Effect of nitrogen and potassium fertilisation on vegetative growth and flowering of mature carob trees (Ceratonia siliqua): variations in leaf area index and water use indices

This work aimed to assess how potassium (K) and nitrogen (N) fertilisation may affect the use of precipitation in terms of vegetative and flowering response of 15-year-old carob trees during a 3-year experiment. A field trial was conducted in 1997, 1998 and 1999 in Algarve (Southern Portugal) in a calcareous soil. Four fertilisation treatments were tested: no fertiliser (control); 0.8 kg N/tree (N treatment); 1 kg K 2 O/tree (K treatment) and 0.8 kg N/tree plus 1 kg K 2 O/tree (NK treatment). No irrigation was applied during the experimental period. Branch length increments were measured every month throughout the growing season and inflorescence number was registered once per year. There was a strong seasonal effect on vegetative growth, since low levels of precipitation (115 mm) during October 1998–March 1999 suppressed the increment in branch length. N supplied to the trees (N and NK treatments) tended to increase water use indices in terms of vegetative growth. No response to K alone was observed in trees fertilised only with K. The number of inflorescences increased throughout the experimental period, particularly for N and NK treatments, and a reduction of the precipitation amount during April, May and June, may also enhance flowering. This knowledge could be important when making decisions concerning fertilisation under dry conditions. The results reported here indicate that tree growth (expressed as the branch growth) and flower production under dry-farming conditions, may be achieved by applying 0.8 kg of N (as ammonium nitrate) per tree during the growing season. However, N uptake and use depends on soil water availability.

Fuzzy model identification by evolutionary, gradient based and memtic algorithms

One of the crucial problems of fuzzy rule modeling is how to find an optimal or at least a quasi-optimal rule base fro a certain system. In most applications there is no human expert available, or, the result of a human expert's decision is too much subjective and is not reproducible, thus some automatic method to determine the fuzzy rule base must be deployed.

Design of neuro-fuzzy models by evolutionary and gradient-based algorithms

All systems found in nature exhibit, with different degrees, a nonlinear behavior. To emulate this behavior, classical systems identification techniques use, typically, linear models, for mathematical simplicity. Models inspired by biological principles (artificial neural networks) and linguistically motivated (fuzzy systems), due to their universal approximation property, are becoming alternatives to classical mathematical models. In systems identification, the design of this type of models is an iterative process, requiring, among other steps, the need to identify the model structure, as well as the estimation of the model parameters. This thesis addresses the applicability of gradient-basis algorithms for the parameter estimation phase, and the use of evolutionary algorithms for model structure selection, for the design of neuro-fuzzy systems, i.e., models that offer the transparency property found in fuzzy systems, but use, for their design, algorithms introduced in the context of neural networks. A new methodology, based on the minimization of the integral of the error, and exploiting the parameter separability property typically found in neuro-fuzzy systems, is proposed for parameter estimation. A recent evolutionary technique (bacterial algorithms), based on the natural phenomenon of microbial evolution, is combined with genetic programming, and the resulting algorithm, bacterial programming, advocated for structure determination. Different versions of this evolutionary technique are combined with gradient-based algorithms, solving problems found in fuzzy and neuro-fuzzy design, namely incorporation of a-priori knowledge, gradient algorithms initialization and model complexity reduction.

Cloning of the Bone Gla Protein gene from the teleost fish Sparus aurata (gilthead seabream). Molecular organization, developmental appearance and evolutionary implications

Tese de Doutoramento, Biologia Molecular, Faculdade de Ciências do Mar e do Ambiente, Universidade do Algarve, 2001