Understanding the Changes in Global Crop Yields Through Changes in Climate and Technology

Najafi, Ehsan; Devineni, Naresh; Khanbilvardi, R.; Kogan, Felix

doi:10.1002/2017ef000690

Cited by 84 publications

(54 citation statements)

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“…Fourth, our estimates of future yield changes are conducted without considering adaptations Huybers 2013, Challinor et al 2014a), CO 2 fertilization effects (Mcgrath andLobell 2013, Deryng et al 2016), technology change (Najafi et al 2018) and other important factors such as pests, weeds and nutrients (Tubiello et al 2007). For example, the negative temperature impacts on crop yields could be reduced through soil mulching (Qin et al 2015), conservation tillage (Karlen et al 2013) and multiple cropping (Seifert and Lobell 2015).…”

Section: Discussionmentioning

confidence: 99%

Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and process-based models

Leng

Hall

2020

Environ. Res. Lett.

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Pervious assessments of crop yield response to climate change are mainly aided with either process-based models or statistical models, with a focus on predicting the changes in average yields, whilst there is growing interest in yield variability and extremes. In this study, we simulate US maize yield using process-based models, traditional regression model and a machine-learning algorithm, and importantly, identify the weakness and strength of each method in simulating the average, variability and extremes of maize yield across the country. We show that both regression and machine learning models can well reproduce the observed pattern of yield averages, while large bias is found for process-based crop models even fed with harmonized parameters. As for the probability distribution of yields, machine learning shows the best skill, followed by regression model and process-based models. For the country as a whole, machine learning can explain 93% of observed yield variability, followed by regression model (51%) and process-based models (42%). Based on the improved capability of the machine learning algorithm, we estimate that US maize yield is projected to decrease by 13.5% under the 2 • C global warming scenario (by~2050 s). Yields less than or equal to the 10th percentile in the yield distribution for the baseline period are predicted to occur in 19% and 25% of years in 1.5 • C (by~2040 s) and 2 • C global warming scenarios, with potentially significant implications for food supply, prices and trade. The machine learning and regression methods are computationally much more efficient than process-based models, making it feasible to do probabilistic risk analysis of climate impacts on crop production for a wide range of future scenarios.

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Section: Discussionmentioning

confidence: 99%

Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and process-based models

Leng

Hall

2020

Environ. Res. Lett.

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“…Lobell and Field (2007) applied regression models, showing that there was a clear negative response of global yields to increased temperatures for wheat, maize, and barley. More recently, Najafi et al (2018) presented a comprehensive global analysis of the changes in the crop yields and how they relate to different large-scale and regional climate variables, climate change variables and technology using a Bayesian multilevel model.…”

Section: Introductionmentioning

confidence: 99%

Stochastically modeling the projected impacts of climate change on rainfed and irrigated US crop yields

Zhu

Troy

Devineni

2019

Environ. Res. Lett.

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Food demands are rising due to an increasing population with changing food preferences, placing pressure on agricultural production. Additionally, climate extremes have recently highlighted the vulnerability of the agricultural system to climate variability. This study seeks to fill two important gaps in current knowledge: how irrigation impacts the large-scale response of crops to varying climate conditions and how we can explicitly account for uncertainty in yield response to climate. To address these, we developed a statistical model to quantitatively estimate historical and future impacts of climate change and irrigation on US county-level crop yields with uncertainty explicitly treated. Historical climate and crop yield data for 1970-2009 were used over different growing regions to fit the model, and five CMIP5 climate projections were applied to simulate future crop yield response to climate. Maize and spring wheat yields are projected to experience decreasing trends with all models in agreement. Winter wheat yields in the Northwest will see an increasing trend. Results for soybean and winter wheat in the South are more complicated, as irrigation can change the trend in projected yields. The comparison between projected crop yield time series for rainfed and irrigated cases indicates that irrigation can buffer against climate variability that could lead to negative yield anomalies. Through trend analysis of the predictors, the trend in crop yield is mainly driven by projected trends in temperature-related indices, and county-level trend analysis shows regional differences are negligible. This framework provides estimates of the impact of climate and irrigation on US crop yields for the 21st century that account for the full uncertainty of climate variables and the range of crop response. The results of this study can contribute to decision making about crop choice and water use in an uncertain future climate.

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“…Each type of model has its own advantages and disadvantages. Statistical models are accurate for the specific locations and conditions of their training data sets, and are a direct way to model yields within those constraints [20,21]. Process models offer a deeper understanding of the cause and effect of the environmental impacts on yields, and they can potentially model future yields outside of historical observations.…”

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Impact of Climate Change on Twenty-First Century Crop Yields in the U.S.

Petersen

2019

Climate

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Crop yields are strongly dependent on the average climate, extreme temperatures, and carbon dioxide concentrations, all of which are projected to increase in the coming century. In this study, a statistical model was created to predict US yields to 2100 for three crops using low and high-emissions future scenarios (RCP 4.5 and 8.5). The model is based on linear regressions between historical crop yields and daily weather observations since 1970 for every county in the US. Yields were found to be most strongly dependent on heat waves, summer average temperatures, and killing degree days; these relationships were hence used to predict future yields. The model shows that warming temperatures will significantly decrease corn and soybean yields, but will not have as strong of an influence on rice. Before accounting for CO 2 fertilization, crops in the high-emissions scenario are predicted to produce 77%, 85%, and 96% of their expected yield without climate change for corn, soybeans, and rice, respectively. When a simple CO 2 fertilization factor is included, corn, a C4 plant, increases slightly, while the yields of the C3 plants (soybeans and rice) are actually predicted to increase compared to today's yields. This study exhibits the wide range of possible impacts of climate change on crop yields in the coming century, and emphasizes the need for field research on the combined effects of CO 2 fertilization and heat extremes.Climate 2019, 7, 40 2 of 17 century [6]. Agriculture is a substantial portion of the economy, and crop yields are highly dependent on temperature. Other impacts are more difficult to put a price on, such as biodiversity and ecosystem loss. The currently accepted cost of carbon dioxide when considering these externalities varies between $37 and $220 per ton emitted [7]. Warming Temperatures Impact AgricultureThe United States produces 41% of the world's corn and 38% of the world's soybeans, two of the four largest crop sources of caloric energy [8]. These crops are thus crucial to food security, and understanding how their yields will change in the next century could help drive more informed policy decisions.The growing world population requires a larger food supply. Historical improvements in crop yields from agricultural technology (e.g., pesticides, fertilizers, farm machinery, gene modification, and shifting of production to large corporations [9]) have kept up with increasing demand for several decades, but it is doubtful that yields will continue to grow at the same rate as they have since 1970. Population is unlikely to stop growing this century, and by 2100 there will be between 9.6 and 12.3 billion people on earth [10]. Research has shown that yields are projected to drop in coming decades due to warming temperatures and the potential emergence of virulent crop diseases [11,12].Crop yields are strongly dependent on the weather and may be predicted from observed weather events during the growing season [13][14][15][16]. Over 60% of yield variability in global breadbaskets can be explained by...

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Understanding the Changes in Global Crop Yields Through Changes in Climate and Technology

Cited by 84 publications

References 69 publications

Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and process-based models

Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and process-based models

Stochastically modeling the projected impacts of climate change on rainfed and irrigated US crop yields

Impact of Climate Change on Twenty-First Century Crop Yields in the U.S.

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