Developing machine learning models with multi-source environmental data to predict wheat yield in China
文献类型：期刊论文
| 作者 | Li, Linchao2,3,4; Wang, Bin3,5; Feng, Puyu1; Liu, De Li5,6; He, Qinsi7; Zhang, Yajie3; Wang, Yakai2; Li, Siyi5,7; Lu, Xiaoliang3; Yue, Chao3 |
| 刊名 | COMPUTERS AND ELECTRONICS IN AGRICULTURE
 |
| 出版日期 | 2022-03-01 |
| 卷号 | 194页码:12 |
| 关键词 | Yield prediction Vegetation indices NIRv Random forest Support vector machine Wheat |
| ISSN号 | 0168-1699 |
| DOI | 10.1016/j.compag.2022.106790 |
| 通讯作者 | Wang, Bin(bin.a.wang@dpi.nsw.gov.au) ; Yang, Guijun(yanggj@nercita.org.cn) |
| 英文摘要 | Crop yield is controlled by different environmental factors. Multi-source data for site-specific soils, climates, and remotely sensed vegetation indices are essential for yield prediction. Algorithms of data-model fusion for crop growth monitoring and yield prediction are complicated and need to be optimized to deal with model uncertainty. This study integrated multi-source environmental variables (e.g., satellite-based vegetation indices, climate data, and soil properties) into random forest (RF) and support vector machine (SVM) models for wheat yield prediction in China. The performance of both RF and SVM models was investigated using different types of vegetation indices associated with other predictors. Relative importance and partial dependence analyses were used to identify the main predictors and their relationships with wheat yield. We found that using remotely sensed vegetation indices improved our model precision, and that near-infrared reflectance of terrestrial vegetation (NIRv) was slightly better than normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) in predicting yield. NIRv was better in detecting climate stress on crops, and could capture more information regarding crop growth and yield formation. Compared with the SVM model, the RF model with NIRv and other covariates had better performance in wheat yield prediction, with R-2 and RMSE being 0.74 and 758 kg/ha respectively. We also found that NIRv from jointing to heading was the most important predictor in determining yield, followed by solar radiation (especially during tillering-heading), relative humidity (during planting-tillering), soil organic carbon, and wind speed (throughout the growing season). In addition, wheat yield exhibited threshold-like responses to most factors based on our RF model. These threshold values can help to better understand how different environmental factors limit wheat yield, which will provide useful information for climate-adaptive crop management. Our findings demonstrated the potential of using NIRv for yield prediction. This approach is broadly applicable to other regions globally using publicly available data. |
| WOS关键词 | CROP YIELD ; CLIMATE-CHANGE ; WINTER-WHEAT ; FOOD-DEMAND ; TEMPERATURE ; IMPACTS ; PHOTOSYNTHESIS ; CLASSIFICATION ; FLUORESCENCE ; TERRESTRIAL |
| 资助项目 | Natural Science Foundation of China[41961124006] ; Natural Science Foundation of China[41730645] ; Natural Science Foundation of China[52079114] ; Natural Sci-ence Foundation of Qinghai[2021-HZ-811] ; National Key Research and Development Program of China[2019YFE0125300] ; National Key Research and Development Program of China[2017YFE0122500] |
| WOS研究方向 | Agriculture ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000784219300002 |
| 出版者 | ELSEVIER SCI LTD |
| 资助机构 | Natural Science Foundation of China ; Natural Sci-ence Foundation of Qinghai ; National Key Research and Development Program of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/175019]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Bin; Yang, Guijun |
| 作者单位 | 1.China Agr Univ, Coll Land Sci & Technol, Beijing 100193, Peoples R China 2.Northwest A&F Univ, Coll Nat Resources & Environm, Yangling 712100, Shaanxi, Peoples R China 3.Northwest A&F Univ, Inst Soil & Water Conservat, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling 712100, Shaanxi, Peoples R China 4.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China 5.Wagga Wagga Agr Inst, NSW Dept Primary Ind, Wagga Wagga, NSW 2650, Australia 6.Univ New South Wales, Climate Change Res Ctr, Sydney, NSW 2052, Australia 7.Univ Technol Sydney, Fac Sci, Sch Life Sci, POB 123, Broadway, NSW 2007, Australia 8.Northwest A&F Univ, Coll Water Resources & Architectural Engn, Yangling 712100, Shaanxi, Peoples R China 9.Changan Univ, Sch Geol Engn & Surveying & Mapping, Xian 710054, Peoples R China 10.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Linchao,Wang, Bin,Feng, Puyu,et al. Developing machine learning models with multi-source environmental data to predict wheat yield in China[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE,2022,194:12. |
| APA | Li, Linchao.,Wang, Bin.,Feng, Puyu.,Liu, De Li.,He, Qinsi.,...&Yu, Qiang.(2022).Developing machine learning models with multi-source environmental data to predict wheat yield in China.COMPUTERS AND ELECTRONICS IN AGRICULTURE,194,12. |
| MLA | Li, Linchao,et al."Developing machine learning models with multi-source environmental data to predict wheat yield in China".COMPUTERS AND ELECTRONICS IN AGRICULTURE 194(2022):12. |
入库方式： OAI收割
来源：地理科学与资源研究所
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