Summary
Agricultural productivity is strikingly low in much of Sub-Saharan Africa, exposing the large fraction of the population who lives from agriculture to low incomes and periods of hunger. Despite high returns to modern agricultural inputs elsewhere, their adoption in the region has been persistently low over the past decades. In the absence of adaptation, climate change is expected to severely impact the agricultural productivity of Sub-Saharan Africa. I propose three projects which will significantly improve our ability to understand the effect of modern agricultural inputs for agricultural productivity, and encourage their efficient adoption. First, existing methods for measuring the outcome of interest are notoriously imprecise, or costly, which is a fundamental limitation for research on agricultural productivity in developing countries. I will develop and evaluate an approach to substantially improve the precision with which researchers can measure agricultural output, at low costs. Second, existing approaches to encourage the adoption of modern agricultural inputs have had muted success. I will test a potentially highly impactful, novel approach: teaching farmers how to assess the returns to agricultural inputs on their own plots. In light of heterogeneous returns to input adoption across plots, this promises to encourage the right farmers to adopt the right inputs for their plots. And third, existing approaches to assess the degree of misallocation of inputs and associated output losses rely on strong functional form assumptions. I will leverage the plot-level estimates of returns generated in the second project as well as the knowledge embedded in bio-physical models of plant growth to quantify the extent of misallocation of inputs in agriculture. This radically new methodology to quantify misallocation will yield substantially more credible estimates, and has the potential to significantly expand the possibilities to learn about the sources of misallocation.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101126144 |
| Start date: | 01-06-2024 |
| End date: | 31-05-2029 |
| Total budget - Public funding: | 1 575 812,00 Euro - 1 575 812,00 Euro |
Cordis data
Original description
Agricultural productivity is strikingly low in much of Sub-Saharan Africa, exposing the large fraction of the population who lives from agriculture to low incomes and periods of hunger. Despite high returns to modern agricultural inputs elsewhere, their adoption in the region has been persistently low over the past decades. In the absence of adaptation, climate change is expected to severely impact the agricultural productivity of Sub-Saharan Africa. I propose three projects which will significantly improve our ability to understand the effect of modern agricultural inputs for agricultural productivity, and encourage their efficient adoption. First, existing methods for measuring the outcome of interest are notoriously imprecise, or costly, which is a fundamental limitation for research on agricultural productivity in developing countries. I will develop and evaluate an approach to substantially improve the precision with which researchers can measure agricultural output, at low costs. Second, existing approaches to encourage the adoption of modern agricultural inputs have had muted success. I will test a potentially highly impactful, novel approach: teaching farmers how to assess the returns to agricultural inputs on their own plots. In light of heterogeneous returns to input adoption across plots, this promises to encourage the right farmers to adopt the right inputs for their plots. And third, existing approaches to assess the degree of misallocation of inputs and associated output losses rely on strong functional form assumptions. I will leverage the plot-level estimates of returns generated in the second project as well as the knowledge embedded in bio-physical models of plant growth to quantify the extent of misallocation of inputs in agriculture. This radically new methodology to quantify misallocation will yield substantially more credible estimates, and has the potential to significantly expand the possibilities to learn about the sources of misallocation.Status
SIGNEDCall topic
ERC-2023-COGUpdate Date
12-03-2024
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