António Xavier

Redistributing Agricultural Data by a Dasymetric Mapping Methodology

This paper examines the adaptation of dasymetric mapping methodologies to agricultural data, including their testing and transposition, in order to recover the underlying statistical surface (i.e., an approximation of the real distribution of data). A methodology based on the ideas of Gallego and Peedell (2001) and on the binary method is proposed. It has several steps: (i) the exclusion of target zones for which no observations exist (binary method), (ii) the application of an iterative process to define the most precise densities for data distribution, and (iii) the stratification/definition of sub-units with homogenous characteristics if the results of the previous step are not satisfactory, and the subsequent application of step two. // The methodology was applied in the Alentejo region of Portugal, using data from the 1999 Agricultural Census. Several counties are used as source zones. The aim was to generate a distribution of agro-forestry occupations as close as possible to reality. Two lines of analysis were followed: (i) application of the methodology simultaneously to all counties (definition of regional densities), and (ii) application of the methodology separately to the different sub-areas with similar characteristics (definition of sub-regional densities). For an easy application of the methodology, a computer tool was created, which allowed the easy optimization, validation, and exportation of the data into a Geographic Information System (GIS). // The results were validated using several error indicators at the county level, as well as in a sample of parishes. We show that the second variant of the methodology yielded more precise results, and is superior for the types of data available. This method yielded maps in which the distribution of the most relevant agro-forestry occupations is closest to reality.This paper examines the adaptation of dasymetric mapping methodologies to agricultural data, including their testing and transposition, in order to recover the underlying statistical surface (i.e., an approximation of the real distribution of data). A methodology based on the ideas of Gallego and Peedell (2001) and on the binary method is proposed. It has several steps: (i) the exclusion of target zones for which no observations exist (binary method), (ii) the application of an iterative process to define the most precise densities for data distribution, and (iii) the stratification/definition of sub-units with homogenous characteristics if the results of the previous step are not satisfactory, and the subsequent application of step two. The methodology was applied in the Alentejo region of Portugal, using data from the 1999 Agricultural Census. Several counties are used as source zones. The aim was to generate a distribution of agro-forestry occupations as close as possible to reality. Two lines of analysis were followed: (i) application of the methodology simultaneously to all counties (definition of regional densities), and (ii) application of the methodology separately to the different subareas with similar characteristics (definition of sub-regional densities). For an easy application of the methodology, a computer tool was created, which allowed the easy optimization, validation, and exportation of the data into a Geographic Information System (GIS). The results were validated using several error indicators at the county level, as well as in a sample of parishes. We show that the second variant of the methodology yielded more precise results, and is superior for the types of data available. This method yielded maps in which the distribution of the most relevant agro-forestry occupations is closest to reality.

Disaggregation of Statistical Livestock Data Using the Entropy Approach

A process of agricultural data disaggregation is developed to address the lack of updated disaggregated data concerning main livestock categories at subregional and county level in the Alentejo Region, southern Portugal. The model developed considers that the number of livestock units is a function of the agricultural and forest occupation, and data concerning the existing agricultural and forest occupation, as well as the conversion of livestock numbers into normal heads, are needed in order to find this relation. The weight of each livestock class is estimated using a dynamic process based on a generalized maximum entropy model and on a crossentropy minimization model, which comprises two stages. The model was applied to the county of Castelo de Vide and their results were validated in cross reference to real data from different sources.