TY  - JA
AU  - Sherpa,S.R.
AU  - Nayak,H.S.
AU  - Rossiter,D.G.
AU  - Craufurd,P.
AU  - Kritee Kritee
AU  - Kumar,V.
AU  - Paudel,G.P.
AU  - Panneerselvam,P.
AU  - Pathak,H.
AU  - Poonia,S.P.
AU  - Singh,B.
AU  - Urfels,A.
AU  - van Es,H.M.
AU  - Gautam,U.S.
AU  - Malik,R.
AU  - McDonald,A.J.
TI  - A data-driven approach for devising and assessing precision nitrogen management strategies applied to wheat systems in India
SN  - 2976-601X
PY  - 2025///
CY  - United Kingdom
PB  - IOP Publishing Ltd
KW  - Nitrogen
KW  - AGROVOC
KW  - Pollution
KW  - Greenhouse gas emissions
KW  - Precision agriculture
KW  - Nitrogen-use efficiency
KW  - Decision support
KW  - Food security
KW  - Smallholders
KW  - Wheat
KW  - India
N1  - Peer review; Open Access
N2  - Limiting nitrogen pollution from crop production is essential for mitigating greenhouse gas emissions and protecting aquatic ecosystems while maintaining food security. Precision nitrogen management (PNM) provides a conceptual framework for achieving yield goals while maintaining nitrogen pollution within planetary boundaries by matching fertilizer rates to specific production conditions. Nevertheless, PNM strategies for smallholder contexts like India, a global nitrogen pollution hotspot, have proven costly to implement and are often ineffective. By combining survey data of production practices from 8705 wheat fields with digital soil mapping, we develop a novel PNM strategy that ‘learns from landscapes’ to generate and evaluate novel decision logic for nitrogen management. With this approach, ex-ante simulations indicate that reductions of 9% in nitrogen use and 16% in N2O emissions can be achieved without compromising yields, saving US$ 28 million per year in subsidies for the Indian state of Bihar alone. In contrast, conventional soil test-based recommendations may increase nitrogen use by 5% without corresponding yield gains. Our method that leverages large-n survey data and predictive modeling may provide a scalable pathway for PNM in similarly complex crop production environments where field and management heterogeneity is high
UR  - https://hdl.handle.net/10883/36345
DO  - https://doi.org/10.1088/2976-601X/ae1839
T2  - Environmental Research: Food Systems
ER  -