Variability of agro climatic regime over homogeneous monsoon regions of India — El Niño and La Niña events

Prediction of vegetation anomalies at regional scales is essential for management of food and water resources. Forecast of vegetation anomalies at 1–3 months lead time can help in decision making. Here we show that normalized difference vegetation index (NDVI) along with other hydroclimatic variables (soil moisture and sea surface temperature) can be effectively used to predict vegetation anomalies in India. The spatiotemporal analysis of NDVI showed significant greening over the region during the period of 1982–2013. The root‐zone soil moisture showed a positive correlation with NDVI, whereas the El Niño–Southern Oscillation index (Nino 3.4) is negatively correlated in most of the regions. We extended this relationship to develop a model to predict NDVI in 1 to 3 months lead time. The predicted vegetation anomalies compare well with observations, which can be effectively utilized in early warning and better planning in water resources and agricultural sectors in India.

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“…[] and Rao et al . [] reported the negative impact of SST on food grain production over various Indian regions.…”

Section: Resultsmentioning

confidence: 99%

Prediction of vegetation anomalies to improve food security and water management in India

Asoka

Mishra

2015

Geophysical Research Letters

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“…The NDVI data has been averaged over Indian region and is used as All India NDVI in the present study. The same data sets were used by the authors to study the vegetal cover over India [18,19]. The NDVI data from 2001 to 2012 has been obtained from the MODIS Terra, supplied by University of Hamburg in Germany in gridded format for Indian region [20].…”

Section: Methodsmentioning

confidence: 99%

“…Since, these global teleconnections can reduce/increase the amount of rainfall over India, this will definitely show an impact on crop yields. Koteswara Rao et al [18], studied the variability of agro climatic parameters over the monsoon homogeneous regions of India and reported that there is a strong impact of ENSO on Indian crop yields. Madhu et al [10], also reported that the water stress conditions initially increase the rate of evapotranspiration and further increase the water scarcity which may lead to short /long term droughts.…”

Section: Sustainability 2019 11 X For Peer Review 3 Of 14mentioning

confidence: 99%

Studies on Crop Yields and Their Extreme Value Analysis over India

et al. 2019

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Trends of rice, wheat, maize, sorghum (jowar) and pearl millet (bazra) yields of India are studied in relation to water irrigation for the period 1951 to 2012. These crop yields have been subjected to correlation with the Normalized Difference Vegetation Index (NDVI), obtained from NOAA Advanced Very High Resolution Radiometer (AVHRR) (for 1982 to 2000) and Moderate Imaging Spectroradiometer (MODIS) Terra (for 2001 to 2012) to understand the linear association among them. Crop products and food inadequacy in percentage along with the average food production rate, available from FAO have been used in the present study. The present study mainly focuses on the estimation of return values of crop yields for different periods using Gumbell Extreme Value analysis. The present study is very important in the context of increased global food demands by 2050 where in many studies report that food production to be doubled by the year 2050 to meet the demands of increasing population. The main results of the study are: (i) significant positive correlations between NDVI and the crop yields during the study period; (ii) rice, maize and jowar yields did not show the required incremental rate while wheat and bajra yields are able to meet the expectations by the 2050. More efforts require to an increase of additional ~8% in the rice yields as the present growth is only ~12% and ought to be enhanced to ~20%.

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