Sensitivity of crop cover to climate variability: insights from two Indian agro-ecoregions.

Crop productivity in India varies greatly with inter-annual climate variability and is highly dependent on monsoon rainfall and temperature. The sensitivity of yields to future climate variability varies with crop type, access to irrigation and other biophysical and socio-economic factors. To better understand sensitivities to future climate, this study focuses on agro-ecological subregions in Central and Western India that span a range of crops, irrigation, biophysical conditions and socioeconomic characteristics. Climate variability is derived from remotely-sensed data products, Tropical Rainfall Measuring Mission (TRMM - precipitation) and Moderate Resolution Imaging Spectroradiometer (MODIS - temperature). We examined green-leaf phenologies as proxy for crop productivity using the MODIS Enhanced Vegetation Index (EVI) from 2000 to 2012. Using both monsoon and winter growing seasons, we assessed phenological sensitivity to inter-annual variability in precipitation and temperature patterns. Inter-annual EVI phenology anomalies ranged from -25% to 25%, with some highly anomalous values up to 200%. Monsoon crop phenology in the Central India site is highly sensitive to climate, especially the timing of the start and end of the monsoon and intensity of precipitation. In the Western India site, monsoon crop phenology is less sensitive to precipitation variability, yet shows considerable fluctuations in monsoon crop productivity across the years. Temperature is critically important for winter productivity across a range of crop and management types, such that irrigation might not provide a sufficient buffer against projected temperature increases. Better access to weather information and usage of climate-resilient crop types would play pivotal role in maintaining future productivity. Effective strategies to adapt to projected climate changes in the coming decades would also need to be tailored to regional biophysical and socio-economic conditions.

[1]  J. Famiglietti,et al.  Satellite-based estimates of groundwater depletion in India , 2009, Nature.

[2]  L. Ziska,et al.  Climate change and rice , 1995 .

[3]  N. Dubash Tubewell capitalism: groundwater development and agrarian change in Gujarat. , 2002 .

[4]  Wang Jing,et al.  Effects of temperature increase and elevated CO2 concentration, with supplemental irrigation, on the yield of rain-fed spring wheat in a semiarid region of China , 2005 .

[5]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[6]  H. Akaike A new look at the statistical model identification , 1974 .

[7]  H. Kaur,et al.  Impact of climate change scenarios on yield, water and nitrogen-balance and -use efficiency of rice–wheat cropping system , 2013 .

[8]  Fulu Tao,et al.  Land surface phenology dynamics and climate variations in the North East China Transect (NECT), 1982–2000 , 2008 .

[9]  Pinki Mondal,et al.  Mapping cropping intensity of smallholder farms: A comparison of methods using multiple sensors , 2013 .

[10]  Kenneth J. Boote,et al.  Temperature Effects on Rice at Elevated CO2 Concentration , 1992 .

[11]  W. Cramer Air pollution and climate change both reduce Indian rice harvests , 2006, Proceedings of the National Academy of Sciences.

[12]  J. Mustard,et al.  Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil , 2008 .

[13]  R. DeFries,et al.  Cropland expansion changes deforestation dynamics in the southern Brazilian Amazon , 2006, Proceedings of the National Academy of Sciences.

[14]  H Meiners,et al.  [Effects of temperature]. , 1973, ZWR.

[15]  V. Ramanathan,et al.  Climate change, the monsoon, and rice yield in India , 2012, Climatic Change.

[16]  Ashutosh Kumar Singh,et al.  Yield and water productivity of rice-wheat on raised beds at New Delhi, India , 2007 .

[17]  James Hansen,et al.  Climate impacts on Indian agriculture , 2004 .

[18]  C. Reason,et al.  On the relative roles of El Nino and Indian Ocean Dipole events on the Monsoon Onset over Kerala , 2011 .

[19]  John C. Daucsavage,et al.  Land processes distributed active archive center product lifecycle plan , 2014 .

[20]  P. Aggarwal,et al.  Simulating impacts, potential adaptation and vulnerability of maize to climate change in India , 2010 .

[21]  R. Wu,et al.  Impacts of the Indian Ocean on the Indian Summer Monsoon-ENSO Relationship , 2004 .

[22]  Walter E. Baethgen,et al.  Contributions of individual variation in temperature, solar radiation and precipitation to crop yield in the North China Plain, 1961–2003 , 2013, Climatic Change.

[23]  K. Cassman,et al.  Rice yields decline with higher night temperature from global warming. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[24]  R. Selvaraju Impact of El Niño–southern oscillation on Indian foodgrain production , 2003 .

[25]  David B. Lobell,et al.  Why are agricultural impacts of climate change so uncertain? The importance of temperature relative to precipitation , 2008, Environmental Research Letters.

[26]  Hiroyuki Ohno,et al.  Spatio-temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers , 2006 .

[27]  John H. Prueger,et al.  Value of Using Different Vegetative Indices to Quantify Agricultural Crop Characteristics at Different Growth Stages under Varying Management Practices , 2010, Remote. Sens..

[28]  I. Pal,et al.  Long-term changes and variability of monthly extreme temperatures in India , 2010 .

[29]  J. Mustard,et al.  The Amazon Frontier of Land-Use Change: Croplands and Consequences for Greenhouse Gas Emissions , 2010 .

[30]  R. P. Singh,et al.  Use of vegetation index and meteorological parameters for the prediction of crop yield in India , 2007 .

[31]  Toshihiro Sakamoto,et al.  Agro-ecological Interpretation of Rice Cropping Systems in Flood-prone Areas using MODIS Imagery , 2009 .

[32]  R. Jones,et al.  Simulated projections for summer monsoon climate over India by a high-resolution regional climate model (PRECIS) , 2011 .

[33]  Climate Change and Rice , 1996 .

[34]  Murari Lal,et al.  Implications of climate change in sustained agricultural productivity in South Asia , 2011 .

[35]  N. H. Ravindranath,et al.  Multi-model climate change projections for India under representative concentration pathways , 2012 .

[36]  A. Sood,et al.  Yield and water productivity of Bt cotton (Gossypium hirsutum) as influenced by temperature under semi-arid conditions of north-western India: field and simulation study , 2012 .

[37]  L. H. Allen,et al.  Temperature Effects on Rice at Elevated C02 , 1992 .

[38]  Christopher O. Justice,et al.  Estimating Global Cropland Extent with Multi-year MODIS Data , 2010, Remote. Sens..

[39]  S. Gadgil,et al.  The Asian monsoon — agriculture and economy , 2006 .

[40]  Koji Kotani,et al.  Climatic impacts across agricultural crop yield distributions: An application of quantile regression on rice crops in Andhra Pradesh, India , 2013 .

[41]  B. Wardlow,et al.  Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains , 2008 .

[42]  Madhya Pradesh,et al.  Census of India 2011 , 2011 .

[43]  R. S. Sengar,et al.  Impact of climate change on Indian agriculture , 2014 .

[44]  Mutlu Ozdogan,et al.  The spatial distribution of crop types from MODIS data: Temporal unmixing using Independent Component Analysis , 2010 .

[45]  B. Wardlow,et al.  Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains , 2007 .

[46]  A. Clemmens,et al.  Cotton response to high frequency surface irrigation , 1998 .

[47]  J. V. Revadekar,et al.  Statistical analysis of the relationship between summer monsoon precipitation extremes and foodgrain yield over India , 2012 .

[48]  J. I. Ortiz-Monasterio,et al.  Extreme heat effects on wheat senescence in India , 2012 .

[49]  Damien Sulla-Menashe,et al.  Winter wheat area estimation from MODIS-EVI time series data using the Crop Proportion Phenology Index , 2012 .

[50]  D. K. Freebairn Did the Green Revolution Concentrate Incomes? A Quantitative Study of Research Reports , 1995 .

[51]  P. Hazell,et al.  Targeting public investments by agro-ecological zone to achieve growth and poverty alleviation goals in rural India , 2000 .

[52]  P. V. Vara Prasad,et al.  Temperature variability and the yield of annual crops , 2000 .

[53]  B. Wardlow,et al.  Using USDA Crop Progress Data for the Evaluation of Greenup Onset Date Calculated from MODIS 250-Meter Data , 2006 .

[54]  C. Mandal,et al.  Agro-Ecological Zones , their Soil Resource and Cropping Systems , 2022 .

[55]  N. Subash,et al.  An investigation into observational characteristics of rainfall and temperature in Central Northeast India—a historical perspective 1889–2008 , 2011 .

[56]  J. Matsumoto,et al.  Effects of rainfall variation on rice production in the Ganges-Brahmaputra Basin , 2009 .

[57]  Linda O. Mearns,et al.  The effect of changes in daily and interannual climatic variability on CERES-Wheat: A sensitivity study , 1996 .

[58]  Xiangming Xiao,et al.  Quantifying the area and spatial distribution of double- and triple-cropping croplands in India with multi-temporal MODIS imagery in 2005 , 2011 .

[59]  I. Pal,et al.  Assessing seasonal precipitation trends in India using parametric and non-parametric statistical techniques , 2011 .

[60]  Vis Taraz Adaptation to climate change: historical evidence from the Indian monsoon , 2017, Environment and Development Economics.

[61]  Raymond Guiteras,et al.  The Impact of Climate Change on Indian Agriculture , 2008 .

[62]  Ron B. H. Wills,et al.  Effects of temperature. , 2007 .

[63]  Aaron Moody,et al.  Trends in vegetation activity and their climatic correlates: China 1982 to 1998 , 2004 .

[64]  Lynn P. Nygaard,et al.  Mapping vulnerability to multiple stressors: climate change and globalization in India , 2004 .

[65]  Jan de Leeuw,et al.  Introducing Multilevel Modeling , 1998 .

[66]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[67]  T. O'riordan,et al.  The violence of the green revolution: Third world agriculture, ecology and politics: Vandana Shiva, 264 pp., 1991, Zed Books, London, £10.95 pbk , 1993 .

[68]  Kevin P. Price,et al.  Multitemporal, Moderate-Spatial-Resolution Remote Sensing of Modern Agricultural Production and Land Modification in the Brazilian Amazon , 2007 .

[69]  T. Sakamoto,et al.  A crop phenology detection method using time-series MODIS data , 2005 .

[70]  R. Das,et al.  Geographical unevenness of India's green revolution. , 1999, Journal of contemporary Asia.