Salt Tolerant Gossypium hirsutum L. Cultivars are Mostly Long-Staple Length Cultivars

Background: Cotton is a major cash crop in the global and, in particular, the Indian markets, playing an important economic role in the textile and oil industries. The cotton plant is one of the highly bred plants that is highly sensitive to salt stress. As cotton is a non-food crop, the availability of non-saline terrain and water for the cultivation of cotton plants is only next to other food crops, thereby posing a need to better understand the salt tolerance of this plant. Gossypium hirsutum L. cultivars MCU 5, LRA 5166, and SVPR 2 were selected based on exomorphic traits like staple length and cropping season so that the genotypic responses to salt stress and salt shock can be compared for interpreting the effects of salinity on in vitro germination. Thus, this study aims to establish genotypic dependence on salinity tolerance. Results: The results affirmed genotypic variation in salinity tolerance, with MCU 5 tolerating salt stress better than LRA 5166 and SVPR 2 in all the observed stages of growth of the plant and the parameters measured. Further salt-tolerant cotton varieties were observed to be long-staple length varieties; staple length is the fiber character of the cotton lint. Moreover, salt tolerance in the vegetative growth stage of cotton plants is not independent of the germination stage of the plant.Conclusion: Nevertheless, the correlation of genotypic dependence to morphological characteristics, in particular, staple length (and cropping season), is of agronomic and commercial significance. Further research by screening and investigating a greater number of cultivars using biochemical and molecular techniques will provide a better understanding of this observed phenotypical relationship to the genotypes of cotton cultivars under salt stress.

Analysing recent meteorological trends and computation of reference evapotranspiration and its effect on crop yields in semi-arid region of Haryana

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.

Intelligent Image Classification for Grading Egyptian Cotton Lint

Egyptian cotton is one of the most important commodities to the Egyptian economy and is renowned globally for its quality, which is currently graded by manual inspection. This has several drawbacks including significant labour requirement, low inspection efficiency, and influence from inspection conditions such as light and human subjectivity. This current work uses a low-cost colour vision system, combined with machine learning to predict the cotton lint grade of the cultivars Giza 86, 97, 90, 94 and 96. Unsupervised and supervised machine learning approaches were explored and compared. Three different supervised learning algorithms were evaluated: linear discriminant analysis, decision trees and ensemble modelling. The highest accuracy models (77.3-98.2%) used an ensemble modelling technique to classify samples within the Egyptian cotton grades: Fully Good, Good, Fully Good Fair, Good Fair and Fully Fair. The unsupervised learning technique k-means showed that human error is more likely to occur when classifying lint belonging to the higher quality grades and underlined the need for an intelligent system to replace manual inspection.

Forecasting cotton lint exports in Nigeria using the autoregressive integrated moving average model

Nigeria was a major global exporter of cotton lint to international market during the colonial and post-colonial era till late 70s when the country fully embraced oil exports to the detriment of the non-oil sector, cotton lint exports inclusive. However, Nigeria is gradually emphasizing agricultural exports again to earn huge foreign exchange, the oil sector having left the country in economic crises. This study utilized time series model particularly, Autoregressive Integrated Moving Average (ARIMA) to make forecasting of cotton lint exports in Nigeria by using 46 yearly observations (1970-2015). The model went through series of investigative and diagnostic tests in order to observe the usefulness of the model. The fitting of the selected ARIMA (2,1,2) model to the time series data, means fitting ARIMA (2,1,2) model of one first order difference. Smaller RMSE, MAE as well as Theil Inequality coefficient are actually preferred and justified that ARIMA (2,1,2) model was justified as adequate for the forecasting of cotton lint exports in Nigeria with AIC value of 20.96771, SIC value of 21.04881, MAPE value of 6751.231, RMSE of 93303.67 and R2 of 0.330951. A thirty-year period ahead of cotton lint exports is predicted. The observations signify a rising trend in exports hence; it will be available especially in the future for foreign trade in the next thirty years. The outcome from the study is valuable for trade organisations and investors in assessing the precariousness of the market structure.

Cotton Gin Stand Machine-Vision Inspection and Removal System for Plastic Contamination: Software Design

The removal of plastic contamination from cotton lint is an issue of top priority to the U.S. cotton industry. One of the main sources of plastic contamination showing up in marketable cotton bales is plastic used to wrap cotton modules produced by John Deere round module harvesters. Despite diligent efforts by cotton ginning personnel to remove all plastic encountered during module unwrapping, plastic still finds a way into the cotton gin’s processing system. To help mitigate plastic contamination at the gin, a machine-vision detection and removal system was developed that utilizes low-cost color cameras to see plastic coming down the gin-stand feeder apron, which upon detection, blows plastic out of the cotton stream to prevent contamination. This paper presents the software design of this inspection and removal system. The system was tested throughout the entire 2019 cotton ginning season at two commercial cotton gins and at one gin in the 2018 ginning season. The focus of this report is to describe the software design and discuss relevant issues that influenced the design of the software.

Oxygation to Unlock Yield Potential of Crops: A Review

Irrigated agriculture has played a vital role in supporting a dramatic increase in global food production over recent decades. However, only 20 per cent of the world’s agricultural land is irrigated. It produces 40 per cent of world’s food supply. Even the traditional practices of irrigation, in whatever form, will have transient of long term depressive effects of soil oxygen content. The depressive effect of irrigation on soil oxygen is higher for a given soil water potential on heavy clay soils (e.g., for vertisols) than on lighter soils Hence plants suffered from sub-optimal oxygen supply in the root zone and causes hypoxia and anoxia. Aeration of subsurface drip irrigation (SDI) has been shown to alleviate soil hypoxia/anoxia by providing air/oxygen to an oxygen-depleted plant root zone. This can be achieved by coupling an air injector venturi to draw air into the subsurface drip irrigation system is known as oxygation/aerogation/air injection. Oxygation assures optimal root function, microbial activity and mineral transformations, which lead to enhanced yield and water use efficiency under hypoxic (anaerobic) conditions. It also improves plant performance and yield under irrigated conditions (i.e. crops such as radish by 9.87 per cent and cotton lint yields by 10 per cent) previously considered to be satisfactory for crop growth and offers scope to offset some of the negative impacts of compaction and salinity related to poor soil aeration on crop growth. The aeration condition of irrigated soils deserves more attention than it has received in the past, if we wish to unlock yield potential constraints by soil oxygen limitations in irrigated areas and enhance the yield potential to meet the future food (and fibre) demand.

Health Concerns of Textile Workers & Associated Community

Background: Workers in the textile industry risk developing various respiratory and pulmonary diseases due to exposure to cotton dust. The particles from the cotton lint are inhaled by the workers and results in the breathing problems including asthma, shortness of breath, cough and tightness in the chest. The poor health of labor contributes to the low productivity of the labor and in serious cases loss of jobs leading to the poverty. Methods: This study explores the health profiles of the textile workers and associated community and contrast them against the health profile of the control group to factor out any confounding factors. The study is conducted on cotton industry in Kasur, Pakistan. We interviewed 207 workers, 226 people from associated community (living in vicinities of weaving units) and 188 people for control group (from areas far away from weaving units and people are not associated with weaving industry) based on stratified random sampling technique. We employed descriptive methods and logistic regression to explore the association between respiratory diseases and weaving workers. Results: Overall, prevalence of postnasal drip, byssinosis, asthma, and chronic bronchitis were 47%, 35%, 20%, and 10% respectively among the workers. These percentages are significantly higher than the control group. Among workers, 43% & 21% feel difficulty in hearing against noisy background and at low volume respectively. Due to bad light arrangements at workstations, 21% & 31% workers are suffering from myopia and hyperopia respectively. Proportions of the workers suffering from continuous headache, skin infection, depression, and low back pain are 28%, 29%, 27% and 44% respectively. Conclusion: Better environment at workstations, use of protective gears and education are the factors which reduce the risk of associated diseases among workers.

Impacts of Effects of Deficit Irrigation Strategy on Water Use Efficiency and Yield in Cotton under Different Irrigation Systems

Irrigated cotton (Gossypium hirsutum L.) growers in the Murray-Darling Basin (MDB) of Australia, are challenged by limited water availability. This modelling-study aimed to determine if deficit irrigation (DI) practices can potentially improve water use efficiency (WUE) for furrow irrigation (FI), overhead sprinkler irrigation (OSI) and subsurface drip irrigation (SDI) systems. We validated the Agricultural Production System sIMulator (APSIM) against observed cotton lint yield and crop biomass accumulation for different management practices. The model achieved concordance correlation coefficients of 0.93 and 0.82 against observed cotton crop biomass accumulation and lint yields, respectively. The model was then applied to evaluate the impacts of different levels of DI on lint yield, WUE across cotton growing locations in the MDB (Goondiwindi, Moree, Narrabri, and Warren), during the period from 1977 to 2017. The different levels of DI for the FI system were no irrigation, full irrigation (TF) and irrigated one out of four, one out of three, one out of two, two out of three and two out of four TF events. For the OSI and SDI systems, DI levels were no irrigation, TF, 20% of TF, 40% of TF, 60% of TF and 80% of TF. Lint yield was maximised under the OSI and SDI systems for most locations by applying 80% of TF. However; modelling identified that WUE was maximised at 60% of full irrigation for OSI and SDI systems. These results suggest there are significant gains in agronomic performance to be gained through the application of DI practices with these systems. For FI, DI had no benefit in terms of increasing yield, while DI showed marginal gains in terms of WUE in some situations. This result is due to the greater exposure to periodic water deficit stress that occurred when DI practices were applied by an FI system. The results suggest that in the northern MDB, water savings could be realised for cotton production under both OSI and SDI systems if DI were adopted to a limited extent, depending on location and irrigation system.