Cotton Farmers Research Articles

Optimizing fertilizer use for sustainable food systems: an evaluation of integrated water-fertilizer system adoption among cotton farmers in China

IntroductionIn the face of escalating apprehensions surrounding food security, the sustainability of food systems, and food quality, the ingenuity of resource management strategies becomes paramount. A key component within these strategies is the enhancement of chemical fertilizer utilization, an element that bears significant weight on agricultural yields and the preservation of our environment. The emergence of Integrated Water-Fertilizer Systems (IWFS) thus presents a significant innovation in boosting the efficiency of chemical fertilizer usage, necessitating in-depth examination.MethodsUtilizing a rigorous analytical framework that combines meta-frontier production function with a Two-Stage Residual Inclusion model, this study delves into the multi-dimensional impacts of IWFS adoption on fertilizer use efficiency among cotton growers in Xinjiang, China.ResultsEmpirical evidence demonstrates that those who have adopted IWFS achieve a fertilizer use efficiency score of 0.452, markedly outstripping the mean score of 0.382 among non-adopters. Intriguingly, efficiency increases proportionately with the size of the farm, hinting at a sophisticated interplay between the adoption of technology and operational parameters.DiscussionThe research further unveils additional benefits including augmented cotton yields and diminished labor inputs among adopters. These multifaceted outcomes bear significant policy implications, highlighting the transformative potential of IWFS in promoting sustainable food systems, bolstering food security, and enhancing food quality.

Long- and Short-Term Memory Model of Cotton Price Index Volatility Risk Based on Explainable Artificial Intelligence.

Market uncertainty greatly interferes with the decisions and plans of market participants, thus increasing the risk of decision-making, leading to compromised interests of decision-makers. Cotton price index (hereinafter referred to as cotton price) volatility is highly noisy, nonlinear, and stochastic and is susceptible to supply and demand, climate, substitutes, and other policy factors, which are subject to large uncertainties. To reduce decision risk and provide decision support for policymakers, this article integrates 13 factors affecting cotton price index volatility based on existing research and further divides them into transaction data and interaction data. A long- and short-term memory (LSTM) model is constructed, and a comparison experiment is implemented to analyze the cotton price index volatility. To make the constructed model explainable, we use explainable artificial intelligence (XAI) techniques to perform statistical analysis of the input features. The experimental results show that the LSTM model can accurately analyze the cotton price index fluctuation trend but cannot accurately predict the actual price of cotton; the transaction data plus interaction data are more sensitive than the transaction data in analyzing the cotton price fluctuation trend and can have a positive effect on the cotton price fluctuation analysis. This study can accurately reflect the fluctuation trend of the cotton market, provide reference to the state, enterprises, and cotton farmers for decision-making, and reduce the risk caused by frequent fluctuation of cotton prices. The analysis of the model using XAI techniques builds the confidence of decision-makers in the model.

RESPONSE OF COTTON CULTIVARS TO SOIL AND FOLIAR APPLIED FERTILIZER IN KABBA, KOGI STATE, NIGERIA

The experiment was carried out at Kabba College of Agriculture experimental field in 2020 and 2021 cropping seasons to evaluate response of cotton cultivars to soil and foliar applied fertilizer. The experiment was performed in a randomized complete block design with two factors arranged in split plot arrangement in three replications. The first experimental factor was a cultivar, include two cotton varieties Hybrid and Lokoja local (abbreviated as C1 and C2) were used. The second experimental factor was the different nitrogen sources applied at five levels, NS1 = No Nitrogen source (control), NS2 = NPK (20:10:10 plus boron) fertilizer applied into soil alone = 80kg NPK & B, NS3 = NPK & B at 50% (40kg N) + Agric Zyme 50% (40 kg N), NS4 = NPK & B at 75% (60kg N) + Agric Zyme at 25% (20kg N) and NS5 = NPK & B at 25% (20kg N) + Agric Zyme at (60kg N). Data were collected on growth characters such as plant height, stem girth, number of leaves and leaf area fresh and dry weight of cotton. Yield characters observed were number of flowers per plant, cotton ball per plant, cotton yield per plant and cotton lint yield. All plots with NPK&B and Agric Zyme produce better growth and yield characters than the control. All plots with NPK&B alone or in combination with Agric Zyme at reduced level produced similar growth characters. Cotton with fertilizer application either singly or combined produced higher number of flowers and cotton balls compared to the control. Cotton grows through hybrid seeds gave more flowers and cotton balls more than Lokoja local seeds used. Cotton with fertilizer application were susceptible to pest attacked compared to cotton grow in the control plots. Cotton grows with the use of hybrid seed gave higher number of aborted flowers than the Lokoja local seed used. Though, cotton plant grows with NPK&B at 80 kg /ha gave the highest yield of cotton. All other plots with combined application of NPK&B and Agric Zyme produced yield comparable to plots with NPK&B at 80 kg /ha. Among cotton with combined application of NPK&B and Agric Zyme, plots treated with NPK & B at 25% (20kg N) + Agric Zyme at 75% (60kg N) gave the greatest yield. Cotton with hybrid seed produced cotton yield higher than the Lokoja local seed. The increment in hybrid cotton yield over Lokoja local seed is 58.52%. Cotton farmers in the study area should grow hybrid cotton using any of the treatment combinations especially NPK & B at 25% (20kg N) + Agric Zyme at 75% (60kg N).

Correction: The heterogeneous effects of climate variability on cotton farming productivity in Burkina Faso

Correction: The heterogeneous effects of climate variability on cotton farming productivity in Burkina Faso

The heterogeneous effects of climate variability on cotton farming productivity in Burkina Faso

The heterogeneous effects of climate variability on cotton farming productivity in Burkina Faso

Costs, Returns, Resource Use Efficiency of American Cotton Production in Irrigated North Western Plain Zone of Rajasthan, India

The study was carried out at Irrigated North Western Plain Zone 1b of Rajasthan in agricultural year 2012-13. This zone covers about 80 percent of the total area of cotton in the state. The study focused on the cost and return, efficiency of resource use and problems faced by the farmers in American cotton production under different farm sizes. Two villages namely 4C and Mirjawala were selected from Sriganganagar tehsil of the zone. Thirty farmers were selected at random in proportion to the total number of farmers in each size group from the list of from American cotton farmers group. The total variable cost of per hectare American cotton was Rs 28985. The yield per ha was 18.70 quintals. The return over variable cost per hectare American cotton was Rs 61436. The Cobb Douglas production function, revealed that the in American cotton farmers were underutilized fertilizer. Non-availability of labour during peak season was reported as highly acute constraint for cotton farmers. The damage caused by insect pests was reported as main problem for American cotton sample farmers.

Is ‘pupae busting’ or destroying overwintering pupae of Helicoverpa spp. (Lepidoptera: Noctuidae) still relevant today in Australian Bt cotton?

AbstractTransgenic Bt cotton was developed to control lepidopteran pests like the cotton bollworm, Helicoverpa armigera. However, there was concern that H. armigera would develop resistance to Bt cotton as this species had developed resistance to many insecticides and Bt toxins. To counter resistance, the cotton industry developed a resistance management plan (RMP) that included techniques to block resistant genes surviving from one season to the next (seasonal quarantining). One such technique is pupae busting, where cotton fields are cultivated after harvest, destroying potentially resistant pupating Helicoverpa spp. While pupae busting was important when there was only one insecticidal gene in Bt cotton, is it still relevant now Bt cotton has three insecticidal Bt genes? To address this question, we reviewed the development of pupae busting as a tool and its role in the current RMP. This included examining the ecology and behavioural characteristics of Helicoverpa spp. that impact on pupae busting efficacy (e.g., diapause, pupal mortality and pupae depth); the effect of soil type and different tillage techniques on pupae busting efficacy; and pupae busting within the context of Australia's current cotton farming system. We also looked at alternative forms of seasonal quarantining, such as using bisexual attract‐and‐kill techniques against adults. We confirmed that soil for pupae busting needs to be checked for moisture, which ideally should be less than the soil plastic limit. Comparisons between reports indicated that under good conditions, ‘go‐devils’ and chisel ploughs were excellent pupae busters. While a bisexual attract‐and‐kill strategy of late season moths has a place within the industry, pupae busting is still the best method in seasonal quarantining and has a good fit within the modern cotton industry, particularly given differences in the biology and ecology of H. armigera and H. punctigera, and the presence of dominant resistance to Bt toxins by H. armigera in China.

Self-reported health effects of pesticides among cotton farmers from the Central-West region in Burkina Faso

Pesticides are chemicals used to control pests with sometime harmful effects on human health. This paper presents results of self-reported health effects experienced by cotton farmers in the Central-West region of Burkina Faso. It was a cross-sectional survey conducted from October to December 2021 among 585 consenting conventional and organic cotton farmers. Data collected included pesticides used, they use conditions, farming practices, experienced health effects. Binary logistic regression was used to find relationships between self-reported health effects and the type of cotton produced. Results showed that all conventional cotton farmers (100%) reported using synthetic pesticides compared to organic ones who using only natural insecticides. Both conventional and organic farmers reported health effects that occurred at least once since they started using pesticides, involving skin effects (85.27% conventional, 65.52% organic), nervous (88.95% conventional, 48,71% organic), respiratory (88.10% conventional, 67.67% organic) systems. 99.72% of conventional farmers vs 46.98% of organic ones reported skin irritation following pesticide use. 69.97% of conventional vs 35.34% of organic cotton farmers reported acute signs such as severe headaches. In univariate and multivariate logistic regressions, severe headaches, dizziness, skin, and ocular effects were significantly associated with conventional farmers compared to organic ones (p < 0.05). There was a significant relationship between vomiting (p = 0.014), diarrhea (p = 0.003) and experience in synthetic pesticide use among conventional farmers. Among organic farmers, there was no significant relationship between health effects reported and experience in organic insecticides use. there was a significant relationship between severe headaches (p = 0.01), rhinitis (p = 0.006), cough (p = 0.0001), skin and ocular irritations (p = 0.007) and the frequency of synthetic insecticides use per year by conventional farmers. Study showed that conventional and organic cotton producers experience the same types of health effects. However, these health effects were significantly associated with conventional farmers compared to organic ones.

Sustainable Development Goals for Textiles and Fashion.

In this paper, each of the 17 Sustainable Development Goals (SDGs) is discussed in the context of textiles and fashion. The necessity of collaborative efforts is accentuated to overcome the climate crisis and human health concerns encountered by the textiles and fashion industry. The concerns over poverty faced by cotton farmers, soil depletion, and toxicity to aquatic life due to microfibres and heavy metals are presented. The paper underlines numerous corrective practises such as the utilisation of African Organic Cotton, Better Initiative Cotton, and others that could enable curtailing poverty and hunger. The requirement for a more corporate and socially responsible textiles and fashion business that would propel SDGs is described with inspiring examples of Inditex, Culthread, Saint Basics, Flamingo's Life, etcetera. More, the greenwashing and ardent necessity of transparency across the fashion value chain is emphasised herein. The importance of reducing inequalities and working in partnership for innovation and justice is highlighted such as apple leather, algal foam, and others. Even more, the production waste and landfill disputes are reviewed. Eventually, the paper concludes with an appeal for mindful and diligent efforts from textiles and fashion consumers, designers, manufacturers, and traders to continually adapt to SDGs even after 2030 as there is no planet B. Also, abiding by the laws of nature as listed herein is found to be the key to reaching SDGs.

Organic Cotton Farming: Exploring Planting Rates for Sustainability and Yield

Organic Cotton Farming: Exploring Planting Rates for Sustainability and Yield

Risk Attitude of Farmers in the Climate Extreme Region of Andhra Pradesh, India

Aim: The objective of the current study was to assess the risk attitude of both insured and non-insured farmers and identify the factors influencing the risk attitude of insured and non-insured farmers.&#x0D; Study Area and Design: An ex-post-facto study was conducted, and the stratified random sampling method was used to collect the data at selected villages in Prakasam district, Andhra Pradesh.&#x0D; Methodology: A sample of 150 farmers of chilli and cotton was collected (90 insured and 60 non-insured). Data regarding the agriculture year 2021-22 was analysed using Moscardi and de Janvry approach. The socioeconomic, institutional and farm characteristics were analysed by using discriminant analysis to identify the factors influencing farmers’ risk attitudes.&#x0D; Results: The present study results showed that the majority of the farmers are risk neutral. In the case of risk preferers insured farmers are more compared to non-insured farmers. Insured chilli farmers more than non-insured chilli farmers in case of risk-averse but, it is the reverse in the case of cotton farmers. Occupation, constraints in getting credit facility, membership of the association, the proportion of crop income to the total farm income and family size of insured chilli farmers and family size, education level membership of the association, constraints in getting credit facility, the proportion of crop income to total farm income of non-insured chilli farmers are significant. Variables like age, occupation, farming experience, constraints in getting credit facility and age, occupation, education level, and farming experience of insured and non-insured cotton farmers were found to be significant. &#x0D; Conclusion: The majority of the farmers (both insured and non-insured) are risk neutral.

Dynamics of Labour Use in Cotton Farming in India: An Economic Appraisal

In India, a large proportion of the workforce is still dependent on the agricultural sector (63.32% employment share in 1990-91, which reduced further to 42.61% in 2019-20. A gradual diminishing pattern has been observed in percentage of agricultural workers to the total workforce in the country and it is projected to reach 25.7% by 2050 creating a critical shortage of farm labour. This paper is intended to understand the dynamic changes in the labour utilization, farm mechanisation and labour productivity across the States in cotton farming during the years 2007 to 2019 by using cost of cultivation scheme data. Eleven major cotton growing States have been selected for the study. The data on cost of cultivation scheme of Government of India was used to calculate the changes in crop profitability, labour use, labour costs and labour productivity indices. In spite of increased growth in wage rate, the labour productivity growth is not that significant in most of the States. Selective mechanization wherein the labour shortage is crucial affecting the crop productivity should be identified and given impetus.

Determination of Energy Consumption and Technical Efficiency of Cotton Farms in Türkiye

This study aimed to examine the efficiency of cotton farms and the energy requirements of the input and output of cotton in Türkiye. Data were collected from 657 cotton farms, and the results showed that the energy input of machinery (28.69%) had the most significant share in the total energy input, followed by electricity (22.79%) and nitrogen (20.75%). The total energy consumption of cotton was 83,869.49 MJ ha−1. In cotton production, the energy use efficiency, energy productivity, specific energy, and net energy were measured to be 0.87, 0.07, 17.31, and −23,043.92 MJ per hectare. Cotton plants consumed more indirect energy (51.99%) than direct energy (48.01%) and more non-renewable energy (89.96%) than renewable energy (10.04%). According to the data envelopment analysis results, the average technical efficiency of cotton farms was 0.84. Inefficient farms can reduce their inputs by approximately 16% without reducing the amount of cotton production. Allocative efficiency, pure technical efficiency, and scale efficiency of cotton farms were determined at 0.570, 0.539, and 0.640, respectively. Human labour, machinery, diesel, nitrogen, and phosphate energy use should be reduced for inefficient farms to become more efficient.

Exploring The Impact of Weather Factors on the Population Trend of Oxycarenus Laetus Kirby (Dusky Cotton Bug) and Natural Enemies in Different Agro-Ecological Zones of Sindh, Pakistan

The research work was carried out in different agro-eco-zones of Sindh, Pakistan at the experimental field area of Cotton Agriculture Research Station at Tandojam, Sakrand, Kotdiji and Sarhad, 2019. The peak population of the Dusky cotton bug (DCB) was recorded at Tandojam. The high population of natural enemies like Chrysoperla, Orius, and Mirid bug was observed from July to September. The peak pest of pest observed averagely at Tandojam and Sakrand (49 to 23%). The abiotic factors like temperature and humidity averagely at Tandojam and Sakrand (32 to 50%) followed by Kotdiji and Sarhad (41 to 18%) recorded ideal for Chrysoperla, Orius, and Mirid bug. The abiotic factors were recorded as favorable for natural population multiplication like temperature and respectively. The weather factors noted optimum for spider population at Tandojam. It was concluded that the peak population of the pest was observed in different agro-eco-zones from September and October.&#x0D;

Efficiency in Cotton Production Across the States in India

Background: Cotton is one of the oldest crops in the world. About 60 million people are directly engaged in cotton textiles and processing works. Cotton is an important cash crop. In some places, it is called “white gold” because it brings in foreign exchange. India is one of the top cotton producers and exporters in the world. 85% of the world’s cotton is cultivated in ten major countries, including India, which is the second-largest producer. India has a competitive advantage in the production of cotton. The study’s objective is to find out the relative yield efficiency between the states of India in cotton farming. Methods: The Secondary data of cotton production is collected from the website of INDIASTAT. The ANOVA is used to calculate the relative yield efficiency of cotton. Result: The paper’s result is calculated using SPSS V22 (Statistical Package for Social Sciences). The result shows that Punjab, Haryana, Gujarat and Orissa have the highest efficiency in cotton yield, while Maharashtra, Karnataka and Andhra Pradesh have the lowest efficiency in cotton yield and the rest states have the average yield. In Maharashtra and Karnataka, rainfall was uneven and soil fertility was not good. The dryland of Karnataka played an important role in less productivity of cotton. Punjab, Haryana and Gujrat had better soil fertility which makes it better for cotton yield. Government should take the necessary steps to increase the average yield of cotton by shifting cotton farming from the low-yield region to the high-yield region. The government should promote cotton production where the yield is higher and demote cotton farming where the yield is lower. Government should also promote “better cotton” farming because it is more sustainable and has a higher yield with a lower cost of cultivation.

Crop Prediction and Disease Detection in Cotton Using Machine Learning

Abstract: Cotton is one of the most important cash crops grown globally, and its yield is critical to the world's economy. Crop prediction and disease detection in cotton are essential for farmers to make informed crop management and production decisions. This project paperproposes a machine learning-based approach to predict cotton yield and detect disease in cotton plants. The proposed approach involves collectingdata from various sources, including weather data, soil data, and satellite images. The collected data will be preprocessed and analyzed to identify patterns and trends. Machine learningmodels such as regression, classification, and clustering will be developed to predict cotton yield and detect disease in cotton plants. The proposed approach will be implemented in a web-based application that will provide farmers with timely and accurate information about the yield and health of their cotton crops. The application will also provide recommendations on crop management practices based on data analysis and predictions. The proposed project aims to provide a cost-effective and efficient solution to cotton farmers for predicting crop yield and detecting disease in cotton plants. The proposed approach can also be extended toother crops, making it applicable to a wide range of agriculture-based industries.

Dynamics of Cotton Farming in Kalahandi District of Odisha

The present research attempted to explore the cost-returns profile and resource use efficiency of cotton cultivation in the Kalahandi district of Odisha by collecting primary data from 300 sample cotton farmers based on their operational landholdings. The resource use efficiency was measured using the Cobb-Douglas production function. The results concluded that the farmers should utilize more bullock labour, pesticide and seed inputs for higher benefits. The major cost components of cotton cultivation were hired human labour (23.89 per cent), followed by fertilizers (12.13 per cent), machine labour (10.09 per cent) and manure (10.02 per cent). The overall cost of cotton cultivation was `25,385 per acre, and the gross return was `56,799 per acre. The benefit-cost ratio of the overall farmers was estimated to be 2.23, indicating the profitability of the cotton crop.

Exposure Levels of Airborne Fungi, Bacteria, and Antibiotic Resistance Genes in Cotton Farms during Cotton Harvesting and Evaluations of N95 Respirators against These Bioaerosols.

The USA is the third-leading cotton-producing country worldwide and cotton farming is common in the state of Georgia. Cotton harvest can be a significant contributor to airborne microbial exposures to farmers and nearby rural communities. The use of respirators or masks is one of the viable options for reducing organic dust and bioaerosol exposures among farmers. Unfortunately, the OSHA Respiratory Protection Standard (29 CFR Part 1910.134) does not apply to agricultural workplaces and the filtration efficiency of N95 respirators was never field-tested against airborne microorganisms and antibiotic resistance genes (ARGs) during cotton harvesting. This study addressed these two information gaps. Airborne culturable microorganisms were sampled using an SAS Super 100 Air Sampler in three cotton farms during cotton harvesting, and colonies were counted and converted to airborne concentrations. Genomic DNA was extracted from air samples using a PowerSoil® DNA Isolation Kit. A series of comparative critical threshold (2-ΔΔCT) real-time PCR was used to quantify targeted bacterial (16S rRNA) genes and major ARGs. Two N95 facepiece respirator models (cup-shaped and pleated) were evaluated for their protection against culturable bacteria and fungi, total microbial load in terms of surface ATP levels, and ARGs using a field experimental setup. Overall, culturable microbial exposure levels ranged between 103 and 104 CFU/m3 during cotton harvesting, which was lower when compared with bioaerosol loads reported earlier during other types of grain harvesting. The findings suggested that cotton harvesting works can release antibiotic resistance genes in farm air and the highest abundance was observed for phenicol. Field experimental data suggested that tested N95 respirators did not provide desirable >95% protections against culturable microorganisms, the total microbial load, and ARGs during cotton harvesting.

A review on Cotton Leaf Disease Detection with Balanced and Imbalanced Data

Cotton is a major crop in India, and its production is essential to the country's economy. However, cotton plants are susceptible to a number of diseases, which can cause significant yield losses. Early detection of these diseases is crucial, but manual identification can be challenging. This paper reviews the use of machine learning for cotton leaf disease detection. A number of classification methods and algorithms are discussed, including convolutional neural networks (CNNs), random forests, and SMOTE. The results show that machine learning can be used to achieve high accuracy in detecting cotton leaf diseases, even in the case of imbalanced datasets. The paper also discusses the importance of balancing class distributions in order to improve model performance. A number of data-level and classifier-level techniques are evaluated, and the results show that the Weighted Random Forest algorithm is effective in handling imbalanced datasets. The findings of this paper suggest that machine learning is a promising tool for the early detection of cotton leaf diseases. This could help to reduce yield losses and improve the profitability of cotton farming in India.

Rhizosphere Soil Bacterial Communities and Bacterial Root Endophytes in Cotton farming is Influence by Cultivation of Transgenic Bt and non-Bt Cotton Crop Varieties

Rhizosphere Soil Bacterial Communities and Bacterial Root Endophytes in Cotton farming is Influence by Cultivation of Transgenic Bt and non-Bt Cotton Crop Varieties