Evaluating area-specific adaptation strategies for rainfed maize under future climates of India

Rao, A V R K; Chandran, M. A. Sarath; Bal, Santanu Kumar; Pramod, V. P.; Manikandan, N.; Raju, B. M. K.; Prabhakar, M.; Kumar, Soora Naresh; Singh, Vinod

doi:10.1016/j.scitotenv.2022.155511

Cited by 11 publications

(8 citation statements)

References 91 publications

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“…Researchers used different bias correction methods to reduce the errors and improve the accuracy of prediction significantly in different parts of the world 47 – 50 . In the present study also, to reduce the biases and improve the accuracy of estimated hourly temperature, three bias correction methods were used and the details of each are discussed below.…”

Section: Methodsmentioning

confidence: 99%

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Identifying appropriate prediction models for estimating hourly temperature over diverse agro-ecological regions of India

Bal

Pramod

Manikandan

et al. 2023

Sci Rep

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The present study tests the accuracy of four models in estimating the hourly air temperatures in different agroecological regions of the country during two major crop seasons, kharif and rabi, by taking daily maximum and minimum temperatures as input. These methods that are being used in different crop growth simulation models were selected from the literature. To adjust the biases of estimated hourly temperature, three bias correction methods (Linear regression, Linear scaling and Quantile mapping) were used. When compared with the observed data, the estimated hourly temperature, after bias correction, is reasonably close to the observed during both kharif and rabi seasons. The bias-corrected Soygro model exhibited its good performance at 14 locations, followed by the WAVE model and Temperature models at 8 and 6 locations, respectively during the kharif season. In the case of rabi season, the bias-corrected Temperature model appears to be accurate at more locations (21), followed by WAVE and Soygro models at 4 and 2 locations, respectively. The pooled data analysis showed the least error between estimated (uncorrected and bias-corrected) and observed hourly temperature from 04 to 08 h during kharif season while it was 03 to 08 h during the rabi season. The results of the present study indicated that Soygro and Temperature models estimated hourly temperature with better accuracy at a majority of the locations situated in the agroecological regions representing different climates and soil types. Though the WAVE model worked well at some of the locations, estimation by the PL model was not up to the mark in both kharif and rabi seasons. Hence, Soygro and Temperature models can be used to estimate hourly temperature data during both kharif and rabi seasons, after the bias correction by the Linear Regression method. We believe that the application of the study would facilitate the usage of hourly temperature data instead of daily data which in turn improves the precision in predicting phenological events and bud dormancy breaks, chilling hour requirement etc.

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Section: Methodsmentioning

confidence: 99%

“…MAPE is less sensitive to extreme values and therefore, it is intuitively more appealing than RMSE. However, for evaluating the performance of model both RMSE and MAPE can be used as many researchers employed the both indices 50 , 57 .…”

Section: Methodsmentioning

confidence: 99%

Identifying appropriate prediction models for estimating hourly temperature over diverse agro-ecological regions of India

Bal

Pramod

Manikandan

et al. 2023

Sci Rep

Self Cite

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“…Process-based crop models employ integrated mathematical methods to describe crop growth status driven by climate, nutrient and water cycling, soil properties, and agricultural management practices (Basso et al, 2016;Rao et al, 2022). These models, including Crop Environment Resource Synthesis (CERES), WOrld FOod STudies (WOFOST), Soil Water Atmosphere Plant Model (SWAP), Environmental Analysis and Remote Sensing (EARS), and the Crop Growth Simulation algorithm (EARS-CGS), have been applied to predict the yield of various crops such as maize, wheat, barley, and millet (Rojas, 2007;Manatsa et al, 2011;Tripathy et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Estimation of maize yield incorporating the synergistic effect of climatic and land use change in Jilin, China

et al. 2023

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Yield forecasting can give early warning of food risks and provide solid support for food security planning. Climate change and land use change have direct influence on regional yield and planting area of maize, but few studies have examined their synergistic impact on maize production. In this study, we propose an analysis framework based on the integration of system dynamic (SD), future land use simulation (FLUS) and a statistical crop model to prefuture maize yield variation in response to climate change and land use change in a region of central Jilin province, China. The results show that the cultivated land is likely to reduce by 862.84 km 2 from 2030 to 2050. Nevertheless, the total maize yield is expected to increase under all four RCP scenarios due to the promotion of per hectare maize yield. the scenarios, RCP4.5 is the most beneficial to maize production, with a doubled total yield in 2050. Notably, the yield gap between different counties will be further widened, which necessitates the differentiated policies of agricultural production and farmland protection, e.g., strengthening cultivated land protection and crop management in low-yield areas, and taking adaptation and mitigation measures to coordinate climate change and production.

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“…El cambio climático afecta la producción de cultivos de maíz, por la inestabilidad de las variables climáticas como; temperatura, precipitación, concentración de dióxido de carbono, radiación solar, etc. El impacto significativo en la fenología de los cultivos, el rendimiento y puede inducir cambios en las prácticas actuales de gestión agrícola, como el riego, el uso de fertilizantes y la fecha de siembra (Subba Rao et al, 2022). El sector agrícola y de producción consideran los propósitos sociales, ecológicos y económicos en un contexto climático y socioeconómico cambiante que deberían desarrollarse de manera equilibrada para disminuir impactos ante la variabilidad climática (Dubois et al, 2020) La agricultura se ha visto afectada negativamente en los últimos años debido a las condiciones ambientales actuales, lo cual en muchos de los casos se ha visto perjudicada en el desarrollo de los cultivos (Georgakopoulos et al, 2016).…”

Section: Introductionunclassified

Zonificación agroecológica del cultivo de maíz (Zea mays) y su adaptabilidad a posibles cambios climáticos en el cantón Buena Fe, provincia de Los Ríos

Penafiel

Lopez²,

Cañarte³

et al. 2022

Ciencia Latina

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El trabajo de investigación tiene un enfoque en los efectos de la variabilidad climática en la agricultura de los cambios en los rendimientos de los cultivos, en especial del maíz, en el cual a través de un estudio de zonificación agroclimática nos permite conocer las zonas más idóneas donde el cultivo se ha desarrollado de manera eficiente, por lo que analizamos Zonas agroecológicas del maíz y su adaptabilidad a posibles escenarios de cambio climático en zonas rurales del cantón Buena Fe, utilizando método inductivo para la estimación de datos de clima futuro obtenidos en WorldClim. Se realizó una investigación no experimental, siendo un diseño transversal donde se analizaron las variables climáticas que influyen en la adaptabilidad del maíz, por lo tanto, se describieron y exploraron diferentes software o programas para el diseño de mapas. En el que se muestra la percepción ciudadana de los agricultores, relacionada con el rendimiento del maíz en un periodo 2016-2021. Se mostraron las áreas ideales con proyección futura de acuerdo a las variables estimadas en un periodo 2061-2080 y 2081-2100, por lo tanto, muestra las estrategias identificadas para la adaptación del cultivo de maíz a los cambios climáticos.

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Evaluating area-specific adaptation strategies for rainfed maize under future climates of India

Cited by 11 publications

References 91 publications

Identifying appropriate prediction models for estimating hourly temperature over diverse agro-ecological regions of India

Identifying appropriate prediction models for estimating hourly temperature over diverse agro-ecological regions of India

Estimation of maize yield incorporating the synergistic effect of climatic and land use change in Jilin, China

Zonificación agroecológica del cultivo de maíz (Zea mays) y su adaptabilidad a posibles cambios climáticos en el cantón Buena Fe, provincia de Los Ríos

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