Enhancing yield and GHG mitigation through site-specific nutrient management for transplanted and direct-seeded rice in Odisha, India

The Rice Crop Manager (RCM), a web-based decision support tool rooted in Site-Specific Nutrient Management (SSNM), provides transformative solutions to address the challenges of fertilizer overuse and underuse in rice production. This study, conducted across diverse agro-ecologies in Odisha, India,...

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Detalles Bibliográficos
Autores principales: Chaudhary, Anjali, Mishra, Ajay Kumar, Venkatramanan, Veluswamy, Sharma, Sheetal
Formato: Journal Article
Lenguaje:Inglés
Publicado: Frontiers Media 2025
Materias:
Acceso en línea:https://hdl.handle.net/10568/174647
Descripción
Sumario:The Rice Crop Manager (RCM), a web-based decision support tool rooted in Site-Specific Nutrient Management (SSNM), provides transformative solutions to address the challenges of fertilizer overuse and underuse in rice production. This study, conducted across diverse agro-ecologies in Odisha, India, evaluates the impact of SSNM under two rice establishment methods—Transplanted Rice (TPR) and Direct-Seeded Rice (DSR)—over six cropping seasons. Results reveal that RCM recommendations consistently increased grain yields by 17–19% compared to traditional Farmer Fertilizer Practices (FFP) while significantly improving nitrogen and potassium use efficiency. SSNM also reduced phosphorus application rates by 8.6–18.1 kg/ha and effectively mitigated critical micronutrient deficiencies, particularly zinc. Additionally, RCM treatments demonstrated reduced greenhouse gas (GHG) emissions compared to FFP, highlighting the role of precision agriculture in mitigating climate impacts. Despite slightly higher initial input costs, RCM delivered greater economic returns through optimized fertilizer use. While TPR exhibited higher yield advantages, DSR emerged as a resource-efficient and mechanization-compatible alternative, though it requires targeted interventions to address challenges such as nitrous oxide emissions. This study underscores the potential of RCM as a scalable, data-driven solution for enhancing productivity, profitability, and environmental sustainability in rice systems.