[Objective] The study aimed to examine the effects of nitrogen, phosphorus, and potassium fertilizers on soil nutrient contents, cotton growth, and yield-related traits in the arid and nutrient-deficient Heilonggang region. [Methods] The experiment was conducted from 2022 to 2024 at the Weixian Experimental Station of the Cotton Research Institute, Hebei Academy of Agriculture and Forestry Sciences. A randomized block design was adopted with five treatments: no fertilizer, conventional fertilization, nitrogen deficiency, phosphorus deficiency, and potassium deficiency. Soil total nitrogen, alkali-hydrolyzable nitrogen, available phosphorus, and available potassium contents were measured after cotton harvest. At the square stage, early flowering stage, peak boll stage, and boll opening stage, dry matter accumulation of aboveground parts and plant nutrient contents were determined, and fertilizer utilization efficiency was calculated. Boll number per plant were counted on July 15, August 15, and September 10. The boll weight and lint percentage were measured after harvest, and the seed cotton yield and lint yield were calculated. [Results] The effects of nitrogen deficiency on soil total nitrogen and alkali-hydrolyzable nitrogen content were not significant. In the three-year experiment, the phosphorus deficiency treatment significantly reduced soil available phosphorus content by 8.5%, 14.6%, and 19.9%, compared with conventional fertilization, while potassium deficiency treatment significantly reduced available potassium content by 10.3%, 18.9%, and 24.6%, respectively. Compared with conventional fertilization, nitrogen uptake of aboveground parts of cotton in the nitrogen deficiency treatment significantly decreased by 35.7%, 35.4%, and 47.1%, while phosphorus uptake of aboveground parts of cotton in the phosphorus deficiency treatment showed no significant difference, and potassium uptake of aboveground parts of cotton in the potassium deficiency treatment significantly decreased by 15.3%, 13.0%, and 21.1% in 2022, 2023, and 2024, respectively. For agronomic efficiency, fertilizer contribution rate, and apparent utilization rate, nitrogen was the most efficient, followed by potassium, with phosphorus showing near-zero efficiency. Seed cotton yield in the nitrogen deficiency treatment significantly decreased by 5.5%, 13.1%, and 25.7%, while phosphorus deficiency had no significant effect on yield, and potassium deficiency led to a reduction of 3.1% over three years. [Conclusion] In the arid and nutrient-deficient Heilonggang region, fertilizer strategy should follow the principle of "applying efficient nitrogen, moderating potassium use, and reducing phosphorus".
[Objective] This study aimed to investigate the effects of different defoliants on the seed vigor and storage substance content of machine-harvested cotton seeds with different maturities, providing references for the rational selection of defoliants in cotton production. [Methods] Field experiments were conducted in Shihezi City, Xinjiang, from 2020 to 2021. The main plot involved the application of two different defoliants: Ruituolong and Yeluokong. The subplots included two cotton materials: an early-maturing variety, Shidamian 268 (S268), and a late-maturing line, Shidamian 451 (S451). On September 27, cotton bolls that had developed for 30 d, 35 d, 40 d, 45 d, and 50 d on August 31 (the first application of defoliant) were harvested. Naturally-opened cotton bolls before defoliant application served as the control (CK). Seed coat color was observed, and seed index, seed specific weight, oil content, and protein content were measured. Seed germination percentage, germination potential, germination index, and vigor index were determined at 18 ℃ and 28 ℃. [Results] Compared with CK, the white seed rate (the proportion of immature seeds) of 30-day bolls and 35-day bolls of S268 significantly increased under Ruituolong and Yeluokong treatments; while the black-brown seed rate (the proportion of mature seeds), seed index, seed specific weight, protein content, oil content, germination percentage, germination index, and vigor index of cotton seeds all significantly decreased in 2020 and 2021. For S451, the white seed rate of bolls at 30 d, 35 d, and 40 d significantly increased; while the black-brown seed rate, seed index, seed specific weight, protein content, oil content, germination percentage (except for the 40-day bolls), germination potential (except for the 40-day bolls at 28 ℃), germination index, and vigor index of seeds all significantly decreased. After spraying Ruituolong and Yeluokong, there were no significant differences in the black-brown seed rate, germination percentage, and germination potential for 45-day and 50-day cotton bolls of S268 and S451 compared with CK; as well as the seed oil content and protein content of 50-day cotton bolls compared with CK. For S268 and S451, compared with Ruituolong, the black-brown seed rate of 30-day bolls, seed index of 50-day bolls, protein content of 35-day bolls, seed germination index of 35-day bolls at 28 ℃, and vigor index of 40-day bolls at 18 ℃ all significantly decreased. [Conclusion] Under the conditions of this experiment, the superior defoliant was Ruituolong. Spraying Ruituolong had a relatively minor impact on seed vigor and the content of storage substances of 45-50 days cotton bolls.
[Objective] This study aimed to analyze the influencing factors of farmers' participation in the futures market, and provide reference for using the futures market to promote the development of the cotton industry. [Methods] A theoretical analysis framework was constructed along the lines of "risk aversion motivation→futures function cognition→futures market participation willingness→futures market participation behavior". The ordered Logit model and multinomial Logit model were comprehensively used to analyze the influencing factors of cotton farmers' futures market participation willingness, and the Logit model was further used to discuss the influence of social capital and intermediary organizations. [Results] The results showed that 70% of farmers have heard of the futures market, but only 5% of farmers participate in the futures market. Farmers' resource endowments, such as education level, planting scale, age, as well as market risk avoidance motivation, futures function cognition, social capital, and intermediary organization development, have important impacts on cotton farmers' participation in the futures market. [Conclusion] Currently, the proportion of farmers participating in the futures market is still very low. The main factors that inhibit cotton farmers from participating in the futures market are insufficient motivation to avoid market risks, unclear understanding of futures functions, lack of demonstration guidance and social capital, and immature development of intermediary organizations.
Transgenic crops, including insect-resistant cotton, have been widely cultivated globally, yielding significant economic and social benefits. Cotton is the foremost natural fiber source and one of the most important cash crops worldwide. The genetic transformation technology mediated by Agrobacterium tumefaciens, based on somatic embryogenesis or organogenesis, has become the primary method for obtaining transgenic plants. Particularly for cotton, a crop that typically regenerates through somatic embryogenesis, this process is not only crucial for the application of modern biotechnology in cotton, but also plays a vital role in genetic improvement and variety innovation in cotton. However, cotton somatic embryogenesis is a complex process; although its regulatory mechanisms have been extensively studied, genotype-dependence and low regeneration efficiency remain significant challenges in cotton genetic transformation. This article provides an overview of the research progress in cotton somatic embryogenesis, summarizing the status of the cotton somatic embryogenesis system, including key influencing factors and molecular mechanisms, and offers perspectives on future research in this area.
This study mainly based on the Xinjiang Statistical Yearbook, the annual global supply and demand information of extra-fine cotton published by the International Cotton Advisory Committee (ICAC), combined with the data provided by agricultural departments of Xinjiang prefectures (autonomous prefectures), Development and Reform Commission of Xinjiang Uygur Autonomous Region, and other relevant institutions. The development status and its influencing factors of sea island cotton production in Xinjiang from 1955 to 2024 were analysed, and relevant recommendations were offered, to provide references for the sustainable development of sea island cotton production in Xinjiang. From 1955 to 2024, the planting area, yield per unit area, and total production of Xinjiang sea island cotton showed inter-annual fluctuations. From 1955 to 1990, the fluctuating growth trend was obvious, while in the 21st century, the planting area and total production fluctuated obviously. Sea island cotton varieties in Xinjiang have fully achieved independent breeding, with 88 excellent varieties developed, of which 20 varieties have been widely promoted and planted, supported by the planting technology of "short, dense, early, film, drip irrigation". It was analysed that the key factors influencing the production of sea island cotton include varieties, technology, economic returns, trade wars, and natural disasters. Sea island cotton production currently faces a historical low point in the 21st century in Xinjiang. To overcome these challenges, both technological support and policy guidance are needed.
[Objective] This study aimed to comprehensively evaluate the drought resistance of major cotton cultivars in southern Xinjiang, screen drought resistance indicators, and identify superior drought-resistant cultivars (lines). [Methods] A sand culture experiment was conducted using 15% PEG6000 to simulate drought stress on 15 cotton cultivars from southern Xinjiang. Phenotypic and physiological traits were investigated, and drought resistance coefficients were calculated. Comprehensive evaluation of drought resistance was performed through correlation analysis, principal component analysis (PCA), entropy-weighted TOPSIS method, and cluster analysis. A multiple stepwise regression model was established to optimize the evaluation system for drought resistance. [Results] Compared with the control, drought stress reduced the germination energy (GE), germination index (GI), root number (RN), main root length (MRL), hypocotyl length (HL), fresh weight (FW), and dry weight (DW) by over 30%, with HL showing the most significant decline (50.55%) and hypocotyl diameter (HD) the least (24.66%). Conversely, superoxide dismutase (SOD) and peroxidase (POD) activities, along with malondialdehyde (MDA) content, increased by over 20%. PCA condensed the original 12 indicators into two independent comprehensive factors, explaining 82.80% of variance, and identified five key drought resistance indicators during germination: MDA, SOD, DW, HD, and GR. Using entropy-weighted TOPSIS, a comprehensive drought resistance index was determined, and cluster analysis classified the 15 cultivars into four drought resistance types, including three high drought-resistant cultivars (Tahe 2, Zhongmian 113, and Xinluzhong 40), four moderate drought-resistant cultivars (Zhongmian 88, CCRI 979, Xinluzhong 61, and Jiumian 20), four drought-tolerant resistant cultivars (Xinluzhong 88, Zhongmian 619, Zhongmian 96A, and Zhongshengmian 17), and four drought-susceptible cultivars (Xinluzao 50, Xinluzhong 67, JBK16, and Xinluzhong 37). A reliable regression model for drought resistance evaluation was established: I=0.74-0.51CMDA+0.15CSOD+0.20CDW+0.24CHD+0.45CGE, with a coefficient of determination of 0.99. [Conclusion] High drought-resistant cultivars exhibited significantly lower stress impacts and higher antioxidant capacity compared to sensitive ones. The entropy-weighted TOPSIS method identified Tahe 2 as the most drought-resistant cultivar. This study established a precise and efficient drought resistance evaluation system, providing a theoretical and technical support for cotton breeding and cultivation under drought conditions.
[Objective] Cotton yield prediction is an important part of cotton production management in our country. The accuracy of boll detection during the boll-opening stage directly affects the precision of yield estimation. In order to solve the problem that the computational complexity of existing cotton boll detection models increases significantly due to the pursuit of detection accuracy, this study proposes a lightweight cotton boll detection model, Slim-YOLOv8n, based on improved YOLOv8n. [Methods] This model takes YOLOv8n as its main framework. On one hand, it integrates a lightweight cross-scale feature fusion network in the neck structure to effectively reduce the dimensionality of multi-scale feature fusion and lower computational complexity. On the other hand, it reconstructs the detection head through reparameterized convolution and the idea of sharing, designing a reparameterized head and a two-stage feature processing stream to maintain accuracy while achieving model lightweighting. [Results] Experimental results show that this model achieves a detection accuracy of up to 98.20%. Compared with the YOLOv8n model, it reduces the number of parameters by 44.84%, computational cost by 39.51%, and model size by 43.34%, verifying the superiority of the model improvement. [Conclusion] Slim-YOLOv8n fully meets the dual demands of high accuracy and lightweighting for boll detection tasks, providing strong technical support for the efficient and precise detection of bolls at the boll-opening stage in cotton yield prediction.
