Abstract
This study is an attempt to identify the socioeconomic factors that affect farmers’ decision-making process regarding the adoption of organic farming. A total of 100 (50 organic and 50 conventional) farmers were interviewed and their demographic, socioeconomic and ecological behaviour differences were studied. The behavioural analysis identified education, environmental concerns and social benefits as the prominent drivers of organic farming, while lack of government support in marketing, managerial and technical spheres as major constraints to its adoption. The probit model confirms that farmers with a smaller size of holding who are educated, younger and practice diversified cropping are more inclined towards organic farming.
Keywords
Introduction
During the second half of the 20th century, to align with the spirit of capitalism, the third world nations transformed their agriculture production from traditional self-sustained farming to commercial farming (based on intensive use of chemical fertilizers, pesticides, weedicides, etc.) (Mazoyer and Roudart, 2006). In Punjab, as in the rest of the world, capitalist and populist policies have extended unconditional support in the form of subsidized inputs (superfluous supply of fertilizer subsidy, negligible irrigation charges, free electricity to farmers, state-funded mechanization, etc.), for conventional farming (famously known as Green Revolution technology) to reap immediate rewards.
In the 1970s and 1980s, due to successful implementation of Green Revolution technology, Punjab emerged as a role model for third world countries and other states of Indian union (Singh, 2011). However, since 1990s, the intensive inputs-driven model of transformation of Punjab’s agriculture has come under severe criticism for socioeconomic and ecological reasons. After 50 years of implementation of Green Revolution technology, as Shiva (1991) argued, Punjab is no more a land of prosperity. The production and productivity growth which increased at a very rapid rate during 1970s and 1980s started showing signs of deceleration during 1990s, and after 2000, it moved towards stagnation. Furthermore, the retreat of the State from agrarian affairs after the introduction of neoliberal reforms in 1991 gave way to the reduction in subsidies to agriculture and deregulation of agricultural input prices. This has increased the cost of cultivation which has further given birth to the socioeconomic problems in the State such as indebtedness and farmers’ suicide (Singh and Singh, 2020). In addition to these socioeconomic upheavals, the Green Revolution was criticized for another reason; high yield could not be obtained without meeting certain conditions like assured irrigation, intensive use of chemical fertilizers, pesticides and higher use of machinery (Glaeser, 1987). All these conditions led to issues such as overexploitation of ground water, chemicalization of soil, monoculture (wheat–paddy cycle) and increase in health problems which have adversely affected the environment and ecological balance in the State.
The magnitude and severity of economic and ecological crisis caused by the chemical-led cultivation call for an urgent need to rethink about an alternative model. This paper argues that in order to reinvigorate the ecological balance of the State, organic farming can emerge as an effective alternative. However, the transformation of production structure from capitalist/commercial agriculture towards organic practices is not an easy task. It requires efforts at multiple levels which in turn require, first, a comparative analysis of the economics of organic and conventional farming and, second, an investigation into the behaviour and attitudes of farmers to adopt organic agriculture.
In the light of these facts, this paper is an attempt to compare the economic and behavioural aspects of organic and conventional farmers. For the purpose of analysis, this article is divided into three parts. The first part provides the review of studies and methodology used for the analysis. The second part compares the cost of and return from wheat cultivation in organic and conventional farms. The last part identifies the drivers and constraints in decision-making process of organic and conventional farmers and presents empirical evidence about farmers’ behaviour. The probit model is used to explore the underlying factors that explain the individual’s willingness to adopt organic farming.
Part I
Review of concerned literature
Conventional agricultural practices have been widely criticized by scholars due to their negative implications for biodiversity, environment and human health. To reconcile with natural resources, organic farming has been presented as an important alternative to conventional agricultural practices. On this account, many attempts were made by the scholars to examine the drivers and constraints behind the decision-making process of cultivators regarding farm practices. Most of the studies regarding the attitude and behaviour of farmers to opt for organic farming have been conducted in the regions of developed countries (New Zealand, Canada, Belgian, the United States, Austria Norway, Greece, etc.). These studies have identified and compared the farmers’ characteristics, beliefs and attitudes towards sustainable farming and conventional farming (Cock, 2005; Comer et al., 1999; Duram, 2000; Egri, 1999; Herath and Wijekoon, 2013; Laepple and Donnellan, 2008; Papadopoulos et al., 2018; Senger et al., 2017; Sullivan et al., 1996; Yazdanpanah et al., 2014). They have found differences in demographic, economic, political and social characteristics; farming experience; farm-related characteristics; capital investment and general environment concerns and attitudes towards use of agrichemicals among organic and conventional farmers. Pinthukas (2015) found that debt, income, bio-physical and knowledge limitations act as major constraints in adopting organic cultivation. In addition to this, the decision-making process to adopt organic farming is also determined by information sources, government policies, profitability concerns and farm organization memberships (Darnhofer et al., 2005; Fairweather, 1999; Hall, 2014; Marsh et al., 2016; Wood et al., 2006). However, there are some comparative studies which have found that age and education are not significantly different for organic and conventional farmers (Khaledi et al., 2010; Lockeretz and Wernick, 1980).
Kerselaers et al. (2007) and Te Pas and Rees (2014) highlighted that profitability, sustainability, potential income change and organic carbon content of soils are higher in organic farming management than conventional management. Tuncer and Boz (2017) argued that organic farmers were more aware about Internet, more active to search information regarding farming and more participative in conferences and workshops than conventional farmers. Finley et al. (2017) highlighted that organic farming has more potential for job creation that can contribute in economic growth. These studies recommended that in order to promote organic farming, the identified factors such as large farms, more education and economic benefits must be encouraged along with sustainable and environment-friendly techniques (Best, 2009; Koesling et al., 2008; Lapple and Rensburg, 2011). The studies conducted by Padel (2001) and Rigby and Caceres (2001) suggested that information and advisory provisions need to be developed to accelerate the adoption rate of organic farming among the farmers. The condition of natural resources and the position of farmers within the system need to be contemplated before the formation of any policy and its adoption.
Among the developing countries, Van Thanh and Yapwattanaphun (2015) and Djokoto et al. (2016) argued about Vietnam and Ghana, respectively, that the adoption of organic farming is determined by age, economic status, education, extension services, feasibility of practices, gender, household size, farming experience, access to extension services and provision of credit facilities. Prihtanti (2016) and Issa and Hamm (2017) highlighted about Central Java Province and Syria, respectively, that motivations behind organic cultivation were low cost of production, health concerns and soil productivity. On the contrary, conventional farmers’ reasons for not opting organic were yield uncertainty, complicated practices and unfamiliarity with the organic production system. Nandi et al. (2015) argued that diverse factors, such as market forces, environmental concerns, political support factors, benefit–cost and community factors, acted as driving forces towards adoption of organic production in Karnataka (India). Among the barriers to conversion, production barriers were most challenging for smallholders, followed by marketing, techno-managerial, economic and financial barriers.
As far as existing literature on Punjab agriculture is concerned, many scholars have made attempts to study the socioeconomic and ecological consequences of Green Revolution technology (Chand, 1999; Johl and Rao, 2002; Mittal et al., 2013; Prasad and Karman, 2017; Sidhu, 2002; Srivastva et al., 2015; Tiwana and Singh, 2017). However, there is hardly any comprehensive attempt to study the drivers of and constrains to organic farming as an alternative to bring Punjab agriculture out of economic distress and ecological crisis. For instance, Charyulu and Dwivedi (2010) only analysed the economics of organic farming as compared with conventional farming among four states. The study has found that in Punjab, net returns favoured organic farming for wheat only, whereas paddy and cotton crops had lower net returns in organic practices than conventional farming. Singh and Grover (2011) also assessed only the economic viability of organic wheat cultivation in Punjab. The study has found that total variable costs under organic farming are lower than inorganic farming and yields of organic wheat are lower than inorganic wheat. However, net returns are more in organic wheat. The present study is a novel attempt to fill the research gap through comprehensive analysis of and behavioural aspects of organic and conventional farmers along with an empirical investigation of socioeconomic determinants. It compares not only the economic determinants but also the educational, environmental, marketing, governmental and technical aspects of conventional and organic farmers.
Database and methodology
This study is based on primary data collected from 100 (50 organic and 50 conventional) farmers in Sangrur district of Punjab State. For the list of organic farmers in Sangrur district, many agencies were inquired such as Punjab Agro Export Corporation Limited, Chandigarh; Krishi Vigyan Kendra (KVK), Kheri and Punjab Agricultural Corporation, Sangrur. The contacts of organic farmers of Sangrur district were obtained from Kheti Virasat Mission (KVM), a non-governmental organization (NGO) promoting organic production in Punjab region. A list of 50 organic farmers for interview was compiled from villages named Akoi Sahib, Amargarh, Badshahpura, Bhalwan, Bhulerheri, Bhutal Kalan, Chatha Nanhera, Chhajla, Choonda, Dasaunda Singh Wala, Daska, Dhandoli Khurd, Gagga, Gobindgarh, Kanjala, Kanoye, Kapial, Kehru, Khanal kalan, Kheri Chahalan, Kheri Kalan, Kotra Amru, Lasoi, Lehragaga, Majhi, Mauli Kalan, Mauran, Nagra, Nanadgarh, Raidhrana, Sangatpura, Sanghreri, Sangrur, Janal, Sarrow and Sunam. The conventional farmers were deliberately selected from the same villages with same landholding size. In total, 50 organic and 50 conventional farmers were surveyed through face-to-face interviews.
Prior to collecting data, the pilot study was conducted with an objective to explore farmers’ attitudes, perceptions, feelings and ideas about organic farming by means of focus group interviews. Three focus groups with eight farmers each were conducted to generate survey questionnaire. Participants discussed (1) the principal advantages and disadvantages of organic farming that could influence their decision to adopt it; (2) the categories of people or social institutions that may encourage or discourage their decision and (3) the circumstances that could potentially facilitate or hinder the adoption of organic farming. While it was tried that each focus group contained equal number of conventional and organic farmers, the numbers varied during the actual discussion. A detailed structured schedule was prepared using the results of the focus group interviews to gather information on sociodemographic characteristics, cost of cultivation, returns and socioeconomics that were considered to be affecting the farmers’ decision about organic farming.
The economics of wheat production is examined using the methodology adopted by Directorate of Economics and Statistics, Department of Agriculture and Cooperation and Commission for Agricultural Costs and Prices (CACP) to examine the economics of wheat cultivation. The cost and return of wheat cultivation are calculated on per acreage area of land across various landholding sizes. The present study is concerned only with the production costs which were directly or indirectly borne by the farmers.
Furthermore, the Theory of Planned Behaviour (TPB) is used to examine the socioeconomic incentives and barriers for organic farmers and the intention of conventional farmers to adopt organic farming. The use of the TPB is justified, first, by the fact that it aims to address the issue of over-simplistic model of the attitude–behaviour relationship through discussing the ‘behavioural approach’ in the context of advances in socio-psychological theory and, second, because relatively few studies focus on the social psychological factors that influence farmers’ decisions on adoption of organic farming in developing countries, like India. For reliability of behaviour determining factors, the consistencies of the attitudinal statements across different factors are checked by the use of Cronbach’s alpha value. The acceptable level for exploratory variables is 0.65 or above. Furthermore, probit regression model is used to figure out the structural factors that influence farmers’ willingness to adopt organic farming.
Part II
Farm profitability: a comparative analysis
In a market-driven farming system, organic farming would be acknowledged as a more viable method of farming by farmers at large if it enhanced the returns or reduced the costs of cultivation. In the state of Punjab, agriculture production structure and relations are dominated by the cost–benefit analysis in pure economic sense and are considered to be the driving force of all major decisions regarding what to produce, how to produce, how much to produce and so on. Thus, it becomes imperative to investigate economic viability of organic farming in comparison with conventional farming. The fundamental objective of the analysis is to examine whether there is any significant difference between the two methods of cultivation in terms of cost and return. It is also important to examine the variation in cost and return from crop cultivation under two methods across various sizes of land holdings.
The cost and return of wheat cultivation under organic and conventional methods have been comprehensively complied and summarized in Tables 1 and 2. It is clear from the data that the operational cost differences between the two methods of cultivation majorly exist in the cost of human labour, fertilizers, pesticides and manure. These differences exist due to greater use of natural resources in organic cultivation in place of market-based inputs. The use of market-based inputs makes up a considerable proportion of the cost of cultivation in case of conventional farmers. In case of organic cultivation, fertilizing and plant protection material are prepared from livestock dung, cow urine, jaggery, gram flour, neem leaves, kitchen residue, compost and so on. All these are easily available in nature and at negligible costs, which accounts for lower costs on such inputs.
Cost of wheat cultivation of sampled farmers (in INR).
Source: Field Survey.
Figures in brackets represent relative difference. Subfields with zero costs (rental value of owned land and rent paid for leased-in land) are omitted. Rent on leased-in land is determined on yearly basis. As wheat takes half a year, the proportion of rent on land for wheat cultivation was taken as half of the total rent on land.
The boldfaced values explain the relative differences of cost between organic and conventional cultivators.
Returns from wheat cultivation of sampled farmers (in INR).
Source: Field Survey.
Figures in brackets show relative difference.
The boldfaced values explain the differences between income from organic and conventional farming.
The data show that more human labour is required to manage organic farming; therefore, it constitutes an important component of cost of organic cultivation. The fixed costs are almost similar in both systems. Moreover, cost of depreciation of implements and interest on fixed capital are almost similar due to high mechanization of Punjab agriculture. Except in case of marginal farmers, the total cost of cultivation is found to be higher on conventional farming than the organic farming.
As far as net returns are concerned, conventional farming has an edge over organic farming, even though costs are higher in conventional farming. Much lower yield of wheat under organic cultivation is responsible for its lower net returns. The higher returns fetched for organic wheat covers only the production costs without adding to returns. The cost differences across different holding sizes appear only in the case of organic farming and that too in the employment of human labour. While marginal and small farmers use more family labour, rich landholders hire labour for assistance at farm. Apart from this, farm holding size does not contribute significantly to cost differentiation in either of the two cultivation methods. Contrary to the established notion, this study found that the net return on organic and conventional farming is not significantly different. In fact, the efficient use of local and natural resources under organic method makes it an economically viable alternative form of cultivation that not only fosters ecological sustainability but also ensures better produce quality for safe consumption.
Part III
Behavioural analysis of sampled organic and conventional farmers
This section is based on the presupposition that the attitudes and behaviour of organic farmers differ from those who practice conventional farming. The organic farmers’ attitudes for organic cultivation are the outcomes of their past and present experience of it. On the other side, conventional farmers frame their intentions regarding organic farming based on the information available to them. In order to examine the role of behaviour in decision-making process of conventional and organic farmers, the TPB developed by Ajzen (1999, 2005) has been applied in the present analysis. According to the TPB, the decision to adopt or abandon organic practices originates from the intention of the farmers, which is influenced by three constructs: attitude, subjective norm and perceived behavioural control (see Figure 1 and Figure 2). The use of these three constructs allows us to identify how farmers evaluate the possibility of shifting towards organic agricultural production on their farms (attitude), verifying the social pressure perceived by farmers to shift (subjective norm) and identifying the perception of farmers of their ability to use this strategy on their farms (perceived behavioural control). Each construct is measured in a direct way. The direct measures are captured by statements which directly assess the opinion of the respondents. Attitude is measured by valuation of responses to several questions like, ‘In your opinion, is organic farming better than the existing pattern?’. Subjective norms which investigate the belief about whether friends, family and others approve of the idea of conversion to organic farming are examined using statements like, ‘Do most people who are important to you think you should produce organically?’, whereas perceived behavioural control assesses whether the farmer thinks it is possible to produce organically in coming years.
Theoretical framework for behavioural analysis of organic farmer.
Theoretical framework for examining behavioural intention of conventional farmers.
Theoretical models
The attitudes of the organic farmers towards their method of cultivation were measured through statements in the schedule, the responses of which were scaled on a 5-point Likert-type scale (1 = strongly disagree and 5 = strongly agree). The lower scores reflect negative response towards the statement and higher scores indicate a positive response. Cronbach’s alpha is a measure of internal consistency, that is, how closely related a set of items are as a group. A commonly accepted rule of thumb is that a value of 0.6–0.7 indicates acceptable reliability, and 0.8 or higher indicates good reliability. High reliabilities (0.95 or higher) are not necessarily desirable, as this indicates that the items may be entirely redundant.
Motivational factors of organic farming
Table 3 provides the mean score of attitudes of organic farmers about the various motivational factors that influence their decision to adopt organic practices. The score on market factors is quite low. This reflects the lack of demand by the retailers and consumers and their unwillingness to pay anything extra for organic products. It can also be seen that lack of government support in marketing is also a major constraint for organic agricultural practices. The statements of the farmers regarding market factors are internally consistent as reflected by Cronbach’s alpha value (0.746).
Mean scores of attitude of organic farmers.
Source: Field Survey.
Moving on to ecological concerns, it can be safely claimed that the existing production technology of agriculture in Punjab is on alert not only due to stagnation of production and productivity of dominant crops but also due to the ecological crisis born out of it. The ecological crisis in rural Punjab is quite visible in the form of depletion of ground water table, alkaline and acidic soil, uranium contamination water, chemicals and pesticides in food chains, health problem associated with chemical agriculture, and so on (Singh, 2011). In such a case, organic farming seems to be a better alternative of its low dependency on chemicals. Moreover, organic farmers are more conscious cultivators when it comes to ecological sustainability. This is well reflected in their responses to all statements regarding ecological benefits of organic agriculture. They seem to well aware of the environmental benefits of their practices.
The examination of cost and benefit factors reflects the disagreement of organic farmers regarding more profitability of organic farming than conventional farming. While respondents are neutral regarding the prices of organic products, improvement in income due to organic farming, most of them agree that there are lower costs in organic cultivation, given that most inputs are naturally available. It can also be seen that sampled organic farmers do not find certification and inspection to be expensive, as these are ensured by the State and Centre authorities in their attempt to promote organic farming in the country. Overall and quite surprisingly, respondents disagree to be earning the higher economic benefits in organic cultivation. It indicates that their adoption decision for organic farming does not depend majorly on economic profits and has negative attitude for economic benefits.
Social aspects of attitudes towards organic farming include family and society-related issues. High score in this domain implies that many farmers opted for organic farming out of health concerns. The support by family also contributes in building a positive attitude towards organic cultivation. While organic farmers evidently take pride in their decision to adopt organic farm practices, the opinion of others in the community varies. Some consider it equivalent to conventional pattern while others agree that it needs a completely different mindset. It was noted during survey that organic farmers kept little contact with conventional fellow farmers.
Barriers to organic cultivation
The barriers to organic farming are analysed under three broad headings: first, production barriers that constitutes constraints faced by organic farmers with respect to yields of crops; second, marketing barriers that include hindrances faced by organic cultivators in selling their produce and, third, technical and managerial barriers which involve the difficulties in attaining the skills and other managerial technicalities, to handle the organic cultivation on their farms.
The data presented in Table 4 show major barriers faced by farmers in the production process under organic farming. First, it is found that farmers face lower yield problems in initial years of production under organic farming. Difficulty to use bio-manures and unwillingness of labour to work with cumbersome organic inputs are also found to be a serious production constraint. The 0.708 value of Cronbach’s alpha reflects reliability of scale in observing the barriers. Furthermore, high scores on statements regarding marketing barriers highlight the difficulties in accessing potential markets for their organic products where they can get remunerative prices. Organic farmers have to frequently go too far places to sell their products. This is difficult for small and marginal farmers who can neither afford nor have access to means of transportation. In the absence of an accessible market, they have to opt for evening urban markets, roadside setting and even convincing local buyers about the benefits of eating organic. To add to it, the perishable nature of the produce leads to the storage problems. In addition to this, many of the respondents have identified new ideas to market their produce, for instance, producing dissimilar crops like turmeric, pulses and flax seeds or adding more value to their raw produce through processed products like porridge and vinegar.
Mean scores of attitudes of organic farmers for production, marketing and managerial barriers.
Source: Field Survey.
Organic farmers are trying to establish their stable markets through collective efforts. Still others consider it as an impediment to increase area under organic farming. The 0.799 value of reliability scale reflects the success of statements in explaining the market barriers. The results show that responses about lack of skills and unavailability of technical assistance on farm management reflect the denial of farmers about technical difficulties acting as a strong barrier to organic farming practices. However, the lack of government support is considered as a notable barrier by the organic farmers. In all, the analysis brings out the strong resolve of organic farmers to live, produce and eat healthy regardless of obstacles in organic cultivation.
Behavioural analysis of conventional farmers towards organic farming
Like on the case of organic farmers, the responses of conventional farmers are also measured on 5-point Likert-type scale for all constructs. For attitudes and perceived behavioural control, Likert-type scale is constructed on agreement values (1 = strongly disagree and 5 = strongly agree, and for measurement of subjective norms, the scale is constructed on the importance given to statements (1 = not at all and 5 = very much importance). Table 5 summarizes the mean scores of attitudes, subjective norms and perceived control behaviour of conventional farmers’ intention to opt for organic farming. The attitudes exhibit the personal responses of conventional farmers to adopt or not to adopt organic farming on their farm in the coming years. The overall score represents a neutral stance as far as attitudes are concerned implying that farmers neither agree for adoption of organic farming nor deny it altogether.
Mean scores of attitudes of conventional farmers on attitudinal construct, subjective norms and perceived control behaviour.
Source: Field Survey.
The subjective norms are perceived social influence regarding adoption of organic farming. It has been found that interpersonal contacts of farmers influence their intention of conversion to organic farming. These interpersonal channels for conversion influence are family members, other farmers, media programmes, governmental extensional services, workshops or other informational events.
Scores on subjective norms reflect the influence of all social agents in forming the intention towards the adoption idea for organic farming. Thus, subjective norms can be perceived as effective tools in framing the opinion of farmers about conversion process. Cronbach’s alpha score reflects the success in measuring subjective norm construct. The perceived behaviour control represents the perceived easiness to overcome the anticipated obstacles in operating farm organically. The lower scores in totality under this construct reflect the anticipated difficulty in succeeding organic farmers. Technical lack is perceived as the most difficult to surpass by the respondents among the obstacles perceived as hard to overcome. Overall, the negative attitude of conventional farmers on perceived behavioural control construct and the neutral response to the attitude construct for adoption for organic cultivation reflects the weak intention of conventional farmers to convert to organic farming. Among the three constraints, subjective norms construct can be considered highly influential for the conversion to organic cultivation. However, due to the lack of demonstration effect of organic farming in the Punjab’s farming community, its influence is minimal in the present trend.
Probit regression model
This paper aims to examine the socioeconomic and demographic factors affecting the decision of the farmer to adopt conventional or organic farming (Table 6). Given the dichotomous nature of the farmer’s decision, limited dependent variable models must be applied for econometric estimation. The objective is to model and estimate the probability that farmers are willing to adopt organic farming conditional upon specific socioeconomic and demographic characteristics. For this kind of discrete binary choice problems, a qualitative response model like the probit or logit model is most appropriate (Johnston and DiNardo, 1997). The non-linear statistical model relates choice probability to explanatory factors. While the logit model is based on the logistic cumulative distribution function (CDF), the probit model is based on the normal CDF. According to Amemiya (1985), there is no compelling reason that can justify, on theoretical grounds, the use of one type of continuous probability distribution over another. For reasons of convenience and previous experience, the standard normal distribution is shown and thus the probit model is selected. In our case, the null hypothesis of no significance implies that farmers, irrespective of their age, family size, farm size, education, and so on, will adopt a given cultivation method. The dependent variable used in this study is the response of the farmers to the question on whether they practised conventional or organic farming. The answers were recorded as 1 (for organic) and 0 (for conventional).
Different socioeconomic and demographic variables used in probit analysis.
Source: Calculated from Filed Survey Data.
Probit models are based on the normal CDF. The farmer’s decision to adopt organic farming or not depends on the unobservable utility index
where
Given the assumption of normal distribution of the error term, the probability that
where P is the probability that the ith household is willing to adopt organic farming given the values of
The probit regression model is expressed as
Several measures provide the goodness of the fit of the probit model. One such measure is the percentage of observations that are correctly predicted by the model (Green, 2000). Other measures of goodness of fit of binary dependent variable models, including the probit model, can be calculated based on the log likelihood values, like, for example, the pseudo R2 proposed by McFadden (1973), or the ‘likelihood ratio index’.
Probit model results and discussion
The probit analysis sheds light on the factors that influence farmers’ willingness to adopt organic farming. The estimation results of the probit model are presented in Table 7. A likelihood-ratio test rejected the hypothesis that the coefficients are jointly zero (Prob > χ2 = 0.00). Likewise, the model passed the goodness-of-fit tests and correctly classified about 90% of the sample.
Probit regression results.
, ** and *** donate statistical significance at the 1 per cent, 5 per cent and 10 per cent levels, respectively.
The coefficient estimates presented in Table 7 provide only the direction of the effect of the independent variables on the dependent variable and not the actual magnitude of the change of probabilities. Among the variables considered, farm size and points of sale are negative and statistically significant at the 10% and 1% significance level, respectively. This clearly suggests that economic stability in terms of higher land ownership and fewer points of sale significantly lowers the likelihood of success of adoption of organic farming. On the contrary, variety of crops and sources of family income are positively and statistically significant at the 1% and 5% significance level, respectively. This confirms that organic farming essentially promotes crop diversification and organic farming system in the study areas is richer in integrating a greater number of crop types. It is also evident from the results that organic cultivation is more likely to be practised in households where income also comes from sources other than agriculture.
For a better understanding of the probit results, marginal effects are obtained since the coefficients of selection equation have no direct interpretation. The marginal effects or marginal probabilities are functions of the probability itself and measure the expected change in the probability of a particular choice being made with respect to a unit change in an independent variable from the mean (Green, 2000). Marginal effect at means gives the partial effect on the dependent variable conditioned on a regressor after setting all the other covariates at their means. If we use margins to get the predicted probabilities for the values of age from 24 to 72 years in increments of 10 while holding other explanatory variables at their means, we find that the predicted probability of adopting organic farming decreases from 0.41 to 0.38 as age increases from 24 to 72 years. Thus, each older age group is less likely to adopt organic farming than the youngest age group. In case of farm size, the results are more dramatic. The predicted probability of adopting organic farming decreases from 0.79 to 0.30 as farm size increases from 1 acre to 51 acres. We can also see that the mean predicted probability of adopting organic farming is only 0.12 if a farmer practices monoculture and increases to 0.98 if the farmer is able to diversify his cropping pattern.
For categorical variables with more than two possible values, for instance, education, the marginal effects show the difference in the predicted probabilities for cases in one category relative to the reference category. Since we have coded 0 = not completed school, the marginal effect for Completed School shows how much more likely educated farmers are to adopt organic cultivation than those who did not complete school. We see that the predicted probability of adopting organic farming is 0.90 for the highest level of education (2 = college or higher) and 0.34 for the lowest level of education (0 = not completed school), holding other explanatory variables at their means. This clearly suggests that higher levels of education significantly affect the likelihood of adoption of organic farming in Punjab. Family type, that is, whether the farmer belongs to a neutral family or a joint family, is found to have no significant effect on willingness to adopt organic cultivation. In our analysis, we define only two points of sale: directly to consumers and government or others (retailers, exporters, etc). We find that the farmer who sells directly to the consumer has a predicted 79% chance of adopting organic cultivation while the farmer who sells his produce to sale points other than the consumer has a lower chance of adopting organic cultivation over conventional cultivation (21.3% chance). Finally, it is observed from the analysis that a farmer whose family income comes from both agriculture and allied activities is most likely to adopt organic farming (69.9% chance) in comparison with those whose family income source is agriculture alone or those whose family income comes from other professions in addition to agriculture and allied activities.
Concluding remarks
The paper submits that organic farming can emerge as an effective alternative to conventional agricultural practices. The study found that economic returns are not a significant barrier to promote organic farming as net return on organic and conventional farming is not significantly different. The behavioural aspects of organic farmers regarded market and economic factors as important barriers that influence their decisions to increase area under organic farming. The motivation for organic farming largely comes from the environmental, social and health concerns. In context of conventional farmers, subjective norms are proved to be the most effective tool in framing the opinion of farmers about conversion towards organic practices. The lack of technical knowledge and confidence to deal with organic practices emerged as important barriers to conversion to organic farming. The results of the probit model show that economic stability in terms of large-scale ownership of land and fewer sale points significantly lowers the likelihood of success of adoption of organic farming. In comparison with conventional farmers, organic farmers are holistic in terms of cropping pattern and crop rotation system that plays an important part to maintain natural productivity of soil. The predicted probability shows that level of education plays a positive and significant role in adopting organic farming.
Organic farming can be regarded as a social countermovement born out of the crisis of conventional farming that requires a genuine policy push, the successfulness of which depends on the effectiveness of motivating farmers to adopt organic farming. The stark absence of proper links between the consumer and organic producer has been pointed out for the tardy growth of organic farming. Incorporation of organic agriculture fully into State marketing and promotion efforts can prove to be especially helpful. The setting up of remunerative prices for organically produced items will ensure income stability and help in enhanced adoption of organic farming.
The organic pattern of agriculture offers a way forward by integrating a variety of crops in the farming system. The state must give greater attention to organic farmers who have the potential to solve the problems of monoculture and facilitate crop diversification. Diversification towards fruits, vegetables, flowers and other high-value and labour-intensive crops can provide adequate income and employment to the farmers dependent on small size of farms. A big part of the environmental damage caused by conventional agriculture originates from the use of chemical inputs (pesticides and over-application of chemical fertilizers). The support to organic farming will lead to use of environmental friendly inputs that can act as one of the most direct policy interventions to address sustainability in agriculture. Comprehensive informational services to organic farmers such as guidance documents, advice, trainings, technical assistance and marketing assistance, technical support or extension services offered by universities will be able to bridge the present information gap. For this, the government can collaborate with NGOs and reach out to the rural communities in the very interiors. Most farmers in the surveyed region responded positively about the support provided by the NGOs. A close collaboration with them will definitely accelerate the adoption of organic farming.
Along with this, dissemination of eco-centric education can play an effective role in bringing about a transformation in agriculture. It will help to create an ecologically conscious community of farmers and consumers. Apart from educating the farmers and upcoming agriculturists, state-led awareness initiatives must also target the adult farmers about the holistic benefits of organic farming. While the young can take the baton forward, the older generation can create an encouraging social environment where organic farming is practised and promoted wholeheartedly.
Footnotes
Funding
The author(s) received no financial support for the research, authorship and/or publication of this article.
