Unraveling Heat Tolerance in Upland Cotton (Gossypium hirsutum L.) Using Univariate and Multivariate Analysis

The ever-changing global environment currently includes an increasing ambient temperature that can be a devastating stress for organisms. Plants, being sessile, are adversely affected by heat stress in their physiology, development, growth, and ultimately yield. Since little is known about the response of biochemical traits to high-temperature ambiance, we evaluated eight parental lines (five lines and three testers) and their 15 F1 hybrids under normal and high-temperature stress to assess the impact of these conditions over 2 consecutive years. The research was performed under a triplicate randomized complete block design including a split-plot arrangement. Data were recorded for agronomic, biochemical, and fiber quality traits. Mean values of agronomic traits were significantly reduced under heat stress conditions, while hydrogen peroxide, peroxidase, total soluble protein, superoxide dismutase, catalase (CAT), carotenoids, and fiber strength displayed higher mean values under heat stress conditions. Under both conditions, high genetic advance and high heritability were observed for seed cotton yield (SCY), CAT, micronaire value, plant height, and chlorophyll-a and b content, indicating that an additive type of gene action controls these traits under both the conditions. For more insights into variation, Pearson correlation analysis and principal component analysis (PCA) were performed. Significant positive associations were observed among agronomic, biochemical, and fiber quality-related traits. The multivariate analyses involving hierarchical clustering and PCA classified the 23 experimental genotypes into four groups under normal and high-temperature stress conditions. Under both conditions, the F1 hybrid genotype FB-SHAHEEN × JSQ WHITE GOLD followed by Ghuari-1, CCRI-24, Eagle-2 × FB-Falcon, Ghuari-1 × JSQ White Gold, and Eagle-2 exhibited better performance in response to high-temperature stress regarding the agronomic and fiber quality-related traits. The mentioned genotypes could be utilized in future cotton breeding programs to enhance heat tolerance and improve cotton yield and productivity through resistance to environmental stressors.

Yield Estimation of High-Density Cotton Fields Using Low-Altitude UAV Imaging and Deep Learning

Background: Different from other parts of the world, China has its own cotton planting pattern. Cotton are densely planted in wide-narrow rows to increase yield in Xinjiang, China, causing the difficulty in the accurate evaluation of cotton yields using remote sensing in such field with branches occluded and overlapped. Results: In this study, low-altitude unmanned aerial vehicle (UAV) imaging and deep convolutional neural networks (DCNN) were used to estimate the yields of densely planted cotton. Images of cotton field were acquired by an UAV at the height of 5 m. Cotton bolls were manually harvested and weighted afterwards. Then, a modified DCNN model was developed by applying encoder-decoder recombination and dilated convolution for pixel-wise cotton boll segmentation termed CD-SegNet. Linear regression analysis was employed to build up the relationship between cotton boll pixels ratio and cotton yield. Yield estimations of four cotton fields were verified after machine harvest and weighting. The results showed that CD-SegNet outperformed the other tested models including SegNet, support vector machine (SVM), and random forest (RF). The average error of the estimated yield of the cotton fields was 6.2%. Conclusions: Overall, the yield estimation of densely planted cotton based on lowaltitude UAV imaging is feasible. This study provides a methodological reference for cotton yield estimation in China.

Evaluation of Thellungiella halophila ST7 for improving salt tolerance in cotton

Background Gossypium hirsutum (upland cotton) is one of the principal fiber crops in the world. Cotton yield is highly affected by abiotic stresses, among which salt stress is considered as a major problem around the globe. Transgenic approach is efficient to improve cotton salt tolerance but depending on the availability of salt tolerance genes. Results In this study we evaluated salt tolerance candidate gene ST7 from Thellungiella halophila, encoding a homolog of Arabidopsis aluminum-induced protein, in cotton. Our results showed that ThST7 overexpression in cotton improved germination under NaCl stress as well as seedling growth. Our field trials also showed that ThST7 transgenic cotton lines produced higher yield under salt stress conditions. The improved salt tolerance of the transgenic cotton lines was partially contributed by enhanced antioxidation as shown by diaminobenzidine (DAB) and nitrotetrazolium blue chloride (NBT) staining. Moreover, transcriptomic analysis of ThST7 overexpression lines showed a significant upregulation of the genes involved in ion homeostasis and antioxidation, consistent with the salt tolerance phenotype of the transgenic cotton. Conclusions Our results demonstrate that ThST7 has the ability to improve salt tolerance in cotton. The ThST7 transgenic cotton may be used in cotton breeding for salt tolerance cultivars.

The estimation of cotton yield based on weather parameters in Maharashtra

ABSTRACT. The cotton yield of 12 years (1975-1987), for five districts in Vidarbha region of Maharashtra, was taken for statistical-regression study. It is found that the higher temperature during first fortnight of September, which is period of budding and flowering is favourable for better yield. The cooler nights during second fortnight of October, when the crop is generally in fruiting stage, also help in good increases in final cotton yield. Higher rainfall, dufing last week of June to first week of July, when the crop is in the germination period, causing logging, reduces the seedling and more number of rainy days in second fortnight of December hamper the bolll bursting and thus al1ecting the cotton yield adversely.