Zhao Zhang

Beijing Normal University

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Fulu Tao

Institute of Geographic Sciences and Natural Resources Research

Yuchuan Luo

Shanghai Institute of Technology

Juan Cao

Beijing Normal University

Jichong Han

Beijing Normal University

Liangliang Zhang

China Geological Survey

Peijun Shi

Beijing Normal University

Jing Zhang

Beijing Normal University

Chenzhi Wang

Leibniz Centre for Agricultural Landscape Research

Reimund P. Rötter

University of Göttingen

Wang Pin

University of Maryland, College Park

Cooperative Institutions

Chinese Academy of Sciences

Beijing Normal University

University of Chinese Academy of Sciences

Natural Resources Institute Finland

Ministry of Agriculture and Rural Affairs

China Agricultural University

Tsinghua University

Chinese Academy of Agricultural Sciences

Tianjin University

State Key Laboratory of Earth Surface Processes and Resource Ecology

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Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

Remote Sensing (2020)

Jichong Han Zhao Zhang Juan Cao Yuchuan Luo Liangliang Zhang

Wheat is one of the main crops in China, and crop yield prediction is important for regional trade and national food security. There are increasing concerns with respect to how to integrate multi-source data and employ machine learning techniques to establish a simple, timely, and accurate crop yield prediction model at an administrative unit. Many previous studies were mainly focused on the whole crop growth period through expensive manual surveys, remote sensing, or climate data. However, the effect of selecting different time window on yield prediction was still unknown. Thus, we separated the whole growth period into four time windows and assessed their corresponding predictive ability by taking the major winter wheat production regions of China as an example in the study. Firstly we developed a modeling framework to integrate climate data, remote sensing data and soil data to predict winter wheat yield based on the Google Earth Engine (GEE) platform. The results show that the models can accurately predict yield 1~2 months before the harvesting dates at the county level in China with an R2 > 0.75 and yield error less than 10%. Support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF) represent the top three best methods for predicting yields among the eight typical machine learning models tested in this study. In addition, we also found that different agricultural zones and temporal training settings affect prediction accuracy. The three models perform better as more winter wheat growing season information becomes available. Our findings highlight a potentially powerful tool to predict yield using multiple-source data and machine learning in other regions and for crops.

Predictive modelling

Growing season

10.3390/rs12020236

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Citations (233)

Climate change, land use change, and China’s food security in the twenty-first century: an integrated perspective

Climatic Change (2008)

Fulu Tao Masayuki Yokozawa Jiyuan Liu Zhao Zhang

10.1007/s10584-008-9491-0

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Citations (50)

International Journal of Disaster Risk Science (2020)

Jing Zhang Zhao Zhang Fulu Tao

Rainfall-related hazards—deficit rain and excessive rain—inevitably stress crop production, and weather index insurance is one possible financial tool to mitigate such agro-metrological losses. In this study, we investigated where two rainfall-related weather indices—anomaly-based index (AI) and humidity-based index (HI)—could be best used for three main crops (rice, wheat, and maize) in China’s main agricultural zones. A county is defined as an “insurable county” if the correlation between a weather index and yield loss was significant. Among maize-cropping counties, both weather indices identified more insurable counties for deficit rain than for excessive rain (AI: 172 vs 63; HI: 182 vs 68); moreover, AI identified lower basis risk for deficit rain in most agricultural zones while HI for excessive rain. For rice, the number of AI-insurable counties was higher than the number of HI-insurable counties for deficit rain (274 vs 164), but lower for excessive rain (199 vs 272); basis risks calculated by two weather indices showed obvious difference only in Zone I. Finally, more wheat-insurable counties (AI: 196 vs 71; HI: 73 vs 59) and smaller basis risk indicate that both weather indices performed better for excessive rain in wheat-planting counties. In addition, most insurable counties showed independent yield loss, but did not necessarily result in effective risk pooling. This study is a primary evaluation of rainfall-related weather indices for the three main crops in China, which will be significantly helpful to the agricultural insurance market and governments’ policy making.

10.1007/s13753-020-00283-w

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Citations (6)

Temporal and spatial changes of maize yield potentials and yield gaps in the past three decades in China

Agriculture Ecosystems & Environment (2015)

Fulu Tao Shuai Zhang Zhao Zhang Reimund P. Rötter

Yield gap

10.1016/j.agee.2015.04.020

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Citations (74)

Modelling the impacts of weather and climate variability on crop productivity over a large area: A new super-ensemble-based probabilistic projection

Agricultural and Forest Meteorology (2009)

Fulu Tao Zhao Zhang Jiyuan Liu Masayuki Yokozawa

Biometeorology

10.1016/j.agrformet.2009.02.015

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Citations (164)

Silver lining to a climate crisis in multiple prospects for alleviating crop waterlogging under future climates

Nature Communications (2023)

Ke Liu Matthew Tom Harrison Hàoliàng Yán De Li Liu Holger Meinke

Extreme weather events threaten food security, yet global assessments of impacts caused by crop waterlogging are rare. Here we first develop a paradigm that distils common stress patterns across environments, genotypes and climate horizons. Second, we embed improved process-based understanding into a farming systems model to discern changes in global crop waterlogging under future climates. Third, we develop avenues for adapting cropping systems to waterlogging contextualised by environment. We find that yield penalties caused by waterlogging increase from 3-11% historically to 10-20% by 2080, with penalties reflecting a trade-off between the duration of waterlogging and the timing of waterlogging relative to crop stage. We document greater potential for waterlogging-tolerant genotypes in environments with longer temperate growing seasons (e.g., UK, France, Russia, China), compared with environments with higher annualised ratios of evapotranspiration to precipitation (e.g., Australia). Under future climates, altering sowing time and adoption of waterlogging-tolerant genotypes reduces yield penalties by 18%, while earlier sowing of winter genotypes alleviates waterlogging by 8%. We highlight the serendipitous outcome wherein waterlogging stress patterns under present conditions are likely to be similar to those in the future, suggesting that adaptations for future climates could be designed using stress patterns realised today.

Waterlogging (archaeology)

Frost (temperature)

Growing season

10.1038/s41467-023-36129-4

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Citations (152)

Spatial pattern and decadal change of agro-meteorological disasters in the main wheat production area of China during 1991–2009

Journal of Geographical Sciences (2014)

Zhao Zhang Wang Pin Yi Chen Shuai Zhang Fulu Tao

Loess plateau

Meteorological disasters

10.1007/s11442-014-1095-1

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Citations (46)

Considerable energy crop production potentials in the Russian Far East

Biomass and Bioenergy (2024)

Zongliang Zhang Jialu Xu S. Jin Huimin Zhuang Shaokun Li

Crop Production

10.1016/j.biombioe.2024.107365

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Tailoring wheat agronomic management to ENSO phases to manage climate variability in Australia at 5-minute resolution

Agricultural and Forest Meteorology (2024)

Juan Cao Zhao Zhang Xie Jun Yuchuan Luo Jichong Han

Biometeorology

Crop Management

Climatic variability

10.1016/j.agrformet.2024.110168

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Study on effect of infiltration paths on water-air permeability coefficients in unsaturated loess

Journal of Hydraulic Engineering (2008)

Zhao Zhang

Three methods for determining the time of stable moisture transfer and three methods for calculating the seepage force are suggested.At the same time,the infiltration test for loess specimens with the same dry density experienced different infiltration paths is carried out.The effect of time for stable moisture transfer and calculation method for seepage force on permeability coefficient is analyzed according to the data of test.The result shows that the calculation method using varying water head and taking the time-variation of seepage force into account is the most stable and best method.On this basis,by applying the method using varying water head the effect of infiltration path on water-air permeability coefficient is analyzed.The result shows that the water-air permeability coefficient is not significantly affected by the infiltration path,but it can not be neglected if the saturation of loess is high.

Infiltration (HVAC)

Saturation (graph theory)

Hydraulic head

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Citations (1)