Nowadays, the climate is undoubtedly one of the main threats to the sustainability of olive
orchards, especially in the case of rainfed traditional production systems. Local warming, droughts,
and extreme weather events are some of the climatological factors responsible for environmental
thresholds in relation to crops being exceeded. The main objective of this study was to investigate the
influence of microclimatic variability on the productivity of traditional olive orchards in a municipality
located in northeastern Portugal. For this purpose, official data on climate, expressed through agrobioclimatic
indicators, and olive productivity for a 21-year historical period (2000–2020) were used
to evaluate potential correlations. In addition, a comprehensive regression analysis involving the
dataset and the following modeling scenarios was carried out to develop regression models and
assess the resulting predictions: (a) Random Forest (RF) with selected features; (b) Ordinary Least-
Squares (OLS) with selected features; (c) OLS with correlation features; and (d) OLS with all features.
For the a and b scenarios, features were selected applying the Recursive Feature Elimination with
Cross-Validation (RFECV) technique. The best statistical performance was achieved considering
nonlinearity among variables (a scenario, R2 = 0.95); however, it was not possible to derive any
model given the underlying methodology to this scenario. In linear regression applications, the best
fit between model predictions and the real olive productivity was obtained when all the analyzed
agro-bioclimatic indicators were included in the regression (d scenario, R2 = 0.85). When selecting
only the most relevant indicators using RFECV and correlation techniques, moderate correlations
for the b and c regression scenarios were obtained (R2 of 0.54 and 0.49, respectively). Based on
the research findings, especially the regression models, their adaptability to other olive territories
with similar agronomic and environmental characteristics is suggested for crop management and
regulatory purposes.
The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for
financial support through national funds FCT/MCTES (PIDDAC) to CIMO (UIDB/00690/2020 and
UIDP/00690/2020) and SusTEC (LA/P/0007/2020). This work was carried out under the Project
“OleaChain: Competências para a sustentabilidade e inovação da cadeia de valor do olival tradicional
no Norte Interior de Portugal” (NORTE-06-3559-FSE-000188), an operation to hire highly qualified
human resources, funded by NORTE 2020 through the European Social Fund (ESF).
