scholarly journals Corrigendum to “Folate content analysis of wheat cultivars developed in the North China Plain” [289 (2019) 377–383]

2021 ◽  
Vol 351 ◽  
pp. 129349
Author(s):  
Bisma Riaz ◽  
Qiuju Liang ◽  
Xing Wan ◽  
Ke Wang ◽  
Chunyi Zhang ◽  
...  
Keyword(s):  
Content Analysis ◽  
North China Plain ◽  
North China ◽  
Wheat Cultivars ◽  
The North ◽  
The North China Plain

Folate content analysis of wheat cultivars developed in the North China Plain

2019 ◽  
Vol 289 ◽  
pp. 377-383 ◽  
Author(s):  
Bisma Riaz ◽  
Qiuju Liang ◽  
Xing Wan ◽  
Ke Wang ◽  
Chunyi Zhang ◽  
...  
Keyword(s):  
Content Analysis ◽  
North China Plain ◽  
North China ◽  
Wheat Cultivars ◽  
The North ◽  
The North China Plain

Performance of New Released Winter Wheat Cultivars in Yield: A Case Study in the North China Plain

2016 ◽  
Vol 108 (4) ◽  
pp. 1346-1355 ◽  
Author(s):  
Wenying Zhang ◽  
Bianyin Wang ◽  
Binhui Liu ◽  
Zhaojin Pang ◽  
Xishen Wang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
North China Plain ◽  
North China ◽  
Wheat Cultivars ◽  
The North ◽  
The North China Plain ◽  
Winter Wheat Cultivars

scholarly journals Pathotype, Resistance Classification, and Seed-Coating Control of Heterodera avenae and H. filipjevi in the North China Plain

Plant Disease ◽  
2020 ◽  
Vol 104 (12) ◽  
pp. 3230-3238
Author(s):  
Jiangkuan Cui ◽  
Yongqing Jiao ◽  
Bo Zhou ◽  
Haohao Ren ◽  
Hao Li ◽  
...  
Keyword(s):  
North China Plain ◽  
North China ◽  
Wheat Yield ◽  
Heterodera Avenae ◽  
Seed Coating ◽  
Net Income ◽  
Wheat Cultivars ◽  
Cyst Nematodes ◽  
The North ◽  
The North China Plain

Heterodera avenae and H. filipjevi are cereal cyst nematodes (CCNs) that infect cereals in 16 provinces of China. CCN populations from Xuchang, Tangyin, Qihe, and Juye were tested using 23 barley, oat, and wheat entries of the International Test Assortment for Defining Cereal Cyst Nematode Pathotypes. H. avenae populations from Tangyin, Qihe, and Juye were classified as pathotype Ha91, and H. filipjevi from Xuchang was classified as a new pathotype similar to pathotype West. Among 42 other winter wheat cultivars, 29 and 30 were differentially susceptible, 13 and 12 were differentially resistant to H. avenae and H. filipjevi, respectively. Three entries were resistant to both species, and three other entries were resistant to H. avenae and moderately resistant to H. filipjevi. Coating wheat seed with abamectin + isopycnic imidacloprid or methylene (bis) thiocyanate + thiamethoxam reduced the number of H. avenae and H. filipjevi cysts by 46 to 56%, increased wheat yield by 9 to 27%, and improved net income by 660 to 2,640 Chinese Yuan ha−1, respectively. Resistant wheat cultivars are scarce in China, and seed coating is considered the most suitable method for controlling CCNs in the North China Plain, where crop rotation cannot be practiced.

ROx Budgets and O3 Formation during Summertime at Xianghe Suburban Site in the North China Plain

2021 ◽  
Author(s):  
Min Xue ◽  
Jianzhong Ma ◽  
Guiqian Tang ◽  
Shengrui Tong ◽  
Bo Hu ◽  
...  
Keyword(s):  
North China Plain ◽  
North China ◽  
The North ◽  
The North China Plain ◽  
Suburban Site

scholarly journals Improving the Resolution and Accuracy of Groundwater Level Anomalies Using the Machine Learning-Based Fusion Model in the North China Plain

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 46
Author(s):  
Gangqiang Zhang ◽  
Wei Zheng ◽  
Wenjie Yin ◽  
Weiwei Lei
Keyword(s):  
Machine Learning ◽  
Data Fusion ◽  
Groundwater Level ◽  
North China Plain ◽  
North China ◽  
Gradient Boosting ◽  
Fusion Model ◽  
The North ◽  
The North China Plain ◽  
Prediction Module

The launch of GRACE satellites has provided a new avenue for studying the terrestrial water storage anomalies (TWSA) with unprecedented accuracy. However, the coarse spatial resolution greatly limits its application in hydrology researches on local scales. To overcome this limitation, this study develops a machine learning-based fusion model to obtain high-resolution (0.25°) groundwater level anomalies (GWLA) by integrating GRACE observations in the North China Plain. Specifically, the fusion model consists of three modules, namely the downscaling module, the data fusion module, and the prediction module, respectively. In terms of the downscaling module, the GRACE-Noah model outperforms traditional data-driven models (multiple linear regression and gradient boosting decision tree (GBDT)) with the correlation coefficient (CC) values from 0.24 to 0.78. With respect to the data fusion module, the groundwater level from 12 monitoring wells is incorporated with climate variables (precipitation, runoff, and evapotranspiration) using the GBDT algorithm, achieving satisfactory performance (mean values: CC: 0.97, RMSE: 1.10 m, and MAE: 0.87 m). By merging the downscaled TWSA and fused groundwater level based on the GBDT algorithm, the prediction module can predict the water level in specified pixels. The predicted groundwater level is validated against 6 in-situ groundwater level data sets in the study area. Compare to the downscaling module, there is a significant improvement in terms of CC metrics, on average, from 0.43 to 0.71. This study provides a feasible and accurate fusion model for downscaling GRACE observations and predicting groundwater level with improved accuracy.

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