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Revista de Ciências Agrárias

versão impressa ISSN 0871-018X

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FREITAS, Teresa R.; SANTOS, João A.; SILVA, Ana P.  e  FRAGA, Hélder. Regional model for predicting almond production in the Trás-os-Montes agrarian region. Rev. de Ciências Agrárias [online]. 2023, vol.46, n.2, pp.11-20.  Epub 01-Jun-2023. ISSN 0871-018X.  https://doi.org/10.19084/rca.31320.

The almond tree (Prunus dulcis) is a species with high economic and social importance in Portugal. In 2020, mainland Portugal produced approximately 32 × 103 t of almonds, for an area of 52340 ha. Currently, the country presents favourable climatic conditions for the development of the species. However, in the future, the effects of climate change may alter the weather patterns and consequently affect the species' productivity. In the present study, a regional model was developed to predict the almond tree productivity, for the Trás-os-Montes. This region, situated in the northeastern of Portugal, is characterized by traditional agricultural practices and rainfed crops. The stepwise method was applied, with multiple regression models. As input variables were used: precipitation, mean temperature, radiation, thermal amplitude, relative humidity and production of the previous year. In the case of climate variables, the historical database - E-OBS (1986-2020) was used, in the future the model - CNRM-CERFACS-CNRM-CM5, RCP4.5 (2021-2080) was used. The values were calculated for the bi-weekly average, from January to May. As a result, the model (R2 = 0.75) highlighted the most representative variables: precipitation in the 2nd fortnight of May, the radiation of the 1st fortnight of January, the radiation of the 1st fortnight of May and the productivity of the previous year. When the model is applied to future data, it indicates that a sharp variation in productivity will occur. This model is a viable tool for production forecasting. To complement the study, the development of new methodologies is suggested.

Palavras-chave : almond tree; stepwise method; precipitation; radiation; future scenarios; historic.

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