Abstract
Drought is an extreme climatic situation where there is a water shortage arising due to sub-normal rainfall, erratic distribution of precipitation, increased water supply demand, etc. India faced several years of drought in last six decades. As Indian agriculture is largely dependent on the monsoon, a slight change affects production as well as crop yield drastically. Statistical analysis is important for mapping the drought prone areas. Raichur district of the northern interior state of Karnataka is a drought-prone region where the economy is mainly based on agriculture. So, the uneven distribution of rainfall as well as the delay in the arrival of the southwest monsoon adversely affects the growth stage of crops which result in a decline in crop production. The effect of drought on the agriculture for the past decade has been analyzed using crop productivity data. When the production rate of Raichur district was studied for the years 1998 to 2009, it was seen that major crops like rice and jowar faced a decline in its production during the years 2002 and 2003, whereas bajra, maize, etc. mostly decreased in the year 2004.
Similar content being viewed by others
References
Akinremi, O. O., McGinn, S. M., & Barr, A. G. (1996). Evaluation of the Palmer drought index on the Canadian prairies. Journal of Climate, 9, 897–905.
Chakraborthy, A., & Sehgal, V. K. (2010). Assessment of agricultural drought using MODIS derived normalized difference water index. Journal of Agricultural Physics, 10, 28–36.
Dabrowska-Zielinska, et.al (2002) Modeling of crop growth conditions and crop yield in Poland using AVHRR-based indices. International Journal of Remote Sensing, 23, 1109–1123
Dracup, J. A., Lee, K. S., & Paulson, E. G., Jr. (1980). On the definition of droughts. Water Resources Research, 16(2), 297–302.
Hielkema, J. U., et al. (1986). Rainfall and vegetation monitoring in the Savanna zone of Democratic Republic Sudan using NOAA Advanced Very High Resolution Radiometer. International Journal of Remote Sensing, 7, 1499–1514.
Justice, C. O., et al. (1985). Analysis of the phenology of global vegetation using meteorological satellite data. International Journal of Remote Sensing, 6, 1271–1318.
Kogan, F. N. (1987a) Vegetation index for areal analysis of crop conditions. Proceedings of 18th Conference on Agricultural and Forest Meteorology, AMS, W. Lafayette, Indiana, on 15–18 September 1987 (Indiana, USA), p 103–106.
Kogan, F. N. (1987b) On using smoothed vegetation time-series for identifying near-optimal climate conditions. Edmonton, Canada (Edmonton, Canada): Proceedings of the 10th Conference on Probability and Statistics AMS, (pp. 81–83)
Kogan, F. N. (1990). Remote sensing of weather impacts on vegetation in non-homogeneous areas. International Journal of Remote Sensing, 11, 1405–1419.
Kogan, F. N. (1995). Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15, 91–100.
Kogan, F. N. (1998). A typical pattern of vegetation conditions in southern Africa during El Nino years detected from AVHRR data using three-channel numerical index. International Journal of Remote Sensing, 19, 3689–3695.
Morgan, R. (1985). The development and application of a drought early warning system in Botswana. Disasters, 9(1), 44–50.
Narasimhan, B., & Srinivasan, R. (2005). Development and evaluation of soil moisture deficit index and evapotranspiration deficit index for agricultural drought monitoring. Agricultural and Forest Meteorology, 133, 69–88.
Sinha, C. P., Chaube, U. C., & Saxena, R. P. (1992). A critical analysis of drought indices for different regions (pp. 10–14). New Delhi: In Proceedings World Congress on Natural Hazards.
Spruce JP, Sader S, et al. (2010). Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks. Remote Sensing of Environment.
Sruthi, S., & Aslam, M. (2014). Vegetation stress analysis using NDVI at drought prone Raichur district, Karnataka. IWRM International Symposium.
Sruthi, S., & Aslam, M. (2015). Agricultural drought analysis using the NDVI and land surface temperature data; a case study of Raichur district. Aquatic Procedia, 4, 1258–1264.
Sun L, A, B Scott W. Mitchella, B, Davidsona A, C. (2011). Multiple drought indices for agricultural drought risk assessment on the Canadian prairies. International Journal of Climatology, 32(11), 1628–1639.
Thenkabail, P. S., Gamage, M. S. D. N. and Smakhtin, V. U. (2004).The use of remote-sensing data for drought assessment and monitoring in Southwest Asia (Research Report 85). Colombo: International Water Management Institute.
Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150.
Tucker, C. J., et al. (1982). Monitoring large scale vegetation dynamics in the Nile delta and river valley from NOAA AVHRR data. Proceedings of the Conference on Remote Sensing of Arid and Semi-Arid Lands, Cairo, Egypt (pp. 973–977). Ann Arbor: Environmental Research Institute of Michigan.
Wan, Z., Wang, P., & Li, X. (2004). Using MODIS land surface temperature and normalized difference vegetation index products for monitoring drought in the southern Great Plains, USA. International Journal of Remote Sensing, 25, 61–72.
Wilhite, D. A., & Glantz, M. H. (1985). Understanding the drought phenomenon: the role of definitions. Water International, 10, 111–120.
Zhu J, Miller AE, Chuck L, Broderson D, Heinrichs T, Parker M (2013) Modis NDVI products and metrics user Manual, Version 1.0.
Acknowledgments
The authors acknowledge the Central University of Karnataka for providing the available facilities to carry out this work. The first author also wishes to acknowledge the thanks to DST (Department of Science and Technology) for providing DST-INSPIRE fellowship.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Swathandran, S., Aslam, M.A.M. Food productivity trend analysis of Raichur district for the management of agricultural drought. Environ Monit Assess 188, 63 (2016). https://doi.org/10.1007/s10661-015-5065-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10661-015-5065-6