Yield Effects of Tissue Culture Bananas in Kenya: Accounting for Selection Bias and the Role of Complementary Inputs

We analyse yield effects of tissue culture (TC) banana technology in the Kenyan small farm sector, using recent survey data and an endogenous switching regression approach. TC banana plantlets, which are free from pests and diseases, have been introduced in East Africa since the late 1990s. Although field experiments show significant yield advantages over traditional banana suckers, a rigorous assessment of impacts in farmers’ fields is still outstanding. A comparison of mean yield levels between TC adopters and non‐adopters in our sample shows no significant difference. However, we find evidence of negative selection bias, indicating that farmers with lower than average yields are more likely to adopt TC. Controlling for this bias results in a positive and significant TC net yield gain of 7%. We also find that TC technology is more knowledge intensive and more responsive to irrigation than traditional bananas. Simulations show that improving access to irrigation could lift TC productivity gains to above 20%. The analytical approach developed and applied here and the finding of negative selection bias may also be relevant for the evaluation of other agricultural technologies.     

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.     

Advancing climate-smart-agriculture in developing drylands: Joint analysis of the adoption of multiple on-farm soil and water conservation technologies in West African Sahel

Water stress and soil infertility are the greatest constraining factors for higher agricultural productivity in drylands, prompting the current interest in soil and water conservation (SWC) practices in water-constrained regions. To provide a more comprehensive understanding of challenges surrounding the adoption of SWC practices in these regions, we used a joint analysis framework combining both multivariate and ordered probit models to analyze adoption-decisions for eleven on-farm SWC practices. Our case study, involving 500 farmers from a representative West African Sahelian zone, revealed that although the adoption of SWC practices is widespread in the West African drylands, there is still an important potential to improve and upscale their specific adoption rates. Almost all farmers (99%) used at least one of the eleven practices considered in this study, whereas specific adoption rates ranged from 5% for contour vegetation barriers to 87% for manure application. More than 70% of the farmers used up to three practices only, and less than 30% used between four to nine practices. Many practices are interdependent, with some practices being complementary and others substitutable. The analysis of the determinants of the adoption and the intensity of adoption revealed that SWC practices are labor-, knowledge- and capital-intensive. We found that the major drivers of farmers’ decisions to adopt, as well as to intensify the use of, most SWC practices are the presence of children (aged 6 to 14) in the household, land holding, land tenure, awareness and training on SWC and access to alternative – but non-agricultural labor constraining – cash sources such as remittance and cash farming. A higher number of migrating household members increases the probability of intensifying the use of SWC practices, but only when this is in line with the household’s land endowment and labor needs for farm activities. This comprehensive study will be of significance for a finer understanding of SWC practices in West African Sahel. More generally, it will likely help policy makers to upscale the adoption of sustainable SWC practices for the advance of climate-smart agriculture in developing drylands.     

Measuring agricultural knowledge and adoption

Understanding the tradeoffs in improving the precision of agricultural measures through survey design is crucial. Yet, standard indicators used to determine program effectiveness may be flawed, and at a differential rate for men and women. We use a household survey from Mozambique to estimate the measurement error from male and female self‐reports of their adoption and knowledge of three practices: intercropping, mulching, and strip tillage. Despite clear differences in human and physical capital, there are no obvious differences in the knowledge, adoption, and error in self‐reporting between men and women. Having received training unanimously lowers knowledge misreports and increases adoption misreports. Other determinants of reporting error differ by gender. Misreporting is positively associated with a greater number of plots for men. Recall decay on measures of knowledge appears prominent among men but not women. Findings from regression and cost‐effectiveness analyses always favor the collection of objective measures of knowledge. Given the lowest rate of accuracy for adoption was around 80%, costlier objective adoption measures are recommended for a subsample in regions with heterogeneous farm sizes.     

Awareness and adoption of conservation agriculture in Malawi: what difference can farmer-to-farmer extension make?

Despite the potential of conservation agriculture (CA) for increased crop yields, energy savings, soil erosion control, and water-use efficiency, smallholder farmers in sub-Saharan Africa have been slow to adopt. Farmer-to-farmer extension (F2FE) may have a role to play in overcoming the information access problems and lack of knowledge that may preclude widespread adoption. This study uses data for 180 lead farmers linked to their 455 followers to investigate how F2FE influences awareness and adoption of CA technologies in Malawi. Results from a bivariate probit model for follower farmer awareness and adoption of the three CA principles (minimum soil disturbance, crop residue retention, and crop diversification) reveal four main findings: First, lead farmer motivation increases their effectiveness at diffusing CA practices to their followers. Second, lead farmer familiarity with and adoption of CA both matter to the spread of CA practices, but familiarity appears more important. Third, lead farmers play a more critical role in increasing awareness than adoption of the CA practices. Finally, F2FE is a complement rather than a substitute for other sources of agricultural extension in Malawi's pluralistic extension system and should support but not replace current systems. Research and policy implications are discussed.     

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.     

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.     

Does IPM Have Staying Power? Revisiting a Potato‐producing Area Years After Formal Training Ended

Integrated pest management (IPM) potentially reduces pesticide use and costs of agricultural production. However, IPM is knowledge intensive and its spread may dissipate over time due to knowledge required for its effective implementation and to competing messages about pest control. We examine IPM spread and adoption several years after formal intensive IPM outreach efforts ceased in a potato‐producing region in Ecuador. We describe adoption patterns and sources of IPM knowledge in 2012 and compare them with patterns that existed when outreach ceased in 2003. Results show that IPM adoption continues in the area but with a lower proportion of farmers fully adopting all practices and a higher proportion adopting low to moderate levels as compared to 2003. Almost all potato farmers in the area use some IPM practices, reflecting a major increase in IPM use. Farmer‐to‐farmer spread has supplanted formal training and outreach mechanisms. IPM adoption significantly lowers pesticide use and saves production costs for adopters.     

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.     

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.     

The impact of water conservation and intensification technologies: empirical evidence for rice farmers in Ghana

This study employs the propensity score matching model to examine the impact of the adoption of water conservation and intensification technologies on farm output and income among small-scale lowland rice farmers in the Northern Region of Ghana. The matching was conducted based on the Mahalanobis distance combined with the propensity score. Balancing tests by checking the mean standardized absolute bias in the matched sample were conducted as well as a sensitivity analysis was conducted to check for hidden bias due to unobservable selection. The empirical results show a positive and significant impact of the adoption of bunds technology on input demand as well as a positive but insignificant impact on output supply and net returns. Adopters of the dibbling technology were found to have higher output supply, whereas no statistically significant difference was found between the incomes of adopters and nonadopters of dibbling seed method. The results, however, reveal a positive and significant effect on output and net returns when the dibbling method is combined with intensified weeding. The estimates were found to be relatively insensitive to hidden bias.     

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.     

Land titling policy and soil conservation in the northern uplands of Vietnam

In Vietnam, a quasi-private property regime has been established in 1993 with the issuance of exchangeable and mortgageable long-term land use right certificates. Using primary qualitative and quantitative data collected in a mountainous district of Northern Vietnam, this paper investigates the role of the land policy in the adoption of soil conservation technologies by farmers. This issue is of crucial importance in the region where population growth and growing market demands have induced farmers to intensify agricultural production. While poverty has been reduced, environmental problems such as soil erosion, landslides, and declining soil fertility have become more severe over the past years. Our findings suggest that despite farmers’ awareness of erosion, soil conservation technologies are perceived as being economically unattractive; therefore, most upland farmers continue to practice the prevailing erosion-prone cultivation system. Focusing on agroforestry as one major soil conservation option, we estimate household and plot-level econometric models to empirically assess the determinants of adoption. We find that the possession of a formal land title positively influences adoption, but that the threat of land reallocations in villages discourages adoption by creating uncertainty and tenure insecurity. The analyses reveal that these two effects interact with each other but are of small magnitude. We conclude that the issuance of land titles is a necessary but not sufficient prerequisite to encouraging the adoption of soil conservation practices. However, current practices remain economically unattractive to farmers. This deficiency needs to be addressed by interdisciplinary research and complemented by strong efforts by local authorities to promote sustainable land use.     

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 climate‐smart practices and its impact on farm performance and risk exposure among smallholder farmers in Ghana

Increased climate variability during the last four decades has made the agricultural environment in many developing countries more uncertain, resulting in increasing exposure to risk when producing crops. In this study, we use recent farm‐level data from Ghana to examine the drivers of individual and joint adoption of crop choice and soil and water conservation practices, and how adoption of these practices impacts on farm performance (crop revenue) and exposure to risks (skewness of crop yield). We employ a multinomial endogenous switching regression model to account for selectivity bias due to both observable and unobservable factors. The empirical results reveal that farmers’ adoption of crop choice and soil and water conservation leads to higher crop revenues and reduced riskiness in crop production, with the largest impact on crop revenues coming from joint adoption. The findings also show that education of the household head, access to extension and weather information influence the likelihood of adopting these practices. Thus, enhancing extension services and access to climate information and irrigation can reduce gaps in adoption of soil and water conservation and crop choice, considered as climate‐smart practices that will eventually improve crop revenues and reduce farmers’ exposure to climate‐related production risks.     

Impact of conservation agriculture technology on household welfare in Zambia

This article examines the determinants and impact of conservation agriculture (CA) technology adoption on farm household welfare in Zambia. To account for selection bias from both observable and unobservable factors, an endogenous switching regression model is employed to estimate the impact of the technology on continuous outcomes like farm output, throughput accounting ratio (TAR), poverty gap, and severity of poverty. A recursive bivariate probit model is however used for the estimation of impact of adoption on a binary outcome like poverty headcount. The empirical findings demonstrate that the adoption of CA technology increases maize output, and farm TAR and reduces household poverty. Moreover, the results reveal that farmers’ years of schooling, social networks, access to credit, extension services, and machinery as well as soil quality positively influence adoption of CA technology.     

Market imperfections,access to information and technology adoption in Uganda: challenges of overcoming multiple constraints

Limited empirical evidence exists on how multiple binding constraints influence the adoption of improved technologies by smallholder farmers. This article uses the case of groundnut variety adoption in Uganda to investigate the role of information, seed supply, and credit constraints in conditioning technology uptake. New data from a household survey in seven groundnut growing districts (n = 945) indicate that 8% of farmers lack information on new varieties, while 18% and 6% of farmers, respectively, cannot adopt mainly due to seed supply and capital constraints. A tobit‐type specification that considers all nonadopters as being uninterested in the technology (i.e., corner solutions) would lead to inconsistent parameter estimates and incorrect conclusions in this context. We therefore estimate a modified multi‐hurdle specification of demand for new varieties, taking into account how information, seed supply, and capital constraints jointly determine adoption probability and intensity. The study reveals new empirical insights on why agricultural technology adoption in Africa has lagged behind: slow uptake is not mainly due to a lack of economic incentives, but rather a reflection of information, seed supply, and credit constraints that prevent farmers from translating their desired demand into adoption of modern varieties. Policy implications are discussed.     

Roles of extension and ethno-religious networks in acceptance of resource-conserving agriculture among Ethiopian farmers

This article analyses roles of social and extension networks in adoption of resource-conserving practices among Ethiopian farmers. We gathered data from 297 randomly sampled households on their agricultural practices, social networks, access to the extension, and geographical location. After examining general determinants of practising resource-conserving agriculture, we employ a two-stage regression with full-maximum likelihood correction for selection bias to establish the roles of general social networks and external professionals in acceptance of conservation techniques. In accordance with previous research, probit regression in the first stage shows that the access to extension increases with farmers’ wealth and the size of their personal networks, and decreases with the distance of their households from village centres. However, after accounting for this unequal access to extension, the second-stage linear regression shows that regardless of education, wealth or geographical location, those whose religion and ethnicity match with their agent, report learning more about conservation from extension sources. Furthermore, farmers who are socially well connected within the community tend to be less receptive to agents’ recommendations regarding resource conservation. Dissemination policy of conservation agriculture should consider the ethnic and religious affinity between farmers and their extension agents. It also needs to pay more attention to socially and geographically isolated individuals.     

Production Risks and Food Security under Alternative Technology Choices in Malawi: Application of a Multinomial Endogenous Switching Regression

Employing nationally representative data, we investigate the impact of Sustainable Intensification Practices (SIPs) on farm households’ food security, downside risk and the cost of risk in Malawi. The analysis relies on a flexible moment‐based specification of a stochastic production function in a multinomial endogenous switching regression framework to correct for the selection bias stemming both from observed and unobserved heterogeneity. A quantile moment approach is used to estimate the cost of risk. After controlling for the effects of unobserved heterogeneity and several observable variables on maize production and downside risk functions, estimation results show that the adoption of SIPs increases food security and reduces downside risk exposure and the cost of risk. We estimate greater food security and larger reduction in downside risk from simultaneous adoption of both crop diversification (maize–legume intercropping and rotations) and minimum tillage, suggesting that there are complementary benefits from these practices. We find most of the cost of risk comes from exposure to downside risk. Our findings imply that in dealing with production risks development agents should encourage the adoption of agronomic and resource‐management practices along with other risk mitigation and food security improving strategies.     

Adoption and welfare impacts of multiple agricultural technologies: evidence from eastern Zambia

Using plot level panel data and multinomial endogenous switching regression, this article analyzes the adoption and welfare impacts of multiple agricultural technologies in eastern Zambia. We adapt a multinomial endogenous switching/treatment effect regression framework to correct for selection bias and endogeneity originating from both observed and unobserved heterogeneity. Results indicate that joint adoption of multiple agricultural technologies had greater impacts on crop yields, household incomes, and poverty than the adoption of individual components of the technology package. Our findings suggest that efforts aimed at raising household incomes and reducing poverty should focus on promoting the adoption of multiple agricultural technologies through provision of improved support services such as extension and input supply.