Combined disaggregation of agricultural land uses, livestock numbers and crops' production: an entropy approach

This paper presents several combined agricultural data disaggregation models in order to recover the farms' land uses, the livestock numbers and main crops' productions. The proposed approach estimates incomplete information at disaggregated level through entropy, using an information prior, and generating information for a combined calculation use of data in the estimation of other variables. The models were applied to the region of Algarve, to some rural pilot areas (Salir-Ameixial-Cachopo and Alcoutim) for livestock data, since this data in some Algarve's inland areas is needed for a European forest fire prevention project, and to the agrarian zones in a more complex framework. The results are promising. They were validated, in cross reference to real data, having proven to be valid and reliable. The total error was small and a considerable level of information heterogeneity was recovered. The total error was about 27,9% for the counties' land uses and 21% for the agrarian zones, and for the livestock it was also acceptable. The level of heterogeneity recovered was always higher than 50%, revealing some improvements regarding previous studies.

Aims of education and well-being as absence of disorder

This thesis offers an original account of what personal well-being can be. Any account of education, it is believed, has to do with and aims at personal well-being. I approach this view on well-being not in a positive but in a negative way. I put forward some items that in certain circumstances can be taken by and called sources or forms of disorder. In the absence of such forms or sources of disorder, I assume that a certain order, prudential or moral, takes place and that constitutes the well-being of the person. The concept of ‘absence of disorder’ is introduced and argued as an educationally appropriate view of personal well-being which is the central educational aim. Therefore, ‘absence of disorder’ is positioned as the central aim of education. This concept is illuminated, for practical reasoning, by a list of seven possible forms of disorder: Comparison, Corruption, Dependency, Division, Fear, Self-disintegration and Violence. As a view of personal well-being, ‘absence of disorder’ is initially rooted in informed desire satisfaction, via the introduction of the concept of entropy. Prudentially, the agent’s informed desire is satisfied by living a life with low build up of entropy or disorder. But, in a second move such a base is also provided by the Levinasinian concept of ‘disinterest’ as a root for ‘what is to be a human’. Such ‘disinterest’ is related to the concepts of love and of ‘action for its own sake’. It is at this final approach that an attempt is made towards the approximation of the ethical and the prudential aspects of social practices. Even if only to some extent successful, the argument is directed to the following conclusion: an education aiming at ‘absence of disorder’ may promote prudential well-being and give us some confidence in simultaneously favouring moral education.

Generate disaggregated soil allocation data using a Minimum Cross Entropy Model

Montado ecosystem in the Alentejo Region, south of Portugal, has enormous agro-ecological and economics heterogeneities. A definition of homogeneous sub-units among this heterogeneous ecosystem was made, but for them is disposal only partial statistical information about soil allocation agro-forestry activities. The paper proposal is to recover the unknown soil allocation at each homogeneous sub-unit, disaggregating a complete data set for the Montado ecosystem area using incomplete information at sub-units level. The methodological framework is based on a Generalized Maximum Entropy approach, which is developed in thee steps concerning the specification of a r order Markov process, the estimates of aggregate transition probabilities and the disaggregation data to recover the unknown soil allocation at each homogeneous sub-units. The results quality is evaluated using the predicted absolute deviation (PAD) and the "Disagegation Information Gain" (DIG) and shows very acceptable estimation errors.