Farmer adoption-based prompt networking and modeling for targeting optimal agro-conservation practices

Shubham Aggarwal, Viven Sharma, Srinivas Rallapalli, Christian Lenhart, Joe Magner

Research output: Contribution to journalArticlepeer-review


Despite the availability of robust agricultural models, the successful on-ground implementation of all Best Management Practices (BMPs) remains elusive due to the lack of consideration for farmers' preferences. This study develops a unique network-based optimization technique coupled with Soil and Water Assessment Tool (SWAT) which delineates combined set of cost-effective BMPs at field-scale based on farmer's conservation identity in Ganges River Basin, India. SWAT was calibrated and validated using 10-year monthly discharge and nitrate data, and the selected BMPs were simulated using sequential uncertainty fitting framework. Farmers belonging to the Shahpur subdistrict were found to have the maximum normalized conservation identity of 90%, whereas the northeastern portion of the watershed contributed a maximum nitrate load of 7.5–9.92 kg/acre to the watershed outlet. An optimized combination of BMPs (riparian buffer-conservation tillage) with an efficiency of 0.28 was obtained through the modified network technique that targeted subbasins of the Morna subdistrict using farmer conservation identities. The results provide valuable guidance for watershed modelers, conservation planners, and researchers in pinpointing regions for targeted farmer training, subsidies, and other initiatives aimed at facilitating prompt field-scale BMP adoption. A flexible feature of the approach is its ability to modify the BMP action plans by replacing even technically feasible, cost-effective combinations based on farmers' preferences.

Original languageEnglish (US)
Article number106060
JournalEnvironmental Modelling and Software
StatePublished - Jun 2024

Bibliographical note

Publisher Copyright:
© 2024 Elsevier Ltd


  • Best management practices
  • Conservation identity
  • Network optimization
  • Nitrate reduction
  • Non-point source pollution


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