Impact of Projected Climate Change on Rice (&lt;i&gt;Oryza sativa&lt;/i&gt; L.) Yield Using CERES-Rice Model in Different Agroclimatic Zones of India

Singh, P. K.; Singh, K. K.; Bhan, Suraj; Baxla, A. K.; Singh, Sompal; Rathore, L. S.; Gupta, Akhilesh

doi:10.18520/cs/v112/i01/108-115

Cited by 19 publications

(7 citation statements)

References 15 publications

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“…However, other crops experienced a positive effect in their yield among which fertilizer use and cropping intensity could have had a huge impact on increasing the crop productivity over the study period. These results are in line with those of past studies on the impact of climate change on various cereal crops, which found that a combined increase in both temperature and CO 2 led to reduced crop productivity in major north Indian states (Singh et al, 2017 ). Especially in the case of temperature, the major adverse impact on the yield of rice was noticed due to increases in the minimum temperature compared to increases in the maximum temperature (Bhatt et al, 2019 ).…”

Section: Resultssupporting

confidence: 92%

Climate change and vulnerability of agribusiness: Assessment of climate change impact on agricultural productivity

Mohapatra

Mohapatra²,

Han³

et al. 2022

Front. Psychol.

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The current study has mapped the impact of changes in different climatic parameters on the productivity of major crops cultivated in India like cereal, pulses, and oilseed crops. The vulnerability of crops to different climatic conditions like exposure, sensitivity, and adaptive indicators along with its different components and agribusiness has been studied. The study uses data collected over the past six decades from 1960 to 2020. Analytical tools such as the Tobit regression model and Principal Component Analysis were used for the investigation which has shown that among climatic parameters, an increase in temperature along with huge variations in rainfall and consistent increase in CO2 emissions have had a negative impact by reducing crop productivity, particularly cereals (26 percent) and oilseed (35 percent). Among various factors, adaptive factors such as cropping intensity, agricultural machinery, and livestock density in combination with sensitivity factors such as average operational land holding size and productivity of cereals, and exposure indicators like Kharif (June-September) temperature, heavy rainfall, and rate of change in maximum and minimum Rabi (October-February) temperature have contributed significantly in increasing crop vulnerability. The agribusiness model needs to be more inclusive. It should pay attention to small and remote farmers, and provide them with inclusive finance that can facilitate the adoption of climate-smart financial innovations, serve the underserved segments, and help them reach the target of a sustainable and inclusive agribusiness model. Though the social, technological, and economic initiatives can enhance the adaptive capacity of farmers, political measures still have a major role to play in providing a healthy climate for agriculture in India through tailored adaptive approaches like the adoption of craft climate adaptation program, dilating the irrigation coverage and location-centric management options. Hence, multidisciplinary and holistic approaches are worth emphasizing for evaluating the future impacts of change in climate on Indian agriculture.

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Section: Resultssupporting

confidence: 92%

Climate change and vulnerability of agribusiness: Assessment of climate change impact on agricultural productivity

Mohapatra

Mohapatra²,

Han³

et al. 2022

Front. Psychol.

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“…Suas variáveis de entrada são mais complexas e o modelo exige a calibração de diversos coeficientes relacionados ao solo, à espécie e ao genótipo (Kiniry et al, 1997). Esse modelo vem sendo utilizado para simular o crescimento e o desenvolvimento da cultura do milho (Lima, 1995;Soler, 2004;Andrade et al, 2009;Soler et al, 2009;Santana et al, 2010;Liu et al, 2011;Jabeen et al, 2017), do arroz (Xiong et al, 2008;Singh et al, 2017), da cana- de-açúcar (Nassif et al, 2012, Morgan e Royce, 2014Pagani et al, 2017), da soja (Justino et al, 2013), da cultura do sorgo (Lopez et al, 2017) e também de sistemas consorciados e de rotação de culturas (Negm et al, 2017;Yin et al, 2017).…”

Section: Decision Support System For Agrotechnology Transfer (Dssat)unclassified

“…Modelos de simulação de culturas agrícolas vêm sendo cada vez mais utilizados na agricultura como ferramenta para identificar e quantificar os impactos na produtividade em função das condições climáticas, do uso de diferentes genótipos e das características do manejo, como irrigação, adubação, características do solo e data de semeadura (Heinemann et al, 2008;Peak et al, 2008;Wang et al, 2012;Costa el al., 2013;Justino et al, 2013;Jabeen et al, 2017;Lopez et al, 2017;Negm et al, 2017;Singh et al, 2017;Yin et al, 2017).…”

Section: Introductionunclassified

Modelos de simulação da cultura do milho - uso na determinação das quebras de produtividade (<em>Yield Gaps</em>) e na previsão de safra da cultura no Brasil

Duarte¹

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Milho 2. Modelos de simulação de cultura 3. Multi-modelos 4. Previsão de safra 5. Yield gap I. Título AGRADECIMENTOS Em meio a essa mistura de euforia e alivio, digna de um ciclista que acaba de completar o último dia de prova no Tour de France, escrevo aqui minhas palavras de gratidão a todo esse período de estudo e desenvolvimento do presente projeto de mestrado. Dentre todos os tropeços, dificuldades e vacilos, os frutos colhidos são de aprendizado, amadurecimento pessoal e novas amizades. Não posso deixar de agradecer aos meus pais, Eliete e Alex, e a minha irmã Nathalia, por toda a confiança e apoio incondicional. Espero um dia poder servir de exemplo e contribuir com alguém assim como vocês fizeram por mim. Agradeço ao professor, orientador e amigo Paulão, sempre presente e dando apoio não apenas nas questões acadêmicas, mas em diversas outras esferas da vida. Agradeço também a CAPES, pelo apoio financeiro, essencial para o desenvolvimento do projeto, a Ana, pela companhia e apoio, principalmente durante esses meses finais e, como não poderia deixar de ser, agradeço a todos os amigos que de alguma forma contribuíram ao longo dessa jornada: Galiña; Nãr-c; Édiño; Tchê; Fernandão; Pino; Fabi; Gustavo; Battisti; Cristiam (e a todo o pessoal da cúpula); Lucão; Qñé; K-pa; Rep. Kazinsky e também a famiglia Xapadão, que me proporcionou experiências únicas e muito contribuiu para meu desenvolvimento pessoal, Agradeço. quebras de produtividade (Yield Gaps) .

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“…In the beginning, the literature primarily focused on developed countries (Kaiser et al 1993;Mendelsohn, Nordhaus, and Shaw 1994;Adams et al 1998;Adams, Hurd, and Reilly 1999;Lewandrowski and Schimmelpfennig 1999;Bryant et al 2000). However, some more recent studies examined climatic impacts on agriculture in developing countries (Sanghi and Mendelsohn 2008;Molua 2009;Deressa and Hassan 2009;Wang et al 2009;Sarker, Alam, and Gow 2012;Poudel and Kotani 2013;Burney and Ramanathan 2014;Singh et al 2017). In general, these studies found evidence of significant impacts of climatic changes on mean yield, growth, and yield variability of different crops, and used these findings to derive implications for adaptation strategy in cropping patterns.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Climatic Impacts on Agricultural Production: Evidence from Rice Yield in Assam, India

Nath¹,

Mandal²

2018

AJAD

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Understanding the nature and extent of climatic impacts on agricultural productivity under a variety of scenarios is extremely important for developing countries, where a sizable portion of the population relies on agriculture for life and livelihood. Thus, this paper presents evidence of heterogeneity in climatic impacts on crop yield in Assam, India. In particular, applying the non-parametric quantile regression technique to district-level data from 1978 to 2005, this study examined heterogeneity in the impacts of temperature and rainfall across seasonal rice varieties (autumn, winter, and summer), agroclimatic (AC) zones, and the distribution of rice yield. The results suggested that, in general, the effects of temperature on yield were not statistically significant for any of the three seasonal rice varieties. However, these effects were not uniform in their magnitudes, signs, and statistical significance across AC zones and yield distribution for each variety of rice. Similarly, there were wide variations in the effects of total precipitation across seasonal varieties, AC zones, and yield distribution. The results also suggested that an increase in temperature variability is beneficial and that rainfall variability is harmful to autumn and winter rice yield. For summer rice, the effects of these two climate variables were positive but statistically insignificant. Given the importance of rice yield for food security and poverty alleviation in Assam, these results could inform the design of appropriate adaptation strategies and public policies to counter the adverse impacts of climate change on agriculture in Assam. Furthermore, since most people in rural areas are engaged in agriculture, these results are important for the sustainability of rural economies.

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Impact of Projected Climate Change on Rice (<i>Oryza sativa</i> L.) Yield Using CERES-Rice Model in Different Agroclimatic Zones of India

Cited by 19 publications

References 15 publications

Climate change and vulnerability of agribusiness: Assessment of climate change impact on agricultural productivity

Climate change and vulnerability of agribusiness: Assessment of climate change impact on agricultural productivity

Modelos de simulação da cultura do milho - uso na determinação das quebras de produtividade (<em>Yield Gaps</em>) e na previsão de safra da cultura no Brasil

Heterogeneous Climatic Impacts on Agricultural Production: Evidence from Rice Yield in Assam, India

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