Seasonal climate forecasts (SCFs) based risk management strategies: A case study of rainfed rice cultivation in India
Seasonal climate forecasts (SCFs) have gained popularity in agriculture for climate risk management studies. The available forms of SCFs are not conducive to decision making because of a mismatch in scales over space and time. In this study, available SCFs were disaggregated using the FResampler1 te...
| Autores principales: | , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Association of Agrometeorologists
2022
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/129180 |
| _version_ | 1855530429592895488 |
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| author | Kushwaha, N. L. Rajput, Jitendra Shirsath, Paresh B. Sena, Dipaka Ranjan Mani, Indra |
| author_browse | Kushwaha, N. L. Mani, Indra Rajput, Jitendra Sena, Dipaka Ranjan Shirsath, Paresh B. |
| author_facet | Kushwaha, N. L. Rajput, Jitendra Shirsath, Paresh B. Sena, Dipaka Ranjan Mani, Indra |
| author_sort | Kushwaha, N. L. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Seasonal climate forecasts (SCFs) have gained popularity in agriculture for climate risk management studies. The available forms of SCFs are not conducive to decision making because of a mismatch in scales over space and time. In this study, available SCFs were disaggregated using the FResampler1 technique to simulate rice yield (cultivar PR 114) under different nitrogen levels and planting dates using DSSAT (Decision Support System for Agrotechnology Transfer) for Sitamarhi district, Bihar, India. Results showed that the late planting of rice predicted the highest yield (3800 kg ha-1) with high variability under SCF (wet) and 200 kg ha-1 application of nitrogen fertilizer. Similarly, for SCF (dry), the late planting of rice simulated high yield (3100 kg ha-1) attributes with 200 kg ha-1 of nitrogen fertilizer. However, rice yield under climatology (3450 kg ha-1) was more than SCF (dry) (3100 kg ha-1). Planting of rice on 15th June 2019 under the SCF (normal) predicted low uncertainty with high mean yields as compared to the mid (05th July 2019), and late (25th July 2019) planting. The present study showed that by applying SCF, we can have a better understanding on “relative” changes in yield attributes with fertilizer doses and planting dates, which may be adopted by the climate adviser to offset the climate risk without compromising productivity. |
| format | Journal Article |
| id | CGSpace129180 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Association of Agrometeorologists |
| publisherStr | Association of Agrometeorologists |
| record_format | dspace |
| spelling | CGSpace1291802025-11-06T13:06:54Z Seasonal climate forecasts (SCFs) based risk management strategies: A case study of rainfed rice cultivation in India Kushwaha, N. L. Rajput, Jitendra Shirsath, Paresh B. Sena, Dipaka Ranjan Mani, Indra climate change decision support systems yields rice risk management Seasonal climate forecasts (SCFs) have gained popularity in agriculture for climate risk management studies. The available forms of SCFs are not conducive to decision making because of a mismatch in scales over space and time. In this study, available SCFs were disaggregated using the FResampler1 technique to simulate rice yield (cultivar PR 114) under different nitrogen levels and planting dates using DSSAT (Decision Support System for Agrotechnology Transfer) for Sitamarhi district, Bihar, India. Results showed that the late planting of rice predicted the highest yield (3800 kg ha-1) with high variability under SCF (wet) and 200 kg ha-1 application of nitrogen fertilizer. Similarly, for SCF (dry), the late planting of rice simulated high yield (3100 kg ha-1) attributes with 200 kg ha-1 of nitrogen fertilizer. However, rice yield under climatology (3450 kg ha-1) was more than SCF (dry) (3100 kg ha-1). Planting of rice on 15th June 2019 under the SCF (normal) predicted low uncertainty with high mean yields as compared to the mid (05th July 2019), and late (25th July 2019) planting. The present study showed that by applying SCF, we can have a better understanding on “relative” changes in yield attributes with fertilizer doses and planting dates, which may be adopted by the climate adviser to offset the climate risk without compromising productivity. 2022-02-11 2023-03-03T16:17:57Z 2023-03-03T16:17:57Z Journal Article https://hdl.handle.net/10568/129180 en Open Access application/pdf Association of Agrometeorologists KUSHWAHA, N. L., RAJPUT, J., SHIRSATH, P. B., SENA, D. R., & MANI, I. (2022). Seasonal climate forecasts (SCFs) based risk management strategies: A case study of rainfed rice cultivation in India. Journal of Agrometeorology, 24(1), 10–17. https://doi.org/10.54386/jam.v24i1.775 |
| spellingShingle | climate change decision support systems yields rice risk management Kushwaha, N. L. Rajput, Jitendra Shirsath, Paresh B. Sena, Dipaka Ranjan Mani, Indra Seasonal climate forecasts (SCFs) based risk management strategies: A case study of rainfed rice cultivation in India |
| title | Seasonal climate forecasts (SCFs) based risk management strategies: A case study of rainfed rice cultivation in India |
| title_full | Seasonal climate forecasts (SCFs) based risk management strategies: A case study of rainfed rice cultivation in India |
| title_fullStr | Seasonal climate forecasts (SCFs) based risk management strategies: A case study of rainfed rice cultivation in India |
| title_full_unstemmed | Seasonal climate forecasts (SCFs) based risk management strategies: A case study of rainfed rice cultivation in India |
| title_short | Seasonal climate forecasts (SCFs) based risk management strategies: A case study of rainfed rice cultivation in India |
| title_sort | seasonal climate forecasts scfs based risk management strategies a case study of rainfed rice cultivation in india |
| topic | climate change decision support systems yields rice risk management |
| url | https://hdl.handle.net/10568/129180 |
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