Influence of neural network training parameters on short-term wind forecasting

This paper investigates factors which can affect the accuracy of short-term wind speed prediction when done over long periods spanning different seasons. Two types of neural networks (NNs) are used to forecast power generated via specific horizontal axis wind turbines. Meteorological data used are for a specific Western Australian location. Results reveal that seasonal variations affect the prediction accuracy of the wind resource, but the magnitude of this influence strongly depends on the details of the NN deployed. Factors investigated include the span of the time series needed to initially train the networks, the temporal resolution of these data, the length of training pattern within the overall span which is used to implement the predictions and whether the inclusion of solar irradiance data can appreciably affect wind speed prediction accuracy. There appears to be a relatively complex relationship between these factors and the accuracy of wind speed prediction via NNs. Predicting wind speed based on NNs trained using wind speed and solar irradiance data also increases the prediction accuracy of wind power generated, as can the type of network selected

Structure and Dynamics of Sheep Systems in Bosnia and Herzegovina

The paper presents the analysis of dynamics and structure of the sheep systems in Bosnia and Herzegovina assuming that they suffered a decrease of animal and farms consistency in the last 6 decades. Since 1991 neither a general nor agricultural censuses were made to provide information about the present state of sheep farming in the country. An analysis of the available statistical records of agricultural trends related to the sheep sector was performed. In addition, a depth questionnaire by consulting national experts was performed in order to obtain relevant information on the spatial distribution, consistency, feeding management, production and environmental impact on the present structure of sheep production systems. A decrease in sheep number was observed over the last six decades, but less than in other species. Six main sheep systems in three biogeographical regions were identified. Differences in animal spatial distribution, production purpose and other characteristics of the systems indicate that the environmental and socio-economic factors throughout the country strongly influence the choice of breeding methods and management. All consulted experts indicated the lack of support for sheep systems in relation to agro environmental management, landscape conservation and biodiversity preservation

Bayesian estimation of genetic parameters for multivariate threshold and continuous phenotypes and molecular genetic data in simulated horse populations using Gibbs sampling

<p>Abstract</p> <p>Background</p> <p>Requirements for successful implementation of multivariate animal threshold models including phenotypic and genotypic information are not known yet. Here simulated horse data were used to investigate the properties of multivariate estimators of genetic parameters for categorical, continuous and molecular genetic data in the context of important radiological health traits using mixed linear-threshold animal models via Gibbs sampling. The simulated pedigree comprised 7 generations and 40000 animals per generation. Additive genetic values, residuals and fixed effects for one continuous trait and liabilities of four binary traits were simulated, resembling situations encountered in the Warmblood horse. Quantitative trait locus (QTL) effects and genetic marker information were simulated for one of the liabilities. Different scenarios with respect to recombination rate between genetic markers and QTL and polymorphism information content of genetic markers were studied. For each scenario ten replicates were sampled from the simulated population, and within each replicate six different datasets differing in number and distribution of animals with trait records and availability of genetic marker information were generated. (Co)Variance components were estimated using a Bayesian mixed linear-threshold animal model via Gibbs sampling. Residual variances were fixed to zero and a proper prior was used for the genetic covariance matrix.</p> <p>Results</p> <p>Effective sample sizes (ESS) and biases of genetic parameters differed significantly between datasets. Bias of heritability estimates was -6% to +6% for the continuous trait, -6% to +10% for the binary traits of moderate heritability, and -21% to +25% for the binary traits of low heritability. Additive genetic correlations were mostly underestimated between the continuous trait and binary traits of low heritability, under- or overestimated between the continuous trait and binary traits of moderate heritability, and overestimated between two binary traits. Use of trait information on two subsequent generations of animals increased ESS and reduced bias of parameter estimates more than mere increase of the number of informative animals from one generation. Consideration of genotype information as a fixed effect in the model resulted in overestimation of polygenic heritability of the QTL trait, but increased accuracy of estimated additive genetic correlations of the QTL trait.</p> <p>Conclusion</p> <p>Combined use of phenotype and genotype information on parents and offspring will help to identify agonistic and antagonistic genetic correlations between traits of interests, facilitating design of effective multiple trait selection schemes.</p