Towards a Vectorial Approach to Predict Beef Farm Performance
Abstract
:1. Introduction
2. Materials and Methods
2.1. Aim and Scope
2.2. The Dataset
2.3. Standard vs. Vectorial Approaches: Genetic Programming
2.4. Standard vs. Vectorial Approaches: Experimental Settings
3. Results
3.1. ST-GP vs. VE-GP
3.2. Comparisons of ST-GP and VE-GP with Other ML Methods
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
PLF | Precision Livestock Farming |
ML | Machine Learning |
ANABORAPI | National Association of Piemontese Cattle Breeders |
GP | Genetic Programming |
ST-GP | Standard Genetic Programming |
VE-GP | Vectorial Genetic Programming |
EA | Evolutionary Algorithm |
KNN | k-Nearest Neighbors |
NN | Neural Network |
LM | Linear Model |
GLMNET | Generalized Linear Model with Elastic Net Regularization |
RNN | Recurrent Neural Network |
LSTM | Long Short-Term Memory |
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FARM | YEAR | PRIMIPAROUS | PLURIPAROUS | HEIFERS | INTERPARTO |
---|---|---|---|---|---|
Farm 1 | 2014 | 22 | 36 | 7 | 365 |
Farm 1 | 2015 | 10 | 46 | 13 | 375 |
Farm 1 | 2016 | 16 | 47 | 12 | 381 |
Farm 1 | 2017 | 14 | 46 | 11 | 375 |
Farm 1 | 2018 | 16 | 47 | 12 | 374 |
Farm 2 | 2014 | 11 | 90 | 9 | 396 |
Farm 2 | 2015 | 10 | 93 | 9 | 391 |
Farm 2 | 2016 | 9 | 95 | 7 | 380 |
Farm 2 | 2017 | 7 | 97 | 10 | 387 |
Farm 2 | 2018 | 9 | 92 | 11 | 385 |
Farm 3 | 2014 | 7 | 42 | 3 | 414 |
Farm 3 | 2015 | 4 | 43 | 4 | 439 |
Farm 3 | 2016 | 4 | 44 | 10 | 452 |
Farm 3 | 2017 | 10 | 44 | 11 | 425 |
Farm 3 | 2018 | 9 | 60 | 4 | 473 |
Variable Name | Variable Description | |
---|---|---|
1 | Consistency for cows, i.e., number of cows | |
2 | Consistency for heifers, i.e., number of heifers | |
3 | Calving interval in days, based on currently pregnant cows | |
4 | Average parity | |
5 | __1 | Age at first calving |
6 | N. of cows that delivered with easy calving | |
7 | N. of primiparous that delivered with easy calving | |
8 | _ | Calving ease (EBV for cows) |
9 | _ | Birth ease (EBV for heifers) |
10 | Birth ease (EBV for A.I. bulls) | |
11 | Calving ease (EBV for A.I. bulls) | |
12 | UBA referred to bovines 6 months–2 years old | |
13 | UBA referred to bovines 4–6 months old | |
14 | N. of dead calves in the first 60 days after birth | |
15 | Total number of calves born | |
16 | Total number of calves born alive | |
17 | Percentage of calves born without defects (e.g., Macroglossia, Arthrogryposis) | |
18 | _ | Consanguinity calculated on future calves |
19 | Y | N. of weaned calves per cow per year (2) |
2017 | 2018 | ||||||
---|---|---|---|---|---|---|---|
COWS | COW_AGE | CALVING_INT | N_CALVING | ||||
FARM 1- | 104 | 3020 | 387 | 60 | 0.95 | ||
FARM 2- | 54 | 3112 | 425 | 54 | 0.9 | ||
FARM 3- | 63 | 2824 | 515 | 48 | 0.69 | ||
… | 49 | 3131 | 466 | 49 | 0.67 | ||
108 | 2766 | 407 | 50 | 0.85 | |||
74 | 3448 | 459 | 62 | 0.84 |
2014–2017 | 2018 | |||||
---|---|---|---|---|---|---|
COWS | COW_AGE | CALVING_INT | ||||
FARM 1- | [98, 101, 107, 104] | [2999, 3001, 2998, 3020] | [391, 391, 380, 387] | 0.95 | ||
FARM 2- | [61, 49, 53, 54] | [3076, 3002, 3056, 3112] | [408, 376, 402, 425] | 0.9 | ||
FARM 3- | [53, 55, 64, 63] | [2799, 2813, 2802, 2824] | [367, 376, 406, 515] | 0.69 | ||
… | [31, 36, 47, 49] | [3102, 3075, 3009, 3131] | [434, 480, 461, 466] | 0.67 | ||
[102, 99, 105, 108] | [2704, 2795, 2789, 2766] | [404, 371, 395, 407] | 0.85 | |||
[69, 71, 75, 74] | [3401, 3388, 3406, 3448] | [387, 367, 373, 459] | 0.84 |
Parameter | Description |
---|---|
ST-GP | |
Maximum number of generations | 40 |
Population size | 250 |
Selection Method | Lexicographic Parsimony Pressure |
Elitism | Keepbest |
Initialization Method | Ramped half and half |
Tournament Size | 2 |
Subtree Crossover Rate | 0.7 |
Subtree Mutation Rate | 0.1 |
Subtree Shrinkmutation Rate | 0.1 |
Subtree Swapmutation Rate | 0.1 |
Maxtreedepth | 17 |
VE-GP | |
Maximum number of generations | 40 |
Population size | 250 |
Selection Method | Lexicographic Parsimony Pressure |
Elitism | Keepbest |
Initialization Method | Ramped half and half |
Tournament Size | 2 |
Subtree Crossover Rate | 0.7 |
Subtree Mutation Rate | 0.3 |
Mutation of aggregate function parameters | 0.2 |
Maxtreedepth | 17 |
ML Technique | Parameters |
---|---|
knn | k = 15 |
nnet | size = 7; decay = 0.2 |
glmnet | = 0.8, = 0.85 |
LSTM | hidden units = 200; epochs = 50; batchsize = 1; learning algorithm = adam. |
Variable | % of Use (ST-GP) | % of Use (VE-GP) |
---|---|---|
X1 | 70% | 100% |
X2 | 10% | 10% |
X3 | 0% | 10% |
X4 | 50% | 0% |
X5 __1 | 0% | 10% |
X6 | 0% | 10% |
X7 | 0% | 10% |
X8 _ | 0% | 0% |
X9 _ | 0% | 0% |
X10 | 10% | 0% |
X11 | 0% | 0% |
X12 | 0% | 0% |
X13 | 20% | 0% |
X14 | 70% | 40% |
X15 | 0% | 80% |
X16 | 60% | 0% |
X17 | 30% | 0% |
X18 _ | 20% | 30% |
Prediction Model | Fitness on Test | N. of Variables | % of Variables |
---|---|---|---|
ST-GP | |||
model 1 | 0.1335 | 9 | 50% |
model 2 | 0.1207 | 6 | 33% |
model 3 | 0.1143 | 11 | 61% |
model 4 | 0.1383 | 8 | 44% |
model 5 | 0.1392 | 7 | 39% |
model 6 | 0.1439 | 7 | 39% |
model 7 | 0.1395 | 8 | 44% |
model 8 | 0.1370 | 6 | 33% |
model 9 | 0.1285 | 15 | 83% |
model 10 | 0.1184 | 7 | 39% |
VE-GP | |||
model 1 | 0.1117 | 5 | 26% |
model 2 | 0.1016 | 3 | 16% |
model 3 | 0.1044 | 9 | 47% |
model 4 | 0.1085 | 8 | 42% |
model 5 | 0.1134 | 3 | 16% |
model 6 | 0.0998 | 8 | 42% |
model 7 | 0.1018 | 4 | 21% |
model 8 | 0.1149 | 4 | 21% |
model 9 | 0.0999 | 8 | 42% |
model 10 | 0.1121 | 3 | 16% |
STGP | KNN | NN | VEGP | GLMNET | LSTM | |
---|---|---|---|---|---|---|
Learning sets | ||||||
Median | 0.1238 | 0.1074 | 0.0967 | 0.1052 | 0.1025 | 0.1011 |
Mean | 0.1220 | 0.1077 | 0.0967 | 0.1054 | 0.1025 | 0.0988 |
Test sets | ||||||
Median | 0.1353 | 0.1151 | 0.1122 | 0.1065 | 0.1057 | 0.1037 |
Mean | 0.1314 | 0.1147 | 0.1128 | 0.1068 | 0.1056 | 0.1034 |
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Abbona, F.; Vanneschi, L.; Giacobini, M. Towards a Vectorial Approach to Predict Beef Farm Performance. Appl. Sci. 2022, 12, 1137. https://doi.org/10.3390/app12031137
Abbona F, Vanneschi L, Giacobini M. Towards a Vectorial Approach to Predict Beef Farm Performance. Applied Sciences. 2022; 12(3):1137. https://doi.org/10.3390/app12031137
Chicago/Turabian StyleAbbona, Francesca, Leonardo Vanneschi, and Mario Giacobini. 2022. "Towards a Vectorial Approach to Predict Beef Farm Performance" Applied Sciences 12, no. 3: 1137. https://doi.org/10.3390/app12031137