Optimizing Dietary Net Energy for Maximum Profitability in Growing- Finishing Pigs

Kansas Agricultural Experiment Station Research Reports, Nov 2017

Feed accounts for a significant portion of swine production cost, with dietary energy alone representing more than half of the total cost. Considering the financial implications of determining the energy content of the diet, the objective of this research project was to develop a tool to accurately estimate the dietary NE content that yields maximum profitability for growing-finishing pigs. A Microsoft Excel®-based model was developed to contrast dietary NE defined by the user with recommended concentrations that are intended to maximize profitability in user defined production and economic scenarios. To calculate pig performance, the model uses prediction equations for ADG and feed efficiency. In addition, the model also uses the NDF content of the diet because of its effect on dressing percentage. For profitability calculations, a non-linear mathematical programming model was designed to select the optimum dietary NE content that yields the greatest income over total cost per pig on a live or carcass basis. The model can be used to predict dietary NE content that yields the highest economic benefit considering dynamic productive and economic scenarios. The model can be downloaded at www.ksuswine.org.

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Optimizing Dietary Net Energy for Maximum Profitability in Growing- Finishing Pigs

Optimizing Dietar y Net Energ y for Max imum Profitability in Growing- Finishing Pigs J. Soto 0 1 M. D. Tokach 0 1 S. S. Dritz 0 1 M. A. Goncalves 0 1 Genus PIC-USA, Hendersonville, TN 0 Department of Animal Science and Industry, Kansas State University , USA 1 Kansas State University , Manhattan , USA Part of the Other Animal Sciences Commons Recommended Citation - See next page for additional authors Article 43 Follow this and additional works at: http://newprairiepress.org/kaesrr This report is brought to you for free and open access by New Prairie Press. It has been accepted for inclusion in Kansas Agricultural Experiment Station Research Reports by an authorized administrator of New Prairie Press. Copyright 2017 Kansas State University Agricultural Experiment Station and Cooperative Extension Service. Contents of this publication may be freely reproduced for educational purposes. All other rights reserved. Brand names appearing in this publication are for product identification purposes only. K-State Research and Extension is an equal opportunity provider and employer. Optimizing Dietary Net Energy for Maximum Profitability in GrowingFinishing Pigs Abstract Feed accounts for a significant portion of swine production cost, with dietary energy alone representing more than half of the total cost. Considering the financial implications of determining the energy content of the diet, the objective of this research project was to develop a tool to accurately estimate the dietary NE content that yields maximum profitability for growing-finishing pigs. A Microsoft Excel®-based model was developed to contrast dietary NE defined by the user with recommended concentrations that are intended to maximize profitability in user defined production and economic scenarios. To calculate pig performance, the model uses prediction equations for ADG and feed efficiency. In addition, the model also uses the NDF content of the diet because of its effect on dressing percentage. For profitability calculations, a non-linear mathematical programming model was designed to select the optimum dietary NE content that yields the greatest income over total cost per pig on a live or carcass basis. The model can be used to predict dietary NE content that yields the highest economic benefit considering dynamic productive and economic scenarios. The model can be downloaded at www.ksuswine.org. Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 License. Cover Page Footnote The authors thank Genus PIC-USA, (Hendersonville, TN) for technical and financial support. Authors J. Soto, M. D. Tokach, S. S. Dritz, M. A. Goncalves, J. C. Woodworth, J. M. DeRouchey, R. D. Goodband, and U. A. Orlando This Finishing Pig Nutrition and Management article is available in Kansas Agricultural Experiment Station Research Reports: http://newprairiepress.org/kaesrr/vol3/iss7/43 Optimizing Dietary Net Energy for Maximum Profitability in Growing Finishing Pigs1 Summary Feed accounts for a significant portion of swine production cost, with dietary energy alone representing more than half of the total cost. Considering the financial implications of determining the energy content of the diet, the objective of this research project was to develop a tool to accurately estimate the dietary NE content that yields maximum profitability for growing-finishing pigs. A Microsoft Excel®-based model was developed to contrast dietary NE defined by the user with recommended concentrations that are intended to maximize profitability in user defined production and economic scenarios. To calculate pig performance, the model uses prediction equations for ADG and feed efficiency. In addition, the model also uses the NDF content of the diet because of its effect on dressing percentage. For profitability calculations, a non-linear mathematical programming model was designed to select the optimum dietary NE content that yields the greatest income over total cost per pig on a live or carcass basis. The model can be used to predict dietary NE content that yields the highest economic benefit considering dynamic productive and economic scenarios. The model can be downloaded at www.ksuswine.org. Introduction Feed accounts for up to 75% of pork production cost, with energy alone representing 50% or more of the total cost.4,5 The knowledge of energy metabolism is essential to predict, optimize, and formulate diets to achieve expected performance. Typically, the DE (digestible energy) and the ME (metabolizable energy) systems are the most common in the US.5 However, the concentration of dietary NE provides the most accurate estimate of the amount of energy available to the pig.6 Acknowledging the difficulties of 1 The authors thank Genus PIC-USA. (Hendersonville, TN) for technical and financial support. 2 Department of Diagnostic Medicine/Pathobiology, College of Veterinary Medicine, Kansas State University. 3 Genus PIC-USA. (Hendersonville, TN). 4 Noblet, J., H. Fortune, C. Dupire, S. Dubois. 1993. Digestible, metabolizable and net energy values of 13 feedstuffs for growing pigs. Anim. Feed Sci. Tech 42:131-149. 5 Patience, John F. 2009. Energy in swine nutrition. Animal industry report: AS 665, ASL R2457. Available at: http://lib.dr.iastate.edu/ans_air/vol655/iss1/80. 6 Noblet, J. 2007. Recent developments in net energy research for swine. Adv. Pork Prod. 18:149-156. measuring NE and limited availability of NE estimates in dietary ingredients, Nitikanchana et al. (2015)7 developed and validated regression equations to predict growth rate and feed efficiency of growing-finishing pigs the NE system. These equations provide a useful estimate for growth performance of pigs fed different dietary NE concentrations. Taking into consideration the financial implications of the energy density of the diet, the objective of this study was to develop a tool to estimate the dietary NE concentration that yields maximum profitability for growing-finishing pigs. Procedures: Building the Model Model Description The NE optimization tool is a Microsoft Excel®-based model. This tool is intended for use by swine nutritionists as a method to contrast current dietary NE concentrations to recommended values that yield maximum profitability. The model is divided into 3 sections: 1) model inputs, with economics, production, and dietary criteria; 2) model calculations and optimization, for growth performance and carcass yield predictions, and profitability indicators; and 3) model outputs with recommended dietary NE concentrations, predicted growth performance, carcass yield, and profitability indicators contrasting current with the estimated ideal dietary NE concentrations. User Input Page Economics and System Performance For calculation of growth performance and profitability, the user is required to enter the following inputs: current ADG (lb), F/G, and carcass yield (%), pork carcass price ($/lb), feeder pig cost ($/pig), facility cost ($/pig/d), and other cost (e.g., veterinary supplies, insurance, etc.). For the growth curve, the user can utilize default values or input a custom growth curve. In addition, the profit determination criteria can be customized by selecting the economic evaluation based on a live- or carcass-basis and marketing pigs on either a fixed time or fixed weight basis. Nutritional Program Specifics In this section, the number of dietary phases is selected (currently the model allows the selection of 4 to 6 phases) along with the BW range per phase. In addition, current, minimum, and maximum NE (kcal/lb) concentrations are specified by the user in each dietary phase. Inputs for minimum and maximum NE will be obtained by diet formulation. With these three NE inputs, the model will calculate 5 equidistant NE values, maintaining the minimum, maximum as well as the current NE value used. Afterward, the user needs to input the feed cost ($/ton) for diets at each NE value in all phases and the percentage of neutral detergent fiber (NDF) associated to each concentration of dietary NE for diet phases 3 and greater. Building the Calculations for Performance and Economics Growth performance prediction equations and SID Lys adequacy This model utilizes the ADG prediction equations developed by Nitikanchana7 et al. Their publication provides two equations: 1) equation with adequate dietary SID Lys 7 Nitikanchana, S., S. Dritz, M. Tokach, J. DeRouchey, R. Goodband, and B. White. 2015. Regression analysis to predict growth performance from dietary net energy in growing-finishing pigs. J. Anim. Sci. 93:2826-2839. (this equation includes BW, dietary NE, and the quadratic term of BW as regressors) and 2) equation with dietary SID Lys at suboptimal values (this equation includes BW, dietary NE, and SID Lys). In the inputs section, the user is required to select if their diets are adequate in SID Lys or not. If diets are deficient, the user needs to input the SID Lys associated to each value of dietary NE in each dietary phase. To calculate ADG, the user provides a current system overall ADG, which is partitioned to a current calculated ADG in each dietary phase with the use of a regression equation developed from a reference population (Table 1). Furthermore, ADG is calculated with the inputs provided by the user (BW and dietary NE in each dietary phase). The difference between both, current and calculated ADG, are added or subtracted to predict ADG, which represents an adjustment to the intercept for the calculated ADG results. To calculate G:F, the model utilizes estimations performed by Beaulieu8 et al., which suggested a 1:1 ratio between feed efficiency and dietary energy concentration. The model utilizes this ratio to calculate the influence of dietary NE on F/G. Comparable to the procedures to calculate predicted ADG, the user provides an overall F/G, and these values are partitioned to a current F/G (as G:F) in each dietary phase with the use of a growth curve from the reference population (Table 1). Feed Cost, SID Lys, and NDF Prediction Equations For the calculated NE values not provided by the user, feed cost, SID Lys, and NDF for energy, are predicted using a set of regression equations that were developed using the least squares estimates method from the Linest function of Microsoft Excel. According to Briand and Carter,9 the Linest function is an alternative to the use of least squares estimator formulas to obtain the best fit under a predefined criterion, and allows combinations with multiple functions to calculate statistics for other linear models. For the feed cost prediction, Linest calculates the slope and intercept from the feed cost associated to each NE value provided by the user. In each dietary phase, a set of five linear regression equations are calculated by combining pairs of consecutive feed cost and associated NE values. The rationale supporting these calculations is to provide exact estimates of feed cost, and consequently more accurate economic estimates. For the NDF prediction, Linest calculates a set of three linear regression equations (linear, quadratic, and cubic) and the equation with the best fit is selected to estimate NDF. The regression equations are calculated by selecting the NDF and associated NE values in each dietary phase from the inputs provided by the user. The equation fit is determined by the adjusted coefficient of determination, intended to account for the number of predictors in the model (Table 2). 8 Beaulieu A., Williams N., and Patience J. 2009. Response to dietary digestible energy concentration in growing pigs fed cereal-grain based diets. J. Anim. Sci 87:965–976. 9 Briand, G. and Carter, H. 2011. Using excel for principles of econometrics: 4th edition. John Wiley & Sons Inc. New York, NY. Comparable to the procedures to calculate predicted NDF, Linest calculates a set of three linear regression equations, and the model with the best fit is selected for estimation of SID Lys. Regression Equations to Predict Carcass Yield This model uses carcass yield prediction equations developed by Soto10 et al., which provides an estimate of the effects of dietary NDF on carcass yield. Building the Linear Programming Model for Optimization in Excel A non-linear mathematical programming (NLP) model was designed to select the optimum values of dietary NE that yields the maximum profitability for growing-finishing pigs. In Microsoft Excel Solver, NLP problems are solved with the generalized reduced gradient (GRG) algorithm. In this model, the objective function is income over total cost (IOTC) on a live- or carcass-basis and is maximized by the optimal value of NE in each dietary phase. In the model, once economic, system performance, weight ranges, and dietary inputs are entered, the GRG algorithm begins the routine at any feasible solution (starting point). Then through multiple iterations across the feasible region, it searches for a solution that provides the value of NE that satisfies the greatest profitability (IOTC) defined in the objective function. When no further possibility for profitability improvement exists, the current solution becomes local optima in relation to nearby points. However, a global optimal solution represents the best possible solution for the objective function.11 To land in the global optima, the model has the GRG in the Solver set up with the Multistart option, which selects several starting points throughout the feasible region, which produces multiple local optima solutions; therefore, increasing the chance of arriving to the global optima solution. The mathematical structure and economic calculations of the model are described in Tables 3 and 4. Application of the Model Scenario Building An example using this model is presented in Tables 5, 6, 7, and 8. In this example, a six-phase feeding program based on corn-soybean meal and dried distillers grains with solubles (DDGS) was used. To generate the NE range, a series of 5 diets per phase were formulated to include 0, 10, 20, 30, and 40% DDGS. In our simulation, the base feeding program used for comparisons had 20% DDGS added throughout all dietary phases. The resulting NE values from the 20% DDGS diets in this simulation were: 1,104, 1,122, 1,130, 1,145, 1,150, and 1,140 Kcal/lb for phases 1, 2, 3, 4, 5, and 6, respectively (Table 5). From phases 3 to 6, resulting NDF values had an average of 13% for diets with a 20% DDGS inclusion. The results of calculations for 5 equidistant NE values and respective NDF values are presented in Table 5. 10 Soto, J.A., M.D. Tokach, S.S. Dritz, M. A. Goncalves, J.C. Woodworth, J.M. DeRouchey, and R.D. Goodband. 2017. Regression Analysis to Predict the Impact of High Insoluble Fiber Ingredient on Carcass Yield. Kansas Agricultural Experiment Station Research Reports: Vol. 3: Iss. 7.  11 Ragsdale, C. 2008. Spreadsheet modeling and decision analysis. 5th edition. Thomson Higher Education. Mason, OH. For scenario building, the following inputs were used: 1) current overall ADG of 2.15 lb; 2) current overall F/G of 2.90; 3) current carcass yield of 73.4%; 4) feeder pig cost of $55.00/pig; 5) facility cost of $0.11/pig/d; and 6) other cost (veterinary supplies, field service personnel, trucking, etc.) of $8.00/pig. Dynamic Scenario Variables Definition To further evaluate the model performance, dried distillers grains with solubles (DDGS) pricing was modified from low-cost ($90.00/ton) to high-priced ($150.00/ ton). Similarly, carcass pricing was also modified from moderate-priced ($0.65/lb) to high-priced ($0.85/lb). For calculation of feed cost the pricing of main ingredients used was: corn $3.48/bu, soybean meal $290.60/ton, and L-Lys $0.69/lb. Resulting feed costs are presented in Table 5. Results and Discussion Scenario Results Considering a scenario with low-priced DDGS and moderate carcass, the model solution suggested that NE should be decreased, thus forcing in 40% DDGS. This decrease is only observed from phases 1 to 5. In phase 6, the model yielded no modification from the current energy value. The recommended NE values worsened ADG, feed efficiency, and carcass yield, nonetheless, the recommend NE values under the conditions of this scenario improved IOTC by $3.75/pig. Interestingly, by only changing the scenario to a high carcass price, the model solution suggested a similar NE decrease in phases 1 to 5 to the previously explained scenario. However, in phase 6 the model suggested the highest energy value, thus switching to a corn-soybean meal-based diet, and improving carcass yield. With the use of the recommend NE values under the conditions of this scenario, IOTC improved by $3.76/pig over the current system performance. Considering a scenario with high-priced DDGS and moderated carcass price, the model solution still suggested that NE should be decreased; however, the extent of this decrease is lower compared to the scenarios described above, particularly for phases 1 and 3. For phases 2 and 4, the recommend NE values remain the lowest, forcing the 40% DDGS diet. For phases 5 and 6, the recommended NE values are increased, particularly for phase 6. The recommended NE values slightly worsened feed efficiency, yet carcass yield was improved. With the use of the recommend NE values under the conditions of this scenario, IOTC improved by $1.26/pig. With a more favorable scenario for carcass price, NE is moderately reduced for phases 1 and 3. For phase 2 the recommended NE value remained the lowest. For phase 4, the model yielded no modification. Like the previous scenario, the recommended NE values are increased for phases 5 and 6, particularly for phase 6. With the use of the recommend NE values under the conditions of this scenario, IOTC improved by $1.56/pig. The model described in this paper can be used to predict the value of dietary NE that yields the greatest economic return to the production system. To evaluate the performance of the model, an example is presented considering different economic scenarios created by modifying DDGS and carcass pricing. Growth performance ADG, g G:F Variable NDF, % SID Lys, % 1 Growth curve reference taken from PIC 337 growing-finishing pigs (PIC internal data). 1 The equation selected for the prediction is the one with the highest adjusted coefficient of determination. Kansas State University Agricultural Experiment Station and Cooperative Extension Service Objective function Income over total cost, live basis MAX (IOTC Live, $/pig): Calculation f(x)= ((Total gain Ph1-6, lb + Feeder pig BW, lb) × Live price, $/lb) – (Feed cost, $/pig + Facility cost, $/pig) – Feeder pig cost, $/pig Subject to: Income over total cost, carcass basis MAX (IOTC Carcass, $/pig): f(x)= (((Total gain Ph1-6, lb + Feeder pig BW, lb) × Predicted carcass yield, $/lb × Carcass price, $/lb) – (Feed cost, $/pig + Facility cost, $/pig)) – Feeder pig cost, $/pig Subject to: Phase 1 Predicted NE ≥ Minimum user NE, Phase 1 Predicted NE ≤ Maximum user NE Phase 2 Predicted NE ≥ Minimum user NE, Phase 2 Predicted NE ≤ Maximum user NE Phase 3 Predicted NE ≥ Minimum user NE, Phase 3 Predicted NE ≤ Maximum user NE Phase 4 Predicted NE ≥ Minimum user NE, Phase 1 Predicted NE ≤ Maximum user NE Phase n Predicted NE ≥ Minimum user NE, Phase n Predicted NE ≤ Maximum user NE Ph1 NE ≥ 0, Ph2 NE ≥ 0, Ph3 NE ≥ 0, Ph4 NE ≥ 0, Phn NE ≥ 0 Phase 1 Predicted NE ≥ Minimum user NE, Phase 1 Predicted NE ≤ Maximum user NE Phase 2 Predicted NE ≥ Minimum user NE, Phase 2 Predicted NE ≤ Maximum user NE Phase 3 Predicted NE ≥ Minimum user NE, Phase 3 Predicted NE ≤ Maximum user NE Phase 4 Predicted NE ≥ Minimum user NE, Phase 4 Predicted NE ≤ Maximum user NE Phase n Predicted NE ≥ Minimum user NE, Phase n Predicted NE ≤ Maximum user NE Ph1 NE ≥ 0, Ph2 NE ≥ 0, Ph3 NE ≥ 0, Ph4 NE ≥ 0, Phn NE ≥ 0 Kansas State University Agricultural Experiment Station and Cooperative Extension Service Total feed cost per phase, $/pig Gain per phase, lb Feed cost per lb of gain, $/pig Total phase intake, lb/pig Feed and facility cost, $/pig Income per pig, $/pig Income over feed cost per phase, $/pig Income over feed and facility cost per phase, $/pig Calculation = Calculated ADG, g/Calculated G:F = (Targeted BW, lb – Initial BW, lb/2.2046)/ (Calculated ADG, g/1000) = (Phase duration, d × (Predicted daily intake, g/d/1000) × (Diet cost, $/ton /2000) × 2.2046) = Calculated ADG, g/1000 × Phase duration, d × 2.2046 = ((Total feed cost by phase, $/pig/ (Targeted BW, lb – Initial BW, lb))) = (Predicted daily intake, g/d/1000) × 2.2046 × Phase duration, d = Total feed cost, $/pig + (Phase duration, d × Facility cost, $/pig/d) = Gain per phase, lb × Live price, $/lb = Income per pig, $/pig – Total feed cost per phase, $/pig = Income per pig, $/pig – Feed and facility cost, $/pig Kansas State University Agricultural Experiment Station and Cooperative Extension Service 6 1,117 132.17 156.17 17.4 1,128 138.92 156.92 15.3 1,1404 146.11 158.11 13.1 1,149 154.40 160.40 11.0 1,159 163.80 163.80 8.8 1 Model calculated 5 equidistant NE levels by phase, keeping minimum, maximum, and currently used NE levels as defined by the user. 2 The feeding program had an inclusion of 20% dried distillers grains with solubles (DDGS) in all dietary phases. 3 Main ingredients pricing: corn $3.48/bu, soybean meal $290.60/ton, L-Lys $0.69/lb. 4 Current levels of NE defined by user. 5 Neutral detergent fiber defined by user for dietary phase 3 and greater. Kansas State University Agricultural Experiment Station and Cooperative Extension Service % ) ) ) .,f .0 .3 .8 .0 .8 .6 if (1 (2 (0 0 0 1 D . m 3 7 1 5 9 9 5 9 9 2 4 5 5 .80 ceo ,0 ,0 ,1 ,1 ,1 ,1 1 1 1 1 1 1 R t n 4 2 0 5 0 0 re 0 2 3 4 5 4 r 1 ,1 ,1 ,1 ,1 ,1 u , 1 1 1 1 1 1 C n 0 i y 5 r 1 a v h t i w KansasStateUniversityAgriculturalExperimentStationandCooperativeExtensionService w D se .s le ,3 sa se ed,12 . h a ifen issab .805 ceom ,0813 ,0917 ,1110 ,1119 ,1216 ,1159 itreayp trayph d g R d ie lllli)(tssrrr/veecceeaaygokuphbdwm iiiiifttssrrreeeccaaaaxgokpdnnnmm 09 .650 546,..iffttrrceeounRC%Dm ,.,)(8301014119 ,.,)(7901212123 ,.,)(0111310118 ,.,)(9111415123 ,.,)(6112510121 ,.,0114410100 lliiiii-fsssssttrreeeeaaaggxoobbpuddhhnnwwm lllllliiiiiifsssssttrrreeeaagoob02uddddhnnw% ..,.,ll-ssteeaa//yyoo960$b90$60bb2unnLLm .isstreeaayybpdh .llisstreeeeaayyvbpdh .llissstrrreeeeeeceeeeeeayggvxppdddnnnnm teeenn sceadn rreunC ,1104 ,1122 ,1130 ,1145 ,1150 ,1140 lliisstrreg liiscounn .r/438$n llreeeygv treeeygn rceodnm ed lb : : dd an co en dn tan edn lsou t/on ,l/b$ lb 57 125 175 210 250 852 li-read adhm :iircgn teedn iitezm rrecu comm itshw ,S$G ssrcaa ,BW to50 to75 to512 to017 to021 to025 eeabnm rragop istepn ifreedn :eopdd teeebnw lee6R. iragdn DD C -srcyoon ifeeegdhn iireagdnn :strreuun eceonmm iffreceen ab ire A T M C R D T d 0.65 Item Current4 Recom.5 Current Recom. Current Recom. ADG, lb 2.15 2.14 2.15 2.14 2.15 2.15 F/G 2.90 2.95 2.90 2.94 2.90 2.92 ADFI, lb 6.24 6.31 6.24 6.30 6.24 6.26 Carcass yield, % 73.4 73.2 73.4 73.7 73.4 74.0 1 A corn-soybean meal-dried distillers grains with solubles-based feeding program with six dietary phases were used for comparisons. 2 The feeding program had an inclusion of 20% dried distillers grains with solubles in all dietary phases. 3 Current: user defined net energy levels by dietary phase. 4 Recommended: optimized net energy levels by dietary phase. Recom. 108.0 673.5 56.50 68.38 177.71 121.22 110.81


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J. Soto, M. D. Tokach, S. S. Dritz, M. A. Goncalves, J. C. Woodworth, J. M. DeRouchey, R. D. Goodband, U. A. Orlando. Optimizing Dietary Net Energy for Maximum Profitability in Growing- Finishing Pigs, Kansas Agricultural Experiment Station Research Reports, 2017,