Crop production potential of rural areas within the European Communities. IV: Potential, water-limited and actual crop production

Economics; European Unio

Crop production potential of rural areas within the European Communities. 3. Soils, Climate and Administrative Regions

Economics; European Unio

Land use in Eduador: a statistical analysis at different aggregation levels.

Land use in Ecuador was investigated by means of statistical analysis with the purpose of deriving quantitative estimates of the relative areas of land use types on the basis of biogeophysical, socio-economic and infrastructural conditions. The smallest spatial units of investigation were 5 by 5 minute (9.25×9.25 km) cells of a homogenous geographical grid covering the whole country. Through aggregations of these cells, a total of six artificial aggregation levels was obtained with the aim of analysing spatial scale dependence of land use structure. For all aggregation levels independent multiple regression models were constructed for the estimation of areas within cells of the land use/cover types permanent crops, temporary crops, grassland and natural vegetation. The variables used in the models were selected from a total of 23 variables, that were considered proxies of biogeophysical, socio-economic and infrastructural conditions driving Ecuadorian land use. A spatial stratification was applied by dividing the country into three main eco-regions. The results showed that at higher aggregation levels, the independent variables explained more of the variance in areas of land use types. In most cases, biogeophysical, socio-economic as well as infrastructural variables were important for the explanation of land use, although the variables included in the models and their relative importance varied between land use types and eco-regions. Also within one eco-region, the model variables varied with aggregation level, indicating spatial scale effects. It is argued that these types of analyses can support the quantitative multi-scale understanding of land use, needed for the modelling of realistic future land use change scenarios that take into account local and regional conditions of actual land use

Spatial autocorrelation in multiscale land use models

Various modelling approaches exist for the simulation and exploration of land use change. Until recently often ordinary statistics were used in studies dealing with spatial data, although several techniques are available to deal with spatial autocorrelation. This article presents the spatial autocorrelation techniqueIn several land use models statistical methods are being used to analyse spatial data. Land use drivers that best describe land use patterns quantitatively are often selected through (logistic) regression analysis. A problem using conventional statistical methods, like (logistic) regression, in spatial land use analysis is that these methods assume the data to be statistically independent. But, spatial land use data have the tendency to be dependent, a phenomenon known as spatial autocorrelation. Values over distance are more similar or less similar than expected for randomly associated pairs of observations. In this paper correlograms of the Moran's I are used to describe spatial autocorrelation for a data set of Ecuador. Positive spatial autocorrelation was detected in both dependent and independent variables, and it is shown that the occurrence of spatial autocorrelation is highly dependent on the aggregation level. The residuals of the original regression model also show positive autocorrelation, which indicates that the standard multiple linear regression model cannot capture all spatial dependency in the land use data. To overcome this, mixed regressive-spatial autoregressive models, which incorporate both regression and spatial autocorrelation, were constructed. These models yield residuals without spatial autocorrelation and have a better goodness-of-fit. The mixed regressive-spatial autoregressive model is statistically sound in the presence of spatially dependent data, in contrast with the standard linear model which is not. By using spatial models a part of the variance is explained by neighbouring values. This is a way to incorporate spatial interactions that cannot be captured by the independent variables. These interactions are caused by unknown spatial processes such as social relations and market effects. (C) 2003 Elsevier Science B.V. All rights reserved

Crop growth model WOFOST applied to potatoes

The WOFOST model was calibrated with an experiment on yield effects of drought in potatoes, using data on weather, soil moisture and crop calendar. Then, crop growth and development were predicted for the next year, using planting date and weather data. The model is described. The adjustments in the standard parameter sets resulting from the calibration are discussed. The model slightly underestimated the leaf area index and hence the tuber growth rate. This may be due to unintended effects of experimental set-up, factors not accounted for in the model, and the too many model parameters in relation to the measured data