Cotton Fields Research Articles

Exogenous application of mepiquat chloride and crop geometry alters cotton growth and yield traits of compact cotton cultivars (Gossypium hirsutum L.)

Mepiquat chloride is widely used as a growth regulator in cotton fields to increase crop yield. The present study investigated the effects of growth regulator (mepiquat chloride) and row spacings on compact cotton's growth and yield attributes. An experiment was conducted during summer and winter seasons of 2023-2024 at Cotton Research Station, Veppanthattai and the field trial was designed as a split-split plot with three main plots (Varieties - CO 17, VPT 2, Suraksha), four sub-plots (crop geometry - 90 x 15 cm, 70 x 15 cm, 90 x 10 cm, 70 × 10 cm), and two sub-sub plots (growth regulators - mepiquat chloride @150 ppm, mepiquat chloride + cyclanilide @400 ppm), each replicated three times. Results concluded that the Suraksha variety showed superior performance with greater plant height, higher biomass, more sympodial branches, higher bolls/m2 and higher seed cotton yield than CO 17 and VPT 2. Wider spacing of 90 cm resulted in greater plant height, more sympodial branches and more bolls/m2 to a significantly rise in dry matter production due to the higher number of plants per unit area. Combining mepiquat chloride with cyclanilide at 400 ppm during square initiation and boll development stages significantly increased sympodial branches and bolls/m2, improving seed cotton yield.In contrast, applying mepiquat chloride alone led to more significant biomass accumulation, increased plant height and longer internodal distances. It was suggested that the Suraksha variety be sown at a spacing of 90 x 15 cm and treated with a combination of mepiquat chloride and cyclanilide. This resulted in a plant architecture well-suited for mechanical harvesting.

First Report of Alternaria alternata Causing Alternaria Leaf Spot of Black Nightshade in China.

Black nightshade (Solanum nigrum L.) belongs to Solanaceae family, which is distributed all over China and has become one of the malignant weeds in cotton field in Xinjiang (Zhang et al., 2020; Zhang et al., 2021). During July 2016 to August 2023, leaf spot was observed on black nightshade in Hami and Shihezi city, Xinjiang, China. The spots appeared as brown circular or irregular, with some small black dots surrounded with yellow halos. Twenty five black nightshade plants were collected from a cotton field (100 m2) for pathogen isolation in Shihezi, July 2023. Leaf samples (2×2 mm) from the boundary between diseased tissue and healthy tissue, sterilized in 70% ethanol for 45 s, dipped in 2% NaClO for 30 s, rinsed three times with sterile distilled water, placed on potato dextrose agar (PDA) after dried and cultivated at 25°C in the dark for 4 days. Twenty-five fungal isolates were obtained and purified by using the single-spore isolation method, and all isolates were found to be similar in morphology, appeared as dark brown and produced dark brown pigmentation on PDA. Conidiophores were black brown, 8 to 58 × 10 to 20 μm, with 1 to 5 transverse septa and 0 to 3 longitudinal septa, the results similar to those of Alternaria alternata isolated from buckwheat (Li et al., 2021). To confirm this identification, total genomic DNA of the isolates were extracted by CTAB method (Doyle et al., 1987). The internal transcribed spacer (rDNA-ITS), Alternaria major allergen gene (Alt α1) and glyceraldehyde-3-phosphate dehydrogenase gene (GAPDH) of the A. alternata 26 were amplified using the primers ITS1/ITS4 (Glass et al., 1995), Altα1-F/Altα1-R (Li et al., 2024) and gpd-F/gpd-R (5'-CAACGGCTTCGGTCGCAT- TG-3' / 5'-GCCAAGCAGTTGGTTGTG-3'), respectively. The rDNA-ITS, Altα1 and GAPDH gene sequences were deposited in GenBank (KX904867, PP263361, PP263360), and showed 100% identity (rDNA-ITS: 572 out of 609 bp; Alt α1: 514 out of 516 bp; GAPDH: 619 out of 619 bp) to A. alternata (KU179665; MW522975; MK451977), respectively. The multi-gene phylogenetic tree showed that the representative isolate was grouped with A. alternata and identified as A. alternata. Black nightshade was used for pathogenicity assay. Fifteen plants at 3 to 4-leaf stage were selected and sprayed with the conidial suspension (1.0×106 spores/mL) of A. alternata 26 after wound the underside of leaves (2 mm length) with inoculating needle. Five plants were sprayed with sterilized water as control. After inoculation, placed the plants in a plastic box and covered with plastic wrap to keep 80% relative humidity for 3 days at 25℃ in the greenhouse, then removed the plastic box. Symptoms of leaf spots appeared within 12 - 15 days that were similar to the symptoms observed in the field, and the fungal pathogen that was re-isolated from symptomatic leaves was identical to original pathogen on the basis of morphological and molecular analysis to fulfill Koch's postulates. This experiment was repeated three times, and the disease incidence was above 80% after inoculation at each time. To our knowledge, this is the first report of A. alternata causing leaf spot of black nightshade in China. This report will help us to monitoring distribution of the disease and providing theoretical basis for disease prevention and control.

Assessing the Pathogenicity of Berkeleyomyces rouxiae and Fusarium oxysporum f. sp. vasinfectum on Cotton (Gossypium hirsutum) Using a Rapid and Robust Seedling Screening Method.

Cotton (Gossypium spp.) is the most important fibre crop worldwide. Black root rot and Fusarium wilt are two major diseases of cotton caused by soil-borne Berkeleyomyces rouxiae and Fusarium oxysporum f. sp. vasinfectum (Fov), respectively. Phenotyping plant symptoms caused by soil-borne pathogens has always been a challenge. To increase the uniformity of infection, we adapted a seedling screening method that directly uses liquid cultures to inoculate the plant roots and the soil. Four isolates, each of B. rouxiae and Fov, were collected from cotton fields in Australia and were characterised for virulence on cotton under controlled plant growth conditions. While the identities of all four B. rouxiae isolates were confirmed by multilocus sequencing, only two of them were found to be pathogenic on cotton, suggesting variability in the ability of isolates of this species to cause disease. The four Fov isolates were phylogenetically clustered together with the other Australian Fov isolates and displayed both external and internal symptoms characteristic of Fusarium wilt on cotton plants. Furthermore, the isolates appeared to induce varied levels of plant disease severity indicating differences in their virulence on cotton. To contrast the virulence of the Fov isolates, four putatively non-pathogenic Fusarium oxysporum (Fo) isolates collected from cotton seedlings exhibiting atypical wilt symptoms were assessed for their ability to colonise cotton host. Despite the absence of Secreted in Xylem genes (SIX6, SIX11, SIX13 and SIX14) characteristic of Fov, all four Fo isolates retained the ability to colonise cotton and induce wilt symptoms. This suggests that slightly virulent strains of Fo may contribute to the overall occurrence of Fusarium wilt in cotton fields. Findings from this study will allow better distinction to be made between plant pathogens and endophytes and allow fungal effectors underpinning pathogenicity to be explored.

Pink bollworm (Pectinophora gossypiella) movement with respect to rosette flowers in cotton: Novel insights for integrated pest management

Aim: This preliminary study aimed to investigate the movement patterns of Pectinophora gossypiella (Saunders) (Lepidoptera: Gelechiidae) larvae in the rosette flowers. Methodology: Field experiments conducted on the migration behavior of pink bollworm larvae with respect to rosette flowers in cotton fields. Observations included monitoring infested flowers, larval stage duration within rosette flowers, and larval movement to soil for pupation, larval mortality post-drop, and larval migration to early bolls, all conducted without insecticide application to reflect natural conditions. Results: The movement of the larvae was tracked and recorded from the start of flowering to the boll formation stage. Nearly, 44.88% of the larvae present on the infested flowers had moved to the soil for pupation, while 14.29% had moved to newly formed bolls on the same rosette flower. Additionally, 30.90% of the larvae had moved to dry leaf debris on the soil, and the remaining 9.93% either escaped or died due to environmental factors such as damp soil. Notably, the study showed that dry soils or light debris accumulation were the most favorable environments for pink bollworm larvae. Interpretation: The present study has generated useful information regarding the movement of PBWs larvae from rosette flowers to bolls that contribute to infestation. Hence, by implementing integrated pest management strategies during the early crop periods between 60 and 100 DAS, green boll infestations can be avoided and economic yields improved. Key words: Larval movement, Pest carryover, Pest management, Rosette flower

The Distribution of Reniform Nematode (Rotylenchulus reniformis) in Cotton Fields in Central Queensland and Population Dynamics in Response to Cropping Regime.

Reniform nematode (Rotylenchulus reniformis) causes significant yield loss in cotton worldwide. In 2012, its detection in the Dawson-Callide region of Central Queensland prompted extensive surveys of cotton fields. The nematode was confirmed in 68% of sampled fields, with populations ranging from 2 to 3870 R. reniformis/200 mL of soil. Soil monitoring revealed increasing populations associated with consecutive cotton crops. However, when corn or sorghum replaced cotton, soil nematode populations significantly decreased. A two-year replicated field trial demonstrated that growing a non-host crop (such as biofumigant sorghum 'Fumig8tor', grain sorghum, or corn) significantly reduced nematode populations in the top 15 cm of soil compared to cotton. Unfortunately, when cotton was replanted the following season, nematode populations rebounded regardless of the previous crop. Only the 'Fumig8tor'-cotton rotation resulted in significantly lower nematode populations than continuous cotton. Vertical soil sampling showed that rotating with a non-host crop significantly reduced nematode densities to a depth of 100 cm compared to cotton. However, when the field was replanted with cotton, nematode populations recovered, unaffected by cropping or soil depth. This study emphasises the importance of monitoring reniform nematodes in cotton soils for early detection and defining distribution patterns within a field, which may improve the effectiveness of management practices. These results suggest that one rotation out of cotton is not sufficient, as populations return to high levels when cotton is grown again. Therefore, two or more rotations out of cotton should be considered to manage this nematode.

Effects of Bt-cotton cultivation on Helicoverpa armigera activity-density in agricultural landscapes in northwestern China.

Cotton bollworm, Helicoverpa armigera (Hübner), threatens many crops. Bacillus thuringiensis (Bt) cotton has been planted to control this severe pest in northern Xinjiang, China. In 2021 and 2022, we monitored the activity densities of H. armigera males using sex pheromone traps in Bt cotton and non-Bt maize fields. We assessed how much of the population variation of cotton bollworm in the fields within the Bt cotton planting area could be explained by (i) landscape composition [including the proportion of Bt cotton among total area of host crops (cotton, maize, wheat and vegetables)], (ii) landscape configuration (patch density - i.e. the number of patches within a given area) across 0.5-2.0 km scales, or (iii) the population density of the previous pest generation. Cotton bollworm activity-density exhibited two distinct peaks annually (mid- to late May and mid-July each year), with the number of males caught during the second peak significantly and positively correlated with the first peak's numbers. The suppressive effect of the proportion of Bt cotton in the landscape on bollworms was more pronounced at larger scales, and patch density had a significant positive effect on bollworm activity density. These findings support (i) the promotion of Bt cotton in northwestern China has reduced and suppressed the occurrence of cotton bollworms at the landscape scale and (ii) the importance of controlling spring populations for effective summer outbreak management, and (iii) that cotton bollworm control should be coordinated at a large scale across multiple crop fields. © 2024 Society of Chemical Industry.

Determining selectivity of isocycloseram and afidopyropen and their compatibility with conservation biological control in Arizona cotton.

Selective tools, including selective insecticides and transgenic cotton, have been crucial in reducing insecticide usage within the integrated pest management (IPM) plan for Arizona cotton. To guide growers effectively, cotton field trials evaluated the effects of the novel insecticides, isocycloseram and afidopyropen against our primary pests, Bemisia argentifolii and Lygus hesperus, and their impacts on nontarget arthropods, including key predators: Collops spp., Orius tristicolor, Geocoris spp., Misumenops celer, Drapetis nr. divergens and Chrysoperla carnea s.l. Assessments involved over 27 arthropod taxa through community analyses, individual predator abundance, and biological control function via predator to prey ratios and a sentinel prey method. Comparisons were made with an untreated check, a proven fully selective insecticide (flonicamid) and acephate-treated positive controls. Overall, relative to the untreated check, afidopyropen showed no significant differences, whereas isocycloseram exhibited some negative impacts, primarily reducing M. celer and Geocoris spp. nymphs, yet it was less detrimental compared with the positive control, acephate, and it did not affect four of the six key predators and most nontarget arthropods. Afidopyropen was classified as a fully selective insecticide and isocycloseram as a partially selective insecticide. Their fit for conservation biological control in Arizona cotton IPM and similar systems is discussed. © 2024 Society of Chemical Industry. Published by John Wiley & Sons Ltd.

Remote detection of water stress in cotton using a center pivot irrigation system-mounted sensor package.

Much research has been invested in infrared temperature (IRT)-based methods for cotton (Gossypium hirsutism L.) water stress detection using in-field sensors, but adoption of these is low, perhaps due to logistical challenges. Alternatively, the Water Deficit Index (WDI) was developed for crop water stress assessment using remote sensors not embedded in the canopy. The objective of this research was to evaluate the performance of a sensor package-including modern IRT and normalized difference vegetation index (NDVI) sensors facing downward at 45˚, and a mini weather station-attached unintrusively to a center pivot irrigation system for detecting cotton water stress using WDI. Sensor packages were evaluated in a two-year field study that included four irrigation treatments (0, 30, 60, and 90% ET replacement) and in two production cotton fields. Overall, the tested system was effective at distinguishing crop water stress among irrigation rates. Comparison of the results to a ground-based station and simulations indicated that WDI overestimated water stress at the highest irrigation rate, but performed well otherwise. Accuracy of the system could be improved by measuring canopy coverage (Fc) from the same vantage point as the IRT and NDVI sensors (from the pivot, downward at a 45˚ angle).

Impact of active root zone soil potassium levels on cotton yield and fiber quality under no tillage.

Potassium deficiency significantly hinders cotton growth and development, adversely affecting yield and fiber quality. Applying potassium fertilizer is a common practice to address potassium deficiency in the soil. However, the effectiveness of potassium fertilizer application depends on the appropriate soil potassium levels in cotton fields. This study used a randomized block design with six different soil potassium levels and conducted experiments across 18 micro-zones in the field. This study aimed to investigate the response of cotton yield and quality to different soil potassium levels, to try to clarify the suitable soil potassium levels for cotton growth, so as to provide practical and effective help for determining the amount of potash fertilizer in the cotton field. The results showed that the seedcotton yield was increasing, with the soil potassium level increased under no tillage. There was no significant difference among K4, K5, and K6 on seedcotton yield, which were significantly higher than K1 and K2. As soil potassium levels increased, the proportion of autumn boll and the proportion of outer boll also increased, indicating that higher soil potassium levels support the better growth and development of cotton in the middle and late stages, leading to increased boll sets and higher yields. Additionally, the available potassium content in the 0-40-cm soil layer was significantly correlated with yield and yield parameters but not with fiber quality indices. It is concluded that K4 treatment could provide sufficient potassium to meet the growth and development needs of cotton. Potassium fertilizer application is recommended when the available potassium content in the 0-40-cm soil layer falls below 122.88 mg kg-1 in the cotton field.

Automatic visual recognition, detection and classification of weeds in cotton fields based on machine vision

Crops and weeds are involved in a continuous competition for equal resources, which may result in a potential decrease in crop yields by up to 31% and an increase in the costs of agricultural inputs by up to 22% of cultivation. Weeds further impact crop production, and their detection is crucial for effective crop management. In this research, we targeted common weeds of cotton field, specifically i) Digitaria sanguinalis (L.) Scop, ii) Amaranthus retroflexus L., iii) Acalypha australis, L., iv) Cephalanoplos segetum, and v) Chenopodium album L. Additionally, image processing techniques such as grayscale conversion, binarization, and Gaussian and morphological filters were also utilized. These methods are based on machine vision and facilitate rapid and straightforward weed detection by segmenting, scrutinizing, and comparing input images. The plant height and area were obtained during cotton planting within 32 days and fitted to develop the growth law concerning planting days for achieving the function of distinguishing cotton from weeds. We conducted recognition experiments by dividing images into four quadrants and categorizing weeds as either inter-row or intra-row. Meanwhile, the inter-row planting information was used to identify weeds, and the leaf pixel area and circularity were used as the identification methods for intra-row weeds, which reduced the algorithm's running time and improved real-time performance. The experimental results indicated that the inter-row weed recognition rate was 89.4%, with an average processing time of 102ms. Whereas in the case of intra-row weeds, the recognition rate was measured at 84.6%, and the overall recognition rate for cotton was 85.0%, with a mean time consumption of 437ms. Furthermore, the present research underscores recent advancements such as machine vision and high-resolution imaging, which have significantly improved the accuracy of automated weed identification in cotton fields while acknowledging ongoing challenges and outlining future opportunities. By Integrating state-of-the-art technology with sustainable agricultural practices, implementing an intelligent system offers a viable approach toward efficient and environmentally friendly weed management in modern agriculture.

Evidence at the landscape level links high predator/pest ratios to biocontrol services against aphids

Compared with external environment interferences, the interaction between arthropod natural enemies and pests is the key inherent driver determining the strength of biocontrol services. However, the extent to which this effect can suppress pests is still inconclusive. We combined two complementary experiments to determine how variation in an assemblage of generalist arthropod predators modified the level of biocontrol services for suppression of a key aphid pest in cotton fields. Using generalized linear mixed effect models (GLMM) and path analysis, we clarified the causal relationship between predator/aphid ratios (PAR) and (1) the resulting biocontrol services index (BSI) and (2) the aphid population growth index (APGI) as seen in field predator exclusion trials. We also measured the effect of PAR on APGI in more commercial cotton fields. Our results, at landscape level, indicate that when PAR values increased one unit, BSI values improved 34.1 %, and reduced aphid population growth 28.3 % (the standardized effect coefficient in path analysis) during two weeks in field cage-exclusion trials. The effect of high predator/aphid ratios on reducing the aphid population growth rates was also significant in more commercial cotton fields, which were sampled over a longer time interval (4 weeks). Our study confirmed that there was a causal relationship between the natural enemy/pest ratio and the level of biological pest control services for predators and aphid population growth in crop fields. PAR values (predator/aphid ratios) can, therefore, be used to predict the level of biocontrol services in this context instead of relying on more labor-intensive cage exclusion studies.

Identification of weeds in cotton fields at various growth stages using color feature techniques

Weeds affect the growth and yield of cotton (Gossypium hirsutum), which is the most prevalent commercially viable non-food crop. Accurate differentiation between weeds and cotton plants is crucial for implementing precise weed-control strategies, optimizing crop yields, and minimizing herbicide usage. In this study, we aimed to investigate the effectiveness of using color feature techniques combined with Otsu's method (OTSU) to distinguish weeds from cotton plants at various growth stages. The results revealed that the image segmentation approach achieved a recognition rate of 74.17% during the second growth stage. In the third growth stage, the weed identification process primarily targeted lambsquarters (Chenopodium album). The standard deviation (SD) difference between the red (R), green (G), and blue (B) components of the plant images revealed that the difference between the SD values of R and B was <5, which could serve as an effective threshold for identifying lambsquarters. Additionally, the recognition rates for cotton and lambsquarters were 71.40% and 92.90%, respectively, and the overall recognition rate reached 82.10% during the identification process. The results of this study have practical implications for weed management practices, such as targeted herbicide application, mitigation of environmental impacts, and contribution to higher crop yields. Future research will focus on refining these methods by exploring advanced image processing techniques that integrate deep learning for automatic feature extraction, which will be evaluated for handling complex agronomic scenarios, such as leaf occlusion, varying growth stages, and image noise.

A global synthesis of nitrous oxide emissions across cotton-planted soils

Globally, acquiring information on region- and crop-specific nitrous oxide (N2O) emissions is vital for establishing effective N2O mitigation strategies. Soil cultivated with cotton (Gossypium hirsutum L.) is an important source of N2O in agricultural production. However, little is known about the magnitudes and main drivers of soil N2O emissions from cotton fields worldwide. In this meta-analysis, we were the first to synthesize 34 peer-reviewed papers (298 observational datasets) to quantify the magnitudes and controlling factors of area-scaled N2O emissions (N2Oarea), direct N2O emission factors (EFd), and yield-scaled N2O emissions (N2Oyield) from the soils of cotton fields and to explore associated potential mitigation strategies. On average, the N2Oarea from global cotton-planted soils was 2.10 kg N ha−1, with a mean EFd of 0.92 %, which is comparable to those reported for cereal crops (e.g., maize, 1.02 %) and the Intergovernmental Panel on Climate Change default value of 1 % for global croplands. The global mean N2Oyield estimated here was 622 g N Mg−1. At the global scale, the variations in all N2O-related indices in the soils of cotton fields were demonstrated to be primarily controlled by climatic conditions (e.g. climate type) and soil properties (e.g., bulk density, pH, C/N or soil texture) rather than by well-recognized management practices (e.g., N fertilization rate). Furthermore, our analysis showed that the application of urease and/or nitrification inhibitors significantly reduced soil N2O emissions while maintaining seed cotton yields. These findings emphasize that cotton production has an obvious climate footprint and provide potential N2O mitigation options for the sustainable intensification of cotton production.

Mulched drip irrigation: a promising practice for sustainable agriculture in China’s arid region

The long-term practice of mulched drip irrigation (MDI) has significantly advanced cotton production in China’s arid regions. In this study, we conducted an evaluation of the changes to key soil environmental factors within cotton fields under MDI, providing a holistic assessment of MDI sustainability. Specifically, long-term MDI demonstrated numerous benefits including improved soil salinity management, nutrient retention, soil structure, and microbial functions. Collectively, these changes contribute to enhanced overall soil quality. Our research also confirms MDI as an effective integrated water-saving technology approach. Affirming the sustainability of widespread MDI promotion, our findings firmly support its potential as a sustainable and viable solution for oasis agriculture in arid lands, conferring long-term benefits to agricultural production in these regions.

Residue Addition Can Mitigate Soil Health Challenges with Climate Change in Drylands: Insights from a Field Warming Experiment in Semi-Arid Texas

Texas cotton production is facing challenges from increased temperatures and extended droughts. We sought to determine whether applying a multi-species grass mulch on the surface of cotton fields in a semiarid region would mitigate some of the negative effects of climate change. We used open-top chambers (OTCs) to mimic climate warming and compared whether the effects of residue addition were similar between dryland and irrigated cotton fields located in the High Plains region of Texas during the summer of 2021. The OTCs raised the average air temperature by 2 °C. Under experimental warming, residue addition increased moisture content in non-irrigated (i.e., dryland) soils (+9.2%) and reduced the daily temperature range (by −1.4 °C) relative to uncovered soils. Furthermore, when pooled across irrigation and warming treatments, the addition of residue increased microbial biomass, soil respiration (+78.2%), and cotton yield (+15.2%) relative to uncovered soils. OTCs further enhanced the residue effects on microbial biomass by 34.9%. We also observed higher soil organic matter, microbial biomass, cotton biomass, and yield in irrigated fields compared to dryland, irrespective of residue addition. Our findings suggest that residue addition in dryland agriculture can mitigate the adverse effects of warming by stabilizing soil microclimates and promoting microbial growth and biomass by providing a more labile source of carbon, which, in turn, could boost the yield of cotton plants.

Inversion Modeling of Chlorophyll Fluorescence Parameters in Cotton Canopy via Moisture Data and Spectral Analysis

The study of chlorophyll fluorescence parameters is very important for understanding plant photosynthesis. Monitoring cotton chlorophyll fluorescence parameters via spectral technology can aid in understanding the photosynthesis, growth, and stress of cotton fields in real time and provide support for cotton growth regulation and planting management. In this study, cotton plot experiments with different water treatments were set up to obtain the spectral reflectance of the cotton canopy, the maximum photochemical quantum yield (Fv/Fm), and the photochemical quenching coefficient (qP) of leaves at different growth stages. Support vector machine regression (SVR), random forest regression (RFR), and artificial neural network regression (ANNR) were used to establish a fluorescence parameter inversion model of the cotton canopy leaves. The results show that the original spectrum was transformed by multivariate scattering correction (MSC), the standard normal variable (SNV), and continuous wavelet transform (CWT), and the model constructed with Fv/Fm passed accuracy verification. The SNV-SVR model at the budding stage, the MSC-SVR model at the early flowering stage, the SNV-SVR model at the full flowering stage, the MSC-SVR model at the flowering stage, and the CWT-SVR model at the full boll stage had the highest estimation accuracy. The accuracies of the three spectral preprocessing and qP models were verified, and the MSC-SVR model at the budding stage, SNV-SVR model at the early flowering stage, MSC-SVR model at the full flowering stage, SNV-SVR model at the flowering stage, and CWT-SVR model at the full boll stage presented the highest estimation accuracies.

Reducing the Sodium Adsorption Ratio Improves the Soil Aggregates and Organic Matter in Brackish-Water-Irrigated Cotton Fields

The assessment of soil health relies on key parameters such as soil aggregates and organic matter content. Therefore, examining the impact of irrigation water ion composition and variations in salinity on soil aggregates and organic matter is imperative, which is key to developing a theoretical basis for the sustainable utilization of saline water resources, particularly in extremely arid regions. This experiment was conducted to investigate the impact of different irrigation water salinity treatments (T3: 3 g/L, T5: 5 g/L, and T7: 7 g/L) on the root zone soil of cotton fields. Each salinity treatment included three variations of the sodium adsorption ratio (SAR) at S10: 10 (mmol/L)1/2, S15: 15 (mmol/L)1/2, and S20: 20 (mmol/L)1/2. Local freshwater irrigation served as the control, resulting in a total of 10 treatments. Our findings show that the soil Ca2+ and Mg2+ content increased with higher irrigation water salinity but decreased with increasing irrigation water SAR. The relative macroaggregate stability and the content of water-stable macroaggregates and soil organic matter (SOM) decreased as the irrigation water salinity and SAR increased. In comparison to T3S20, T5S10 did not improve the soil Na+ content but significantly increased the soil Ca2+ content by 147.76%, while the water-stable aggregate and SOM saw a notable increase of 7.66% and 9.86%, respectively. Reducing the SAR in brackish water lessens its negative impact on soil aggregates in cotton fields. This is primarily because Ca2+ counteracts the dispersive effect of high Na+ concentrations and promotes aggregate formation. Irrigation water with a salinity of 3 g/L and an SAR of 10 (mmol/L)1/2 positively affected the stabilization of soil aggregates and organic matter.

Abundance and diversity of cotton insect pests under repeatedly cultivated cotton fields of Tanzania.

Cotton production in Tanzania is facing significant challenges due to insect pests that cause extensive damages to the crop. The most notable pests include the African bollworm (Heliothis armigera Hubner), Spiny bollworm (Earias biplaga Walker), Cotton stainers (Dysdercus sidae (Herrich-Schaeffer), Cotton Aphids (Aphis gossypii Glover), Thrips (Thrips tabaci Lindeman), Jassids (Amrasca biguttula, Bigutula), Leafhoppers (Cicadellidae jassidae), and Whiteflies (Bemisia tabaci Genn). If left uncontrolled, these pests can cause up to 60% damage to the crop. Despite the importance of cotton and the fact that most of these pests are endemic, there are scanty knowledge on the dynamics and distribution of cotton pests across the seasons of the year and crop's phenological growth stages (germination, vegetative growth, flowering and boll formation) in areas under repeated cultivation of the crop in Tanzania. Here we report on the influence of seasons and cotton's phenological stages on the abundance, diversity, distribution and richness of cotton insect pests. The study was conducted in the Misungwi district for two cotton-growing seasons, using the UKM08 cotton variety. Stick traps and handpicking methods were deployed in catching the cotton insect pests. On average, a total of 8,500 insect specimen of diverse families and species were collected every season. The four dominant species among the collected were Aphis gossypii (17.37%), Amrasca biguttula (11.42%), Nezara viridura (10.7%), and Bemisia tabacci (10.68%). Both cotton phenological growth stages and seasons significantly (p<0.05) influenced the abundance, diversity, distribution and richness of cotton insect pests. In particular, the phenological growth stage 3 exhibited greater diversity of insect pests. The pests' distribution patterns remained relatively uniform across the crop growth stages. Findings from the present study could contribute to developing sustainable pest management strategies in areas under repeated cotton production in Tanzania and elsewhere.

Optimal phosphorus proportion increases cotton yields in low nitrogen environments by enhancing mid-upper canopy biomass and nitrogen utilization

To elucidate the physiological mechanism of optimal phosphorus (P) proportion under reduced nitrogen (N) environments on cotton yield, dry matter (DM) spatiotemporal distribution, and N utilization. A three-year experiment was conducted with reduced N (299 kg hm−2, N3), four proportions of P fertilizer (25:75 % at squaring and bloom-bolling stages (P1), 50 %:50 % (P2), 75 %:25 % (P3), 100 %:0 % (P4)), and a traditional N application (402.5 kg hm−2, N2) with P2 as control. Under the N3 treatment, the seed yield of P2 was 6.7–24.5 % higher than other P, and the yield of N3P2 was 1.6–2.6 % lower than N2P2. During the whole growth period, P2 demonstrated 12.4–46.9 % higher leaf and reproductive organs dry matter (LDM and RoDM) compared to other P, and primarily due to more mid-upper DM. Throughout the growth period, N3P2 maintained dominant LDM and exhibited RoDM 2.5 % higher at late-full bolling stage (LFB) compared to N2P2. Trends in leaf N content and reproductive organ N content were similar to DM, with N3P2 consistently maintaining higher levels. Regarding N metabolism activities, N3P2 displayed 8.5–16.1 % higher activity of nitrate reductase, 9.5–21.9 % higher activity of glutamine synthetase, and 7.5 %-15.6 % higher activity of glutamate synthase compared to other P across all growth stages, with no significant differences observed with N2P2. Moreover, N3P2 and N2P2 exhibited higher relative expression of NR, GS1, and ferredoxin-dependent glutamate synthase compared to other treatments. In Conclusion, at 25 % reduced N (N3), optimizing the proportion of P fertilizer (P2, 50 %:50 %) could lead to higher yields due to enhanced N re-utilization capabilities in the later growth stage and increased distribution of DM in leaves and reproductive organs, particularly in the middle and upper layers (especially the fruiting branches). The study findings hold significant theoretical implications for achieving sustainable and efficient production in drip-irrigated cotton fields under reduced N management.

Adoption of Sustainable Cotton Farming Practices: A Pathway to Eco-Friendly Agriculture

Cotton is one of the key cash crops in Pakistan, significantly contributing to the economy and supporting the livelihoods of the farming community. However, this promising sector faces major threats from global warming and climate change. A study was conducted in District Muzaffargarh to assess the adoption of sustainable cotton farming practices. This area was chosen as it is a major cotton-producing region in Pakistan. A total of 270 cotton growers were randomly selected for data collection. The data were analyzed using the Statistical Package for Social Sciences (SPSS). Additionally, five focus group discussions were conducted to gather qualitative insights. The results revealed that a limited number of cotton varieties were cultivated in the region, with SS, CKC, IUB, BS, and NIAB being the most common. An overwhelming majority of the farmers had cotton fields of up to 5 acres. Hand sowing was the most common planting method. Among water stewardship practices, irrigation after weather forecasts, cleaning of watersheds, and reduced use of flood irrigation were prevalent. In terms of soil improvement practices, foliar spraying, soil analysis, reduced pesticide usage, and reliance on farmyard manure were widely adopted. Regarding Integrated Pest Management (IPM) practices, rotavating crop residue, proper disposal of cotton sticks, the use of recommended pesticide doses, and the application of appropriate active ingredients were the most common practices.