Assessing the influence of climate model biases in predicting yield and irrigation requirement of cassava
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ORIGINAL ARTICLE
Assessing the influence of climate model biases in predicting yield and irrigation requirement of cassava Raji Pushpalatha1 · Byju Gangadharan1 Received: 12 August 2020 / Accepted: 31 October 2020 © Springer Nature Switzerland AG 2020
Abstract The present study is conducted to assess the influence of climate model biases in the predictions of yield and water requirement of cassava in one of the major cassava growing regions in India. Simple linear bias correction methods are used for temperature, and non-linear corrections are used for other meteorological variables. The WOFOST and CROPWAT models are used to predict the crop yield and water requirement of cassava using the scenarios of 2030, 2050, and 2070 for the representative concentration pathway 4.5 derived from the Long Ashton Research Station Weather Generator (LARS-WG). The percentage change in crop yield predictions with and without bias corrections of meteorological variables ranges from 7.6 to 10.8%, 1.6 to 5.4%, and − 3.0 to 4.0% respectively for 2030, 2050, and 2070. The bias corrections made an increment in the gross irrigation requirements of cassava with 16.5, 17.8, and 16.0% in 2030, 2050, and 2070 respectively, compared to the values without bias corrections. The outcome of this study indicates that raw meteorological variables directly from the climate models result over-/underestimation of yield and irrigation requirements of cassava, and the bias corrections help to issue reliable crop yield predictions. Results show zero yield reductions of cassava until 2050, and beyond that, there can be reductions in the crop yield. The gross irrigation requirements of cassava increase in the future to achieve higher productivity. However, this study needs to extend to other major growing regions in India to derive a general conclusion. Keywords Climate model · Biases · Crop model · Future scenario · Yield prediction · Gross irrigation
Introduction The impact of climate change on agriculture and its influence on food security are some of the ongoing research areas worldwide (Toros et al. 2019; Islam et al. 2020; Madhukar et al. 2020). To get a clear understanding of the influence of climate change on crop yield, agricultural researchers depend on climate model outputs to predict the crop yield for different climatic scenarios. Global climate models are available to downscale the climate data for different scenarios, which can be projected into the crop models to get crop yield predictions. However, the reliability of these outcomes needs further verification, as small biases in climate model outputs can result in significant variations in the crop yield (Hawkins et al. 2013; Liu et al. 2018; Galmarini et al. 2019). For example, overestimation/underestimation of maximum * Byju Gangadharan [email protected] 1
ICAR—Central Tuber Crops Research Institute, Thiruvananthapuram 695 017, Kerala, India
temperature can reduce/increase the crop yield due to the influence of heat stresses (Ruiz-Ramos et al. 2011). Therefore, bias corrections a
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