The Mediterranean basin is characterized by hot and dry summers, which are aggravated by climate change, leading to water shortages for irrigation purposes. Olive trees (Olea europea L.) which are the most common cultivation in the area, while tolerant to drought conditions, are often irrigated due to the fact that they suffer from water deficits with negative impacts on yield. The knowledge of the onset and water stress intensity, essentially determines the accuracy of an irrigation schedule that avoids yield reduction and waste of water. Water stress can be quantified by stress coefficient Ks, as suggested by Food and Agriculture Organization (FAO). Combinations and adjustments of the FAO method with field measured data have been used to calculate Ks more precisely and apply it on single experimental fields. The FAO method and a validation method were compared and evaluated with in situ measurements in two different olive orchards in terms of tree age and irrigation system. The results indicate that the FAO method fails to distinguish the differences between cultivars, attributed mainly to the tree age and irrigation system, rendering almost a similar slope of Ks and calculating a nearly simultaneous onset of stress, which was not confirmed by pre-dawn water potential (Ψ_PD) measurements.

The forecast of irrigation requirements in commercial olive orchards in the era of climate change is at the forefront of scientific research. Simplified models that are based on monitoring soil and plant water status, along with microclimatic variables are well established. In the present study, an attempt was made to correlate the olive tree sap fluxes and the theoretical grass water losses, as expressed by reference evapotranspiration (ETo) in mild to moderate water-stress conditions. The water flow in the soil–plant–air continuum was monitored using soil water and thermal dissipation probes (TDP), which have a comparatively low cost and satisfactory reliability, while microclimatic variability was monitored by a meteorological station placed within the experimental orchard. The assessment of water stress was conducted via a stress coefficient (Ks), which was determined according to soil water availability, and validated with measurements of pre-dawn water potential and stomatal conductance. The results suggest the existence of an exponential correlation (R² = 0.869) between daily plant transpirational losses and reference evapotranspiration, while the methodology’s applicability is verified by the validation process (R² = 0.804 and RMSE = 0.579 L per day).

Adulteration of high-value agricultural products is a critical issue worldwide for consumers and industries. Discrimination of the geographical origin can verify food authenticity by reducing risk and detecting adulteration. Between agricultural products, beans are a very important crop cultivated worldwide that provides food rich in iron and vitamins, especially for people in third-world countries. The aim of this study is the construction of a map of the locally characteristic isotopic fingerprint of giant beans, “Fasolia Gigantes-Elefantes PGI”, a Protected Geographical Indication product cultivated in the region of Kastoria and Prespes, Western Macedonia, Greece, with the ultimate goal of the discrimination of beans from the two areas. In total, 160 samples were collected from different fields in the Prespes region and 120 samples from Kastoria during each cultivation period (2020–2021 and 2021–2022). The light element (C, N, and S) isotope ratios were measured using Isotope Ratio Mass Spectrometry (IRMS), and the results obtained were analyzed using chemometric techniques, including a one-way ANOVA and Binomial logistic regression. The mean values from the one-way ANOVA were δ¹⁵N_AIR = 1.875‰, δ¹³C_V-PDB = −25.483‰, and δ³⁴S_V-CDT = 4.779‰ for Kastoria and δ¹⁵N_AIR = 1.654‰, δ¹³C_V-PDB = −25.928‰, and δ³⁴S_V-CDT = −0.174‰ for Prespes, and showed that stable isotope ratios of C and S were statistically different for the areas studied while the Binomial logistic regression analysis that followed correctly classified more than 78% of the samples.

Olive trees have a unique reproductive pattern marked by biennial fruiting. This study examined the repercussions of alternate fruit bearing on the water relations of olive trees and the associated ecophysiological mechanisms. The experiment spanned two consecutive years: the “ON” year, characterized by a high crop load, and the “OFF” year, marked by minimal fruit production. Key ecophysiological parameters, including sap flow, stomatal conductance, and photosynthetic rate, were monitored in both years. Pre-dawn water potential was measured using continuous stem psychrometers and the pressure chamber technique. Biochemical analyses focused on non-structural carbohydrate concentrations (starch, sucrose, and mannitol) and olive leaves’ carbon-stable isotope ratio (δ¹³C). Results revealed a higher leaf gas exchange rate during the “ON” year, leading to an average 29.3% increase in water consumption and a 40.78% rise in the photosynthetic rate. Higher water usage during the “ON” year resulted in significantly lower (43.22% on average) leaf water potential. Sucrose and starch concentrations were also increased in the “ON” year, while there were no significant differences in mannitol concentration. Regarding the carbon-stable isotope ratio, leaves from the “OFF” year exhibited significantly higher δ¹³C values, suggesting a higher resistance to the CO₂ pathway from the atmosphere to carboxylation sites compared to the “ON” year plants.

In this study, we aimed to develop a sampling method that could be used in geographical discrimination studies of Protected Geographical Indication (PGI) dry beans (Phaseolus vulgaris L.) by considering the geoclimatic variability within the cultivation zone of the analyzed product. The Regional Unit of Kastoria in Greece, a major area of protected designation origin of pulse crops, was selected for detailed investigation. Meteorological data were collected from five weather stations in different subregions of Kastoria (Argos Orestiko, Kalochori, Lakkomata, Lithia, and Polykarpi), over a period of six years (2015 to 2020), along with data of soil texture. The collected data were analyzed in order to determine statistically significant differences among the subregions with regard to the aforementioned parameters. A seasonality pattern was observed for all subregions concerning the microclimate, which splits the data into two clusters. Moreover, a significant variation of the soil textures was revealed, frequently affecting farmers’ decision regarding agronomic practices, leading to the unique stable-isotope ratios and multi-elemental composition. This study guides the dry bean sample collection and will enable the designation of the boundaries of protected origin regions and enable future sampling schemes for stable-isotope and multi-elemental analysis.

Greek giant beans, also known as “Gigantes Elefantes” (elephant beans, Phaseolus vulgaris L.,) are a traditional and highly cherished culinary delight in Greek cuisine, contributing significantly to the economic prosperity of local producers. However, the issue of food fraud associated with these products poses substantial risks to both consumer safety and economic stability. In the present study, multi-elemental analysis combined with decision tree learning algorithms were investigated for their potential to determine the multi-elemental profile and discriminate the origin of beans collected from the two geographical areas. Ensuring the authenticity of agricultural products is increasingly crucial in the global food industry, particularly in the fight against food fraud, which poses significant risks to consumer safety and economic stability. To ascertain this, an extensive multi-elemental analysis (Ag, Al, As, B, Ba, Be, Ca, Cd, Co, Cr, Cs, Cu, Fe, Ga, Ge, K, Li, Mg, Mn, Mo, Na, Nb, Ni, P, Pb, Rb, Re, Se, Sr, Ta, Ti, Tl, U, V, W, Zn, and Zr) was performed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Bean samples originating from Kastoria and Prespes (products with Protected Geographical Indication (PGI) status) were studied, focusing on the determination of elemental profiles or fingerprints, which are directly related to the geographical origin of the growing area. In this study, we employed a decision tree algorithm to classify Greek “Gigantes Elefantes” beans based on their multi-elemental composition, achieving high performance metrics, including an accuracy of 92.86%, sensitivity of 87.50%, and specificity of 96.88%. These results demonstrate the model’s effectiveness in accurately distinguishing beans from different geographical regions based on their elemental profiles. The trained model accomplished the discrimination of Greek “Gigantes Elefantes” beans from Kastoria and Prespes, with remarkable accuracy, based on their multi-elemental composition.