首页|Spatial heterogeneity of county-level grain protein content in winter wheat in the Huang-Huai-Hai region of China
Spatial heterogeneity of county-level grain protein content in winter wheat in the Huang-Huai-Hai region of China
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NSTL
Elsevier
? 2022 Elsevier B.V.Timely and accurate forecasting of crop grain protein content (GPC) is helpful in planning to acquire the desired target protein levels. A geographically weighted regression (GWR) model was estimated based on meteorological factors to predict the winter wheat GPC at the county level. In the Huang-Huai-Hai region, the grain protein content of winter wheat increased by 0.29% for every 1° increase in latitude. GPC prediction with this model was more precise than that of the multiple linear regressions (MLR) model. The correlation coefficient (R) and Akaike information criterion (AIC) value ranges were 0.26 ~ 0.66 and 1573.86 ~ 1710.70 for the GWR, and 0.06 ~ 0.46 and 1670.18 ~ 1939.76 for the MLR, respectively. Except for radiation in March (RAD03), radiation in April (RAD04) and radiation in May (RAD05), the sensitivity index of other monthly weather indicators to GPC had a high correlation with latitude. With 36° north latitude (L) as the limit, the correlation between RAD03 (RL<36 ° = 0.36, RL>36 ° = ?0.29), RAD04 (RL<36 ° = 0.31, RL>36 ° = ?0.35) and RAD05 (RL<36 ° = 0.20, RL>36 ° = ?0.20) with latitude all showed an opposite trend. We highlight that spatial information needs to be considered when predicting county-level winter wheat GPC.
European Center for Medium-range Weather Forecasts (ECMWF) meteorological dataGeographically weighted regressionGrain protein contentSpatial heterogeneity
Wang B.、Liang J.、Zhao Y.、Zhao C.、Li Z.、Yang G.、Hu X.、Duan D.、Fu Y.
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Cereal Quality Supervision and Testing Center in Ministry of Agriculture
National Agricultural Technology Extension and Service Center
National Engineering and Technology Center for Information Agriculture Nanjing Agricultural University
Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs Information Technology Research Center Beijing Academy of Agriculture and Forestry Sciences
NSTL
Elsevier
