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Models for the forecasting of productive and qualitative responses of Panicum maximum Jacq. based on climatic variables


Models for forecasting the production of forage plants and of the qualitative characteristics of the forage produced throughout the year can be valuable tools in planning and taking decisions in animal production systems based on pastures, as well as in the study of these responses by means of simulation in research environments. Such models, however, need to be developed and proposed according to proven criteria of validity and applicability to a range of scenarios and, in general, are more solid and inclusive the more they encapsulate the decisive variables of the processes about which one hopes to make deductions. Data on the production and variation of the qualitative characteristics of the forage produced should be generated in simulations in such a way as to be coherent with the real values obtained in field conditions. To this end, the independent variables chosen for the forecasting of these responses should be biologically consistent with the processes on which they have an effect. The objective of the present study is to characterize the principal productive and qualitative responses of five cultivations of Panicum maximum (Jacq.), with the objective of creating a database of the agronomic characteristics and the nutritional value of these forages, such as accumulation of forage, morphological and chemical-bromatological composition, digestibility and physical characterization. With this information in hand, we will evaluate forecasting models of productive and qualitative responses against climatic variables such as heat units (degree-days) and heat units modified by the photoperiod (photothermic units). With this, it is hoped to be possible to rationalize the biological processes, detaching them from less consistent procedures, such as the use of chronological scales (calendar) to determine the management of ideal harvest time for these forages. Seasonal variations in the responses measured in the field will be compared against the results simulated by the models for the purpose of evaluating the forecasting power of these models. (AU)

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