Cotton Lint Yield Research Articles

Identifying causes of crop yield variability with interpretive machine learning

Improving subtropical cotton production by using late winter sowing to reduce climatic risk

Evaluation of genetic behavior of some Egyption Cotton genotypes for tolerance to water stress conditions

Yield data of major crops and corresponding meteorological trends for the last forty-five years (1972-2016) were analysed for arid region (Hisar) of Haryana. Reference evapotranspiration (ET0) for the region was calculated based on Penman-Monteith equation. Meteorological parameters were subjected to Man-Kendall (MK) test for testing the significance and Sen’s slope estimator for estimating the magnitude of trend. Similarly, variability index was employed for computing variability in seasonal and annual weather parameters. Yield data was also subjected to MK test to estimate the annual increasing/decreasing trend over the years. During the last 45 years wind speed, sunshine hours and reference evaporation declined at a rate of 5%, 3.3% and 2% year-1 respectively while minimum temperature increased at 1.8% year-1. Average rainfall deficit of 1122 mm over evapotranspiration (ET0) was observed although it registered a declining trend owing to decline in ET0. The increasing trend in yield was found to be more in kharif season crops as compared to the same during rabi season. Cotton lint yield increased at a maximum rate (17.5% year-1) followed by pearl millet (7.8% year-1), rice (3.1% year-1) and barely (2.7% year-1) while no significant trend was observed in wheat, gram and pigeon pea yield during the study period.

Non-optimal application of nitrogen (N) fertilizer in cotton (Gossypium hirsutum L.) production systems often results from a producer’s uncertainty in predicting the N rate that ensures maximum economic return. Residual soil nitrate-N (NO3-N) is also often unaccounted for in fertilizer management decisions. In this study, the lint yield and profitability of two cotton cultivars (FiberMax FM 958 and Deltapine DP 1646 B2XF) were compared across five N fertilizer treatments [0 kg ha−1 (control), 45 kg ha−1 (N-45), 90 kg ha−1 (N-90), 135 kg ha−1 (N-135), 180 kg ha−1 (N-180)] from 2018 to 2020. For both cultivars, additional N fertilizer on top of the control treatment did not increase the lint yield of cotton. For each year, both control and N-45 treatments resulted in the greatest revenue above variable costs (RAVC) values for all cultivars. The improved N partitioning efficiency in newer cultivars and the high levels of residual soil NO3-N allowed sustained plant growth and yield even with reduced N application. Overall, the results show the advantage of reducing N inputs in residual N-rich soils to maintain yield and increase profits. These findings are important in promoting more sustainable agricultural systems through reduced chemical inputs and maintained soil health.

A six-by-six complete F1 Gossypium hirsutum, L. diallel cross of three pre-screened drought tolerant and three drought susceptible varieties (CRIS-134, CRIS-342, SINDH-1, NIAB-78, SADORI and BH-160) was evaluated for genetic parameters during 2009 at Sindh Agriculture University farm, Tandojam. The characters studied were number of bolls per plant, sympodial branches per plant, seedcotton yield per plant and lintcotton yield per plant. The objective of such study was to assess the effect of irrigation stress on the genetic inheritance pattern of above quantitative traits as to how far the genetic parameters are affected due to irrigation stress in the F1 diallel generation. Irrigation treatments were four; normal seven irrigations schedule, five irrigations, four irrigations (medium stress) and three irrigations up to 150 days of crop maturity (stress conditions). CRIS-134 in seven, Sadori in five and CRIS-342 in four and three irrigations treatments were the most recessive parents contributing increasing boll number into their progenies while BH-160 in seven, CRIS-342 in five and Sindh-1 in four and three irrigations treatments proved to be the most dominant parents responsible for contributing decreased boll number per plant into their progenies. Seedcotton per plant was partial dominant in seven irrigations treatment while it inherited as an overdominant trait in five, four and three irrigations respectively. BH-160 was the most recessive of all with increased sympodia contributing attributes in seven and four irrigations whereas Niab-78 in five and CRIS-342 in stress were the most recessive parents. Sindh-1 was the most dominant parent in seven, five and three irrigation treatments while CRIS-342 in four irrigations yielded decreased sympodia contributing attributes into their progenies. Sindh-1 in seven, BH-160 in five and three and CRIS-342 in four irrigations treatments proved to be the most recessive parents with increasing seedcotton yield attributes while CRIS-342 in seven and five and Sindh-1 in four and three irrigations were the most dominant parents contributing decreased seedcotton yield into their progenies. Inheritance trend of lintcotton per plant was similar to that of seedcotton yield per plant.

Cotton physiological and agronomic response to nitrogen application rate

Abstract Without reliable seasonal climate forecasts, farmers and managers in other weather-sensitive sectors might adopt practices that are optimal for recent climate conditions. To demonstrate this principle, crop simulation models driven by a dense meteorological network were used to identify climate-optimal planting dates for US Southern High Plains (SHP) unirrigated agriculture. This method converted large samples of SHP growing season weather outcomes into climate-representative cotton and sorghum yield distributions over a range of planting dates. Best planting dates were defined as those that maximized median cotton lint (April 24) and sorghum grain (July 1) yields. Those optimal yield distributions were then converted into corresponding profit distributions reflecting 2005–19 commodity prices and fixed production costs. Both crops’ profitability under variable price conditions and current SHP climate conditions were then compared based on median profit and loss probability, and through stochastic dominance analyses that assumed a slightly risk-averse producer.

Responses of greenhouse gas emissions to different straw management methods with the same amount of carbon input in cotton field

Abstract In cotton (Gossypium hirsutum L.) production systems, mineral N is the most managed nutrient because of its mobility and therefore optimal use of N is an important goal for sustainable management of cotton. Soil characteristics influence the availability of N for plant growth and therefore spatial variability in soil should be considered for optimal N fertilization. The study was established in a 4‐yr rotation with 2 yr of bahiagrass (Paspalum notatum Flueggé) followed by peanut (Arachis hypogaea L.) and cotton. A 2‐yr study was conducted to develop zone‐specific in‐season N recommendations based on soil electrical conductivity (EC) variations captured by Veris MSP3. Each year, nitrogen calibration strips (NCS) were established in the cotton quadrant to inform in‐season N fertilization rate decisions. The NCS informed in‐season N rates varied from 34 to 101 kg N ha–1 for two EC zones. In 2019, NCS informed N rate (34 kg N ha–1) was a 50% reduction from the standard N rate of 67 kg ha–1 with no loss in cotton lint yield in zone 1. In zone 2, in‐season N application was reduced to 50 kg N ha–1 from the standard N rate with no loss in cotton lint yield. Nitrogen calibration strips have the potential to be used as an on‐farm tool to assist growers in making optimal in‐season N application decisions.

Abstract Across the U.S. Cotton Belt, potassium (K) deficiency symptoms in cotton (Gossypium hirsutum L.) have become more common over the past decade. In 2015–2017, an experiment was conducted in Alabama, Arkansas, Louisiana, Mississippi, North Carolina, Oklahoma, South Carolina, two regions in Texas, and Virginia for a total of 23 site‐years. The objectives were (a) to quantify soil K levels at‐depth in representative soils where cotton is commonly grown in major cotton production regions with observed K deficiencies; and (b) to evaluate the effects of application method and K rates on cotton lint yield, loan value, and return on fertilizer investment. Granular and liquid potassium chloride were broadcast or injected, respectively, 2–4 wk prior to planting at 0, 45, 90, 135, and 180 kg K2O ha−1. Locations other than Texas and Oklahoma generally had soil K levels <less than 150 mg kg−1, the Mehlich III critical K level, and thus, a yield response to applied K fertilizer was expected. However, among the 23 site‐years, a treatment effect was determined at 5 site‐years. Two of those, Williamson County, Texas, and Virginia endured severe moisture stress and resulted in low yields (<526 kg lint ha−1). A positive lint yield response to knife‐injected 0–0–15 was determined in 2015 at the Lubbock County, Texas, location—a location with high yield (>1,653 kg lint ha−1). Inconsistent yield responses among locations indicate that K dynamics in the soil–cotton plant system are not well understood and deserve continued investigation.

Abstract The environmental impact of genetically modified crops has been extensively investigated. However, few reports on the influence of transgenic traits on genetic structure have been reported in the literature. It is unknown how or if transgenic cultivars have affected genotypic variation in upland cotton (Gossypium hirsutumL.) since its rise to dominance in cotton production. In this study, the genotypic variance components,g, of lint yield (LY) and fiber quality were compared among transgenic and nontransgenic cotton in the USDA Regional High Quality (RHQ) tests from 2002 through 2018. The popular transgenic and nontransgenic cultivars/lines developed by the major private and public cotton breeding programs in the United States during this period were included. Testing cycles within the RHQ protocol consist of standardized control cultivars plus experimental entries. Variance components were dissected in each testing year within six such testing cycles. Lint yield of the transgenic cotton was generally higher than nontransgenic cotton. Fiber quality of the nontransgenic cotton was generally higher than the transgenic cotton. For LY, the proportion ofgto the total variance was lower in the transgenic cotton than in the nontransgenic cotton, but the difference diminished in the recent two cycles. The proportion ofgwas lower in the transgenic cotton compared with the nontransgenic cotton for fiber length, fiber strength, fiber uniformity, and micronaire. The discrepancy between the two types of cotton in the RHQ tests reflects the influences of differential breeding schemes in the private and public breeding programs on means and genotypic variance of LY and fiber quality.

Nitrogen and plant density effects on growth, yield performance of two different cotton cultivars from different origin

The present study was conducted to select cotton stable cultivars with high productivity across various environments. Nine Egyptian cotton cultivars were grown in a split-plot randomized complete block design with three replications consisted of six different environments (2 years × 3 sowing dates) to identify the high yield stability cultivars under these conditions. Pooled analysis of variance for; number of bolls/plant, seed cotton yield and lint yield revealed significant differences due to cultivars, environments and their interactions. Results revealed that the cultivars Dandara and Giza 90 were considered as superior cultivars under different environmental conditions due to their high number of bolls/plant, seed and lint yield traits across different environments when compared with grand mean for these traits beside acceptable stability parameters (bi near to one, S2di non-significant, α stability value not significantly differed from zero and the λ statistic was not significantly differed from one). Therefore, it could be used in breeding programs for development of high yield stable genotypes across environments for future use. Also, principal component analysis (PCA) showed that Dandara and Giza 90 cultivars were located near all studied traits and environments (stable cultivars over different environments). According to our results the two cultivars (Dandara and Giza 90) can be recommended to be uses under a wide range of environmental conditions and use in breeding programs for development of high yield stable genotypes across environments for future use.

Soil and soil organic carbon effects on simulated Southern High Plains dryland Cotton production

Abstract Two conventional upland cotton (Gossypium hirsutum L.) germplasm lines, NC18‐05 (Reg. no. GP‐1082, PI 697272) and NC18‐06 (Reg. no. GP‐1083, PI 697273), were developed by the Department of Crop and Soil Sciences at North Carolina State University. The lines were bred for fiber elongation within yield‐competitive phenotypes. The two lines were derived from a randomly mated population using multiple parental lines. Both NC18‐05 and NC18‐06 produced equivalent or higher lint than commercial cultivars ‘DP393’, ‘SG747’, and ‘UA48’ during 2 yr in Clayton, NC. Germplasm line NC18‐05 produced 1507.2 kg ha–1 of lint, which was 36.2% higher than DP393 and 29.4% higher than UA48 (p < .05). Germplasm line NC18‐06 produced 1428.6 kg ha–1 of lint, which was 29.1% higher than DP393 and 22.6% higher than UA48 (p < .05). However, neither line yielded more lint per hectare than SG747 or the average of the parental lines (p > .05). Both NC18‐05 and NC18‐06 exhibited equal or higher fiber elongation values (6.0–49.2%) than the commercial cultivar controls. These two lines had higher lint percentages than UA48 (p < .05). NC18‐06 also demonstrated stronger fiber than DP393 and SG747 (p < .05). These two germplasm lines offer breeders a new source of exceptional fiber elongation before break within a high‐yielding background.

Cotton crop, plays a significant role in Pakistan's economy by ruling a prominent place in edible oil and local textile industry. Phosphorus (P) inaccessibility and deficiency of soil organic matter are the key restraints for low crop productivity in cotton. Therefore, a two years field study was designed during 2014-15, to explore the influence of phosphate solubilizing bacteria (PSB), farmyard manure (FYM), poultry manure (PM) and inanimate sources of P on various physiological, growth, yield and quality parameters of cotton crop at CCRI Multan. Field responses of seeds inoculated with two distinctive phosphate solubilizing bacteria (PSB) strains viz. S0 = control, S1 =strain-1, S2 = strain-2 and eight organic, inorganic P sources viz., P0= control, P1 = 80 kg ha-1 P from inorganic source, P2 = 80 kg ha-1 P from FYM, P3 = 80 kg ha-1 P from PM, P4 = 40 kg ha-1 P from FYM + 40 kg ha-1 P from inorganic source, P5 = 40 kg ha-1 P from PM + 40 kg ha-1 P from inorganic source, P6 = 80 kg ha-1 P from FYM + 40 kg ha-1 P from inorganic source, P7 = 80 kg ha-1 P from PM + 40 kg ha-1 P from inorganic source and P8 = 40 kg ha-1 P from FYM + 40 kg ha-1 P from PM were evaluated. Results revealed that inoculation of seeds with PSB and collective use of inorganic and organic sources of P had considerably increased the yield contributing attributes in cotton. However, the treatment P7 (80 kg P ha-1 from PM + 40 kg P ha-1 from inorganic source) in coincidence with seeds inoculated with PSB (S1) produced taller plant, maximum boll weight, significantly higher LAI and CGR. Significantly higher seed cotton yield, lint yield, fiber length and maximum BCR of 1.95 and 1.81 was also obtained from the P7 treatment during both crop-growing seasons. In conclusion, combined use of 80 kg P ha-1 from PM + 40 kg P ha-1 from inorganic source and cotton seeds inoculated with strain-1 improved phosphorus uptake ensuing in greater consumption of photo-assimilates for maximum growth and yield.

Interactive effect of nitrogen fertilizer and plant density on photosynthetic and agronomical traits of cotton at different growth stages.

Identifying the factors dominating the spatial distribution of water and salt in soil and cotton yield under arid environments of drip irrigation with different lateral lengths

Abstract 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.