Dynamic Linear Models for the Study of Agricultural Systems

This volume presents five studies for modeling agro-industrial development at a regional and enterprise level with the basic methodological framework of dynamic linear programming. The first two papers focus on methodological problems. Section 1 describes a dynamic linear modeling framework for studying agro-industrial development. The introduction of uncertainties in linear dynamic agricultural models is discussed in Section 2. Concrete modeling projects using dynamic linear programming are reported in the next three papers. A farm level application of dynamic Linear models is presented in Section 3. Section 4 gives an account of the Silistra case study where the dynamic linear model elaborated at IIASA was used as the basic methodology for planning the region's agro-industrial development. Finally, Section 5 describes a dynamic linear water demand model

Numerical optimization of recursive systems of equations with an application to optimal swine genetic selection

A new dynamic programming method is developed for numerical optimization of recursive systems of equations, in which continuous choice variables determine the allowed choices in subsequent stages of the problem. The method works by dynamically creating bubbles, or subspaces, of the total search space, allowing the indexing of states visited for later use, and taking advantage of the fact that states adjacent to a visited state are likely to be visited. The method thereby allows search of spaces far larger than would traditionally be permitted by memory limitations. The search allows an infinite planning horizon, and tests at each stage to determine whether further optimization is worth the costs, reverting to a default choice when no longer profitable. The method is applied to the quantitative genetics problem of finding the optimal selection choices for quantitative traits using an identified locus, using the present discounted value of all generations. The method is then applied to the Estrogen Receptor Gene (ESR) to find the economic value of testing for this particular gene