Cotton is widely cultivated globally because it provides natural fibre for the textile industry and human use. To identify quantitative trait loci (QTLs)/genes associated with fibre quality and yield, a recombinant inbred line (RIL) population was developed in upland cotton. A consensus map covering the whole genome was constructed with three types of markers (8295 markers, 5197.17 centimorgans (cM)). Six fibre yield and quality traits were evaluated in 17 environments, and 983 QTLs were identified, 198 of which were stable and mainly distributed on chromosomes 4, 6, 7, 13, 21 and 25. Thirty-seven QTL clusters were identified, in which 92.8% of paired traits with significant medium or high positive correlations had the same QTL additive effect directions, and all of the paired traits with significant medium or high negative correlations had opposite additive effect directions. In total, 1297 genes were discovered in the QTL clusters, 414 of which were expressed in two RNA-Seq data sets. Many genes were discovered, 23 of which were promising candidates. Six important QTL clusters that included both fibre quality and yield traits were identified with opposite additive effect directions, and those on chromosome 13 (qClu-chr13-2) could increase fibre quality but reduce yield; this result was validated in a natural population using three markers. These data could provide information about the genetic basis of cotton fibre quality and yield and help cotton breeders to improve fibre quality and yield simultaneously.
Global food production faces immense pressure, much of which can be attributed to climate change. A detailed evaluation of the impact of climate change on the yield of staple crops in Kazakhstan, a major food exporter, is required for more scientific planting management. In this study, the Mann–Kendall test and Theil–Sen Median slope were used to determine climate trends and staple food yields over the past 30 years; random forest was used to analyze the importance of monthly climatic factors; states were classified according to climatic factors through systematic clustering method; and lastly, the influence of climate on yield was analyzed using panel regression models. The upward trend in wind speed and potato yield throughout Kazakhstan was apparent. Furthermore, barley and wheat yields had increased in the southeast. We determined that for wheat, frostbite should be prevented after the warmer winters in the high-latitude areas. Except for July–August in the low-latitude areas, irrigation water should be provided in the other growth periods and regions. As similar effects were reported for barley, the same preventive measures would apply. For potatoes, tuber rot, caused by frost or excessive precipitation in May, should be prevented in high-latitude areas; soil dryness should be alleviated during the germination and seedling stages in low-latitude areas; and irrigation and cooling should be maintained during tuber formation and maturation. Furthermore, hot dry air in March and April could damage the crops.
Climate change, greenhouse gas emissions, and food security have put forward higher requirements for sustainable agricultural development. Agricultural ecological efficiency (AEE) is an important indicator to evaluate the sustainable development of agriculture. Low carbon agriculture promotes sustainable agricultural development. Agricultural carbon sinks are an important output of agricultural production, but they have not been fully reflected in the current research on agricultural ecological efficiency. In this study, agricultural carbon sinks are considered as one of the expected outputs of AEE. The data envelopment method was used to measure the AEE of 31 provincial-level administrative regions in China from 2000 to 2019, and the AEE of China was compared with and without carbon sinks. The Gaussian kernel function was used to estimate the time evolution of regional differences in AEE. A geodetector model was used to detect the drivers of spatial differentiation of AEE in China. The results showed that considering agricultural carbon sinks as one of the expected measurement outputs brings the estimated AEE closer to reality. From 2000 to 2019, China’s AEE showed an upward trend, and the efficiency value increased from 0.48 to 0.95, an increase of 97.92%. The spatial distribution pattern of AEE in China was Northeast > West > Central > East, with obvious differences among provinces. The industrialization level, urban–rural gap, agricultural economic level, agricultural disaster rate, and urbanization level were the leading driving forces for the spatial differentiation of AEE in China. The research will help to reveal the dynamic characteristics, spatial differentiation characteristics, and driving factors of China’s agricultural ecological efficiency, and provide a scientific reference for the realization of sustainable agricultural development and high-quality development.
Pumped storage power stations not only serve as a special power load but also store excess electricity from the power system, significantly reducing the curtailment of wind and solar power. This dual function ensures the stable operation of the power grid and enhances its economic benefits. The scheduling optimization problem of a combined wind–solar–pumped storage system is addressed in this study, and an optimization scheduling model is proposed with the objective of maximizing total system revenue. The model is designed to comprehensively account for the generation revenues from wind power, photovoltaic power, thermal power, and pumped storage, as well as the penalty costs associated with pollutant emissions. To address the limitations of traditional algorithms, which are prone to being trapped in local optima and exhibit slow convergence, an improved bat algorithm was developed. The algorithm is enhanced through the use of chaotic mapping to expand the initial solution space, the incorporation of adaptive step-size updates to improve convergence efficiency, and the integration of the Cauchy function to strengthen global search capabilities, thereby effectively avoiding local optima. Simulation results have demonstrated that the improved algorithm achieves significant improvements over traditional bat algorithms and particle swarm optimization (PSO) in terms of optimization efficiency, with total revenue increases of 21.9% and 24.6%, respectively. The optimized scheduling plan is shown to fully utilize the flexible regulation capabilities of pumped storage, mitigating the adverse effects of wind and photovoltaic output fluctuations on grid operations and achieving a balanced trade-off between economic and environmental objectives.
The characteristics of fertility of 208s, a paoto-thermo-senisitive genic male sterile line in rice, and the heterosis of its F1 hybrids was analyzed. The results showed that 208s is a PTGMS line in rice with low sterility-inducing critical temperature. It is characteristic of high outcrossing rate and fine grain quality. Its F1 hybrids showed strong heterosis and fine grain quality. It is a fine-quality and practicubility PTGMS lines in breeding fine-quality two-line bybrid rice.
All-polymer solar cells (all-PSCs), while having the merits of materials robustness, high mechanical flexibility, and low sensitivity of photovoltaic efficiencies to thickness variation, still suffer from non-satisfactory photovoltaic performance. This...
Estimating above-ground biomass (AGB) and leaf area index (LAI) is crucial for determining crop developmental status and estimating grain yield, thus facilitating high-throughput phenotyping of maize (Zea Mays L.). Recently, LAI and AGB estimation models using an unmanned aerial vehicle (UAV) have been widely used to monitor crop growth. However, few studies have reported on the quantitative analysis of improved accuracy in biomass and leaf area index estimation using undistorted images obtained through inverse projection transformation algorithms, as well as predicting the biomass and leaf area index of different crops during various growth stages by replacing traditional data augmentation methods with images captured from different aerial perspectives. The results showed that when using undistorted image at single solar zenith angle of 0°, the accuracy of AGB and LAI estimation was improved by 20.3% and 7.3%, respectively, than that of the models estimated from orthoimages. The undistorted images with various aerial perspectives dataset was 9 ~ 15 times larger than the orthomosaic image dataset, and could be used as a new training data that is similar to the original data, but with variations. The AGB and LAI estimation model constructed using the data augmentation method achieved better performance with higher average R2 (0.98). Model performance was increased by 40% and 16.7% for estimating AGB and by 19.3% and 11.3% for estimating LAI based on undistorted images, respectively, in comparison to models based on orthophotos and undistorted images due to the increased diversity and quantity of training data. The results showed that the canopy structure including canopy coverage and plant height, characterized by 96.9%, was the most reliable indication for estimating AGB and LAI, while other RGB sensor-derived feature combinations contributed only 3.1% to the estimation. Spectrum information took the second place, and the texture information was the weakest. The results showed that the data processing framework using data augmentation based on various aerial perspectives' images can improve the performance of crop growth estimation and provide a mechanism for precision agriculture under field conditions
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