Search

Copyright of Terra Latinoamericana is the property of Sociedad Mexicana de la Ciencia del Suelo A.C. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Technological and mathematical advances have provided opportunities to investigate new approaches for the holistic quantification of complex biological systems. One objective of these approaches, including the multi-inverse deterministic approach proposed in this paper, is to deepen the understanding of biological systems through the structural development of a useful, best-fitted inverse mechanistic model. The objective of the present work was to evaluate the capacity of a deterministic approach, that is, the multi-inverse approach (MIA), to yield meaningful quantitative nutritional information. To this end, a case study addressing the effect of diet composition on sheep weight was performed using data from a previous experiment on saccharina (a sugarcane byproduct), and an inverse deterministic model (named Paracoa) was developed. The MIA successfully revealed an increase in the final weight of sheep with an increase in the percentage of corn in the diet. Although the soluble fraction also increased with increasing corn percentage, the effective nonsoluble degradation increased fourfold, indicating that the increased weight gain resulted from the nonsoluble substrate. A profile likelihood analysis showed that the potential best-fitted model had identifiable parameters, and that the parameter relationships were affected by the type of data, number of parameters and model structure. It is necessary to apply the MIA to larger and/or more complex datasets to obtain a clearer understanding of its potential. [ABSTRACT FROM AUTHOR]

Copyright of Terra Latinoamericana is the property of Sociedad Mexicana de la Ciencia del Suelo A.C. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista de Investigación Agraria y Ambiental is the property of Revista de Investigacion Agraria y Ambiental and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Modern researchers working in applied animal science systems have faced issues with modelling huge quantities of data. Modelling approaches that have previously been used to model biological systems are having problems to adapt to increased number of publications and research. So as to develop new approaches that have the potential to deal with these fast-changing complex conditions, it is relevant to review modern modelling approaches that have been used successfully in other fields. Therefore, this paper reviews the potential capacity of new integrated applied animal-science approaches to discriminate parameters, interpret data and understand biological processes. The analysis shows that the principal challenge is handling ill-conditioned complex models, but an integrated approach can obtain meaningful information from complementary data that cannot be obtained from present applied animal-science approaches. Furthermore, it is shown that parameter sloppiness and data complementarity are key concepts during system behaviour restrictions and parameter discrimination. Additionally, model evaluation and implementation of the potential integrated approach are reviewed. Finally, the objective of an integral approach is discussed. Our conclusion is that these approaches have the potential to be used to deepen the understanding of applied animal systems, and that exist enough developed resources and methodologies to deal with the huge quantities of data associated with this science. The data available are increasing, but diversity makes it difficult to obtain meaningful information because current research approaches are not specifically designed for complex systems. It is possible to develop new applied animal science approaches if researchers integrate modern approaches specifically designed for dealing with high quantities of diverse data, system complexity and significant abstraction. This approach implies a more efficient resource and data use, deeper system understanding, and therefore a more relevant and faster response to the society. [ABSTRACT FROM AUTHOR]