A Dynamic Tool to Describe Lamb Growth and Its Use as a Decision Support System
Abstract
:Simple Summary
Abstract
1. Introduction
2. Methods
2.1. Model Framework
2.2. Diet Composition
2.3. Simulation Procedure
- Input Data: The initial input data consisted of the animal’s initial body mass (kg), standard final mass (kg), age (days), sex (1 or 1.15), and diet metabolizable energy concentration (Mcal.kg−1).
- Nutritional Requirements: The model calculated the nutritional requirements for maintenance (Mcal EM·day−1) and estimated the dry matter intake (kg·day−1). The metabolizable energy intake above maintenance (dimensionless) was then calculated, given the metabolizable energy available for growth (Mcal EM·day−1).
- Energy Content of Gain: Based on the metabolizable energy available for growth and the degree of maturity, the energy content of gain (Mcal·day−1) was estimated as the sum of the retained energy from protein (Mcal·day−1) and the retained energy from fat (Mcal·day−1). The protein in gain (g·day−1) and the fat in gain (g·day−1) were then calculated based on the retained energy from protein and fat, respectively. These values were accumulated in the total body protein (kg) and total body fat (kg).
- Net Energy on Gain: Based on the proportion of energy retained as protein, the kG was calculated and multiplied by the metabolizable energy to gain, giving the net energy on gain.
- Average Daily Gain: The average daily gain was estimated using the net energy available for growth. This calculation considers the initial body mass and its respective maintenance energy requirement. This change was automatically computed to the animal body mass, closing the feedback loop, and dynamically influencing the intake capacity and nutritional requirements over time.
2.4. Model Evaluation as a Support Decision Tool
Reference | Treatments | Observations | SFM a (kg) | Genotype |
---|---|---|---|---|
[23] | 4 | 20 | 55 | Pelibuey |
[24] | 3 | 18 | 100 | Santa Inês |
[21] | 2 | 16 | 105 | Mutton Merino |
[25] | 2 | 14 | 80 | Awassi |
[26] | 3 | 54 | 81 | Mehbraban |
[27] | 6 | 36 | 35 | Taleshi |
[28] | 4 | 24 | 100 | Santa Inês |
[29] | 4 | 24 | 100 | Santa Inês |
[30] | 3 | 27 | 100 | Santa Inês |
[31] | 1 | 10 | 105 | Dorper × Katadhin |
[32] | 4 | 80 | 80 | Chall |
[33] | 4 | 24 | 100 | Santa Inês |
[34] | 5 | 45 | 100 | Santa Inês |
[35] | 3 | 72 | 119 | Hampshire |
[36] | 6 | 36 | 80 | Chall |
[37] | 4 | 24 | 81 | Mehbraban |
[38] | 4 | 40 | 75 | Pelibuey × Katahdin |
Item | Mean | Standard Deviation | Minimum | Maximum |
---|---|---|---|---|
Days on feed | 68.85 | 18.99 | 35.00 | 105.00 |
Dietary ME (Mcal·day−1 of DM) | 2.54 | 0.21 | 2.10 | 2.96 |
Initial body mass (kg) | 25.85 | 5.90 | 18.05 | 39.10 |
Final body mass (kg) | 39.94 | 8.87 | 29.15 | 61.70 |
Dry matter intake (kg·day−1) | 1.30 | 0.30 | 0.87 | 2.33 |
Average daily gain (kg·day−1) | 0.22 | 0.05 | 0.10 | 0.31 |
3. Results
Model Evaluation
- The mean bias (MB) was 0.656 kg, corresponding to 1.64% of the average final body mass of 39.94 kg;
- The mean square error of prediction (MSE) was 9.68;
- The root mean square error of prediction (RMSE) was 3.11 kg;
- The mean bias (UM), systematic bias (US), and random error (UC) values were 0.044, 0.024, and 0.93, respectively.
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
References
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Variable | Unit | Description |
---|---|---|
a | Average simulated value | |
a | ith observed or measured value | |
dimensionless | Standard deviation of the Y variable | |
a1 | NEM kg 0.75 | Maintenance requirement |
ADG | g·day−1 | Average daily gain |
AF | % | Proportion of empty body fat |
Age | Days | Current age |
AP | % | Proportion of empty body protein |
BCS | 1 to 5 | Body condition score |
BM | kg | Body mass |
CP | g·day−1 | Crude protein |
DBF(t) | kg | Dynamic body fat |
DBM(t) | kg | Dynamic body mass |
DBP(t) | kg | Dynamic body protein |
DMI | kg·day−1 | Dry matter intake |
EBM | kg | Empty body mass |
EVG | Mcal·day−1 | Energy content of gain |
kG | dimensionless | Partial efficiency of ME to NE for growth |
kM | dimensionless | Partial efficiency of ME to NE for maintenance |
L | dimensionless | Intake above maintenance |
MB | dimensionless | Mean bias |
ME | Mcal.kg−1 | Metabolizable energy |
MEG | Mcal·day−1 | ME available for growth |
MEI | Mcal·day−1 | ME intake |
MEM | Mcal·day−1 | ME requirement for maintenance |
MP | g·day−1 | Metabolizable protein |
MPM | g·day−1 | MP for maintenance |
MSE | a | Mean square error of prediction |
NEG | Mcal·day−1 | Net energy available for growth |
P | dimensionless | Degree of maturity |
r | dimensionless | Pearson correlation coefficient. |
R2 | dimensionless | Coefficient of determination |
REFat | Mcal·day−1 | Retained energy from fat |
REP | dimensionless | Proportion of retained energy as protein |
REprot | Mcal·day−1 | Retained energy from protein |
RMSE | % | Root mean square error of prediction |
S | dimensionless | Factor. 1 to female and castrated male and 1.15 to intact males |
SBM | kg | Shrunk body mass |
SBM0.75 | kg | Metabolic SBM |
SFM | kg | Standard final mass |
t0 | Days | Initial time |
TE | Mcal | Total energy |
TF | kg | Total body fat |
ti | Days | Time in i moment |
TP | kg | Total body protein |
UC | dimensionless | MSE decomposition into random error |
UM | dimensionless | MSE decomposition into mean bias |
US | dimensionless | MSE decomposition into systematic bias |
unit | Sample size |
Variable | Equation | Equation Number |
---|---|---|
ADG | (1) | |
AF | (2) | |
AP | (3) | |
DMI a | (4) | |
DBM(t) b | (5) | |
DBF(t) b | (6) | |
DBP(t) b | (7) | |
EBM | (8) | |
EVG | (9) | |
kG | (10) | |
MEM c | (11) | |
MPM | (12) | |
NEG | (13) | |
REFat | (14) | |
REP | (15) | |
REProt | ( | (16) |
SBM | (17) | |
TE | (18) | |
TF | (19) | |
TP | (20) |
Item/Ingredient | Low-Energy Diet (%) | High-Energy Diet (%) |
---|---|---|
Wheat Bran | 1.50 | 0.00 |
Maize Meal | 30.00 | 38.00 |
Sunflower Oilcake | 1.64 | 4.46 |
Groundnut Oilcake | 3.33 | 0.00 |
Limestone | 0.07 | 0.58 |
Urea | 0.70 | 0.59 |
Maize Germ | 0.00 | 9.31 |
Supermax Premix | 0.00 | 10.45 |
Vitamin and Minerals Premix | 0.21 | 0.21 |
Monocalcium Phosphate | 0.00 | 0.11 |
Salt | 0.50 | 0.50 |
Citrus Ruman Flavor | 0.02 | 0.02 |
Ammonium Chloride | 0.75 | 0.75 |
Taurotec | 0.03 | 0.03 |
NaOH Wheat Straw | 21.26 | 15.00 |
Lucerne Hay | 35.00 | 15.00 |
Molasses Cuber | 5.00 | 5.00 |
Chemical Composition | Content | Content |
Ash (%) | 9.49 | 8.44 |
Crude Fat (%) | 2.18 | 5.59 |
Crude Protein (%) | 14.29 | 14.56 |
Acid Detergent Fiber (%) | 24.74 | 17.78 |
Neutral Detergent Fiber (%) | 44.17 | 35.92 |
Gross Energy (Mcal kg−1) | 3.82 | 4.00 |
Metabolizable Energy (Mcal kg−1) | 2.36 | 2.89 |
Calcium (%) | 0.44 | 0.74 |
Phosphorus (%) | 0.27 | 0.36 |
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Amaral, R.M.; Rodrigues, M.T.; Schultz, E.B.; Reis, C.E.R. A Dynamic Tool to Describe Lamb Growth and Its Use as a Decision Support System. Animals 2024, 14, 2246. https://doi.org/10.3390/ani14152246
Amaral RM, Rodrigues MT, Schultz EB, Reis CER. A Dynamic Tool to Describe Lamb Growth and Its Use as a Decision Support System. Animals. 2024; 14(15):2246. https://doi.org/10.3390/ani14152246
Chicago/Turabian StyleAmaral, Rafael Marzall, Marcelo Teixeira Rodrigues, Erica Beatriz Schultz, and Cristiano Eduardo Rodrigues Reis. 2024. "A Dynamic Tool to Describe Lamb Growth and Its Use as a Decision Support System" Animals 14, no. 15: 2246. https://doi.org/10.3390/ani14152246