The estimation of Crop Land Productivity based on satellite and biophysical modelled data

<p><span dir="ltr" role="presentation">To increase the efficiency of agricultural activities related to the crop production, it is</span><br role="presentation" /><span dir="ltr" role="presentation">important to assess the potential yield of territory, especially in the context of climate change. The</span><br role="presentation" /><span dir="ltr" role="presentation">estimation of potential yield consists in determining the maximum level of biological or economic</span><br role="presentation" /><span dir="ltr" role="presentation">productivity of crop under the constraints of soil fertility, climate, and the consequences of</span><br role="presentation" /><span dir="ltr" role="presentation">anthropogenic impact on the agroecosystem.</span><br role="presentation" /><span dir="ltr" role="presentation">In this study disclosed the methodology of crop potential yield estimation based on</span><br role="presentation" /><span dir="ltr" role="presentation">Productivity Index of Cropland (PIC) in Ukraine. This index can identify the spatial distribution</span><br role="presentation" /><span dir="ltr" role="presentation">pattern of a cropland productivity from a field to a district/region or a country level. Major</span><br role="presentation" /><span dir="ltr" role="presentation">components of the PIC, are the average NDVI based on remote sensing data and the average</span><br role="presentation" /><span dir="ltr" role="presentation">simulated yield storage for specific crops. The average maximum of NDVI values on pixel level for</span><br role="presentation" /><span dir="ltr" role="presentation">15 years have been obtained from MODIS and shows biomass of plants, that related to yield with a</span><br role="presentation" /><span dir="ltr" role="presentation">spatial resolution of 250 by 250 m.</span><span dir="ltr" role="presentation">&#160;</span><span dir="ltr" role="presentation"> The spatial distribution of NDVI values shows a spatial</span><br role="presentation" /><span dir="ltr" role="presentation">inhomogeneity of soil types and topography that is mainly related to soil moisture (one of the main</span><br role="presentation" /><span dir="ltr" role="presentation">drivers of yield). The simulated yield storage of specific crops shows joint influence of weather</span><br role="presentation" /><span dir="ltr" role="presentation">parameters and soil type on yield. In this research, the WOFOST model has been used to simulate</span><br role="presentation" /><span dir="ltr" role="presentation">yield storage on climatic grids 9 by 9 km for 15years. Then, calculated average values of model</span><br role="presentation" /><span dir="ltr" role="presentation">output have been interpolated to pixel level of MODIS NDVI data and combined with these values.</span><br role="presentation" /><span dir="ltr" role="presentation">Final step is cluster analysis which have been used to partition PIC values on pixel level to the</span><br role="presentation" /><span dir="ltr" role="presentation">clusters with a range of PIC values from 0 to 1. PIC is a good indicator of the potential cropland</span><br role="presentation" /><span dir="ltr" role="presentation">productivity and can be builds for the different time periods. The PIC allows to determine the</span><br role="presentation" /><span dir="ltr" role="presentation">suitability and productivity of agricultural crops from field level and among different areas for</span><br role="presentation" /><span dir="ltr" role="presentation">demarcating and identifying agricultural regions. The farmers, planners and policy makers will be</span><br role="presentation" /><span dir="ltr" role="presentation">able to make decisions by considering the outcome of the PIC that would lead to better performance</span><br role="presentation" /><span dir="ltr" role="presentation">in the agricultural sector according to the climate change.</span></p>