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 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.
| Main Authors: | , , , , |
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| Format: | Article biblioteca |
| Language: | English |
| Published: |
Association of Agrometeorologists
2022
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| Subjects: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, FResampler1, Seasonal Climate Forecasts, Decision Support System for Agrotechnology Transfer, CLIMATE CHANGE, DECISION SUPPORT SYSTEMS, YIELDS, RICE, RISK MANAGEMENT, Institutional, |
| Online Access: | https://hdl.handle.net/10883/22388 |
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| Summary: | 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. |
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