Modern Concepts and Techniques for Better Cotton Production

Ghaffar, Abdul; Habib-ur-Rahman, Muhammad; Ali, Hafiz Rizwan; Haider, Ghulam; Ahmad, Saeed; Fahad, Shah; Ahmad, Shakeel

doi:10.1007/978-981-15-1472-2_29

Cited by 6 publications

(6 citation statements)

References 123 publications

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“…As with interaction between environments and sowing positions had significant effect in 2021 the highest and lowest values (0.024 and 0.017 g plant -1 day -1 ) were obtained from E1 X RP12 and E4 X RP3, respectively in 2021, while non-significant effect on this trait in 2022. Most researchers believe sampling from central parts and avoid sampling from borders Bennis et al [38] , This research is traying to verify the credibility of this prevailing idea and take the whole population as a sample instate of smaller number of plants to represent the community as usually is done , that to minimize the bias of sampling process via minimizing the experimental errors at least and tried to do the best during sampling and data collection and analysis to minimize the effect of extraneous factors to as minimum as possible Ghaffar et al [20] , and since the data are different in the their nature , types and scales , cluster analysis was adopted to judge the results after standardizing of all the data , row position 7 was regarded as a central row position where its neighbor rows 6 and 8 regarded as central rows and the terminal row one and 13 regarded as border rows that are taken in account as guard rows , so every row position resembles a sampling position or sampling site .…”

Section: Influence Of Environments and Row Positions On Crop Growth R...mentioning

confidence: 99%

“…Dhir et al [19] revealed that among various planting times, and row directions very little variation was recorded among observed and simulated maximum leaf area index. The choice of sample strategies is critical in agricultural statistics as it lays the groundwork for dependable data that are essential for making decisions [20] . Selecting the appropriate locations for the statistical sample is a crucial choice that involves balancing the centrally located plants in a plot against the peripheral plants.…”

Section: Introductionmentioning

confidence: 99%

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Influence of micro-environment on the growth characteristics of cotton (Gossypium hirsutum L.) with reference to row positions

Muhammad,

Marof

2024

PJBAS

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This experiment was conducted at Grdarasha Experimental Field, University of Salaheddin, Erbil (36.11 o N, 44.01 o E), to study the growth performance of cotton cultivar Coker 310 under four micro environmental conditions. The levels were composed of row directions and sowing dates as main plot (E1-E4) in split plot of Randomized Complete Block Design (RCBD). A total of 13 row positions (RP1-RP13) of different central and outer positions were included in sub plots, those rows had dual roles, as a principle constitute of the sub-plot and as a bilateral use to check the reality of the sampling statistic concept that suggests not to depend on the samples from terminal parts. with three replicates, during two successive growing seasons in 2021 and2022. Results showed that the highest value was recorded from (E1and E2) for both seasons. The highest values were recorded of weight dry matter from (E3 and E4), leaf area from (E3), leaf area index from (E3 and E4), crop growth rate and relative growth rate from (E4 and E1) at 60 DAS f or both seasons. at 90 DAS the highest values of weight dry matter, leaf area, crop growth rate was recorded form (E4) and leaf area index from (E3) in the first season, while the second season at 90DAS the highest values of weight dry matter, leaf area, leaf area index, crop growth rate was recorded from (E3) and relative growth rate was recorded from (E4). The row positions (RP8 and RP5) superiority in plant height for both yeas. (RP1 and RP9) recorded superiority in weight dry matter, (RP1 and RP8) recorded superiority in leaf area, (RP2 and RP8) recorded superiority in leaf area index and (RP10 and RP3) in relative growth rate at 60DAS for both years. The highest values at 90DAS of weight dry matter, leaf area and leaf area index were recorded from (RP9 and RP10) for both years. The highest values of crop growth rate were recorded from (RP9 and RP11) at 60 and 90 DAS for both years. The highest value of relative growth rate at 90DAS was recorded from (RP12)

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Section: Influence Of Environments and Row Positions On Crop Growth R...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Influence of micro-environment on the growth characteristics of cotton (Gossypium hirsutum L.) with reference to row positions

Muhammad,

Marof

2024

PJBAS

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“…The global climate is changing due to various anthropogenic factors that influence temperature regimes (Ahmad et al, 2020 ). There are several mechanisms, including phenological, physiological, morphological, and biochemical mechanisms, that plants exhibit for their survival under high-temperature conditions (Ghaffar et al, 2020 ) since plants are sessile in nature, which limits them to a specific range of responses to external stimuli that vary at different stages of growth and have flexible relevance to physiological and cellular mechanisms of protection and acclimatization (Ahmad et al, 2021a ). The prime stress indications (e.g., variations in temperature, ionic effects, osmotic effects, membrane uncertainty) would activate the signaling and transcript control that triggers the mechanism of stress responses to restore the homeostasis and repair the plasma membrane.…”

Section: Mechanism Of Heat Stress Tolerance In Plantsmentioning

confidence: 99%

“…In oilseed crops, the best way to improve stress tolerance is to associate different candid proteins that are physiologically significant. In oilseed crops, proteomic identification has been made by using both the gel-free and gel-based proteomics approaches (Chandramouli and Qian, 2009 ), and separation is done by using the most frequently used gel-based strategies, including one-dimensional gel electrophoresis (SDS-PAGE) (Han et al, 2013 ; Messaitfa et al, 2014 ; Li et al, 2020 ) and 2D-polyacrylamide gel electrophoresis (2-DGE) (Ghaffar et al, 2020 ). MS techniques like ESI and MALDI TOF proteomic-based experiments are very accurate and more precise due to the accessibility of many genomic sequences of many organisms and EST information.…”

Section: Mechanism Of Heat Stress Tolerance In Plantsmentioning

confidence: 99%

Adaptation Strategies to Improve the Resistance of Oilseed Crops to Heat Stress Under a Changing Climate: An Overview

et al. 2021

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Temperature is one of the decisive environmental factors that is projected to increase by 1. 5°C over the next two decades due to climate change that may affect various agronomic characteristics, such as biomass production, phenology and physiology, and yield-contributing traits in oilseed crops. Oilseed crops such as soybean, sunflower, canola, peanut, cottonseed, coconut, palm oil, sesame, safflower, olive etc., are widely grown. Specific importance is the vulnerability of oil synthesis in these crops against the rise in climatic temperature, threatening the stability of yield and quality. The natural defense system in these crops cannot withstand the harmful impacts of heat stress, thus causing a considerable loss in seed and oil yield. Therefore, a proper understanding of underlying mechanisms of genotype-environment interactions that could affect oil synthesis pathways is a prime requirement in developing stable cultivars. Heat stress tolerance is a complex quantitative trait controlled by many genes and is challenging to study and characterize. However, heat tolerance studies to date have pointed to several sophisticated mechanisms to deal with the stress of high temperatures, including hormonal signaling pathways for sensing heat stimuli and acquiring tolerance to heat stress, maintaining membrane integrity, production of heat shock proteins (HSPs), removal of reactive oxygen species (ROS), assembly of antioxidants, accumulation of compatible solutes, modified gene expression to enable changes, intelligent agricultural technologies, and several other agronomic techniques for thriving and surviving. Manipulation of multiple genes responsible for thermo-tolerance and exploring their high expressions greatly impacts their potential application using CRISPR/Cas genome editing and OMICS technology. This review highlights the latest outcomes on the response and tolerance to heat stress at the cellular, organelle, and whole plant levels describing numerous approaches applied to enhance thermos-tolerance in oilseed crops. We are attempting to critically analyze the scattered existing approaches to temperature tolerance used in oilseeds as a whole, work toward extending studies into the field, and provide researchers and related parties with useful information to streamline their breeding programs so that they can seek new avenues and develop guidelines that will greatly enhance ongoing efforts to establish heat stress tolerance in oilseeds.

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“…According to Ghaffar et al [ 17 ], it is possible to increase the profits from raising cotton and guarantee sustainability if precision technologies of agriculture are adopted. The overlapping of productivity maps and soil fertility, monitoring via satellites and drones, or even the localized application of products can provide significant economic and environmental gains and even reduce the use of pesticides by more than 50% [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of Cotton Leaf Lesions Using Deep Learning Techniques

Caldeira

Santiago

Teruel

2021

Sensors

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The use of deep learning models to identify lesions on cotton leaves on the basis of images of the crop in the field is proposed in this article. Cultivated in most of the world, cotton is one of the economically most important agricultural crops. Its cultivation in tropical regions has made it the target of a wide spectrum of agricultural pests and diseases, and efficient solutions are required. Moreover, the symptoms of the main pests and diseases cannot be differentiated in the initial stages, and the correct identification of a lesion can be difficult for the producer. To help resolve the problem, the present research provides a solution based on deep learning in the screening of cotton leaves which makes it possible to monitor the health of the cotton crop and make better decisions for its management. With the learning models GoogleNet and Resnet50 using convolutional neural networks, a precision of 86.6% and 89.2%, respectively, was obtained. Compared with traditional approaches for the processing of images such as support vector machines (SVM), Closest k-neighbors (KNN), artificial neural networks (ANN) and neuro-fuzzy (NFC), the convolutional neural networks proved to be up to 25% more precise, suggesting that this method can contribute to a more rapid and reliable inspection of the plants growing in the field.

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Modern Concepts and Techniques for Better Cotton Production

Cited by 6 publications

References 123 publications

Influence of micro-environment on the growth characteristics of cotton (Gossypium hirsutum L.) with reference to row positions

Influence of micro-environment on the growth characteristics of cotton (Gossypium hirsutum L.) with reference to row positions

Adaptation Strategies to Improve the Resistance of Oilseed Crops to Heat Stress Under a Changing Climate: An Overview

Identification of Cotton Leaf Lesions Using Deep Learning Techniques

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