Home

This paper evaluates a low-cost, customized soil nutrient management advisory service in India. As a methodological contribution, we examine whether and in which settings satellite measurements may be effective at estimating both agricultural yields and treatment effects. The intervention improves self-reported fertilizer management practices, though not enough to measurably affect yields. Satellite measurements calibrated using OLS produce more precise point estimates than farmer-reported data, suggesting power gains. However, linear models, common in the literature, likely produce biased estimates. We propose an alternative procedure, using two-stage least squares. In settings without attrition, this approach obtains lower statistical power than self-reported yields; in settings with differential attrition, it may substantially increase power. We include a “cookbook” and code that should allow other researchers to use remote sensing for yield estimation and program evaluation.

One of the challenges in evaluating the impact of agronomy training programs, particularly on downstream impacts such as yield, is identifying a sample of farmers who are likely to participate in the training. We assess farmers’ participation in a farm business training activity before the agronomy training intervention as a sample identification mechanism. The screening activity was designed to appeal to the same group of farmers targeted by a coffee agronomy training program, while having minimal impact on the program’s goal of increasing coffee yields. A three-session training on farm business management was conducted in 22 study villages in central Uganda. Coffee agronomy training was then offered in half of these villages, based on random assignment. The results show that 52% of coffee farmers who attended the first business training session subsequently attended agronomy training, compared to 22% of those identified through a census. Applying these results to the design of a large ongoing randomized controlled trial, we find that using a self-selected sample reduces the minimum detectable effect of agronomy training on coffee yield to 15.83%, compared to 38% if population-based sampling were used.

Providing information at scale about improved agricultural practices to smallholder farmers remains a challenge in most developing countries. Traditional dissemination methods like in-person meetings or radio programming can be costly to scale or offer too generic information. Moreover, while most agronomic recommendations focus on maximizing crop yields, farmers weigh multiple other factors when making farming decisions, such as the profitability of investments and risks. The proliferation of mobile phones has shifted these trends. Mobile agriculture extension can cost-effectively provide tailored suggestions to farmers and improve their use of information. This case study describes the use of digital extension technologies to support farmers in a number of contexts. We draw insights from various studies and the experience of Precision Development on the importance of human-centered design, monitoring, and continuous experimentation. The chapter also discusses the ecosystem of stakeholders for digital agriculture, concerns relating to privacy and financing, and how mobile services can be used to facilitate social learning.

We evaluate at scale the impact of a digital agricultural advisory service reaching millions of smallholder farmers, in an eastern state of India. We randomized the rollout of the service among 13,675 rice farmers within five districts, and measure the impact on agricultural outcomes using both survey and remote sensing data. Using survey data, we find that access to the digital service leads to significant improvements in farmers’ knowledge and adoption of recommended practices, a modest increase in rice yield and harvest, and a large reduction in the likelihood of rice crop loss on average. Further analyses suggest that the treatment impact is concentrated in areas hit by some weather shocks, increasing harvest by up to 9% and reducing severe crop loss by up to 21% in affected areas. We use vegetation indices (VIs) to construct an objective yield measure for all farmers in the study sample and confirm that our key survey results are robust against differential attrition, reporting biases, and survey sample selection. While the VI-predicted yield provides valuable validation of survey results, our analysis highlights the need for methodological improvements in the effective application of remote sensing data to measure program impacts on agricultural outcomes.

We evaluate at scale the impact of a digital agricultural advisory service reaching millions of smallholder farmers, in an eastern state of India. We randomized the rollout of the service among 13,675 rice farmers within five districts, and measure the impact on agricultural outcomes using both survey and remote sensing data. Using survey data, we find that access to the digital service leads to significant improvements in farmers’ knowledge and adoption of recommended practices, a modest increase in rice yield and harvest, and a large reduction in the likelihood of rice crop loss on average. Further analyses suggest that the treatment impact is concentrated in areas hit by some weather shocks, increasing harvest by up to 9% and reducing severe crop loss by up to 21% in affected areas. We use vegetation indices (VIs) to construct an objective yield measure for all farmers in the study sample and confirm that our key survey results are robust against differential attrition, reporting biases, and survey sample selection. While the VI-predicted yield provides valuable validation of survey results, our analysis highlights the need for methodological improvements in the effective application of remote sensing data to measure program impacts on agricultural outcomes.

Weather-induced risk reduces farmers’ incomes, and climate change is increasing such risk. One promising intervention to mitigate risk is high-quality, probabilistic, short-to-medium-range rainfall forecasts, which predict weather between zero and fifteen days ahead. For forecasts to be effective, however, farmers have to understand and act on them. This paper evaluates how farmers use probabilistic forecasts and form beliefs about their accuracy in a lab-in-the-field experiment. In scenarios that mimic real-world decision making, we find that farmers update their beliefs about the (in)accuracy of forecasts following false alarms, where forecasts erroneously predict events. Farmers who experience false alarms perform worse in subsequent rounds of incentivized experimental games, and report a lower willingness-to-pay for a real world weather forecast service in an incentive-compatible Becker–DeGroot–Marschak elicitation. Light-touch interventions to improve probability comprehension and make climate change salient have limited impact on farmer decision-making, with positive effects that are mitigated by the incidence of false alarms.

Mobile technology has the potential to increase the efficiency and the usage of financial services for the poor. Many of these services are, however, traditionally delivered in a group setting. Digitization may then disrupt the existing social architecture, leaving its overall effect uncertain. Using a randomized experiment in the Philippines, I examine how the introduction of mobile banking in group microfinance affects savings behavior of existing clients and find that the average daily balance and frequency of deposits declined by 20% over two years.

Climate change induced rainfall and temperature variability pose a substantial economic risk to smallholder dairy farmers in developing countries. Rainwater harvesting tanks may help farmers adapt to climate uncertainty. Previous work has found that Asset Collateralized Loans (ACLs) help farmers purchase water tanks in Kenya (Jack et al, 2019). In this project, we are working with two dairy cooperatives in Rift Valley, Kenya, to evaluate the impact of ACLs for water tanks on milk production and climate adaptation outcomes. 

Farmers in low and middle-income countries face multiple barriers to optimizing agricultural decisions. Farmers often don’t have access to actionable information to adapt their practices, and learning from experience is challenging as the benefits associated with behavior changes are stochastic and observed with a lag. Leaf Color Chart (LCC) is a decision support tool to help farmers assess leaf nitrogen needs in real-time, and optimize fertilizer use, aiming to improve profits while minimizing environmental impact. We will conduct a field experiment in Maharashtra, India, to estimate the efficacy and cost-effectiveness of LCC with digital support. In the first year, we will rigorously assess the impact of LCCs on cotton farmers’ nitrogen fertilizer use and production costs; in the second year, we will determine the cost-effectiveness of scalable LCC distribution mechanisms.