Swine-production simulation model to predict body weight gain.*

Transcription

1 Swine-production simulation model to predict body weight gain.* R. Cañas 1, 2, C. León-Velarde 2, R. Quiroz 2, J. Guerrero 2, J. Osorio 2, E. Murillo 2, D. Pezo 3 1. Biotecnología Agropecuaria (BTA. S.A.), Santiago, Chile (rcanas@bta.cl). 2. International Potato Center (CIP), Lima, Perú. 3. International Livestock Research Institute (ILRI), Addis Ababa, Ethiopia. * Partially supported by the Virtual Lab. Project. Abstract A swine-production simulation model was developed, using as exogenous variables animal genetic potential, feed characteristics and environmental conditions inside the pen. The model is specifically applicable to: a) animals with a body weight ranging from 15 to 120 kg); b) a group of females and castrated males; c) cases where feed is characterized in terms of metabolizable energy (ME kg -1 ), dry matter (%), crude fiber (%), lysine (%), methionine+cystine (%), threonine (%), tryptophan (%); and d) animals that are confined. With regard to the model s endogenous variables, food intake is determined by animal weight, pen temperature, and the fiber, energy and dry matter content of the diet. Using the exogenous variables, the model determines the animal s requirements and balances this against total nutrient intake. Diet protein quality was estimated by comparing the amino acid availability in the diet to the amino acid content in muscle protein. The model was validated with data from commercial operations in Chile, Peru and the Philippines, and from experimental trials, which included animals with different genetic growth potentials, ranging from very low (70 g protein-deposit day -1 ) to very high (150 g protein-deposit day -1 ). The model s predictions were in close agreement with experimental data; the error was less than 10%. This tool is very useful in identifying the most profitable options from among different feeding strategies used in different swine production systems. The most sensitive variable was the genetic potential growth of the swine. For each bio-economic scenario defined by the user, a response surface was developed using a central composite rotatable design. The flexibility and the user-friendliness of the software make it a good tool to help in identifying research gaps, in making appropriate management decisions, in facilitating extension work and in conducting training in animal production. Keywords: Swine model, swine simulation, pig model, swine production systems. Introduction State-of-the-art knowledge in swine science and technology can be systematically synthesized in a swine-production simulation model, which can be used to predict, with adequate levels of precision, an animal s performance under different environmental conditions and feeding regimes. Such a model is a tool that is useful for quickly identifying profitable options from amongst the different feeding strategies used in different production systems. Methodology The exogenous variables used in this model are 1. Animal characteristics, in terms of the average animal weight and genetic potential.

2 2. Environmental conditions, in terms of the pen s size (m 2 ), temperature, floor characteristics, isolation to wind, and the number of animals per pen; 3. Feed characteristics, in terms of the following contents: dry matter (%), metabolizable energy (Mcal ME kg -1 ), crude fiber (%), and available amino acids i.e. lysine (%), methionine+cystine (%), threonine (%) and tryptophan (%). The model s endogenous variables are: Real food intake (RFi) The potential feed intake (PFI, in kg), is estimated using the following equation: PFI = (( *(1-EXP( *PT)*0.96)/ME)*FCMS)-FCRTCMS where: PT = Animal protein mass (kg); see Whittemore (1986) ME = Metabolizable energy per kg of food FCMS = Diet dry matter correction factor, estimated by the equation *Diet % dry matter FCRTCMS = 0.001*Animal weight (kg)*(eet-mct) where: EET = Effective environmental temperature ( o C) and MCT = Maxima critical temperature ( o C), both estimated using the equations of Whittemore (1986). The RFI is estimated multiplying the PFI by two correction factors: (1) Diet crude fiber content (CF, in %), estimated by the equation *CF (2) Animal space in pen, estimated by the equation *Animal weight (kg)*area of the pen (m 2 ) Once the RFI (kg) is calculated, each nutrient intake is estimated multiplying the RFI for the feed characteristics as endogenous variable. Potential protein weight gain (PPWG) The potential protein weight gain is a function of the animal s genetic potential for weight gain (POTGEN) and the protein quality of the diet (PQ). It is estimated as: PPWG (kg day -1 ) = (POTGEN*1000)*(1/CRPRT) where POTGEN is the amount of potential protein that the animal can deposit depending on its genetic characteristics or quality (Table 1). Table 1. Potential protein deposit (g day -1 ) depending on genetic quality. Genetic quality Potential protein depot (g day -1 ) Very low 70 Low 90 Medium 110 High 130 Very High 150 Genetic quality is defined as ranging from very low (equivalent to wild boar, with a potential protein deposit of 70 g day -1 ) to very high (which corresponds with the

3 genetic quality of a commercial breed available from various companies in the year 2001). The protein quality (PQ) of the diet is estimated by comparing the actual intake of each amino acid with the amino acid content of the protein deposit, which is assumed to be constant and independent of animal genetic quality (Table 2). Table 2. Amino acid content of animal s protein deposit. Amino acids Content in protein depot (%) Lysine 7.8 Metheonine + cystine 3.8 Threonine 5.1 Tryptophan 1.4 Once the RFI of each nutrient and the PPWG have been calculated, the model balances nutrient intake against nutrient expenses to determine the quantity of nutrients available for deposit. Once the energy covers the ecological maintenance requirements (EMR), and protein depot, the surplus is used for fat deposition (PFD), in accordance with the animal s genetic characteristics. Energy expenditure (EE) Energy expenditure, expressed as metabolizable energy (ME, in Mcal day -1 ), is estimated for different physiological processes using the equations below. Maintenance requirements (EMM) = *(0.17*BW)^0.74)*0.95 (Pomar et al., 1991) where BW= Body weight (kg). Temperature regulation (TR) = *BW^0.75*(Tc-Te) (Whittemore, 1986) where: BW = Body weight (kg) Tc = Minimal critical temperature ( o C), estimated as 27-(0.6*PC) where PC = Heat production (Mcal day -1 ), estimated by: EMM+(7.41*PPWG )+(3.35*PFD) Te = Effective temperature, estimated by T*Ve*Vi where: T = Pen temperature ( o C) Ve = Wind velocity factor; this depends on the exposure of the pen its value ranges from 0.6 (outdoor conditions) to 1.0 (completely indoors/enclosed) Vi = Pen-floor-characteristics factor; this depends on the material of the floor its value ranges from 0.7 (bare soil) to 1.4 (straw). The harvesting cost (HC) is the amount of ME the animal expends in obtaining its food. Under ad libitum and confined conditions, this value is a constant, and is equivalent to 10% of EMM (Cañas et al., 2003). Ecological Maintenance Requirements (EMR) = EMM+HR+HC

4 The energy costs (in Mcal day -1 ) assumed for deamination, protein deposition and fat deposition are listed below. Deamination cost (MEDEAM) = Mcal per kg of deaminated protein. Energetic cost of protein depot (MEPD) = Mcal per kg of protein depot. Energetic cost of fat depot (MEFORFD) = Mcal per kg of fat depot. Energy available for fat deposition (MEASFAT) is calculated as follows: MEASFAT = (RFI*ME)-EMM-MEDEAM-MEPD-MEFORFD Fat deposition (FD)(kg day -1 ) = MEASFAT* Protein deposition Protein deposition is the balance between PPWG and protein available for production (PAP). PAP (g day -1 ) = (PI*PQ)-PMR where: PI (kg day -1 ) = Daily protein intake PQ (%) = Protein quality PMR (kg day -1 ) = ENDPROT+MFPROT+SURFPROT where: ENDPROT (kg day -1 ) = Endogenous protein, estimated by the equation 0.146*BW^0.75*6.25*PQ MFPROT (kg day -1 ) = 68*CONMS*(1-DIGCONS)/PQ where: CONMS = Dry matter intake (kg) DIGCONS = Food intake digestibility SURFPROT (kg day -1 ) = (0.1125*BW^0.75)/PQ. Lean deposition Lean deposition (LPD) is the amount of lean weight gain obtained by protein deposition, which depends on animal weight, and is estimated by the equation: LPD (kg day -1 ) = ( *LN(BW))/100 Daily weight gain Daily weight gain (g day -1 ) = LPD + FD Model restrictions The conditions for which the model should be used are restricted to : animals must be confined with a body weights ranging from 15 to 120 kg and in a group of female and castrated male. In addition, feed must be characterized in terms of the following contents: dry matter (%), metabolizable energy (ME kg -1 ), crude fiber (%), and available amino acid as lysine (%), methionine+cystine (%), threonine (%), tryptophan (%).

5 Validation The model was validated using data from 17 different experimental treatments, which involved different diets (in terms of DM, ME, CF and lysine), animal weights, animal genetic potentials and environmental conditions. Using the results of Table 3, the factorcorrected model (FCM) was determined using the methodology of Cañas and Baldwin (1973). The formula used was: FCM = [SQR{(SUM Y 2 SUM X 2 )+(SUM Y * SUM X)/N}/(N-1)] / AVRG(Y) Where: X = observed daily weight gain, Y = simulated daily weight gain SQR = square root of; AVRG = average). Model variation was calculated as +/- 4.21%, using the formula Model variation (%) = 100 *(FCM-1.0). Table 3. Real and simulated daily weight gain a of animals in different experimental treatments (kg day -1 ). Weight gain (kg day -1 ) Treatment Observed Simulated a Each observed weight gain is an average calculated from measurements of at least 8 animals. Sensitivity analysis Results of a sensitivity analysis showed that the most sensitive model parameter is the animal s genetic quality, in terms of potential growth. For this reason then, in order to use the model, it is vital that the genetically determined potential growth of the animals is known.

6 Bibliography. Cañas C., R.; and L.R. Baldwin The lactational efficiency complex in rats Ph D thesis. Graduate Division of University of California. Davis. Cañas C., R.; R.A. Quiroz; C. León Velarde; A. Posadas Quantifying energy dissipation in grazing animals. Determination of energy harvesting cost. IX World Conference in animal production. Brasil- October. Edmond, M.S., B.E. Arenston and G.A. Mente Effect of protein levels and space allocation on performance of growing-finishing pigs. Journal of Animal Science 76: Pomar, C., L.H. Dewey and F. Minvielle Computer simulation model of swine production system: l. modeling the growth of young pigs. Journal of Animal Science 69: Robles, C.A. and C. Aguilar Modelo de simulación de predicción del desempeño productivo y evaluación de alternativas de manejo en cerdos en etapa de crianza y engorda. Tesis de Magister. Grupo de Sistemas. Universidad Católica de Chile. Whittemore, C.T An approach to pig growth modeling. Journal of Animal Science 63: