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Cover crop adoption is increasing among growers with the occurrence of herbicide-resistant weed species. A field study conducted at three sites from autumn 2021 through the crop harvest in 2022 in Alabama aimed to evaluate the combined effect of cover crop residue and herbicides for weed control and improved cotton lint yield. The experiment was conducted in split-plot design with main plots consisting of six cover crop treatments: cereal rye, crimson clover, oat, radish, cover crop mixture, and winter fallow. The subplots included four herbicide treatments: (i) preemergence, pendimethalin + fomesafen, (ii) postemergence, dicamba + glyphosate + S-metolachlor, (iii) preemergence followed by postemergence, and (iv) nontreated (NT) check. Cover crops, excluding radish, exhibited greater weed biomass reduction than winter fallow with corresponding herbicide treatments of either preemergence, postemergence, or preemergence + postemergence as compared to control (winter fallow and NT check). Considering preemergence + postemergence treatment, cereal rye, crimson clover, oat, and cover crop mixture provided >95% weed biomass reduction as compared to control. Looking at the overall effect of cover crop, cereal rye outperformed and showed greater weed biomass reduction than radish relative to control. Preemergence + postemergence herbicide treatment resulted in greater lint yield than other treatments. Cotton in cereal rye plots had a greater lint yield than in winter fallow at one out of three locations. In conclusion, integrating herbicides and incorporating high-residue cover crops such as cereal rye is an effective weed management strategy to control troublesome weeds. Nomenclature: Dicamba; fomesafen; glyphosate; pendimethalin; S-metolachlor; cereal rye, Secale cereale L.; cotton, Gossypium hirsutum L.; crimson clover, Trifolium incarnatum L.; oat, Avena strigosa Schreb.; radish, Raphanus sativus L.

Cotton is one of the most important industrial crops in the world. Though widely cultivated in Sicily (Italy) in the past, cotton growth on the island has disappeared today due to a complex variety of agronomic, economic and socio-political reasons. In recent years, increased interest in natural fibers worldwide has led to a revival in cotton plants in the Mediterranean area. The aim of this paper was to assess the response of Gossypium hirsutum L. cultivars to different environments and sowing times. Elsa and Juncal were selected from the most promising cotton cultivars regarding earliness and productivity. Plants were tested with three sowing times and in two Sicilian environments. Cotton yield and yield components were significantly affected by experimental station, sowing time and cultivar. Lint yield of cultivars was 1.60 t ha−1 on average, and the highest value of 1.99 t ha−1 was obtained from an early sowing time. The three indices of agronomic earliness varied significantly based on treatments. In conclusion, the evaluation of response genotype-by-environment under different sowing times could represent a strategy to obtain optimal cotton seed and lint yields, although other general aspects, such as labor costs, land availability and capital resources, should be also considered when evaluating the reintroduction of the species in Sicily.

Early detection of within‐field yield variability for high‐value commodity crops, such as cotton (Gossypium spp.), offers growers potential to improve decision‐making, optimize yields, and increase profits. Over recent years, publicly available datasets have become increasingly available and at a resolution where within‐field yield prediction is possible. However, the viability of using these datasets with machine learning to predict within‐field cotton lint yield at key growth stages are largely unknown. This study was conducted on two cotton fields, located near Mungindi, New South Wales, Australia. Three years of yield data, soil, elevation, rainfall, and Landsat imagery were collected from each field. A total of 12 models were created using: (a) two machine learning algorithms: random forest (RF) and gradient boosting machines (GBM); (b) three growth stages: squaring, flowering, and boll‐fill; and (c) two different amounts of variables: all variables and the optimal variables determined by a recursive feature elimination (RFE). Results showed a strong agreement between predicted and observed yields at flowering and boll‐fill when more information was available. At flowering and boll‐fill, root mean square error (RMSE) ranged between 0.15 and 0.20 t ha−1 and Lin's concordance correlation coefficient (LCCC) ranged between 0.50 and 0.66, with RF providing superior results in most cases. Models created using the optimal variables determined by the RFE provided similar results compared to using all variables, allowing greater model accuracy and resolution with targeted sampling. Overall, these findings indicate significant potential of publicly available datasets to predict within‐field cotton yield and guide decision‐making in‐season.

Cotton, with cellulose-enriched mature fibers, is the largest source of natural textiles. Through a map-based cloning strategy, we isolated an industrially important lint fiber development gene (Li 3) that encodes an MYB-MIXTA-like transcription factor (MML) on chromosome D12 (GhMML4_D12). Virus-induced gene silencing or decreasing the expression of the GhMML4_D12 gene in n2NSM plants resulted in a significant reduction in epidermal cell prominence and lint fiber production. GhMML4_D12 is arranged in tandem with GhMML3, another MIXTA gene responsible for fuzz fiber development. These two very closely related MIXTA genes direct fiber initiation production in two specialized cell forms: lint and fuzz fibers. They may control the same metabolic pathways in different cell types. The MIXTAs expanded in Malvaceae during their evolution and produced a Malvaceae-specific family that regulates epidermal cell differentiation, different from the gene family that regulates leaf hair trichome development. Cotton has developed a unique transcriptional regulatory network for fiber development. Characterization of target genes regulating fiber production has provided insights into the molecular mechanisms underlying cotton fiber development and has allowed the use of genetic engineering to increase lint yield by inducing more epidermal cells to develop into lint rather than fuzz fibers.

This study quantifies the effects of wide row spacing on lint yield, yield components, and fiber quality of upland cotton (Gossypium hirsutum L.). An experiment was conducted in Tifton, GA in 2021 and 2022, where cotton was planted in six replications of 91‐, 122‐, 152‐, and 183‐cm row spacings. Following defoliation, 1.8 m was hand harvested from each plot, and boll density plant−1 and density ha−1 were determined for sympodial and monopodial branches. Additional measurements included fruit and lint yield distribution assessments and intra‐boll yield components including seed cotton weight boll−1, seed index, seed boll−1, lint weight seed−1, seed surface area (SSA), fiber density, and single fiber weight. Lint yield was reduced 20% in the 183‐cm row spacing compared to the 91‐cm row spacing. The 91‐cm rows had the lowest number of sympodial bolls plant−1 with 152‐ and 183‐cm rows demonstrating a 24%–28% increase in sympodial bolls plant−1. Sympodial bolls ha−1 were reduced 22% in the 183‐cm row spacing compared to the 91‐cm row spacing. There were no differences in bolls ha−1 or lint yield ha−1 with respect to monopodial growth. There were no differences in seed cotton weight boll−1, seeds boll−1, fiber density, single fiber weight, or turnout. Seed index and SSA were increased in the 183‐cm row spacing. Lint weight seed−1 was reduced in the 91‐cm row spacing. Core Ideas Cotton produced in 152‐ and 183‐cm row spacings produces more sympodial bolls plant−1 than traditional 91‐cm rows. Sympodial bolls ha−1 were reduced in 183‐cm row spacings as compared to 91‐cm row spacings. With a reduction in sympodial bolls ha−1, lint yield is sacrificed in the 183‐cm row spacing. Wide row spacing improved fruit retention but did not affect lint yield. Even with more monopodial bolls plant−1 in the wider rows, there are no differences in monopodial bolls ha−1.

Success stories of wide row cotton production have generated producer interest across the cotton belt. The objective of this study was to quantify the effect of row spacing on (1) cotton growth and development and (2) lint yield and fiber quality in Georgia. The cotton cultivar Deltapine DP 1646 (Bollgard 2, XtendFlex; Bayer Crop Science) was planted in 2021 and 2022 in Tifton, GA, at row spacings of 91 cm, 122 cm, 152 cm, and 183 cm. Although canopy closure was delayed in wider row spacings compared to the 91‐cm spacing, all other crop growth and development trends did not differ among row spacings. Similarly, row spacing had no effect on the incidence of boll rot/hard lock. The 91‐cm row spacing yielded 1734 kg lint ha−1. The 183‐cm row spacing reduced yield by 15% compared with the grower's standard row spacing of 91 cm. Fiber quality parameters did not differ among row spacing treatments. Seed costs can be reduced in all wide row systems evaluated for cotton, and the 122 and 152‐cm row spacings had similar yields to the 91‐cm row spacing. However, Georgia growers produce peanuts (Arachis hypogea L.) and corn (Zea mays L.), the most common rotational crops for cotton producers, using a 91‐cm row spacing. Consequently, the simplest transition to wide row production would be the 183‐cm spacing, which incurred yield penalties in the current study. Thus, a 91‐cm row spacing is still the most feasible option for Georgia cotton producers. Core Ideas Wide row cotton production has potential benefits for Georgia growers. Planting cotton on 183‐cm row spacings reduces seeding rate 50% from 91‐cm row spacings, with plant populations being reduced 46%. Canopy closure was delayed in wider row spacings compared to the grower standard. All other crop growth and development measurements were similar across four row spacings. Lint yield was reduced 15% in 183‐cm row spacings as compared to 91‐cm row spacings.

Cotton fiber is the most important source of cellulose for the global textile industry. These hair-like single-celled trichomes develop from ovule epidermis. They are classified into long spinnable lint and short fuzz. A key objective in the cotton industry is to breed elite cultivars with fuzzless seeds carrying high lint yield. Molecular basis underlying lint and fuzz initiation remains obscure. Recent studies indicate fiber initiation is under the control of MYB-bHLH-WDR (MBW) transcription factor complex. Based on molecular genetic studies and gene expression patterns linking fiber phenotypes, we propose that specific but different sets of MBW genes are required to precisely regulate the initiation of the lint and fuzz fibers. Emerging evidence further points to sugar signaling as a ‘hair-tonic’ to boost fiber initiation through interaction with MBW complex and auxin signaling. An integrative model is provided as a conceptual framework for future studies to dissect the molecular network responsible for cotton fiber initiation.

Background Salinity is one of the most significant environmental factors limiting the productivity of cotton. However, the key genetic components responsible for the reduction in cotton yield in saline-alkali soils are still unclear. Results Here, we evaluated three main components of lint yield, single boll weight (SBW), lint percentage (LP) and boll number per plant (BNPP), across 316  G. hirsutum accessions under four salt conditions over two years. Phenotypic analysis indicated that LP was unchanged under different salt conditions, however BNPP decreased significantly and SBW increased slightly under high salt conditions. Based on 57,413 high-quality single nucleotide polymorphisms (SNPs) and genome-wide association study (GWAS) analysis, a total of 42, 91 and 25 stable quantitative trait loci (QTLs) were identified for SBW, LP and BNPP, respectively. Phenotypic and QTL analysis suggested that there was little correlation among the three traits. For LP, 8 stable QTLs were detected simultaneously in four different salt conditions, while fewer repeated QTLs for SBW or BNPP were identified. Gene Ontology (GO) analysis indicated that their regulatory mechanisms were also quite different. Via transcriptome profile data, we detected that 10 genes from the 8 stable LP QTLs were predominantly expressed during fiber development. Further, haplotype analyses found that a MYB gene ( GhMYB103 ), with the two SNP variations in cis-regulatory and coding regions, was significantly correlated with lint percentage, implying a crucial role in lint yield. We also identified that 40 candidate genes from BNPP QTLs were salt-inducible. Genes related to carbohydrate metabolism and cell structure maintenance were rich in plants grown in high salt conditions, while genes related to ion transport were active in plants grown in low salt conditions, implying different regulatory mechanisms for BNPP at high and low salt conditions. Conclusions This study provides a foundation for elucidating cotton salt tolerance mechanisms and contributes gene resources for developing upland cotton varieties with high yields and salt stress tolerance.

Cotton is a widely planted commercial crop in the world. Enhancing fiber yield and quality is a long-term goal for cotton breeders. Our previous work has demonstrated that fine promotion of auxin biosynthesis in ovule epidermis, by overexpressing FBP7pro::iaaM , has a significant improvement on lint yield and fiber fineness. Lately, transgenic cottons overexpressing GhROP6 variants modify mature fiber length by controlling GhPIN3a-mediated polar auxin transport in ovules. Here, this study showed that all these GhROP6- related cottons displayed unsatisfactory agronomic performance in field conditions. Yet extra auxin supply could promote their fiber development, suggesting inadequate auxin supply in the ovules. Thus, these cottons were integrated with enhanced auxin synthesis by crossing with FBP7pro::iaaM cotton. All the transgene-stacked cottons exhibited synergetic effects on cotton yield (seedcotton yield, lint yield, and lint percentage) and quality (length, strength, and micronaire). Notably, comparing to the FBP7pro::iaaM background, the transgene-stacked cotton co-expressing FBP7pro::iaaM and CA-ghrop6 (constitutively active GhROP6 ) exhibited a 12.6% increase in seedcotton yield and a 19.0% increase in lint yield over a three-year field trial, and simultaneously resulted in further improvement on fiber length, strength, and micronaire. Collectively, our data provide a potential strategy for genetic improvement on cotton fiber yield and quality.

Background Upland cotton ( Gossypium hirsutum L.) is the most economically important species in the cotton genus ( Gossypium spp.). Enhancing the cotton yield is a major goal in cotton breeding programs. Lint percentage (LP) and boll weight (BW) are the two most important components of cotton lint yield. The identification of stable and effective quantitative trait loci (QTLs) will aid the molecular breeding of cotton cultivars with high yield. Results Genotyping by target sequencing (GBTS) and genome-wide association study (GWAS) with 3VmrMLM were used to identify LP and BW related QTLs from two recombinant inbred line (RIL) populations derived from high lint yield and fiber quality lines (ZR014121, CCRI60 and EZ60). The average call rate of a single locus was 94.35%, and the average call rate of an individual was 92.10% in GBTS. A total of 100 QTLs were identified; 22 of them were overlapping with the reported QTLs, and 78 were novel QTLs. Of the 100 QTLs, 51 QTLs were for LP, and they explained 0.29–9.96% of the phenotypic variation; 49 QTLs were for BW, and they explained 0.41–6.31% of the phenotypic variation. One QTL ( qBW-E-A10-1 , qBW-C-A10-1 ) was identified in both populations. Six key QTLs were identified in multiple-environments; three were for LP, and three were for BW. A total of 108 candidate genes were identified in the regions of the six key QTLs. Several candidate genes were positively related to the developments of LP and BW, such as genes involved in gene transcription, protein synthesis, calcium signaling, carbon metabolism, and biosynthesis of secondary metabolites. Seven major candidate genes were predicted to form a co-expression network. Six significantly highly expressed candidate genes of the six QTLs after anthesis were the key genes regulating LP and BW and affecting cotton yield formation. Conclusions A total of 100 stable QTLs for LP and BW in upland cotton were identified in this study; these QTLs could be used in cotton molecular breeding programs. Putative candidate genes of the six key QTLs were identified; this result provided clues for future studies on the mechanisms of LP and BW developments.