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Pazhanivelan, S.
- Rainfall and Temperature Projections and their Impact Assessment Using CMIP5 Models under Different RCP Scenarios for The Eastern Coastal Region of India
Abstract Views :304 |
PDF Views:89
Authors
S. Vijayakumar
1,
A. K. Nayak
1,
A. P. Ramaraj
2,
C. K. Swain
1,
V. Geethalakshmi
3,
S. Pazhanivelan
3,
Rahul Tripathi
1,
N. S. Sudarmanian
3
Affiliations
1 ICAR-National Rice Research Institute, Cuttack 753 006, IN
2 International Crops Research Institute for the Semi-Arid Tropics, Hyderabad 502 324, IN
3 Tamil Nadu Agricultural University, Coimbatore 641 003, IN
1 ICAR-National Rice Research Institute, Cuttack 753 006, IN
2 International Crops Research Institute for the Semi-Arid Tropics, Hyderabad 502 324, IN
3 Tamil Nadu Agricultural University, Coimbatore 641 003, IN
Source
Current Science, Vol 121, No 2 (2021), Pagination: 222-232Abstract
Trend analysis of annual rainfall over the coastal districts of Odisha, India showed statistically nonsignificant increasing trend in all districts, except Ganjam. Whereas the maximum and minimum temperature showed significant increasing trend. Warming in these districts is mainly due to increasing minimum temperature during summer and rainy season, and maximum temperature during winter. Future climate projection results revealed, the annual mean rainfall is expected to change by 0.1–2.2%, –0.3–0.7% and 1.5–3.2% (RCP 4.5), and 3.6–7.9%, 3.7–6.6% and 8.5–14% (RCP 8.5) during the near (2011–39), mid (2040–69) and late (2070–99) centuries respectively. Anticipate climate change will have a marginal impact on total rainfall, and a major impact on its distribution. The annual mean minimum temperature is expected to increase by 0.60–0.73°C, 0.71–0.88°C, 1.20–1.42°C (RCP 4.5), and 1.77–2.14°C, 1.56–1.68°C, 3.06–3.73°C (RCP 8.5) during near, mid and late centuries respectively. Similarly, the annual mean maximum temperature is expected to increase by 0.61–0.66°C, 0.68–0.72°C and 1.35–1.55°C (RCP 4.5), and 1.79–1.97°C, 1.73–2.01°C and 3.08–3.44°C (RCP 8.5) during near, mid and late centuries respectively. Season-wise projection revealed that the change in rainfall and temperature is expected to be more in winter and summer under both the RCP scenarios. The projected future climate change will have both positive and negative impacts on agriculture. The negative impacts are expected to be more pronounced during kharif in comparison to rabi.Keywords
Climate Projection, Coastal Districts, Rainfall, Temperature, Trend Analysis.Full Text
References
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- Spatial prediction of leaf chlorophyll content in cotton crop using drone-derived spectral indices
Abstract Views :169 |
PDF Views:90
Authors
P. Shanmugapriya
1,
K. R. Latha
1,
S. Pazhanivelan
2,
R. Kumaraperumal
3,
G. Karthikeyan
4,
N. S. Sudarmanian
5
Affiliations
1 Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore 641 003, India, IN
2 Water Technology Centre, Tamil Nadu Agricultural University, Coimbatore 641 003, India, IN
3 Department of Remote Sensing and GIS, Tamil Nadu Agricultural University, Coimbatore 641 003, India, IN
4 Department of Plant Pathology, Tamil Nadu Agricultural University, Coimbatore 641 003, India, IN
5 Krishi Vigyan Kendra, Aruppukottai 626 107, India, IN
1 Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore 641 003, India, IN
2 Water Technology Centre, Tamil Nadu Agricultural University, Coimbatore 641 003, India, IN
3 Department of Remote Sensing and GIS, Tamil Nadu Agricultural University, Coimbatore 641 003, India, IN
4 Department of Plant Pathology, Tamil Nadu Agricultural University, Coimbatore 641 003, India, IN
5 Krishi Vigyan Kendra, Aruppukottai 626 107, India, IN
Source
Current Science, Vol 123, No 12 (2022), Pagination: 1473-1480Abstract
Crop health monitoring and assessment have become more successful with the advent of remote sensing technology in agriculture. Using this technology, retrieving information about crop biophysical parameters on a non-destructive basis at spatial and temporal scales has been made possible. Several drone-derived spectral vegetation indices (VIs) have assessed crop growth status in a larger farming area. In this study, we generated VI maps for a cotton field area in the Tamil Nadu Agricultural University, Coimbatore, India. The ground-truth chlorophyll data (SPAD-502 Minolta meter) were collected from the field on the same day of drone image acquisition. Pearson correlation analysis and regression analysis were done for validation and accuracy of the ground-truth chlorophyll data and VIs. The study reveals that obtaining near real-time chlorophyll content using high spatial resolution drone images is quick and reliableKeywords
Chlorophyll content, cotton crop, drone, multi-spectral images, spectral indices.Full Text
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- Effect of temperature on brown planthopper Infestation in rice using hyperspectral remote Sensing
Abstract Views :89 |
PDF Views:59
Authors
S. Sivaranjani
1,
V. Geethalakshmi
1,
S. Pazhanivelan
1,
J. S. Kennedy
1,
S. P. Ramanathan
1,
R. Gowtham
2,
K. Pugazenthi
1
Affiliations
1 Tamil Nadu Agricultural University, Coimbatore 641 003, India., IN
2 Indian Farmers Fertilizers Cooperative Limited, Coimbatore 641 003, India., IN
1 Tamil Nadu Agricultural University, Coimbatore 641 003, India., IN
2 Indian Farmers Fertilizers Cooperative Limited, Coimbatore 641 003, India., IN
Source
Current Science, Vol 124, No 10 (2023), Pagination: 1194-1200Abstract
Hyperspectral remote sensing captures images in multiple wavelengths and is widely used to detect plant stress in agriculture. A study was conducted on brown planthopper (BPH) infestation in rice at various temperature regimes (15°C, 20°C, 25°C, 30°C and 35°C). The experimentation was done in the Environmental Control Chamber, Tamil Nadu Agricultural University, Coimbatore, India. The field spectroradiometer and vegetation indices were used to study the early and late infestations of BPH in rice. The results reveal that reflectance at certain wavelengths (550, 670 and 700 nm) indicates plant stress. Among the vegetation indices, MCARI performed better than NDVI, PRI, NDRE and SR for the detection of early and late infestation of BPH. Hence, hyperspectral reflectance from rice has been used to detect pest damage and improve management policies.Keywords
Brown planthopper, hyperspectral sensor, Plant stress, rice, vegetation indices.Full Text
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