USING MULTIPLE LINEAR REGRESSIONS TO DERIVE CROPLAND AND PASTURE PROPORTION MAPS IN ROMANIA - DIANA DOGARU; GHEORGHE KUCSICSA (Revue Roumaine de Géographie)
The spatial pattern of agricultural lands is an important part of the assessments regarding land management and its societal consequences, especially when considering the increasing demand for food and stronger environmental change impacts. As a subsequence, integrative studies based on complex spatial models simulating biogeochemical and physical processes that estimate yield gaps, crops efficiency or agricultural water resources use are relevant for providing trustful information required by stakeholders from different governance levels, and whose interests center on land use and its societal implications. The present paper is about the creation of a dataset representing the distribution of cropland and pasture proportions at 1 km resolution grid cell in Romania, around the year 2012. The geospatial dataset was developed by fusing the statistical agricultural data provided by the TEMPO Online Service of the National Institute of Statistics with the CORINE 2006 Land Use / Land Cover geospatial data. The two input datasets were linked through multiple linear regressions using a backward selection method. In this way, the statistical proportion of croplands and pastures of each Local Administrative Units (LAU2) is explained by all significant CORINE Land Use / Land Cover classes. The results show a high agreement between the observed proportions and the linear models’ estimates, particularly in the case of croplands (i.e. 94% of the proportions are correctly estimated) as well as for pastures (i.e. 84% of the observed values). Moreover, the graphical representation of the difference between the estimated values and the observed proportions, at LAU2 level, shows that such differences, either overestimated or underestimated, are below 10 percentage points in most of the cases. The newly developed geospatial dataset could be particularly useful as an input dataset for integrative models of atmosphere-plantsoil processes simulation as well as for a wide range of specific topic-oriented syntheses and assessments on agricultural land use issues.