yangtze river valley
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Toxins ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 23
Author(s):  
Ling Wang ◽  
Dong Xu ◽  
Yunxin Huang ◽  
Huazhong Zhou ◽  
Weiguo Liu ◽  
...  

Transgenic crops producing Bacillus thuringiensis (Bt) toxins are widely planted for insect control, but their efficacy may decrease as insects evolve resistance. Understanding the genetic basis of insect resistance is essential for developing an integrated strategy of resistance management. To understand the genetic basis of resistance in pink bollworm (Pectinophora gossypiella) to Bt cotton in the Yangtze River Valley of China, we conducted an F2 screening for alleles associated with resistance to the Bt (Cry1Ac) protein for the first time. A total of 145 valid single-paired lines were screened, among which seven lines were found to carry resistance alleles. All field parents in those seven lines carried recessive resistance alleles at the cadherin locus, including three known alleles, r1, r13 and r15, and two novel alleles, r19 and r20. The overall frequency of resistance alleles in 145 lines was 0.0241 (95% CI: 0.0106–0.0512). These results demonstrated that resistance was rare and that recessive mutation in the cadherin gene was the primary mechanism of pink bollworm resistance to Bt cotton in the Yangtze River Valley of China, which will provide a scientific basis for implementing targeted resistance management statics of pink bollworm in this region.


The Holocene ◽  
2021 ◽  
pp. 095968362110666
Author(s):  
Jie Yu ◽  
Yanyan Yu ◽  
Haibin Wu ◽  
Wenchao Zhang ◽  
Hui Liu

The contribution of early human activity to the increase in atmospheric CH4 content during the middle to late-Holocene is still debated. The quantitative reconstruction of past changes in land use by early rice agriculture is a key to resolving the issue, because large uncertainties still exist in current prehistoric land use estimates, owing to a lack of direct records. In this study, we used the combination of archaeological data (the area and distribution of archaeological sites) and an improved prehistoric land use model (PLUM) to determine the spatiotemporal changes in land use by rice agriculture throughout the Yangtze River Valley, China, which was the origin and centre of the development of rice cultivation. The results indicate that the area devoted to rice agriculture increased during 10–2 ka BP, and that a significant increase occurred at ~5 ka BP accompanied by a spatial expansion from the northern part of the valley to the entire valley. However, the rice land use area decreased slightly during 4–3 ka BP but then increased after 3 ka BP. We estimate that the CH4 emissions from the rice cultivated area in the Yangtze River Valley increased from ~0.001 (±0.001) to ~1.3 (±0.6) Tg/year during 10–2 ka BP, and the resulting atmospheric CH4 concentrations increased from ~0.004 (±0.002) to ~4.1 (±2.0) ppb, which accounted for 3 (±2)–9 (±5) % of the ‘anomalous atmospheric CH4 increase’ during 5–2 ka BP.


2021 ◽  
Vol 33 (8) ◽  
pp. 101599
Author(s):  
Muhammad Ishaq Asif Rehmani ◽  
Chengqiang Ding ◽  
Ganghua Li ◽  
Syed Tahir Ata-Ul-Karim ◽  
Adel Hadifa ◽  
...  

2021 ◽  
Vol 264 ◽  
pp. 105853
Author(s):  
Kejun He ◽  
Ge Liu ◽  
Renguang Wu ◽  
Sulan Nan ◽  
Jingxin Li ◽  
...  

2021 ◽  
Vol 35 (6) ◽  
pp. 1148-1148
Author(s):  
Licheng Wang ◽  
Xuguang Sun ◽  
Xiu-Qun Yang ◽  
Lingfeng Tao ◽  
Zhiqi Zhang

Water ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 3294
Author(s):  
Chentao He ◽  
Jiangfeng Wei ◽  
Yuanyuan Song ◽  
Jing-Jia Luo

The middle and lower reaches of the Yangtze River valley (YRV), which are among the most densely populated regions in China, are subject to frequent flooding. In this study, the predictor importance analysis model was used to sort and select predictors, and five methods (multiple linear regression (MLR), decision tree (DT), random forest (RF), backpropagation neural network (BPNN), and convolutional neural network (CNN)) were used to predict the interannual variation of summer precipitation over the middle and lower reaches of the YRV. Predictions from eight climate models were used for comparison. Of the five tested methods, RF demonstrated the best predictive skill. Starting the RF prediction in December, when its prediction skill was highest, the 70-year correlation coefficient from cross validation of average predictions was 0.473. Using the same five predictors in December 2019, the RF model successfully predicted the YRV wet anomaly in summer 2020, although it had weaker amplitude. It was found that the enhanced warm pool area in the Indian Ocean was the most important causal factor. The BPNN and CNN methods demonstrated the poorest performance. The RF, DT, and climate models all showed higher prediction skills when the predictions start in winter than in early spring, and the RF, DT, and MLR methods all showed better prediction skills than the numerical climate models. Lack of training data was a factor that limited the performance of the machine learning methods. Future studies should use deep learning methods to take full advantage of the potential of ocean, land, sea ice, and other factors for more accurate climate predictions.


2021 ◽  
Vol 9 ◽  
Author(s):  
Ruonan Zhang ◽  
QuCheng Chu ◽  
Zhiyan Zuo ◽  
Yanjun Qi

Based on the Lagrangian particle dispersion model, HYSPLIT 4.9, this study analyzed the summertime atmospheric moisture sources and transportation pathways affecting six subregions across China. The sources were: Midlatitude Westerly (MLW), Siberian-Arctic regions (SibArc), Okhotsk Sea (OKS), Indian Ocean (IO), South China Sea (SCS), Pacific Ocean (PO), and China Mainland (CN). Furthermore, the relative contributions of these seven moisture sources to summertime precipitation in China were quantitatively assessed. Results showed that the CN precipitation source dominates the interannual and interdecadal variation of precipitation in most subregions, except Southwest and South China. The Northeast China vortex and Pacific–Japan (PJ) teleconnection, which transport water vapor from the MLW, OKS and PO sources, are crucial atmospheric systems and patterns for the variation of precipitation in Northeast China. The interannual variation of precipitation in Northwest and North China is mainly dominated by mid–high-latitude Eurasian wave trains, which provide the necessary dynamical conditions and associated moisture transport from the MLW and SibArc sources. In addition, an enhanced western North Pacific subtropical high (WNPSH) accompanied by the East Asian–western North Pacific summer monsoon and PJ teleconnection, transports extra moisture to North China from the SCS and PO sources, as well to the Yangtze River Valley and South China. The Indian summer monsoon (ISM) is also critically important for the interdecadal change in precipitation over the Yangtze River Valley and South China, via the southwesterly branch of moisture transport from the IO source. The interdecadal changes in precipitation over Southwest China are determined by the IO and SCS sources, via enhanced WNPSH coupling with a weakened ISM. These results suggest that the interdecadal and interannual variations of moisture sources contribute to the attendant variation of summertime precipitation in China via large-scale circulation regimes in both the mid–high and lower latitudes.


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