Cotton Farmers Research Articles

The impact of green control technology adoption on cotton farmers’ welfare in Xinjiang

The impact of green control technology adoption on cotton farmers’ welfare in Xinjiang

Testing the Agreement Between a Traditional and UAV-Based Method for Quantifying Skips in Suboptimal Cotton Stands

Highlights Agreement in the mean difference between the traditional and the UAV-based method only occurred in poor stands. Effects of different sampling sizes between methods were evident in mediocre-to-good stand assessments. Abstract. When suboptimal cotton stands occur, growers face the decision to accept or reject the stand. The replanting decision is difficult because the tradeoffs associated with replanting expenditures and reduced yields are difficult to objectively assess. Traditional methods like visual assessments and manual counts of cotton stands are commonly used to support a replanting decision. Typically, manual counts of skip size and frequency will provide more accurate assessments of the stand than visual assessments, but they are cumbersome to conduct and may not provide clear evidence that a replant is needed. Still, manual counts are popular among cotton farmers and the scientific community. Skip counts generated with the help of unmanned aerial vehicles (UAV) are less popular among cotton growers but provide more coverage and a larger sampling size across a given field. Therefore, UAVs have the potential to overcome the limitations associated with traditional methods. The motivation behind this study is to inform readers if manual methods can still be used for accurate decision-making regarding the replanting decision. More specifically, we study the interchangeability, or agreement, between a manual and a UAV-based method using Bland-Altman plots. Each method quantified skips greater than or equal to 0.91 m at different sampling sizes. Treatment plots varied in their stand counts, skip size, and skip frequency. Agreement between both methods was only found in the lowest stand treatment, where skips of large sizes were predominant. Conversely, methods disagreed in the higher stand where skips greater than or equal to 0.91 m were scarce. Keywords: Agriculture, Altman, Bland, Drone, Gaps, Precision, Remote, Sensing, UAS.

Assessment of Livelihood Security and Constraints Encountered by Small and Marginal Cotton Growers

The study examines the constraints faced by cotton growers in achieving livelihood security in the Guntur district of Andhra Pradesh, India. The investigation used a Livelihood Security (LS) index comprising seven elements, including food security, economic security, health security, educational security, social security, institutional security, and infrastructural security. Data were collected from 120 cotton farmers using structured interviews, and statistical analysis was employed to assess the level of livelihood security and identify constraints. The findings indicate that a substantial proportion of farmers have medium to high levels of livelihood security, but certain challenges persist. Among the constraints, lack of knowledge of technical skills and relevant technologies, low market prices for products and inadequate knowledge of marketing stand out as significant obstacles. Addressing these challenges can contribute to enhancing the overall well-being and livelihood security of cotton growers in the region.

Regional integrated assessment of climate change impact on cotton production in a semi-arid environment

Climate change has a negative impact on the productivity of agricultural crops at local, regional and global levels. Foodstuff security and sustainable livelihood of cotton farmers in the core cotton growing region in Punjab, Pakistan is under threat because of decreased yield due to climate change. The quantification of the integrated impact assessment of climate change for developing adaptation approaches for cotton is vital for improving productivity at a regional level and improving food security at a national level. Two crop models were evaluated with on-farm survey data of 165 farms employing stratified random sampling techniques. Representative agricultural pathways (RAPs) were developed for characterizing future cotton production. Global climate models (GCMs) depicted a rise of 3.6 and 4.3°C for maximum and minimum temperature, respectively, along with a decreased in rainfall of 600 mm under Representative Concentration Pathway (RCP) 8.5. The expected temperature rise for the hot-dry climate would cause a reduction in productivity of 35.3 and 39.2% by mid-century for RCP4.5 and RCP8.5, respectively, according to the Decision Support System for Agrotechnology Transfer (DSSAT) model, while the Agricultural Production System Simulator (APSIM) showed a reduction of 51.1 and 59.6%, respectively. Increases under the current adaptation of a 15% increase in each of nitrogen and planting density ranged from 1.1 to 6.3% for DSSAT and 2.6 to 8.2% for APSIM. Climate-adapted cotton productivity was projected to rise from 18.7 to 35.9% for DSSAT and from 13.8 to 42% for APSIM for all GCMs. Results showed that current and future cotton systems are adversely impacted by climate change; however, climate-change-adapted management approaches could offset possible reductions in productivity. Sustained cotton productivity in the core cotton zone requires capacity building amongst farmers, enabling them to improve their crop management in the face of seasonal climate variability and future climate change.

Cost Analysis of Biopesticides and Chemical Insecticides: Implications for Cotton Farmers in South Africa

Cost Analysis of Biopesticides and Chemical Insecticides: Implications for Cotton Farmers in South Africa

Development of new efficient technology for extraction of fine dust impurities from cotton

To date, the problem of creating an effective technology for the extraction of fine dust pollution from cotton has not been solved. This article provides a solution to this problem. The socio-economic significance of the study lies in the fact that the proposed device is associated with significant savings in energy costs for cleaning cotton from dust. After the contaminated cotton enters the threshing floor, under great pressure, fine trash settles firmly inside the cotton. It makes it very difficult to process and clean it in cotton factories. Therefore, the cleaning process of cotton has to be repeated several times. The cost of energy consumed during cleaning increases significantly and the cotton fiber quality is reduced. The device proposed in the article cleans the collected cotton from impurities during machine harvesting. The process of cleaning cotton at this stage is very effective. As a result, the cost of harvested cotton is reduced, which increases the income of cotton farmers and workers employed in cotton factories. In addition, it causes an increase in the quality of textile products. Another socio-economic significance of the device proposed in the article is that it can be installed on a cotton harvester and in other places. In addition, it can be used not only to clean cotton but also to clean other substances from fine dust. As a result of practical research, technology has been developed to extract small contaminants from cotton.

Economic Analysis and Resource Use Efficiency of Cotton Production in Haryana

The study was conducted on the economic analysis of cotton crop and its returns in two districts viz. Sirsa and Hisar of Haryana selected purposely having the highest area. The collected data was used to calculate the cost and returns and resource use efficiency of cotton crop in Haryana. The cost benefit ratio for the study area came out as 1:1.22, 1:1.04 and 1:1.13 Sirsa, Hisar and overall, respectively. The findings concluded that resource use efficiency of the cotton farms is showing decreasing returns to scale in both Sirsa (0.419) and Hisar (0.413) districts, which means that there is no scope for improvement in the yield of cotton and there is over-utilization of the resources for the cotton cultivation in Haryana.

A Smart Solution for Sustainable Cotton Farming: A Machine Learning Approach for Visual Recognition of Leaf Diseases

Cotton leaf diseases pose significant threats to sustainable farming practices, leading to yield losses and economic burdens for cotton growers worldwide. In this paper, we propose a smart solution for efficient and accurate detection of cotton leaf diseases using machine learning techniques. Our approach leverages a convolutional neural network (CNN) architecture specifically designed for visual recognition of leaf diseases. To train and optimize the CNN model, we employ a genetic algorithm that enhances the learning process and improves classification performance. The proposed model is trained and evaluated on a comprehensive dataset containing six classes of cotton leaf diseases, namely Aphids, Army worm, Bacterial Blight, Powdery Mildew, Target spot, and healthy leaves. Experimental results demonstrate the effectiveness of our proposed method, achieving an overall accuracy of 97% on the test set. Comparative analyses with existing studies and methodologies reveal the superior performance of our approach, showcasing its potential for practical implementation in the field of cotton leaf disease detection. The outcomes of this study have significant implications for farmers, agronomists, and agricultural organizations, enabling them to make informed decisions and take timely actions to protect their crops and enhance productivity.

Economic Assessment of Sustainable Cotton Production in Maharashtra, India

Cotton, the king of fibers is often quoted as ‘White Gold’ because of its higher commercial values. It is a primary raw material in the textile industries. Cotton, cotton yarn, cotton fabrics and garments have substantial demand in the global market. Cotton is the most important fibre crop of India playing a dominant role in its agrarian and industrial economy. In the present study efforts have been made to study the cost, returns, profitability of organic and conventional cotton. The primary data required for the study were collected during year 2021-22 from 320 cotton growers of Maharashtra. Simple statistical tools like averages and percentages were used in analysing the collected data and standard cost concepts was used for analysis. The result of the study examined that, the total cost of production (cost ‘C’) of cotton was worked out to Rs. 87633 and Rs. 72683 in conventional irrigated and conventional rainfed cotton cultivation, respectively, the per hectare conventional rainfed cotton production was worked out to be 13.18 qtls. The per hectare conventional irrigated cotton production was worked out to be 20.95 qtls. The benefit-cost ratio was estimated to 2.09 and 1.77 for irrigated and rainfed respectively, This indicated that, organic irrigated cotton production was more profitable than organic rainfed cotton. Organic cotton farming promises to be a more sustainable form of agriculture that is aimed at producing food in a more environmentally friendly, economically viable and socially just way. Organic methods also have wider social and environmental benefits that come from the use of sustainable methods.

Analysis of Resource Use Efficiency and Constraints of Cotton Production in Odisha, India

The present study entitled "Analysis of resource use efficiency and constraints of cotton production in Odisha" was undertaken with the major objectives to analyse resource use efficiency in cotton cultivation and to elicit the constraints faced by the farmers in production and marketing of cotton in the study area. This study used both secondary and primary data. Multi-stage random sampling was taken for this study. The districts selected for study were Kalahandi, Balangir and Rayagada of Odisha state. A total number of 120 cotton farmers i.e 71 farmers from small and 49 farmers from large size groups were selected at random based on probability proportion. Analytical tools like Tabular analysis, Cobb-Douglas production function and Garrett’s ranking were used for the analysis. Cobb-Douglas production function was used taking Y i.e. yield of cotton as dependent variable and X1, X2, X3, X4, X5, X6 i. e. for total human labour, farm power cost, manure cost, fertilizer cost, cost of pesticides, area under the crop respectively as independent variable. Among these variables, fertilizer, pesticide, human labour and machine labour were positive and significant for small farmers and manure, fertilizer, pesticide and machine labour were positive and significant for large farmers. Most of the significant resources were proved to be efficient. The major constraints in cotton yield as reported by farmers were high cost of labour, high cost of seeds, lack of regulated markets etc. in the study area. For the sustainable growth of cotton crop in the state, timely policy interventions were required by promoting contract farming and providing price incentives, so that cotton farmers of Odisha will get better profit for their crop and prosperity of agriculture in the state.

Biodiversity and Food Diversity of Farms Using Agroecology in Benin Cotton Areas

Farms biodiversity and food diversity of agricultural households are evaluated to understand some agroecology effects on cotton farms. This is a part of socio-economic analysis of the agroecological  transition underway in cotton zones of Benin. The surveys covered 509 farmers in 5 municipalities: Banikoara, Kandi, Ouassa-Péhunco, Parakou and Savalou. Data collected are crops diversity, livestock diversity, natural vegetation, trees and pollinators of each farm and the various food consumed within 24 hours before the survey on each farm. Scores obtained by farm on each criterion were used to calculate the farm biodiversity index and household food diversity index; and the averages by type. Student's Chi--square test and Kruskal Wallis multiple comparison test were used to compare index averages, to analyze difference between farm types according to their ''Test'' or ''Control'' status. Analysis based on ''Tool Agroecology Performance Evaluation (TAPE)'' method shows that surveyed farms are unsustainable (biodiversity index <50%) regardless of status, except farms of type 5 Test which have a better result and significantly different from the Controls. But their diet remains acceptable (5≥food diversity index<7); difference observed on most types except 1 and 4. This means that agroecology is not yet bearing full fruit in the study areas and that farming households are still vulnerable to food insecurity. However, there are clear differences between the types of farms and differences between individuals of the same type. These few disparities observed highlight the potential of agroecology to improve households’ food situation if the process is intensified.

AN OVERVEW OF BREEDING FOR DROUGHT STRESS TOLERANCE IN COTTON

Drought is a main non-living factor that causes severe crop yield loss globally. Given the strengthening and reappearance of drought events and their impacts, it's important to deepen our understanding as a key to subsidizing mechanisms for drought training and mitigation plans. Pakistan is ranked maximum of the top 5 biggest cotton manufacturers, the seventh largest material producer international, and cotton contributes 10% to the country-wide GDP compared to the overall agriculture area GDP percentage of 18.9%. Cotton farming performs a tremendous role in presenting direct livelihood to 11 million farmers. The cotton crop, in particular, is confined to northern, imperative, and southern zones, with approximately 90 in keeping with cent of the area coming beneath 3 zones. Regardless of this, its cumulative, not apparent impact and multidimensional nature significantly impact the cotton plant’s morphological, physiological, biochemical, and molecular attributes with a detrimental impact on photosynthetic capability. Dealing with water scarcity, plants evolve various complicated resistance and edition mechanisms, including physiological and biochemical responses, which range with species stage. The sophisticated adaptation mechanisms and regularity community that improve the water stress tolerance and version in plants are briefly discussed. Growth pattern and structural dynamics, reduction in transpiration loss via altering stomatal conductance and distribution, leaf rolling, root-to-shoot ratio dynamics, root duration increment, accumulation of like-minded solutes, enhancement in transpiration performance, osmotic and hormonal regulation, and behind-schedule senescence are the techniques that are followed using cotton plant life underneath water deficit. Approaches for drought stress resistance we develop transgenic cotton plants which which can tolerate drought stress to improve cotton quality with good yield.

Extent of Adoption of Recommended Practices of Cotton Cultivation by the Cotton Farmer

Aims: To know the adoption level of recommended cultivation practices of cotton crop.
 Study Design: The research design used for this study was an Ex-post Facto.
 Place and Duration of Study: The study was conducted in Khandwa district of Madhya Pradesh and duration 2020-2022.
 Methodology: The study was conducted in Khandwa district of Madhya Pradesh a total of 200 farmers selected by following the proportionate random sampling technique. An interview schedule was prepared for collection data relating to the adoption of recommended practices by the respondents.
 Results: The study, concludes that, with respect to the adoption, majority of the farmers (69.00%) had a medium level of adoption of recommended improved practices of cotton cultivation while, an equal number (15.50%) of the respondents had low and high level of adoption of the same practices. Among the socio-economic and personal, communication and psychological characteristics of the respondents, age, education, family size, family type, land holding, annual income, farm mechanization, extension participation, information seeking behaviour, cosmopoliteness, economic motivation, scientific orientation and risk orientation indicated significant association with the extent of adoption of the recommended practices of cotton.
 Conclusion: The study concluded that majority of the cotton growers were low adopted recommended cotton practices.

Effect of agricultural extension on cotton farmer's efficiency in arid and semi‐arid areas of Burkina Faso

AbstractThis study shows that farmers in arid and semi‐arid land can improve their efficiency if they have access to agricultural extension services. An exhaustive survey was used to obtain data from 423 cotton producers in the arid zones and 144 of wetlands in Burkina Faso. The method of the directional distance function modeling a multi‐output technology was used. The results show that cotton farmers in the arid zones are not technically more inefficient than those in wetlands. On average, arid zone producers have a technical inefficiency of 0.056 while that of wetland producers is around 0.052. The agricultural extension, defined as receiving visits and advice from technical agents, is important for producers because it helps farmers choose soil and water conservation techniques and fertilizers adapted to the Agro‐climatic conditions of their respective zones. Agriculture extension also helps farmers of the arid zones to reduce their technical inefficiency by advising them to cultivate small agricultural plots. On average, in these zones, the size of an agricultural plot is around 1.03 ha compared to those of wetlands which is about 1.6 ha.

Small unopened cotton boll counting by detection with MRF-YOLO in the wild

Accurate detection and counting of unopened cotton bolls at the early stage of cotton maturation is an effective way to develop crop load management and harvesting strategies in advance. However, robust and effective detection and counting of unopened bolls under complicated field conditions is a challenging task. In this study, we propose a deep learning method with multi-receptive field extraction based on YOLOX, called MRF-YOLO, to detect and count small targets, which we validate on a cotton boll dataset collected from a cotton farm. In the target detection part, a multi-scale residual block and an attention module are first introduced to enhance the extraction of cotton boll feature details. Second, a multi-receptive field extraction module is added to reduce the loss on small targets in the deep network. Finally, a small target detection layer is integrated to improve detection precision. Compared with existing methods, the proposed model achieves a 14.86% improvement in the average accuracy AP50:95 and AP50 of 92.75% while maintaining a high processing speed. In the counting part, we propose a detection-based counting method using MRF-YOLO. The mean squared error and coefficient of determination (R2) are 1.06 and 0.92, respectively. MRF-YOLO can be extended to a wide range of small target crop detection, enabling reliable yield predictions under real field conditions.

Constraints Faced by Cotton Farmers in Management of Cotton Cultivation in Madhya Pradesh, India

The study was conducted in ten purposively selected villages of Khandwa district of Madhya Pradesh on the basis of large area under cotton crop and a total of 200 farmers selected by the proportionate random sampling technique. An interview schedule was prepared for collection data relating to constraint faced by cotton growers. They faced in growing cotton to lack of knowledge about improved scientific practices in cotton crop (89.50%), insufficient training programs (88.00%), insufficient demonstration of improved technology (83.50%), lack of technical knowledge about pheromone and sticking trap (81.00%), high price & poor quality of Bt. cotton seeds (79.50%), lack of information regarding upcoming abnormal weather conditions (78.00%), lack of coordination between farmer and agriculture institution (76.50%), scarcity of labour at the plucking stage (73.50%), a decline in the yield of cotton due to insects (71.00%), lack of technical knowledge about the recommended insecticide, pesticide, fungicide and herbicide (68.00%), high costs of fertilizers (65.50%), uneven electricity supply (65.00%), lack of expertise in identifying the crop's pests, diseases, and weeds (63.50%). Farmer has not proper knowledge about improved cultivation practices of cotton crop. So famer should be provided the various training programmes be organized to provide practical knowledge and field demonstrations should be arranged on improved practices.

Spatial Variability of Bt Cotton On-farm Situation: A Survey

The survey was carried out during November 2015 in selected cotton fields of seven villages viz., Kalmala, Jagir Venkatapur and Ijapur of Raichur district and Gogi, Ulalkal, Hothpet and Maddarki villages of Yadgir districts covering part of TBP and UKP irrigation commands, respectively. The villages were selected based on the predominance of cotton area. Farmers plots were visited and location were recorded using GPS and observations were made on management practices, leaf reddening incidence and per cent leaf reddening, NDVI values, chlorophyll content, anthocyanin content and seed cotton yield. The spatial variability maps were generated using “krigging”, and interpolation method under GIS environment using observations on spatial spectral variability in NDVI, SPAD (chlorophyll content), anthocyanin content, leaf reddening index, reddening percentage and seed cotton as influenced by irrigation ecosystem, soil type and foliar spray of 19:19:19, date of sowing and cultivars. The data was further subjected for correlation studies. There is a lot of variation in the seed cotton yields among Bt cotton farmers across the Upper Krishna project and Tunga Bhadra Project irrigation commands of the state. Soils cultivated and cultivars used often remain the same the difference in yield could be due to nutritional management, the occurrence of leaf reddening, other factors, or combination of many of these attributing characteristics. Since leaf health is indicative of plant health and yield in turn, leaf health could be assessed through leaf spectral reflectance viz, NDVI, SPAD, and LRI. Based on this a survey was undertaken in November 2015 at Raichur (in three taluks) and Yadgir (in four taluks) districts falling under TBP and UKP irrigation commands, respectively, taking in all 90 farmers. The spectral observations were made on standing field crops besides leaf samples were collected to estimate leaf anthocyanin in the laboratory and GIS mapping was done using ESRI-made ArcGIS version 10.4 from Sujala-III, Remote Sensing and GIS laboratory, College of Agriculture, Raichur, was used to import GPS data and field data attachment for spatial analysis of leaf reddening incidence in cotton. The results on Spectral observations on NDVI, SPAD, leaf reddening index, reddening percentage in addition to leaf anthocyanin content varied due to locations, irrigation or otherwise, foliar spray, fertilization level, date of sowing, and cultivars.The leaf chlorophyll content is an important biometric character, the content quality, and duration (stability) indicate general crop health and ultimately yield directly if not influenced by the rate and efficiency of translocation to sink (kapas yield in case of cotton), therefore, it is often used to monitor real-timeN fertilization (LCC, SPAD, Green Seeker) as well as to forecast crop condition and yield. Periodic spectral observations on SPAD and NDVI are useful indicators of crop health and performance in commercial crop like cotton which could be extrapolated to field scale for crop management and production forecast.

Knowledge Gain of PJTSAU Agricultural Videos of YouTube Channel in Central Telangana Zone

Electronic media plays an important role in communication and dissemination process. This helps in disseminating the technologies and connect the people from one end to other end by getting them together with media facilities. Through internet the farmers, department offi cials and scientists can gain instant access to the most advanced research technologies can discuss their research problems with each other. Thus, PJTSAU Agricultural videos of YouTube channel is providing knowledge on crop production and protection aspects of diff erent crops to the farming community. Majority of the respondents had medium knowledge at pre-exposure stage (53.3%) and medium level at post exposure stage (66.6%) with regard to paddy production and protection aspects. A majority of the respondents were in the category of medium level at pre-exposure (71.6%) and high level at post exposure stage (75.0%) with regard to Cotton production and protection aspects. The study revealed that there was a signifi cant diff erence between pre and post knowledge gain of paddy and cotton farmers.

Efficacy of Talaromyces flavus microcapsule in controlling cotton important fungal diseases

Purpose: Evaluation of efficacy of Talaromyces flavus microcapsule in controlling cotton important fungal diseases and introducing Talaromyces flavus for using in the cotton fields. Methods: In this research focused on the application and efficiency evaluation of two new formulations of T. flavus microcapsules (TF-Co-G-1: Isolate 1 obtained from the Gorgan cotton farm) in suspension and powder forms, which were compared with the fungicide Talaromin® in farmers' fields. In this research, two cotton farms (Karkandeh and Hashemabad) with a history of infection with VW and DO were selected in two different regions of Golestan province. Each of the fields with an area of 1000 m2 was divided into seven equal parts. In each part, the treatments were applied separately, including microcapsule suspension with soil application, microcapsule suspension as seed impregnation, microcapsule powder with soil application, microcapsule suspension as seed impregnation, Talaromin® fungicide with soil application, Talaromin® as seed impregnation, and a control. The efficiencies of different treatments in terms of controlling the studied diseases and strengthening different growth indicators were evaluated by comparing the means of nine measured traits, including three traits for disease indicators (the percentage healthy seedlings for DO and the incidence rate and severity percentage of VW). To this end, the mixed analysis of variance (ANOVA) was first performed for two regions using the MS TAT C software, and the ANOVA of means was performed for each region separately with the significant effect of treatment × location. Results and conclusion: The results showed that among the studied formulations, powder microcapsules with seed impregnation by 32% and Talaromin® with soil application by 59% were respectively the most effective treatments for a significant increase in the percentage of healthy seedlings and a significant decrease in the severity of VW. Therefore, the obtained results indicate that the technical knowledge to produce T. flavus microcapsule formulations with two forms of suspension and powder can be transferred to manufacturing companies to carry out the commercialization and mass production process. Originality/value: Production of Talaromyces flavus microcapsule.

RETRACTED ARTICLE Fuzzy multicriteria analysis of “Better Cotton” farmers’ adoption and experts’ recommendation on cotton pest and disease management practices

RETRACTATION The SJAR Editorial Board, following the Guide to Good Practices of Editorial CSIC and the international recommendations of editorial ethics, due to a conflict of duplicity, has agreed to formally retract the following article: Fuzzy multicriteria analysis of “Better Cotton” farmers’ adoption and experts’ recommendation on cotton pest and disease management practices (2022), by Absar M. Jiskani, Manzoor A. Abro, M. Ibrahim Khaskheli and Khadim H. Wagan. Spanish Journal of Agricultural Research, Volume 20, Issue 4, e1006. https://doi.org/10.5424/sjar/2022204-18953. The retractation is based on the following: in addition to authorship and funding disputes that we cannot arbitrate, the article was submitted to two journals at the same time: Spanish Journal of Agricultural Research (SJAR) and Academia Journal of Agricultural Research (AJAR). This evidence came to light once the article was published in SJAR. The article was also published in AJAR 10(3), 042-053, March 2022: Fuzzy multi-critaria analysis of better Cotton farmers' adoption and experts' recommendation on Cotton pest and disease management practices, by Absar Mithal Jiskani, Noor Nabi Bhutto and Abdul Rehman, https://www.academiapublishing.org/journals/ajar/content/2022/Mar.htm, DOI: 10.15413/ajar.2021.0128. Currently there is neither access to the pdf nor there is any information. Only the author Absar M. Jiskani is common to both articles. Both journals indicate in their instructions the same. In the case of AJAR (https://www.academiapublishing.org/journals/ajar/policy.htm), “Authors must not submit the same work, in whole or in part, to two places of publication at the same time, or at any time while the manuscript is under review at ACADEMIA PUBLISHING”. In the case of SJAR (https://revistas.inia.es/index.php/sjar/about/submissions): “Submission of a manuscript implies the work described has not been published previously in any language (except in a book of abstracts, in the proceedings of a scientific meeting or as part of a thesis); the work is not under consideration for publication elsewhere, etc. “. When comparing the submitted version to SJAR to the article published in AJAR (pdf file supplied by the claimant), both articles present more than 80% similarity, with the abstract, tables and figures being identical. This similarity is unacceptable.