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Standard experimental designs are geared toward point estimation and hypothesis testing, while bandit algorithms are geared toward in-sample outcomes. Here, we instead consider treatment assignment in an experiment with several waves for choosing the best among a set of possible policies (treatments) at the end of the experiment. We propose a computationally tractable assignment algorithm that we call “exploration sampling,” where assignment probabilities in each wave are an increasing concave function of the posterior probabilities that each treatment is optimal. We prove an asymptotic optimality result for this algorithm and demonstrate improvements in welfare in calibrated simulations over both non-adaptive designs and bandit algorithms. An application to selecting between six different recruitment strategies for an agricultural extension service in India demonstrates practical feasibility.

We evaluate causal impacts of a large-scale agricultural extension program for smallholder women farmers on technology adoption and food security in Uganda through a regression discontinuity design that exploits an arbitrary distance-to-branch threshold for village program eligibility. We find eligible farmers used better basic cultivation methods, achieved improved food security. Given minimal changes in adoption of relatively expensive inputs, we attribute these gains to improved cultivation methods that require low upfront monetary investment. Farmers also modified their shock-coping methods. These results highlight the role of information and training in boosting agricultural productivity among poor farmers and, indirectly, improving food security.

Low adoption of agricultural technologies holds large productivity consequences for developing countries. Many countries hire agricultural extension agents to communicate with farmers about new technologies, even though a large academic literature has established that information from social networks is a key determinant of product adoption. We incorporate social learning in extension policy using a large-scale field experiment in which we communicate to farmers using different members of social networks. We show that communicator own adoption and effort are susceptible to small performance incentives, and the social identity of the communicator influences others’ learning and adoption. Farmers appear most convinced by communicators who share a group identity with them, or who face comparable agricultural conditions. Exploring the incentives for injection points in social networks to experiment with and communicate about new technologies can take the influential social learning literature in a more policy-relevant direction.

Agricultural extension programs have changed significantly over the past four decades. What has changed and why? Have these changes affected adoption of innovations by farmers? What if anything should policy makers and extension agencies do differently, particularly in developing countries? Structural changes in agriculture, new types of agricultural technologies, tight public budgets, efforts to decentralize government, and emerging information and communication technologies (ICT) have led to pluralistic and, in some cases, lower-cost extension and advisory services that combine public and private mechanisms for financing and implementing extension activities. Farmer groups and virtual networks play a growing role in technology diffusion, and extension services can exploit these networks using the latest ICT approaches.

In this article, we investigate the role of information transmission in promoting agricultural technology adoption and diffusion through extension services and social learning. We develop a theoretical model of technology adoption and diffusion, which we then empirically apply, using duration analysis, on a micro-dataset consisting of recall data covering the period 1994–2004 for olive-producing farms from Crete, Greece. Our findings suggest that both extension services and social learning are strong determinants of technology adoption and diffusion, while the effectiveness of each of the two informational channels is enhanced by the presence of the other.

This article investigates whether public investments that led to improvements in road quality and increased access to agricultural extension services led to faster consumption growth and lower rates of poverty in rural Ethiopia. Estimating an Instrumental Variables model using Generalized Methods of Moments and controlling for household fixed effects, we find evidence of positive impacts with meaningful magnitudes. Receiving at least one extension visit reduces headcount poverty by 9.8 percentage points and increases consumption growth by 7.1 percentage points. Access to all-weather roads reduces poverty by 6.9 percentage points and increases consumption growth by 16.3 percentage points. These results are robust to changes in model specification and estimation methods.

This study aims to explore the factors and challenges influencing Agricultural Extension Model Sites (AEMSs) in improving irrigated wheat production in Iran. An exploratory survey was conducted to investigate changes related to pre-planting, planting, harvesting, and post-harvesting activities by examining farmers who are members of wheat AEMSs. The goal was to understand the effects and challenges faced by these sites in agriculture. The study revealed that challenges at model sites are grouped into six categories: technical-structural, planning, political-motivational, financial and credit, agronomic, and cooperation and coordination factors. These six factors accounted for 68.55% of the variance in AEMSs challenges. The most significant effects of AEMSs included improving the efficiency of agricultural activities, and increasing farmers’ trust in agricultural extension agents by providing practical solutions to enhance productivity. The main practical implication is that the Agricultural Institute of Education and Extension in Iran can objectively introduce farmers to the best agricultural techniques and accelerate the adoption of innovations in agriculture by creating and expanding these sites for various products and topics. The study offers new insights into agricultural technology transfer models and addresses a gap in the existing literature regarding model sites as an Iranian agricultural extension initiative. This is particularly significant in the context of international academic literature. This study investigates the factors and challenges associated with the Agricultural Extension Model Sites (AEMS) approach, which has been designed and implemented in agricultural extension in Iran. While similar extension models are utilized internationally, AEMS demonstrates a superior ability to persuade farmers to adopt optimal agricultural practices. This paper presents evidence of the effectiveness of agricultural extension models, particularly in enhancing the productivity of the strategically important wheat crop.

New and improved agricultural technologies can transform lives, particularly the lives of smallholder farm households in Asia who are highly dependent on agriculture. However, there are large gender disparities in the adoption of such technologies. Many barriers exist in achieving gender equity in access to and adoption of agricultural technologies, from sociocultural norms and deeply rooted beliefs about gender roles to lack of agency and lack of resources to implement policies. Notwithstanding these barriers, the case for promoting gender-inclusive adoption of technology is strong. In this paper, we outline the rationale for improving women’s adoption of agricultural technology and discuss the pitfalls of failing to include women in the technology-adoption agenda. We then explore the policy implications and suggest various strategies that promote gender-equitable outcomes and that can be used to mainstream gender in agricultural technology adoption efforts to convert policy statements to practical and effective actions.

Agriculture plays a crucial role in the economy of Burkina Faso and other sub-Saharan African countries. It provides food for the population and is the primary source of employment in rural areas. The government of Burkina Faso has implemented a 2018–2027 Agro-Sylvo-Pastoral production sector policy to improve agricultural productivity and reduce poverty. This study uses a dynamic computable general equilibrium model linked to a microsimulation model, which evaluates the impact of subsidizing investment in agricultural capital on production, income, and poverty under three different fiscal financing scenarios. The results indicate that the agricultural mechanization subsidy program effectively reduces poverty, but the extent of the reduction varies depending on the financing method used. Financing the subsidy by reducing nonproductive public expenditure further reduces poverty, especially in rural areas. However, it is essential to supplement the subsidy policy with an agricultural extension service program for the program to have long-lasting effects.

Standard models of hierarchy assume that agents and middle managers are better informed than principals. We estimate the value of the informational advantage held by supervisors—middle managers—when ministerial leadership—the principal—introduced a new monitoring technology aimed at improving the performance of agricultural extension agents (AEAs) in rural Paraguay. Our approach employs a novel experimental design that elicited treatment-priority rankings from supervisors before randomization of treatment. We find that supervisors have valuable information—they prioritize AEAs who would be more responsive to the monitoring treatment. We develop a model of monitoring under different scales of treatment roll-out and different treatment allocation rules. We semiparametrically estimate marginal treatment effects (MTEs) to demonstrate that the value of information and the benefits to decentralizing treatment decisions depend crucially on the sophistication of the principal and on the scale of roll-out.