Percorrer por autor "Monteiro, Gabriela Oliveira de Aquino"
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- Interpreting Machine Learning Models with SHAP Values: Application to Crude Protein Prediction in Tamani Grass PasturesPublication . Monteiro, Gabriela Oliveira de Aquino; Difante, Gelson dos Santos; Montagner, Denise Baptaglin; Euclides, Valéria Pacheco Batista; Castro, Marina; Rodrigues, Jéssica Gomes; Pereira, Marislayne de Gusmão; Ítavo, Luís Carlos Vinhas; Campos, Jecelen Adriane; Costa, Anderson Bessa da; Matsubara, Edson TakashiMachine learning models such as XGBoost show strong potential for predicting pasture quality metrics like crude protein (CP) content in tamani grass (Panicum maximum). However, their 'black box' nature hinders practical adoption. To address this limitation, this study applied SHapley Additive exPlanations (SHAP) to interpret an XGBoost model and uncover how management practices (grazing interval, nitrogen fertilization, and pre- and post-grazing heights) and environmental factors (precipitation, temperature, and solar radiation) jointly influence CP predictions. Data were divided into 80% for training/validation and 20% for testing. Model performance was assessed with stratified 5-fold cross-validation, and hyperparameters were tuned via grid search. The XGBoost model yielded a Pearson correlation coefficient (r) of 0.78, a mean absolute error (MAE) of 1.45, and a coefficient of determination (R2) of 0.57. The results showed that precipitation in the range of 100-180 mm increased the predicted CP content. Application of 240 kg N ha-1 year-1 positively affected predicted CP, whereas a lower dose of 80 kg N ha-1 year-1 had a negative impact, reducing predicted levels of CP. These findings highlight the importance of integrated management strategies that combine grazing height, nitrogen fertilization, and grazing intervals to optimize crude protein levels in tamani grass pastures.
- Machine learning models for crude protein prediction in Tamani grass pasturesPublication . Monteiro, Gabriela Oliveira de Aquino; Difante, Gelson dos Santos; Montagner, Denise Baptaglin; Euclides, Valéria Pacheco Batista; Castro, Marina; Rodrigues, Jéssica Gomes; Pereira, Marislayne de Gusmão; Santana, Juliana Caroline Santos; Itavo, Luis Carlos Vinhas; Nantes, Rafael Torres; Campos, Jecelen Adriane; Costa, Anderson Bessa da; Matsubara, Edson TakashiUnderstanding forage quality is essential for meeting animal demands and optimizing production. This study aimed to: (i) test the applicability of machine learning models with tabular data such as climate variables, light interception (LI), nitrogen dose (N dose), interval between grazing (GI), and pre- (HPRE) and post-grazing height (HPOST) to predict leaf crude protein (CP) content of tamani grass pastures; (ii) identify which variables contribute most to CP prediction. A set of 90 instances was used with 80% for training and validation and 20% for testing. The hyperparameters were adjusted with grid-search on the training set. We tested Linear Regression (LR), Multilayer Perceptron (MLP), Decision Trees(DT), Random Forest (RF), and XGBoost. The MLP (r=0.75, R2 =44.18%, MAE=1.55), RF (r=0.78, R2 =49.07%, MAE=1.59) and XGBoost (r=0.78, R2 =56.65% MAE=1.45) models presented the best prediction results (p<0.001). The variables most important in predicting CP content were GI, followed by N dose, HPRE and HPOST. XGBoost outperformed other tested models (p<0.001). Tabular data, including N dose, GI, HPRE, HPOST, LI, and climatic variables, is a viable alternative for predicting CP. In conclusion, the results of this study suggest that management practices may have a greater influence on the chemical composition of Tamani grass than environmental conditions, although further research with larger and more diverse datasets is needed to confirm these findings.