A price endogenous analysis of agricultural intervention policies: The case of Turkey

Government intervenes into agricultural markets in various forms in order to achieve planned targets. The instruments used for this purpose usually have important welfare distributional effects. This article quantifies the social and economic consequences of intervention policies in a modeling framework. The model is of mathematical programming type which uses the principle of surplus maximization.Publisher's Versio

A price endogenous sector model for Turkish agriculture for an analysis of agricultural intervention policies.

Ph.D. - Doctoral Progra

Incorporating spatial criteria in optimum reserve network selection

Considering the spatial location of sites that are to be selected for inclusion in a protected reserve network may be necessary to facilitate dispersal and long-term persistence of species in the selected sites. This paper presents an integer programming (IP) approach to the reserve network selection problem where spatial considerations based on intersite distances are taken into account when selecting reserve sites. The objective is to reduce the fragmentation of preserved sites and design a compact reserve network. Two IP formulations are developed which minimize the sum of pairwise distances and the maximum intersite distance between all sites in the reserve network, respectively, while representing all species under consideration. This approach is applied to a pond invertebrate dataset consisting of 131 sites containing 256 species in Oxfordshire, UK. The results show that significant reductions in reserve fragmentation can be achieved, compared with spatially unrestricted optimum reserve selection, at the expense of a small loss in reserve efficiency

Optimizing Conservation Planning for Multiple Cohabiting Species

Conservation planning often involves multiple species occupying large areas including habitat sites with varying characteristics. For a given amount of financial resources, designing a spatially coherent nature reserve system that provides the best possible protection to targeted species is an important ecological and economic problem. In this paper, we address this problem using optimization methods. Incorporating spatial criteria in an optimization framework considering spatial habitat needs of multiple species poses serious challenges because of modeling and computational complexities. We present a novel linear integer programming model to address this issue considering spatial contiguity and compactness of the reserved area. The model uses the concept of path in graph theory to ensure contiguity and minimizes the sum of distances between selected sites and a central site in individual reserves to promote compactness. We test the computational efficiency of the model using randomly generated data sets. The results show that the model can be solved quite efficiently in most cases. We also present an empirical application of the model to simultaneous protection of two cohabiting species, Gopher Tortoise and Gopher Frogs, in a military installation in Georgia, USA

Selection of a minimum-boundary reserve network using integer programming

In the conservation literature, heuristic procedures have been employed to incorporate spatial considerations in reserve network selection with the presumption that computationally convenient optimization models would be too difficult or impossible to formulate. This paper extends the standard set-covering formulation to incorporate a particular spatial selection criterion, namely reducing the reserve boundary to the extent possible, when selecting a reserve network that represents a set of target species at least once. Applying the model to a dataset on the occurrence of breeding birds in Berkshire, UK, demonstrated that the technique resulted in significant reductions in reserve boundary length relative to solutions produced by the standard set-covering formulation. Computational results showed that moderately large reserve network selection problems could be solved without issue. Alternative solutions may be produced to explore trade-offs between boundary length, number of sites required or alternative criteria

Optimal Design of Compact and Functionally Contiguous Conservation Management Areas

Compactness and landscape connectivity are essential properties for effective functioning of conservation reserves. In this article we introduce a linear integer programming model to determine optimal configuration of a conservation reserve with such properties. Connectivity can be defined either as structural (physical) connectivity or functional connectivity; the model developed here addresses both properties. We apply the model to identify the optimal conservation management areas for protection of Gopher Tortoise (GT) in a military installation, Ft. Benning, Georgia, which serves as a safe refuge for this ‘at risk’ species. The recent expansion in the military mission of the installation increases the pressure on scarce GT habitat areas, which requires moving some of the existent populations in those areas to suitably chosen new conservation management areas within the boundaries of the installation. Using the model, we find the most suitable and spatially coherent management areas outside the heavily used training areas

Data from: How large spatially-explicit optimal reserve design models can we solve now? an exploration of current models’ computational efficiency

Spatially-explicit optimal reserve design models select best sites from a set of candidate sites to assemble nature reserves to protect species (or habitats) and these reserves display certain spatial attributes which are desirable for species. These models are formulated with linear 0-1 programming and solved using standard optimization software, but they were run on different platforms, resulting in discrepant or even conflicting messages with regard to their computational efficiency. A fair and accurate comparison of the convenience of these models would be important for conservation planners who use these models. In this article we considered eight models presented in the literature and tested their computational efficiency using randomly generated data sets containing up to 2000 sites. We focused on reserve contiguity and compactness which are considered crucial to species persistence. Our results showed that two of those models, namely Williams (2002) and Önal et al. (2016), stand out as the most efficient models. We also found that the relative efficiency of these models depends on the scope of analysis. Specifically, the Williams (2002) model solves more of the test problems when contiguity is the only spatial attribute and a large subset of the candidate sites needs to be selected. When compactness is considered also, the Önal et al. (2016) model generally performs better. Large scale models are found to be difficult to solve in a reasonable period of time. We discussed factors that may affect those models' computational efficiency, including model size, share of selected sites, model structure, and input data. These results provide useful insight and guidance to conservation practitioners and researchers who focus on spatial aspects and work with large-scale data sets

Data from: How large spatially-explicit optimal reserve design models can we solve now? an exploration of current models’ computational efficiency

Spatially-explicit optimal reserve design models select best sites from a set of candidate sites to assemble nature reserves to protect species (or habitats) and these reserves display certain spatial attributes which are desirable for species. These models are formulated with linear 0-1 programming and solved using standard optimization software, but they were run on different platforms, resulting in discrepant or even conflicting messages with regard to their computational efficiency. A fair and accurate comparison of the convenience of these models would be important for conservation planners who use these models. In this article we considered eight models presented in the literature and tested their computational efficiency using randomly generated data sets containing up to 2000 sites. We focused on reserve contiguity and compactness which are considered crucial to species persistence. Our results showed that two of those models, namely Williams (2002) and Önal et al. (2016), stand out as the most efficient models. We also found that the relative efficiency of these models depends on the scope of analysis. Specifically, the Williams (2002) model solves more of the test problems when contiguity is the only spatial attribute and a large subset of the candidate sites needs to be selected. When compactness is considered also, the Önal et al. (2016) model generally performs better. Large scale models are found to be difficult to solve in a reasonable period of time. We discussed factors that may affect those models' computational efficiency, including model size, share of selected sites, model structure, and input data. These results provide useful insight and guidance to conservation practitioners and researchers who focus on spatial aspects and work with large-scale data sets