Mapeamento digital de classes e atributos de solos: métodos, paradigmas e novas técnicas.

Panorama geral sobre os métodos de mapeamento de solos e/ou de suas propriedades, assim como sobre as principais técnicas quantitativas usadas.

Revisão sobre funções de Pedotransferência (PTFs) e novos métodos de predição de classes e atributos do solo.

Revisão sobre o uso das funções de pedotransferência e discussão sobre os vários tipos de PTFs. Diferentes abordagens e alguns princípios são considerados para desenvolver PTFs. Um conceito de sistema de inferência de solo é proposto (SINFERS), em que funções de pedotransferência são as regras do conhecimento, para serem usadas como ferramentas de inferência. É fornecida extensa bibliografia para consulta e expansão do conhecimento e uso da metodologia de pedotransferência.

Mapa de classes de terra pra irrigação do Projeto Jusante - área 3 - Glória/BA.

2008

Acid damage to vegetation following the Laki Fissure eruption in 1783 - an historical review

Grattan, John and Pyatt, Brian. 'Acid damage to vegetation following the laki fissure eruption in 1783 - an historical review' The Science of the Total Environment. 26 August 1993. 151 pgs 241-247

Vegetation and Topographic Control of Wind-blown Snow Distributions in Distributed and Aggregated Simulations for an Arctic Tundra Basin

Essery, RLH & JW, Pomeroy, (2004). Vegetation and topographic control of wind-blown snow distributions in distributed and aggregated simulations. Journal of Hydrometeorology, 5, 735-744.

Granularity and disaggregation in compositional modelling with applications to ecological systems

J. Keppens and Q. Shen. Granularity and disaggregation in compositional modelling with applications to ecological systems. Applied Intelligence, 25(3):269-292, 2006.

Speeding up the learning of equivalence classes of Bayesian network structures

R. Daly, Q. Shen and S. Aitken. Speeding up the learning of equivalence classes of Bayesian network structures. Proceedings of the 10th International Conference on Artificial Intelligence and Soft Computing, pages 34-39.

Using ant colony optimisation in learning Bayesian network equivalence classes

R. Daly, Q. Shen and S. Aitken. Using ant colony optimisation in learning Bayesian network equivalence classes. Proceedings of the 2006 UK Workshop on Computational Intelligence, pages 111-118.

A Framework for the Scoring of Operators on the Search Space of Equivalence Classes of Bayesian Network Structures

R. Daly and Q. Shen. A Framework for the Scoring of Operators on the Search Space of Equivalence Classes of Bayesian Network Structures. Proceedings of the 2005 UK Workshop on Computational Intelligence, pages 67-74.

Locational distribution of gene functional classes in Arabidopsis thaliana

Riley, M. C., Clare, A., King, R. D. (2007). Locational distribution of gene functional classes in Arabidopsis thaliana. BMC Bioinformatics 8, Article No: 112 Sponsorship: EPSRC / RAEng

Appreciating ecological complexity: Habitat contours as a conceptual landscape model

Joern Fischer, David B. Lindermayer, and Ioan Fazey (2004). Appreciating Ecological Complexity: Habitat Contours as a Conceptual Landscape Model. Conservation Biology, 18 (5)pp.1245-1253 RAE2008

Spatial distribution of upland beetles in relation to landform, vegetation and grazing management

Dennis, P., Aspinall, R. J., Gordon, I. J. (2002). Spatial distribution of upland beetles in relation to landform vegetation and grazing management. Basic and Applied Ecology, 3 (2), 183?193. Sponsorship: SEERAD RAE2008

The ecological effects of exotic disease resistance genes introgressed into British gooseberries

Warren, J. and James, P. (2006). The ecological effects of exotic disease resistance genes introgressed into British gooseberries. Oecologia 147(1),69-75. RAE2008

Adaptive capacity and learning to learn as leverage for social-ecological resilience

Ioan Fazey, John A. Fazey, Joern Fischer, Kate Sherren, John Warren, Reed F. Noss, Stephen R. Dovers (2007) Adaptive capacity and learning to learn as leverage for social?ecological resilience. Frontiers in Ecology and the Environment 5(7),375-380. RAE2008

A model for the simulation of macroalgal population dynamics and productivity

A mathematical model to simulate the population dynamics and productivity of macroalgae is described. The model calculates the biomass variation of a population divided into size-classes. Biomass variation in each class is estimated from the mass balance of carbon fixation, carbon release and demographic processes such as mortality and frond breakage. The transitions between the different classes are calculated in biomass and density units as a function of algal growth. Growth is computed from biomass variations using an allometric relationship between weight and length. Gross and net primary productivity is calculated from biomass production and losses over the period of simulation. The model allows the simulation of different harvesting strategies of commercially important species. The cutting size and harvesting period may be changed in order to optimise the calculated yields. The model was used with the agarophyte Gelidium sesquipedale (Clem.) Born. et Thur. This species was chosen because of its economic importance as a the main raw material for the agar industry. Net primary productivity calculated with it and from biomass variations over a yearly period, gave similar results. The results obtained suggest that biomass dynamics and productivity are more sensitive to the light extinction coefficient than to the initial biomass conditions for the model. Model results also suggest that biomass losses due to respiration and exudation are comparable to those resulting from mortality and frond breakage. During winter, a significant part of the simulated population has a negative net productivity. The importance of considering different parameters in the productivity light relationships in order to account for their seasonal variability is demonstrated with the model results. The model was implemented following an object oriented programming approach. The programming methodology allows a fast adaptation of the model to other species without major software development.