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Animal nutrition is a very large area of study where mathematical modeling is a success, as in many areas. In addition to shaping the main physiological processes underlying the farm animals yielding, the objective function optimization issue (i.e. total expenditure on fodder) arises both in terms of protein and vitamin's intake of nutrients and in terms of their cost. Optimising feed rations through mathematical modeling monitors the amount of food an animal needs throughout the day, so that the objective function (also called goal function of efficiency function) be minimum or maximum depending on the problem requirements. In order to solve mathematical model one needs fodder varieties that are part of the fodder basis and this feed (in kg) must be purchased or produced, and the solving method we use is the Solver tool of Microsoft Office Excel software. In order to optimize the objective function one should observe the following restrictions concerning the provisioning of total feed expressed in nutrients: provisioningof the necessary fodder by groups of fodder, observing proportions in fodder structure by groups of fodder, complying with fodderplants culture technologies and taking into account the availability of land designated for the forage and fodder crops. The mathematical model consists of three parts: the system of restrictions, non-negativity conditions and objective function. This paper shows the effectiveness of the Solver IT tools in dealing with many linear programming issues, including the establishment of feed rations as ensuring a good and well balanced food in terms of nutrition ensures both a good maintenance of the animals and an increased production. [ABSTRACT FROM AUTHOR]

Factor analysis (principal components analysis is a factor method) of downsizing the complexity of data and fixing the number of principal components to be retained in the final model. In this paper we present the usefulness of Principal Components Analysis in the agriculture. Data we have used in this paper were taken from a public database, namely the National Institute of Statistics of Romania. [ABSTRACT FROM AUTHOR]

This paperwork presents a model of transportation network which uses a package of programs allowing the creation, processing, integration, analysis and posting of geographical data at various levels. ArcGIS was used, extension ArcGIS Network Analyst, with which the area of influence of an economic objective on any location from the map may be identified, representing the network, as well as the data base obtained following the Digital Romania National Project. In the model created by the road network 16 points of interests were added on the map of Cluj County, divided into two themes: Agricultural holdings: Aghireş, Baciu, Beliş, Berind, Floreşti, Huedin, Steluţa; Livestock holdings: Apahida, Cojocna, Copăceni, Dej, Iclod, Floreşti, Răscruci, Someşeni, Tureni. The modelling of transportation network from this paperwork contains three phases: a) The travel of path through all points of interest having as starting and return point the city of Cluj-Napoca without barriers and without the optimization option; b) The introduction of several restrictions on the previous path; c) The optimization of path with barriers, as well as without them. [ABSTRACT FROM AUTHOR]

Principal Component Analysis is a method factor - factor analysis - and is used to reduce data complexity by replacingmassive data sets by smaller sets. It is also used to highlight the way in which the variables are correlated with eachother and to determining the (less)latent variableswhich are behind the (more)measured variables. These latent variables are called factors, hence the name of the methodi.e. factor analysis. Our paper shows the applicability of Principal Components Analysis (PCA) in livestock area of study by carrying out a researchon some physiological characteristics in the case of tencow breeds. By using PCA only two factors have been preserved, concentrating over 80% of their information from the four variables in question, one factor concentrating weight and height and the other factor concentrating trunk circumference and weight at calving, respectively. [ABSTRACT FROM AUTHOR]