A dynamic adoption model with Bayesian learning: an application to U.S. soybean farmers

Adoption of agricultural technology is often sequential, with farmers first adopting a new technology on part of their lands and then adjusting their use of the new technology in later years based on what was learned from the initial partial adoption. Our article explains this experimental behavior by using a dynamic adoption model with Bayesian learning, in which forward‐looking farmers take account of future impacts of their learning from both their own and their neighbors’ experiences with the new technology. We apply the analysis to a panel of U.S. soybean farmers surveyed from 2000 to 2004 to examine their adoption of the genetically modified (GM) seed technology. We compare the results of the forward‐looking model to that of a myopic model, in which farmers maximize current benefits only. Results suggest that the forward‐looking model fits data better than the myopic model does. And potential estimation biases arise when fitting a myopic model to forward‐looking decision makers.     

Early adoption,experience, and farm performance of GM corn seeds

The current article explores the characteristics that distinguish early from late adopters of GM corn and measures the productivity impacts of early adoption, for a sample of farmers in Minnesota and Wisconsin. The results of the adoption analysis confirm that size, education as well as specialization are positively correlated with early adoption. In addition, these results also show that farms that are mostly worked by family labor but hire some off‐farm labor are more likely to adopt GM seeds earlier in the diffusion process. The productivity analysis demonstrates the superiority of stacked varieties. At the same time, we find no evidence of a direct impact of experience on yields. Given the previously documented impact of early adoption on the use of stacked varieties, we conclude that experience plays a role through the adoption of these new technologies but does not play a role in allowing the producer to use the technology more efficiently, once it has been adopted.     

Bias from Farmer Self‐Selection in Genetically Modified Crop Productivity Estimates: Evidence from Indian Data

In the continuing debate over the impact of genetically modified (GM) crops on farmers of developing countries, it is important to accurately measure magnitudes such as farm‐level yield gains from GM crop adoption. Yet most farm‐level studies in the literature do not control for farmer self‐selection, a potentially important source of bias in such estimates. We use farm‐level panel data from Indian cotton farmers to investigate the yield effect of GM insect‐resistant cotton. We explicitly take into account the fact that the choice of crop variety is an endogenous variable which might lead to bias from self‐selection. A production function is estimated using a fixed‐effects model to control for selection bias. Our results show that efficient farmers adopt Bacillus thuringiensis (Bt) cotton at a higher rate than their less efficient peers. This suggests that cross‐sectional estimates of the yield effect of Bt cotton, which do not control for self‐selection effects, are likely to be biased upwards. However, after controlling for selection bias, we still find that there is a significant positive yield effect from adoption of Bt cotton that more than offsets the additional cost of Bt seed.     

Risk, uncertainty, and learning in adoption of a crop innovation

Risk and uncertainty have often been suggested as causes of poor adoption of rural innovations, but empirical evidence has been scarce. This study focuses on a new crop‐type, chickpeas, in Western Australia to gather such evidence. The empirical models developed are based on a theoretical framework that conceptualizes adoption as a dynamic decision process involving information acquisition and learning‐by‐doing by growers who vary in their managerial abilities, risk preferences, and their perceptions of the profitability and riskiness of the innovation. Learning encompasses improvements in skill as well as reductions in uncertainty. An annual face‐to‐face survey of over 100 farmers was conducted over 3 years, eliciting the farmers' risk attitudes and their subjective distributions of yields and prices. Two limited dependent variable models, Tobit and Probit, are used to estimate the empirical model. There is a high degree of goodness‐of‐fit for both models. The study provides strong empirical support for the primarily economic character of the adoption decision, and highlight the importance of economic risk in the process. The two risk‐related factors with greatest impact on the adoption decision were risk aversion and relative riskiness of the innovation. Risk aversion tended to reduce adoption, and to do so to a greater extent as relative riskiness and scale increased. Results also reveal the key role that trialing of the innovation plays in adoption.     

Irrigation Technology Adoption and Gains from Water Trading under Asymmetric Information

We develop a water allocation and irrigation technology adoption model under the prior appropriation doctrine with asymmetric information among heterogeneous farmers and between farmers and water authorities. We find that adverse selection reduces the adoption of modern irrigation technology. We also show that even with asymmetric information, incentives for water trade exist and lead to additional technology adoption with gains to all parties. This suggests that under asymmetric information, a thin secondary market improves the allocation of water resources and induces additional adoption of modern irrigation technologies.     

Farmers’ objectives as determinants of organic farming adoption: the case of Catalonian vineyard production

This article assesses the decision to adopt organic farming practices. We use Duration Analysis (DA) to determine why farmers adopt organic farming practices and what influences the timing of adoption. We extend previous studies by including farmers’ objectives, risk preferences, and agricultural policies as covariates in the DA model. The Analytical Hierarchy Process is used as a multicriteria decision‐making methodology to measure farmers’ objectives. The empirical analysis uses farm‐level data from a sample of vineyard farms in the Spanish region of Catalonia. Farmers’ objectives are found to influence the conversion decision. Moreover, farmers who are not risk averse are more likely to adopt organic farming. Results point to the policy changes that have been most relevant in motivating adoption of organic practices.     

Forecasting the Adoption of GM Oilseed Rape: Evidence from a Discrete Choice Experiment in Germany

This paper explores farmers’ willingness to adopt genetically modified (GM) oilseed rape prior to its commercial release and estimates the ‘demand’ for the new technology. The analysis is based upon choice experiments with 202 German arable farmers. A multinomial probit estimation reveals that GM attributes such as gross margin, expected liability from cross pollination, or flexibility in returning to conventional oilseed rape significantly affect the likelihood of adoption. Neighbouring farmers’ attitudes towards GM cropping and a number of farmer and farm characteristics were also found to be significant determinants of prospective adoption. Demand simulations suggest that adoption rates are very sensitive to the profit difference between GM and non‐GM rape varieties. A monopolistic seed price would substantially reduce demand for the new technology. A monopolistic seed supplier would reap between 45% and 80% of the GM rent, and the deadweight loss of the monopoly would range between 15% and 30% of that rent. The remaining rent for farmers may be too small to outweigh possible producer price discounts resulting from the costs of segregating GM and non‐GM oilseed rape along the supply chain.     

Welfare impacts of maize–pigeonpea intensification in Tanzania

This article examines the driving forces behind farmers’ decisions to adopt improved pigeonpea and maize and estimates the causal impact of technology adoption on household welfare using data obtained from a random cross‐section sample of 613 small‐scale farmers in Tanzania. We use seemingly unrelated and recursive bivariate probit regressions to test the endogeneity and joint decision making of pigeonpea–maize production. A double hurdle model is used to analyze the determinants of the intensity of technology adoption conditional on overcoming seed access constraints. To address the impact of adoption on welfare, the article employs both propensity score matching and switching regression techniques. Results from bivariate probit models show that unobservable factors cause both decisions to be correlated but the finding does not support the conjecture that both decisions are made jointly. Overall the analysis of the determinants of adoption identifies inadequate local supply of seed, access to information, human capital, and access to private productive asset as key constraints for pigeonpea technology adoption. The causal impact estimation from both the propensity score matching and switching regression suggests that maize/pigeonpea adoption has a positive and significant impact on income and consumption expenditure among sample households.     

Institutional factors and farmers’ adoption of conventional,organic and genetically modified cotton in Burkina Faso

Organic farming and genetically modified (GM) crops technologies are currently being promoted as alternatives to conventional farming that is seen as unsustainable. However, institutional constraints can impede the adoption of even the most sustainable technology. This paper analysed the effect of institutional factors on farmers’ adoption of conventional, organic and GM cotton in Burkina Faso. Building on the expected utility model and institutional theory, a multinomial logistic regression was performed using farmers’ survey data from the 2014–2015 production season. The results showed that subsidies on fertiliser and credit for cereals production, the power of farmers’ association and that of the cotton company favoured the adoption of conventional and GM cotton at the expense of organic cotton. In order to succeed, organic cotton projects need to include components that help farmers to access organic fertilisers for cereals production. They also need to involve the cotton companies that are the most powerful stakeholders of the cotton sector. Extension services are necessary for both organic and GM cotton adoption. Other important factors to consider include farmers’ education, the potentials of the technologies, the good agro-ecological conditions, the continued involvement of women, the availability of virgin lands and the closeness of farmers to their farms.     

Heterogeneous information exposure and technology adoption: the case of tissue culture bananas in Kenya

Classical innovation adoption models implicitly assume homogenous information flow across farmers, which is often not realistic. As a result, selection bias in adoption parameters may occur. We focus on tissue culture (TC) banana technology that was introduced in Kenya more than 10 years ago. Up till now, adoption rates have remained relatively low. We employ the average treatment effects approach to account for selection bias and extend it by explicitly differentiating between awareness exposure (having heard of a technology) and knowledge exposure (understanding the attributes of a technology). Using a sample of Kenyan banana farmers, we find that estimated adoption parameters differ little when comparing the classical adoption model with one that corrects for heterogeneous awareness exposure. However, parameters differ considerably when accounting for heterogeneous knowledge exposure. This is plausible: while many farmers have heard about TC technology, its successful use requires notable changes in cultivation practices, and proper understanding is not yet very widespread. These results are also important for other technologies that are knowledge‐intensive and require considerable adjustments in traditional practices.     

Gender and the dynamics of technology adoption: Empirical evidence from a household-level panel data

Very few empirical studies account for the dynamic nature of the agricultural technology adoption decision and none of these explores if this dynamic nature depends on the gender of the decision maker. Using four waves of a household-level Ugandan panel data, this is the first empirical analysis to account for self-learning (one's own adoption experience) in explaining current adoption decision in a developing country context, and the first to study the interaction between self-learning and gender. Technology adoption is defined as adoption of hybrid seed, inorganic fertilizer, or pesticides. Our results indicate that the dynamic panel data Probit model is superior to its static counterpart in the sense that self-learning, captured by lagged technology adoption indicators, is by far the most important determinant of technology adoption. We also find a weaker impact of self-learning for female-headed households than male-headed households. Female-headed households face fewer learning opportunities, which produce a lower self-learning impact in later periods, further exacerbating the gap in technology adoption among male- and female-headed households.     

Information Barriers to Adoption of Agricultural Technologies: Willingness to Pay for Certified Seed of an Open Pollinated Maize Variety in Northern Uganda

We examine smallholder farmers’ willingness to pay for agricultural technology and whether information is a constraint to adoption of certified maize seed in Northern Uganda. The uptake of improved maize varieties by smallholder farmers in Uganda remains persistently low, despite the higher yield potential compared to traditional varieties. A recently growing body of literature identifies information constraints as a potential barrier to adoption of agricultural technologies. We used incentive compatible Becker‐DeGroot‐Marschak auctions to elicit willingness to pay for quality assured improved maize seed by 1,009 smallholder farmers, and conducted a randomised evaluation to test the effect of an information intervention on farmers’ knowledge of seed certification. Our results show that the randomised information treatment enhanced farmers’ knowledge of certified seed. However, using the information treatment as an instrumental variable for knowledge, we find no evidence of a causal effect of knowledge on willingness to pay, suggesting that even though farmers are information constrained, this constraint does not affect adoption of certified seed directly. Nevertheless, only 14% of sampled farmers were willing to pay the market price, which corresponds closely with actual observed demand for certified seed in the previous season. This suggests that there are other barriers to adoption than information and awareness.     

Farmer-to-farmer technology diffusion and yield variation among adopters: the case of improved cowpea in northern Nigeria

This article examines the magnitude and sources of yield variation among adopters of improved cowpea varieties in northern Nigeria promoted through farmer‐to‐farmer diffusion. The results reveal important efficiency differences between the lead farmers who have contacts with breeders and the follower farmers who get technology and information from the lead farmers. Differential adoption of the package of seed, insecticide, fertilizer, and recommended cereal‐cowpea cropping pattern provides much of the explanation for yield variation among adopters. The component often missing, and hence accounting for much of the yield variation, is the crop management technology relating to the cereal‐cowpea cropping pattern. No efficiency variation is attributed to the source of technology and information, such as whether improved cowpea was obtained from breeders or lead farmers. Technology source has a rather indirect influence on efficiency through its effect on package adoption where breeders promote greater package adoption among the lead farmers than the lead farmers do among the follower farmers. Possible ways of disseminating crop management technological information through the farmer‐to‐farmer technology diffusion are recommended to better exploit the yield and profitability potentials of improved cowpea varieties in northern Nigeria.     

New Maize Variety Adoption in Mozambique: A Spatial Approach

Farmers in developing countries can dramatically improve their productivity by adopting new plant varieties. Yet, informational barriers often mean adoption rates remain low. In this study, we focus on how learning from others represents one means of removing informational barriers. We capture the effect of social learning through an explicitly spatial econometric model applied to farm‐level maize adoption rates in Mozambique. We find that social learning is significant and can help explain the apparent clustering of adoption among farmers. Agencies interested in promoting variety adoption, therefore, would be well served to leverage the strength of existing information networks, rather than imposing solutions that work against interfarmer information flow.     

The impact of social networks on hybrid seed adoption in India

This article adds to the literature about the impact of social networks on the adoption of modern seed technologies among smallholder farmers in developing countries. The analysis centers on the adoption of hybrid wheat and hybrid pearl millet in India. In the local context, both crops are cultivated mainly on a subsistence basis, and they provide examples of hybrid technologies at very different diffusion stages: while hybrid wheat was commercialized in India only in 2001, hybrid pearl millet was launched in 1965. The analysis is based on surveys of wheat and millet farmers in the state of Maharashtra. Comprehensive data on farmer characteristics and social interactions allow for identifying individual networks, thereby improving upon previous research approaches that employed village-level variables as proxies for network effects. Using econometric models, we find that individual social networks play an important role for technology adoption decisions. While village-level variables may be used as suitable proxies at later diffusion stages, they tend to underestimate the role of individual networks during early phases of adoption.     

Interaction between crop insurance and technology adoption decisions: The case of wheat farmers in Chile

This paper examines relationships between crop insurance and input technology decisions among Chilean wheat farmers. Using nationwide farm‐level data, a bivariate probit model is estimated. We investigate the extent to which the adoption of production input technologies is associated with farmers’ participation in the insurance program. We find that relationships between insurance and technology decisions are significant only for family farmers. In particular, there is a negative relationship between participation in the insurance program and the adoption of modern irrigation. Interpretations based on the role of input technologies on insurance adoption and adverse selection behaviours are discussed.     

Adoption of farm‐based irrigation water‐saving techniques in the Guanzhong Plain,China

This article analyses adoption of farm‐based irrigation water saving techniques, based on a cross‐sectional data set of 357 farmers in the Guanzhong Plain, China. Approximately 83% of the farmers use at least one farm‐based water‐saving technique. However, the traditional, inefficient techniques border and furrow irrigation are still prevalent whereas the use of advanced, more efficient techniques is still rather rare. We develop and estimate an adoption model consisting of two stages: awareness of water scarcity and intensity of adoption. We find that awareness of water scarcity and financial status enhance adoption of more advanced techniques whereas access to better community‐based irrigation infrastructure discourages it. We furthermore find both community‐based irrigation infrastructure and farm‐based irrigation water‐saving techniques have mitigating effects on production risk. From the results it follows that adoption can be stimulated via financial support and via extension aimed at enhancing awareness of water scarcity.     

Stochastic Technology, Risk Preferences, and Adoption of Site-Specific Technologies

A model of farmer decision making is developed to determine the extent to which uncertainties about soil fertility and weather affect the value of site-specific technologies (SSTs) using jointly estimated risk and technology parameters. Uncertainty can lead risk-averse farmers to apply more fertilizers and generate more pollution than in the certainty case. Ignoring uncertainty and risk aversion would overestimate the economic and environmental benefits of SSTs and underestimate the subsidy required to induce adoption. Accounting for uncertainties and risk preferences might explain the low observed adoption rates of SSTs. Improving the accuracy of SSTs would increase the incentives for adoption.     

Incentive-Compatible Pollution Control Policies under Asymmetric Information on Both Risk Preferences and Technology

This article proposes a method to accommodate asymmetric information on farmers' risk preferences in designing voluntary environmental policies. By incorporating stochastic efficiency rules in a mechanism design problem, the government can find incentive-compatible policies by knowing only the general class of risk preferences among farmers. The model also accounts for hidden information on technology types and input use. The method is applied empirically to simulate a pollution control program in New York. Results suggest that participation incentives would be inadequate for many risk-averse producers if the government does not account for the diversity in risk preferences.     

Greenhouse gas emissions and mitigation potential from fertilizer manufacture and application in India

Crop management research is increasingly involving farmers in evaluating new technologies, identifying adoption constraints and opportunities for improving farm performance to produce more sustainable impact. ICRISAT and its partners worked with farmers in Malawi and Zimbabwe during the 1999/2000 and 2000/2001 seasons to evaluate a range of ‘best bet’ soil fertility and water management technologies and evaluate the impact of farmer participatory research. Although there was some variation in methods implemented at different sites, the study found that there is a basis for a comparison of methods. Community entry and participatory approaches that engage farmers in decision making throughout the research-development-diffusion-innovation process have higher setup costs compared to traditional ‘top-down’ approaches. But they improve efficiency, both in technology development and in building farmers' capacity for experimentation and collective learning. This results in the development of more relevant technologies, joint learning among farmers, researchers and extensionists and better impact. To make farmer participatory research projects more sustainable and introduce them on a wide scale, the study recommends that public and NGO investments be targeted to building district and village-level innovation clusters.