Niemi, Jarkko K.

Partanen, Kirsi

Pietola, Kyösti

Sevón-Aimonen, Marja-Liisa

Jukuri

A dynamic programming model for optimisingthe timing of replacement of sowsJarkko K. Niemi1, Marja-Liisa Sevón-Aimonen2, Kirsi Partanen3 and Kyösti Pietola11 MTT Economic Research, Latokartanonkaari 9, FI-00790 Helsinki, Finland jarkko.niemi@mtt.fi, kyosti.pietola@mtt.fi  2 MTT Biotechnology and Food Research, FI-31600 Jokioinen, Finlandmarja-liisa.sevon-aimonen@mtt.fi3 MTT Animal Production Research, Tervamäentie 179, FI-05840 Hyvinkää, Finland, kirsi.partanen@mtt.fiPoster prepared for presentation at the Agricultural & Applied Economics Association 2010 AAEA, CAES, & WAEA Joint Annual Meeting, Denver, Colorado, July 25-27, 2010Copyright 2010 by Jarkko K. Niemi, Marja-Liisa Sevón-Aimonen, Kirsi Partanen and Kyösti Pietola. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies.IntroductionApproximately half of sows are removed from the stock each year. Early culling of sows is an important economic and animal welfare problem. It is associated with genetic traits of sows and animal welfare issues such as the prevalence of animal diseases, inappropriate feeding, stress and housing environment - issues which can reduce also piglet growth and increase their mortality. Rational producer can decide to replace the least productive sows after having evaluated their genetic and phenotypictraits. However, animal’s performance is unknown a priori for both the current sow and gilts which may replace it. Thisuncertainty carries a cost and it can impact the timing of replacement considerably.Our goal is to estimate how information about the piglet yield contributes to the timing of replacement, and howimprovements in the animal’s genetic traits impact the replacement pattern and return on capital invested in the facility.A dynamic programming model for optimising the timing of replacement of sowsJarkko K. Niemi1), Marja-Liisa Sevón-Aimonen2), Kirsi Partanen3) and Kyösti Pietola1)1) MTT Economic Research, Latokartanonkaari 9, FI-00790 Helsinki, Finland, jarkko.niemi@mtt.fi, kyosti.pietola@mtt.fi  2) MTT Biotechnology and Food Research, FI-31600 Jokioinen, Finland, marja-liisa.sevon-aimonen@mtt.fi3) MTT Animal Production Research, Tervamäentie 179, FI-05840 Hyvinkää, Finland, kirsi.partanen@mtt.fiModelWe use a recursive stochastic dynamic programming model to appraise genetic traits in the sow. The model maximisesreturn on sow space unit by optimising the voluntary replacement policy. The state variables are parity number andlitter size. The transition equations are parameterized with data about 12197 sows born in 2002. Statistical market pricedata are also used. Litter size and involuntary culling rate (exogenous shocks such as disease) can be represented by:Litter sizeParity+1=Litter sizeParity + f(Litter size, Parity, Farm effect, Breed, Genotype, other factors) + random term (i.i.d.),Probability of involuntary culling = f(Parity, ∆(Litter size), ∆(Mortality), Breed, Genotype, Price vector, other factors),where ∆(.) is the difference between observed value and expected value of variable inside the parenthesis1). Litter size equation is estimated with OLS whereas the probability of involuntary culling is estimated in a binary response model (probit). Data from experiments conducted at MTT parameterize piglet performance. Results & DiscussionThe optimal replacement rule responds inelastically and the value function elastically to changes in prices. The sow’sgenetic ability to repeatedly produce a large number of live piglets and the ability to stay in the herd increase the valueof sow place (Fig.1). Involuntary culling of primiparous sows particularly contributes to the overall replacement cost andinfertility and leg disorders are the most important stated reasons to cull the sow.Small litter, high piglet mortality, the sow’s age (parity number) and poor genetic index significantly (p<0.05) impact thedecision to cull the sow. Piglet price also contributes to replacement decision, whereas feed price is less important.Incentives for voluntary replacement increase rapidly only after the 6th farrowing, because the optimal replacementpolicy is driven by involuntary replacements and uncertainty about future piglet yield (Fig. 2).Fig. 2. Replacement policy conditional on piglet yield and parity number.Fig. 1. Estimated benefits from improved genetics (the magnitude of change is given in parenthesis; * denotes a change which is Poster prepared for presentation at the Agricultural & Applied Economics Association’s 2010AAEA, CAES & WAEA Joint Annual Meeting, Denver, Colorado, July 25-27, 2010.ConclusionInformation about piglet yield in current parity has little value when used to forecast the next litter, because therepeatability of high and low piglet yield in successive farrowings is low. This reduces the benefits from earlyreplacement of poorly productive sows. Durable animal investments require that animal breeding programmes, housingand management practices pay attention to yield, piglet mortality and other performance measures in early parities.Future research should examine returns on investments in animal-friendly production technologies.1) e.g.Kristensen, A.R. & Søllestedt, T.A. (2004). A sow replacement model using Bayesian updating in a three-level hierarchic Markov process: II. Optimization model. Livestock Production Science 87, 25-36.1 35 79 111510152000.20.40.60.81.0Piglet yieldParity #Prob(Replace)0.0 0.5 1.0 1.5 2.0€ per pigletExpected litter size (+0.85 piglets) *..……………Pre-weaning piglet mortality (-0.32 piglets) *..….The size of the first litter (+0.85 piglets) *...…......Involuntary culling rate (-10%)………………......Variation (s.d.) of litter size (-10%)…….……......Repeatability coefficient of litter size (-10%)...…equal to the genetic standard deviation of the measure).

A dynamic programming model for optimising the timing of replacement of sows

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