A framework for decision support related to infectious diseases in slaughter pig fattening units

Toft, Nils; Kristensen, Anders Ringgaard; Jørgensen, Erik

doi:10.1016/j.agsy.2004.07.017

Cited by 18 publications

(11 citation statements)

References 13 publications

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“…For pigs observed during short time intervals, growth has been defined simply by linear functions with the intercept representing initial BW and the slope expressing an increase in BW (Toft et al, 2005). For pigs observed over longer time intervals, periods with faster and slower growth rates have been expressed in the form of Gompertz functions (Niemi et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Analyses of body weight patterns in growing pigs: a new view on body weight in pigs for frequent monitoring

Stygar

Dolecheck

Kristensen

2018

Animal

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Frequent BW monitoring of growing pigs can be useful for identifying production (e.g. feeding), health and welfare problems. However, in order to construct a tool which will properly recognize abnormalities in pigs' growth a precise description of the growth process should be used. In this study we proposed a new model of pig growth accounting for daily fluctuations in BW. Body weight measurements of 1710 pigs (865 gilts and 843 barrows) originating from five consecutive batches from a Danish commercial farm were collected. Pigs were inserted into a large pen (maximum capacity = 400) between November 2014 and September 2015. On average, each pig was observed for 42 days and weighed 3.6 times a day when passing from the resting to feeding area. Altogether, 243,160 BW measurements were recorded. A multilevel model of pig growth was constructed and fitted to available data. The BW of pigs was modeled as a quadratic function of time. A diurnal pattern was incorporated into the model by a cosine wave with known length (24 h). The model included pig effect which was defined as a random autoregressive process with exponential correlation. Variance of within-pigs error was assumed to increase with time. Because only five batches were observed, it was not possible to obtain the random effect for batch. However, in order to account for the batch effect the model included interactions between batch and fixed parameters: intercept, time, square value of time and cosine wave. The gender effect was not significant and was removed from the final model. For all batches, morning and afternoon peaks in the frequency of visits to the feeding area could be distinguished. According to results, pigs were lighter in the morning and heavier in the evening (minimum BW was reached around 1000 h and maximum around 2200 h). However, the exact time of obtaining maximum and minimum BW during the day differed between batches. Pigs had access to natural light and, therefore, existing differences could be explained by varying daylight level during observations periods. Because the diurnal amplitude for pig growth varied between batches from 0.9 to 1.4 kg, BW monitoring tools based on frequent measurements should account for diurnal variation in BW of pigs. This proposed description of growth will be built into a monitoring tool (a dynamic linear model) and applied to farm data in future studies.

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Section: Introductionmentioning

confidence: 99%

Analyses of body weight patterns in growing pigs: a new view on body weight in pigs for frequent monitoring

Stygar

Dolecheck

Kristensen

2018

Animal

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“…Similar result has been confirmed in several studies (cf. Jorgensen 1993, Rantala 2004, Toft et al 2005, which is due to the fact that producers can compensate for the value of lost growth potential of slowly growing pigs by increased rotation of animal stock and improved productivity of replacement pigs. Roemen and de Klein (2000) used a Markov decision model to study the production problem of how to organise delivery of groups of pigs.…”

Section: Marginal Revenuementioning

confidence: 99%

“…The final example of income uncertainty is that by Toft et al (2005), who show that the optimal delivery policy depends on such risk factors as the possibility of animal disease. When disease pressure increases, producer can benefit from marketing a larger share of animals at earlier growth stages.…”

Section: Uncertainty Of Slaughter Incomementioning

confidence: 99%

A dynamic programming model for optimising feeding and slaughter decisions regarding fattening pigs

Niemi¹

2006

AFSci

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Costs of purchasing new piglets and of feeding them until slaughter are the main variable expenditures in pig fattening. They both depend on slaughter intensity, the nature of feeding patterns and the technological constraints of pig fattening, such as genotype. Therefore, it is of interest to examine the effect of production technology and changes in input and output prices on feeding and slaughter decisions. This study examines the problem by using a dynamic programming model that links genetic characteristics of a pig to feeding decisions and the timing of slaughter and takes into account how these jointly affect the quality-adjusted value of a carcass. The state of nature and the genotype of a pig are known in the analysis. The results suggest that producer can benefit from improvements in the pigs genotype. Animals of improved genotype can reach optimal slaughter maturity quicker and produce leaner meat than animals of poor genotype. In order to fully utilise the benefits of animal breeding, the producer must adjust feeding and slaughter patterns on the basis of genotype. The results also suggest that the producer can benefit from flexible feeding technology. Typically, such a technology provides incentives to feed piglets with protein-rich feed. When the pig approaches slaughter maturity, the share of protein-rich feed in the diet gradually decreases and the amount of energy-rich feed increases. Generally, the optimal slaughter weight is within the weight range that pays the highest price per kilogram of pig meat. The optimal feeding pattern and the optimal timing of slaughter depend on price ratios. Particularly, an increase in the price of pig meat provides incentives to increase the growth rates up to the pigs biological maximum by increasing the amount of energy in the feed. Price changes and changes in slaughter premium can also have large income effects.;

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“…When uncertainty exists about the state variables (e.g., when they are unobservable or partially observable), the sequential approach of MLHMP makes it possible to apply Bayesian updating between decision epochs to model learning effects (Kristensen 1993). Primarily developed as a decision support tool for herd management, MLHMP has been applied to decision problems within a herd concerning (endemic) infectious diseases, where disease control decisions must be made simultaneously with production and delivery decisions (Toft et al 2005).…”

Section: Introductionmentioning

confidence: 99%

A new decision support framework for managing foot-and-mouth disease epidemics

Kristensen

Mourits

et al. 2010

Ann Oper Res

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Animal disease epidemics such as the foot-and-mouth disease (FMD) pose recurrent threat to countries with intensive livestock production. Efficient FMD control is crucial in limiting the damage of FMD epidemics and securing food production. Decision making in FMD control involves a hierarchy of decisions made at strategic, tactical, and operational levels. These decisions are interdependent and have to be made under uncertainty about future development of the epidemic. Addressing this decision problem, this paper presents a new decision-support framework based on multi-level hierarchic Markov processes (MLHMP). The MLHMP model simultaneously optimizes decisions at strategic, tactical, and operational levels, using Bayesian forecasting methods to model uncertainty and learning about the epidemic. As illustrated by the example, the framework is especially useful in contingency planning for future FMD epidemics.Keywords Foot-and-mouth disease (FMD) · Epidemic control · Decision support · Bayesian forecasting · Multi-level hierarchic Markov processes (MLHMP) L. Ge ( ) LEI Wageningen UR, Hollandseweg 1, 6706KN,

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A framework for decision support related to infectious diseases in slaughter pig fattening units

Cited by 18 publications

References 13 publications

Analyses of body weight patterns in growing pigs: a new view on body weight in pigs for frequent monitoring

Analyses of body weight patterns in growing pigs: a new view on body weight in pigs for frequent monitoring

A dynamic programming model for optimising feeding and slaughter decisions regarding fattening pigs

A new decision support framework for managing foot-and-mouth disease epidemics

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