Yun Bao , Pol Llagostera , Daniel Babot , Lluís M. Plà-Aragonès
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引用次数: 0
Abstract
Context:
Modern fattening farms require effective interdisciplinary decision-making tools to ensure profitability.
Objective:
This paper introduces a mixed integer linear programming (MILP) - based decision tool, to optimize the transportation strategy selecting the most profitable slaughterhouse and exploring the impact of key factors such as pig prices, feed costs, and mortality rates.
Methods:
The proposed tool is based on a mixed integer linear programming (MILP) model, the pig growth modeling by Gompertz model and the payment grid of different slaughterhouses.
Results and conclusions:
Our findings confirm the selection of the most suitable slaughterhouse significantly enhances the economic outcome, and that the current increment in slaughter weight to about 120 kg leads to a larger fattening period. Piglet mortality did not affect the optimal delivery plan but: for every 1% increase in mortality rate, there was a loss of 0.9 ct €/kg. In addition, four growth curves fit from experimental data are used to compare their optimal marketing strategies. The base case scenario outperformed the official profits reported to the sector by 12.49 ct €/kg liveweight. Pigs with low growth generated losses of -0.42 ct €/kg while medium growth pigs with 4.24 ct €/kg outperformed higher growth pigs (1.29 ct €/kg). The feeding cost advantage makes medium-growth pigs more profitable. The optimal fattening period was 22 weeks except for medium growth pigs (which was from 20 to 21 weeks).
Significance:
With the rise of digital farms and precision livestock farming, Decision Support Systems (DSS) based on accurate growth curves plays a key role in slaughterhouse selection and developing effective marketing strategy, outperforming industry averages.
期刊介绍:
Agricultural Systems is an international journal that deals with interactions - among the components of agricultural systems, among hierarchical levels of agricultural systems, between agricultural and other land use systems, and between agricultural systems and their natural, social and economic environments.
The scope includes the development and application of systems analysis methodologies in the following areas:
Systems approaches in the sustainable intensification of agriculture; pathways for sustainable intensification; crop-livestock integration; farm-level resource allocation; quantification of benefits and trade-offs at farm to landscape levels; integrative, participatory and dynamic modelling approaches for qualitative and quantitative assessments of agricultural systems and decision making;
The interactions between agricultural and non-agricultural landscapes; the multiple services of agricultural systems; food security and the environment;
Global change and adaptation science; transformational adaptations as driven by changes in climate, policy, values and attitudes influencing the design of farming systems;
Development and application of farming systems design tools and methods for impact, scenario and case study analysis; managing the complexities of dynamic agricultural systems; innovation systems and multi stakeholder arrangements that support or promote change and (or) inform policy decisions.