An optimization model for the management of a South African game ranch

Joubert, Johan W.; Luhandjula, M. K.; Ncube, Ozias; Roux, Gys le; Wet, Febe de

doi:10.1016/j.agsy.2006.03.006

Cited by 12 publications

(8 citation statements)

References 13 publications

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“…For instance, Mayer et al (1999;2001) successfully used genetic algorithms for a single objective beef production model. A range of agricultural planning problems also extended this reasoning towards multi-objective models (Joubert et al, 2007;Francisco and Ali, 2006;Raju and Kumar, 1999;Mainuddin et al, 1997). A limitation of these studies was that each problem was solved as a single objective model using conventional goal programming or compromise programming methods.…”

Section: Methodsmentioning

confidence: 99%

Multi-criteria evolutionary algorithm optimization for horticulture crop management

West

2019

Agricultural Systems

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Climate variability requires adaptive production systems in agriculture often resulting in significant irreversible investments. Cultivar replacement programs in horticulture orchards that substitute older varieties for more heat-and drought-resilient varieties have enterprise values that are highly sensitive to the timing of such investments. Farm-level replacement programs are subject to multiple constraints around debt serviceability, operating costs, the replacement cycle and the rate of degradation of the existing orchard. The maximization of enterprise value subject to multiple constraints can be reduced to a multiobjective optimization problem. Over long horizons this optimization process generates a very-large solution space. Using a multi-objective evolutionary algorithm we examine uncertainties around climatic effects and the timing of investments for horticultural operations and derive the optimal times to adapt using cultivar replacement techniques. We find that naive switching decisions using traditional valuation methods are found to be suboptimal and can initiate poor decisions, potentially undermining adaptation efforts. We further show that opposing economic and climatic conditions can adversely impact enterprise value based on mistiming the investment decision. Application of the GA solver is demonstrated using a vector-based GIS to a farm where individual portions of an orchard are subject to varying rates of production, degradation and age.

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Section: Methodsmentioning

confidence: 99%

Multi-criteria evolutionary algorithm optimization for horticulture crop management

West

2019

Agricultural Systems

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“…Further, the National Department of Agriculture (NDA) reports that the conversion rate from commercial livestock farming to game ranching in the Eastern Cape Province (ECP) alone is between 25 to 30 percent (NDA, 2009). It has been argued that the production of wild animals, which are keystone species or natural capital in an area, could promote environmental management in rangelands (Barnes, 1998;Kreuter and Workman, 1997;Milton et al, 2003;Joubert et al, 2007). Rosenzweig (2003: 201) has termed this "reconciliation ecology" and argues that it "discovers how to modify and diversify anthropogenic habitats so that they harbour a wide variety of wild species … [and further] seeks techniques to give many species back their geographical ranges without taking away ours."…”

Section: Springbuck Ranching and The Conservation Of Rangelandsmentioning

confidence: 99%

“…Thirdly, game animals have a natural ability to survive under harsh arid environments and thus are pertinent repositories of biodiversity and finally; they are more efficient users of local vegetation. Moreover, wild animals also possess other consumptive and non-consumptive benefits such as meat production and ecotourism, respectively (Gibson, 2009;Rosenzweig, 2003;Milton et al, 2003;Joubert et al, 2007). It is assumed that the re-introduction of game in fragile rangelands would aid in their recovery particularly if they (game) are keystone species or natural capital (Milton et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

“…Yet regardless of the government's investment in considerable research, policy and action plans, Karoo rangelands continue to linger under a cloud of controversy on whether the current livestock farming systems are the best means through which ecological sustainability and ecosystem's health can be realised (Vetter, 2009). One way to achieve ecological diversity in rangelands is through the production of wildlife or keystone species (Barnes, 1998;Kreuter and Workman, 1996;Milton et al, 2003;Joubert et al, 2007;Tomlinson et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

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Economics of meat production from springbuck in the Eastern Cape Karoo

Dlamini

Fraser²,

Grové

2012

Agrekon

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The feasibility of springbuck based meat production in the EC Karoo was analysed through a stochastic budgeting model, while overtly taking cost and price risk into consideration. Monte Carlo simulation of a springbuck based meat production enterprise was used to quantify the risks that would be faced by springbuck ranchers. Springbuck ranching has been proven a viable alternative in the production of highly nutritious and healthy meat (venison) that is on high demand in European markets and more recently with a promising and growing local demand as well. The results indicate that in the Eastern Cape Karoo, springbuck ranching for meat production is a viable business. As the call for more environmentally friendlier rangelands utilisation economic systems intensifies, rangelands owners in the EC Karoo have a practicable option. At the very least, an alternative to broaden their incomes with springbuck based meat production exists.

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“…Often these problems involve making the most efficient use of available resources including money, time, machinery, staff, inventory, and land and water. These problems have been solved by using different optimization techniques from the researchers from worldwide (Anwar and Clarke, 2001;Ayvaz and Karahan, 2008;Azaiez et al, 2005;Bar et al, 2009;Bardossy et al, 1990;Chiadamrong and Kawtummachai, 2008;de Vries and Anwar, 2006;Easa and Hossain, 2008;Hsu and Wei, 2007;Hsu et al, 2008;Hugo, 2004;Joubert et al, 2007;Kale et al, 2008;Karmakar and Mujumdar, 2006;Majone et al, 2010;Mattilal and Duley, 1998;Pathumnakul et al, 2011;Ravirala and Grivas, 1995;Sadeghpour et al, 2006;Salazar et al, 2005;Stray et al, 2012;Wei and Hsu, 2009;Zhang and Huang, 2011).…”

Section: Introductionmentioning

confidence: 99%