Promoting Sustainable Agricultural Practices Through Rental Markets

Award Period
to
Award Amount
$78,570
Agency Name
Massachusetts Institute of Technology-MIT
Award Number
S5112
PI First Name
Kelsey
PI Last Name
Jack
Area/s of Research
Climate Change Science
Abstract

Agricultural productivity gains and the commercialization of the agricultural sector may negatively impact the long-term health of the environment. For example, in northern India, mechanized harvesting of rice paddy fields has increased farmer profits, but has also created massive air quality problems since it is connected with the practice of burning crop residue to clear fields after harvest. The study population consists of Punjabi paddy farming villages for which satellite imagery was able to identify as areas with a high rate of burning in 2018. The project provides farmers in these villages with conditional payments based on verifying that the farmer did not burn his or her fields.

Implementation of the conditional payment program was rolled out in 2019, with villages randomly assigned to a control group or to one of four treatment arms, which varied the level and timing of payments. Survey data collection is largely complete, and field monitoring has been completed on treated fields that enrolled in the program. To complete the analysis, measures of field burning are required for the un-monitored fields. These will be obtained through remote sensing imagery.

The award to UCSB will support the remote sensing work, including (a) modification of an algorithm for measuring burn scars, (b) processing of Sentinel-2 imagery for the study districts, (c) calibration of results to best match “ground truthed” monitoring data, (d) development of one or more datasets to be used as outcomes for the randomized trial and (e) careful documentation of the work.

Timeline and Deliverables
Timeline: July 1, 2020 – June 30, 2021

Deliverables by the end of the award period include:

  • Code for the remote sensing algorithm, adjusted to the study setting
  • Output data covering all study fields, assigned to either a binary “burn” variable or a continuous probability of burn variable
  • Documentation of the remote sensing methodology, calibration steps and output