Quantifying the consequences of nutritional strategies aimed at decreasing phosphorus excretion from pig populations: a modeling approach

Abstract

There is a global imperative to reduce phosphorous (P) excretion from pig systems. In this study, a previously validated deterministic model was modified to be stochastic, in order to investigate the consequences of different management strategies on P excretion by a group of growing pigs. The model predicts P digestion, retention and excretion from feed composition and growth parameters that describe a specified pig phenotype. Stochasticity was achieved by introducing random variation in the latter. The strategies investigated were: (1) changing feed composition frequently in order to match more closely pig digestible P (digP) requirements to feed composition (phase feeding) and (2) grouping pigs into light and heavy groups and feeding each group according to the requirements of their group average BW (sorting). Phase feeding reduced P excretion as the number of feeding phases increased. The effect was most pronounced as feeding phases increased from 1 to 2, with a 7.5% decrease achieved; the increase in phases from 2 to 3 was associated with a further 2.0% reduction. Similarly, the effect was more pronounced when the feed targeted the population requirements for digP at the average BW of the first third, rather than the average requirements at the mid-point BW of each feeding sequence plan. Increasing the number of feeding phases increased the percentage of pigs that met their digP requirements during the early stages of growth and reduced the percentage of pigs that were supplied <85% of their digP requirements at any stage of their growth; the latter may have welfare implications. Sorting of pigs reduced P excretion to a lesser extent; the reduction was greater as the percentage of pigs in the light group increased from 10% to 30% (from 1.5% to 3.0% reduction, respectively). This resulted from an increase in the P excreted by the light group, accompanied by a decrease in the P excreted by the remaining pigs. Sorting increased the percentage of light pigs that met their dig P requirements, but only slightly decreased the percentage of heavy pigs that met these requirements at any point of their growth. Exactly the converse was the case as far as the percentage of pigs that were supplied <85% of their digP requirements were concerned. The developed model is flexible and can be used to investigate the effectiveness of other management strategies in reducing P excretion from groups of pigs, including precision livestock feeding.

The consequences of introducing stochasticity in nutrient utilisation models: the case of phosphorus utilisation by pigs

“…The model outputs for a single pig are as follows: (1) average daily gain and food intake (FI), (2) body composition, including P retained and (3) soluble and insoluble, and thus total, P excreted. Random variation in the model has been included in the animal-related inputs and described in detail by Symeou et al (3) . In addition, in this study, we introduced random variation in feed ingredient composition, variation in the uniformity of the feed arising from mixing and investigated the interactions of these stochastic introductions with the variation in pig genetic traits.…”

“…Some models deal with variation between individual pigs and in aspects of the environment (1)(2)(3) , but none has dealt with uncertainty in feed composition at a particular point in time or over time. There are several reasons why the latter may be important.…”

“…The growth parameters considered to vary between the pig phenotypes were protein at maturity (Pr m ), lipid:protein ratio at maturity (LPr m ) and the scaled maturity rate (B*) (1)(2)(3)24,25) . A justification for the choice of these parameters, the lack of correlation between them and why they are able to account for both growth rate and body composition in individual pigs is given in the study by Symeou et al (3) .…”

“…The mean of B* were estimated at 0·0392 and (SD 0·0078) kg/d, respectively, from the calculations of Brossard et al (27) for the data of Rivest (28) ; the details of how this estimate was derived are also given in the study by Symeou et al (3) . Finally, the mean of LPr m were derived from Knap and Rauw (29) to be 1·50 and (SD 0·315) kg/kg, which were in turn adapted from the study by Doeschl-Wilson et al (30) .…”

“…The initial BW of the pigs (BW 0 ) was in accordance with the methodology of Wellock et al (2) , having an average BW 0 of 30 kg, and their chemical composition was calculated assuming that the pigs had their ideal composition set by the genotype (31) . The values of B*, Pr m and LPr m were assumed to be uncorrelated and normally distributed (1)(2)(3)24,25) .…”