JOURNAL ARTICLE

Avoiding Fields on Fire: Information Dissemination Policies for Environmentally Safe Crop-Residue Management.

  • Published In: Management Science (INFORMS), 2025, v. 71, n. 8. P. 6683 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Farahani, Mehdi H.; Dawande, Milind; Janakiraman, Ganesh; Wang, Shouqiang 3 of 3

Abstract

This article focuses on reducing agricultural open burning—a major source of CO₂ and black-carbon emissions—by optimizing information disclosure about the scheduling of Happy Seeders, a low-soil-tillage machine that enables sowing without burning crop residue. The government assigns a limited number of Happy Seeders to process farms sequentially, and farmers decide whether to wait for the machine or burn residue based on their beliefs about the schedule and associated yield losses from delayed sowing. The study proposes a class of "dilatory policies," which withhold schedule information until a chosen switch period before full disclosure, and demonstrates that such policies outperform both full-disclosure and no-disclosure approaches in minimizing burning. Using data from the rice-wheat system in northwestern India, the optimal dilatory policy is shown to reduce crop-residue burning by about 17%, significantly lowering CO₂ and black-carbon emissions, with further gains expected as Happy Seeder availability increases. The analysis also extends to heterogeneous farmer patience levels and considers government objectives balancing environmental impact and farmer welfare, finding that earlier disclosure benefits farmers more, while delayed disclosure better reduces burning.

Additional Information

  • Source:Management Science (INFORMS). 2025/08, Vol. 71, Issue 8, p6683
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2025
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2021.03030
  • Accession Number:187706385
  • Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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