The effect of information sources on technology adoption and modification decisions

This article focuses on adoption and modification decisions on improved maize storage technologies in southern Benin. Modification implies changing a technology to adapt to farmers' circumstances. A sample selection framework is used to account for selectivity bias as some farmers were not aware of the new technologies. Using this framework, the study investigates the effect of alternative information sources on adoption and modification decisions. Farmers are either informed by extension agents or by other farmers. The empirical results show that there are differences in adoption and modification decisions between these two groups.     

Estimating Input Complementarities with Unobserved Heterogeneity: Evidence from Ethiopia

The level of technology adoption is often characterised as low in Africa. Recent evidence, however, points to the coexistence of substantial heterogeneity across farm households and the lack of a suitable mix of inputs for farmers to take advantage of input complementarities. We use a random coefficients multivariate probit model to quantify the complementarities between agricultural inputs and alternative forms of unobserved heterogeneity effects in modeling farmers' technology adoption decisions. The empirical analysis reveals that, conditional on various types of unobserved heterogeneity effects, farmers' technology adoption decisions exhibit strong complementarity for some inputs. The analysis also reveals substantial unobserved heterogeneity effects. We show that ignoring these behavioural features (unobserved heterogeneity and input complementarity) has important implications in quantifying the effect of some policy interventions that are meant to facilitate technology adoption. In particular, ignoring these features leads to significant overestimation of the effectiveness of extension services.     

How crop insurance influences agrochemical input use: Evidence from cotton farmers in China

Crop insurance is critical in risk management in global agricultural production (e.g. by helping stabilise farmers' incomes in the long term and reducing risk-bearing costs). In this paper, using field survey data on cotton farmers in Xinjiang, China, we examine the influence of crop insurance on farmers' behaviours regarding agrochemical inputs and aim to investigate the synergy between crop insurance and reductions in fertiliser and pesticide usage. We find evidence that crop insurance significantly negatively affects farmers' use of fertilisers and pesticides, as well as significantly positively affects their adoption of green agricultural technologies (GAT) that can replace or complement traditional fertilisers and pesticides. Moreover, our results reveal that compared with small-scale farmers, crop insurance has a stronger effect on large-scale farmers' use of agrochemicals. Finally, when the insured amount is higher or the relative deductible is lower, farmers are more likely to reduce fertiliser and pesticide usage and adopt GAT. Overall, this paper scientifically identifies crop insurance can improve farmers' agrochemical input behaviour, by reducing farmers' use of traditional agrochemical inputs and increasing their adoption of GAT, which is of great significance for ensuring the safety of the agricultural ecological environment.     

The influence of farm input subsidies on the adoption of natural resource management technologies

Farm input subsidies are often criticised on economic and ecological grounds. The promotion of natural resource management (NRM) technologies is widely seen as more sustainable to increase agricultural productivity and food security. Relatively little is known about how input subsidies affect farmers’ decisions to adopt NRM technologies. There are concerns of incompatibility, because NRM technologies are one strategy to reduce the use of external inputs in intensive production systems. However, in smallholder systems of Africa, where the average use of external inputs is low, there may possibly be interesting complementarities. Here, we analyse the situation of Malawi's Farm Input Subsidy Program (FISP). Using panel data from smallholder farm households, we develop a multivariate probit model and examine how FISP participation affects farmers’ decisions to adopt various NRM technologies, such as intercropping of maize with legumes, use of organic manure, water conservation practices and vegetative strips. As expected, FISP increases the use of inorganic fertilizer and improved maize seeds. Yet, we also observe a positive association between FISP and the adoption of certain NRM technologies. For other NRM technologies, we find no significant effect. We conclude that input subsidies and the promotion of NRM technologies can be compatible strategies.     

The complex dynamics of smallholder technology adoption: the case of SRI in Madagascar

This article explores the dynamics of smallholder technology adoption, with particular reference to a high‐yielding, low external input rice production method in Madagascar. We present a simple model of technology adoption by farm households in an environment of incomplete financial and land markets. We then use a probit model and symmetrically censored least squares estimation of a dynamic tobit model to analyze the decisions to adopt, expand, and disadopt the method. We find that seasonal liquidity constraints discourage adoption by poorer farmers. Learning effects—both from extension agents and from other farmers—exert significant influence over adoption decisions.     

Tradeoffs and complementarities in the adoption of improved seeds,fertilizer, and natural resource management technologies in Kenya

There is widespread consensus that agricultural technology has an important role to play for poverty reduction and sustainable development. There is no consensus, however, about the types of technologies that are best suited for smallholder farmers in Africa. While some consider natural resource management (NRM) technologies as most appropriate, others propagate input intensification with a stronger role of the private sector. In the public debate, these two strategies are often perceived as incompatible. Environmental non‐governmental organizations in particular consider low‐external input strategies as the only sustainable form of agriculture, a view that has considerable influence on policymakers and the international donor community. Most existing research studies on smallholder innovation focus on the adoption of individual technologies, so that comparisons between different types of technologies in the same context are not easily possible. We use representative data from maize‐producing households in Kenya and a multivariate probit model to analyze the adoption of different types of technologies simultaneously. Results indicate that NRM technologies and strategies that build on external inputs are not incompatible. Interesting complementarities exist, which are not yet sufficiently exploited because many organizations promote either one type of technology or the other, but rarely a combination of both.     

Insurance for Technology Adoption: An Experimental Evaluation of Schemes and Subsidies with Maize Farmers in Mexico

For farmers in developing countries, the combination of both risk aversion and the lack of insurance is often a major impediment to adoption of productivity‐enhancing technologies, such as higher yielding hybrid seed. In a framed field experiment with Mexican maize farmers, we investigate whether bundling hybrid seed with an insurance scheme can increase its adoption, while also controlling for risk aversion. We test insurance schemes with different levels of risk coverage and premium subsidies and find that (1) all schemes significantly increase the degree of adoption of the higher yielding seed, (2) partial insurance schemes perform worse than full insurance, (3) weather index insurance with geographical basis risk performs no worse than indemnity insurance, and (4) premium subsidies significantly increase the adoption effect of indemnity insurance, but not that of index insurance.     

Measuring Farm and Market Level Economic Impacts of Improved Maize Production Technologies in Ethiopia: Evidence from Panel Data

While it is often recognised that agricultural technology adoption decisions are intertwined and best characterised by multivariate models, typical approaches to examining adoption and impacts of agricultural technology have focused on single technology adoption choice and ignored interdependence among technologies. We examine farm‐ and market‐level impacts of multiple technology adoption choices using comprehensive household survey data collected in 2010/11 and 2012/13 in Ethiopia. Economic surplus analysis combined with panel data switching endogenous regression models are used to compute the supply shift parameter (K‐shift parameter), while at the same time controlling for the endogeneity inherent in agricultural technology adoption among farmers. We find that our improved technology set choices have significant impacts on farm‐level maize yield and maize production costs, where the greatest effect appears to be generated when various technologies are combined. The change in maize yield and production costs results in an average 26.4% cost reduction per kilogram of maize output (the K‐shift parameter). This increases the producer and consumer surpluses by US$ 140 and US$ 105 million per annum, respectively. These changes in economic surplus help to reduce the number of poor people by an estimated 788 thousand per year. We conclude that deliberate extension efforts and other policies that encourage integration of technologies are important for maize technologies to yield their full potential at both farm and market levels.     

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.     

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.     

Farmers’ risk preferences and their climate change adaptation strategies in the Yongqiao District,China

Farmers’ risk preferences play an important role in agricultural production decisions. This study characterizes risk preferences among farmers in Yongqiao and determines how these risk preferences are related to their choices regarding climate change adaptation strategies. We find that most farmers in the study area were aware of climate change. They were taking measures to protect their livelihoods against perceived changes to the local climate. The risk experiment result shows that the representative subject was risk averse, and women were more risk averse than men. The relationships between farmers’ risk preferences and different climate change adaptation choices were different. Farmers’ risk aversion was negatively and significantly related with adaptation strategies on planting new crop varieties and adopting new technology, but it had a significantly positive effect on purchasing weather index crop insurance. The results also indicate that the level of education, farming experience, farm size, household income and perception of climate change impacts influence farmers’ adaptation decisions.     

When knowledgeable neighbors also share seedlings: diffusion of banana cultivation in Tanzania

Networks have been found to increase adoption of technology by providing information about the new technology (e.g., Conley and Udry, 2010). However, little is known about provision of necessary inputs for adoption through networks. Using data from an intervention in Tanzania, I discuss how a farmer's network can also affect the adoption of improved banana cultivation by providing seedlings. A solidarity chain principle obliged project farmers to pass on improved seedlings to other farmers free of charge. I provide a theoretical framework to guide intuition for the empirical results and suggest an empirical distinction between information and input provision through networks. Empirically, I find support for network effects through provision of both information and inputs, jointly boosting the network effect on adoption: a farmer is 39 percentage points more likely to adopt banana cultivation if there is at least one banana grower in the farmer's network compared to none. In this setting, it is not possible to fully disentangle the input and information channel. While the findings suggest that a solidarity chain principle could be an interesting avenue to boost local diffusion of new technologies, more research is needed to unpack the causal impact and the interplay with existing input markets.     

The Adoption and Profitability of rbST on Connecticut Dairy Farms

This work estimates probit and tobit models of the adoption of recombinant bovine somatotropin (rbST) on Connecticut dairy farms and then endogenizes that adoption in estimates of milk production and farm profit rates. The work improves on the current literature by allowing the rbST decision to be both continuous and contingent on other technology adoption decisions. The results show that larger farms, with more productivity technologies, and with younger, more educated farmers, are more likely to adopt rbST. While rbST is shown to significantly increase milk production, there is no evidence that it increases profits on a per cow basis.     

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.     

Risk,learning, and technology adoption

This article explores how decision makers learn and use information, with an application to the adoption of biotechnology in agriculture. The empirical analysis relies on experimental and survey data measuring risk preferences, learning processes, and the adoption of genetically modified (GM) seeds among U.S. grain farmers. While controlling for risk aversion, we link individual learning rules with the cognitive abilities of each decision maker and their actual GM adoption decisions. We find evidence that very few individuals are Bayesian learners, and that the population of farmers is quite heterogeneous in terms of learning rules. This suggests that Bayesian learning (as commonly assumed in the analysis of agricultural technology adoption) is not an appropriate characterization. In addition, we do not find a strong relationship between observed learning styles and the timing of GM seed adoption. To the extent that learning is a key part of the process of technology adoption, this suggests the presence of much unobserved heterogeneity in learning among farmers.     

Productivity and Land Enhancing Technologies in Northern Ethiopia: Health, Public Investments, and Sequential Adoption

The adoption of more efficient farming practices and technologies that enhance agricultural productivity and improve environmental sustainability is instrumental for achieving economic growth, food security, and poverty alleviation in sub-Saharan Africa. Our research examines the interaction between public investments, community health, and adoption of productivity and land enhancing technologies by households in the northern Ethiopian state of Tigray. Agricultural technology adoption decisions are modeled as a sequential process where the timing of choices can matter. We find that time spent sick and opportunity costs of caring for sick family members are significant factors in adoption. Sickness, through its impact on household income and labor allocation decisions for healthcare and other activities, significantly reduces the likelihood of technology adoption. Our findings suggest that agencies working to improve agricultural productivity and land resource conservation should consider not only the financial status of potential adopters, but also their related health situation.     

The impact of safety nets on technology adoption: a difference‐in‐differences analysis

This article contributes to a growing body of empirical literature relating credit constraints and incomplete insurance to investment decisions. We use panel data from rural Ethiopia to investigate whether participation in a safety net program enhances fertilizer adoption. Using a difference‐in‐differences estimator and inverse propensity score weighting, we find that participation in Ethiopia's food‐for‐work (FFW) program increased fertilizer adoption in the short run, but not in the long run. Results also indicate that the intensity of fertilizer usage increased with livestock holdings for FFW‐participant households, providing some evidence that the intervention helped asset‐rich farm households more than asset‐poor households. We find no significant effects of free distribution on fertilizer adoption or intensification. Our results are consistent with the hypothesis that safety nets can be viewed as mechanisms that allow households to take on more risk to pursue higher profits. The results highlight the importance of safety net programs, their effectiveness in ensuring farmers that they will be protected against uninsured shocks, and how that assurance can translate into productivity‐enhancing behavior.     

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.     

Factors Affecting Farmers’ Crop Insurance Participation in China

China's latest crop insurance program, launched in 2007, provides an excellent opportunity to explore the factors affecting farmers’ crop insurance purchase decisions, particularly decision making when crop insurance was first introduced into rural communities. This study surveyed all households in Kuangjiaqiao Village, Changde, Hunan Province, China over a four‐year period, from 2007 to 2010. Using basic regression models for cross‐sectional analysis and advanced models to consider lag effects, this study identifies the dominant factors influencing farmers’ crop insurance decisions. Results indicate farmers developed a dynamic adaptive process toward the new crop insurance. Farmers initially made relatively arbitrary decisions that were significantly influenced by community insistence or pressure to conform. Then, farmers gradually established more rational decision‐making mechanisms in which yield volatility, education, and engagement experience became statistically significant. The focus on the initial stages of the crop insurance program from this study helps improve our understanding of the demands within this rapidly growing market in China.     

Household nutrition and income impacts of using dairy technologies in mixed crop–livestock production systems

Technologies like improved breeds of dairy cows and improved forages have the potential to significantly increase dairy cow productivity and farmers’ profits in developing countries. However, adoption of such technologies has been low in Ethiopia, despite numerous efforts to disseminate the technologies in the past. Some studies argue that adoption of technologies is low because welfare effects of the technologies could be insignificant or negative to certain groups of farmers. This article employed propensity score matching and inverse probability weighting estimator with regression adjustment to examine the difference in household nutrition and income between adopters and nonadopters of dairy technologies in rural Ethiopia. We find that adoption of cross‐bred dairy cows and improved forages increases household nutrition and income. The significant household nutrition and income impact for adopters support the notion that many Ethiopian smallholders have not adopted dairy technologies because adopters and nonadopters of dairy technologies have inherent differences in welfare outcome potentials. The results suggest that interventions that enhance access to farm resources and address barriers to input and output value chains could improve adoption of dairy technologies.