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Kumar, Himanshu
- Physiological, biochemical and molecular manifestations in response to seed priming with elicitors under drought in cotton
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Authors
Affiliations
1 ICAR-Central Institute for Cotton Research, Nagpur 441 108, India; Sam Higginbottom University of Agriculture, Technology and Sciences, Allahabad 221 007, India
2 ICAR-Central Institute for Cotton Research, Nagpur 441 108, India
3 Sam Higginbottom University of Agriculture, Technology and Sciences, Allahabad 221 007, India
1 ICAR-Central Institute for Cotton Research, Nagpur 441 108, India; Sam Higginbottom University of Agriculture, Technology and Sciences, Allahabad 221 007, India
2 ICAR-Central Institute for Cotton Research, Nagpur 441 108, India
3 Sam Higginbottom University of Agriculture, Technology and Sciences, Allahabad 221 007, India
Source
Current Science, Vol 123, No 5 (2022), Pagination: 658-666Abstract
Water stress has a detrimental effect on growth and development, which alters physio-biochemical activities. Seed priming with elicitors such as methyl jasmonate and paclobutrazol can mitigate the impact of drought stress. Therefore, pot-culture studies were conducted with drought-tolerant (DTS-155) and drought-susceptible (IC-357055) cotton genotypes to assess the seed priming effects of elicitors (methyl jasmonate and paclobutrazol) on the physio-biochemical changes and gene expression. The dose (50, 100, 150 and 200 mM) and time interval (1.5 and 2.5 h) experiments of both the elicitors were performed separately. On the basis of germination, seedling growth and vigour, a 150 mM elicitor for 1.5 h time interval was found to be the best. Biochemical and physiological parameters confirmed an increase in relative water content, total antioxidant activities, chlorophyll, superoxide dismutase, catalase and proline under drought stress in both the genotypes, but a decrease in lipid peroxidation. Among the elicitors, methyl jasmonate improved drought tolerance as compared to paclobutrazol. Gene expression studies with Rub-S, Rub-L and Osmotin confirmed the results. Transcript abundance of Osmotin and Rub-L was upregulated under drought stress in both the genotypes and was highest in methyl jasmonate primed samples. These findings suggest that priming with methyl jasmonate enhances drought tolerance in cottonKeywords
Drought responsive gene, Gossypium hirsu-tum, methyl jasmonate, paclobutrazol, seed primingReferences
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- Utilizing Machine Learning Algorithm, Cloud Computing Platform and Remote Sensing Satellite Data for Impact Assessment of Flood on Agriculture Land
Abstract Views :46 |
PDF Views:41
Authors
Affiliations
1 ICAR-National Dairy Research Institute, Karnal 132 001, IN
2 Lovely Professional University, Phagwara 144 001, IN
3 Space Applications Centre, Indian Space Research Organizations, Ahmedabad 380 015, IN
4 Commissionerate of Rural Development, Government of Gujarat, Gandhinagar 382 010, IN
1 ICAR-National Dairy Research Institute, Karnal 132 001, IN
2 Lovely Professional University, Phagwara 144 001, IN
3 Space Applications Centre, Indian Space Research Organizations, Ahmedabad 380 015, IN
4 Commissionerate of Rural Development, Government of Gujarat, Gandhinagar 382 010, IN
Source
Current Science, Vol 125, No 8 (2023), Pagination: 886-895Abstract
Floods are one of the most devastating natural disasters that cause immense damage to life, property and agriculture worldwide. Recurring floods in Bihar (a state in eastern India) during the monsoon season impact the agro-based economy, destroying crops and making it difficult for farmers to prepare for the next season. To mitigate the impact of floods on the agricultural sector, there is a need for early warning systems. Nowadays, remote sensing technology is used extensively for monitoring and managing flood events, which is also used in the present study. The random forest (RF) machine learning (ML) algorithm has also been used for land-use classification, and its output is used as an input for flood impact assessment. Here, we have analysed the flood extents and their impact on agriculture using Sentinel-1 SAR, Sentinel-2 and Planet Scope optical imageries on the Google Earth Engine (GEE) cloud computing platform. The present study shows that floods severely impacted a large part of Bihar during the monsoon seasons of 2020 and 2021. About 701,967 ha of land (614,706 ha agricultural land) in 2020 and 955,897 ha (851,663 ha agricultural land) in 2021 were severely flooded. An inundation maps and area statistics have been generated to visualise the results, which can help the government authorities prioritize relief and rescue operations.Keywords
Agriculture, Cloud Computing Platforms, Floods, Machine Learning Algorithm, Remote Sensing Data.References
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