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Trial Title
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Before
Impact of a Digital Agriculture Advisory Program in Odisha
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After
Evaluating the Impact of a Digital Agriculture Advisory Program in India
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Abstract
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Before
With the rapid increase in access to mobile phones, there is an increasing effort among governments and policymakers to provide timely and customized agricultural information via mobile phones. However, rigorous evidence on how and for whom such interventions work is still limited. This study aims to understand whether and how agricultural extension delivered via mobile phones increases the agricultural productivity and profitability of smallholder farmers. The research is motivated by the large and persistent yield gap that exists among poor farmers in developing countries. We plan to examine this question using a large-scale randomized controlled trial over a two-year period in Odisha, India. Moreover, this study will test low-cost approaches to measuring yields. Specifically, it will assess the feasibility of using satellite images to estimate farmers’ rice yields in Odisha. This work will help address a common empirical challenge of evaluating digital extension and other agricultural interventions.
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After
With the rapid increase in access to mobile phones, there is an increasing effort among governments and policymakers to provide timely and customized agricultural information via mobile phones. However, rigorous evidence on how and for whom such interventions work is still limited. This study aims to understand whether and how agricultural extension delivered via mobile phones increases the agricultural productivity and profitability of smallholder farmers. The research is motivated by the large and persistent yield gap that exists among poor farmers in developing countries. We plan to examine this question using a large-scale randomized controlled trial over a two-year period in one state of India. Moreover, this study will test low-cost approaches to measuring yields. Specifically, it will assess the feasibility of using satellite images to estimate farmers’ rice yields in an eastern state of India. This work will help address a common empirical challenge of evaluating digital extension and other agricultural interventions.
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Trial End Date
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Before
September 01, 2023
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After
December 31, 2023
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Last Published
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Before
November 21, 2021 05:07 PM
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After
April 04, 2022 05:27 AM
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Intervention End Date
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Before
December 31, 2022
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After
April 30, 2023
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Experimental Design (Public)
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Before
We use a randomized controlled trial (RCT) to investigate the impact of a digital agricultural advisory service on smallholder farmers’ yield. We plan to recruit eligible households cultivating rice across 1,300 villages in 19 blocks using a random-walk method and randomly select half of the households to receive a free digital advisory service.
We selected 19 blocks in five districts in the state for inclusion in the study. All the included blocks had low (<18.5 percent) “penetration” of the information service, as measured by the estimated share of farmers that were enrolled prior to the study, and were covered by the same satellite path. Within these 19 blocks, we selected all villages that met the following criteria during the 2011 Census of India: (i) classified as “rural”; (ii) had more than 50 households; (iii) located in a panchayat with service “penetration” below 10 percent. A total of 1,313 or 39 percent of the villages met these conditions and constituted our study sites. In each village, we plan to select 5 - 10 percent of households using a random-walk sampling method. Households will be eligible for inclusion in the study if they meet a set of criteria and are willing to allow the research team to collect the GPS coordinates of their primary rice plot.
The eligible households (one person per household) will be invited to participate in a baseline survey, including questions on demographics, farm characteristics, agricultural knowledge and practice, yield, and GPS coordinates of the primary rice plot.
We then randomize those farmers who have completed the baseline survey into control and treatment groups. Randomization is conducted at the individual farmer level, and stratified by panchayat and an indicator for participants’ self-reported productivity at baseline being above median.
Farmers in the treatment group start to receive free digital advisory service in the coming agriculture season. Moreover, those farmers are administered a phone-based compensation and service enrollment survey, which collects necessary information to send farmer compensation for their baseline survey participation and explains the digital advisory service to farmers.
The study began implementation of the random walk sampling method in March 2021, but suspended all field operations on 23rd April following a steep rise in COVID-19 cases in the state. 5,204 households were enrolled in the study before the fieldwork suspension (“initial” sample, or cohort 1), of which half were randomized to the treatment group and invited to enroll in the information service via phone. 2,571 (99.5%) members of the treatment group (2,602 farmers) opted-in to the service and began receiving advisory messages from 9th June. As the COVID-19 situation alleviated in the study location, we resumed our field activities in October, 2021. We use the same random-walk method in the same locations as previously and plan to sample a further 8,000 - 10,000 households.
At the end of 2021-22 and 2022-23 Kharif seasons, we will conduct a midline survey and an endline survey with the initial sample of farmers, allowing us to measure their knowledge, practice, yield and profit.
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After
We use a randomized controlled trial (RCT) to investigate the impact of a digital agricultural advisory service on smallholder farmers’ yield. We recruited eligible households cultivating rice across approximately 903 villages in 19 blocks using a random-walk method and randomly selected half of the households to receive a free digital advisory service.
We selected 19 blocks in five districts in the state for inclusion in the study. All the included blocks had low (<18.5 percent) “penetration” of the information service, as measured by the estimated share of farmers that were enrolled prior to the study, and were covered by the same satellite path. Within these 19 blocks, we selected all villages that met the following criteria during the 2011 Census of India: (i) classified as “rural”; (ii) had more than 50 households; (iii) located in a panchayat with service “penetration” below 10 percent. A total of 1,313 or 39 percent of the villages met these conditions and constituted our study sites. In each village, we selected 5 - 10 percent of households using a random-walk sampling method. Households are eligible for inclusion in the study if they meet a set of criteria and are willing to allow the research team to collect the GPS coordinates of their primary rice plot.
The eligible households (one person per household) are invited to participate in a baseline survey, including questions on demographics, farm characteristics, agricultural knowledge and practice, yield, and GPS coordinates of the primary rice plot.
We then randomize those farmers who have completed the baseline survey and have not registered for this study’s digital extension service before into control and treatment groups. Randomization is conducted at the individual farmer level, and stratified by panchayat group, baseline survey verison, an indicator for participants’ self-reported productivity at baseline being above the median or not, and (for cohort 2 only) an indicator for participants’ gps measured plot size at baseline being above the median or not.
Farmers in the treatment group start to receive free digital advisory service in the coming agriculture season post their baseline survey. Moreover, those farmers are administered a phone-based compensation and service enrollment survey, which collects necessary information to send farmer compensation for their baseline survey participation and explains the digital advisory service to farmers.
The study began implementation of the random walk sampling method in March 2021, but suspended all field operations on 23rd April following a steep rise in COVID-19 cases in the state. 5,204 households were enrolled in the study before the fieldwork suspension (aka “initial” sample, or cohort 1), of which half were randomized to the treatment group and invited to enroll in the information service via phone. 2,571 (99.5%) members of the treatment group (2,602 farmers) opted-in to the service and began receiving advisory messages from 9th June. As the COVID-19 situation alleviated in the study location, we resumed our field activities in October, 2021. We used the same random-walk method in the same locations as previously and sampled a further 8,471 households (aka. cohort 2).
After the 2021-22 and 2022-23 Kharif seasons, we will conduct a midline survey and an endline survey with cohort 1 farmers, allowing us to measure their knowledge, practice, yield and profit.
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Planned Number of Observations
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Before
Approximate 15,000 rice farmers.
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After
Approximate 13,600 rice farmers.
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Sample size (or number of clusters) by treatment arms
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Before
Control group: 7,500 farmers.
Treatment group: 7,500 farmers.
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After
Control group: approximate 6,800 farmers.
Treatment group: approximate 6,800 farmers.
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Power calculation: Minimum Detectable Effect Size for Main Outcomes
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Before
If this study achieves its planned number of observations (15,000), it will be able to detect a 3.2% or 0.04 standard deviation increase in self-reported rice yields and a 0.74% increase in vegetation index, with 80% power at 5% significance.
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After
If this study achieves its planned number of observations (13,600), it will be able to detect a 2.8% increase in self-reported rice yields and a 0.78% increase in vegetation index, with 80% power at 5% significance.
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