Optimising finishing pig delivery weight: participatory decision problem analysis
F. Leen A B , A. Van den Broeke A , M. Aluwé A , L. Lauwers A B , S. Millet A and J. Van Meensel A C
+ Author Affiliations
- Author Affiliations
A Institute for Agricultural and Fisheries Research (ILVO), Burg. Van Gansberghelaan 115, Box 2, 9820 Merelbeke, Belgium.
B Department of Agricultural Economics, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Ghent, Belgium.
C Corresponding author. Email: jef.vanmeensel@ilvo.vlaanderen.be
Animal Production Science 58(6) 1141-1152 https://doi.org/10.1071/AN16098
Submitted: 16 February 2016 Accepted: 15 November 2016 Published: 18 January 2017
Abstract
The seemingly straightforward question of optimal pig delivery weight is more complex than meets the eye. Despite abundant research insights, the industry continues to request additional applied scientific decision support on the delivery weight problem. The current objective is to investigate whether and how the complex decision of delivery weight can be reshaped (reframed) into a more tangible and comprehensible system of factors that matter for making the right decision. We used a participatory decision problem analysis, which resulted in modelling blueprints that incorporate factors prioritised by stakeholders for determining optimal delivery weights. How to efficiently organise such a ‘problem reframing process’ is case-specific: it depends on the objective, the initial problem understanding by the stakeholders, and their learning potential. Efficient co-learning is a prerequisite for successful participatory problem analysis. Our study reveals that the first step in such a process of ‘problem reframing’ should therefore be to answer the question of how to effectively organise co-learning among stakeholders and researchers, instead of starting with a correct and detailed representation of the problem. Useful guidelines for participatory problem reframing processes are (1) providing sufficient participatory learning steps, (2) having few and clearly defined objectives per learning step, (3) providing adapted learning tools per step, (4) establishing a common language and (5) deliberately choosing stakeholders based on prior knowledge of the problem or its context, potential motivation or incentives to be part of the participatory process step and potential role in up-scaling the co-learning process to a larger group of beneficiaries.
Additional keywords: optimisation, stakeholder participation.
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