Towards a decision support tool with an individual-based model of a pig fattening unit

Cadero, Alice; Aubry, Alexia; Dourmad, Jean-Yves; Salaün, Yvon; Garcia-Launay, Florence

doi:10.1016/j.compag.2018.02.012

Cited by 16 publications

(9 citation statements)

References 20 publications

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“…Using the Ecoalim dataset ( Wilfart et al, 2016 ) of the AGRIBALYSE database, the environmental impacts of the diet ingredients were estimated by applying the ReCiPe Method ( Huijbregts et al, 2017 ). A distance of 100 km was assumed for the transport of the ingredients of the diets from the farm to the feed factory, a distance of 500 km for cereals ( Garcia-Launay et al, 2018 ), and a distance of 30 km ( Cadero et al, 2018 ) for transport from the feed factory to the pig farm, using the Ecoinvent version 3.1 database (attributional life cycle inventories).…”

Section: Methodsmentioning

confidence: 99%

Economic and environmental assessments of combined genetics and nutrition optimization strategies to improve the efficiency of sustainable pork production

Soleimani

Hermesch

Gilbert

2021

Journal of Animal Science

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We evaluated the economic and environmental impacts of strategies that incorporated selection for pig feed:gain and dietary optimization based on a single or multiple objectives tailored to meet the population’s nutritional requirements, with the goal to optimize sustainable farm feed efficiency. The economic and environmental features of the strategy were evaluated using life cycle assessment (LCA) and bio-economic models. An individual trait-based LCA model was applied to evaluate global warming potential (GWP), terrestrial acidification potential (AP), freshwater eutrophication potential (EP), and land occupation (LO) of the combined genetics and nutrition optimization to produce 1kg of live pig weighing 120kg at the farm gate. A parametric individual trait-based bio-economic model was developed and applied to determine the cost breakdown, revenue and profit to be gained from a 120kg live pig at the farm gate. Applying the combined genetics and nutrition optimization, the individual performance traits of pigs from two genetic lines with contrasted levels of feed efficiency were simulated with InraPorc in response to diets formulated for least cost, least environmental impacts, or minimum combination of cost and environmental impacts objectives, and accounting for the nutritional requirements of each line. Significant differences in the environmental impacts (P < 0.0001) and profit (P < 0.05) between lines predicted the same reference diet showed that selection for feed efficiency (residual feed intake, RFI) in pigs improves pig production sustainability. When pig responses were simulated with their line optimized diets, except for EP, all the line environmental impacts were less (P < 0.05) than with the reference diet. The high correlations of feed conversion ratio (FCR) with the environmental impacts (> 0.82) and the profit (< -0.88) in both lines underline the importance of feed efficiency as a lever for the sustainability of pig production systems. Implementing combined genetics and nutrition optimization, the inherent profit and environmental differences between the genetic lines was predicted to be reduced from 23.4% with the reference diet to 7.6% with the diet optimized jointly for economic and environmental objectives (joint diet). Consequently, for increased pig sustainability, diet optimization for sustainability objectives should be applied to cover the specific nutritional requirements arising in the herd from the pigs’ genetic level.

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Section: Methodsmentioning

confidence: 99%

Economic and environmental assessments of combined genetics and nutrition optimization strategies to improve the efficiency of sustainable pork production

Soleimani

Hermesch

Gilbert

2021

Journal of Animal Science

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“…The model of the pig fattening unit considers individual variability in performance among pigs, farmers feeding practices and animal management, and estimates environmental impacts (through Life Cycle Assessment) and economic results of the unit (Cadero et al, 2018a). This model provides reliable estimates of the fattening unit performance (Cadero et al, 2018b). A virtual experiment was designed with 96 scenarios, resulting from different options for batch interval (7 vs. 35 days), management of lightest pigs (use or no use of a buffer room), feed rationing (ad libitum, restriction to 2.5 kg/d) and sequence plans (two-phase, daily-phase), scale of application of the feeding programs (i.e.…”

Section: Methodsmentioning

confidence: 99%

Proceedings of the 9th Workshop on Modelling Nutrient Digestion and Utilization in Farm Animals (MODNUT)

2019

Advances in Animal Biosciences

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“…Smart Livestock Farming (SLF) is one of applications of PA that supports real-time monitoring of productions, health, and welfare of livestock, and to ensure optimal yield, to increase the management capacity of animals. A variety of sensors and actuators and decision-making tools, and IoT technologies are used in SLF (Cadero et al 2018). Electronic identification systems such as ear tags, ruminal boluses, and sub-cutaneous radio-frequency identification; on-animal sensors like accelerometers, global positioning systems, and social activity loggers; and stationary management systems, milking parlor, and related technologies and flock management software are used in SLF (Vaintrub et al 2021).…”

Section: Examples From Digital Agriculturementioning

confidence: 99%

Digital Transformation for Sustainable Future - Agriculture 4.0 : A review

Dayioğlu

Türker

2021

Tarım Bilimleri Dergisi

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Towards a decision support tool with an individual-based model of a pig fattening unit

Cited by 16 publications

References 20 publications

Economic and environmental assessments of combined genetics and nutrition optimization strategies to improve the efficiency of sustainable pork production

Economic and environmental assessments of combined genetics and nutrition optimization strategies to improve the efficiency of sustainable pork production

Proceedings of the 9th Workshop on Modelling Nutrient Digestion and Utilization in Farm Animals (MODNUT)

Digital Transformation for Sustainable Future - Agriculture 4.0 : A review

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