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Quantifying fluctuations in winter productive cropped area in the Central Indian Highlands
Authors:Pinki Mondal  Meha Jain  Mateusz Zukowski  Gillian Galford  Ruth DeFries
Institution:1.Department of Ecology, Evolution and Environmental Biology,Columbia University,New York,USA;2.Department of Environmental Earth System Science,Stanford University,Stanford,USA;3.Department of Applied Physics and Applied Mathematics,Columbia University,New York,USA;4.Gund Institute for Ecological Economics, Rubenstein School of Environment and Natural Resources,University of Vermont,Burlington,USA
Abstract:The Central Indian Highland landscape (CIHL) represents a complex, diverse, and highly human-modified system. Nearly half the landscape is cropland, yet it hosts 21 protected areas surrounded and connected by forests. Changing farming practices with increasing access to irrigation might alter this intensifying landscape in the near future particularly in light of weather variability. We analyzed a decade of remote sensing data for cropping patterns and climatic factors combined with census data for irrigation and demographic factors to understand winter cropping trajectories in the CIHL. We quantified ‘productive cropped area’ (PCA), defined as the area with planted crop that is green at the peak of the winter growing season. We find three primary trajectories in PCA—increasing, fluctuating, and decreasing. The most dominant trend is fluctuating PCA in two-thirds of the districts, ranging from ~2.11 million to ~3.73 million ha between 2001 and 2013, which is associated with village-level access to irrigation and local labor dynamics. In 58 % of all districts, clay soils were associated with winter cropping (p < 0.05). Increasing irrigation is associated with increased winter PCA in most (94 %) districts (p < 0.00001). We find strong negative association between PCA and land surface temperature (LST) in most (66 %) districts (p < 0.01). LST closely corresponds to daytime mean air temperature (p < 0.001) for available meteorological stations. Fine-scale meteorological and socioeconomic data, however, are needed to further disentangle impacts of these factors on PCA in this landscape.
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