Submitted by Aprajit Mahajan on
This project will provide incentives for smallholder farmers in India to adopt agricultural interventions expected to increase soil carbon sequestration. The long-term objective is to examine the feasibility of a program that provides farmers pay-outs from commercial firms dealing in carbon credits based on improved practices and to measure the efficacy of regenerative agricultural practices in sequestering carbon on a wide scale.
A recent body of evidence suggests that certain agricultural practices (e.g., limited tillage, residue retention, modified fertilizer choices, cover crops, and biochar) can increase soil carbon sequestration or reduce GHG emissions (Koyama et al., 2016; Chaun et al., 2017; Runkle et al., 2018; Tang et al., 2019; Babu et al., 2020). Most of this research was conducted under tightly controlled conditions. Some key questions remain unanswered:
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To what extent can farmers, particularly in poor countries, be persuaded to adopt such regenerative agricultural practices particularly if the effect on yields and revenues is unclear?
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To what extent is the carbon sequestration observed in tightly controlled studies replicable in field settings? Answering this question also requires us to adapt and test measurement protocols for SOC in field settings.
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How can remotely sensed data help complement or substitute for field activities (soil sampling and crop cuts) for measurement of SOC and biomass?
We address these questions with a pilot of 100 small farmers in India. We will provide farmers with payouts based on the adoption of regenerative agricultural practices and improvements in soil carbon content. If the pilot is successful we plan to conduct an RCT around an intervention providing farmers with payments from firms selling carbon credits on a commercial basis.
The general function and responsibilities of the position include:
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conduct background research and literature reviews.
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collect remote sensing data.
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clean and manage datasets.
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conduct statistical analyses.
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calibrate and validate biogeochemistry models.
The ideal candidate will have the following qualifications:
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Computer skills including experience with statistical software such as Python, R, and/or Stata
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Strong quantitative background.
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Strong motivation, self-direction, and organizational skills.
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Background in economics is a plus, but not necessary. (Candidates with strong technical backgrounds who are looking for more exposure to economics are welcomed.)
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Demonstrated academic excellence.