Glyphosate-resistant Palmer amaranth (Amaranthus Palmeri S. Wats.) caused cotton growers to increase the use of preemergence (PRE) herbicides. These herbicides can cause crop injury, which increases the susceptibility window of cotton to thrips (Thysanoptera: Thripidae). With the use of PREs in cotton, management strategies for ThryvOn varieties are unknown. In this study, PRE herbicides at multiple rates (none, 1x, and 2x) were evaluated for ThryvOn (Deltapine [DP] 2211 B3TXF) and non-ThryvOn (DP 2127 B3XF) varieties with or without a foliar insecticide treatment (acephate [200 g ai ha-1] applied at one-leaf stage) to determine the combined effects of herbicide injury and thrips management on the growth of ThryvOn and non-ThryvOn cotton. Experiments were conducted in Tifton, GA, and Prattville, AL, in 2023 and 2024. Generally, herbicide injury was greater in DP 2127 B3XF and thrips injury was greater for any treatment of DP 2127 B3XF than any DP 2211 B3TXF treatment. Herbicide injury early in the season did not increase thrips injury in ThryvOn cotton. Plant heights, total nodes, shoot fresh biomass, and lint yield were only impacted by cultivar. Although one cultivar had ThrvyOn and the other did not, differences cannot be attributed to the trait alone as the two varieties evaluated were not isolines. Fruit retention and cutout date were not significantly different. Therefore, these results indicate agronomic management should not generally differ between ThryvOn and non-ThryvOn cotton.

Abstract The interactive effects of nitrogen (N) and mepiquat chloride (MC) on yield and key physiological traits have received limited attention in the primary literature. This study was aimed to (1) evaluate the effects of N rate and MC application strategy on yield‐altering physiological processes, lint yield, and fiber quality and (2) quantify relationships between lint yield and physiological responses at peak bloom and cutout stages. A field experiment was conducted in 2023 and 2024 near Tifton, GA, using a split‐plot design with three N rates (0, 135, and 270 kg N ha −1 ) and three MC strategies (control, moderate, and aggressive). Variables assessed included lint yield, gin turnout, fiber quality traits, fraction of intercepted photosynthetically active radiation (IPAR f ), radiation use efficiency, above‐ground biomass (AGB), N uptake, and crop growth rate. Nitrogen fertilization increased lint yield (57‐91%), IPAR f , AGB, and N uptake, with no yield benefit above 135 kg N ha −1 . Yield increased by 18%–25% with MC application in 2024, but no impact was observed in 2023. No significant N × MC interaction was observed for yield. Regression analysis showed that IPAR f and AGB at peak bloom were the most consistent predictors of lint yield. Nitrogen and MC effects on fiber quality varied by year. Nitrogen application had a consistent positive effect on yield and physiological traits. MC showed variable effects on lint yield across years. IPAR f and AGB and N uptake at the peak bloom are important indicators of N‐ or MC‐management‐driven yield variation in cotton.

The United States (U.S.) leads the world in fresh market sweet corn (Zea mays subsp. Mayes L.) production. The state of Georgia is one of the leading U.S. producers, with over 9,000 ha of sweet corn harvested annually at an economic value of more than USD 106 million in 2023. Failure to meet sweet corn water needs may lead to yield and quality losses. Therefore, growers tend to over-irrigate this valuable crop to mitigate potential losses. Excess irrigation can lead to soil health problems, water table decline, nutrient and fertilizer runoff, and inefficient water use. SmartIrrigation CropFit (SI CropFit) is a smartphone-based platform that provides irrigation scheduling recommendations for maize, cotton, peanut, and soybean based on real-time crop water needs. SI CropFit makes irrigation scheduling decisions based on the Food and Agricultural Organization (FAO)-56 method of estimating daily crop water use, also described as crop evapotranspiration (ETc), by multiplying Penman-Monteith evapotranspiration (ETo) by a crop coefficient (Kc). This work aimed to develop an ETc-based irrigation scheduling model for sweet corn grown in the southeastern U.S. that could be incorporated into SI CropFit. Soil moisture data collected over four growing seasons from 12 grower-managed sweet corn fields located in southwestern Georgia were utilized to estimate daily crop water use of sweet corn. These estimates were then used to develop an empirical Kc model based on accumulated heat units (growing degree days). This model was field-tested for two growing seasons and ultimately incorporated into SI CropFit.

In some peanut (Arachis hypogaea L.) producing regions, growth and photosynthesis-limiting low and high temperature extremes are common. Heat acclimation potential of photosynthesis and respiration is a coping mechanism that is species-dependent and should be further explored for peanut. The objectives of the current study are (1) to evaluate the response of photosynthesis, its component processes, and respiration to low and high temperatures, and (2) to determine the heat acclimation potential of photosynthesis and respiration during early vegetative growth of peanut. Peanut was exposed to four different growth temperature regimes: (1) optimum temperature (30/20 degrees C day/night), (2) low temperature (20/15 degrees C), (3) moderately high temperature (35/ 25 degrees C), and (4) a high temperature extreme (40/30 degrees C). Low temperature and both high temperatures caused substantial reductions in growth and net photosynthetic rate. Mesophyll conductance and RuBP regeneration colimited net photosynthetic rate under low temperature. Rubisco carboxylation was the most negatively impacted biochemical processes by high temperatures; however, diffusional limitations were not evident under high temperature conditions. Photosynthesis did not acclimate to high temperatures, while respiration and photorespiration exhibited heat acclimation. The inability of photosynthesis to acclimate to high temperature is likely a major constraint to early season growth in peanut.

High resolution three-dimensional (3D) point clouds enable the mapping of cotton boll spatial distribution, aiding breeders in better understanding the correlation between boll positions on branches and overall yield and fiber quality. This study developed a segmentation workflow for point clouds of 18 cotton genotypes to map the spatial distribution of bolls on the plants. The data processing workflow includes two independent approaches to map the vertical and horizontal distribution of cotton bolls. The vertical distribution was mapped by segmenting bolls using PointNet++ and identifying individual instances through Euclidean clustering. For horizontal distribution, TreeQSM segmented the plant into the main stem and individual branches. PointNet++ and Euclidean clustering were then used to achieve cotton boll instance segmentation. The horizontal distribution was determined by calculating the Euclidean distance of each cotton boll relative to the main stem. Additionally, branch types were classified using point cloud meshing completion and the Dijkstra shortest path algorithm. The results highlight that the accuracy and mean intersection over union (mIoU) of the 2-class segmentation based on PointNet++ reached 0.954 and 0.896 on the whole plant dataset, and 0.968 and 0.897 on the branch dataset, respectively. The coefficient of determination (R2) for the boll counting was 0.99 with a root mean squared error (RMSE) of 5.4. For the first time, this study accomplished high-granularity spatial mapping of cotton bolls and branches, but directly predicting fiber quality from 3D point clouds remains a challenge. This method provides a promising tool for 3D cotton plant mapping of different genotypes, which potentially could accelerate plant physiological studies and breeding programs.

Estimating cotton fiber quality early in the season, or its field variability, is impractical due to limitations in current methods, and it has not been widely explored. Similarly, few studies have tried estimating the parameters contributing to in-season cotton yield using UAV-based sensors. Thus, this study aims to explore the potential of using UAV-based multispectral images to estimate important in-season parameters, such as intercepted photosynthetically active radiation (IPAR), cotton height, the number of mainstem nodes, leaf area index (LAI), and end-of-the-season yield and cotton fiber quality parameters. Research trials were carried out in 2018 and 2020 in two experimental fields. In both years, a randomized complete block design was used with three cotton cultivars (2018), three plant growth regulators (2020), and three different irrigation levels to promote variability (both years). Cotton growth parameters were collected throughout the season on the same dates as UAV flights. Yield and fiber quality data were collected during harvest. The VI-based models used in this study were mostly sensitive to differences in cotton growth and final yield but less sensitive in detecting variation in cotton fiber quality indicators, such as length, strength, and micronaire, early in the season. The best performing regression model among the three fiber quality indicators was achieved in 2020, using a combination of four VIs, which explained 68% of the micronaire variability at 71 DAP. Results from this study also showed that multispectral-based VIs can be applied as early as the squaring stage at around 44 DAP to estimate most cotton growth indicators and final lint yield. Multiple linear regression validation models for height using NDVI, GNDVI, and RDVI obtained an R2 of 0.62, and for LAI using MSR and NDVI an R2 of 0.60. For lint yield, the best regression model combined four VIs and explained 66% of the yield variability. The ability to capture the variability in important growth and yield parameters early in the season can provide useful insights on potential crop performance and aid in in-season decisions.

Upland cotton (Gossypium hirsutum) faces the challenge of limited genetic diversity in the elite or improved gene pool. To address this issue, we explored alleles contributed by five ‘converted’ exotic lines sampling most of the undomesticated botanical races of G. hirsutum, in BC1F2 and F3 populations. Joint analysis of all populations along with population-specific analyses identified 38 unique QTL for six different fiber quality traits. At 15 of these loci, DES56 or the elite allele improved upon all the exotics. For another 15, only a single of the five exotics improved upon the elite allele, suggesting the rare alleles that may not have been sampled in the cotton domestication or improvement. At the remaining 8 QTL, multiple exotic lines contributed the superior allele, suggesting that DES56 (and by extension the elite gene pool) has chronically poor alleles at these loci. Converted strains T1046, T326, and T063 showed the highest potential for contributions to cotton fiber quality breeding programs. Upper Half Mean Length and Fiber Strength showed multiple QTL regions affecting both traits simultaneously, while the Uniformity Index showed the smallest heritability values. The estimation of pairwise genetic distances for six parental lines indicates that DES56 has a higher genetic similarity with each exotic line than the exotic lines have with each other. Most of the detected QTL were ‘minor’ (explaining less than 10% of variance) supporting the implementation of genomic selection techniques to utilize the cumulative effects of most of these QTL distributed genome-wide. Finally, some regions were consistently unfavorable for exotic introgression such as on chromosomes A13 and D09, indicating the possible genome-wide haplotypes that may combine the benefits of a history of scientific breeding of the elite gene pool.

Bronze wilt was an issue for the cotton industry in the 1990s but mentions of bronze wilt in the literature were minimal until cotton leafroll dwarf virus (CLRDV) was observed across the cotton belt beginning in 2017. In 2024, bronze wilt symptoms were observed at high levels in Southwest Georgia. University of Georgia on-farm cotton variety trials (10 varieties evaluated across 19 locations) were used to quantify susceptibility to expression of bronze wilt symptoms and impacts on lint yield. Overall, the four varieties evaluated were determined to express bronze wilt symptoms; an additional variety outside the trial program was determined to be susceptible based on observations in grower fields. Differences in symptom severity were observed among locations, with some showing significant yield impacts, whereas others were unaffected. Averaged across non-yield-limiting locations (14 out of 19), 2 to 3% symptomatic plants were observed in symptomatic varieties: symptoms increased to 28 to 35% averaged across yield-limiting locations (5 out of 19). Where yield was affected, bronze wilt symptoms reduced lint yield 16 to 32% in the four susceptible varieties. Across all locations, a negative linear relationship was observed within susceptible varieties: a 1% increase in symptoms resulted in a 0.54% decrease in relative lint yield. These data are the first from replicated research to document yield losses associated with bronze wilt symptoms. Future research should evaluate in-field variety screening methods, genetics of susceptibility to expression of bronze wilt symptoms, and controlled environment experiments to replicate symptoms.

Limited genetic diversity for fiber quality and yield traits in the improved gene pool for Upland cotton motivates the exploration of exotic materials. In the present study, we carried out QTL mapping for 2 fiber yield components in five BC1F2 and one F3 resembling intermated population(s) involving 5 ‘converted’ exotic lines of Upland cotton and an elite cultivar “DES56,” as a common parent in all populations. The results indicated a higher frequency of dominant QTLs than additive QTLs. However, the prevalence of negative heterotic effects shows few opportunities to utilize these exotic lines for hybrid production. Most of the dominant QTLs were overdominant. In addition, all additive QTLs for lint percentage indicated the DES56 allele to be superior. These results suggest that selection practices need to be carried out in the later generations along with the implementation of precise marker-assisted selection to avoid the undesirable heterotic effects of these regions and reduce the linkage drag from undesirable alleles in the exotic segments. Moreover, the population-specific analysis and joint analysis showed the inflation of QTL parameters with a decrease in sample size, indicating the importance of larger sample sizes in mapping populations. Overall, the potential for using these exotic race stocks for hybrid production targeting yield components is limited.

Context: Georgia is one of the largest cotton producer in the United States. Genotype x environment analysis have been previously performed, although there still exists a gap in knowledge related to i) newer varieties and ii) characterization of environmental potential in relation to meteorological patterns during the growing season. Objectives: i) to quantify the effects of environment, genotype, and management on yield and quality; ii) to evaluate the performance and responsiveness of different genotypes to different environments, and iii) to identify environmental conditions with increased cotton lint yield or quality parameters. Method: Studies were conducted in 73 site-years as part of a variety trial program. In all the site-years, 22 cotton varieties were evaluated, of which twelve were present in at least 45 site-years. We performed analysis of variance, variance component, Finlay-Wilkinson, and conditional inference tree, to achieve our objectives. Results: The environment had a greater impact on yield and fiber quality (length, strength, uniformity and micronaire) than did genotype. We generate recommendations on variety selection according to each environment index. Conditional inference tree identified temperature and stage duration in squaring and boll opening as the most important variables and stages for affecting micronaire, yellowness, length, and uniformity. Conclusions: Our results will help farmers selecting the proper variety, considering not only their potential but also their main goal (yield or quality). As newer cotton genotypes are introduced yearly, we propose to continue working with these datasets to develop an online application to help farmers to identify and select the best genotype for their environment.

Bronze wilt was an issue for the cotton industry in the 1990s but mentions of bronze wilt in the literature were minimal until cotton leafroll dwarf virus (CLRDV) was observed across the cotton belt beginning in 2017. In 2024, bronze wilt symptoms were observed at high levels in farm cotton variety trials (10 varieties evaluated across 19 locations) were used to quantify susceptibility to expression of bronze wilt symptoms and impacts on lint yield. Overall, the four varieties evaluated were determined to express bronze wilt symptoms; an additional variety outside the trial program was determined to be susceptible based on observations in grower fields. Differences in symptom severity were observed among locations, with some showing significant yield impacts, whereas others were unaffected. Averaged across non-yield-limiting locations (14 out of 19), 2 to 3% symptomatic plants were observed in symptomatic varieties: symptoms increased to 28 to 35% averaged across yield-limiting locations (5 out of 19). Where yield was affected, bronze wilt symptoms reduced lint yield 16 to 32% in the four susceptible varieties. Across all locations, a susceptible varieties: a 1% increase in symptoms resulted in a 0.54% decrease in relative lint yield. replicate symptoms.

Extreme temperatures and cultivar variation in seed characteristics affect stand establishment, early‐season growth, and photosynthetic processes. However, studies addressing temperature responses of similarly adapted cultivars of contrasting seed traits are needed. This study assessed the effect of temperature and cultivar on growth, physiological responses, and photosynthetic thermotolerance in cotton ( Gossypium hirsutum L.) seedlings and identified important plant traits contributing to seedling vigor. Two similarly adapted cotton cultivars with contrasting seed sizes were grown in a controlled environment under 4 day/night temperature regimes, 20/15, 30/20, 35/25, and 40/30°C, for 4 weeks. Growth analysis, chlorophyll fluorescence, and gas exchange data were collected. Rapid induction fluorescence × incubation temperature experiments were used to define temperature thresholds, causing a 15% decline in photosynthetic efficiencies ( T 15 ). The large‐seeded cotton cultivar exhibited higher values for most growth traits. The 20/15°C temperature had the lowest values for all growth traits, and 30/20 and 35/25°C were optimal for shoot growth. Leaf area was the most important driver of seedling vigor, and whole‐canopy photosynthesis could be a more accurate predictor of biomass accumulation than net carbon assimilation per unit leaf area. Plants grown at 20/15°C had the lowest net carbon assimilation rates, primarily due to metabolic impairment. Photosystem I (PSI) and photosystem II (PSII) had greater heat tolerance than inter‐photosystem electron transport. The thermotolerance of all thylakoid responses increased as the early‐season growth temperature increased, and there were cultivar differences in acclimation potential for PSI.

Light Detection and Ranging (LiDAR) technology can be used to assess canopy height in cotton (Gossypium hirsutum L.), but standardized data acquisition and processing guidelines are lacking. Accurate canopy height estimation is crucial in cotton for optimizing growth regulator application and maximizing yield. The main goal of this study was to determine the optimal unmanned aerial vehicle flight settings—altitude and speed—and assess specific processing parameters’ impact on data accuracy, processing time, and file size. Nine flight settings comprising three altitudes (12.2 m, 24.4 m, and 48.8 m) and three speeds (4.8 km/h, 9.6 km/h, and 14.4 km/h) were tested. LiDAR data were processed using DJI Terra software (v. 4.1.0), where two user-defined processing steps were examined: point-cloud thinning via grid size sub-sampling (0, 10, 20, 30, 40, and 50 cm) and slope classification (flat, gentle, and steep). The optimal flight altitude was 24.4 m, with no effect of flight speed. Grid sub-sampling up to 20 cm produced balanced accuracy, processing time, and file size. The choice of slope category had no significant effect on LiDAR-derived canopy height. These findings contribute to the development of standardized LiDAR data acquisition and processing guidelines for cotton to support crop management decision.

Drought can greatly limit carbon assimilation in plants. However, different species have distinct photosynthetic components governing limitations to photosynthesis exposed to drought conditions. Furthermore, intra-species variations in photosynthetic response to drought is also expected. Information on underlying limitations to carbon assimilation in peanut (Arachis hypogaea L.) has been controversial. Therefore, this study aimed to verify potential drought tolerance associated with the photosynthetic process within ten diverse peanut genotypes grown under drought as well as to determine the limitation to carbon assimilation in these genotypes and identify parameter(s) that can be used as a reference indicator of photosynthesis response to drought intensity. Experiments were conducted in 2017 and 2018 using rainout shelters to impose drought for 40 days starting 34 days after planting. Ten peanut genotypes were planted in two blocks, one fully irrigated and one under drought stress during reproductive development. Photosynthetic measurements were taken at 25 and 40 days after onset of stress. C76-16 was identified as the most tolerant genotype due to improved plasticity by downregulating photosynthesis under mild drought stress (25 progressive days under drought) and upregulating multiple photosynthetic component processes under more severe drought (40 days under drought) to sustain photosynthesis. The primary limitation to photosynthesis across all peanut genotypes was stomatal conductance, whereas non-stomatal factors (photochemical reactions) were nearly unaffected by mild drought. In addition, stomatal conductance and electron flux to CO2 assimilation contributed most to drought tolerance in peanut genotypes. Moreover, these two photosynthetic component processes can be jointly used as reference indicators of photosynthetic status of peanut under varying drought intensities.

Various tillage systems have limitations on soil health, such as the degradation of soil structure and organic matter under conventional tillage (CT) systems, as well as short-term soil compaction in conservation tillage systems. A 3-year field experiment was established to evaluate the integration of cover crop (CC) and organic amendments (OAs) into CT and strip tillage (ST) systems, and their impact on soil properties and cotton (Gossypium hirsutum L.) productivity. The CC was cereal rye (Secale cereale), and the combined application of animal manure and biochar constituted the OA. In the third year, differences in soil compaction between the CT and ST systems were observed when the measurements were made after tillage. Moreover, integrating CC and OA under the CT and ST systems increased the soil depth to compaction zones. Soil compaction was observed at 27.5-cm depth under CT, at 30-cm depth under CT integrated with CC and OA, at 10-cm depth under ST, and at 15-cm depth under ST integrated with CC and OA, using 2 MPa as the threshold. In general, the integration of CC and OA tended to increase soil respiration, organic matter, and available nutrients, but the effects were not consistent across years and soil depth. Despite differences in the various soil health properties, the management systems had minimum impact on cotton productivity and fiber quality, indicating the ST was effective in preparing the seedbed. Moreover, the differences in soil properties were not at yield-limiting levels within 3 years of the study. A 3-year assessment of integrating cover crops and organic amendments into different tillage systems was made. Differences in soil compaction between the different tillage systems were observed after tillage operation. Integrated systems increased soil depth to compaction zones under the different tillage systems by the third year. Integrated systems effects on soil respiration, organic matter, and nutrients were not consistent over time. The integrated systems had no significant impact on cotton yield and fiber quality in all 3 years.

Yield improvement in cotton could be accelerated through selection for functional yield drivers such as interception of cumulative photosynthetically active radiation ( n-ary sumation IPAR), radiation use efficiency (RUE), and harvest index (HI). However, information on the extent to which these traits vary in cotton in the southeastern United States is limited. It was hypothesized that functional yield drivers would vary significantly within a diverse cotton collection. This study was conducted in Tifton and Athens, GA, and included a total of 4 site-years. Lint yield, total biomass production, n-ary sumation IPAR, RUE, and HI were all affected by genotype. Biomass was more strongly correlated with RUE than n-ary sumation IPAR. Even among the highest yielding genotypes, values for functional yield drivers (biomass and harvest index) differed significantly, indicating that high yields could be achieved by differentially manipulating these underlying traits. However, when considered for all genotypes, only HI exhibited a significant positive correlation with yield. Boll production and intra-boll yield components were also affected by genotype. When considered across upland genotypes, lint per boll, lint per seed, and lint percent were strongly associated with HI and lint yield, whereas boll mass and seed number per boll were not. We conclude that the genotypes evaluated in the current study achieve high lint production per boll and lint yields by manipulating different yield drivers. However, lint yield was primarily maximized through an increase in HI due to increases in boll production and within-boll distribution of biomass to fiber, not due to increases in total biomass production or boll size. Lint yield varied significantly across a diverse collection of cotton genotypes. Maximum lint yields were achieved by manipulating different functional yield drivers. Across all upland genotypes, harvest index was most strongly associated with lint yield. Harvest index was most associated with sympodial boll numbers, fiber per boll, and fiber per seed.

Improving shade tolerance is critical for development of new turfgrass cultivars in the United States. Comparing turfgrass coverage under reduced sun exposure is a popular and effective method for determining shade tolerance, but requires years to evaluate. The objectives were to (i) compare phenotypical differences of experimental genotypes and cultivars of bermudagrass (Cynodon spp.), St. Augustinegrass (Stenotaphrum secundatum), and zoysiagrass (Zoysia spp.) grown under 73% shade and (ii) identify whether genetic improvement for shade adaptation was made in these species. This 3-year study conducted in Tifton, GA, found phenotypic differences among genotypes within species for turfgrass coverage when exposed to 73% shade. The experimental bermudagrass, 11-T-56, possessed the superior combination of high green turfgrass coverage, low canopy height, and season long dark green color under shade. Experimental genotypes in St. Augustinegrass exhibited genetic improvement compared to commercially available cultivars; however, these genotypes should be examined under more intense shade to elicit differences before further selection. Performance of experimental zoysiagrass genotypes from several turfgrass breeding programs did not indicate significant improvement in the shade persistence within Zoysia spp. There appears to be genetic differences in the speed at which newer zoysiagrasses can initially spread by rhizomes and stolons when grown under shade. Further research should be conducted to determine if juvenile growth is an indicator of shade tolerance under natural tree shade or structures. Overall, results indicated that canopy heights cannot be used to directly predict shade tolerance but can be used to identify turfgrasses with reduced mowing frequency requirements. Experimental bermudagrass, 11-T-56, had greater coverage, lower canopy height, and darker green color under shade. Experimental St. Augustinegrasses showed genetic improvement compared to commercial cultivars grown under shade. Newer zoysiagrasses can initially spread faster when grown under shade compared to older cultivars. Research identified turfgrasses with reduced mowing needs when grown in shade environments.

Water deficit stress limits net photosynthetic rate (AN), but the relative sensitivities of underlying processes such as thylakoid reactions, ATP production, carbon fixation reactions, and carbon loss processes to water deficit stress in field-grown upland cotton require further exploration. Therefore, the objective of the present study was to assess (1) the diffusional and biochemical mechanisms associated with water deficit-induced declines in AN and (2) associations between water deficit-induced variation in oxidative stress and energy dissipation for field-grown cotton. Water deficit stress was imposed for three weeks during the peak bloom stage of cotton development, causing significant reductions in leaf water potential and AN. Among diffusional limitations, mesophyll conductance was the major contributor to the AN decline. Several biochemical processes were adversely impacted by water deficit. Among these, electron transport rate and RuBP regeneration were most sensitive to AN-limiting water deficit. Carbon loss processes (photorespiration and dark respiration) were less sensitive than carbon assimilation, contributing to the water deficit-induced declines in AN. Increased energy dissipation via non-photochemical quenching or maintenance of electron flux to photorespiration prevented oxidative stress. Declines in AN were not associated with water deficit-induced variation in ATP production. It was concluded that diffusional limitations followed by biochemical limitations (ETR and RuBP regeneration) contributed to declines in AN, carbon loss processes partially contributed to the decline in AN, and increased energy dissipation prevented oxidative stress under water deficit in field-grown cotton.

AbstractWith grower interest in skip and wide row cotton systems, varietal performance in such systems has become a major question. An experiment was conducted in 2022 and 2023 in Tifton and Midville, GA, evaluating three row arrangements (standard 91‐cm row spacing, 2 × 1 skip row, and 183‐cm row spacing or wide row) and four commercially available varieties (Stoneville [ST] 5091 Bollgard 3 Xtendflex [B3XF], Phytogen [PHY] 400 Widestrike 3 Roundup Flex Enlist [W3FE], DynaGro [DG] 3799 B3XF, and Deltapine [DP] 1840 B3XF). There were no interactions between variety and row arrangement for any response variable, indicating the best variety for standard row spacings would also be the best variety in alternative row arrangements. Plant populations were reduced 32% and 53% in 2 × 1 skip‐row and wide‐row systems, respectively, compared to standard row arrangements, which accomplishes the major goal of these systems in reducing seed cost. Boll rot and hard lock were reduced in wide row treatments only, which could benefit cotton growers in the lower Southeast. However, reductions in lint yield were associated with 2 × 1 skip row (all site years) and wide row arrangements (three out of four site‐years) compared to the grower standard. Differences among varieties were observed in plant heights, lint yield, and fiber quality, which is to be expected. These results confirm much of the work conducted on skip and wide row cotton systems and indicate that for growers in the lower Southeast to achieve maximum lint yields, standard row arrangements are superior to alternative row arrangements.

Drought stress and nitrogen (N) deficiency are important abiotic stresses that severely limit net photosynthetic rate (AN). A number of studies have investigated the underlying physiological limitations to AN in response to water deficit or N deficiency; however, the relative sensitivities of photosynthetic component processes and carbon loss processes to combined drought and N deficiency in field-grown cotton (Gossypium hirsutum L.) have not been explored. Therefore, the objective of the present study was to determine the effects of combined water deficit and nitrogen deficiency on the underlying physiological processes driving AN in field-grown cotton. Water-deficit stress caused substantial reductions in AN, but reductions in AN were greater under optimum N conditions (74%) than under N deficiency (22%). Decreased CO2 diffusion, RuBP regeneration, and Rubisco carboxylation were major contributors to a decline in AN due to water-deficit stress. Reductions in Rubisco carboxylation and RuBP regeneration were the greatest drivers of N deficiency-induced decline in AN. Lower CO2 diffusion and Rubisco carboxylation were main constraints to AN due to combined water deficit and N deficiency. Regarding carbon loss processes, both dark respiration and photorespiration under water-deficit stress or N deficiency, and only photorespiration under combined water deficit and N deficiency, contributed to declines in AN. Increased non-photochemical quenching and/or photorespiration prevented photoinhibition of photosystem II under stress conditions. Overall, response of photosynthesis to water-deficit stress was dependent on N availability, and rate-limiting physiological processes contributing to declines in AN were dependent on the type of prevailing stress. Response of photosynthesis to water-deficit stress was dependent on nitrogen rate. CO2 diffusion, RuBP regeneration, and Rubisco carboxylation were major limitations to photosynthesis under water deficit. Rubisco carboxylation and RuBP regeneration co-limited photosynthesis under nitrogen deficiency conditions. CO2 diffusion and Rubisco carboxylation limited photosynthesis under combined water deficit and nitrogen deficiency. Non-photochemical quenching and/or photorespiration prevented photodamage to photosystem II under stress conditions.