Detecting and quantifying zero tillage technology adoption in Indian smallholder systems using Sentinel-2 multi-spectral imagery

Abstract

Zero tillage (ZT), an important component of Conservation Agriculture, has enormous potential to curb emissions from residue burning, increase soil organic carbon and water retention, reduce land preparation costs and increase the long-term productivity and profitability of the farming system. Despite the promise of ZT, little is known about how widely it has been adopted at regional scales in smallholder systems, where management is heterogeneous. Identifying ZT diffusion patterns across space and time along with other popular tillage technologies, such as conventional tillage (CT) and shallow tillage (ST), helps to target and disseminate the most effective technologies and estimate their climate change mitigation potential. Acknowledging the complexities involved in distinguishing ZT from CT and ST in smallholder fields, this study utilized an innovative two-step change detection method leveraging early-season Sentinel-2 multi-spectral imagery. We developed and applied our model in the Indian state of Punjab over three years (2020–2022). Our method outperformed traditional binary classification models, achieving 81 % accuracy. The analysis indicated that areas under different tillage types changed over time across Punjab. Specifically, from 2020 to 2021, we found a 33 % and 4 % decrease in ZT and CT, respectively. However, a 29 % increase is observed in CT adoption. On the other hand, from 2021 to 2022, the adoption rates for ZT and CT increased by 18 % and 2 %, respectively, while ST adoption decreased by 12 %. Overall, this study demonstrates the potential use of early season Sentinel-2 imagery to map the adoption of tillage practices in smallholder systems. Our approach can provide large-scale information on technology uptake, aiding policies to implement carbon markets and the scaling up of sustainable agricultural practices in India.

Keywords

• Zero tillage
• Sentinel-2
• Conservation agriculture
• Google Earth Engine
• Random forest