Abstract
To increase the efficiency of agricultural activities related to the crop production, it isimportant to assess the potential yield of territory, especially in the context of climate change. Theestimation of potential yield consists in determining the maximum level of biological or economicproductivity of crop under the constraints of soil fertility, climate, and the consequences ofanthropogenic impact on the agroecosystem.In this study disclosed the methodology of crop potential yield estimation based onProductivity Index of Cropland (PIC) in Ukraine. This index can identify the spatial distributionpattern of a cropland productivity from a field to a district/region or a country level. Majorcomponents of the PIC, are the average NDVI based on remote sensing data and the averagesimulated yield storage for specific crops. The average maximum of NDVI values on pixel level for15 years have been obtained from MODIS and shows biomass of plants, that related to yield with aspatial resolution of 250 by 250 m.  The spatial distribution of NDVI values shows a spatialinhomogeneity of soil types and topography that is mainly related to soil moisture (one of the maindrivers of yield). The simulated yield storage of specific crops shows joint influence of weatherparameters and soil type on yield. In this research, the WOFOST model has been used to simulateyield storage on climatic grids 9 by 9 km for 15years. Then, calculated average values of modeloutput have been interpolated to pixel level of MODIS NDVI data and combined with these values.Final step is cluster analysis which have been used to partition PIC values on pixel level to theclusters with a range of PIC values from 0 to 1. PIC is a good indicator of the potential croplandproductivity and can be builds for the different time periods. The PIC allows to determine thesuitability and productivity of agricultural crops from field level and among different areas fordemarcating and identifying agricultural regions. The farmers, planners and policy makers will beable to make decisions by considering the outcome of the PIC that would lead to better performancein the agricultural sector according to the climate change.
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