The value of precision feeding technologies for grow–finish swine

Livestock Science 129 (2010) 13–23

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Livestock Science j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / l i v s c i

The value of precision feeding technologies for grow–finish swine Jarkko K. Niemi a,⁎, Marja-Liisa Sevón-Aimonen b, Kyösti Pietola a, Kenneth J. Stalder c a b c

MTT Agrifood Research Finland, Economic Research, Luutnantintie 13, FI-00410 Helsinki, Finland MTT Agrifood Research Finland, Biotechnology and Food Research, FI-31600 Jokioinen, Finland Department of Animal Science, Iowa State University, Ames, Iowa, 50011-3150, USA

a r t i c l e

i n f o

Article history: Received 12 January 2009 Received in revised form 2 November 2009 Accepted 9 December 2009 Keywords: Pig Phase feeding Soybean meal Barley Stochastic dynamic programming

a b s t r a c t Feed and piglet are the largest variable cost items and important factors affecting the profitability of grow–finish pork production in most pork producing countries world-wide. Although a pig's potential to utilize energy and protein depends on its weight, age and genetic line, grow–finish pigs are typically fed with only 1 to 4 diets during the grow–finish phases of production. The goal of this study is to examine whether there are differences in feeding patterns, the timing of slaughter and return on pig space unit between 1) multi-phase feeding in which the quantity and protein content of the feed and the timing of slaughter are optimized on a daily basis, and 2) two-phase feeding in which feed quantity and slaughter decision are optimized daily but feed composition is predetermined for a given weight range. A stochastic dynamic programming model was used to evaluate different scenarios. The model maximizes return on capital invested in production facilities, which is critical for the competitiveness of pork production. The bio-economic model explicitly characterizes the growth process of grow–finish pigs and optimizes the timing of slaughter and daily amounts of protein and energy used in the feed. The growth of lipid mass and protein mass in the pig in a grow–finish group are modelled separately. The results suggest that a switch from the twophase feeding to the multi-phase feeding shifts feeding regimen towards increased protein inclusion in the diets. It increases annual return on the operation by €1.35–€1.88 per pig space unit. © 2009 Elsevier B.V. All rights reserved.

1. Introduction Feed and piglet are the largest variable production cost items for grow–finish pork production. They can represent as much as 90% of the variable cost. Feeding, the timing of slaughter and genetic line also impact the quality and the market value of the carcass, and hence greatly impact the profitability of pork production. Literature suggests that producer benefits from splitting the grow–finish feeding schemes into two, three or even more phases so that each phase has a different feed composition (Glen, 1983; Boland et al., 1999; Campos, 2003; Alexander et al., 2006). Modern technologies allow segregating the pigs according to their production potential and then adjusting feeding continuously ⁎ Corresponding author. Tel.: +358 40 358 0487; fax: +358 6 414 1504. E-mail address: jarkko.niemi@mtt.fi (J.K. Niemi). 1871-1413/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.livsci.2009.12.006

during the growth period so that the potential is fully utilized. Employing this type of multi-phase feeding system has environmental benefits because nutrient run-offs from pork production can be reduced by not providing the pigs nutrients in excess of their growth potential (cf. Kornegay and Harper, 1997). It is also important to recognize that the capacity to utilize nutrients varies between pigs (Pomar et al., 2003). Equipment is available to monitor pig weight and provide accurate diets even on an individual pig basis, but so far the technology is costly. Moreover, there is potential for real-time control of pigs' growth rates, and possibly back fat depth (Parsons et al., 2007). Despite potential benefits of multi-phase feeding, grow– finish pigs in Finland and many other pork producing countries are typically fed 1–4 different diets while growing them from approximately 25 kg body weight (BW) to slaughter weight. Pigs close to slaughter weight are fed

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with larger amounts of feed per day, but the feed is less expensive and its protein and other nutrient content are less dense than in diets fed to pigs at lower BW. Besides the feed price, an important issue in the diet regimen design is how the feed influences the growth, carcass quality and meat quality of grow–finish pigs. Experimental studies have shown that restricted provision of energy during grow–finish production phase can increase the share of lean meat in the carcass by 2–3 percentage points when compared to the case when ad libitum feeding is utilized (e.g. Affentranger et al., 1996; Ramaekers et al., 1996; Quiniou et al., 1999). The concept of optimization in the pigs' diet regimen design has traditionally focused on biological results, such as maximizing the daily weight gain, rather than on truly economic concepts, such as maximizing the return on capital, that is decisive for the competitiveness of modern highly capital-intensive pork production. Focusing on the biological concepts only does not reveal the true economic potential of the genetic line. Moreover, feeding and slaughter decisions should be studied simultaneously, because there is an interaction between the economically optimal feeding regimen and slaughter timing (e.g. Chavas et al., 1985; Niemi, 2006). It alters results, if this interaction and the fact that production technology affects economically optimal diet is ignored. However, previously-published, peer-reviewed literature which examined these aspects jointly is scarce. The present study contributes to the literature by examining whether there are differences in feeding patterns, the timing of slaughter and the return on capital invested in pig space unit facility between 1) multi-phase feeding in which the quantity and protein content of the feed and the timing of slaughter are optimized on a daily basis and 2) two-phase feeding in which the quantity of feed and the timing of slaughter are optimized daily, but feed composition is predetermined for a given range of BW. The impact of market situation is also examined. The analysis was conducted using a structural-form stochastic dynamic programming model, which maximizes the value of facility per grow–finish pig space unit by optimizing slaughter decision and the amounts of energy and protein-rich feed. The bio-economic model characterizes the pigs' growth process explicitly by taking into account weight, carcass composition and genotype of pigs. It takes into account interactions between feeding, genotype and quality-adjusted carcass value and accounts for the distribution of carcass weight, carcass leanness and growth potential of pigs within the group of grow–finish pigs. The data are based on animal experiments (e.g. Sevón-Aimonen, 2001) and previously-published, peer-reviewed literature (e.g. Fuller et al., 1989; Quiniou et al., 1999; Collin et al., 2001). 2. Dynamic programming model 2.1. Characterization of the optimization problem The economic optimization model is based on a recursive process, which includes a description of the biological growth process when all-in–all-out procedure is utilized. The producer's goal is to choose 1) the quantity of each feed provided to the pigs and 2) the timing of slaughter of the pigs so that the choices maximize the value of the pig space unit facility. The pig space unit refers to grow–finish building

space required to house a pig. The standard can be, for instance, that 3.5 pigs are grown successively from 25 kg BW to the optimal slaughter weight per pig space unit per year. The economic model is based on a dynamic optimisation, which follows the Bellman equation (Bellman, 1957) of the form: Vt ðxt Þ = maxfRt ðxt ; ut Þ + βEðVt ut

+ 1 ðxt + 1 ÞÞg

for t = 1; ::; T ð1Þ

subject to: xt

+ 1

= gðxt ; ut ; εÞ

ðinitial state givenÞ

x1 given VT

+ 1 ðxT

ðtransition equationsÞ

+ 1Þ given

ðthe terminal valueÞ;

where Vt(.) is the optimal value function; xt is the current state vector; the subscript t is the time index, the time unit being 1 day; ut is the control vector; Rt(.) is the one-period return function (revenues minus expenses); β is the discount factor; E (.) is the expectations operator; Vt + 1(xt + 1) is the next-period value function; g(.) is the pig growth model or the slaughtering decision; ε refers to the variation in the pigs' carcass composition and growth; VT + 1(xT + 1) is the value of the pig space unit after the terminal period T, and x1 is the state at the beginning of the planning horizon. T must be set large enough to get both the value function and the decision rules to converge. The model characterizes the state of a group of pigs so that the weights and performance traits of a group of pigs are distributed around the average pig. The mean and the variance of growth potential as well as expected carcass value and the variance of market value for the pork carcass are known. Since the pigs can grow in the state space differently, there is a spectrum of management patterns for a group of grow–finish pigs over time. The live weight (xweight ) of an individual pig is a t function (Eq. (6)) of two independent state variables, viz. the lipid mass in the whole body (xLt ) and the protein mass in the whole body (xPt ). These variables determine the qualityadjusted market value of a carcass, which a producer can influence through feeding and other management strategies. The next two sections represent the main structure of the growth model and Table 1 gives an indication where each parameter refers to. For further information on the single pig growth model, see Niemi (2006). 2.2. Transition equations The pig growth model simulates how lipid and protein deposited into the body responds to the amounts of nutrients provided to the pigs in their diets. Pork producer supplies protein (uprot , measured in lysine) and energy (uener ) in feeds t t by controlling the amounts of barley with amino acid supplementation and soybean meal. Barley and soybean meal are used in the analysis as they are the most common sources of protein and energy in feeds in Finnish grow–finish pork operations, and amino acid supplementation is used to balance the amino acid composition of the diet. The model can be easily transformed to optimize other feed ingredients such as maize and soybean meal which are fed to grow–finish pigs in many countries around the world.

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Growth is obtained, when energy and protein needs for the pig's body maintenance are subtracted from energy and protein supplied in the feed, and the resulting energy and protein is converted into lipid and protein mass, respectively. A producer is allowed to apply unrestricted feeding, which implies that there is enough protein and/or energy in the feed so that a pig can grow according to its genetic and environmental growth capacity. Alternatively, the producer is able to restrict the amount of protein, energy, or both so that the daily BW gain is below the pig's growth potential. In this case, growth is determined by the availability of protein and energy in the feed rather than the pig's genetic and environmental growth capacity.

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The decision to slaughter the pigs in the group (ucull = 1) t implies that the state of nature in the subsequent period (xt + 1) is set at the state of a 25 kg piglet, as it represents the beginning of a new group of grow–finish pigs using the pig space unit. If the producer continues to grow the pig at time t (i.e. ucull = 0), the transition equation for the growth of t protein mass is: P

xt

P

+ 1

weightθ3

= xt −θ1 xt

prot

+ θ2 ut

;

ð2Þ

where θj, j = 1,2,3 are parameters and the diet is balanced so that lysine is the limiting amino acid. Before implementing

Table 1 Parameter values (± SE) used as benchmark values in a stochastic dynamic programming model to evaluate the value of precision feeding technologies. Source: Adapted from Niemi (2006). Symbol i

θ θ2 θ3 θ4 θ5 θ6 θ7 θ8 θ9 θ10 θ11 θ12 α∧ φL αп φP θ13 θ14 θ15 θ16 θ17 θ18 θ19 θ20 θ21

λt

β

Description of related variable unit of variable a

Lysine for body maintenance, kg per 1 kg BW Deposition of protein in feed into body (in lysine units), kg lysine per 1 kg protein mass b Power term for kg BW, related to the amount of lysine needed for maintenance a Deposition of energy in feed into body, MJ energy per 1 kg lipid mass c Energy needed for body maintenance, MJ per kg BW d Power term for kg BW, related to MJ energy needed for body maintenance d Adjustment of energy to protein growth, MJ energy per 2.5 kg protein mass growth potential e Adjustment of energy to protein growth, MJ per one unit change in related parameter set e Adjustment of energy to protein growth, MJ per 1 kg gain of protein mass e Adjustment of energy to protein growth, MJ per 1 kg gain of lipid mass e Subtraction of maintenance energy from energy used to protein mass growth, MJ per kg BW e Subtraction of maintenance energy from energy used to protein mass growth, MJ per kg BW e Adult weight of lipid mass, kg f Maturing rate of lipid mass, kg per day f Adult weight of protein mass, kg f Maturing rate of protein mass, kg per day f Loss of meat at slaughter, percentage points (constant) f The effect of BW on the loss of meat at slaughter, percentage points per 1 kg BW f Weight of water in the carcass, kg water per 1 kg protein mass f Weight of ash in the carcass, kg ash per 1 kg protein mass f Weight of water in the carcass, power term for kg water per 1 kg protein mass f Weight of ash in the carcass, power term for kg water per 1 kg protein mass f The share of red meat in the carcass, percentage points (constant) f Increase in the share of red meat, percentage points per 1 kg protein mass f Decrease in the share of red meat, percentage points per 1 kg lipid mass f The amount of lysine in feed, g per 1 kg soybean meal g The amount of lysine in feed, g per 1 kg barley (includes lysine supplementation) g The amount of energy in feed, MJ NE per 1 kg soybean meal g The amount of energy in feed, MJ NE per 1 kg barley g Minimum requirement for the ratio 1 kg lipid mass growth per 1 kg protein mass growth Pig meat price, €/kg h + Adjustment price, € per one percentage point increase in the carcass' red meat percentage − Discount, € per kg for each additional kg deviating from the target range Price of a 25 kg piglet, €/piece Slaughter premium for carcasses 61 kg, € per slaughtered carcass Price of barley, €/kg (includes the cost of amino acid supplementation) Price of soybean meal, € per kg Daily discount rate, %

Value

SE

0 8.33 0.75 0.03 0.03 0.6 0.24 0.36 23.6 39.3 0.1 1.02 52.85 0.01 29.69 0.01 30.81 0.04 5.11 0.2 0.87 0.95 55.7 0.9 0.53 23.9 5.3 8.44 9.07 1.20 1.34 0.02 0.02 55.00 15.00 0.14 0.31 99.98%

– – – – – – – – – – – – 15 0 5.3 0 – – 0.267 0.019 0.01 0.02 3.01 0.19 0.05 – – – – – – – – – – – – –

– Means that the information is not available or not reported here. BW refers to body weight. a Fuller et al. (1989). b Agricultural Research Council (1981) and Fuller et al. (1989). c One kilogram lipid mass contains 39.3 MJ energy and 1 kg protein mass contains 23.6 MJ energy (e.g. Whittemore, 1998, pp. 281). d Collin et al. (2001). e Derived using the results of Quiniou et al. (1999) and estimation results from the Finnish growth experiment (Sevón-Aimonen, 2001). f Estimated from the growth experiment data (see Sevón-Aimonen, 2001). Meat percentage formula was updated in 2007. g MTT (2006). h Base price paid for a carcass weighting 71–89 kg (target range) and containing 59% red meat (lean meat). Adjustment based on the red meat content is computed: (red meat percentage of the carcass − 59) × adjustment price. For carcasses weighing <71 kg an additional adjustment is: (carcass weight − 71) × discount. For carcasses weighing > 89 kg an additional adjustment is: (89 − carcass weight) × discount. i Value is 0.3 g per 1 kgBW.

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the model, it was verified that lysine was the first limiting amino acid and that the amounts of other amino acids besides lysine were as balanced with lysine as possible. The growth of lipid mass is adjusted according to the availability of energy, maintenance requirements based on BW, and the share and growth potential of protein mass in the body. On the condition that the pig is not slaughtered at day t, the transition equation for lipid mass is: L

xt

L

+ 1

ener

= xt + θ4 ut

weight θ6

−θ5 ðxt

P

P

L

ener

Þ −m ðxt ; xt ; ut

Þ;

ð3Þ

where mP ðxPt ; xLt ; uener Þ = θ7 ΔxPt t + ðθ8 ΔxPt ðuener −θ12 ðxweight Þθ6 ÞÞ = ðθ9 ΔxPt + θ10 ΔxLt −θ11 ðxweight Þθ6 Þ; t t t

where θj, j = 4,…,12 denote parameters and ΔxPt and ΔxLt refer to the growth potential of xPt and xLt , respectively. The values of ΔxPt and ΔxLt are obtained from Eq. (4) as the difference xt + 1i − xit. Energy cost of protein deposition mP(xtP, xLt , uener ) is a t function of a linear slope (θ7ΔxtP), which quantifies demand for energy for protein synthesis as a function of protein mass in the basic situation, and a non-linear part, which adjusts demand for energy used in the protein synthesis according to the current state of nature and current growth rates of lipid and protein. Non-linearity is due to the fact that the growth potential of protein starts decreasing earlier than the growth potential of lipid mass. The amount of energy in feeds is measured using the net energy system, which accurately characterizes the energy value of feed (e.g. Noblet et al., 1994; Moehn et al., 2005). Defining all the protein to be used in protein synthesis does not result in any losses of generality because converting excess protein first into energy and then into lipid mass is inefficient (Whittemore 1998, p. 343) and therefore, the algorithm excludes it from the set of the optimal feeding policies. The role of this assumption is examined with calculations applied on model results. Moreover, the assumption about the distribution of pigs in the group around the average pig implicitly takes into account that pigs can differ in their use of energy and protein. The maximum growth of both lipid and protein mass is restricted to the pig's growth potential, which is represented by the derivative of the Gompertz function. In this case, corresponding transition equation simplifies to:

2.3. Modelling variation in pigs A feature of dynamic programming is that the state variables contain enough information about the pigs for the program to be able to make the optimal decisions conditional on current information contained by the state variables. The distributions of the state variables and the parameters of growth potential are used to approximate variation in both the quality-adjusted market value and growth patterns of pigs in a grow–finish pig group. Variation in growth rates and carcass characteristics is defined proportional to the state variable. It is simulated using Eqs. (2)–(5), which imply that variation in the pigs' weight is a function of the state of the average pig and the distribution of parameters αi and φi in the studied pig group. In order to simulate lipid and protein mass for an individual pig h = 1,..,n, the procedure uses the means, standard errors and mutual correlations of parameters αi and φi. The average pig is denoted as h = n/2. In order to simulate the distribution of weights, let xit,h, i = {P, L}, denote lipid and protein mass in the pig h. For each pig, the state of body component i can be characterized by: i

i

=

i i i i xt ðγ −ϕ lnxt Þ;

ð4Þ

meat

xt;h

ð5Þ

weight

= ð1−ðθ13 −θ14 xt;h

weight

Þ = 100Þxt;h

;

ð6Þ

where xt;h

P

where γi = 1 + φilnαi, where i = {P, L} and αi and φi are parameters referring to adult weight (weight at maturity) and maturing rate of the pig, respectively. The minimum amount of energy that can be provided to the pigs is restricted so that the growth of lipid mass must exceed the growth of protein mass multiplied by the parameter λt (cf. Whittemore, 1998, pp. 68–70). Although this restriction can be relaxed, it is justified, because providing inadequate energy can result in stress, tail biting, and other abnormal behaviour in the pig.

i

where αih and φih refer to as adult weight and maturing rate for pig h (values drawn from the normal distribution); parameter ki(h) is the ratio between adult weight and initial weight of a newly born piglet h; τ(h) describes how much younger or older piglet h is at 25 kg BW when compared to the average pig at 25 kg BW; η is the maturity parameter for the average pig at a given state. τ(h) + η equals pig h age when it is fed ad libitum. The maturity parameters τ(h) and η are solved with a standard optimization procedure by setting BW of individual pig drawn from the group of pigs to the desired target. For the average pig, τ(h) equals zero, but for other pigs in the group it can be negative or positive. In the numerical simulation, the pigs were divided into ten groups each of which characterized part of the full parameter distribution. In order to estimate the market value of an individual pig, meat information on the carcass weight (xt,h , Eq. (6)) and the share P L of red meat (ρ(xt,h ,xt,h ), Eq. (7)) in the pig was determined:

weight

i xt + 1

i

xt;h = αh expð−k ðhÞ expð−ϕh ðτðhÞ + ηÞÞÞ;

P

L

P

θ17

= ðxt;h + xt;h + θ15 ðxt;h Þ L

P

P

θ18

+ θ16 ðxt;h Þ

L

ρðxt;h ; xt;h Þ = θ19 + θ20 xt;h −θ21 xt;h ;

Þ = 0:95;

ð7Þ

where θj, j = 13,…,21 are parameters. 2.4. Data and scenarios The growth potential of pigs was obtained from the analysis of growth experiments conducted with Finnish Yorkshire and Landrace crossbreed pigs (Sevón-Aimonen, 2001) at MTT Pig Research station, Hyvinkää, Finland. Other

J.K. Niemi et al. / Livestock Science 129 (2010) 13–23

data were obtained from peer-reviewed literature referred in Table 1. One-period returns accounted for upon slaughter included the income from selling pigs to slaughter plus related subsidies paid upon slaughter minus the price of the piglet at 25 kg. Quality-adjusted market value of the carcass was determined by using Eqs. (6) and (7) and a pricing system used by the slaughterhouse as explained in Table 1. Otherwise, one-period returns accounted for the cost of feeding soybean meal, barley, amino acid supplementation and minerals (including feed wastage 5%). Other costs, such as labour, energy and facility costs, were assumed to be constant over time. Thus, they cancel out in the optimization and in comparing returns to alternative technologies. Feeding patterns, the timing of slaughter and the value of pig space unit were solved for the multi-phase feeding and the two-phase feeding. Both technologies optimize the amount of feed and the slaughter decisions on a daily basis, but only multi-phase feeding can adjust protein content of the feed daily. When using the two-phase feeding, feed composition is predetermined so that a producer is able to feed pigs with one feed mixture before they have reached certain BW and with another feed mixture thereafter. Soybean meal content of both mixtures and the weight at which the dietary switch is made are optimized. Table 1 represents benchmark values of the most important parameters used in the analysis. Sensitivity analysis was carried out with respect to the price parameters and the parameter characterizing variation in the pig group by increasing each parameter value by 10% (ceteris paribus). The following increased parameter values were examined: pig meat base price at €1.47 per kg; piglet price at €60.50 per piece; the price of barley–amino acid supplementation mixture at €0.16 per kg; soybean meal price at €0.34 per kg; and the standard errors of maturing rate and adult weight parameters increased by 10%. In addition, sensitivity analysis examined the less restricted energy diet where λt (the minimum requirement for lipid mass growth) decreased from 1.2 to 1.0. The role of unbalanced amino acid composition of feed was assessed with calculations applied on model results. The amounts of essential amino acids required for body maintenance and growth were calculated relative to lysine (Board on Agriculture National Research Council, 1998). Essential amino acids were 45% of the total protein requirement and nonessential amino acids were 55% of the total protein requirement. It was verified for eight most important amino acids, which portion in the feed was known, that lysine was the amino acid that limited the growth. Essential amino acids supplied in excess of the pig's needs were counted as nonessential amino acids and their contribution to lipid mass growth was counted by using daily growth and feed intake data simulated by the model for BW range 25 kg to 107 kg.

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of soybean meal in feed at each 1% range. Linear interpolation was applied between the evaluation points. The results are useful for comparing alternative scenarios rather than for examining individual scenarios separately. There are alternative ways to solve the dynamic programming model (e.g. Judd, 1997). The present study uses a technique where the value function at the start of the subsequent batch (‘terminal reward’, cf. Kure, 1997) is given. The given value is iterated until the first-order conditions for the model convergence are satisfied (i.e. Vt (xt) = Vt + 1(xt + 1) and ut(xt) = ut + 1(xt + 1)). The given value for the optimal value function is the capitalized expected productive value of the grow–finish facility calculated per pig space unit over the period t = 1,..,T, when the unit is initially populated with newly weaned piglets. The value function reported for the full (infinite) time horizon (T) can be converted into 25 years period value by multiplying it by 0.787, or to a 1 year period value by multiplying it by 0.060. 3. Results 3.1. Feeding patterns The optimal feeding patterns suggest that a producer should increase the daily ration of barley–amino acid supplementation and to decrease the share of soybean meal in the diet provided to the group of pigs as the pigs grow (Figs. 1 and 2). The result is mainly due to the fact that the pig's energy requirements for maintenance increase when it becomes heavier. It is also related to the fact that the pig's potential to utilize protein increases until the growth potential curve has reached the inflection point, which is approximately at 71 kg BW or 55 days after the start at 25 kg BW (given that unrestricted feeding is applied). The two-phase feeding provides more barley to the pigs than does the multi-phase feeding. Moreover, it provides less

2.5. Solution technique The optimization problem was solved numerically using an algorithm programmed in Matlab 7.1 (Mathworks Inc., USA). Seeded Monte Carlo simulation was used to simulate the distribution of pigs within a group of grow–finish pigs. The weight variables were evaluated discretely at each 0.5 kg range, the amount of barley at each 50 g range, and the share

Fig. 1. Benchmark for the amount of barley (kg per day per pig) for an average genotype pig fed with two-phase or multi-phase feeding at weight range 25 kg-slaughter weight, when the quantity of the feed, the protein content of the feed and the timing of slaughter of a pig group is optimized with a stochastic dynamic programming model.

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Fig. 2. Benchmark for the amount of soybean meal in feed (kg per day per pig) for an average genotype pig fed with two-phase or multi-phase feeding at weight range 25 kg-slaughter weight, when the quantity of the feed, the protein content of the feed and the timing of slaughter of a pig group is optimized with a stochastic dynamic programming model.

soybean meal at the end of both feeding phases than the multi-phase feeding (Fig. 1 and 2). In most scenarios, 2–3 kg barley and 1 kg soybean meal per slaughtered pig was saved when using the multi-phase feeding approach as opposed to the two-phase feeding (Table 2). On a daily basis, 32 g barley and less than 10 g soybean meal was saved. Some scenarios resulted in an opposite result due to differences in the timing of slaughter between the two feeding technologies evaluated in this study. The difference in the amount of saved barley was at the largest, 50 g per day, at the beginning of both feeding phases and it diminished towards the end of the feeding phases (Fig. 1). The optimal first-phase diet was fed until 70 kg BW. This differs with the Finnish feeding recommendations (MTT, 2006), which suggest changing the diet already at 55 kg or

60 kg. If the second phase was set to start at 55 kg or 60 kg, the two-phase feeding diet would then have contained approximately one percentage point more soybean meal than in Fig. 2. Moreover, the difference to the multi-phase feeding in the amount of barley in the beginning of the second phase would have been larger than in Fig. 1. A shortened firstphase feeding period shifted the optimal feeding regime under the two-phase feeding towards ad libitum feeding. The model tended to feed pigs enough protein so that an average pig was able to approximately reach the maximum protein mass growth, whereas the growth of lipid mass was restricted below the pig's potential particularly at the end of the grow–finish period. An increase in soybean meal price (barley price) increased (decreased) the energy density of the feed. The feeding patterns generated by the present study generally fed more energy per day for pigs weighing less than 55 kg when compared to the Finnish feeding recommendations (MTT, 2006), but not for pigs close to their slaughter weight. Feeding patterns were adjusted according to the market prices (Table 2). Adjustments under the multi-phase feeding can be characterized as parallel shifts (up/down) of the feed inclusion curves illustrated in Figs. 1, 2 and 3. An increase in barley price reduced its use and increased soybean meal content of feed throughout the grow–finish period (the substitution effect). An opposite adjustment was true for an increase in soybean meal price. However, due to already restricted feeding, the amount of barley fed per slaughtered pig was quite unaffected by barley price. The impacts of price changes on the daily amount of barley feeding were stronger under the two-phase feeding than the multi-phase feeding. The effects were particularly visible around 70 kg BW. In contrast to this, price changes resulted in larger adjustments in the soybean meal content of the feed under the multi-phase feeding than under the two-phase feeding. The most elastic response was due 10% increase in

Table 2 The live weight (SW) at which it is optimal to switch from the first diet to the second diet under the two-phase feeding, and the amount of soybean meal and barley (kg per day on average for pigs below and above the switching weight and kg per slaughtered pig) fed with two-phase or multi-phase feeding technology using different market scenarios for an average group of pigs. Scenario

Two-phase feeding Benchmark Variation + 10% Pig meat price + 10% Piglet price+ 10% Barley mixture price + 10% Soybean meal price + 10% Minimum requirement for lipid:protein growth set at λt = 1 Multi-phase feeding Benchmark Variation + 10% Pig meat price + 10% Piglet price+ 10% Barley mixture price + 10% Soybean meal price + 10% Minimum requirement for lipid:protein growth set at λt = 1

Switch at kg (SW)

Soybean meal in feed

Barley in feed

Below SW

Above SW

kg per pig

Below SW

Above SW

kg per pig

70.0 70.0 65.0 70.0 70.0 70.0 65.0

0.36 0.36 0.37 0.35 0.35 0.35 0.41

0.29 0.29 0.29 0.29 0.29 0.26 0.32

30.2 30.2 29.9 30.1 30.1 29.2 34.9

2.09 2.10 2.03 2.08 2.08 2.09 1.84

2.89 2.89 2.90 2.89 2.89 2.92 2.68

223 223 222 222 222 224 213

– – – – – – –

0.36 0.36 0.37 0.36 0.36 0.36 0.41

0.28 0.27 0.32 0.26 0.28 0.27 0.33

29.5 29.1 31.7 29.1 29.5 28.8 35.2

2.06 2.06 2.01 2.06 2.06 2.06 1.80

2.85 2.87 2.83 2.86 2.85 2.85 2.59

220 221 221 226 220 220 210

J.K. Niemi et al. / Livestock Science 129 (2010) 13–23

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Fig. 3. The amount of barley (kg per day) 1) fed to a group of average pigs under the two-phase feeding in the benchmark scenario (solid curve) and in six alternative scenarios (dotted curves)2). 1) Downward shift at the end of each curve shows the slaughter date for the group of grow–finish pigs. 2) The only difference between the benchmark scenario and alternative scenario in each figure is that price parameter indicated above the figure is 10% higher in the alternative scenario than in the benchmark scenario or that minimum requirement for lipid mass growth:protein mass growth in the alternative scenario is 1.0 instead of 1.2.

pig meat price. It shortened the first-phase diet under the two-phase feeding, reduced per day allowance of barley (Table 2), and increased soybean meal content of feed. When the minimum requirement for lipid mass growth (λt) decreased from 1.2 to 1.0, the amount of soybean meal per slaughtered pig and soybean meal content of feed increased. In contrast to this, the amount of barley fed per slaughtered pig decreased by approximately 10 kg (Table 2). The pig's weight when the dietary switch occurred under the two-phase feeding also decreased. Optimized multi-phase feeding pattern for the benchmark scenario can be approximated as a function of the pig's weight, a time index and the minimum requirement for lipid mass growth: 2

barley weight weight weight uˆ t ðxt ; d; λt = 1:2Þ = 457 + 46:7 xt −0:20 xt 2

−5:03d + 0:04d ;

ð9Þ

2

soy weight weight weight ; d; λt = 1:2Þ = 280 + 2:05 xt −0:04 xt uˆ t ðxt 2

+ 4:29d−0:02d ;

ð10Þ

(xweight , d, λt) refers to the amount of barley fed where ûbarley t t weight (g per day), ûsoy , d, λt) is the amount of soybean meal t (xt fed (g per day) and d is the number of days passed after 25 kg BW. As observed daily weight gain gave an indication of the genetic potential of individual group of pigs, pigs were be fed differently depending on which BW the group has reached within certain time.

3.2. Slaughter weight, carcass leanness and growth rates The optimal slaughter weight was dominated by the meat pricing scheme. There were no clear differences in slaughter weights or the duration of grow–finish periods between twophase and multi-phase feeding of an average pig group, nor large differences between the sensitivity analysis scenarios. Both feeding technologies resulted in slaughtering the average pig at 108–109 kg BW after a 91-day grow–finish period, and finished pigs contained 59.8% red meat. However, these measures and weight gain of individual pig groups were impacted by whether the individual group had the potential to grow fast or slow (Fig. 4). Regarding the average pig's growth pattern, an increase in the piglet price, soybean meal price or the minimum requirement for lipid mass growth tended to increase slaughter weight by less than 1 kg, whereas an increase in the pig meat price had an opposite impact. A 10% increase in the pig meat price shortened the grow–finish period by 1 day. Decreasing in the minimum requirement for lipid mass growth (λt) from 1.2 to 1.0 prolonged grow–finish period by 3 days and increased carcass leanness by 1.1 percentage point. Increased variation in the pigs' growth potential parameters increased the optimal slaughter weight of the average pig, as prolonged growing period was a means to increase the number of pigs in the group fitting into the target weight range. The pigs' genotype affected the optimal slaughter weight, carcass leanness and grow–finish period duration (Table 3). The two-phase feeding tended to increase variation in slaughter weights and reduce carcass leanness within a group of grow–finish pigs when compared to the multi-phase feeding. When the piglet price increased, the multi-phase

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Fig. 4. Simulated development of the pig's live weight (kg) and its gain (kg per day) in the benchmark scenario for ten different genotypes for the period 25 kg to slaughter weight using either the two-phase feeding or the multi-phase feeding technology.

feeding raised a group of fast-growing pigs longer (i.e. to heavier slaughter weights) and produced leaner carcasses from pigs having high capacity to deposit protein mass than

the two-phase feeding. Increased variation in the pigs' growth potential parameters increased the variation of red meat percentage and slaughter weight.

Table 3 The minimum and maximum value of the optimal slaughter weight (kg per pig per group of grow–finish pigs), time from 25 kg until slaughter date (days) and red meat percentage in the set of ten genotype groups modelled to characterize the joint parameter distribution of grow–finish pigs simulated under the two-phase and the multi-phase feeding. Scenario

Growing time

Slaughter weight

Lean meat %

Min

Max

Min

Max

Min

Max

Two-phase feeding Benchmark Variation + 10% Pig meat price + 10% Piglet price + 10% Barley mixture price + 10% Soybean meal price + 10% Minimum requirement for lipid:protein growth set at λt = 1

81 80 81 81 81 81 84

93 92 91 93 93 93 94

107.7 108.1 107.1 108.1 108.1 108.3 107.1

109.7 109.0 109.4 109.6 109.2 109.8 109.3

59.6 59.5 59.4 59.6 59.6 59.5 60.9

61.7 61.8 61.4 61.8 61.8 61.7 63.0

Multi-phase feeding Benchmark Variation + 10% Pig meat price + 10% Piglet price + 10% Barley mixture price + 10% Soybean meal price + 10% Minimum requirement for lipid:protein growth set at λt = 1

81 80 81 83 81 81 85

93 92 91 93 93 93 94

107.7 108.0 107.1 108.1 108.0 107.9 107.1

109.7 109.0 109.4 110.6 109.2 109.8 109.3

59.6 59.5 59.4 59.6 59.6 59.5 60.9

62.0 62.1 61.7 62.2 62.0 62.1 63.3

J.K. Niemi et al. / Livestock Science 129 (2010) 13–23

Variation in growth potential parameters caused much greater differences in performance than the feeding technology. The average daily gain (ADG) for average group pigs was 920 g per day under both technologies. The performance of a pig in a group was examined by dividing the group of pigs into ten subgroups that characterized the growth parameter distribution. The fastest growing subgroup among these ten subgroups gained 110 g more BW per day than the subgroup representing the average pig. Discrete changes of the diet composition under the two-phase feeding reduced the growth of most genotypes in the beginning of the second phase. Restricted feeding reduced particularly the growth rate for the fast-growing (high-potential) pig groups (Fig. 4). Due to restricted feeding, growth rates depended more on the growth potential of lipid mass than the growth potential of lipid mass. Some amino acids were supplied momentarily in excess of the pig's protein mass growth and maintenance requirements. The quantity of unbalanced protein that could be converted into lipid mass was calculated for an average pig in the benchmark scenario. The multi-phase feeding was simulated to supply 4.583 kg protein per pig in excess of the balanced diet. This corresponded to 0.998 kg (4.7%) increase in lipid mass per slaughtered pig. As expected, the diet of the twophase feeding was less balanced. It supplied 5.999 kg protein (corresponds to 1.3 kg (6.0%) lipid mass) in excess of the pig's protein requirements. However, these differences had only a small impact on differences between scenarios, because the unbalanced protein was treated similarly in each scenario. The main pattern to analyze model results in this study was to compare results between scenarios. Hence, the unbalance of protein did not have a significant impact on our main comparative results. Moreover, if a correction coefficient would have been assigned to transform the unbalanced protein into lipid, then the model was able to adjust feeding patterns a little. This would further reduce the bias by being able to reduce the difference between currently simulated lipid mass at slaughter and the one simulated using a correction coefficient by more than 50%. 3.3. The value of the facility per pig space unit A switch from the two-phase feeding to the multi-phase feeding in the benchmark scenario increased the value of the modelled facility per grow–finish pig space unit by €27 for an infinite-horizon period (Table 4). This corresponds to a €1.60 increase in annual return or €21 increase in return on pig space unit over the 25 years period. When taking into account sensitivity analysis, switching from the two-phase feeding to the multi-phase feeding provided annually €1.35–€1.88 in additional return on pig space unit. On a 500 pig grow–finish farm, for instance, the pork producer benefited €675–€940 per year from switching to multi-phase feeding. These values corresponded to approximately 3% improvement in agricultural income of an average Finnish pig farm (cf. MTT, 2007). An increase in the piglet or feed prices or in the minimum requirement for the growth of lipid mass increased the benefits of multi-phase feeding. Values in Table 4 are compensation for additional labour, maintenance and investment costs which are incurred as a result of switching to a more flexible feeding technology.

21

Table 4 Net economic benefit (€ per pig space unit) of changing from a two-phase feeding to a multi-phase feeding system and the impact of model parameters (income effect) on the value function under the two-phase and the multiphase feeding in a study used to determine the value of different feeding technologies. Scenario

Benefit from the multi-phase technology (€)

Benchmark Variation + 10% Pig meat price + 10% Piglet price+10% Barley mixture price +10% Soybean meal price +10% Minimum requirement for lipid:protein growth set at λt = 1

27 26 22 31 28 28 23

Income effect (€) of a market change Two-phase

Multi-phase

0 −7 724 − 358 − 182 − 59 56

0 −7 719 − 353 − 180 − 57 52

Value functions are normalized with respect to the benchmark scenario or technology. The second column (benefit from the multi-phase technology) indicates difference in the value of pig space unit under the multi-phase feeding as opposed to the value of pig space unit under the two-phase feeding. Both values are obtained using the parameter assumption given in the first column. Negative (positive) values in the last two columns (income effects) indicate that the value of pig space unit is smaller (larger) when using parameter assumption mentioned in the first colum than the value obtained using the benchmark scenario assumption. Feeding technology is the same in both cases. Hence, market effect for the benchmark scenario is zero.

They include the option value to adjust the timing of slaughter according to carcass quality and weight. Additional return for switching from two-phase feeding to multi-phase feeding was estimated at €27 per pig space unit. This corresponds to 4.5% of normative investment cost needed to build up a pig space unit facility in the 3 rd quarter of 2008 (updated from MAFF, 2006). In other words, the optimal multi-phase feeding is estimated to increase returns to capital invested in pig space facility by 4.5%, provided that the investment outlays as such do not significantly differ between the technologies. As noted above, the model suggests feeding the first feed mixture of the two-phase feeding until 65–70 kg BW. Benefits from the prolonged first feeding phase, when compared to optimizing feeding when the diet is switched at 55 or 60 kg BW, were €0.32 and €0.15, respectively, per pig space unit per year. Changes in the market prices had, in most scenarios, a greater impact on the return on pig space unit than the feeding technology. The value function responded particularly elastically to changes in pig meat price. A 10% increase in pig meat price increased annual return on pig space unit by about €43. Input prices also had a relatively strong impact on returns to pig space unit. Relaxing the minimum requirement for lipid mass growth (λt) from 1.2 to 1.0 increased the annual returns by €3.15– €3.35 per pig space unit as it allowed to increase carcass leanness. A 10% increase in variation in pigs (in an all-in–allout operation) decreased return on pig space unit by about €0.40 per year. The impact was largely the result of increased variation in carcass values, because an increase in the variation of growth rates actually benefits producers when they have the option to respond to new information, when it

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is obtained from the production process. Increased variation in pigs decreased the quality-adjusted market value of an individual group of pigs. The meat pricing scheme applied in the present study pays the highest pig meat price for quite a wide range in BW. Hence, based on previous studies (e.g. Kure, 1997; Boys et al., 2007), benefits from segregated slaughtering of grow–finish pigs in a group would have been quite limited. It is also likely that the meat pricing scheme applied by the slaughterhouse impacts the benefits provided by the multi-phase feeding technology. 4. Discussion This paper simulates the economic returns to the twophase and the multi-phase feeding technologies using a structural-form dynamic programming model. Rather than maximizing the weight gain, it focuses on maximizing return on capital invested in the pig space unit. The benefit of the model is that it can explicitly take into account genotype, the optimal timing of slaughter, the optimal amount of energy and protein provided in feed and when evaluating technologies, also accounts for stochastic aspects in the production process. Barley and soybean meal were used in the current analysis, but the model can be easily transformed to optimize other feed ingredients. The results are more robust in comparing between different scenarios than providing absolute values for each scenario separately. The results suggest that benefits from a switch from the two-phase feeding to the multi-phase feeding in an all-in–allout grow–finish operation are €1.35–€1.88 per pig space unit per year. This large potential gains from a continuously adjusted feeding correspond to about 4.5% return on capital invested in the facilities. Our estimates are between those of Boland et al. (1999) and Campos (2003) for shifting from onephase to two-phase feeding. The present work is in agreement with previous work where the most notable economic benefit has been obtained when shifting from onephase to two-phase feeding. Since many feeding equipments allow producers to adjust feeding gradually during the grow– finish phase, the results encourage producers to use this feature. At the end, the benefits depend on the accuracy of feeding applied at the farm and the prices. Prices are affected by the current market situation and farm-specific production contract premiums. For instance, farms which have put their production contract out to tender and have succeeded to receive a 10% contract premium on top of average pig meat price, benefit less from the multi-phase feeding than farms receiving the average price. They can benefit less because when the price of pig meat relative to the input prices increases, it is more important to pay more attention to the output than the inputs. In contrast to this, the return on capital invested in the pig space unit increases. Producers should therefore take greater care in pricing dietary ingredients and negotiating market prices than just implementing more precise feeding technology. Moreover, if production becomes more profitable due to increased pig meat prices or decreasing barley price, it is worthwhile to increase energy supply and growth rates of pigs by substituting barley for soybean meal. In multi-phase feeding, the share of protein in feed decreases continuously whereas in two-phase feeding the

feed ration is restricted to two alternative regimes with one discrete switch in the feed composition between these regimes. The challenge of the two-phase feeding, as illustrated also by (Kornegay and Harper, 1997), is to find a diet which provides pigs with a sufficient amount of protein without excessive amount of energy particularly in the middle of the grow–finish period, where pigs have the largest growth potential of protein mass. One option is to continue feeding pigs with a protein-rich feed until 65–70 kg BW. The protein content of both feeds, and hence their price, can thereafter be decreased. Such an adjustment is beneficial, although the benefits are limited, because the first feed is generally quite costly and the quantity of the second feed is large. Another benefit is that the pig can be given less energy between 55 and 70 kg BW, which may increase carcass leanness slightly, thus giving access to higher quality-price premiums. The results above resemble the way multi-phase feeding adjusts feed composition and feed price according to the pig's growth potential. Multi-phase feeding allows smooth adjustment of feed composition, starting from high protein concentration in the beginning of the grow–finish period and ending at low protein concentration at the end of grow– finish period. When compared to Finnish feeding recommendations (MTT, 2006), the model suggests feeding of pigs at the beginning of the grow–finish period with a protein-rich feed mixture and large amounts of feed per day. The result that some production potential is left unused is similar to results obtained in previous studies (Jean dit Bailleul et al., 2000). However, as the current study pays attention to the protein content of the feed, it is typical for this study that the provision of energy is restricted more strongly than the provision of protein. By the slaughter date, the degree of energy rationing increases gradually from zero at the beginning of the grow– finish phase at approximately 25 kg BW to almost 30% below the potential at slaughter. The option to segregate feeding and slaughter timing by the genotype or sex of the animals results in feeding pattern differences. When information for different genetic lines becomes more detailed, producers can decrease nutrient and economic losses due to excessive feeding. Producers can also gain a higher value per group of grow–finish pigs, because both reduced energy feeding and reduced dispersion of leanness and weights of a slaughtered group of grow–finish pigs impact quality-price discounts suffered upon marketing. An interesting result for the present scenario is that adjusting feeding according to animal's weight is more effective than adjustment based on pigs' age only. The weight-based adjustment can be used to implicitly take into account differences in the growth rates and particular genetic line of pigs being fed. This is supported by the fact that BW is often used as an indicator of how much energy, protein and minerals a pig needs for growth and maintenance. This was the case in the present model. Producers should therefore implement feeding curves as a function of BW rather than time. Although segregated slaughter or split-sex feeding was not explicitly evaluated here, variation in slaughter weights supports the views of Kure (1997) and Boys et al. (2007), that it is profitable to consider splitting pig slaughter in a group of pigs according to their weight. An important question for further studies is how the accuracy of carcass quality and BW

J.K. Niemi et al. / Livestock Science 129 (2010) 13–23

measurement affect feasibility of continuously-adjustable feeding and segregated slaughter timing, and how restricted energy supply affects animal welfare, carcass quality and its variation. The model could also be extended by examining the amino acid supply in more detail. Pomar et al. (2003), among others, pointed out that it is important to consider large groups of pigs rather than individual pigs. As supported by results of Parsons et al. (2007), monitoring and real-time control of pig growth is challenging. However, as far as variation in pigs plays a major role in the grow–finish phase of pork production and it is possible to at least partially control carcass quality and growth, precision technologies have the potential to improve profitability. 5. Conclusion In conclusion, producers benefit from adjusting feeding continuously and smoothly rather than discretely in only a few phases, although large variations in pigs and market situations can impact a return on investment. Modern feeding equipment often provides an option to use a continuous feeding curve and producers should consider using this feature. There are many other details in which the economic performance of grow–finish pork production can be improved and losses due to one less economic choice be reduced. Even the two-phase feeding can be improved by decreasing protein content of the feed and prolonging the first phase. Furthermore, the feeding of pigs can be planned and adjusted according to the weight and genetic line of pigs rather than age or time only. Acknowledgements Funding from the Ministry of Agriculture and Forestry in Finland, Raisio group Plc., The Finnish Cultural Foundation, Finnish Animal Breeding Association and the Central Union of Agricultural Producers and Forest Owners (MTK) is gratefully acknowledged. The authors thank Professor Anders R. Kristensen from The University of Copenhagen, Department of Large Animal Sciences, for discussions and suggestions regarding modelling. We also thank an anonymous referee for invaluable comments. References Affentranger, P., Gerwig, C., Seewer, G.J.F., Schwörer, D., Künzi, N., 1996. Growth and carcass characteristics as well as meat and fat quality of three types of pigs under different feeding regimens. Livest. Prod. Sci. 45, 187–196. Agricultural Research Council, 1981. The Nutrient Requirements of Pig. Commonwealth Agricultural Bureaux, Farnham Royal. Alexander, D.L.J., Morel, P.C.H., Wood, R.D., 2006. Feeding strategies for maximising gross margin in pig production. In: Pintér, J.D. (Ed.), Global Optimization: Scientific and Engineering Case Studies. Nonconvex Optimization and Its Applications, vol. 85. Springer Science+Business Media, New York, pp. 33–43.

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