Towards a decision support tool with an individual-based model of a pig fattening unit
Authored by A Cadero, A Aubry, J Y Dourmad, Y Salaun, F Garcia-Launay
Date Published: 2018
DOI: 10.1016/j.compag.2018.02.012
Sponsors:
No sponsors listed
Platforms:
R
Model Documentation:
Other Narrative
Model Code URLs:
https://zenodo.org/record/890623#.WcuRoWekFcg
Abstract
European pig production is encountering many economic and environmental
challenges. To address these challenges, farmers need tools to assess
the sustainability of their production systems and to make changes to
ensure their sustainability. Decision support tools can help farmers to
simulate and understand the influence of their management practices on
production system performance. In a previous article, we described a
dynamic pig fattening unit model that considers individual variability
in pig performance, farmers' practices and animal management and
estimated environmental impacts (through Life Cycle Assessment) and
economic results of the unit. This model is intended to be included in a
decision support tool, which requires appropriate parsmeterisation for
on-farm application and assessment to guarantee the quality of
predictions. The objective of the present article is to develop a
process to adequately parameterise a model for on-farm use, apply it to
the pig unit model, and evaluate it using external data from commercial
farms. Twenty-one pig farms were surveyed in western France in 2015 to
collect data on animal performance, batch and shipping management, and
farming practices. The parameterisation process was divided into six
steps which correspond to incremental parsmeterisation of the model
using data collected in the survey. The first step consists of
parameterising the inputs related to farm infrastructure and management.
The second step consists of setting initial mean weight and age of pigs
at the beginning of fattening equal to those observed on each farm. The
third step consists of three successive parameterisations for targeted
slaughter weight, mean protein deposition, and mean feed intake. Steps
four, five and six are iterations of step three. Each input
parameterisation step improved predictions, with a decrease in the
squared bias, non-unity slope and lack of correlation between predicted
and observed data. For slaughter weight (SW), the root mean squared
error (RMSE) decreased from 3.25 to 0.83 kg (i.e. from 2.8 to 0.7\% of
mean SW). For average daily gain (ADG), the RMSE decreased from 58.9 to
14.3 g live weight (LW)/day (i.e. from 7.3 to 1.8\% of ADG). For the
feed conversion ratio (FCR), the RMSE decreased from 0.22 to 0.03 kg
feed/kg LW (i.e. from 7.8 to 1.1\% of mean FCR). Considering the final
RMSE values, the parameterisation process developed appears suitable for
calibrating the model for future use in a decision support system.
Tags
Farm management
Performance
Decision support system
Parameterisation
Farming systems
Variability
Weight
Predictions
Feed-intake
Nutrition
Model evaluation
On-farm survey
Growing-finishing pigs
Swine
Inraporc