scholarly journals DTR in Winter Wheat Growing Regions of China: CMIP6 Models Evaluation and Comparation

2021 ◽  
Author(s):  
Wenqiang Xie ◽  
Shuangshuang Wang ◽  
Xiaodong Yan
Keyword(s):  
Winter Wheat ◽  
Climate Models ◽  
Climate Model ◽  
Wheat Yield ◽  
Human Society ◽  
Climate Data ◽  
Growing Seasons ◽  
Yield And Quality ◽  
Important Concern ◽  
Winter Wheat Yield

Abstract Winter wheat is widely planted in China. The changes of winter wheat yield and quality are related to the food security of human society. Climate change has an important impact on the yield and quality of winter wheat. Diurnal temperature range (DTR) is an important factor affecting the yield and protein content of winter wheat. Furthermore, climate model is one of the main sources of error in crop model simulations of yields. Therefore, how to improve the accuracy of climate data has become an important concern for scholars.Previous model evaluations for the entire country or region cannot answer which model is suitable for the estimation of future winter wheat yield. Therefore, we evaluated the ability of climate models to simulate DTR within the range of winter wheat growing regions in China to identify the most suitable climate models for winter wheat yield and quality projections. The results show that CMIP6 models can basically reproduce the DTR of winter wheat-growing regions in China, but there are discrepancies in the simulations between nationwide and winter wheat-growing regions. EC-Earth3-Veg has the best simulation of climate DTR for wheat-growing regions (TS=0.848) and nationwide (TS=0.842), and ACCESS-CM2 has the strongest ability to simulate the annual growing season DTR (TS=0.46). In summary, in the estimation of future winter wheat yield, attention should be given to the selection of models suitable for the actual growing regions and the growing seasons of winter wheat.

scholarly journals WINTER WHEAT YIELD AND QUALITY DEPENDING ON THE LEVEL OF NITROGEN, PHOSPHORUS AND POTASSIUM FERTILIZATION

2019 ◽  
Vol 65 (2) ◽  
Author(s):  
Vera RAJICIC ◽  
Jelena MILIVOJEVIC ◽  
Vera POPOVIC ◽  
Snezana BRANKOVIC ◽  
Nenad DJURIC ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Wheat Yield ◽  
Yield And Quality ◽  
Potassium Fertilization ◽  
Winter Wheat Yield ◽  
Phosphorus And Potassium ◽  
Nitrogen Phosphorus

The influence of mineral fertilization on winter wheat yield and quality

2006 ◽  
Vol 34 (1) ◽  
pp. 429-432 ◽  
Author(s):  
Daniela Horvat ◽  
Zdenko Loncaric ◽  
Vladimir Vukadinovic ◽  
Georg Drezner ◽  
Blazenka Bertic ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Wheat Yield ◽  
Mineral Fertilization ◽  
Yield And Quality ◽  
Winter Wheat Yield

scholarly journals Identifying the Contributions of Multi-Source Data for Winter Wheat Yield Prediction in China

2020 ◽  
Vol 12 (5) ◽  
pp. 750 ◽  
Author(s):  
Juan Cao ◽  
Zhao Zhang ◽  
Fulu Tao ◽  
Liangliang Zhang ◽  
Yuchuan Luo ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Satellite Data ◽  
Large Scale ◽  
Wheat Yield ◽  
Yield Variability ◽  
Yield Prediction ◽  
Climate Data ◽  
Light Gradient ◽  
Source Data ◽  
Winter Wheat Yield

Wheat is a leading cereal grain throughout the world. Timely and reliable wheat yield prediction at a large scale is essential for the agricultural supply chain and global food security, especially in China as an important wheat producing and consuming country. The conventional approach using either climate or satellite data or both to build empirical and crop models has prevailed for decades. However, to what extent climate and satellite data can improve yield prediction is still unknown. In addition, socio-economic (SC) factors may also improve crop yield prediction, but their contributions need in-depth investigation, especially in regions with good irrigation conditions, sufficient fertilization, and pesticide application. Here, we performed the first attempt to predict wheat yield across China from 2001 to 2015 at the county-level by integrating multi-source data, including monthly climate data, satellite data (i.e., Vegetation indices (VIs)), and SC factors. The results show that incorporating all the datasets by using three machine learning methods (Ridge Regression (RR), Random Forest (RF), and Light Gradient Boosting (LightGBM)) can achieve the best performance in yield prediction (R2: 0.68~0.75), with the most individual contributions from climate (~0.53), followed by VIs (~0.45), and SC factors (~0.30). In addition, the combinations of VIs and climate data can capture inter-annual yield variability more effectively than other combinations (e.g., combinations of climate and SC, and combinations of VIs and SC), while combining SC with climate data can better capture spatial yield variability than others. Climate data can provide extra and unique information across the entire growing season, while the peak stage of VIs (Mar.~Apr.) do so. Furthermore, incorporating spatial information and soil proprieties into the benchmark models can improve wheat yield prediction by 0.06 and 0.12, respectively. The optimal wheat prediction can be achieved with approximately a two-month leading time before maturity. Our study develops timely and robust methods for winter wheat yield prediction at a large scale in China, which can be applied to other crops and regions.

Pea green manure management affects organic winter wheat yield and quality in semiarid Montana

2011 ◽  
Vol 91 (3) ◽  
pp. 497-508 ◽  
Author(s):  
P. R. Miller ◽  
E. J. Lighthiser ◽  
C. A. Jones ◽  
J. A. Holmes ◽  
T. L. Rick ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Green Manure ◽  
Wheat Yield ◽  
Manure Management ◽  
Yield And Quality ◽  
Winter Wheat Yield
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