Incorporating machine learning with biophysical model can improve the evaluation of climate extremes impacts on wheat yield in south-eastern Australia

Feng, Ping; Wang, Bin; Liu, De Li; Waters, Cathy; Yu, Qiang

doi:10.1016/j.agrformet.2019.05.018

Cited by 138 publications

(73 citation statements)

References 75 publications

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“…The results showed that ML and DL methods definitely outperformed linear regression (LASSO) across AEZs, largely attributed to their ability to extract the complicated relationships between the predictors and the target variable [14,17,73]. Intuitively, we noticed that ML methods had better performance than the LSTM network across AEZs, especially in zone IV.…”

Section: Comparing the Performances Of Linear ML And Dl Methods In mentioning

confidence: 81%

Combining Optical, Fluorescence, Thermal Satellite, and Environmental Data to Predict County-Level Maize Yield in China Using Machine Learning Approaches

Zhang

Luo

et al. 2019

Remote Sensing

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Maize is an extremely important grain crop, and the demand has increased sharply throughout the world. China contributes nearly one-fifth of the total production alone with its decreasing arable land. Timely and accurate prediction of maize yield in China is critical for ensuring global food security. Previous studies primarily used either visible or near-infrared (NIR) based vegetation indices (VIs), or climate data, or both to predict crop yield. However, other satellite data from different spectral bands have been underutilized, which contain unique information on crop growth and yield. In addition, although a joint application of multi-source data significantly improves crop yield prediction, the combinations of input variables that could achieve the best results have not been well investigated. Here we integrated optical, fluorescence, thermal satellite, and environmental data to predict county-level maize yield across four agro-ecological zones (AEZs) in China using a regression-based method (LASSO), two machine learning (ML) methods (RF and XGBoost), and deep learning (DL) network (LSTM). The results showed that combining multi-source data explained more than 75% of yield variation. Satellite data at the silking stage contributed more information than other variables, and solar-induced chlorophyll fluorescence (SIF) had an almost equivalent performance with the enhanced vegetation index (EVI) largely due to the low signal to noise ratio and coarse spatial resolution. The extremely high temperature and vapor pressure deficit during the reproductive period were the most important climate variables affecting maize production in China. Soil properties and management factors contained extra information on crop growth conditions that cannot be fully captured by satellite and climate data. We found that ML and DL approaches definitely outperformed regression-based methods, and ML had more computational efficiency and easier generalizations relative to DL. Our study is an important effort to combine multi-source remote sensed and environmental data for large-scale yield prediction. The proposed methodology provides a paradigm for other crop yield predictions and in other regions.

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Section: Comparing the Performances Of Linear ML And Dl Methods In mentioning

confidence: 81%

Combining Optical, Fluorescence, Thermal Satellite, and Environmental Data to Predict County-Level Maize Yield in China Using Machine Learning Approaches

Zhang

Luo

et al. 2019

Remote Sensing

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“…Besides process-based crop models, we use ML and a traditional multiple linear regression model to simulate maize yield. Here, the Random Forest algorithm (Breiman 2001) which has been successfully used in previous studies (Hoffman et al 2018, Feng et al 2019, Vogel et al 2019 is adopted. The Random Forest algorithm is a non-parametric ML method and relies on an ensemble of decision trees through two randomization steps: (1) each decision tree is constructed based on a bootstrapped sub-sample dataset, with the decision rule depending on a random sub-set of candidate predictor variables; (2) These processes are repeated at every decision split to overcome the limitations of single decision tree, thus avoiding the potential overfitting issue (Breiman 2001).…”

Section: Machine Learning and Regression Modelmentioning

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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“…Such information can improve profitability, environmental quality and marketing decisions (Brandes et al, 2016;Johnson et al, 2016). Current efforts to forecast seasonal crop yields include field surveys, expert judgement, remote sensing, statistical models, and processbased simulation models (Basso et al, 2013;Feng et al, 2019;Hammer et al, 1996;Prasad et al, 2006;Ines et al, 2013). There are trade-offs among approaches in terms of accuracy, explanatory power, and desired scale and resolution (Basso & Liu, 2019).…”

Section: Introductionmentioning

confidence: 99%

Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt

et al. 2020

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We used the Agricultural Production Systems sIMulator (APSIM) to predict and explain maize and soybean yields, phenology, and soil water and nitrogen (N) dynamics during the growing season in Iowa, USA. Historical, current and forecasted weather data were used to drive simulations, which were released in public four weeks after planting. In this paper, we (1) describe the methodology used to perform forecasts;(2) evaluate model prediction accuracy against data collected from 10 locations over four years; and (3) identify inputs that are key in forecasting yields and soil N dynamics. We found that the predicted median yield at planting was a very good indicator of end-of-season yields (relative root mean square error [RRMSE] of ∼20%). For reference, the prediction at maturity, when all the weather was known, had a RRMSE of 14%. The good prediction at planting time was explained by the existence of shallow water tables, which decreased model sensitivity to unknown summer precipitation by 50-64%. Model initial conditions and management information accounted for Abbreviations: APSIM, Agricultural Production Systems sIMulator; RRMSE, relative root mean square error.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. one-fourth of the variation in maize yield. End of season model evaluations indicated that the model simulated well crop phenology (R 2 = 0.88), root depth (R 2 = 0.83), biomass production (R 2 = 0.93), grain yield (R 2 = 0.90), plant N uptake (R 2 = 0.87), soil moisture (R 2 = 0.42), soil temperature (R 2 = 0.93), soil nitrate (R 2 = 0.77), and water table depth (R 2 = 0.41). We concluded that model set-up by the user (e.g. inclusion of water table), initial conditions, and early season measurements are very important for accurate predictions of soil water, N and crop yields in this environment. Neil Huth from CSIRO for their support with the APSIM model, Iowa State University students () for assistance with data collection and managing the field experiments. We also thank the APSIM Initiative for making the software publicly available and for ensuring software quality. ORCIDSotirios V. Archontoulis https://orcid.org/0000-0001-7595-8107 Mark A. Licht https://orcid.org/0000-0001-6640-7856 Kendall R. Lamkey

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Incorporating machine learning with biophysical model can improve the evaluation of climate extremes impacts on wheat yield in south-eastern Australia

Cited by 138 publications

References 75 publications

Combining Optical, Fluorescence, Thermal Satellite, and Environmental Data to Predict County-Level Maize Yield in China Using Machine Learning Approaches

Combining Optical, Fluorescence, Thermal Satellite, and Environmental Data to Predict County-Level Maize Yield in China Using Machine Learning Approaches

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

Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt

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