Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States

Li, Yan; Guan, Kaiyu; Schnitkey, Gary; DeLucia, Evan H.; Peng, Bin

doi:10.1111/gcb.14628

Cited by 414 publications

(302 citation statements)

References 60 publications

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“…Most variables exhibit expected behaviors. The response curve of rainfall suggests that both drought conditions and excessive rainfall are harmful to yield (Lobell et al 2014, Li et al 2019a. High maximum temperature of July harms yield, especially when it exceeds 33.5 ºC.…”

Section: Yield Response To Climatic and Environmental Variablesmentioning

confidence: 99%

Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest

Kang

Özdoğan

Zhu

et al. 2020

Environ. Res. Lett.

150

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Crop yield estimates over large areas are conventionally made using weather observations, but a comprehensive understanding of the effects of various environmental indicators, observation frequency, and the choice of prediction algorithm remains elusive. Here we present a thorough assessment of county-level maize yield prediction in U.S. Midwest using six statistical/machine learning algorithms (Lasso, Support Vector Regressor, Random Forest, XGBoost, Long-short term memory (LSTM), and Convolutional Neural Network (CNN)) and an extensive set of environmental variables derived from satellite observations, weather data, land surface model results, soil maps, and crop progress reports. Results show that seasonal crop yield forecasting benefits from both more advanced algorithms and a large composite of information associated with crop canopy, environmental stress, phenology, and soil properties (i.e. hundreds of features). The XGBoost algorithm outperforms other algorithms both in accuracy and stability, while deep neural networks such as LSTM and CNN are not advantageous. The compositing interval (8-day, 16-day or monthly) of time series variable does not have significant effects on the prediction. Combining the best algorithm and inputs improves the prediction accuracy by 5% when compared to a baseline statistical model (Lasso) using only basic climatic and satellite observations. Reasonable county-level yield foresting is achievable from early June, almost four months prior to harvest. At the national level, early-season (June and July) prediction from the best model outperforms that of the United States Department of Agriculture (USDA) World Agricultural Supply and Demand Estimates (WASDE). This study provides insights into practical crop yield forecasting and the understanding of yield response to climatic and environmental conditions.

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Section: Yield Response To Climatic and Environmental Variablesmentioning

confidence: 99%

Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest

Kang

Özdoğan

Zhu

et al. 2020

Environ. Res. Lett.

150

View full text Add to dashboard Cite

show abstract

“…Importantly, where sufficient observed yield data exist, statistical models may provide additional information about yield sensitivity to climate due to their ability to account for the effects of unobserved farmer management practices or indirect weather-related drivers of yield losses that cannot be simulated by process-based models (e.g. mechanical damage by hail or wind, pests, diseases, extreme rainfall etc) (Roberts et al 2017, Li et al 2019.…”

Section: Introductionmentioning

confidence: 99%

Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change

Parkes

Higginbottom

Hufkens

et al. 2019

Environ. Res. Lett.

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Weather shocks, such as heatwaves, droughts, and excess rainfall, are a major cause of crop yield losses and food insecurity worldwide. Statistical or process-based crop models can be used to quantify how yields will respond to these events and future climate change. However, the accuracy of weather-yield relationships derived from crop models, whether statistical or process-based, is dependent on the quality of the underlying input data used to run these models. In this context, a major challenge in many developing countries is the lack of accessible and reliable meteorological datasets. Gridded weather datasets, derived from combinations of in situ gauges, remote sensing, and climate models, provide a solution to fill this gap, and have been widely used to evaluate climate impacts on agriculture in data-scarce regions worldwide. However, these reference datasets are also known to contain important biases and uncertainties. To date, there has been little research to assess how the choice of reference datasets influences projected sensitivity of crop yields to weather. We compare multiple freely available gridded datasets that provide daily weather data over the Indian sub-continent over the period 1983-2005, and explore their implications for estimates of yield responses to weather variability for key crops grown in the region (wheat and rice). Our results show that individual gridded weather datasets vary in their representation of historic spatial and temporal temperature and precipitation patterns across India. We show that these differences create large uncertainties in estimated crop yield responses and exposure to variability in growing season weather, which in turn, highlights the need for improved consideration of input data uncertainty in statistical studies that explore impacts of climate variability and change on agriculture.

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“…Conversely, well-irrigated fields show little sensitivity to high atmospheric temperature 8,12,[15][16][17] . A further complication is that excess precipitation can cause yield damage through waterlogging, increased pests and fungal pathogens, and loss of nitrogen fertilizer 14,22,24 . Insurance data over the US suggest that maize yield damage associated with excessive precipitation is of the same magnitude as that associated with drought conditions 24 .…”

mentioning

confidence: 99%

Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields

Rigden¹,

Mueller²,

Holbrook³

et al. 2020

Nat Food

161

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Understanding the response of agriculture to heat and moisture stress is essential to adapt food systems under climate change. Although evidence of crop yield loss with extreme temperature is abundant, disentangling the roles of temperature and moisture in determining yield has proved challenging, largely due to limited soil moisture data and the tight coupling between moisture and temperature at the land surface. Here, using well-resolved observations of soil moisture from the recently launched Soil Moisture Active Passive satellite, we quantify the contribution of imbalances between atmospheric evaporative demand and soil moisture to maize yield damage in the US Midwest. We show that retrospective yield predictions based on the interactions between atmospheric demand and soil moisture significantly outperform those using temperature and precipitation singly or together. The importance of accounting for this water balance is highlighted by the fact that climate simulations uniformly predict increases in atmospheric demand during the growing season but the trend in root-zone soil moisture varies between models, with some models indicating that yield damages associated with increased evaporative demand are moderated by increased water supply. A damage estimate conditioned only on simulated changes in atmospheric demand, as opposed to also accounting for changes in soil moisture, would erroneously indicate approximately twice the damage. This research demonstrates that more accurate predictions of maize yield can be achieved by using soil moisture data and indicates that accurate estimates of how climate change will influence crop yields require explicitly accounting for variations in water availability.

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Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States

Cited by 414 publications

References 60 publications

Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest

Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest

Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change

Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields

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