Journal articles: 'Bioeconomic modelling'

1

Cacho, Oscar J. "Systems modelling and bioeconomic modelling in aquaculture." Aquaculture Economics & Management 1, no. 1-2 (March 1997): 45–64. http://dx.doi.org/10.1080/13657309709380202.

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Shepherd, J. G., and C. W. Clark. "Bioeconomic Modelling and Fisheries Management." Biometrics 42, no. 3 (September 1986): 683. http://dx.doi.org/10.2307/2531229.

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Wilson, Berry, and Colin W. Clark. "Bioeconomic Modelling and Fisheries Management." Journal of Business & Economic Statistics 4, no. 3 (July 1986): 392. http://dx.doi.org/10.2307/1391581.

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Gatto, Marino. "Bioeconomic modelling and fisheries management." Ecological Modelling 42, no. 2 (August 1988): 161–62. http://dx.doi.org/10.1016/0304-3800(88)90114-7.

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Spulber, Daniel F., and Colin W. Clark. "Bioeconomic Modelling and Fisheries Management." Journal of the American Statistical Association 82, no. 397 (March 1987): 357. http://dx.doi.org/10.2307/2289196.

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Mangel, Marc. "Bioeconomic Modelling and Fisheries Management." Mathematical Biosciences 84, no. 1 (May 1987): 121–22. http://dx.doi.org/10.1016/0025-5564(87)90046-0.

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Foley, Naomi S., Claire W. Armstrong, Viktoria Kahui, Eirik Mikkelsen, and Siv Reithe. "A Review of Bioeconomic Modelling of Habitat-Fisheries Interactions." International Journal of Ecology 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/861635.

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This paper reviews the bioeconomic literature on habitat-fisheries connections. Many such connections have been explored in the bioeconomic literature; however, missing from the literature is an analysis merging the potential influences of habitat on both fish stocks and fisheries into one general, overarching theoretical model. We attempt to clarify the nature of linkages between the function of habitats and the economic activities they support. More specifically, we identify theoretically the ways that habitat may enter the standard Gordon-Schaefer model, and nest these interactions in the general model. Habitat influences are defined as either biophysical or bioeconomic. Biophysical effects relate to the functional role of habitat in the growth of the fish stock and may be either essential or facultative to the species. Bioeconomic interactions relate to the effect of habitat on fisheries and can be shown through either the harvest function or the profit function. We review how habitat loss can affect stock, effort, and harvest under open access and maximum economic yield managed fisheries.

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Iorgulescu, Raluca I., John M. Polimeni, and Mariana Balan. "Bioeconomic sustainability and modelling energy systems." Progress in Industrial Ecology, An International Journal 9, no. 1 (2015): 46. http://dx.doi.org/10.1504/pie.2015.069840.

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Perez-Valdes, Gerardo A., Vibeke S. Nørstebø, May-Britt Ellingsen, Jukka Teräs, and Adrian T. Werner. "Bioeconomic Clusters—Background, Emergence, Localization and Modelling." Sustainability 11, no. 17 (August 24, 2019): 4611. http://dx.doi.org/10.3390/su11174611.

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Industrial Clusters, especially those based on biologically sourced materials and their derivative products, can play an important role in the global shift to more sustainable production methods and ecological economic systems. The concept of cluster, however, is difficult to define and study. This paper presents quantitative methods based on Input-Output and Operations Research analysis to establish and plan cluster operations and complement that with qualitative reflections on the nature of these clusters. The purpose is to bring together both dimensions and demonstrate their complementarity, with social and policy aspects being as important considerations as techno-economic-driven ones. Using a case study, hypothetical clusters using numerical methods are created; the clusters produced by numerical methods point to and raise important issues related to the need to utilize qualitative analysis in conjunction to pure economic motives while designing/planning industrial clusters.

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Liyana, Nor Isma, and Moe Shwe Sin. "Bioeconomic Modelling in Sustainable Fisheries Management of Commercial Marine Fisheries in Kelantan, Malaysia." International Journal of Finance, Economics and Business 1, no. 2 (June 30, 2022): 141–57. http://dx.doi.org/10.56225/ijfeb.v1i2.29.

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Bioeconomic modelling is an important issue in sustainable fisheries management of commercial marine fisheries in Kelantan, Malaysia. Previous studies focus on the impact of trawling on fisheries, tourism and the socio-environment, which overfishing is a direct threat to local fishing communities. In addition, bottom trawl fishing may affect fishing, environment and socio-economic management objectives. Moreover, fishing activities lead to changes in the structure of marine habitats and affect the diversity, composition, biomass and productivity of related biota. Finally, the previous research discussed on challenges of the fisheries industry in peninsular Malaysia. Studies focused on bioeconomic modelling in sustainable management of commercial marine fisheries in terms of fishing gear, climate changes, and anthropogenic disturbances are still limited. This study aims to investigate the sustainability of marine fish production and analyse the potential effect of climate changes and anthropogenic disturbances that affect fisheries activities. The theory and practice of the bioeconomic surplus production model by Gordon - Schafer (GS) are used to calculate the total biology and economic value. The result of the study indicated that trawl nets, anchovy purse seinses, climate changes and anthropogenic disturbances affect the sustainable management of commercial marine fisheries in Kelantan, Malaysia.

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Nelson, Rohan, Philip Kokic, and Holger Meinke. "From rainfall to farm incomes—transforming advice for Australian drought policy. II. Forecasting farm incomes." Australian Journal of Agricultural Research 58, no. 10 (2007): 1004. http://dx.doi.org/10.1071/ar06195.

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Australian drought policy is focussed on providing relief from the immediate effects of drought on farm incomes, while enhancing the longer term resilience of rural livelihoods. Despite the socioeconomic nature of these objectives, the information systems created to support the policy have focussed almost exclusively on biophysical measures of climate variability and its effects on agricultural production. In this paper, we demonstrate the ability of bioeconomic modelling to overcome the moral hazard and timing issues that have led to the dominance of these biophysical measures. The Agricultural Farm Income Risk Model (AgFIRM), developed and tested in a companion paper, is used to provide objective, model-based forecasts of annual farm incomes at the beginning of the financial year (July–June). The model was then used to relate climate-induced income variability to the diversity of farm income sources, a practical measure of adaptive capacity that can be positively influenced by policy. Three timeless philosophical arguments are used to discuss the policy relevance of the bioeconomic modelling. These arguments are used to compare the value to decision makers of relatively imprecise, integrative information, with relatively precise, reductionist measures. We conclude that the evolution of bioeconomic modelling systems provides an opportunity to refocus the analytical support for Australian drought policy towards the rural livelihood effects that matter most to governments and rural communities.

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Choquenot, David, Simon J. Nicol, and John D. Koehn. "Bioeconomic modelling in the development of invasive fish policy." New Zealand Journal of Marine and Freshwater Research 38, no. 3 (August 2004): 419–28. http://dx.doi.org/10.1080/00288330.2004.9517249.

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Llorente, Ignacio, and Ladislao Luna. "Bioeconomic modelling in aquaculture: an overview of the literature." Aquaculture International 24, no. 4 (December 14, 2015): 931–48. http://dx.doi.org/10.1007/s10499-015-9962-z.

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Carrasco, L. R., J. D. Mumford, A. MacLeod, J. D. Knight, and R. H. A. Baker. "Comprehensive bioeconomic modelling of multiple harmful non-indigenous species." Ecological Economics 69, no. 6 (April 2010): 1303–12. http://dx.doi.org/10.1016/j.ecolecon.2010.02.001.

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15

Chaudhuri, Kripasindhu. "A bioeconomic model of harvesting a multispecies fishery." Ecological Modelling 32, no. 4 (July 1986): 267–79. http://dx.doi.org/10.1016/0304-3800(86)90091-8.

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Pomeroy, Robert, Boris E. Bravo Ureta, Daniel Solis, and Robert J. Johnston. "Bioeconomic modelling and salmon aquaculture: an overview of the literature." International Journal of Environment and Pollution 33, no. 4 (2008): 485. http://dx.doi.org/10.1504/ijep.2008.020574.

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Samanta, G. P., Debasis Manna, and Alakes Maiti. "Bioeconomic modelling of a three-species fishery with switching effect." Journal of Applied Mathematics and Computing 12, no. 1-2 (January 2003): 219–31. http://dx.doi.org/10.1007/bf02936194.

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18

Maravelias, Christos D., Richard Hillary, John Haralabous, and Efthymia V. Tsitsika. "Stochastic bioeconomic modelling of alternative management measures for anchovy in the Mediterranean Sea." ICES Journal of Marine Science 67, no. 6 (March 12, 2010): 1291–300. http://dx.doi.org/10.1093/icesjms/fsq018.

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Abstract Maravelias, C. D., Hillary, R., Haralabous, J., and Tsitsika, E. V. 2010. Stochastic bioeconomic modelling of alternative management measures for anchovy in the Mediterranean Sea. – ICES Journal of Marine Science, 67: 1291–1300. The purse-seine fishery for anchovy in the Aegean Sea consists of two main fleet segments (12–24 and 24–40 m vessels); this paper investigates economically and biologically preferable effort and capacity scenarios for the fishery. Attention is paid to a bioeconomic analysis of fleets composed of segments with varying levels of efficiency (in terms of catch rate) and costs (fixed and variable) and the role this might play in optimal effort allocation at a fleet level. An age-structured stochastic bioeconomic operating model for Aegean anchovy (Engraulis encrasicolus) is constructed. It attempts to account robustly for the multiple uncertainties in the system, including (i) the effort–fishing mortality relationship, (ii) the selectivity, and (iii) the stock–recruit dynamics of the population. A method is proposed for determining the economically optimal level of long-term effort in a fishery such as this, with similar characteristics in terms of stock dynamics, fishery, and markets. Lower values of effort and capacity are predicted to yield greater future profit when viewing the fleet in its entirety, but even lower values may be advisable to maintain the long-term biological integrity of the stock. The results may prove useful in balancing the productivity of the stock with the harvesting capacity of the fleet, while managing to ensure the long-term profitability of the fleet along with the sustainability of the resource.

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Kokic, Philip, Rohan Nelson, Holger Meinke, Andries Potgieter, and John Carter. "From rainfall to farm incomes—transforming advice for Australian drought policy. I. Development and testing of a bioeconomic modelling system." Australian Journal of Agricultural Research 58, no. 10 (2007): 993. http://dx.doi.org/10.1071/ar06193.

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In this paper we report the development of a bioeconomic modelling system, AgFIRM, designed to help close a relevance gap between climate science and policy in Australia. We do this by making a simple econometric farm income model responsive to seasonal forecasts of crop and pasture growth for the coming season. The key quantitative innovation was the use of multiple and M-quantile regression to calibrate the farm income model, using simulated crop and pasture growth from 2 agroecological models. The results of model testing demonstrated a capability to reliably forecast the direction of movement in Australian farm incomes in July at the beginning of the financial year (July–June). The structure of the model, and the seasonal climate forecasting system used, meant that its predictive accuracy was greatest across Australia’s cropping regions. In a second paper, Nelson et al. (2007, this issue), we have demonstrated how the bioeconomic modelling system developed here could be used to enhance the value of climate science to Australian drought policy.

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20

ANDERSON, LEE G. "A BIOECONOMIC ANALYSIS OF MARINE RESERVES." Natural Resource Modeling 15, no. 3 (June 28, 2008): 311–34. http://dx.doi.org/10.1111/j.1939-7445.2002.tb00092.x.

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21

Pang, H., M. Makarechian, J. A. Basarab, and R. T. Berg. "Application of a dynamic simulation model on the effects of calving season and weaning age on bioeconomic efficiency." Canadian Journal of Animal Science 79, no. 4 (December 1, 1999): 419–24. http://dx.doi.org/10.4141/a99-021.

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A dynamic simulation model, Alberta Beef Production Simulation System (ABPSS), which includes herd inventory, nutrient requirements, forage production, and economic submodels, was used to compare bioeconomic efficiency in spring and fall calvings and different weaning ages (220, 200, 180, 160, and 140 d). Comparisons were made assuming a mature cow weight of 550 kg and a peak milk yield of 8.2 kg d−1. The first day of calving was assumed to be 28 March and 8 September for spring- and fall-calving cows, respectively. Bioeconomic efficiency was measured as the net return per cow (total return minus total cost). Fall calving in Alberta generally results in longer exposure of young calves to extreme cold weather after calving, and therefore total annual DMI and feed cost were higher in the fall-calving season group than in the spring-calving season group. Bioeconomic efficiency improved as weaning age increased from 140 to 220 d in both calving seasons. For weaning age of 200 d or less, spring calving was more efficient than fall calving. However, at a weaning age of 220 d, fall calving had higher bioeconomic efficiency than spring calving, primarily due to higher market prices for fall-born calves. This indicated that interactions of calving season by weaning age was an important factor affecting bioeconomic efficiency. It must be noted that the model was developed based on experimental results and data from the liteature, and due to the unavailability of suitable data the model could not be validated. We suggest that the ABPSS model has the potential for providing a useful decision-making tool for simultaneous consideration of many factors in an integrated system and for evaluating the effects of alternative management strategies on profitability of beef production systems. Key words: Beef cattle, simulation and modelling, production system, calving season, weaning age, bioeconomic efficiency

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Trenbath, B. R. "MIDAS, A bioeconomic model of a dryland farm system." Ecological Modelling 45, no. 2 (April 1989): 155–57. http://dx.doi.org/10.1016/0304-3800(89)90092-6.

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23

Hildén, Mikael, and Veijo Kaitala. "Comprehensive sensitivity analysis of a bioeconomic stock-recruitment model." Ecological Modelling 54, no. 1-2 (May 1991): 37–57. http://dx.doi.org/10.1016/0304-3800(91)90097-k.

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Din, Qamar, A. M. Yousef, and A. A. Elsadany. "Stability and Bifurcation Analysis of a Discrete Singular Bioeconomic System." Discrete Dynamics in Nature and Society 2021 (July 13, 2021): 1–22. http://dx.doi.org/10.1155/2021/6679161.

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The main concern of this paper is to discuss stability and bifurcation analysis for a class of discrete predator-prey interaction with Holling type II functional response and harvesting effort. Firstly, we establish a discrete singular bioeconomic system, which is based on the discretization of a system of differential algebraic equations. It is shown that the discretized system exhibits much richer dynamical behaviors than its corresponding continuous counterpart. Our investigation reveals that, in the discretized system, two types of bifurcations (i.e., period-doubling and Neimark–Sacker bifurcations) can be studied; however, the dynamics of the continuous model includes only Hopf bifurcation. Moreover, the state delayed feedback control method is implemented for controlling the chaotic behavior of the bioeconomic model. Numerical simulations are presented to illustrate the theoretical analysis. The maximal Lyapunov exponents (MLE) are computed numerically to ensure further dynamical behaviors and complexity of the model.

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Shwiff, S., C. Aenishaenslin, A. Ludwig, P. Berthiaume, M. Bigras-Poulin, K. Kirkpatrick, L. Lambert, and D. Bélanger. "Bioeconomic Modelling of Raccoon Rabies Spread Management Impacts in Quebec, Canada." Transboundary and Emerging Diseases 60, no. 4 (June 19, 2012): 330–37. http://dx.doi.org/10.1111/j.1865-1682.2012.01351.x.

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Flaaten, Ola, and Einar Mjølhus. "Nature Reserves as a Bioeconomic Management Tool: A Simplified Modelling Approach." Environmental and Resource Economics 47, no. 1 (April 22, 2010): 125–48. http://dx.doi.org/10.1007/s10640-010-9368-3.

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Menz, K. M., and P. Grist. "Increasing rubber planting density to shade Imperata: a bioeconomic modelling approach." Agroforestry Systems 34, no. 3 (June 1996): 291–303. http://dx.doi.org/10.1007/bf00046929.

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Kvamsdal, Sturla F., José M. Maroto, Manuel Morán, and Leif K. Sandal. "Bioeconomic modeling of seasonal fisheries." European Journal of Operational Research 281, no. 2 (March 2020): 332–40. http://dx.doi.org/10.1016/j.ejor.2019.08.031.

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Melià, Paco, and Marino Gatto. "A stochastic bioeconomic model for the management of clam farming." Ecological Modelling 184, no. 1 (May 2005): 163–74. http://dx.doi.org/10.1016/j.ecolmodel.2004.11.011.

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Alexander, Robert R., and David W. Shields. "Using land as a control variable in density-dependent bioeconomic models." Ecological Modelling 170, no. 2-3 (December 2003): 193–201. http://dx.doi.org/10.1016/s0304-3800(03)00226-6.

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ROUTLEDGE, RICK. "MIXED-STOCK VS. TERMINAL FISHERIES: A BIOECONOMIC MODEL." Natural Resource Modeling 14, no. 4 (June 28, 2008): 523–39. http://dx.doi.org/10.1111/j.1939-7445.2001.tb00072.x.

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CRIDDLE, KEITH R., and MARK HERRMANN. "A STATE SPACE BIOECONOMIC MODEL OF PACIFIC HALIBUT." Natural Resource Modeling 21, no. 1 (March 5, 2008): 117–47. http://dx.doi.org/10.1111/j.1939-7445.2008.00003.x.

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Pradhan, T., and K. S. Chaudhuri. "Bioeconomic Modelling of a Single Species Fishery with Gompertz Law of Growth." Journal of Biological Systems 06, no. 04 (December 1998): 393–409. http://dx.doi.org/10.1142/s021833909800025x.

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A single species fishery model has been developed using the Gompertz law of population growth and the CPUE (Catch-per-unit-effort) hypothesis. The dynamical and the bionomic steady states were determined and their natures were examined from the biological as well as economic view points. The optimal harvest policy is discussed by taking the fishing effort as a dynamic control variable. The results are compared with those of the Schaefer model [10].

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Gowen, Rebecca, and Steven G. Bray. "Bioeconomic modelling of woody regrowth carbon offset options in productive grazing systems." Rangeland Journal 38, no. 3 (2016): 307. http://dx.doi.org/10.1071/rj15084.

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Agricultural land has been identified as a potential source of greenhouse gas emissions offsets through biosequestration in vegetation and soil. In the extensive grazing land of Australia, landholders may participate in the Australian Government’s Emissions Reduction Fund and create offsets by reducing woody vegetation clearing and allowing native woody plant regrowth to grow. This study used bioeconomic modelling to evaluate the trade-offs between an existing central Queensland grazing operation, which has been using repeated tree clearing to maintain pasture growth, and an alternative carbon and grazing enterprise in which tree clearing is reduced and the additional carbon sequestered in trees is sold. The results showed that ceasing clearing in favour of producing offsets produces a higher net present value over 20 years than the existing cattle enterprise at carbon prices, which are close to current (2015) market levels (~$13 t–1 CO2-e). However, by modifying key variables, relative profitability did change. Sensitivity analysis evaluated key variables, which determine the relative profitability of carbon and cattle. In order of importance these were: the carbon price, the gross margin of cattle production, the severity of the tree–grass relationship, the area of regrowth retained, the age of regrowth at the start of the project, and to a lesser extent the cost of carbon project administration, compliance and monitoring. Based on the analysis, retaining regrowth to generate carbon income may be worthwhile for cattle producers in Australia, but careful consideration needs to be given to the opportunity cost of reduced cattle income.

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Pascoe, S. "Bioeconomic model, fisheries management, multi-objective modelling, goal programming, Common Fisheries Policy." European Review of Agriculture Economics 28, no. 2 (June 1, 2001): 161–85. http://dx.doi.org/10.1093/erae/28.2.161.

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Sana, Shib Sankar, Debabrata Purohit, and Kripasindhu Chaudhuri. "Joint project of fishery and poultry – A bioeconomic model." Applied Mathematical Modelling 36, no. 1 (January 2012): 72–86. http://dx.doi.org/10.1016/j.apm.2011.04.031.

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Kvamsdal, Sturla, José M. Maroto, Manuel Morán, and Leif K. Sandal. "A bridge between continuous and discrete-time bioeconomic models: Seasonality in fisheries." Ecological Modelling 364 (November 2017): 124–31. http://dx.doi.org/10.1016/j.ecolmodel.2017.09.020.

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Bunting, Stuart W. "Confronting the realities of wastewater aquaculture in peri-urban Kolkata with bioeconomic modelling." Water Research 41, no. 2 (January 2007): 499–505. http://dx.doi.org/10.1016/j.watres.2006.10.006.

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Robinson, Nick, Xiaoxu Li, and Ben Hayes. "Testing options for the commercialization of abalone selective breeding using bioeconomic simulation modelling." Aquaculture Research 41, no. 9 (August 2010): e268-e288. http://dx.doi.org/10.1111/j.1365-2109.2010.02528.x.

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van Walsum, Paul, John Helming, Louis Stuyt, Eric Schouwenberg, and Piet Groenendijk. "Spatial planning for lowland stream basins using a bioeconomic model." Environmental Modelling & Software 23, no. 5 (May 2008): 569–78. http://dx.doi.org/10.1016/j.envsoft.2007.08.006.

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Knoke, Thomas, and Thomas Seifert. "Integrating selected ecological effects of mixed European beech–Norway spruce stands in bioeconomic modelling." Ecological Modelling 210, no. 4 (February 2008): 487–98. http://dx.doi.org/10.1016/j.ecolmodel.2007.08.011.

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Marchal, Paul, L. Richard Little, and Olivier Thébaud. "Quota allocation in mixed fisheries: a bioeconomic modelling approach applied to the Channel flatfish fisheries." ICES Journal of Marine Science 68, no. 7 (June 6, 2011): 1580–91. http://dx.doi.org/10.1093/icesjms/fsr096.

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Abstract Marchal, P., Little, L. R., and Thébaud, O. 2011. Quota allocation in mixed fisheries: a bioeconomic modelling approach applied to the Channel flatfish fisheries. – ICES Journal of Marine Science, 68: 1580–1591. A simulation modelling approach is used to assess the respective performances of different regimes of quota allocation (fixed or transferable), quota ownership (owned or not by fishers), and taxation for catching fish above quota. The simulations account for a variety of fleet behaviours (ranging from fixed by tradition to dynamic economics-driven). The modelling framework is applied to the Channel flatfish mixed fisheries. Transferable quota allocation regimes would particularly benefit small netters and beam trawlers, which would achieve a profit of €50–150 million without compromising the conservation of eastern Channel sole, but it could impair the sustainability of other stocks. If quota is owned by fishers, the least fishing-efficient fleet stops fishing, but makes substantial profit from leasing quotas to beam trawlers and to small and large netters, which remain actively fishing. The highest economic return for quota owners (€200–300 million) is achieved when effort allocation is fixed by tradition. The profit achieved by small netters is greatest when fleets are almost entirely economics-driven. Increasing overquota landing taxes generally leads to conservation benefits for all stocks, but at the expense of lower profitability for the fishery overall.

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CAI, LIMING, XUEZHI LI, and XINYU SONG. "MODELING AND ANALYSIS OF A HARVESTING FISHERY MODEL IN A TWO-PATCH ENVIRONMENT." International Journal of Biomathematics 01, no. 03 (September 2008): 287–98. http://dx.doi.org/10.1142/s1793524508000242.

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In this paper, a harvesting fishery model in a two-patch environment: one free-fishing zone and the other one reserved zone where fishing is strictly prohibited, is proposed and analyzed. The existence of possible biological steady states, along with their local stability, instability and global stability is discussed. The existence of bioeconomic equilibrium is derived. An optimal harvesting policy is also given by applying pontryagin's maximum principle.

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Li, Meng, Boshan Chen, and Huawen Ye. "A bioeconomic differential algebraic predator–prey model with nonlinear prey harvesting." Applied Mathematical Modelling 42 (February 2017): 17–28. http://dx.doi.org/10.1016/j.apm.2016.09.029.

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ASHFIELD, A., M. WALLACE, M. MCGEE, and P. CROSSON. "Bioeconomic modelling of compensatory growth for grass-based dairy calf-to-beef production systems." Journal of Agricultural Science 152, no. 5 (August 22, 2013): 805–16. http://dx.doi.org/10.1017/s0021859613000531.

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SUMMARYFeed makes up c. 0·7 of total variable costs on Irish beef farms. A period of reduced growth (caused by nutritional restriction) followed by a period of accelerated growth (compensatory growth) can be used to take advantage of lower cost feedstuffs (grazed grass) during the grazing season. The Grange Dairy Beef Systems Model (GDBSM) was modified to capture more accurately the implications of compensatory growth and, thus, the energy demand of beef cattle was partitioned into energy required for maintenance and energy required for growth. For the current study, three production systems were evaluated where the male progeny of dairy cows were finished as steers at 24 (S24), 28 (S28) and 30 (S30) months of age. Three different live weight gains (RESLWG; 0·4, 0·6 and 0·8 kg/day), reflecting different levels of nutritional restriction, were simulated during the first winter feeding period (November–February) for S24 and during the second winter feeding period for S28 and S30. This allowed the effect of different live weight gains during a nutritional restriction period on farm profitability to be determined. Results indicated that for S24 the most profitable RESLWG was 0·6 kg/day. However, for S28 and S30 the most profitable systems were RESLWG of 0·4 kg/day. Financial performance of all systems was very sensitive to variation in beef carcass and calf prices but less sensitive to concentrate and fertilizer price variation. Furthermore, sensitivity analysis showed that the level of maintenance energy reduction and the duration of this reduction had a modest impact on results. The GDBSM is demonstrated as a quantitative framework for simulating compensatory growth and determining its effects on the profitability of dairy calf-to-beef production systems.

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Puga, Rafael, Sergio Hernández Vázquez, Juana López Martinez, and María E. de León. "Bioeconomic modelling and risk assessment of the Cuban fishery for spiny lobster Panulirus argus." Fisheries Research 75, no. 1-3 (September 2005): 149–63. http://dx.doi.org/10.1016/j.fishres.2005.03.014.

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Trijoulet, Vanessa, Helen Dobby, Steven J. Holmes, and Robin M. Cook. "Bioeconomic modelling of grey seal predation impacts on the West of Scotland demersal fisheries." ICES Journal of Marine Science 75, no. 4 (January 12, 2018): 1374–82. http://dx.doi.org/10.1093/icesjms/fsx235.

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Abstract The role grey seals have played in the performance of fisheries is controversial and a cause of much debate between fishers and conservationists. Most studies focus on the effects of seal damage to gears or fish and on prey population abundance but little attention is given to the consequences of the latter for the fisheries. We develop a model that quantifies the economic impact of grey seal predation on the West of Scotland demersal fisheries that traditionally targeted cod, haddock and whiting. Three contrasting fishing strategy scenarios are examined to assess impacts on equilibrium fleet revenues under different levels of seal predation. These include status quo fishing mortality (SQF, steady state with constant fishing mortality), open access fishing (bioeconomic equilibrium, BE) and the maximum economic yield (MEY). In all scenarios, cod emerges as the key stock. Large whitefish trawlers are most sensitive to seal predation due to their higher cod revenues but seal impacts are minor at the aggregate fishery level. Scenarios that consider dynamic fleet behaviour also show the greatest effects of seal predation. Results are sensitive to the choice of seal foraging model where a type II functional response increases sensitivity to seal predation. The cost to the fishery for each seal is estimated.

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Bastardie, Francois, J. Rasmus Nielsen, O. R. Eigaard, H. O. Fock, P. Jonsson, and V. Bartolino. "Competition for marine space: modelling the Baltic Sea fisheries and effort displacement under spatial restrictions." ICES Journal of Marine Science 72, no. 3 (December 1, 2014): 824–40. http://dx.doi.org/10.1093/icesjms/fsu215.

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AbstractMaritime spatial planning (MSP) and fishery management may generate extra costs for fisheries by constraining fishers activity with conservation areas and new utilizations of the sea. More energy-efficient fisheries are also likely to alter existing fishing patterns, which already vary from fishery to fishery and from vessel to vessel. The impact assessment of new spatial plans involving fisheries should be based on quantitative bioeconomic analyses that take into account individual vessel decisions, and trade-offs in cross-sector conflicting interests. We use a vessel-oriented decision-support tool (the DISPLACE model) to combine stochastic variations in spatial fishing activities with harvested resource dynamics in scenario projections. The assessment computes economic and stock status indicators by modelling the activity of Danish, Swedish, and German vessels (>12 m) in the international western Baltic Sea commercial fishery, together with the underlying size-based distribution dynamics of the main fishery resources of sprat, herring, and cod. The outcomes of alternative scenarios for spatial effort displacement are exemplified by evaluating the fishers's abilities to adapt to spatial plans under various constraints. Interlinked spatial, technical, and biological dynamics of vessels and stocks in the scenarios result in stable profits, which compensate for the additional costs from effort displacement and release pressure on the fish stocks. The effort is further redirected away from sensitive benthic habitats, enhancing the ecological positive effects. The energy efficiency of some of the vessels, however, is strongly reduced with the new zonation, and some of the vessels suffer decreased profits. The DISPLACE model serves as a spatially explicit bioeconomic benchmark tool for management strategy evaluations for capturing tactical decision-making in reaction to MSP.

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AKPALU, WISDOM, EDWIN MUCHAPONDWA, and PRECIOUS ZIKHALI. "Can the restrictive harvest period policy conserve mopane worms in southern Africa? A bioeconomic modelling approach." Environment and Development Economics 14, no. 5 (October 2009): 587–600. http://dx.doi.org/10.1017/s1355770x0900518x.

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ABSTRACTThe mopane worm, which is the caterpillar form of the Saturnid moth Imbrasia belina Westwood, is – like other edible insects and caterpillars – a vital source of protein in southern African countries. The worms live and graze on mopane trees, which have alternative uses. With increasing commercialization of the worm, its management, which was hitherto organized as a common property resource, has been degraded to almost open access. This paper uses a bioeconomic modelling approach to show that for some optimal allocation of the mopane forest stock, the restrictive harvest period policy advocated by community leaders may not lead to sustainable harvesting of the worm.

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Elliston, Lisa, and Liangyue Cao. "An agent-based bioeconomic model of a fishery with input controls." Mathematical and Computer Modelling 44, no. 5-6 (September 2006): 565–75. http://dx.doi.org/10.1016/j.mcm.2006.01.010.

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Journal articles on the topic 'Bioeconomic modelling'

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