Sure, you can read the following article; you might find something helpful in it as it is related to the research you want to work on.

https://www.researchgate.net/publication/375733912_Forecasting_Sales_of_Iraqi_Dates_Using_Artificial_Intelligence

To use Decision Tree, Gradient Boosting, and XGBoost in a regression model for predicting agricultural CO2 emissions, consider the following:

1. Decision Tree:

- Pros: Interpretable, handles non-linear relationships

- Cons: Prone to overfitting

- Factors: Tree depth, min samples per leaf, max features

2. Gradient Boosting:

- Pros: High performance, handles diverse data types

- Cons: Sensitive to outliers, computationally intensive

- Factors: Number of estimators, learning rate, max depth

3. XGBoost:

- Pros: Fast, optimized for performance

- Cons: Complex hyperparameter tuning

- Factors: n_estimators, max_depth, learning_rate, subsample

Key considerations:

- Feature selection and engineering

- Cross-validation for model evaluation

- Hyperparameter tuning

- Handling missing data and outliers

- Balancing model complexity and interpretability

Recommended resources:

1. "The Elements of Statistical Learning" by Hastie, Tibshirani, and Friedman

2. "Applied Predictive Modeling" by Kuhn and Johnson

3. Research paper: "Machine learning applications in agricultural and forest phenology" by Morellato et al. (2016)

4. Case study: "Predicting Agricultural Emissions of Nitrous Oxide Using Machine Learning Algorithms" by Shahhosseini et al. (2021)

These resources should provide a solid foundation for your research project.