Book of Extended summaries ISDA

International Conference on Reimagining Rainfed Agro-ecosystems: Challenges &
Opportunities during 22-24, December 2022 at ICAR-CRIDA, Hyderabad
clay soil, 2001 in shallow clay soil and during 2004 and 2015 in shallow loam soils. In deep
loam soils the crop performance was near-to-normal during the entire study period. Whereas
in greengram unusually moist situations existed irrespective of the soil type and depth during
2010 and 2013. However, the same situation was noted during 2008 and 2015 in both deep and
shallow loam soils and during 2000, 2003, 2005 and in deep loam soils during 2007 alone.
The relation established between rainfall and VegDRI also reported that the linear function
represented data more than 50 percent during the entire study period. The predictive efficiency
of the linear term (b) and the determination coefficient (R 2 ) was high in redgram and moderate
to high in greengram. The total explained variation in the estimation of the crop performance
by VegDRI index in relation to rainfall was 0.58 to 0.96. The explained variation of more than
0.75 for the entire study period makes VegDRI index dependable in the estimation of redgram
performance in response to soil moisture availability and is more than 0.5 for greengram.
Conclusions
From the above results it can be inferred that, input from the spectral signatures of the crop
canopy besides ground-based information had made VegDRI more agreeable for different soil
types, soil depths and crop types. A similar result was mentioned by Won-Ho Nam et al., where
PDSI delayed or lagged in the drought assessment since the data depend on soil moisture,
whereas VegDRI-SKorea was useful for more timely detection of improvement in drought
conditions compared to the station-based self-calibrated PDSI. Crops like greengram being
sturdier and with quick canopy coverage might have been analysed easily by VegDRI for crop
condition based on moisture availability. According to Brown et al., (2008) any subtle change
in vegetation conditions when there is low green biomass can result in greater fluctuations in
NDVI values as compared to later in the growing season when slight changes in vegetation
with higher biomass result in less change in the NDVI values.
References
Brown, J. F., Wardlow, B.D., Tadesse, T., Hayes, M. J., and Reed, B. C. 2007. The vegetation
drought response index (VegDRI): A new integrated approach for monitoring drought
stress in vegetation, In Press. GI Sci. Rem. Sens.
Palmer, W. C. 1965. Meteorological drought. Office of Climatology Research Paper 45,
Weather Bureau, Washington DC, 58 pp.
Wilhite, D. A. and Glantz, M. H. 1985. Understanding the drought phenomenon: The role of
definitions. Water Int., 10:111–120
Won-Ho Nam, Tsegaye Tadesse, Brian D, Wardlow, Michael Hayes J, Mark D. Svoboda, Eun-
Mi Hong, Yakov A. Pachepsky and Min-Won Jang. 2018. Developing the vegetation drought
717 | Page Emerging approaches (RS, AI, ML, Drones etc) for crop management &assessment