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Journal of Animal Science - Animal Production

The economic and environmental value of genetic improvements in fattening pigs: An integrated dynamic model approach1

 

This article in JAS

  1. Vol. 93 No. 8, p. 4161-4171
     
    Received: Mar 16, 2015
    Accepted: June 17, 2015
    Published: August 3, 2015


    2 Corresponding author(s): jarkko.niemi@luke.fi
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doi:10.2527/jas.2015-9011
  1. J. K. Niemi 2*,
  2. M.-L. Sevón-Aimonen,
  3. A. H. Stygar33 and
  4. K. Partanen§44
  1. * Natural Resources Institute Finland (Luke), Economics and Society, Kampusranta 9, FI-60320 Seinäjoki, Finland
     Natural Resources Institute Finland (Luke), Green Technology, FI-31600 Jokioinen, Finland
     MTT Agrifood Research Finland, Economic Research, Latokartanonkaari 9, FI-00790 Helsinki, Finland
    § MTT Agrifood Research Finland, Animal production research, FI-31600 Jokioinen, Finland

Abstract

The selection of animals for improved performance affects the profitability of pig fattening and has environmental consequences. The goal of this paper was to examine how changes in genetic and market parameters impact the biophysical (feeding patterns, timing of slaughter, nitrogen excretion) and economic (return per pig space unit) results describing pig fattening in a Finnish farm. The analysis can be viewed as focusing on terminal line breeding goals. An integrated model using recursive stochastic dynamic programming and a biological pig growth model was used to estimate biophysical results and economic values. Combining these models allowed us to provide more accurate estimates for the value of genetic improvement and, thus, provide better feedback to animal breeding programs than the traditional approach, which is based on fixed management patterns. Besides the benchmark scenario, the results were simulated for 5 other scenarios. In each scenario, genotype was improved regarding daily growth potential, carcass lean meat content, or the parameters of the Gompertz growth curve (maturing rate [φi], adult weight of protein [αp], and adult weight of lipid mass [αL]). The change in each parameter was equal to approximately 1 SD genetic improvement (ceteris paribus). Increasing φi, αp, daily growth potential, or carcass lean meat content increased the return on pig space unit by €12.60, €7.60, €4.10, or €2.90 per year, respectively, whereas an increase in αL decreased the return by €3.10. The genetic improvement in αL and φi resulted in the highest decrease in nitrogen excretion calculated in total or per kilogram of carcass gain but only under the optimal feeding pattern. Simulated changes in the Gompertz growth function parameters imply greater changes in ADG and lean meat content than changes in scenarios focusing on improving ADG and lean meat content directly. The economic value of genetic improvements as well as the quantity of nitrogen excreted during the fattening period largely depends on feeding. Improved genotypes can require changes in pig management pattern. Estimating the influence of the genotype on the nitrogen excretion without considering changes in the management pattern can result in flawed conclusions. To improve overall economic performance and to decrease the environmental footprint of fattening pig production, the pig producer can adjust the herd management pattern according to the pigs’ genetics.



INTRODUCTION

Pig producers can gain substantial economic benefits when genetic traits such as the feed conversion ratio, the piglet yield per sow, carcass leanness, or ADG of a growing pig are improved (e.g., De Vries, 1989; Ollivier, 1998; Houska et al., 2004; Serenius et al., 2007). Considerable environmental benefits are available if genetic improvements to reduce the environmental runoffs and appropriate nutrient management are applied (Kornegay and Harper, 1997; Kanis et al., 2005).

The economic importance of genetic traits is often appraised by estimating the “economic value” of traits (e.g., De Vries, 1989). This refers to an approach where animal production is modeled by using a predetermined production function or productivity measure, and then the value of an individual genetic trait is solved as the marginal effect, economic values can be biased because studies often ignore that when the traits change, management patterns should be adjusted accordingly. This is the case even if the optimal management pattern clearly depends on the pig’s genetic line, and it is seldom optimal to maximize only weight gain (cf. Boland et al., 1999; Bailleul et al., 2000; Niemi, 2006). A more robust approach, would be to combine economic optimization and biological models. In fields such as analyzing how to manage different genotypes optimally, departures (e.g., Boland et al., 1993; see also Burt, 1993) have been taken to account for adjustments in management according to genotype.

The goal of this study was to estimate the economic value of genetic improvements in the fattening pig by integrated modeling. We contribute to the literature by analyzing the marginal values of heritable attributes in pigs with a dynamic programming model that accounts for adjustments in swine management when the genetics change. We examine how such changes affect nitrogen (N) excretion. We focus on changes in a dynamic programming model was used to estimate the value of genetic changes in the Gompertz growth function parameters (φi, αp, αL), the lean meat percentage, and ADG potential.


MATERIALS AND METHODS

Dynamic Programming Model

The dynamic programming model used in this study is based on the multiphase feeding model presented by Niemi et al. (2010). The structure of the model is described here to an extent that is relevant to understand how the value of pig genetics has been appraised.

The economic optimization model solves how much energy and protein is provided to the pigs each day and when harvesting of the pigs occurs. The objective is to maximize the value of a finishing pig space over the planning horizon. The economic model of the problem follows the Bellman equation (Bellman, 1957).subject to (the pig growth model). and are given (initial weights and the terminal value), where is the optimal value function, is the state vector, t is an index for the time period, ut is the control vector, Rt () is the 1-period return function (net cash flow), 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 impact of a harvesting decision and the state of nature, is the value of a pig space unit after the terminal period T, and is the state at the beginning of the planning horizon.

The state vector consists of 1) a set of parameters describing the economic and biological process that is being modeled (e.g., prices and growth parameters) and 2) pigs’ weight measures. The set of parameters is fixed for each model run but can vary between different runs. By contrast, weight measures can change over the course of the optimization. The weight measures are described by the lipid (xL,t) and protein (xP,t) mass in the pig and the distribution of these measures in the herd. Body weight is represented as a function of lipid and protein mass in the pig, as described by Niemi (2006). The state vector includes information on genetic parameters describing growth (φi, αp, αL) as defined in the subsequent sections, market prices used in the analysis, and variation in pigs. Within-herd variation in the weights xL,t and xP,t and in the growth of pigs was modeled by assuming that both the weight and the growth of pigs are distributed around the average pig. The magnitude of variation at each state of nature was obtained by simulating the Gompertz growth function.

The control vector includes:

  • 1) the decision whether to harvest the pigs (ucull,t = {0,1}),

  • 2) the decision how much protein (uprot,t) to give to the pigs each day, and

  • 3) the decision how much energy (uener,t) to give to the pigs each day.

The model evaluates multiple options for feed quantity and the protein:energy ratio each day and allows for the application of different feeding patterns. The harvesting decision is a binary variable {harvest, not harvest} whereas the feeding decisions are modeled as discrete variables. The model assumes all-in-all-out practice. Due to computational issues, it was not possible to study marketing in several batches over time (e.g., Boys et al., 2007). Feasible ranges of protein and energy feeding were determined so that the model did not produce corner solutions. The amount of protein is measured by assuming lysine as the first limiting amino acid (ileal digestible lysine) and all other amino acids equally limiting. The step size of the protein control grid is 0.93 g per MJ NE. The step size of the energy control grid is 0.465 MJ NE.

The decision-maker can apply ad libitum feeding, which results in maximal growth as it implies that there is enough protein and energy in the feed for the pig to grow according to its genetic growth capacity. Alternatively, the producer can restrict the amount of protein, energy, or both so that the 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 growth capacity. However, it was assumed that feeding cannot be restricted below a threshold proposed by Whittemore (1998). It was specified that the lipid mass growth:protein mass growth ratio must exceed 0.5 and that the growth of lipid mass growth can fall a maximum of 20% below the genetic potential. These constraints were applied to prevent pigs from suffering due to stress, tail biting, or other abnormal behaviors that may arise due to heavily restricted feeding.

Single-period returns, measured on a daily basis, are the net revenues from selling pigs at harvest (slaughter weight measured as carcass weight) minus the purchase price of a piglet and the feed costs, depending on whether the pig is harvested and how it is fed on that day.

Finnish slaughterhouses value carcasses according to their weight and the share of lean meat. To estimate the market value of an individual pig, expressions for the carcass weight (xmeat,t) and the share of lean meat (ρ[xP,t,xL,t]) in the pig as a function of the state variables were determined.where the expression within brackets on the right in Eq. [3] refers to lipid mass divided by protein mass, water, and ash in the pig, respectively. Hence, it is the ratio of lipid mass divided by nonlipid mass (protein, water, ash) of the body. Equation [2] was obtained from Niemi (2006), and Eq. [3] was re-estimated by using data and methods reported by Sevón-Aimonen (2001). The specification in Eq. [3] is more robust than the earlier specification used by Niemi (2006) because it ensures that the lean meat percentage is assessed correctly over a wider range of carcasses than before.

The least costly feed ratios were determined based on the mechanistic biological growth simulation model, conditional on the genetic parameters used in each model run. Bellman’s principle of optimality (Bellman, 1957) implies that feeding and the timing of harvest are adjusted according to what is observed in the group of pigs; That is, the future is uncertain and it impacts the timing of harvest and feeding whether the pigs turn out to be poorly or highly productive types.

Transition Equations

The pig growth model simulates how lipid and protein deposited into the body responds to the amount of nutrients provided to the pigs in their diets. Hence, the growth model provides the transition equations to the model. Growth is obtained when energy and protein needs for the pig’s body maintenance are subtracted from what is supplied in the feed, and the resulting energy and protein are converted into lipid and protein mass, respectively.

In the model, the Gompertz function is assumed to capture the maximum feed intake and the pigs’ potential to use protein and energy in feed. The maximum growth of both lipid and protein mass is restricted to the pig’s growth potential. The potential is represented by the derivative of the Gompertz function as derived by Niemi (2006). To estimate unknown model parameters, the nonlinear Gompertz function was fitted by breed, sex, and feeding using SAS 9.1 (SAS Inst. Inc., Cary, NC) and the NLIN procedure on data from an animal experiment described in a subsequent section. In the estimated model, the BW of pigs were described bywhere xi,t is the protein and lipid BW of the pig in kilograms, i = {P, L}, xW,t is BW, τ is the age of the animal in days, and αi, ki, and φi are parameters referring to an adult’s BW (kg at maturity), BW at birth (kg), and maturing rate of a body component (lipid or protein) in the pig (kg/d), respectively. Estimated parameters were pooled to represent the Finnish slaughter pig population. φi was assumed to be the same for both body components (see, e.g., Knap, 2000). As we focus on the pigs’ growth until slaughter, parameters αi are growth curve parameters that provide the best fit to the growth curve until slaughter.

The decision to slaughter the pigs in the group (ucull,t = 1) implies that the state of nature in the subsequent period (xt+1) is set to correspond to a 25-kg piglet, as it represents the average weight of pigs in a new group of grow-finish pigs at the onset of this group. If the producer continues to grow the pigs at time t (i.e., ucull,t = 0), the transition equations for the protein and lipid mass in the average pig are based on Eq. [2], [3], and [4] in Niemi et al. (2010). In this case, the next period state variable is defined as the current state (i.e., weight) plus the growth rate of a tissue component (protein and lipids) during a time period. Therefore, the corresponding transition equation derived from Gompertz Eq. [4] simplifies towhere .

Both αi and φi are critical to this study because genetic traits are specified through them. An increase in the φi implies that the ADG potential of lipid and protein mass increases constantly throughout the growing period. The value of a φi parameter is assumed to be the same for both lipid and protein mass. An increase in an ai parameter increases the ADG potential of the respective body component, but it also determines how quickly the maximum daily gain can be obtained and how the daily gain potential decreases after the date it reaches the maximum.

Nitrogen Excretion

To illustrate the possibilities in reducing N pollution from pig production, the effects of genetic improvements and the market situation on N output in feces and urine was calculated. The calculation was made post-optimization, and it utilized nutrient runoff equations estimated by Vu et al. (2009).where diOM is digestibility of OM (coefficient), dPROT is dietary CP (g/kg DM), DMI is measured in kilograms per day, and BW is measured in kilograms. Nitrogen excretion was estimated based on the optimal feeding pattern for protein and energy intake. DMI, dPROT, and diOM were calculated by assuming a well-defined diet based on the use the most common sources of protein and energy in Finnish pork operations, i.e., barley and soybean meal. The feed costs were also based on a similar diet as explained below.

Data and Scenarios

Scenario analysis was conducted with respect to genotype and market scenarios. First, a baseline market scenario and a benchmark genotype scenario were determined. The baseline market prices used in the model were average prices estimated using monthly market price information for 2010 to 2012. A slaughterhouse meat price grid was constructed based on price quotations reported by Grisportalen (2014), except the base price, which was calculated based on the average price reported by Tike (2014). The prices of energy and protein provided to the pigs in feed were estimated in 2 steps. First, the average prices of grains and other feed ingredients in the period 2010 to 2012 were applied to formulations of compound feeds commercially available in Finland. Second, it was estimated how the protein content of the feed affects the price of feed. Hence, the prices of both energy and protein (lysine) components in the feed were obtained. These price parameters take into account all the ingredients that well-designed feeds contain in proportion to energy or protein.

The benchmark genotype scenario was determined using a dataset based on an animal experiment, where 3 different terminal hybrids were fed according to their appetite. Finnish Landrace sows were mated with Finnish Yorkshire, Norwegian Duroc, or Swedish Hampshire boars. The numbers of different crossbred pigs in the experiment were 70, 72, and 42, respectively (equal number of gilts and castrated males). The pigs were raised at MTT Agrifood Research Finland in Hyvinkää (M.-L. Sevón-Aimonen, unpublished data). They were slaughtered on average at 113.1 kg live weight (SD = 3.44) and at 148.5 d of age (SD = 8.81).

After having estimated the parameter values of the Gompertz function, a hypothetical genetic line representing a typical breed combination in Finland was postulated. This genetic line, referred to as the benchmark genotype in Table 1, represents the “average” across all breeds. In addition, the model takes into account heterogeneity of pigs in the pig population. This is represented by the distribution of the Gompertz function parameters. To account for this variation in the postulated genotypes, SD and the variance-covariance matrix (Koivula et al., 2008) of Finnish test station pigs were used when simulating in the pigs for each scenario.


View Full Table | Close Full ViewTable 1.

The parameters of the Gompertz function used in alternative genotype scenarios

 
Genotype scenario1 Maturing rate φi Adult weight of protein mass αP Adult weight of lipid mass aL Lean meat, % ADG, kg/d
Benchmark 0.0136 35 59.5 60.0 1.003
High lean meat % 0.0134 37.0356 54.38 61.0 1.003
High ADG 0.0141 35.7902 61.3553 60.0 1.060
High adult weight of protein mass 0.0136 40.295 59.5 61.2 1.084
High adult weight of lipid mass 0.0136 35 74.465 58.4 1.052
High maturing rate 0.01565 35 59.5 60.0 1.154
1The genetic potential of pigs in other scenarios differs from the benchmark scenario. The difference is equal to approximately 1 (genetic) standard deviation of the relevant parameter. The change in potential is determined under ad libitum feeding and before applying the optimization program. Lean meat percentage and ADG are measured at 110 kg live weight.

Five alternative scenarios in Table 1 for pigs of improved genotype were determined by increasing the genetic potential (i.e., assuming ad libitum feeding) in each relevant parameter (lean meat percent, ADG, adult weight of protein mass [aP], adult weight of lipid mass [αL], and maturing rate [φi]) when compared to the benchmark. The change in respective parameter was equal to approximately 1 SD genetic improvement (ceteris paribus, hereafter referred to as 1 SD change; see, e.g., Serenius et al., 2007; Koivula et al., 2008). The average improvements were simulated without changing other parameters. Since genotype was specified through the parameters of the Gompertz function, high ADG and high lean meat percentage scenarios were determined by adjusting the growth function parameters (φi, αL, aP). These parameters were selected so that when ADG was increased, lean meat percentage was kept constant and contrary. As Table 1 illustrates, an increase in lean meat percentage and ADG is associated with high adult weight of protein mass. By contrast, reduced lean meat percentage and increased ADG are associated with high adult weight of protein mass, and only increased (although substantially increased) ADG is associated with high maturing rate.

Since the market situation of pig-fattening operations was poor during the period of 2010 to 2012, alternative market scenarios considered situations where either the price of piglets, energy in feed or lysine in feed was decreased, or the price of pigmeat was increased by 1 SD from the baseline scenario. A change of 1 SD in these parameters was based on their variation in 2010 to 2012. In addition, a scenario with a higher target weight range than in the baseline scenario was examined. Table 2 represents the scenarios.


View Full Table | Close Full ViewTable 2.

Price parameters examined in the sensitivity analysis1

 
Parameter values used in the analysis
Variable Baseline scenario Alternative scenario2
Price of pigmeat at the farm gate, €/kg3 1.53 1.6983
Price of a 25-kg piglet, €/kg 57 52.44
Price of NE, €/MJ NE4 0.099 0.0782
Price of lysine, €∙g−1×MJ−1 NE4 0.022 0.0169
Target range3, kg 76 to 92.5 85 to 105
One point of lean meat percentage3 0.02
Discount due to undesired weight, €/kg3 0.02
Daily discount rate, % 99.89
1Sources: Tike (2014), Grisportalen (2014), and authors’ own calculations.
2Each parameter was increased or decreased by a factor of 1 SD (±SD). Price variation was as observed during the period of 2010 to 2012.
3Base price of pigmeat is applied for carcasses that are within the “target range” and contain 60% lean meat. For each additional kilogram deviating from the target range, an additional €0.02 discount is charged. For each percentage point of lean meat below (above) 60%, an additional €0.02 price discount (premium) is applied.
4The price of compound (€/MJ) feed is: price of NE + price of lysine ´ lysine content of feed.

The model was programmed in Matlab 8.0 (The MathWorks Inc., Natick, MA) and solved numerically for 6 genotype scenarios representing benchmark genotype and genetic improvements in the following 5 traits: lean meat percentage, ADG potential, adult weight of protein mass, adult weight of lipid mass, and maturing rate of the pig. Our analysis did not examine the feed conversion ratio explicitly, but it was implicitly included in the traits affecting the production potential of pigs.

The model was solved with a backward recursion and value function iteration method. The value of the objective function and the values of decision variables (feeding and slaughter decisions) were also simulated for different market scenarios (Table 2) and for the policy of having vs. not having the option to apply restricted feeding. The value functions were converted to annual (euros per pig space per year) or to per kilogram of pigmeat basis. Finally, the effect of genetic improvements and the market situation of N output in feces and urine were calculated.


RESULTS AND DISCUSSION

Optimal Growth Performance and Feeding

Genetic improvements in the examined traits had only a small impact on the optimal slaughter weight (Fig. 1), which is to a large extent determined by a slaughterhouse’s carcass quality pricing grid. By contrast, the duration of the fattening period (Fig. 1), the optimal feeding pattern (Fig. 2), and the carcass lean meat percentage (Table 3) were influenced by the improvements.

Figure 1.
Figure 1.

The development of the pig’s live weight (kg) for different genotype scenarios after the optimization program was applied.

 
Figure 2.
Figure 2.

The feeding pattern for different genotype scenarios after the optimization program was applied.

 

View Full Table | Close Full ViewTable 3.

Average daily gain, lean meat percentage, and nitrogen (N) excretion as a result of the optimization program for different genotypes and simulation results for nonoptimal scenarios where pigs were fed ad libitum

 
Baseline scenario
Ad libitum scenario: simulation results
Genotype1 ADG, kg/d Lean meat, % N excretion2, kg/pig ADG, kg/d Lean meat, % N excretion3, kg/pig
Benchmark 0.89 63.3 3.69 0.98 59.7 3.54
High lean meat % 0.9 64.0 3.79 0.95 60.6 3.52
High ADG 0.93 63.2 3.68 1.00 59.2 3.57
High adult weight of protein mass 0.97 63.7 3.82 0.97 60.0 3.54
High adult weight of lipid mass 0.9 62.0 3.49 1.02 58.4 3.60
High maturing rate 1.02 62.9 3.54 1.00 58.9 3.59
1Other genotypes differ from the benchmark genotype. The difference is approximately equal to 1 SD genetic increase in the relevant trait. Genetic change is determined before the optimization program has been applied.
2Calculated for a grow-finish pig, from 25 kg BW to slaughter.
3Calculated based on optimal decision pattern for ad libitum feeding scenario under the condition of improved market situation for a grow-finish pig, from 25 kg BW to slaughter after 91 d of fattening.

The examined genetic improvements, except for the scenario with high lean meat percentage, resulted in a shorter fattening time and greater daily feed amounts relative to the benchmark scenario. Moreover, with optimized feeding and slaughter timing, the scenarios with high maturing rate, and the high adult weight of protein mass resulted in a higher ADG than improving that trait directly (Fig. 1). This result is because of larger changes in the shape of the growth function under the high maturing rate and high adult weight of protein mass scenarios than under an improved ADG scenario.

The differences in the optimal growth rate between genotypes were also reflected in the optimal feeding pattern. According to the optimal feeding and slaughter pattern, the pigs with a high maturing rate or adult weight of lipid mass were to be fed with greater daily amounts of energy in feed, while genotypes with a high maturing rate or adult weight of protein mass received larger daily amounts of lysine in relation to megajoules of NE. Moreover, the differences in optimal feeding increase when the pigs approach their optimal harvest weight (Fig. 2). These results suggest that using a fixed feeding, such as in the study of Houska et al. (2004), where the adjustment of diet according to the genotype was not taken into consideration, can affect the value of traits. A further improvement to our model would be to consider marketing pigs in several batches over time as split marketing can provide economic benefits when the herd is heterogeneous (Boys et al., 2007; Niemi and Sevón-Aimonen, 2009).

The improved genetics together with an optimized feeding pattern influenced carcass quality. The scenario with high lean meat percentage resulted in the lowest deposition of fat. By contrast, scenarios with elevated parameter values of growth curve resulted in either a higher (scenario for high adult weight of protein mass) or lower (scenarios for high adult weight of lipid mass or maturing rate) lean meat percentage (Table 3).

Differences in Economic Performance and Economic Values

Regarding economic results, pigs with a high maturing rate yield the highest and pigs with a high adult weight of lipid mass yield the lowest return on pig space unit (Table 4). These results were obtained under restricted-feeding regimes. The average return on total assets in the Finnish pig husbandry industry between 2010 and 2012 was negative (Niemi and Alhstedt, 2012), which was due to relatively high feed prices, meat price being low relative to feed costs, and the termination of subsidy payments to the industry. The market situation was reflected in our study where ad libitum feeding resulted in a negative value function for all studied genotypes. By contrast, positive value functions were obtained under restricted feeding. Restricted feeding can be economically beneficial because it reduces energy intake and can increase the energy conversion ratio and lean meat percentage of carcasses (Ramaekers et al., 1996). Taking into account the difficult market situation of pig farms, we focused on economically feasible options. Therefore, only results for restricted feeding are presented.


View Full Table | Close Full ViewTable 4.

Economic results (value function converted to euros per pig space per year; impact of genotype scenario on net returns of €/100 kg pigmeat) under the optimal fattening and harvest pattern for different genotypes and sensitivity of economic values on market assumptions.

 
Baseline scenario
Difference in the value function between the benchmark genotype and other genotypes for alternative scenarios measured as €/100-kg pigmeat2
Genotype1 Net return, €∙pig space unit−1∙yr−1 Baseline scenario Increase price of pigmeat Decrease price of piglet Decrease price of MJ NE Decrease price of lysine Elevated target BW
Benchmark 7.2
High lean meat % 10.1 0.92 1.09 0.95 1.34 0.78 1.13
High ADG 11.3 1.27 2.24 1.55 1.91 1.43 1.66
High adult weight of protein mass 14.8 2.31 3.90 2.76 3.76 2.45 3.13
High adult weight of lipid mass 3.1 −1.25 −0.52 −0.99 −1.22 −0.81 −1.00
High maturing rate 19.8 3.70 6.28 4.51 5.30 4.12 4.45
1Other genotypes differ from the benchmark scenario. The difference is equal to approximately 1 SD genetic increase in the trait before the optimization program is applied.
2The impact represents the difference to the baseline scenario and it represents the effect of one SD increase in the price of pigmeat, decrease in the piglet, MJ NE or lysine price and the effect of a change in the target slaughter weight.

To facilitate the comparison of annual economic results, the value function was converted to an annual basis. The value of approximately 1 genetic SD increase in the lean meat percentage was estimated at €2.9 per pig space unit per year (difference in the value function between lean meat percentage scenario and the benchmark), whereas the value of increased ADG potential was estimated at €4.1 per pig space unit per year. These values corresponded to €0.92 and €1.27 per 100 kg of pigmeat, respectively. The value of increasing the adult weight of protein mass and maturing rate was estimated at €7.6 and €12.6 per pig space unit per year, respectively. By contrast, an increase in the adult weight of lipid mass reduced the value of a pig space unit by €4.1 (Table 4).

Market prices fluctuate over time and that can influence the economic values of traits. An increase in the price of pigmeat, energy in feed, or lysine in feed increased the values of traits. Increasing the target slaughter weight also increased the values of traits. By contrast, an increase in piglet price reduced the value of high ADG and adult weight of protein mass. The results suggest that the value of the ADG potential and parameters indicating the shape of the Gompertz growth curve (φi, aP, αL) were particularly affected by the price of pigmeat. The values of high ADG potential (€2.24 per 100 kg meat), adult weight of protein mass (€3.90), and maturing rate (€6.28) were 1.6- to 1.8-fold at the increased pigmeat price when compared to the baseline market scenario. The value of the ADG potential, maturing rate, and adult weight of protein mass was also strongly affected by the price of megajoules of NE, whereas the value of adult weight of lipid mass was more affected by the price of protein. The value of the lean meat percentage was affected the most by the price of megajoules of NE and change in the target slaughter weight range (Table 4).

Results suggest that the economic values of lean meat percentage and adult weight of protein mass behave similarly to some extent. This can be expected because the high adult weight of protein in our scenarios also implies genetic potential for high lean meat percentage (in addition to high ADG). Likewise, the economic values of lean meat percentage, adult weight of protein mass, and maturing rate have similarities. This is due to the fact that these 2 parameters of the growth function (aP, φi) contribute the most to ADG and lean meat percentage. By contrast, the adult weight of lipid mass is of less value because of the restricted-feeding and optimization program and because lipid mass growth potential is associated with reduced lean meat percentage.

Economic values of parameters related to the length of the fattening period, in particular, depend on the pigmeat price. If producers have an opportunity to sell the fast-growing stock at a high price, it can be valuable to them. In contrast to this, the prices of feed inputs are important for the value of genetically lean carcasses. Our approach was to identify traits that have the largest economic potential (in relation to other traits) and thus provide guidance on the benefits of improvements in different parameters. As h2 and the effort needed to gain the benefits can differ by trait, the costs associated with the breeding program itself should also be taken into account.

Using growth function parameters in an animal breeding program is likely to have higher data requirements, and thus the program may incur more costs than when using more conventional measures. However, many progeny testing stations already have electronic feeding equipment that can be equipped with scales to measure the pigs multiple times at only a small extra cost. Although changes in the Gompertz growth function parameters imply substantially larger changes in ADG and lean meat percentage than simulated changes in ADG and lean meat scenarios, they seem feasible because breeds can already cause larger differences in parameter values than the differences between those in our scenarios.

Our results suggest that the economic value for genetic improvements in the lean meat percentage and especially in the ADG are higher than estimated in previous studies (e.g., Serenius et al., 2007). This difference is partly due to methodological aspects and partly to market scenarios. As it was noticed by Houska et al. (2004), economic values calculated for different countries or conditions are difficult to compare due to different market assumptions. Our results also suggest that market prices can have a substantial impact on economic values of traits. The authors are not aware of studies in which the economic importance of the parameters of the Gompertz function in pig fattening would have been assessed previously.

Nitrogen Excretion

The optimal results suggest that the genetically high adult weight of protein mass and lean meat percentage increase the manure N output that the pig produces between 25-kg BW and slaughter BW by 3.5 and 2.7%, respectively. By contrast, an increase in the adult weight of lipid mass, maturing rate, or potential for ADG decreased fecal and urinary N by 5.4, 4.1, or 0.3% per pig, respectively (Table 3). However, these results slightly differ when N excretion per cumulative kilogram of carcass gain is examined (Table 5). When compared to the benchmark value, a genetic improvement in the maturing rate, adult weight of lipid mass, and potential for ADG resulted in a 4.0, 3.8, and 0.9% decrease in manure N output per kilogram of carcass gain, respectively. In the scenario with a high lean meat percentage, N excretion per 1 kg of carcass daily gain was 1.7% higher than in the benchmark scenario, whereas for high adult weight of protein mass, a difference of only 0.3% was simulated (Table 3). Therefore, the quantity of N excreted per kilogram of carcass meat produced depends largely on production efficiency. In that respect, our results agree with Kanis et al. (2005), who concluded that efficiency traits such as ADG and lean meat percentage have environmental and, therefore, societal value.


View Full Table | Close Full ViewTable 5.

Sensitivity of nitrogen (N) excretion to assumptions regarding the market situation

 
Baseline scenario
Increase in cumulative N excretion, g/kg of carcass gain in alternative scenarios compared to baseline scenario2
Genotype1 Cumulative N excretion3, g/kg of carcass gain Increase price of pigmeat Decrease price of piglet Decrease price of MJ NE Decrease price of lysine Elevated target weight
Benchmark 56.77 −1.59 −0.78 −0.06 −0.07 2.75
High lean meat % 57.76 −2.11 −1.04 −0.05 −0.25 2.86
High ADG 56.27 −1.59 −0.88 −0.01 −0.20 2.86
High adult weight of protein mass 56.97 −1.08 −0.79 −0.15 −0.07 3.35
High adult weight of lipid mass 54.63 −0.13 −0.09 0.31 0.53 2.97
High maturing rate 54.51 −1.05 0.05 0.26 0.07 3.91
1Other genotypes differ from the benchmark scenario. The difference is equal to approximately 1 SD genetic increase in the relevant trait. Genetic change is determined before the optimization program is been applied.
2The impact represents the difference in the baseline scenario, and it represents the effect of a 1 SD increase in the price of pigmeat, piglet, megajoules NE, or lysine or the effect of a change in the target slaughter weight.
3Calculated for a grow-finish pig, from 25 kg BW to slaughter.

The N excretion for the baseline scenario, presented in Table 3, was calculated for the optimal feeding pattern in a restricted-feeding scenario. However, previously reported values for N excretion for fattening pig production (e.g., Shirali et al., 2012; Saintilan et al., 2013) were calculated for ad libitum feeding patterns. Therefore, to compare the results of N excretion in an optimal solution with data from the literature, the N runoff from fattening production under the condition of unrestricted feeding was also simulated. According to those simulations, the N excretion in the ad libitum scenario ranged from 3.52 kg/pig for the high lean meat percentage genotype to 3.6 kg for the high adult weight of lipid mass genotype (Table 3). Hence, the total N excretion for tested genotypes was slightly higher than in the study of Saintilan et al. (2013) who reported 2.61 to 3.36 kg, but it was lower than Shirali et al. (2012) who reported 5.35 kg of N per pig. However, the potential explanations for these differences could be the various lengths of the growing period (from 77 to 102 d) and higher BW at the beginning of the fattening period (around 35 to 60 kg) in cited studies compared to our simulation scenario (91 d and 25 kg for all genotypes).

An increase in N excretion for the pigs that are genetically predisposed to a higher lipid-to-protein ratio is not surprising because when feeding the same composition of diet during the whole fattening period, the protein that cannot be utilized for protein mass growth (especially in the later stage of fattening for pigs with lower genetic potential for protein growth) will be excreted as N (Clemens and Ahlgrimm, 2001). In addition, the efficiency of lysine utilization declines after the maximum gain is achieved (Gahl et al., 1994). To minimize the N excretion of fattening pigs, the amino acid content of the diet should also be improved. Therefore, assessing the influence of the genotype on N excretion without taking changes in management into account can lead to misleading conclusions.

In our study, environmental effects are post-optimization calculations, and environmental considerations are not taken into account when optimizing feeding. An important factor that could be taken into account in further analysis is that manure has an economic value, which can be negative or positive. Another aspect that should be taken into consideration while calculating N excretion is piglet production. The present analysis considers only pig fattening as a separate activity. Therefore, the results cannot be interpreted as the total N runoff from pigmeat production. Including piglet production as well as considering other environmental aspects such as phosphorous and carbon output or water use for evaluation of the environmental burden of pig fattening could be an extension of our model.

Results suggest that there are limited opportunities to control manure N output (Table 5). For example, the increased price of the pigmeat scenario resulted in a shorter fattening period and a decrease in N excretion. The elevated target weight extended the time of the fattening operation and increased the N load per 1 kg of carcass gain. Although changes in feed prices influenced the feeding pattern, they did not cause large changes in N excretion. These results are mainly related to the amount of N associated with the daily weight gain and the amount of protein fed to the pigs. Therefore, a decrease in the input prices (piglet and feeding prices) in the high maturing rate scenario resulted in small changes in feeding but not a substantially shorter duration of feeding and, hence, only slightly higher N excretion than the baseline scenario.

Applicability of the Model and Model Results in the Breeding Program

We have presented an integrated model approach that characterizes the pigs’ growth process explicitly for different genotypes by taking into account BW, carcass composition, and interactions between genotype, feeding, and quality-adjusted carcass value. The model also accounts for the distribution of carcass weight, carcass lean meat percentage, and the growth potential of pigs within the different genetic group of grow-finish pigs. Therefore, this model aims to meet the suggestions of Pomar et al. (2003) regarding the fact that simulating variability in pigs must be an integral part of model design.

Gompertz growth curve parameters, used in the presented model to predict growth, are characterized by high genetic and phenotypic correlation, h2, and genetic variance (Koivula et al., 2008). High correlation may imply that more effort is needed to achieve an increase in the trait. These parameters also provide useful information on the growth patterns of pigs. Therefore, the selection for those parameters could significantly improve the effectiveness of the breeding program. Moreover, compared to the traditional way of calculating the “economic value” of traits (e.g., De Vries, 1989) the use of growth models in estimating economic weights decreases the risk of underestimating traits that are directly associated with profit. For example, as discussed in the study by Hermesch et al. (2003), economic weight for feed intake derived from a traditional economic model is always negative since a reduction in feed intake, assuming no changes in growth rate and lean meat percentage, reduces production costs. Therefore, deriving economic weights based only on an economic model used in conventional breeding programs may result in a continuous decrease in feed intake in pigs. In contrast, the use of a growth model allows for the direct selection of production traits so that feed intake, feed efficiency, and lean meat growth are optimized with respect to given overall objective such as maximal economic return. That conclusion is also in line with the study of Hoque et al. (2007), who noticed that the selection for residual feed intake (feed consumed above or below the requirements for production and maintenance) should be included in breeding programs for pigs to make genetic improvements in the efficiency of gain.

Combining stochastic dynamic modeling with the growth model allowed us to produce more accurate estimates for economic values of selected genetic parameters than before and, thus, better feedback for animal breeding programs than the traditional approach. Our analysis suggests that it is important to improve the maturing rate and adult weight of protein mass. Our results can be viewed as focusing on terminal line breeding goals. These traits jointly promote high growth potential throughout the growing period and the carcass lean meat percentage on harvest. An improved maturing rate could also reduce the environmental burden of pig production. Hence, it is important to improve both carcass leanness and ADG potential. It can provide valuable information if breeding goals also pay attention to the shapes of the growth curves. Our approach can be further developed to study the influence of genetic traits and feeding on the total environmental burden of pig-fattening production and its interaction with economics. In future studies, our approach could be further developed to help improve animal breeding programs.

Conclusions

In conclusion, maturing rate, adult weight of protein mass and ADG are the most valuable traits in pigs. To best utilize new genetics, the economic values of genetic traits should rather be evaluated with integrated bioeconomic models that take into account the option to adjust the management patterns than models based on fixed management. An integrated approach can provide more accurate estimates for the economic values of traits and enhance the competitiveness of pig production. Besides benefits, research also needs to consider the costs needed to obtain genetic improvements.

 

References

Footnotes


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