makboard/ArableLandSuitability
This repository contains machine learning models for estimating cropland types (irrigated, non-irrigated, no crop) to assess climate change impact on agricultural land suitability across various carbon emission scenarios.
This project helps agricultural planners and climate change researchers understand how cropland types (irrigated, non-irrigated, or no crop) might shift due to future climate change. It takes historical climate data and future emissions scenarios to predict the geographical distribution of different croplands. The output shows maps and statistical analyses of probable cropland changes, helping users visualize and quantify climate change impacts on agriculture.
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Use this if you need to analyze and visualize the potential shifts in agricultural land suitability under various climate change projections.
Not ideal if you are looking for real-time crop yield predictions or localized farm management recommendations.
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Mar 12, 2024
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