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Ramanathan, S. P.
- Drought intensity and frequency analysis using SPI for Tamil Nadu, India
Abstract Views :177 |
PDF Views:86
Authors
S. Kokilavani
1,
S. P. Ramanathan
1,
Ga. Dheebakaran
1,
N. K. Sathyamoorthy
1,
N. K. Maragatham
1,
R. Gowtham
1
Affiliations
1 Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore 641 003, IN
1 Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore 641 003, IN
Source
Current Science, Vol 121, No 6 (2021), Pagination: 781-788Abstract
To assess the drought hazard for different agro-climatic zones of Tamil Nadu (TN), India, the present study deals with temporal trend and spatial pattern of drought over the period 1981–2019. Standardized Precipitation Index (SPI) has been used to detail the geographical variations of drought intensity, duration and frequency at multiple time steps. The spatial rainfall variability of the Southwest monsoon (SWM) ranged from 69.3 mm (Tuticorin) to 772.8 mm (the Nilgiris), and that for the Northeast monsoon (NEM) ranged from 277.8 mm (Krishnagiri) to 825.9 mm (Nagapattinam), while annual rainfall variability ranged from 558.8 mm (Tuticorin) to 1466.8 mm (the Nilgiris) for TN. Irrespective of all the regions, the frequency of moderate drought occurrence was higher compared to other drought nomenclature. The NEM season recorded on par and higher number of drought occurrences with respect to SWM season. Out of 39 years, TN experienced severely dry to extremely dry climate during 2002. The result underlines the potential of SPI in drought identification and also revealed that the rainfall is strongly linked to drought policies and measures implemented for the state.Keywords
Northeast monsoon, rainfall, southwest monsoon, spatial variability, standardized precipitation indexFull Text
References
- IPCC, Working Group, I Contribution to the IPCC Fifth Assessment Report on Climate Change 2013: The Physical Science Basis – Summary for Policymakers, Intergovernmental Panel on Climate Change, Stockholm, 2013.
- Lesk, C., Rowhani, P. and Ramankutty, N., Influence of extreme weather disasters on global crop production. Nature, 2016, 529, 84–87.
- NRC, Adapting to the Impacts of Climate Change: America’s Climate Choices, National Academies Press, Washington, DC, USA, 2010.
- Agha Kouchak, A., Feldman, D., Hoerling, M., Huxman, T. and Lund, J., Recognize anthropogenic drought. Nature, 2015, 524, 409–411.
- Dai, A., Increasing drought under global warming in observations and models. Nature Climate Change, 2013, 3, 52–58.
- Trenberth, K. E., Dai, A., Schrier, G., Jones, P. D., Barichivich, J., Briffa, K. R. and Sheffield, J., Global warming and changes in drought. Nature Climate Change, 2014, 4, 17–22.
- Dai, A., Drought under global warming: a review. Climate Change, 2011, 2(1), 45–65.
- Kamble, M. V., Ghosh, K., Rajeevan, M. and Samui, R. P., Drought monitoring over India through normalized difference vegetation index (NDVI). Mausam, 2010, 61, 537–546.
- CRED, Country profile of natural disasters, EM-DAT: The International Disaster Database, Centre for Research on the Epidemiology of Disasters, 2016.
- Thomas, T., Nayak, P. C. and Ghosh, N. C., Spatiotemporal analysis of drought characteristics in the Bundelkhand region of Central India using the standardized precipitation index. J. Hydrol. Eng., 2015, 20(11), 05015004-1–05015004-12.
- Mishra, A. K. and Singh, V. P., Drought modelling – a review. J. Hydrol., 2011, 403(1–2), 157–175.
- Hosseinizadeh, A., Seyed Kaboli, H., Zareie, H., Akhondalim, A. and Farjad, B., Impact of climate change on the severity, duration, and frequency of drought in a semi-arid agricultural basin. Geoenviron. Disasters, 2015, 2, 23.
- Hayes, M. J., Wilhelmi, O. V. and Knutson, C. L., Reducing drought risk: bridging theory and practice. Nat. Hazards Rev., 2004, 5(2), 106–113.
- Guttman, N. B., Comparing the Palmer drought index and the standardized precipitation index. J. Am. Water Resour. Assoc., 1998, 34(1), 113–121.
- Pramudya, Y. and Onishi, T., Assessment of the standardized precipitation index (SPI) in Tegal city, Central Java, Indonesia. IOP Conf. Series: Earth Environ. Sci., 2018, 129, 012019.
- Kogan, F. N., Contribution of remote sensing to drought early warning. In Early Warning Systems for Drought Preparedness and Drought Management (eds Wilnite, D. A. and Wood, D A.), World Meteorological Organization, Geneva, 2000, pp. 75–87.
- Hayes, M. J., Svoboda, M., Wall, N. and Widhalm, M., The Lincoln Declaration on drought indices: universal meteorological drought index recommended. Bull. Am. Meteorol. Soc., 2011, 92(4), 485–488.
- McKee, T. B., Doesken, N. J. and Kleist, J., The relationship of drought frequency and duration to time scales. In 8th Conference on Applied Climatology, Anaheim, CA, USA, 1993, pp. 179–184.
- Morid, S., Smakhtin, V. and Moghaddasi, M., Comparison of seven meteorological indices for drought monitoring in Iran. Int. J. Climatol., 2006, 26, 971–985.
- Kokilavani, S., Panneerselvam, S. and Dheebakaran, Ga, Centurial rainfall analysis for drought in Coimbatore city of Tamil Nadu, India. Madras Agric. J., 2019, 106(7–9), 484–487.
- Ramaraj, A. P., Kokilavani, S., Manikandan, N., Arthirani, B. and Rajalakshmi, D., Rainfall stability and drought valuation (using SPI) over southern zone of Tamil Nadu. Curr. World Environ., 2015, 10(3), 928–933.
- 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
References
- Oghaz, M. M. D., Razaak, M., Kerdegari, H., Argyriou, V. and Remagnino, P., Scene and environment monitoring using aerial im-agery and deep learning. IEEE, 2019.
- Childs, N. and LeBeau, B., Rice Outlook, Report, FAO, 2022.
- Saravanakumar, V., Lohano, H. D. and Balasubramanian, R., A dis-trict-level analysis for measuring the effects of climate change on production of rice: evidence from southern India. Theor. Appl. Cli-matol., 2022, 150(3–4), 941–953.
- Min, S., Lee, S. W., Choi, B.-R., Lee, S. H. and Kwon, D. H., In-secticide resistance monitoring and correlation analysis to select appropriate insecticides against Nilaparvata lugens (Stål), a migra-tory pest in Korea. J. Asia-Pac. Entomol., 2014, 17(4), 711–716.
- Cabauatan, P. Q., Cabunagan, R. C. and Choi, I.-R., Rice viruses transmitted by the brown planthopper Nilaparvata lugens. In Planthoppers: New Threats to the Sustainability of Intensive Rice Production Systems in Asia, IRRI Books, International Rice Res-earch Institute, 2009, pp. 357–368.
- Mohapatra, S. D. et al., Eco-smart pest management in rice farming: prospects and challenges. Oryza, 2019, 56(Special Issue), 143–155.
- Muller, A., Prakash, A., Lazutkaite, E. M. D., Amdihun, A. and Ouma, J., Scientific linkages between climate change and (trans-boundary) crop pest and disease outbreaks. In TMG Working Paper, 2022, p. 29.
- Abd El-Ghany, N. M., Abd El-Aziz, S. E. and Marei, S. S., A review: application of remote sensing as a promising strategy for insect pests and diseases management. Environ. Sci. Pollut. Res., 2020, 27, 33503–33515.
- Wang, F. M., Huang, J. F. and Wang, X. Z., Identification of optimal hyperspectral bands for estimation of rice biophysical parameters. J. Integr. Plant Biol., 2008, 50(3), 291–299.
- Katsoulas, N., Elvanidi, A., Ferentinos, K. P., Kacira, M., Bartzanas, T. and Kittas, C., Crop reflectance monitoring as a tool for water stress detection in greenhouses: a review. Biosyst. Eng., 2016, 151, 374–398.
- Curran, P., Principles of Remote Sensing, Longman, London, UK, 1985.
- Prasannakumar, N. R., Chander, S., Sahoo, R. N. and Gupta, V. K., Assessment of brown planthopper (Nilaparvata lugens) damage in rice using hyperspectral remote sensing. Int. J. Pest Manage., 2013, 59(3), 180–188.
- Seager, S., Turner, E. L., Schafer, J. and Ford, E. B., Vegetation’s red edge: a possible spectroscopic biosignature of extraterrestrial plants. Astrobiology, 2005, 5(3), 372–390.
- Yang, C. M. and Chen, R. K., Differences in growth estimation and yield prediction of rice crop using satellites data simulated from near ground hyperspectral reflectance. J. Photogramm. Remote Sensing, 2007, 12(1), 93–105.
- Liu, X. D. and Sun, Q. H., Early assessment of the yield loss in rice due to the brown planthopper using a hyperspectral remote sensing method. Int. J. Pest Manage., 2016, 62(3), 205–213.
- Liu, J., Han, J., Chen, X., Shi, L. and Zhang, L., Nondestructive de-tection of rape leaf chlorophyll level based on vis–NIR spectroscopy. Spectrochim. Acta Part A, 2019, 222, 117202.
- Huang, J., Liao, H., Zhu, Y., Sun, J., Sun, Q. and Liu, X., Hyper-spectral detection of rice damaged by rice leaf folder (Cnaphalo-crocis medinalis). Comput. Electron. Agric., 2012, 82, 100–107.
- Abdel-Rahman, E. M., Ahmed, F. B., van den Berg, M. and Way, M. J., Potential of spectroscopic data sets for sugarcane thrips (Fulmekiola serrata Kobus) damage detection. Int. J. Remote Sens-ing, 2010, 31(15), 4199–4216.
- Madasamy, B., Balasubramaniam, P. and Dutta, R., Microclimate-based pest and disease management through a forewarning system for sustainable cotton production. Agriculture, 2020, 10(12), 641.
- Penuelas, J., Gamon, J. A., Griffin, K. L. and Field, C. B., Assessing community type, plant biomass, pigment composition, and photo-synthetic efficiency of aquatic vegetation from spectral reflectance. Remote Sensing Environ., 1993, 46(2), 110–118.
- Daughtry, C. S. T., Walthall, C. L., Kim, M. S., DeColstoun, E. B. and McMurtrey Iii, J. E., Estimating corn leaf chlorophyll concen-tration from leaf and canopy reflectance. Remote Sensing Environ., 2000, 74(2), 229–239.
- Rouse, J. W., Haas, R. H., Schell, J. A. and Deering, D. W., Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec. Publ., 1974, 351(1), 309.
- Gates, D. M., Keegan, H. J., Schleter, J. C. and Weidner, V. R., Spectral properties of plants. Appl. Opt., 1965, 4(1), 11–20.
- Fitzgerald, G. J., Rodriguez, D., Christensen, L. K., Belford, R., Sadras, V. O. and Clarke, T. R., Spectral and thermal sensing for nitrogen and water status in rainfed and irrigated wheat environ-ments. Precis. Agric., 2006, 7, 233–248.
- Carter, G. A., Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. Remote Sensing, 1994, 15(3), 697–703.
- Yang, C. M., Cheng, C. H. and Chen, R. K., Changes in spectral characteristics of rice canopy infested with brown planthopper and leaffolder. Crop Sci., 2007, 47(1), 329–335.
- Sahoo, R. N., Ray, S. S. and Manjunath, K. R., Hyperspectral re-mote sensing of agriculture. Curr. Sci., 2015, 108(5), 848–859.
- Hunt Jr, E. R. and Rock, B. N., Detection of changes in leaf water content using near-and middle-infrared reflectances. Remote Sens-ing Environ., 1989, 30(1), 43–54.
- Clarke, A. and Fraser, K. P. P., Why does metabolism scale with temperature? Funct. Ecol., 2004, 18(2), 243–251.
- Taylor, R. A. J., Herms, D. A., Cardina, J. and Moore, R. H., Climate change and pest management: unanticipated consequences of trophic dislocation. Agronomy, 2018, 8(1), 7.
- Hannigan, S., Nendel, C. and Krull, M., Effects of temperature on the movement and feeding behaviour of the large lupine beetle, Sitona gressorius. J. Pest Sci., 2022, 1–14.
- Priyadarshini, S., Ghosh, S. K. and Nayak, A. K., Field screening of different chilli cultivars against important sucking pests of chilli in West Bengal. Bull. Environ., Pharmacol. Life Sci., 2019, 8(7), 134–140.
- Yan, T., Xu, W., Lin, J., Duan, L., Gao, P., Zhang, C. and Lv, X., Com-bining multi-dimensional convolutional neural network (CNN) with visualization method for detection of Aphis gossypii Glover infec-tion in cotton leaves using hyperspectral imaging. Front. Plant Sci., 2021, 12, 604.
- Polivova, M. and Brook, A., Detailed investigation of spectral vegeta-tion indices for fine field-scale phenotyping. Vegetation Index Dyna-mics, 2021.
- Huang, J. R., Sun, J. Y., Liao, H. J. and Liu, X.-D., Detection of brown planthopper infestation based on SPAD and spectral data from rice under different rates of nitrogen fertilizer. Precis. Agric., 2015, 16, 148–163.
- Vanegas, F., Bratanov, D., Powell, K., Weiss, J. and Gonzalez, F., A novel methodology for improving plant pest surveillance in vine-yards and crops using UAV-based hyperspectral and spatial data. Sensors, 2018, 18(1), 260.
- Pinter Jr, P. J., Hatfield, J. L., Schepers, J. S., Barnes, E. M., Moran, M. S., Daughtry, C. S. T. and Upchurch, D. R., Remote sensing for crop management, 2003.
- Broge, N. H. and Leblanc, E., Comparing prediction power and sta-bility of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing Environ., 2001, 76(2), 156–172.
- de Lima, I. P., Jorge, R. G. and de Lima, J. L. M. P., Remote sensing monitoring of rice fields: Towards assessing water saving irrigation management practices. Front. Remote Sensing, 2021, 2, 762093.
- Kurbanov, R. and Zakharova, N., Justification and selection of vegetation indices to determine the early soybeans readiness for harvesting. EDP Sciences, 2021.
- Luo, J., Huang, W., Zhao, J., Zhang, J., Zhao, C. and Ma, R., Detecting aphid density of winter wheat leaf using hyperspectral measure-ments. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sensing, 2013, 6(2), 690–698.
- Prabhakar, M., Prasad, Y., Desai, S. and Thirupathi, M., Spectral and spatial properties of rice brown plant hopper and groundnut late leaf spot disease infestation under field conditions. J. Agrome-teorol., 2013, 15, 57–62.
- Sogawa, K., The rice brown planthopper: feeding physiology and host plant interactions. Ann. Rev. Entomol., 1982, 27(1), 49–73.
- Watanabe, T. and Kitagawa, H., Photosynthesis and translocation of assimilates in rice plants following phloem feeding by the planthopper Nilaparvata lugens (Homoptera: Delphacidae). J. Econ. Entomol., 2000, 93(4), 1192–1198.
- Liu, J. L., Yu, J. F., Wu, J. C., Yin, J. L. and Gu, H. N., Physiological responses to Nilaparvata lugens in susceptible and resistant rice varie-ties: allocation of assimilates between shoots and roots. J. Econ. Entomol., 2008, 101(2), 384–390.
- Vanitha, K., Suresh, S. and Gunathilagaraj, K., Influence of brown planthopper Nilaparvava lugens feeding on nutritional biochemis-try of rice plant. ORYZA – Int. J. Rice, 2011, 48(2), 142–146.