Impacts of climate change on agriculture: Evidence from China

Chen, Shuai; Chen, Xiaoguang; Xu, Jintao

doi:10.1016/j.jeem.2015.01.005

Cited by 315 publications

(167 citation statements)

References 31 publications

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“…For example, the spatiotemporal uncertainty can be used to evaluate whether or not GCM precipitation projections are more uncertain in high altitudes as compared to coasts or one can analyze whether precipitation uncertainty during El Niño events is higher or not than that associated with other large‐scale climatic events. The SREV metric has also been used for a range of applications that require an assessment of situations where uncertainty may be high or low [ S. Chen et al ., 2014; Woldemeskel et al ., ].…”

Section: Introductionmentioning

confidence: 92%

Quantification of precipitation and temperature uncertainties simulated by CMIP3 and CMIP5 models

et al. 2016

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Assessment of climate change impacts on water resources is extremely challenging, due to the inherent uncertainties in climate projections using global climate models (GCMs). Three main sources of uncertainties can be identified in GCMs, i.e., model structure, emission scenario, and natural variability. The recently released fifth phase of the Coupled Model Intercomparison Project (CMIP5) includes a number of advances relative to its predecessor (CMIP3), in terms of the spatial resolution of models, list of variables, and concept of specifying future radiative forcing, among others. The question, however, is do these modifications indeed reduce the uncertainty in the projected climate at global and/or regional scales? We address this question by quantifying and comparing uncertainty in precipitation and temperature from 6 CMIP3 and 13 CMIP5 models. Uncertainty is quantified using the square root of error variance, which specifies uncertainty as a function of time and space, and decomposes the total uncertainty into its three constituents. The results indicate a visible reduction in the uncertainty of CMIP5 precipitation relative to CMIP3 but no significant change for temperature. For precipitation, the GCM uncertainty is found to be larger in regions of the world that receive heavy rainfall, as well as mountainous and coastal areas. For temperature, however, uncertainty is larger in extratropical cold regions and lower elevation areas.

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Section: Introductionmentioning

confidence: 92%

Quantification of precipitation and temperature uncertainties simulated by CMIP3 and CMIP5 models

et al. 2016

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“…Wheat is the most widely grown crop in the world [3] and the first source of calories ultimately delivered to humans as food, on par with rice [4]. There is mounting evidence, notably from statistical yield models that estimate the weather-or climate-yield relationship from historical data, that climate change will negatively affect maize yields in key producing regions [2,[5][6][7][8][9]. In contrast, evidence regarding the effects of rising temperatures on wheat yields still relies heavily on process-based approaches [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…The present study provides the first evidence of negative effects of warming on wheat and barley yields in Western Europe based on a flexible statistical model. 8 Following [5], our regression analysis estimates the crop yield effects of changes in time exposure to a large number of temperature intervals, controlling for non-monotonic precipitation effects and imposing only moderate structure on the shape of the temperature-yield relationship. For winter crops, we estimate separate marginal impacts for fall months, winter months, and the warm season (spring and summer months).…”

Section: Introductionmentioning

confidence: 99%

Negative impacts of climate change on cereal yields: statistical evidence from France

Gammans

Mérel

Ortiz‐Bobea

2017

Environ. Res. Lett.

152

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In several world regions, climate change is predicted to negatively affect crop productivity. The recent statistical yield literature emphasizes the importance of flexibly accounting for the distribution of growing-season temperature to better represent the effects of warming on crop yields. We estimate a flexible statistical yield model using a long panel from France to investigate the impacts of temperature and precipitation changes on wheat and barley yields. Winter varieties appear sensitive to extreme cold after planting. All yields respond negatively to an increase in spring-summer temperatures and are a decreasing function of precipitation about historical precipitation levels. Crop yields are predicted to be negatively affected by climate change under a wide range of climate models and emissions scenarios. Under warming scenario RCP8.5 and holding growing areas and technology constant, our model ensemble predicts a 21.0% decline in winter wheat yield, a 17.3% decline in winter barley yield, and a 33.6% decline in spring barley yield by the end of the century. Uncertainty from climate projections dominates uncertainty from the statistical model. Finally, our model predicts that continuing technology trends would counterbalance most of the effects of climate change.

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“…Beliefs, attitudes and cultural influences affect farmers' behaviors [26,27], and an understanding of farmers' beliefs, concerns and willingness to adapt is necessary for political, economic and social action towards risks arising from climate change [28]. Most of the literature on climate change in Turkey deals with impact studies [18][19][20][21][22][23]25] and none of the studies focused on the risk perceptions from the farmers' perspective in the GAP (The Southeastern Anatolian Project) Region andŞanlıurfa.…”

Section: Introductionmentioning

confidence: 99%

Farmers’ Risk Perception towards Climate Change: A Case of the GAP-Şanlıurfa Region, Turkey

Aydoğdu

Yenigün

2016

Sustainability

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Abstract:The purpose of this research is to evaluate farmers' risk perception towards climate change in dry farming areas of theŞanlıurfa province in Southeastern Anatolia Project (GAP) Region, Turkey, and their willingness to pay for adaptation practices and explore the potential factors that contribute to the understanding of climate change. The main material of this research comes from a sample of 466 farmers among 32,809 inŞanlıurfa who were chosen via a simple random sampling method. Sampling was conducted in 2014 and participants were interviewed face to face by questionnaires and local interviewers were used in order to maximize the reliability of the results. The logistic regression and fit tests were used for analysis. The results indicate that 53% of farmers have a risk perception about climate change and 62% of farmers accept paying for adaptation practices to reduce its potential effects. Explanatory affecting factors, such as age, agricultural income, amount of land owned, farming experience, agricultural credit usage, household numbers, non-agricultural manpower, education level and water perception, significantly explained the risk perceptions. This study is one of the first of its type in GAP-Şanlıurfa, Turkey. Therefore, the results could be helpful for decision-and policy-makers to develop adaptive strategies.

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Impacts of climate change on agriculture: Evidence from China

Cited by 315 publications

References 31 publications

Quantification of precipitation and temperature uncertainties simulated by CMIP3 and CMIP5 models

Quantification of precipitation and temperature uncertainties simulated by CMIP3 and CMIP5 models

Negative impacts of climate change on cereal yields: statistical evidence from France

Farmers’ Risk Perception towards Climate Change: A Case of the GAP-Şanlıurfa Region, Turkey

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