Seasonal climate forecasts (SCFs) based risk management strategies: A case study of rainfed rice cultivation in India

N.L Kushwaha,Jitendra Rajput,Paresh B Shirsath,Indra Mani,D.R Sena

doi:10.54386/jam.v24i1.775

Abstract

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.

Highlights

Seasonal climate forecasts (SCFs) have gained popularity in agriculture for climate risk management studies
In the late planting schedule, the likelihood of an Seasonal Climate Forecasts (SCFs) condition is beneficial for yield realization over the normal climatology
Available SCFs were disaggregated using FResampler1 method to run the simulation for the rice yield for best risk management strategies

Summary

Introduction

Seasonal climate forecasts (SCFs) have gained popularity in agriculture for climate risk management studies. Various decision-making tools such as crop simulation models were coupled to the SCFs, have shown promise strengthening in the strategic and operational assessments of uncertainties due to contingent behaviour of the climate during the growing seasons (Han et al, 2017) This can offset the risk without compromising with the productivity. One of the legacy tools, CSM-CERES-Rice module of Decision Support System for Agrotechnology Transfer (DSSAT) had demonstrated its capabilities in being integrated to SCFs. Several investigations in the past had applied crop simulation models using historical daily weather data which could not integrate SCFs due to the mismatch in scales over space and time (Eitzinger et al, 2017; Liu et al, 2017; Ma et al, 2020, Dar et al, 2017; Patel et al, 2006; Patel et al, 2008 and Alejo, 2020)” SCFs provide seasonal climate probability in tercile form which can be used to generate synthetic daily weather files needed for crop models in a forecast mode (Han et al, 2017)

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Conclusion