Vítor Martinho
Vítor João Pereira Martinho 0000-0001-9967-7940 • F510-903F-51FA • vdmartinho@esav.ipv.pt
Vítor João Pereira Martinho
0000-0001-9967-7940 • F510-903F-51FA • vdmartinho@esav.ipv.pt
Vítor João Pereira Martinho 0000-0001-9967-7940 • F510-903F-51FA • vdmartinho@esav.ipv.pt
Martinho VJPD (2025) Five models and ten predictors for energy costs on farms in the European Union. Open Agriculture, 10(1): 20250441.
Martinho VJPD, Brózdowski J (2025) Suggestions for resin research under the COST Action EU-PoTaRCh. Open Res Europe, 4:272. https://doi.org/10.12688/openreseurope.18988.2
Martinho VJPD (2025) Economic growth in the Sub-Saharan African world region: convergence theory. Competitiveness Review, 35(1): 137–157. https://doi.org/10.1108/CR-11-2023-0270
Martinho VJPD (2024) Economic growth in the Sub-Saharan African world region: convergence theory. Competitiveness Review, 11: 0270. https://doi.org/10.1108/CR-11-2023-0270
Martinho VJPD, Ferreira AJD, Cunha C, Pereira JLS, Sánchez-Carreira MC, Castanheira NL, Ramos TB. (2024) Soil legislation and policies: Bibliometric analysis, systematic review and quantitative approaches with an emphasis on the specific cases of the European Union and Portugal. Heliyon, 10: e34307. https://doi.org/10.1016/j.heliyon.2024.e34307
Martinho VJPD, Rodrigues RN (2024) Bioenergy relations with agriculture, forestry and other land uses: Highlighting the specific contributions of artificial intelligence and co-citation networks. Heliyon, 10: e26267. http://dx.doi.org/10.1016/j.heliyon.2024.e26267
Martinho VJPD, Rodrigues RN (2024) Bioenergy relations with agriculture, forestry and other land uses: Highlighting the specific contributions of artificial intelligence and co-citation networks. Heliyon, 10: e26267. http://dx.doi.org/10.1016/j.heliyon.2024.e26267
Tenreiro RN, Martinho VJPD (2024) Tourism in rural areas as a broader concept: Some insights from the Portuguese reality. Open Agriculture, 9: 20220345. https://doi.org/10.1515/opag-2022-0345
Martinho VJPD (2024). Applying Artificial Intelligence to Predict Crop Output. In Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector. Chapter 1, pp. 19-33. Springer Nature Switzerland.