Vítor João Pereira Martinho
0000-0001-9967-7940 • F510-903F-51FA • vdmartinho@esav.ipv.pt
Martinho VJPD (2024). Important Indicators for Predicting Crop Protection Costs. In Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector. Chapter 1, pp. 75-85. Springer Nature Switzerland.
Martinho VJPD (2024). Machine Learning Methodologies, Wages Paid and the Most Relevant Predictors. In Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector. Chapter 1, pp. 99-110. Springer Nature Switzerland.
Martinho VJPD (2024). Predicting the Total Costs of Production Factors on Farms in the European Union. In Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector. Chapter 1, pp. 49-60. Springer Nature Switzerland.
Martinho VJPD (2024). Predictive Artificial Intelligence Approaches of Labour Use in the Farming Sector. In Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector. Chapter 1, pp. 125-135. Springer Nature Switzerland.
Martinho VJPD (2024). Predictive Machine Learning Approaches to Agricultural Output. In Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector. Chapter 1, pp. 1-17. Springer Nature Switzerland.
Martinho VJPD (2024). Predictive Machine Learning Models for Livestock Output. In Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector. Chapter 1, pp. 35-47. Springer Nature Switzerland.
Martinho VJPD (2024). Predictors of Interest Paid in the European Union’s Agricultural Sector. In Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector. Chapter 1, pp. 111-123. Springer Nature Switzerland.
Martinho VJPD (2024). The Most Adjusted Predictive Models for Energy Costs. In Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector. Chapter 1, pp. 87-97. Springer Nature Switzerland.
Martinho VJPD (2024). The Most Important Predictors of Fertiliser Costs. In Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector. Chapter 1, pp. 61-73. Springer Nature Switzerland.
Martinho VJPD. (2023) Fertiliser cost prediction in European Union farms: Machine-learning approaches through artificial neural networks. Open Agriculture, 8, 1–13.
