Next Article in Journal
Soil Aggregate Stability Under Freeze–Thaw Cycles in Mollisols as Evidenced by 15N Distribution
Previous Article in Journal
Research on Non Destructive Detection Method and Model Op-Timization of Nitrogen in Facility Lettuce Based on THz and NIR Hyperspectral
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 15
Issue 10
10.3390/agronomy15102262
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Modeling the Impact of Future Temperature Increases on Olive Oil Accumulation Patterns in the Iberian Peninsula

by
José Manuel Cabezas
1,
José Osmar Alza
1,
Raúl de la Rosa
2,
Cristina Santos
1,
Mercedes del Río-Celestino
1,* and
Ignacio Jesús Lorite
1
1
Department of Natural and Forest Resources, IFAPA Alameda del Obispo, Avda. Menéndez Pidal s/n, 14004 Córdoba, Spain
2
Department of Plant Breeding, IAS-CSIC, Avda. Menéndez Pidal s/n, 14005 Córdoba, Spain
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(10), 2262; https://doi.org/10.3390/agronomy15102262
Submission received: 16 July 2025 / Revised: 13 September 2025 / Accepted: 19 September 2025 / Published: 24 September 2025
(This article belongs to the Section Horticultural and Floricultural Crops)
Browse Figures
Versions Notes

Abstract

Oil content is a critical component of yield production in Mediterranean olive orchards, but it has received limited attention in modeling olive cultivation under extreme weather conditions. To address this gap, statistical and regression models based on multiple oil content measurements from field trials conducted with representative olive cultivars in the Guadalquivir basin (southern Iberian Peninsula), together with the latest future climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for the Iberian Peninsula, were integrated to improve the modeling of its behavior under future climate conditions. Temperature was the most influential factor affecting the olive oil accumulation pattern. Summer temperature was negatively correlated with the onset of oil accumulation, the accumulation rate, and the maximum oil content (MOC), while it was positively correlated with the date at which MOC was reached. When these relationships were combined with CMIP6 climate projections, inland southern Spain emerge as one of the most affected areas in the Iberian Peninsula. In the near future period (2040–2069), projected climate warning is expected to result in an earlier onset of oil accumulation, delays of up to 33 days in reaching MOC, and reductions in MOC of up to 17.5 percentage points, corresponding to an average olive oil yield loss of up to 30.3%, considering only the olive oil yield loss associated with the reduction in MOC. These changes vary in intensity depending on the location, cultivar, climate period and the greenhouse gas emission scenario considered. This study confirms the critical importance of temperature in olive oil production, highlights the need to incorporate functions that account for the effects of rising temperature on MOC, and emphasizes the identification of adaptation measures to cope with increasing temperatures and more frequent heat waves.

1. Introduction

The production of olive oil from olive orchards is the economic and social mainstay of many Mediterranean regions, especially those located in the southern Iberian Peninsula. Currently, about 3 Mha of olive orchards are cultivated in the Iberian Peninsula, which represents about 30% of the total worldwide area [1,2]. However, the expansion into new areas and the effects of climate change mean that these agricultural systems are more frequently affected by severe droughts and heat waves [3,4]. The impact of these new weather conditions on yield, oil accumulation and oil quality could threaten the sustainability of many Mediterranean olive areas [5,6].
Phenology is a relevant component for assessing the impact of elevated temperatures on olive orchards. Some of these impacts are the advancement of the onset of flowering [3], flower damage caused by heat waves during this stage [7], as well as a higher probability of flowering failure due to the lack of chilling requirements [3]. Similarly, high temperatures during the oil accumulation stage affect the oil accumulation pattern, alter the harvesting schedule, and lead to relevant reductions in oil content and fatty acid composition [8,9,10,11].
The importance of assessing the optimal harvest date has been previously highlighted, as delays in harvesting have been associated with a reduction in olive oil quality parameters [12,13]. The criteria for optimizing olive harvest date should then consider maximizing oil yield and quality, with a harvest date late enough to ensure high oil content but early enough to achieve high olive oil quality [13]. The importance of olive oil composition has increased in recent years, both to increase the profitability of these systems and to highlight the health benefits of olive oil. For all these reasons, the decision on the harvest date is crucial for farmers. Traditionally, in many olive-growing areas of Spain, the olive harvest season has started on a fixed date, typically extending from December to February [14], which has severely affected olive oil quality. In contrast, earlier harvest dates have been adopted in modern and/or quality-oriented orchards.
An additional negative effect of rising temperatures is the increased water demand [15]. Following a regulated deficit irrigation strategy, irrigation yields excellent results in terms of olive oil production when applied at the onset of the oil accumulation stage [16], which occurs from August to October in southern Spain. This timing is critical, as olive trees are particularly sensitive to water stress during the oil accumulation stage [17,18]. An earlier onset of oil accumulation, favored by high temperatures, shifts this critical stage to the summer period [19], and consequently, an increase in irrigation requirements could be generated.
The interaction of severe weather conditions with critical components of olive oil production, such as maximum oil content or irrigation requirements, makes the modeling of oil accumulation patterns particularly relevant in studies aimed at the agronomic improvement of olive production [20]. Despite the established correlation between weather conditions and olive oil accumulation patterns [21], this component has not been fully addressed in previous approaches [22]. To date, research has mainly led to the development of conceptual models predicting oil content from fruit dry weight measurements [13]. Consequently, most olive crop simulation models have primarily focused on evaluating fruit yield while assuming a fixed oil content percentage [4]. However, the reality is far more complex. Several studies have highlighted that analyzing oil accumulation patterns provides a more reliable strategy for determining the optimum harvest time than relying solely on fruit skin color [22,23]. Although the number of studies addressing this aspect is still limited, research assessing oil accumulation under future weather scenarios is even scarcer.
The literature indicates that, while physiological and descriptive research exists to characterize oil accumulation rates, comprehensive mechanistic or empirical models remain scarce. Earlier research proposed and tested a conceptual model for predicting fruit oil content from inexpensive measurements of fruit dry weight, which could be readily adopted by producers to determine the ideal timing for harvest [13]. Additional work has examined the seasonal accumulation of oil in both traditional and super-high density olive orchards, and its modeling using image-based linear models [24]. Along the same lines, a single non-destructive maturity index based on the absorbance spectrum has been applied to intact olives of the Leccino cultivar [25]. In recent years, significant progress has been made in optimizing farming practices through UAVs, multispectral and thermal imaging, and a variety of sensors (e.g., proximal, remote, optical, in planta), mainly for decision-making in irrigation, fertilization, and pest control [26]. Nonetheless, their application to the estimation of olive oil remains limited.
In this context, prioritizing research on phenology and other concepts related to the interactive effects of temperature and other weather components will improve olive tree models to increase knowledge of the effects of climate change on olive systems [27]. Thus, the use of improved simulation models, including approaches to assess oil accumulation patterns adapted to Mediterranean olive orchards, would allow the evaluation of new production areas, the assessment of potential impacts related to climate change, or the development of site-specific adaptation measures. To address these needs, this study aimed to identify and assess the effects of weather conditions on olive oil accumulation patterns in orchards across the Iberian Peninsula, under both current and projected climate scenarios, by defining response functions for the main olive cultivars experimental data collected under a wide range of weather conditions. These findings will serve as basis for extending the analyses to other regions of the Mediterranean basin with similar climatic characteristics.

2. Materials and Methods

2.1. Plant Material and Experimental Layout

Olive oil accumulation patterns were evaluated in five olive orchards in southern Spain with contrasting field and weather conditions (Antequera, Baena, Córdoba, La Rambla and Úbeda; Figure 1), for the period 2017–2020, for the cultivars ‘Arbequina’, ‘Hojiblanca’, ‘Koroneiki’ and ‘Picual’. Deficit irrigation management has been implemented in Córdoba, Ubeda and La Rambla, and rainfed management in Baena and Antequera. The olive orchards in Antequera, Baena and Ubeda were planted in 2008, in Córdoba in 2010, and in La Rambla between 2004 and 2012, depending on the cultivar. The planting spacing was 7 × 7, except in Baena, where it was 8 × 8 m. In terms of canopy shape, all trees resembled a typical spheroid, as they are young, single-trunk olive trees with moderate pruning. Canopy diameter was approximately 4 m in all orchards, except in Baena, where it was around 3 m. The soil type was similar across all orchards, consisting of relatively deep loam-clay soils.
Daily weather data for each olive orchard were collected from nearby weather stations belonging to the Andalusian Weather Stations Network [28] and from automated weather stations installed within the experimental fields. The sites of the olive orchards show a wide range of temperatures during summer period (Table 1), with maximum temperature differences among them of 3–4 °C, the hottest site being Córdoba and the coolest being Antequera (Table 1).
Each experiment (Table 1) consisted of 4 randomly selected blocks within each commercial plot, with 4 trees in each block. In order to eliminate components that could interfere with the genotype × environment interaction and considering that previous studies have pointed to fruit load as one of the factors affecting oil accumulation [29,30], all trees analyzed had a similar fruit load, with a medium fruit load depending on tree size. Thus, in all treatments, the fruit load was around 40 kg per tree, except for Baena with a fruit load of around 25–30 kg per tree. The fields with a lower fruit load were discarded. This was the reason for the missing cultivar/location/year in Table 2. In addition, flowering phenology was recorded through repeated visits to the fields (Table S1).
To define the curves describing the oil accumulation pattern, between 6 and 14 harvest dates were considered (scheduled every 10 days, approximately on the 5th, 15th and 25th of July, August, September, October, November and December), with 3–4 replicates per date, resulting in more than 1600 oil content measurements. Approximately 200 g of fruits (around 1% of the total fruit load) were collected from evenly distributed parts of the canopy, covering all positions and orientations.

2.2. Method for Determining the Oil Content

The samples of 200 g collected from each monitored block were taken to the laboratory, where three subsamples of 25 g each were extracted. These subsamples were dried in an oven at 105 °C for 40 h. The samples were then reweighed to determine water lost. The oil content in the dry matter of the samples was then measured using a Bruker Minispec Nuclear Magnetic Resonance instrument (Bruker BioSpin GmbH & Co. KG, Ettlingen, Germany) [22].

2.3. Procedure for Defining the Oil Accumulation Pattern

The dynamics of oil accumulation follow a sigmoidal type curve, regardless of the cultivar [16]. However, a simplification of this curve was considered in this study. Therefore, a bilinear model was constructed, with a first linear phase in which the oil increases at a constant rate (i.e., the slope of oil accumulation; ROA) until it reaches its maximum oil content (MOC) on the date of maximum oil content (DMOC), and a second phase in which the oil content remains constant [21,31] (Figure 2). To obtain these three values (DMOC, ROA and MOC), each bilinear model was processed by the Solver add-in in Excel Microsoft 365 (version 2508) to minimize the RMSE between it and the 6–14 measurements taken for each of the 53 experimental fields (4 cultivars × 4 seasons × 5 locations; Table 1).
To determine the date of start of oil accumulation (DSOA), oil content data were fitted to a second-order polynomial function of day of year (DOY), expressed as y(x) = ax2 + bx + c. The coefficients (a, b, c) were estimated separately for each field experiment by maximizing the coefficient of determination (R2) to ensure the best fit with observed oil contents. DSOA was defined as the DOY corresponding to the onset of oil accumulation, i.e., the point at which the fitted curve intersected zero (Figure 2). This approach provided an accurate description of the entire oil accumulation curve (average R2 around 0.98), reliably capturing its initiation and representing a clear improvement over previous approaches that modeled oil accumulation only after its onset [21,31]. The length of oil accumulation (LOA) was then calculated as the interval between DMOC and DSOA (Figure 2).

2.4. Statistical Analysis

A fully randomized factorial analysis of variance with one replication and unbalanced data (Table 1) was performed to assess the effects of weather conditions on olive oil accumulation patterns in orchards, so the sum of squares was calculated using the marginal method (Type III) [32]. Normality of the data distribution and homogeneity of variances between cultivar groups were checked using the Shapiro–Wilk and Levene tests, respectively. Date of start of oil accumulation (DSOA), date of maximum oil content (DMOC), length of oil accumulation period (LOA), rate of oil accumulation (ROA), and maximum oil content (MOC) were the variables, while the sources of variation considered were years (n = 4), locations (n = 5), and cultivars (n = 4).
Preliminary statistical tests were conducted to assess the existence or absence of significant two-way interactions (Cultivar × Year, Cultivar × Location) using the “car” package in R (version 3.1-3) [33]. Because the data were unbalanced, it is not possible to estimate the Year × Location interaction or the three-way interaction (Year × Location × Cultivar). Therefore, only the two-way interactions Cultivar × Location and Cultivar × Year were evaluated, and no significant interactions among main factors were detected for either case (Table S2). Based on these results, ANOVA considered only the main effects without interactions. To compare the corrected means, the Tuckey test at 5% was used. All these analyzes were carried out using STATISTIX 9.0 software (Analytical Software, Tallahassee, FL, USA).
On the other hand, daily weather data from the meteorological stations associated with each olive orchard, such as maximum, minimum and average temperatures, rainfall, maximum, minimum and average atmospheric humidity, solar radiation, chilling hours and growing degree hours were used as predictor variables, as monthly means or sums. To reduce the number of variables, different dimensionality reduction techniques such as Principal Component Analysis (PCA) and Multiple Linear Regression (MLR), were used to select only those predictor variables with the greatest influence on the dependent variables (DSOA, DMOC, LOA, ROA and MOC). A bootstrap resampling method with 1000 replicates and replacement was performed [34,35] to estimate regression coefficients, correlation values, and their respective significance levels between the dependent variable and each independent variable using the “boot” package in R (version 3.1-3) [36]. Confidence intervals at 90%, 95%, and 99% levels were calculated using the percentile method, and significance thresholds were determined based on whether these intervals excluded zero.

2.5. Modeling of the Oil Accumulation Pattern Under Historical and Future Climate Projections

Historical and future weather data were obtained from the Inter-Sectorial Impact Model Intercomparison Project (ISIMIP) [37,38]. The ISIMIP3b protocol was considered in the study and provides bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) climate forcings for Historical, SSP1-RCP2.6, SSP3-RCP7.0 and SSP5-RCP8.5 conditions [39] with a spatial resolution of 0.5° × 0.5° (around 55.7 km × 42 km for Spain). Five datasets from the ISIMIP3b protocol were considered (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0 and USKESM1-0-LL) [40] for the Historical (H; 1974–2005), Near Future (NF; 2040–2069) and Far Future (FF; 2070–2099) time periods. These 30-year windows were selected to ensure consistency with previous ISIMIP phases and to rely on a time span with reliable observational data coverage. The use of other intermediate periods is uncommon in ISIMIP-based impact studies, as analyses usually focus on 30-year windows centered on mid- and end-century.
Regression models were defined based on the field observations described in Section 2.1. The regression outputs DMOC and MOC, derived from each of the climate models described in Section 2.5, were averaged by time period and gas emission scenario. This procedure was applied to each of the 242 cells that constitute the olive-growing area of the Iberian Peninsula (Figure 1). The collection of simulation results from the five climate models described in Section 2.5 formed a climate model ensemble, which was used to capture uncertainties and provide a more robust estimate of future climate projections.

3. Results

3.1. Component Analysis

The ANOVA of the 53 oil accumulation patterns described in Table 1 and Section 2.1 showed that the date of start of oil accumulation (DSOA) and the length of the oil accumulation period (LOA) were significantly influenced by year and location, but not by cultivar (Table 3). In contrast, the maximum oil content (MOC) was significantly affected by year and location, but not for cultivar, while the rate of oil accumulation (ROA) was influenced only by cultivar (Table 3). Finally, the date of maximum oil content (DMOC) was not significantly affected by year, location or cultivar (Table 3).
Considering only the statistically significant correlations, DSOA and DMOC were negatively (−0.82 **) and positively (+0.63 **) correlated with LOA, respectively, and DMOC was inversely correlated with ROA (−0.67 **). Finally, ROA was negatively correlated with LOA (−0.57 **) and positively correlated with MOC (+0.30 *).
Considering the Tukey test, none of the cultivars analyzed showed significant differences at the 5% level for DSOA and DMOC, which ranged from 196 to 206 DOY (15–25 July) and 310 to 319 DOY (6–15 November), respectively (Table 4). However, for LOA, ‘Hojiblanca’ exhibited statistically significant higher values than ‘Picual’ (123 days vs. 105 days), while the others cultivars did not differ significantly (Table 4). For ROA, ‘Picual’ showed statistically significant higher values than ‘Koroneiki’ (0.36 vs. 0.30), whereas no significant differences were observed among the remaining cultivars (Table 4). Finally, for MOC, no significant differences were found among the cultivars analyzed (Table 4).
When comparing mean values across years using the Tukey test, no significant differences at the 5% level were found for DMOC (ranging from 311 to 320 DOY) or ROA (ranging from 0.3 to 0.35). In contrast, for MOC, the Tukey test revealed two distinct groups: years with higher oil content, led by 2017, and the remaining years, which showed significantly lower oil contents (Table 4).
Similarly, when comparing mean values across locations using the Tukey test, no significant differences at the 5% level were observed for DMOC (ranging from 311 to 318 DOY) and ROA (ranging from 0.3 to 0.35). In contrast, for MOC, the Tukey test again revealed two groups of locations, those with higher oil content, led by Baena, and, at the other extreme, Córdoba and Úbeda, with the significantly lower oil contents (Table 4).

3.2. Olive Oil Accumulation Pattern Functions Based on Summer Temperatures

As a result of the statistical analyses described in Section 2.4, monthly average temperature was identified as the critical component for defining olive oil accumulation pattern functions, showing lower performance for the other weather components.
The date of start of olive oil accumulation (DSOA) showed an inverse correlation with temperature: it occurred earlier with higher minimum and mean temperatures in July for ‘Arbequina’ and ‘Picual’, respectively (Table 5).
On the other hand, the date of maximum oil content (DMOC) was directly correlated with temperature, occurring later with higher minimum and mean temperatures in September for ‘Arbequina’ and ‘Picual’, respectively (Table 5). Similarly, the rate of oil accumulation (ROA) was inversely related to September temperatures, slowing down with higher minimum and mean temperatures for ‘Arbequina’ and ‘Picual’, respectively (Table 5). Finally, an inverse relationship was found between the maximum oil content (MOC) and summer temperatures for both cultivars, with MOC decreasing as the maximum temperature in August increased (Table 5).

3.3. Future Projections of the Olive Oil Accumulation Pattern

3.3.1. Temperature Projections

From the analysis of these dataset inputs, high spatial variability was found across the Iberian Peninsula, with maximum temperatures projected for the Middle Guadalquivir basin and neighboring areas to the north reaching around 39 °C and 43 °C for the NF and FF time periods, respectively. Maximum temperatures are lower in the rest of the Iberian Peninsula, reaching 34–38 °C in coastal areas, and 32–34 °C in islands (Figure 1). These projections of future weather conditions, especially for the NF period, approximately match the temperatures observed during the field experiments considered in this study (Table 1).

3.3.2. Date of Maximum Oil Content (DMOC)

According to the relationships between oil accumulation parameters and temperature (Section 3.2) and climate projections for the Iberian Peninsula (Section 2.5), the predicted date of maximum oil content (DMOC) in representative olive growing areas (Figure 1) in the Near Future period under the SSP5-RCP8.5 scenario for ‘Arbequina’ and ‘Picual’ is expected to be around 330 DOY (26 November) and 322 DOY (18 November), respectively. The variability of DMOC between the selected sites for ‘Arbequina’ and ‘Picual’ was 22 and 10 days, respectively (Figure 3 and Table S3). Compared to the Historical period, delays of about 28 and 11 days were found in the Near Future period, for ‘Arbequina’ and ‘Picual’, respectively (Figure 3 and Table S3). Similarly, in the Far Future period under the SSP5-RCP8.5 scenario, DMOC is expected to be around 351 DOY (17 December) and 331 DOY (27 November), for ‘Arbequina’ and ‘Picual’, respectively, representing differences of 49 and 20 days compared to the Historical period. However, the variability of DMOC between sites remained in 24 and 9 days, respectively (Table S3).
When analyzing the DMOC results from the individual climate model outputs included in the ensemble, both the standard deviation (SD) and the range (difference between maximum and minimum values) increased under the SSP5-RCP8.5 scenario for both ‘Picual’ and ‘Arbequina’. The SD values were higher for ‘Arbequina’ than for ‘Picual’, with comparable values in the Near Future and Far Future periods (Tables S4 and S5). Comparisons between inland and coastal sites revealed no substantial differences (Tables S4 and S5).

3.3.3. Maximum Oil Content (MOC)

The maximum oil content (MOC) in representative olive growing areas (Figure 1) in the Near Future period under the SSP5-RCP8.5 scenario for ‘Arbequina’ and ‘Picual’ is expected to be around 46.5 and 45.2%, respectively, with a range between 42.8 and 50.8% and 36.4–55.7%, respectively (Figure 4 and Table 6). The MOC reductions compared to the Historical period were approximately −5.4 and −13.1 percentage points, respectively (Figure 4 and Table 6). These reductions in MOC imply reductions in olive oil yield of about −10.5 and −22.8%, respectively. Similarly, in the Far Future period under the SSP5-RCP8.5 scenario, MOC is expected to be around 43.2 and 37.2%, ranging between 39.0 and 47.6%, and 27.2 and 48.0%, for ‘Arbequina’ and ‘Picual’, respectively, detecting differences in −8.7 and −21.1 percentage points compared to the Historical period (Table 6). These reductions implied olive oil yield reductions of about −16.9 and −36.7%, respectively.
When the results were analyzed spatially, the highest MOC values for ‘Arbequina’ and ‘Picual’ in the Historical period were found in coastal areas (mild summers), and conversely, the lowest MOC values were found in the Guadalquivir basin and other inland areas (very hot summers) (54.5% vs. 49.3% for ‘Arbequina’ and 64.6% vs. 52.0% for ‘Picual’), with limited differences between areas (Table 6). A similar pattern was found for future weather conditions, also with a relevant role of cultivar and GHG scenario. Thus, the MOC values for ‘Arbequina’ for the Far Future period with the SSP5-RCP8.5 scenario were around 39.6 and 46.7% in inland areas (very hot summers), and coastal areas (mild summers), respectively, implying a reduction in olive oil yield of around −14.2 and −19.6%, respectively, compared to the Historical period (Table 6). For ‘Picual’ the MOC reductions were more severe, with MOC values of around 28.6 and 45.8% in inland areas (very hot summers) and coastal areas (mild summers), respectively, with olive oil yield reductions due to oil content reductions of around −45.0 and −28.9%, compared to the Historical period (Table 6). Differences between coastal and inland areas increased in the future climate scenarios, especially for ‘Picual’. MOC and olive oil yield reductions, and differences between areas were smaller for the rest of the climate projections (Figure 4 and Table 6).
When analyzing the MOC results from the individual climate model outputs included in the ensemble, both the standard deviation (SD) and the range (difference between maximum and minimum values) increased under the SSP5-RCP8.5 scenario for both ‘Picual’ and ‘Arbequina’. The SD values were higher for ‘Picual’ than for ‘Arbequina’, with comparable values in the Near Future and Far Future periods (Tables S6 and S7). Comparisons between inland and coastal sites revealed no substantial differences (Tables S6 and S7).

4. Discussion

Crop modeling to assess olive oil production is a complex task that requires the inclusion of key aspects of olive tree physiology, such as phenology and transpiration [22]. Previous studies have examined the effects of climate change on olive phenology [3], irrigation requirements, and olive production in the Mediterranean region [4], as well as productivity and other economic factors [41]. Collectively, these studies have contributed to the development of models such as AdaptaOlive [4]. However, oil content in fruits has so far been modeled only to a limited extent. To fill the knowledge gap in modeling the dynamics of olive oil accumulation, we monitored 53 cultivar/location/year combinations in five locations in southern Spain under contrasting meteorological conditions. The results, although limited by issues related to unbalance data that prevented the performance of certain statistical analyses, showed that temperature had a significant effect on oil accumulation patterns, specifically on the dates of the start of oil accumulation (DSOA) and of the maximum oil content (DMOC), the rate of oil accumulation (ROA), and the maximum oil content (MOC), with a stronger influence than other factors such as precipitation, thereby confirming previous findings [8,21,42]. The changes in temperature not only reduced the final olive oil production but also altered the harvest date and the onset of oil accumulation. Thus, DSOA was earlier with warmer temperatures (also found by Benlloch-González et al. [43]), and therefore oil accumulation occurred during the summer, the period with the highest temperatures [16]. This indicates a shift in irrigation practices, particularly when the strategy widely adopted in Mediterranean orchards—regulated deficit irrigation [15]—is implemented, as it results in higher irrigation demands. Under conditions of limited water availability for agriculture, this represents an additional adverse consequence of rising summer temperatures.
In addition to the correlation with weather conditions, the role of cultivar also had a significant influence on the components of the oil accumulation pattern, with differences in the effect of temperature depending on the cultivar. This variability between cultivars has already been found by Trentacoste et al. [31] or Miserere et al. [44]. Particular attention should be paid to MOC, a critical component of olive oil yield. Thus, the statistically significant influence of environment and cultivar found in our study is in agreement with previous studies [13,45], who indicated that the final oil content depends on the interaction between growing conditions and the genetic potential of the cultivar. The negative effect of high summer temperatures on the MOC of ‘Arbequina’ and ‘Picual’ has been previously reported for both cultivars, but also for many others [8,21]. However, our study also found that the effect of temperature on MOC was different for ‘Arbequina’ or ‘Picual’, the latter being much more sensitive to high summer temperatures. These different responses show the complexity of this process. Moreover, this complexity is also reflected in some divergences between studies; some studies have shown that MOC varies between cultivars and is largely unaffected by season [31], but other studies have reported a decrease in oil content with summer temperature [44] or year [46].
Consideration of these functions under future weather conditions has defined significant changes in olive oil accumulation patterns in the Iberian Peninsula, particularly in inland areas of southern Spain. In these areas, severe reductions in olive oil yield of up to 45% have been observed, resulting from the decrease in oil content caused by heat events and rising summer temperatures. These values reflect the high relevance of olive oil content in olive yield modeling. However, this component has so far been considered in a very limited way in the recently developed olive simulation models [4] and is therefore worryingly underestimated.
These impacts require the evaluation, development and promotion of new site-specific adaptation measures to ensure the sustainability of Mediterranean olive orchards. Currently, the correct choice of cultivar in terms of oil accumulation pattern has been identified as a valuable adaptation measure [44]. However, adaptation to high temperatures has not yet been included as a selection criterion in olive breeding programs [47], and few cultivar evaluations for heat stress have been reported [42]. This is despite the fact that heat stress is considered to be the dominant abiotic stress compared to drought [9]. In our study, ‘Arbequina’ and ‘Picual’ were affected by high summer temperatures, during the oil accumulation stage, reducing the accumulation rate and the maximum oil content. The influence of air temperature on oil content has been previously reported by comparing normal and artificial warming [11,40,41,46] or using high-temperature environments [9,10,30,44]. However, previous reports have also shown cultivar differences in this sensitivity to temperatures [42]. In fact, in our study, ‘Arbequina’ showed less variability than ‘Picual’, possibly due to its lower plasticity, as shown by previous studies [30]. However, other cultivars as ‘Coratina’ has shown to have lower variability than ‘Arbequina’ [10,48]. This indicates that further evaluation with wider number of cultivars for sensitivity to high temperatures are needed to determine the ones better adapted to climate warming. Those reported genetic differences in sensitivity to high temperatures have been related to a downregulation of genes involved in triacylglycerol biosynthesis [46] and to several transcription factors [44].
Another group of adaptation measures is based on the high spatial variability within the Iberian Peninsula in terms of the impact of future weather conditions on critical components of olive oil yield. This adaptation measure consists of identifying the areas that will be more affected by temperature, such as the analysis carried out by Gabaldón-Leal et al. [3] for olive flowering limits, and then where site-specific adaptation strategies will be required. The development of maps showing the impact of future temperature increases on olive oil accumulation patterns is the basis for the development of site-specific adaptation measures. This approach has been performed for other crops such as almonds [49], but to the best of our knowledge this is the first time it has been performed for aspects related to olive oil accumulation patterns. Counterbalancing the potential benefits of these adaptation measures with their implementation costs, their adaptation is feasible only for new plantations, whereas their application in existing olive orchards is not economically viable.
Despite significant progress in assessing the impacts of climate change on olive oil production and promoting adaptation strategies, numerous uncertainties remain in olive crop modeling under future climate projections. These uncertainties hinder the development of effective strategies to ensure the sustainability of Mediterranean olive orchards. Crop modeling is based on field experiments under a wide range of weather conditions, but previous studies have warned of the effects of extreme or anomalous temperature events, such as heat stress or mild winter temperatures, on olive phenology and yield [7]. The wide range of temperatures recorded in our field measurements mitigates these concerns, but the limited number of years and locations for each cultivar makes it difficult to resolve all uncertainties in the results and requires additional field observations. Another source of uncertainty is related to future changes in phenology, which could alter the response functions of olive trees. In the case of olive oil accumulation patterns, the predicted advance in DSOA and delay in DMOC due to increased temperatures maintain the summer period as the critical period for oil accumulation, regardless of the time period or RCP considered. This fact validates the functions obtained in our study, as oil accumulation during summer will remain in the future regardless of the temperature increase. However, the validity of the prediction could be threatened if future temperatures are outside the temperatures considered for the development of the response functions, and then projections for the Far Future period with RCP8.5 scenario must be considered with caution. Consideration of sites with current extreme summer temperatures (such as Córdoba or Úbeda) reduces these effects, but experimentation under controlled weather conditions based on climate projections are needed to confirm these results.
An additional source of uncertainty arises from the coarse spatial resolution of the ISIMIP3b climate model projections (0.5° × 0.5°; approximately 55.7 km × 42 km for Spain). Although ISIMIP3 applies bias correction and statistical downscaling to mitigate these effects, the coarse resolution remains a source of uncertainty. To address this limitation, HighResMIP models from CMIP6 and CORDEX-EUR11 provide downscaled projections at 25 km and 12.5 km, respectively, better capturing local topography, coastal influences, and extremes. Future studies will use the AdaptaOlive model—including the oil accumulation modules developed in the present work—to evaluate the impacts of employing climate models with different spatial resolutions. Regarding climate models, another source of uncertainty arises from the variability in outputs across the different models that make up the ensemble, reflecting their structural and parametric differences. This uncertainty is expected to be higher under SSP5-RCP8.5 scenarios, and therefore results from these scenarios should be interpreted with greater caution. However, no differences in uncertainty associated with variations among climate model outputs were observed between inland and coastal areas.
Temperature has been shown to be a critical component in olive oil accumulation, but other relevant components such as water stress could be the cause of the observed differences in behavior between cultivars, locations or seasons [17,31]. Water stress and its interaction with temperature were not considered in our study and should be analyzed in the future under controlled conditions in a climate-regulated greenhouse. Such research would improve our understanding of the processes involved in olive oil production, with particular emphasis on the combined effects of high temperatures and severe water stress during the pit hardening phase. Furthermore, in order to avoid possible interactions of the oil accumulation pattern with other components than temperature, such as crop load, a special effort was made to select for harvest olive trees with medium crop load and very similar between cultivars, locations and years. However, in contrast to the clear effect of fruit load on fruit size, the effect on oil concentration is less consistent [29]. Previous studies have found significant correlations between fruit load and the rates of fruit dry weight and oil concentration [30], fruit filling rate [31] and maturity index [50]. However, in studies analyzing trees with high, medium and low fruit loads, the dynamics of oil concentration and final fruit oil concentration were not affected by fruit load [31]. Similarly, Lavee and Wodner [45] showed that the relative oil content in the mesocarp was independent of size and yield level, and Gucci et al. [51] showed that crop load had no effect on the oil content of the mesocarp in deficit irrigated trees. However, other studies indicated that fruit oil concentration decreased with high crop load [29].
Therefore, integrating new field experiments across a wider range of weather conditions, including the extreme high temperatures predicted by climate models, together with the assessment of genotype × environment interactions, the collection of longer field data series, and advances in modeling and spatial analysis under both current and future weather conditions, will help to overcome the present limitations of adaptation measures aimed at mitigating oil content reductions caused by summer temperatures. In addition, the current trend of converting traditional orchards systems to high density ones could be a good opportunity to identify and use cultivars with high level of tolerance to high temperatures. This could enhance the resilience of modern orchards to the influence of climate warming in oil content reported here. The evaluation and promotion of these measures will improve the sustainability of Mediterranean olive orchards under future weather conditions.

5. Conclusions

Temperature and water availability have traditionally been considered key factors in olive oil production. Thanks to the analysis of 53 oil accumulation patterns under contrasting weather conditions in southern Spain, temperature has been confirmed as a critical factor in the assessment of olive oil yield. Thus, the increase in temperature predicted for the Iberian Peninsula in the Near Future period will have a significant impact on olive oil accumulation patterns, especially in southern Spain. For this area, a delay in the end of olive oil accumulation of up to about 31 days, an increase in irrigation requirements, and a sharp reduction in oil content of about 15 percentage points have been reported, implying an average reduction in olive oil yield of about 28% for this factor alone, with large differences in response depending on the cultivar analyzed. Furthermore, the effects of increased temperatures were found to be highly spatially variable, threatening the sustainability of olive orchards in the warmest areas of the Iberian Peninsula.
Therefore, in view of the results obtained in this study, the inclusion of cultivar-specific functions in olive simulation models, taking into account the effect of temperature on oil accumulation, will improve the assessment of olive oil yield under current and future weather conditions. These improved simulation models will assess the impact of climate change on olive yield and on the development of site-specific adaptation measures, such as cultivar selection, identification of suitable olive growing areas or optimization of harvesting dates. At present, however, these adaptation measures are not aimed at mitigating the effects of increased temperatures on olive oil production, so additional efforts are needed to consider this component in conjunction with water stress in the development of new adaptation measures.
The results obtained in this study regarding the differences between cultivars and the spatial distribution of the effects of increasing temperature on olive oil yield will contribute to the development of these new adaptation measures. However, due to the uncertainty associated with the spatial variability of future weather conditions and the ability of simulation models to reproduce the behavior of Mediterranean olive orchards under future weather conditions, further studies are needed. The integration of new experiments under controlled weather conditions combining rising temperatures, heat waves and droughts, data management and modeling, constitute new lines of future research to ensure the sustainability of Mediterranean olive orchards.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy15102262/s1, Table S1. Flowering date (in DOY) for each experimental trial considered in the study. Table S2. Preliminary statistical tests: Shapiro–Wilk, Levene, and simple interactions (ns = not significant). df: degrees of freedom. Table S3. DMOC (expressed in DOY) at representative sites across the Iberian Peninsula for the cultivars ‘Arbequina’ and ‘Picual’, evaluated under Historical (H), Near Future (NF), and Far Future (FF) periods, and across the SSP1-RCP2.6, SSP3-RCP7.0 and SSP5-RCP8.5 scenarios. Table S4. Standard deviation of DMOC within the climate model ensemble at representative sites across the Iberian Peninsula for the cultivars ‘Arbequina’ and ‘Picual’, under the Near Future (NF) and Far Future (FF) periods, and across SSP1-RCP2.6 (26), SSP3-RCP7.0 (70) and SSP5-RCP8.5 (85) scenarios. Table S5. Range of DMOC (in days), calculated as the difference between the maximum and minimum within the ensemble, at representative sites across the Iberian Peninsula for the cultivars ‘Arbequina’ and ‘Picual’, under the Near Future (NF) and Far Future (FF) periods, and across the SSP1-RCP2.6 (26), SSP3-RCP7.0 (70) and SSP5-RCP8.5 (85) scenarios. Table S6. Standard deviation of MOC within the climate model ensemble at representative sites across the Iberian Peninsula for the cultivars ‘Arbequina’ and ‘Picual’, under the Near Future (NF) and Far Future (FF) periods, and across SSP1-RCP2.6 (26), SSP3-RCP7.0 (70) and SSP5-RCP8.5 (85) scenarios. Table S7. Range of MOC (in days), calculated as the difference between the maximum and minimum within the ensemble, at representative sites across the Iberian Peninsula for the cultivars ‘Arbequina’ and ‘Picual’, under the Near Future (NF) and Far Future (FF) periods, and across the SSP1-RCP2.6 (26), SSP3-RCP7.0 (70) and SSP5-RCP8.5 (85) scenarios. Figure S1. Measures of oil accumulation in Antequera site for Arbequina (a), Hojiblanca (b), Koroneiki (c) and Picual (d) during the season 2017; Figure S2. Measures of oil accumulation in Baena site for Arbequina (a), Hojiblanca (b), Koroneiki (c) and Picual (d) during the season 2017; Figure S3. Measures of oil accumulation in Cordoba site for Arbequina (a), Hojiblanca (b), Koroneiki (c) and Picual (d) during the season 2017; Figure S4. Measures of oil accumulation in La Rambla site for Arbequina (a), Hojiblanca (b), and Koroneiki (c) during the season 2017; Figure S5. Measures of oil accumulation in Ubeda site for Arbequina (a), Hojiblanca (b), Koroneiki (c) and Picual (d) during the 2017 season.

Author Contributions

Data curation, formal analysis, investigation, writing—original draft, writing—review and editing, J.M.C.; formal analysis, methodology, visualization, writing—review and editing, J.O.A.; conceptualization, formal analysis, funding acquisition, methodology, writing—review and editing, R.d.l.R.; formal analysis, resources, visualization, writing—review and editing, C.S.; funding acquisition, methodology, writing—review and editing, M.d.R.-C.; conceptualization, formal analysis, funding acquisition, methodology, software, writing—original draft, writing—review and editing, I.J.L. All authors have read and agreed to the published version of the manuscript.

Funding

This study has been supported by funding from projects PR.AVA23.INV2023.038 and PR.AVA23.INV2023.039 by the Junta de Andalucía, co-financed with FEDER funds, and by Qualifica Project [QUAL21_023 IAS], Junta de Andalucía.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. FAOSTAT. Food and Agriculture Data. Food and Agriculture Organization of the United Nations. Available online: https://www.fao.org/faostat/en/#data (accessed on 13 June 2024).
  2. MAPA. Encuesta Sobre Superficies y Rendimientos de Cultivos. Resultados Provisionales Nacionales y Autonómicos. ESYRCE; Ministerio de Agricultura, Pesca y Alimentación: Madrid, Spain, 2023. [Google Scholar]
  3. Gabaldón-Leal, C.; Ruiz-Ramos, M.; de la Rosa, R.; León, L.; Belaj, A.; Rodríguez, A.; Santos, C.; Lorite, I.J. Impact of changes in mean and extreme temperatures caused by climate change on olive flowering in southern Spain. Int. J. Climatol. 2017, 37, 940–957. [Google Scholar] [CrossRef]
  4. Cabezas, J.M.; Ruiz-Ramos, M.; Soriano, M.A.; Gabaldón-Leal, C.; Santos, C.; Lorite, I.J. Identifying adaptation strategies to climate change for Mediterranean olive orchards using impact response surfaces. Agric. Syst. 2020, 185, 102937. [Google Scholar] [CrossRef]
  5. Gómez-Limón, J.A.; Picazo-Tadeo, A.J.; Reig-Martínez, E. Eco-efficiency assessment of olive farms in Andalusia. Land Use Policy 2012, 29, 395–406. [Google Scholar] [CrossRef]
  6. Ponti, L.; Gutierrez, A.P.; Ruti, P.M.; Dell’Aquila, A. Fine-scale ecological and economic assessment of climate change in the Mediterranean Basin reveals winners and losers. Proc. Natl. Acad. Sci. USA 2014, 111, 15. [Google Scholar] [CrossRef] [PubMed]
  7. Koubouris, G.C.; Metzidakis, I.T.; Vasilakakis, M.D. Impact of temperature on olive (Olea europaea L.) pollen performance in relation to relative humidity and genotype. Environ. Exp. Bot. 2009, 67, 209–214. [Google Scholar] [CrossRef]
  8. García-Inza, G.P.; Castro, D.N.; Hall, A.J.; Rousseaux, M.C. Responses to temperature of fruit dry weight, oil concentration, and oil fatty acid composition in olive (Olea europaea L. var. ‘Arauco’). Eur. J. Agron. 2014, 54, 107–115. [Google Scholar] [CrossRef]
  9. Ben-Ari, G.; Biton, I.; Many, Y.; Namdar, D.; Samach, A. Elevated Temperatures Negatively Affect Olive Productive Cycle and Oil Quality. Agronomy 2021, 11, 1492. [Google Scholar] [CrossRef]
  10. Miserere, A.; Searles, P.S.; Rousseaux, M.C. Influence of experimental warming on the rate and duration of fruit growth and oil accumulation in young olive trees (cvs. Arbequina, Coratina). Plants 2023, 12, 1942. [Google Scholar] [CrossRef] [PubMed]
  11. Hamze, L.M.; Miserere, A.; Molina, M.S.; Maestri, D.; Searles, P.S.; Rousseaux, M.C. Influence of environmental growth temperature on tocopherol and sterol oil concentrations in olive fruit. J. Sci. Food Agric. 2023, 102, 2741–2749. [Google Scholar] [CrossRef]
  12. Dag, A.; Harlev, G.; Lavee, S.; Zipori, I.; Kerem, Z. Optimizing olive harvest time under hot climatic conditions of Jordan Valley, Israel. Eur. J. Lipid. Technol. 2014, 116, 169–176. [Google Scholar] [CrossRef]
  13. López-Bernal, A.; Fernandes-Silva, A.A.; Vega, V.A.; Hidalgo, J.C.; León, L.; Testi, L.; Villalobos, F.J. A fruit growth approach to estimate oil content in olives. Eur. J. Agron. 2021, 123, 126206. [Google Scholar] [CrossRef]
  14. Galán, C.; García-Mozo, H.; Vázquez, L.; Ruiz, L.; Díaz de la Guardia, C.; Domínguez-Vilches, E. Modelling olive crop yield in Andalusia, Spain. Agron. J. 2008, 100, 98–104. [Google Scholar] [CrossRef]
  15. Iniesta, F.; Testi, L.; Orgaz, F.; Villalobos, F.J. The effects of regulated and continuous deficit irrigation on the water use, growth and yield of olive trees. Eur. J. Agron. 2009, 30, 258–265. [Google Scholar] [CrossRef]
  16. Torres, M.; Pierantozzi, P.; Searles, P.; Rousseaux, M.C.; García-Inza, G.; Miserere, A.; Bodoira, R.; Contreras, C.; Maestri, D. Olive cultivation in the southern hemisphere: Flowering, water requirements and oil quality responses to new crop environments. Front. Plant Sci. 2017, 8, 1830. [Google Scholar] [CrossRef]
  17. Lavee, S.; Wodner, M. Factors affecting the nature of oil accumulation in fruit of olive (Olea europaea L.) cultivars. J. Hortic. Sci. 1991, 66, 583–591. [Google Scholar] [CrossRef]
  18. Moriana, A.; Orgaz, F.; Pastor, M.; Fereres, E. Yield responses of a mature olive orchard to water deficits. J. Am. Soc. Hortic. Sci. 2003, 128, 425–431. [Google Scholar] [CrossRef]
  19. Fernández, J.E.; Perez-Martin, A.; Torres-Ruiz, J.M.; Cuevas, M.V.; Rodriguez-Domínguez, C.M.; Elsayed-Farag, S.; Morales-Sillero, A.; García, J.M.; Hernandez-Santana, V.; Diaz-Espejo, A. A regulated deficit irrigation strategy for hedgerow olive orchards with high plant density. Plant Soil. 2013, 372, 279–295. [Google Scholar] [CrossRef]
  20. Rondanini, D.P.; Castro, D.N.; Searles, P.S.; Rousseaux, M.C. Contrasting patterns of fatty acid composition and oil accumulation during fruit growth in several olive varieties and locations in a non-Mediterranean region. Eur. J. Agron. 2014, 52, 237–246. [Google Scholar] [CrossRef]
  21. Navas-López, J.F.; León, L.; Trentacoste, E.R.; De la Rosa, R. Multi-environment evaluation of oil accumulation pattern parameters in olive. Plant Physiol. Biochem. 2019, 139, 485–494. [Google Scholar] [CrossRef]
  22. López-Bernal, A.; Morales, A.; García-Tejera, O.; Testi, L.; Orgaz, F.; De Melo-Abreu, J.P.; Villalobos, F.J. OliveCan: A process-based model of development, growth and yield of olive orchards. Front. Plant Sci. 2018, 9, 632. [Google Scholar] [CrossRef]
  23. Beltrán, G.; Del Río, C.; Sánchez, S.; Martínez, L. Seasonal changes in olive fruit characteristics and oil accumulation during ripening process. J. Sci. Food Agric. 2004, 84, 1783–1790. [Google Scholar] [CrossRef]
  24. Montanaro, G.; Carlomagno, A.; Petrozza, A.; Cellini, F.; Manolikaki, I.; Koubouris, G.; Nuzzo, V. Predicting oil accumulation by fruit image processing and linear models in traditional and super high-density olive cultivars. Front. Plant Sci. 2024, 15, 1456800. [Google Scholar] [CrossRef]
  25. Cinosi, N.; Portarena, S.; Almadi, L.; Berrettini, A.; Torres, M.; Pierantozzi, P.; Villa, F.; Galletti, A.; Famiani, F.; Farinelli, D. Use of portable devices and an innovative and non-destructive index for in-field monitoring of olive fruit ripeness. Agriculture 2023, 13, 194. [Google Scholar] [CrossRef]
  26. Tsouros, D.C.; Bibi, S.; Sarigiannidis, P.G. A review on uav-based applications for precision agriculture. Information 2019, 10, 349. [Google Scholar] [CrossRef]
  27. Villalobos, F.J.; López-Bernal, A.; García-Tejera, O.; Testi, L. Is olive crop modelling ready to assess the impacts of global change? Front. Plant Sci. 2023, 14, 1249793. [Google Scholar] [CrossRef]
  28. Gavilán, P.; Lorite, I.J.; Tornero, S.; Berengena, J. Regional calibration of Hargreaves equation for estimating reference ET in a semiarid environment. Agric. Water Manag. 2006, 81, 257–281. [Google Scholar] [CrossRef]
  29. Fernández, F.J.; Ladux, J.L.; Hammami, S.B.M.; Rapoport, H.F.; Searles, P.S. Fruit, mesocarp, and endocarp responses to crop load and to different estimates of source: Sink ratio in olive (cv. Arauco) at final harvest. Sci. Hortic. 2018, 234, 49–57. [Google Scholar] [CrossRef]
  30. Hamze, L.M.; Rousseaux, M.C.; Searles, P.S.; Trentacoste, E.R. Fruit growth and oil accumulation of olive (Olea europaea L.) cultivars at different locations along a latitudinal-altitudinal gradient. Horticulturae 2024, 10, 1339. [Google Scholar] [CrossRef]
  31. Trentacoste, E.R.; Puertas, C.M.; Sadras, V.O. Modelling the intraspecific variation in the dynamics of fruit growth, oil and water concentration in olive (Olea europaea L.). Eur. J. Agron. 2012, 38, 83–93. [Google Scholar] [CrossRef]
  32. Monleon-Getino, A. ANOVA Caso no Balanceado: Suma de Cuadrados de Tipo I, II, III y IV; Technical report; Barcelona, Spain, 2017. [Google Scholar] [CrossRef]
  33. Fox, J.; Weisberg, S. An R Companion to Applied Regression. Package “Car”; Sage: Thousand Oaks, CA, USA, 2019; Available online: https://CRAN.R-project.org/package=car (accessed on 9 September 2024).
  34. Davison, A.C.; Hinkley, D.V. Bootstrap Methods and Their Application, Chapter 5; Cambridge University Press: Cambridge, MA, USA, 1997. [Google Scholar]
  35. Olivoto, T.; Lúcio, A.D.C.; Souza, V.Q.; Nardino, M.; Diel, M.I.; Sari, B.G.; Krysczun, D.K.; Meira, D.; Meier, C. Confidence interval width for Pearson’s correlation coefficient: A Gaussian-independent estimator based on sample size and strength of association. Agron. J. 2018, 110, 503–510. [Google Scholar] [CrossRef]
  36. Peng, R.D. Package ‘Simpleboot’: Simple Bootstrap Routines. R Package. Version 1.1-8. 2024. Available online: https://github.com/rdpeng/simpleboot (accessed on 15 May 2024).
  37. Frieler, K.; Lange, S.; Piontek, F.; Reyer, C.P.O.; Schewe, J.; Warszawski, L.; Zhao, F.; Chini, L.; Denvil, S.; Emanuel, K.; et al. Assessing the impacts of 1.5 °C global warming—Simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b). Geosci. Model. Dev. 2017, 10, 4321–4345. [Google Scholar] [CrossRef]
  38. Rosenzweig, C.; Arnell, N.W.; Ebi, K.L.; Lotze-Campen, H.; Raes, F.; Rapley, C.; Smith, M.S.; Cramer, W.; Frieler, K.; Reyer, C.P.O.; et al. Assessing inter-sectoral climate change risks: The role of ISIMIP. Environ. Res. Lett. 2017, 12, 010301. [Google Scholar] [CrossRef]
  39. Riahi, K.; Van Vuuren, D.P.; Kriegler, E.; Edmonds, J.; O’Neill, B.C.; Fujimori, S.; Bauer, N.; Calvin, K.; Dellink, R.; Fricko, O.; et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Glob. Environ. Change 2017, 42, 153–168. [Google Scholar] [CrossRef]
  40. Lange, S.; Büchner, M. ISIMIP3b bias-adjusted atmospheric climate input data (v1.1). ISIMIP Repository, 15 May 2021. [Google Scholar] [CrossRef]
  41. Cabezas, J.M.; Ruiz-Ramos, M.; Soriano, M.A.; Santos, C.; Gabaldón-Leal, C.; Lorite, I.J. Impact of climate change on economic components of Mediterranean olive orchards. Agric. Water Manag. 2021, 248, 106760. [Google Scholar] [CrossRef]
  42. Nissim, Y.; Shlobert, M.; Biton, I.; Many, Y.; Doron-Faigenboim, A.; Zemach, H.; Hovav, R.; Kerem, Z.; Avidan, B.; Ben-Ari, G. High temperature environment reduces olive oil yield and quality. PLoS ONE 2020, 15, e0231956. [Google Scholar] [CrossRef] [PubMed]
  43. Benlloch-Gonzalez, M.; Sanchez-Lucas, R.; Bejaoui, M.A.; Benlloch, M.; Fernández-Escobar, R. Global warming effects on yield and fruit maturation of olive trees growing under field conditions. Sci. Hortic. 2019, 249, 162–167. [Google Scholar] [CrossRef]
  44. Miserere, A.; Searles, P.S.; Rousseaux, M.C. Oil yield components and biomass production responses to warming during the oil accumulation phase in young olive trees. Sci. Hortic. 2022, 291, 110618. [Google Scholar] [CrossRef]
  45. Lavee, S.; Wodner, M. The effect of yield, harvest time and fruit size on the oil content in fruits of irrigated olive trees (Olea europaea), cvs. Barnea and Manzanillo. Sci. Hortic. 2004, 99, 267–277. [Google Scholar] [CrossRef]
  46. Mailer, R.J.; Ayton, J.; Conlan, D. Influence of harvest timing on olive (Olea europaea) oil accumulation and fruit characteristics under Australian conditions. J. Food Agric. Environ. 2007, 5, 58–63. [Google Scholar]
  47. Rallo, L.; Díez, C.M.; Morales-Sillero, A.; Miho, H.; Priego-Capote, F.; Rallo, P. Quality of olives: A focus on agricultural preharvest factors. Sci. Hortic. 2018, 233, 491–509. [Google Scholar] [CrossRef]
  48. García-Inza, G.P.; Hernández, M.L.; Miserere, A.; Sicardo, M.D.; Martínez-Rivas, J.M.; Rousseaux, M.C. Transcriptional regulation of oil synthesis and fatty acid desaturation in olive fruit under elevated growth temperature. Physiol. Plant. 2024, 176, e14149. [Google Scholar] [CrossRef]
  49. Lorite, I.J.; Cabezas-Luque, J.M.; Arquero, O.; Gabaldón-Leal, C.; Santos, C.; Rodríguez, A.; Ruiz-Ramos, M.; Lovera, M. The role of phenology in the climate change impacts and adaptation strategies for tree crops: A case study on almond orchards in Southern Europe. Agric. For. Meteorol. 2020, 294, 10814. [Google Scholar] [CrossRef]
  50. Dag, A.; Karem, Z.; Yogev, N.; Zipori, I.; Lavee, S.; Ben-David, E. The influence of time of harvest and maturity index on olive oil yield and quality. Sci. Hortic. 2011, 127, 358–366. [Google Scholar] [CrossRef]
  51. Gucci, R.; Lodolini, E.; Rapoport, H.F. Productivity of olive trees with different water status and crop load. J. Hortic. Sci. Biotech. 2007, 82, 648–656. [Google Scholar] [CrossRef]
Agronomy 15 02262 g001
Figure 1. Average daily maximum temperature (in °C) in August for the Near Future (A) and Far Future (B) periods under the SSP5-RCP8.5 scenario, showing the locations of the 5 commercial olive orchards considered in the study (yellow circles), and of representative sites for climate change analyses (green circles). The gray shaded area indicates the current olive orchard area, and the black closed contour in the south, the Guadalquivir basin.
Figure 1. Average daily maximum temperature (in °C) in August for the Near Future (A) and Far Future (B) periods under the SSP5-RCP8.5 scenario, showing the locations of the 5 commercial olive orchards considered in the study (yellow circles), and of representative sites for climate change analyses (green circles). The gray shaded area indicates the current olive orchard area, and the black closed contour in the south, the Guadalquivir basin.
Agronomy 15 02262 g001
Agronomy 15 02262 g002
Figure 2. Schematic diagram of the oil accumulation pattern based on [21], defining the date of start of oil accumulation (DSOA), date of maximum oil content (DMOC), length of oil accumulation period (LOC), rate of oil accumulation (ROA) and maximum oil content (MOC) considering a bilinear (solid line) and a polynomial (dotted line) approach.
Figure 2. Schematic diagram of the oil accumulation pattern based on [21], defining the date of start of oil accumulation (DSOA), date of maximum oil content (DMOC), length of oil accumulation period (LOC), rate of oil accumulation (ROA) and maximum oil content (MOC) considering a bilinear (solid line) and a polynomial (dotted line) approach.
Agronomy 15 02262 g002
Agronomy 15 02262 g003
Figure 3. Delay of the date of maximum oil content (DMOC, in days) for ‘Arbequina’ (A) and for ‘Picual’ (B) for the Near Future period with SSP5-RCP8.5 scenario compared to the Historical period. In both figures the numbers indicate the DMOC in day of year (DOY) for each cultivar.
Figure 3. Delay of the date of maximum oil content (DMOC, in days) for ‘Arbequina’ (A) and for ‘Picual’ (B) for the Near Future period with SSP5-RCP8.5 scenario compared to the Historical period. In both figures the numbers indicate the DMOC in day of year (DOY) for each cultivar.
Agronomy 15 02262 g003
Agronomy 15 02262 g004
Figure 4. Reduction in maximum oil content (MOC; in percentage points) for ‘Arbequina’ (A) and for ‘Picual’ (B) for the Near Future period with the SSP5-RCP8.5 scenario compared to the Historical period. In both figures, the numbers indicate the final oil accumulation (in percent) for each cultivar.
Figure 4. Reduction in maximum oil content (MOC; in percentage points) for ‘Arbequina’ (A) and for ‘Picual’ (B) for the Near Future period with the SSP5-RCP8.5 scenario compared to the Historical period. In both figures, the numbers indicate the final oil accumulation (in percent) for each cultivar.
Agronomy 15 02262 g004
Table 1. Mean maximum daily temperature recorded for the summer period (July; TmaxJL, August; TmaxAG, and September; TmaxS) during 2017–2020 at the 5 sites included in the study.
Table 1. Mean maximum daily temperature recorded for the summer period (July; TmaxJL, August; TmaxAG, and September; TmaxS) during 2017–2020 at the 5 sites included in the study.
LocationsTmaxJLTmaxAGTmaxS
(°C)(°C)(°C)
Antequera35.135.430.6
Baena36.736.530.5
Córdoba37.037.332.4
La Rambla35.637.332.6
Úbeda37.336.731.1
Table 2. Year, location and cultivar (Arb; Arbequina, Hoj; Hojiblanca, Kor; Koroneiki, Pic; Picual) considered in each trial.
Table 2. Year, location and cultivar (Arb; Arbequina, Hoj; Hojiblanca, Kor; Koroneiki, Pic; Picual) considered in each trial.
LocationAntequeraBaenaCórdobaLa RamblaÚbedaTotal
Year
2017ArbArbArbArbArb19
HojHojHojHojHoj
KorKorKorKorKor
PicPicPic Pic
2018Arb ArbArb 12
Hoj HojHoj
Kor KorKor
Pic PicPic
2019Arb Arb Arb11
Hoj
KorKorKor Kor
PicPic Pic
2020Arb Arb Arb11
Hoj Hoj
Kor Kor Kor
Pic Pic Pic
Total1561471153
Table 3. ANOVA from a factorial analysis in a completely randomized design of date of start of oil accumulation (DSOA), date of maximum oil content (DMOC), length of oil accumulation period (LOA), rate of oil accumulation (ROA), the maximum oil content (MOC) and date of year (DOY). ** Significant at 1% level. * Significant at 5% level. df: degrees of freedom; SV: source of variation; CV: coefficient of variation; MS: mean square.
Table 3. ANOVA from a factorial analysis in a completely randomized design of date of start of oil accumulation (DSOA), date of maximum oil content (DMOC), length of oil accumulation period (LOA), rate of oil accumulation (ROA), the maximum oil content (MOC) and date of year (DOY). ** Significant at 1% level. * Significant at 5% level. df: degrees of freedom; SV: source of variation; CV: coefficient of variation; MS: mean square.
DSOA
(DOY)		DMOC
(DOY)		LOA
(days)		ROAMOC
(%)
SVdfMS MS MS MS MS
Years3836**224ns735*0.0035ns64**
Locations4578**95ns859*0.0046ns67**
Varieties3214ns151ns631ns0.0076*27ns
Error42126 87 241 0.0019 11
Total52
CV 5.6 3.0 13.7 13.4 7.3
Grand Mean 200 313 113 0.33 45
Table 4. Cultivar, year and location means comparisons using the Tukey test for date of start of oil accumulation (DSOA), date of maximum oil content (DMOC), length of oil accumulation period (LOA), rate of oil accumulation (ROA) and maximum oil content (MOC). Values followed by the same letter are not significantly different (p < 0.05).
Table 4. Cultivar, year and location means comparisons using the Tukey test for date of start of oil accumulation (DSOA), date of maximum oil content (DMOC), length of oil accumulation period (LOA), rate of oil accumulation (ROA) and maximum oil content (MOC). Values followed by the same letter are not significantly different (p < 0.05).
DSOA
(DOY)		DMOC
(DOY)		LOA
(days)		ROA
(-)		MOC
(%)
MeanMeanMeanMeanMean
Cultivars
Arbequina198A311A113AB0.33AB46.9A
Hojiblanca196A319A123A0.32AB44.1A
Koroneiki201A314A113AB0.30B43.8A
Picual206A310A105B0.36A45.4A
Years
2017203A311A108AB0.34A48.1A
2018205A320A116AB0.30A44.1B
2019206A312A105B0.32A44.0B
2020187B311A124A0.35A44.0B
Locations
Antequera201A313A111BC0.34A46.5A
Baena185B313A129A0.33A49.2A
Córdoba199AB318A119AB0.30A42.9B
La Rambla208A311A103C0.33A44.3AB
Úbeda207A312A104C0.35A42.4B
Table 5. Cultivar, number of entries (n), correlation coefficient (Corr.), its significance (Sig.), coefficient of determination (R2) and the regression formula relating the main summer temperature variables to the date of start of oil accumulation (DSOA), date of maximum oil content (DMOC), rate of oil accumulation (ROA) and maximum oil content (MOC). ** Significant at 1% level.
Table 5. Cultivar, number of entries (n), correlation coefficient (Corr.), its significance (Sig.), coefficient of determination (R2) and the regression formula relating the main summer temperature variables to the date of start of oil accumulation (DSOA), date of maximum oil content (DMOC), rate of oil accumulation (ROA) and maximum oil content (MOC). ** Significant at 1% level.
Simple Regression Formula
CultivarnCorr.Sig.R2y=β(Variable)+α
Arbequina14−0.52**0.30DSOA=−3.22 **(TminJL)+261
Picual13−0.52**0.31DSOA=−4.25 **(TmeanJL)+324
Arbequina140.49**0.26DMOC=3.41 **(TminS)+256
Picual130.56**0.34DMOC=8.17 **(TmeanS)+118
Arbequina14−0.75**0.58ROA=−0.03 **(TminS)+0.90
Picual13−0.59**0.37ROA=−0.04 **(TmeanS)+1.21
Arbequina14−0.37**0.17MOC=−1.32 **(TmaxAG)+94.8
Picual13−0.48**0.26MOC=−3.19 **(TmaxAG)+162
Table 6. Maximum oil content (MOC; %) for representative sites in the Iberian Peninsula for ‘Arbequina’ and ‘Picual’ under Historical (H), Near Future (NF) and Far Future (FF) periods, and for the SSP1-RCP2.6 (2.6), SSP3-RCP7.0 (7.0) and SSP5-RCP8.5 (8.5) scenarios.
Table 6. Maximum oil content (MOC; %) for representative sites in the Iberian Peninsula for ‘Arbequina’ and ‘Picual’ under Historical (H), Near Future (NF) and Far Future (FF) periods, and for the SSP1-RCP2.6 (2.6), SSP3-RCP7.0 (7.0) and SSP5-RCP8.5 (8.5) scenarios.
Picual
LocationHNF_2.6NF_7.0NF_8.5FF_2.6FF_7.0FF_8.5
Beja60.651.248.947.551.343.339.7
Jerez61.254.853.452.955.250.248.0
Córdoba49.740.137.636.440.632.528.5
Úbeda53.742.439.037.442.832.327.2
Reus70.359.957.055.760.650.246.2
Mérida52.542.239.938.342.734.030.0
Antequera56.346.644.142.947.139.135.1
Lorca62.454.251.650.754.846.943.1
Arbequina
LocationHNF_2.6NF_7.0NF_8.5FF_2.6FF_7.0FF_8.5
Beja52.848.948.047.449.045.744.2
Jerez53.150.549.849.650.648.547.6
Córdoba48.344.343.342.844.641.239.6
Úbeda50.045.343.943.345.541.139.0
Reus56.852.551.350.852.948.546.9
Mérida49.545.244.343.645.441.840.2
Antequera51.047.046.045.547.243.942.3
Lorca53.650.249.148.750.447.245.6
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Cabezas, J.M.; Alza, J.O.; de la Rosa, R.; Santos, C.; del Río-Celestino, M.; Lorite, I.J. Modeling the Impact of Future Temperature Increases on Olive Oil Accumulation Patterns in the Iberian Peninsula. Agronomy 2025, 15, 2262. https://doi.org/10.3390/agronomy15102262

AMA Style

Cabezas JM, Alza JO, de la Rosa R, Santos C, del Río-Celestino M, Lorite IJ. Modeling the Impact of Future Temperature Increases on Olive Oil Accumulation Patterns in the Iberian Peninsula. Agronomy. 2025; 15(10):2262. https://doi.org/10.3390/agronomy15102262

Chicago/Turabian Style

Cabezas, José Manuel, José Osmar Alza, Raúl de la Rosa, Cristina Santos, Mercedes del Río-Celestino, and Ignacio Jesús Lorite. 2025. "Modeling the Impact of Future Temperature Increases on Olive Oil Accumulation Patterns in the Iberian Peninsula" Agronomy 15, no. 10: 2262. https://doi.org/10.3390/agronomy15102262

APA Style

Cabezas, J. M., Alza, J. O., de la Rosa, R., Santos, C., del Río-Celestino, M., & Lorite, I. J. (2025). Modeling the Impact of Future Temperature Increases on Olive Oil Accumulation Patterns in the Iberian Peninsula. Agronomy, 15(10), 2262. https://doi.org/10.3390/agronomy15102262

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop