of Nadia district, a contrasting picture is observed regarding the quantity of retention between organic and conventional farmers. The conventional farmers in Nadia have retained a smaller portion of their produced crops to meet their consumption requirements. There is no such retention for the organic farmers in this district. The organic farmers in Nadia have adopted contract farming. As a part of the contractual agreement, the organic farmers in Nadia have to sell their entire quantity of production to the contracting company. So, they could not retain any portion of the produced quantity for home consumption or any other purposes.

A closer look at the average farmgate prices reveals a noticeable inter-district variation in prices between organic and conventional crops in the study region. Specifically, average farmgate prices are lower for organic farmers in Purulia and Purba Bardhaman districts, whereas in the Nadia district, prices received by the organic farmers are substantially higher than their conventional counterparts. For the conventional farmers, price realisation is highest in Purba Bardhaman, followed by Nadia and Purulia districts. An inter-district comparison reveals that the average price received by the farmers (both organic and conventional) in Purulia is the lowest among the surveyed districts. Since the majority of the farmers (especially organic farmers) in Purulia are poor and engaged in subsistence farming, they are compelled to sell most of their produce immediately after harvest to meet their cash obligations. This results in distress sales which explain the lower price realisation in this district. On the other hand, the average farmgate price is found to be substantially higher for the farmers in Purba Bardhaman and Nadia districts. The higher price realisation for the farmers in Purba Bardhaman (both organic and conventional) and Nadia (organic) districts can be attributed to their choice of crop variety. The farmers (engaged in commercial agriculture) in these two districts grow ‘aromatic’ rice varieties that attract higher prices. These rice varieties capture higher prices, which positively affects farmers’ marketing efficiency. However, the yields of these varieties are substantially lower, which has a negative effect on marketing efficiency. This is because the lower yield of these varieties reduces the marketed surplus of the farmers, which in turn increases per unit marketing costs. On the other hand, the conventional farmers in Nadia and the farmers (both organic and conventional) in Purulia grow a ‘non-aromatic’ rice variety, which carries a relatively lower price. However, an inter-district comparison shows that the farmers (conventional) engaged in commercial agriculture in Nadia received a substantially higher price (also with considerably higher yields) as compared to the farmers in Purulia.

A district-wise comparison of efficiency values between the two categories of farmers (i.e., organic and conventional) is carried out. Results show a substantially lower efficiency value of the organic farmers (7.39) in the Purulia district as compared to their conventional counterparts (8.49). Similarly, in Purba Bardhaman district, the marketing efficiency value of the organic farmers (9.58) is found to be substantially lower than their conventional counterparts (12.80). The lower efficiency for the organic farmers in these two districts may be attributed to the higher marketing costs, which are not compensated by higher prices for their organic produce. On the other hand, opposite results are witnessed in the Nadia district. The efficiency values of organic farmers (10.12) in Nadia district are considerably higher than their conventional counterparts (9.33). It suggests that, given the resources used, the organic farmers in Nadia district are getting better marketing output (i.e., price) as compared to their conventional counterparts.

There is also a considerable district-wise variation in the marketing efficiency of organic farmers. Among the organic farmers, the marketing efficiency value is highest in Nadia (10.12), followed by Purba Bardhaman (9.58) and Purulia (7.39) districts (Table 2). The variation in marketing efficiency can be explained by the marketing choices of the organic farmers. In the context of our study, it has been observed that marketing choices (involving selection of crop variety, marketing channel and type of certification) played a key role in influencing the marketing efficiency of organic farmers in the study region.

The highest marketing efficiency value of the organic farmers in Nadia reflects their better marketing choices. These choices include selecting crop varieties that attract higher prices, choosing a marketing channel that captures organic price premiums and selecting a certification system that reflects the superior quality¹² of organic produce. Survey evidence shows that the organic farmers in Nadia district had entered into an agreement to sell their organic produce to the contracting company at a pre-agreed price. The selection of the contracting company as the marketing channel proved to be a smart choice, as it gave the organic farmers in Nadia access to attractive markets and assisted them in capturing price premiums. In addition, the contracting company made some key choices that helped the organic farmers in Nadia to get better prices for their produce. For instance, based on the inputs from the contracting company, the organic farmers in Nadia decided to grow rice varieties (i.e., aromatic rice) that have a better market demand and attract higher prices. The other notable marketing choice made by the contracting company is to certify the organic products of the farmers in Nadia under the third-party certification system so that the organically grown crops reflect superior quality. It has been seen that consumers of organic foods often set their perception about the quality of foods based on the type of certification system used in certifying these organic foods. Therefore, the quality aspect of organic produce can be established from the choice of certification arrangements. Consumers are less apprehensive about the quality of organic foods certified under the third-party certification system as it is carried out by independent agencies. As a result, third-party certification has a wider acceptability to consumers both at the domestic and international levels. Therefore, the farmers’ strategy to get their products certified under the third-party certification may have played a part in higher price realisation in the Nadia district.

On the other hand, the lowest marketing efficiency value for the organic farmers in the Purulia district is a reflection of poor marketing strategies. These involve poor choice of crop variety and the inability to search for marketing channels that capture price premiums. The lower efficiency scores in Purulia may also be a reflection of the lower quality of organic produce grown by the farmers in the district. The organic farmers in Purulia follow the PGS certification, which has a lower acceptability, as it employs a peer-group monitoring system in the organic certification process. Therefore, consumers of organic foods are more apprehensive about the quality of organic foods certified under PGS. Hence, organic products certified under PGS attract lower prices.

The marketing efficiency value of the organic farmers in Purba Bardhaman is slightly lower than that of the Nadia district, but it is substantially higher than the organic farmers in Purulia district. Survey evidence indicates that the organic farmers in Purba Bardhaman district had a better marketing strategy regarding their choice of crop variety. The crop varieties grown by the organic farmers in Purba Bardhaman attract higher prices, unlike Purulia district. This explains the higher marketing efficiency value of the organic farmers in Purba Bardhaman district as compared to Purulia district. However, the organic farmers in Purba Bardhaman district were poor in other marketing strategies involving the choice of PGS certification and the inability to search for marketing channels that capture price premiums. These choices may have resulted in lower marketing efficiency value than Nadia district.

Estimates of Farm-level Marketing Efficiency: Data Envelopment Analysis

An analysis of the marketing efficiency of each farmer in the study region is carried out. An output-oriented DEA model (under the assumption of Variable Returns to Scale) is used.

Table 3 presents the marketing efficiency scores for the farmers in the study region. It shows that the farmers in the study region have an overall mean marketing efficiency score of 0.667. It implies that, on average, the farmers can increase their marketing output level by 33.3% to become fully marketing efficient. Based on their marketing efficiency scores, the farmers are categorised into three different classes: low-efficiency (marketing efficiency score below 0.494), moderate-efficiency (score between 0.494 and 0.840) and high-efficiency (efficiency score above 0.840). The frequency distribution of the marketing efficiency scores for the farmers in the study region (Table 3) shows that the majority of the farmers (approximately 67%) fall under the moderate-efficiency class. Moreover, a sizable portion of total farmers in the study region (i.e., 19%) belong to the high-efficiency class. The rest of the farmers (15%) belong to the low-efficiency class.

Table 3.

Frequency Distribution of Marketing Efficiency Scores of Farmers in the Study Region.

Efficiency Classes	Organic Farmers (N = 123)	Conventional Farmers (N = 107)	Overall (N = 230)
Efficiency Classes	VRS-ME	VRS-ME	VRS-ME
0–0.493	23 (18.70)	11 (10.28)	34 (14.78)
0.494–0.840	68 (55.28)	85 (79.44)	153 (66.52)
0.841–1	32 (26.02)	11 (10.28)	43 (18.70)
Summary Statistics of Marketing Efficiency
Maximum	1.000	1.000	1.000
Minimum	0.329	0.385	0.329
Mean (µ)	0.691	0.639	0.667*
Standard deviation (σ)	0.196	0.137	0.173*

Source: Field survey, 2017–18.

Notes: The statistical analysis has been made using DEAP statistical package.

The efficiency classes are determined on the basis of the µ and σ value denoted by the ^* signs.

Figures within the parenthesis denote the respective percentage of farmers belonging to a particular efficiency class.

VRS-ME: Marketing Efficiency under Variable Returns to Scale.

A category-wise breakup of the marketing efficiency score reveals that the mean marketing efficiency scores for the organic and conventional farmers in the study region are 0.691 and 0.639, respectively. It implies that, on average, the organic and the conventional farmers in the study region can increase their marketing output level by 30.9% and 36.1%, respectively, to attain full marketing efficiency. A comparison between the two categories of farmers (i.e., organic and conventional) suggests that the mean marketing efficiency score is higher for the organic farmers in the study region. It implies that given the resources used, the organic farmers in the study region are achieving better marketing output compared to their conventional counterparts.

Determinants of Farmers’ Marketing Efficiency: Truncated Bootstrap Regression

The second stage of the analysis considers some farm-specific variables as determinants of the marketing efficiency scores (from the DEA model). A single truncated bootstrap regression model has been fitted by including the responses collected from 230 sample farmers (including both organic and conventional farmers) from all three surveyed districts. The result of the regression is presented in Table 4.

Table 4.

Results of Truncated Bootstrap Regression.

Dependent Variable: ME
Method: Truncated Bootstrap Regression
Variable	Coefficient	Bootstrap Std Error	Prob.
CONSTANT	0.5352904^*	0.0337232	0.000
ECO_STATUS	0.08205^*	0.0174972	0.000
EDUCATION	0.0053383^***	0.0030494	0.080
FARMING_EXP	–0.0013846^**	0.0006318	0.028
FARM_SIZE	0.2097417^*	0.0616341	0.001
DISTRICT 1	–0.166805^*	0.0146989	0.000
DISTRICT 2	–0.125957^*	0.0337232	0.000

Source: Field survey, 2017–18.

Notes: ^*Significant at 1%, ^**Significant at 5%, ^***Significant at 10%.

The model is strongly significant with a Wald χ² value of 465.87. Table 4 highlights that marketing efficiency is significantly related to economic status, level of education, farming experience, farm size and two district dummies.¹³ The positive effect on economic status suggests that farmer households above the poverty line are more marketing efficient than households below the poverty line. This suggests better marketing strategies on the part of economically affluent farmer households in the study region. Interestingly, regression results show that the level of education¹⁴ attained by the head of the household positively affects their marketing efficiency. It suggests that farmers who have more education are more marketing efficient than the farmers who have fewer years of formal schooling. This is because farmers who have more education tend to possess better negotiation skills (Pham et al. 2019), which is essential for the realisation of higher prices (thus higher marketing efficiency).

The results also reveal that the farming experience of the head of the household negatively affects marketing efficiency. It implies that relatively experienced farmers have lower bargaining power in generating remunerative prices for their produce (Singbo et al. 2014). One possible explanation of this phenomenon could be that experienced farmers often lack the courage¹⁵ to search for better alternative marketing outlets. Besides, producers are often concerned about maintaining a good relationship with the marketers to secure their selling opportunities in the long term and hence agree to take a lower price temporarily (Lancioni 2005). Findings in Table 4 show that the size of the landholding of the farmer household positively affects marketing efficiency. It implies that households having larger size of landholdings are better at negotiating higher prices for their crops.

Table 4 shows that both of the district dummy variables are significant at a 1% level. It suggests that the heterogeneity of marketing arrangements has a strong influence on farmers’ marketing efficiency. Survey evidence shows a diverse set of marketing arrangements (involving decisions relating selection of crop variety, preference of certification and choice of marketing channel) adopted by the farmers in the surveyed districts. Regarding the choice of crop variety, survey evidence shows that the farmers in the Nadia and Purba Bardhaman districts grow high-value rice, whereas the farmers in the Purulia district cultivate subsistence (or low-value) rice. The choice of certification also varies among the organic farmers in the study region. The organic farmers in Purulia and Purba Bardhaman districts are certified under PGS certification, while the organic farmers in Nadia are certified under the third-party certification system. Regarding the choice of marketing channel, evidence reveals that the farmers in Nadia are using specialised marketing channels such as the contracting company (all of the organic farmers) and govt. mandis (few conventional farmers), whereas the farmers in Purulia and Purba Bardhaman districts are solely relying on middlemen to sell their produce. Moreover, the district dummies had a negative effect on marketing efficiency, which indicates that marketing efficiency is lower for the farmers in Purulia and Purba Bardhaman district relative to the reference district (Nadia). Therefore, the analysis suggests that farmers who produce high-value crops and sell these crops through specialised marketing channels (such as the contracting company) are more marketing efficient than those selling subsistence (or low-value) crops to middlemen. This result highlights that better choices regarding marketing arrangements can improve farmers’ marketing efficiency.

Conclusion and Policy Implications

The article assessed the efficiency of the organic farmers in the three districts (namely, Purulia, Purba Bardhaman and Nadia) of West Bengal in exploring the available marketing opportunities. First, an attempt has been made to explore the existing marketing channels in the study region. The result shows a separate marketing channel for organic crops only in the Nadia district. On the other hand, there was no such specialised linkage¹⁶ with the organic market in Purulia and Purba Bardhaman districts. As a result, the organic farmers in these two districts were compelled to use the same marketing channel as conventional farmers (which also meant selling organic crops at the price of conventional crops, hence no price premium). This highlights the severity of marketing problems faced by the organic farmers in Purulia and Purba Bardhaman district.

The higher marketing efficiency score is an outcome of better marketing activities, which involves producing high-value crops, choosing a certification system that reflects the quality of output, and putting time and effort into negotiation with the existing marketing channel or searching for better marketing channels to get higher prices for their crops. Evidence reveals that given the level of resources they use, organic farmers in Purulia and Purba Bardhaman districts are facing marketing problems. On the other hand, a higher marketing efficiency score for the organic farmers in Nadia reflects their ability to generate higher prices for their produce through better marketing activities. The result also reveals that the economic status of the farmer household, farmer’s level of education, years of farming experience and the size of landholding of the farmer household are the major determinants of marketing efficiency. Moreover, the significance of district dummy variables shows that farmers’ marketing efficiency significantly depends on their choice of marketing arrangement.

The study has also made some policy recommendations aimed at improving the efficiency of organic farmers in exploring the available marketing opportunities in the study region. To improve the marketing efficiency of the organic farmers, agricultural extension services (such as providing crop advisory and training relating to production, value addition and marketing activities) may be provided. In this context, the supporting agencies (especially those working under the PKVY eco-system) in the study region may be called into action to play a more active role in handholding the organic farmers in their marketing and other value-adding activities. In addition, looking at the lower bargaining power of the organic farmers (due to the lack of existence of specialised organic marketing channels), there is a need to strengthen the organic producers-market linkage in the study region. Marketing cooperatives may be set up by aggregating the products of small-scale organic farmers to improve the bargaining strength of the farmers. Besides, the use of e-commerce platforms (like the recently launched ‘Jaivik Kheti’ portal of the government of India) can be promoted to establish a direct linkage between organic producers and consumers and ensure organic farmers get the best prices for their products. The government may also introduce the provision for a price support mechanism (popularly known as MSP) for organic crops so that organic farmers get adequate prices for their crops. It has also been observed that the choice of certification has a significant role in higher price realisation for organic farmers. Considering the high cost of third-party certification systems, the economically efficient PGS certification can be promoted. However, necessary steps should be taken to build the consumers’ trust in PGS certification so that organic farmers certified under PGS have adequate marketing opportunities.

The study has certain limitations. In identifying the marketing system of organic farming in the study region, it has solely relied on the in-depth interviews of farmers. The study did not collect information from market participants and consumers in the study region. However, to get a broader picture of the marketing mechanism of organic farming in the study region, there is a need to examine the organic value chain from farm-gate to consumers’ table. Besides, the study is confined to a single crop (i.e., Kharif rice) and covers 123 organic farmer households in three districts of West Bengal, namely Purulia, Purba Bardhaman and Nadia. The replication of this study across more districts and different crop types is expected to generalise the study’s findings.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Notes

ORCID iDs

Nilojyoti Koner

Arindam Laha

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