Physiological and biochemical study of the drought tolerance of 14 main olive cultivars in the Mediterranean Basin

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These cultivars are Arbequina, Arbosana, Chemlali, Cornicabra, Cornezuelo de Jaén, Empeltre, Frantoio, Hojiblanca, Koroneiki, Manzanilla de Sevilla, Martina, Picual, Sikitita1 and Sikitita 2. All of them are certified by the World Olive Germplasm Bank of Córdoba (Spain). They are predominant cultivars in the olive groves of different locations throughout the Mediterranean basin, and they were subjected to total water deficit for a minimum of 14 days and a maximum of 42 days in the present study. Data such as chlorophyll content, soil moisture and specific leaf area were gathered. Photosynthetic parameters measured at the respective saturation irradiance of each cultivar were also analysed: assimilation rate, transpiration, stomatal conductance, photosynthetic efficiency, photochemical and non-photochemical quenching, photonic flux density, electron transference ratio, efficient use of water and amount of proline and malondialdehyde as indicators of oxidative stress. In addition to the control, two different experimental conditions were analysed: moderate drought, after 14 days of lack of irrigation, and severe drought, after 28 to 42 days of total absence of irrigation, depending on the tolerance of each cultivar. Based on the results, the cultivars were characterised and divided into four groups according to their drought tolerance: tolerant, moderately tolerant, moderately sensitive and sensitive to drought. This work represents the first contribution of drought tolerance of a considerable number of olive cultivars, with all of them being subjected to the same criteria and experimental conditions for their classification. water stress olive cultivars drought tolerance photosynthesis physiological study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Key message Classification of 14 olive cultivars of great economic importance according to their drought tolerance, based on the analysis of numerous physiological, photosynthetic and biochemical parameters. 1. Introduction Climate change is strongly related to crop productivity, both directly and indirectly. Its consequences on the different environmental factors have caused important changes, such as temperature and precipitation variability, changes in the distribution of plant diseases, different seasonal patterns, impacts on the soil and the seas, desertification, etc., which reduce crop yield by up to 70%. The scarcity of water, resulting from the progressive increase in temperatures, is one of the most worrying problems in agricultural science. At a physiological, morphological and biochemical level, plants have undergone changes in their metabolism that have affected their turgor, growth, Rubisco activity, chlorophyll content, photosystems and the functionality of the latter. Numerous plants synthesise and accumulate compatible solutes or osmolytes, also known as osmoprotectants, which are small and highly soluble non-toxic molecules that protect the cells against adverse conditions and stabilise the proteins and nucleic acids and membranes, producing the osmotic equilibrium. Research in metabolomics and transcriptomics reports that plants overexpressing osmolyte biosynthesis or metabolic genes showed enhanced stress tolerance (Nahar et al., 2016 ). Water stress affects the correct development of the physiological and metabolic processes of the plant, responding to the appearance of a stress situation according to a sequential model that includes four phases (Shabala, 2017 ): 1) alarm phase, in which the plant detects environmental changes and prepares itself to combat stress; 2) resistance phase: the plant sets off mechanisms or synthesises osmolytes that increase its tolerance; 3) depletion phase, in which the individual is not able to generate more resistance or withstand stress; and 4) regeneration phase, if the stress has finished and the plant can recover from the damage sustained. The olive tree (Olea europaea L.) has been historically considered as a tree endowed with great rusticity and resistance to adverse conditions. However, the great changes that are currently occurring in the different climatic factors, such as large increases in temperatures, prolonged periods of drought combined with irregular and abundant punctual rains, produce damage to the crops and alterations in the growth and development of the plantations, including olive crops. The most affected growth phase of the olive grove is flowering, which is very susceptible to temperature, and it is currently taking place ealier than usual in the spring months. Consequently, the stage of development and maturation of the fruit also occurs earlier, before the end of summer. The high temperatures of recent years in the spring months cause the blossom to fall, leading to less productive crops with poor fruit quality and low yields (Malhi et al., 2021 ). In Andalusia, two varieties represent 80% of olive crops: Picual (60%) and Hojiblanca (20%), which are the most productive and versatile. However, many other varieties are identified and characterised as more adapted to certain local climates, soil types, or difficult terrain (Barbuzano, 2020 ). There is a strikingly small number of publications about olive trees and the drought tolerance of different cultivars using physiological, photosynthetic and spectrophotometric studies. Bacelar et al. (2007) published a review of the effects of drought on photosynthesis, transpiration, stomatic conductance and water use efficiency of several Portuguese olive cultivars. A study was carried out on two cultivars, i.e., Arbequina and Manzanilla de Sevilla, during the pre-flowering and flowering periods; according to the results of these authors, it seems that both cultivars behaved similarly against drought (Pierantozzi et al., 2013 ). Other studies provide information on the morphological changes suffered by two cultivars, Chemlali and Meski, with lack of water (Tangu, 2014 ), and on the variation in the phenolic content of Meski under water stress (Mechri et al., 2020 ). Another work related to drought analysed the tolerance of four cultivars (two Persian cultivars: Fishomi and Dezful and two Greek cultivars: Amigdalolia and Conservolia) to water deficit. The higher drought tolerance of these plants was related to the higher concentration of soluble carbohydrates, proline, potassium, and calcium in their leaves (Karimi et al., 2018 ). There are also enzymatic analyses of proteins related to oxidative stress in Koroneiki grown under different foliar treatments and subjected to water deficit (Denaxa et al., 2020 ). According to the work of Bacelar et al. ( 2009 ), cultivars native to dry regions, such as Cobrancosa, Manzanilla and Negrinha, have a greater capacity to acclimate to drought conditions than cultivars originating in regions with a more temperate climate, like Arbequina and Blanqueta. Faraloni et al. ( 2011 ) measured chlorophyll fluorescence on detached olive leaves subjected to dehydration in vitro in 24 cultivars. Boughalleb et al. ( 2011 ) compared two cultivars against drought, i.e., Zalmati and Chemlali, concluding that the latter variety is much more resistant than Zalmati. Boussadia et al. ( 2008 ) measured photosynthetic gas exchange and fluorescence parameters in Koroneiki and Meski to obtain their tolerance to drought. According to Mousavi et al. ( 2019 ), Koroneiki is a drought-sensitive cultivar. The objective of this work was the physiological and biochemical characterisation of 14 olive cultivars, which were exposed to water deficit for six weeks, without any irrigation: Arbequina (AE), Arbosana (AO), Chemlali (CH), Cornezuelo de Jaén (CJ), Cornicabra (C), Empeltre (E), Frantoio (F), Hojiblanca (H), Koroneiki (K), Martina (M), Manzanilla de Sevilla (MS), Picual (P), Sikitita 1 (S1) and Sikitita 2 (S2). We also evaluated the degree of tolerance and adaptation of each of the selected cultivars to drought, through the measurement of their response to oxidative stress with photosynthetic and ecophysiological tests over time. On the one hand, photosynthetic parameters of each cultivar were measured, including chlorophyll content, soil moisture, leaf area, saturation irradiance, maximal photosynthetic activity, stomatal conductance, transpiration, PSII efficiency, photochemical and non-photochemical quenching, water use efficiency and instantaneous water use efficiency. Furthermore, the degree of tolerance and adaptation of each selected cultivar to drought was evaluated, using spectrophotometric tests to quantify proline and malondialdehyde, in order to determine which of the analysed cultivars are the most tolerant or sensitive to drought. 2. Material and methods 2.1. Plant material The cultivars selected for the study are some of the most important worldwide due to the large extensions of cultivation that they occupy in the Mediterranean basin. These cultivars are certified by the World Olive Germplasm Bank of Córdoba (WOGBC), and they are cultivated and provided by Cordoplant Viveros: Picual, Arbequina, Hojiblanca, Manzanilla de Sevilla, Empeltre, Cornicabra, Cornezuelo de Jaén, Arbosana, Sikitita 1, Sikitita 2, Martina, Koroneiki, Chemlali and Frantoio. The experiment was carried out in an Aralab growth chamber, model 12000PHL-LED, regulated by a FITOLOG 9000 control software incorporated into the installation. Environmental conditions: temperature (21ºC/18ºC, day/night), relative humidity (50%), photoperiod (16 h light/8 h dark), irradiance (165 µmol·m 2 ·s − 1 ), and CO 2 concentration (400 ppm). Irrigation was applied on alternate days by sprinkler only to control samples (six samples per cultivar). The individuals subjected to drought did not receive any irrigation for at least 28 days (in the case of the most drought-sensitive cultivars) and up to 42 days (in the case of the most drought-resistant cultivars). Samples from Koroneiki and Empeltre were taken after 28 days of drought; Hojiblanca after 30 days; Cornezuelo de Jaén, Picual, and Arbequina after 35 days; and finally, the rest of the cultivars endured the absence of water until harvested after 42 days. Leaves of control samples (5–6 leaves/cultivar) were collected, as well as leaves in two different times of drought: moderate drought (MD), after 14 days without receiving any irrigation, and in the last day of severe drought (SD) before their irrigation, which was after 28 days in all cultivars and up to 42 days in the most resistant cultivars. All the samples were collected from at least three different samples and frozen in liquid nitrogen. Subsequently, the plant material was ground using a ball mill, always in a liquid nitrogen environment to avoid thawing, and was stored in an ultra-freezer at -80ºC. 2.2. Photosynthetic parameters determination 2.2.1. Chlorophyll content and soil humidity To measure the chlorophyll content of the leaves, an M-100 Apogee Instrument was used. The device uses equations that allow it to measure any plant species (Parry et al., 2014 ). Chlorophyll content per leaf surface (µmol/m 2 ) was quantified each week by performing 10 measurements to obtain the mean in each case. The quantification was made in triplicate in three different samples per week. To ensure that the plants were experiencing increasing drought over time, soil moisture was measured with a multi-probe using a sensor for temperature, humidity and electrical conductivity of the soil, verifying the percentage of water in the soil during the course of the experiment in drought. 2.2.2. Leaf area, photosynthetic parameters and water use efficiency A scanner was used to measure the leaf area, collecting ​​six leaves from three random control individuals in each cultivar, employing the Image J software, thus obtaining an average for each variety. To generate the light-response data, experiments were conducted to measure photosynthesis–light curves in triplicate, weekly, on the mature, fully expanded leaves of the 14 previously mentioned olive cultivars from November 2021 to March 2022. A LI-6800 Portable Photosynthesis System (68H-581003/68C-571003) with a fluorometer was used to this end. Light response data were gathered using nine different incident light levels: 2000, 1500, 1000, 750, 500, 250, 100, 50 and 0 µmol m − 2 s − 1. Leaf temperature was selected at 21°C and CO2 flux at 400 ppm. Relative humidity was 60–65% during the quantum yield experiments. Data were collected at 0.5 Hz for the full light response curves. The auto-programmes were set to run for 60 to 180 s at a given light level before moving to the next light level. At each light level, the final 40 s of data were used as the representative steady-state data population that was subsequently analysed. Previously, steady-state modulated chlorophyll fluorescence was determined in the leaves of three different plants of each cultivar since the beginning of the experiment. Leaves were dark-adapted overnight to obtain F0 (minimum fluorescence), Fm (maximal fluorescence dark-adapted), and A Dark (respiration of the plant in the dark), and then we calculated Fv (variable fluorescence, equivalent to Fm-F0 dark-adapted) and Fv/Fm (maximum quantum yield of ΦPSII dark-adapted). With the light response curves, we calculated the saturation irradiance and, at this point, we obtained the maximal fluorescence light-adapted (Fm’), variable fluorescence Fv´ (equivalent to Fm´-F0) and the quantum yield of PSII electron transport (ΦPSII), equivalent to (F´m -F)/F´m. Data on maximal photosynthesis (A, µmol CO2 m-2 s-1) at the specific saturation irradiance of each cultivar in each experimental condition (control, MD and SD), intracellular CO2 concentration content (Ci, ppm), stomatal conductance (gsw, mol H2O m-2 s-1) and transpiration rate (E, mol H2O m-2 s-1) were gathered. Moreover, LiCor 6800 calculated the electron transport radius (ETR), quantity of light absorbed (Qabs), photochemical quenching (qP) (i.e., the percentage of light used by the photosystems to produce energy), and NPQ (non-photochemical quenching or percentage of light heat dissipated by carotenoids). 2.3. Proline and malondialdehyde quantification Analysis of proline concentration was performed following the protocol of Sofo et al. ( 2004 ) modified. To 0.1 g of the ground plant tissue, 2 ml of 3% sulfosalicylic acid was added. Subsequently, the samples were taken to a thermostatic bath at 100ºC for 20 minutes and centrifuged at 2500 rpm for 7 minutes at room temperature. The supernatant was obtained and two 300 µl aliquots were collected from it, to which 300 µl of milli-Q water and 2 ml of the reaction mixture were added. The samples were incubated at 100°C for 25 minutes and subsequently cooled down on ice. Finally, 4 ml of toluene was added, and the samples were stirred and allowed to stand for 15 minutes until the phases were differentiated, selecting the upper phase to measure absorbance at 520 nm. The spectrophotometric determination of proline was performed with the extrapolation of the data on a standard curve generated from a 10mM solution of proline. MDA was measured according to the protocol of Lozano-Rodríguez, E. et al. (1997), with some modifications. The TCA-TBA-HCl reagent was prepared extemporaneously, consisting of 15% (w/v) trichloroacetic acid (TCA), 0.37% (w/v) 2-thiobarbituric acid (TBA), 2.4% (v/v) concentrated hydrochloric acid (HCl) and 0.01% (w/v) butyl hydroxytoluene (BHT). To 0.1 g of the plant tissue of each sample, 1.5 ml of the TCA-TBA-HCl reagent was added and incubated in a thermostated bath at 95°C for 30 minutes. The samples were quickly cooled in a container with ice and, subsequently, they were centrifuged at 12,000 g and 4ºC for 15 minutes, collecting the supernatant, where the absorbance at 532 nm and 600 nm was measured. The amount of MDA in each sample was calculated using the Lambert-Beer equation, using the following equation: A = ɛ · c · l, where A corresponds to the difference of the absorbances, ɛ is the molar extinction coefficient (1.56·105 M-1·cm-1), c is the concentration of malondialdehyde, and, finally, l corresponds to the distance in cm that the light from the spectrophotometer travels when passing through the sample (1 cm). All samples were measured in triplicate. 2.4. Statistical analyses All data were processed using the statistical programme IBM® SPSS® Statistics v.27. The data were treated and preprocessed before the statistical analysis, from the photosynthesis-light curves of each cultivar. The data selected were those obtained by the Li-Cor 6800 in each parameter at their saturating irradiance, which was different in each cultivar. We worked with datasets grouped into two intervals: in the case of MD situations, the data obtained at 7, 14 and 21 days were used, and in SD, the data obtained at 21, 28 and 35 days were used. The ANOVA comparison test was applied. To identify the significance between the drought treatments in each cultivar, a post hoc multiple-comparison test was performed, assuming equal variances and using Bonferroni’s and Tukey’s HSD tests. 3. Results and discussion This is a physiological, photosynthetic and biochemical study of the drought tolerance of 14 olive cultivars widely represented in the olive groves of the Mediterranean basin. The plants were subjected to a total absence of irrigation for a maximum of 42 days. Not all cultivars withstood the extreme degree of drought: some varieties had to be irrigated at 28 days, such as Empeltre and Koroneiki; Hojiblanca was irrigated at 30 days; Cornezuelo de Jaén, Picual, and Arbequina were irrigated at 35 days; and, finally, Arbosana, Chemlali, Cornicabra, Frantoio, Martina, Manzanilla de Sevilla, Sikitita 1 and Sikitita 2 withstood the absence of water until they were harvested at 42 days. This ability to withstand water deficit already provides valuable information that helps to deduce which varieties are more tolerant to drought and which are more sensitive. Once the samples were collected and frozen until they were processed, significant results were obtained for almost all the parameters measured, which allowed us to make decisions about their drought tolerance. 3.1. Quantification of chlorophylls and humidity of the soil Chlorophyll measurements were made in the 14 olive cultivars mentioned throughout the 6 weeks of the experiment. The measurements were made one day per week, thereby obtaining data at 0, 7, 14, 21, 28, 35 and 42 days after irrigation. Chlorophyll levels are negatively affected by the passage of time, decreasing progressively with water deficit (Table 1 ). Some cultivars such as Cornicabra (56.9%), Empeltre (55.6%) and Hojiblanca (52%) reflect a loss of photosynthetic pigments in the leaf at the end of the experiment of more than 50% with respect to the initial values, while decreasing in the rest of the cultivars, although less drastically (Table 1 ). Table 1 Chlorophyll concentration (µmol/m2) of each cultivar and % decrease of pigments in severe drought with respect to the control samples. Chlorophyll amount over time Control Moderate Drought Severe Drought AE 513.14 412.82 322.00 AO 738.53 638.33 448.43 CH 315.09 748.50 593.77 CJ 357.62 323.66 286.48 C 885.74 368.50 382.00 E 658.61 431.79 292.57 F 615.26 470.18 362.03 H 364.65 248.64 174.95 K 602.68 501.16 420.22 MS 250.36 293.09 230.86 M 844.03 732.40 581.52 P 450.24 415.65 295.18 S1 622.05 497.94 316.45 S2 420.75 453.67 365.38 A two-way ANOVA was performed to determine whether water deficit (control vs. moderate/severe drought) and cultivar type (AE, AO, CH, CJ, C, E, F, H, K, MS, M, P, S1, S2) have a significant effect (p < 0.001) on chlorophyll concentration. The results revealed that both water deficit (p < 0.001) and cultivar type (p < 0.001) had a statistically significant effect on chlorophyll concentration. Figure 1 represents the concentration of chlorophyll in each cultivar grouped by treatments (Control, MD and SD). In the statistical analysis, the majority of the cultivars present statistical differences between the three treatments, with some exceptions: C has homogeneous data in all the experimental conditions; in the case of CJ and P, only the conditions of SD present significant differences compared to the rest; MS is different in MD; and S2 only presents differences between MD and SD. These data are in line with those published by Bacelar et al. (2007) and Brito et al. ( 2019 ) about the progressive decrease in pigments suffered by the olive tree under water stress. Measurements of soil humidity throughout the 6 weeks of the experiment were carried out using a multiprobe to corroborate the degree of water deficit suffered by all cultivars. It was found that the % of water reached almost the limit of the permanent wilting index in the 14 cultivars, being less than 5% in all cases in the last days of the experiment. In general, it was possible to verify that this is a characteristic of the soil and not of the plant, since the behaviour was very similar in all the cultivars (Fig. 2 ). 3.2. Leaf area, photosynthetic parameters and water use efficiency The measurement of the leaf area (LA) for each cultivar was evaluated from six leaves collected from three control individuals per cultivar studied, in order to ascertain the leaf area under normal "ad libitum" irrigation conditions and determine whether a greater surface area of the leaves involves a greater amount of pigments per leaf, and thus greater photosynthesis, transpiration and chlorophyll content. Comparison by Euclidean analysis was made to calculate the similarity index between cultivars, obtaining a dendrogram with notable differences in their leaf area and identifying the existence of six clusters (Fig. 3 ): Cluster A (Sikitita 2, Martina, Koroneiki and Arbequina); Cluster B (Sikitita 1, Frantoio and Chemlali); Cluster C (Arbosana, Empeltre and Picual); Cluster D (Hojiblanca and Conezuelo de Jaén); Cluster E (Cornicabra); and, finally, Cluster F (Manzanilla de Sevilla). The cluster with the highest LA is C, which includes values in the range of 5.04–5.79 cm 2 , followed by clusters A (4.36–5.37 cm 2 ), D (3.98–4.4 cm 2 ), B (3.15–4.06 cm 2 ) and E (3.98 cm 2 ), with group F showing the lowest LA (2.57cm 2 ). Using the Li-Cor 6800 photosynthetic analyser, measurements were made in triplicate from the cultivars at their saturation irradiance (Isat) calculated. Data on photosynthesis (A), transpiration (E), stomatal conductance of water (gsw), and Isat are represented in Fig. 4 . Regarding the assimilation rate of CO 2 (Fig. 4 a), a generalized behaviour of a sudden decrease in assimilation was observed in all cultivars as the water deficit increased, being very low when the plants were in SD. The majority of the cultivars showed significant differences (p < 0.01) between the three conditions, except for CH, MS, M and S2, where only a significant change was observed in control, MD, SD and control, respectively, with respect to the rest of conditions. The most active cultivars were CH, E, H, AE and P, with activities greater than 10 µmoles of CO 2 fixed per m 2 s under control conditions. These varieties also had a high rate of transpiration and stomatal conductance under ad libitum water conditions. In the case of F, K and MS, assimilation was much lower, with values of less than 5 µmoles of CO 2 fixed per m 2 s. However, these last three cultivars behaved in the same way in MD, increasing their photosynthesis considerably, which did not occur in the rest of the cultivars. In SD, the least active cultivars were P, E, F and CH, which showed practically no photosynthesis, with C and S2 being the most active cultivars in this condition. Transpiration rate (E) (Fig. 4 b) was similar to assimilation (A). Cultivar M, with a high rate of transpiration, and S2 in control conditions are two of the most tolerant cultivars. Figure 4 c represents stomatic conductance. In this parameter, all the cultivars present significant differences in all the treatments. Photosynthesis is directly related to transpiration and stomatal conductance, thus, in general, the plants with higher assimilation of CO 2 are also the cultivars with the best transpiration and stomatal conductance. As the lack of water in the plants increased, these three parameters decreased considerably. These results match those described by Brito et al. ( 2019 ) in a review of the effects of drought on the olive tree. M and S2 stand out as two of the cultivars with the highest transpiration and stomatal conductance, which, at the same time, appear to be very tolerant to drought. Finally, Fig. 4 d displays the saturation irradiance, in which assimilation (A) is maximal for each experimental condition (control, MD and SD). In this case, the majority of the cultivars had significant differences between the three treatments, except for AO, MS and S2. In these three cultivars, there were no significant differences in the Isat between the degrees of drought. The varieties with the highest Isat in the control samples were CH, K and H, which exceeded 1200 µmoles m − 2 s − 1 . Although CH is a very drought-tolerant cultivar, neither H nor K are. In the case of K, it is a very sensitive cultivar with one of the highest Isat, although its assimilation is quite low compared with the rest of the cultivars. In general, the high irradiance that most olive cultivars need (above 800 µmoles m − 2 s − 1 in 9 of the 14 cultivars in control conditions) indicates the high resistance of this species to drought and their acclimation to the Mediterranean climate. This Isat decreases sharply with the lack of water. In this way, the highest Isat does not reach 500 µmoles m − 2 s − 1 when the plants suffer from moderate water deficit, except in M and C, which are two of the most drought-tolerant cultivars. In SD, the Isat is below 400 µmoles m − 2 s − 1 for all the cultivars. Other important photosynthetic parameters were also calculated. The Fv/Fm ratio is the value of the maximum photochemical efficiency of the dark-adapted PSII, that is, when all the cofactors that constitute the PSII are in a reduced state. After the first pulse of light, which induces the activation of all the photosynthetic processes and when these reach a steady state, the Fs value is obtained, which represents the baseline fluorescence value when the photosynthetic material is adapted to the light. At this time, the application of a saturating light pulse results in the maximum emission of fluorescence in the presence of light (Fm'). Baseline fluorescence in a dark-adapted state is called Fo, and baseline fluorescence in a light-adapted state is Fo'. Thus, variable fluorescence (Fv) is Fm-Fo and the maximal fluorescence in a light-adapted state is Fm', with Fv’/Fm’ being the value of the excitation capture efficiency by open PSII reaction centres in the light-adapted state (Fv'/Fm'), which is calculated as (Fm'-Fo')/Fm', also called photosynthetic efficiency. Fluorescence quenching has a photochemical and a non-photochemical contribution. Photochemical (qP) quenching is due to the light-induced activation of enzymes involved in carbon cycling and stomata opening, and non-photochemical (NPQ) quenching is due to an increase in the yields of carbon heat dissipation. Non-photochemical quenching is a process used by plants and algae to protect themselves from excess light, and it consists in the deactivation of excited chlorophyll (Chl*) molecules by internal conversion to the ground state (heat dissipation). This deactivation competes with two other photochemical processes: the emission of fluorescence from (Chl*) and the transfer of electrons in the photosynthetic process. Non-photochemical quenching reduces the formation of excited chlorophyll triplets and consequently decreases the formation of reactive oxygen species. In Fig. 5 a, Fv/Fm has homogeneous values in most of the cultivars (8 of the 14). Contrarily, CJ, E and P showed significant differences in all the treatments. The rest of the varieties (F, K and S2) presented a variable behaviour. With respect to Fig. 5 b, Fv’/Fm’ represent the photosynthetic efficiency of the cultivars. This parameter is always lower than Fv/Fm. In the most tolerant cultivars (CH, C, M and S2), there were no significant differences in Fv’/Fm’ with the increase of drought, presenting very homogeneous data. K, which is a sensitive cultivar, showed homogeneous photosynthetic efficiency along the period of water deficit. The rest of the cultivars obtained heterogeneous data in the three conditions, showing a progressive decrease. The most photosynthetically efficient cultivars in both MD and SD conditions were F and CJ, which are cultivars with a low assimilation value. These two cultivars obtained the highest qP in MD and SD (Fig. 5 c). However, it can be observed that almost all cultivars had good photosynthetic efficiency in general, demonstrating, once again, that the olive tree is very well adapted to water deficit, and thus to the Mediterranean climate. Photochemical quenching (qP) (Fig. 5 c) distinguished two groups: those in which the differences were significant in all conditions (AE, CJ, C, E, F, H, M and P), and those in which the control was significantly different from the two drought conditions (CH, K). In AO, MD was a different condition, and in S1, SD was significantly different from the other two conditions. Note that qP increased with the lack of water. This utilisation of light to produce energy in the light reactions of photosynthesis and to activate enzymes involved in the Calvin cycle and its increase with water deficit indicates that the % of incident light on the plant was used differently in control plants compared to water-stressed plants. It should be taken into account that the amount of light absorbed by the plant decreased in water deficit (Fig. 6 b), thus the lower amount of effective light reaching the plant was used to produce energy through the light reactions of photosynthesis in most of the cultivars, and therefore it is not dissipated as heat. On the contrary, as expected, the trend of NPQ with water deficit showed a progressive decrease (Fig. 5 d). There are two sets of data: AO, CJ, E, F, H, P and S1 were heterogeneous in all situations, decreasing with water deficit, whereas the rest of the cultivars showed no differences in NPQ with the degree of drought. Figure 6 a shows PhiPS2, which is calculated as (1-Fs)/Fm’ and represents the effective quantum efficiency of the PSII to produce the photolysis of the water and the oxidation of plastoquinone. It obtained very homogeneous values in the cultivars for the three experimental conditions, except for P and E in the SD, where this parameter decreased to values below 0.6. Regarding the intercellular concentration of CO 2 in the leaf (Ci) (Fig. 6 c), the cultivars showed very similar values in the three experimental conditions, with a slight tendency to increase progressively with the lack of water, except for AO and E, where the results were homogeneous. The exception is C, where Ci diminished with MD and SD. P and F in SD showed a significantly high concentration of CO 2 in their leaves, although the Qabs in both cultivars were practically null under SD conditions. However, Brito et al. ( 2019 ) describe a decrease in the intercellular concentration of CO 2 in the leaves as the drought increases. Qabs is Photon Plux Fotonic Density absorbed by the leaf, which decreased sharply with the water deficit in the majority of the cultivars. It is important to highlight that the plants that absorbed the highest light quantity in the control samples (CH, H and K) were not the ones that had the greatest CO 2 assimilation or the most efficient photosynthesis. For example, M, with a relatively low Qabs, was quite photosynthetically efficient. CJ, whose Qabs is very low under MD and SD conditions, presented the highest Fv`/Fm´ ratio among all the cultivars studied under these two experimental conditions. Figure 6 d is the ETR or photosynthetic electron transport rate at PS1, whose pattern is similar to that of Qabs in the cultivars. C and P were the cultivars with the best ETR in MD and SD. From the assimilation and transpiration data, instantaneous water use efficiency (WUEi) was calculated. The relationship between assimilation and stomatic conductance is the intrinsic water use efficiency (WUE). Both parameters were calculated throughout time (Fig. 7 ). The objective was to verify those varieties which, in drought, showed a better use of water. In Fig. 7 a, the most efficient cultivars in SD are P, CJ, C and CH, with WUE values greater than 150. This behaviour is maintained in MD for CJ and F. Curiously, CJ was one of the most drought-sensitive cultivars, although, as can be observed, it was also one of the most efficient in the use of water. The cultivars that showed the worse efficiency in control conditions were H, M and S2, which are three of the most drought-tolerant cultivars. Brito et al. ( 2018 ) indicate that WUE typically increases with MD. In this work, Fig. 7 a shows an increasing tendency in all cultivars for MD. In control conditions, the cultivars with the greatest WUE were CH and MS. In the case of WUEi (Fig. 7 b), P remained significantly the most efficient cultivar in the use of water during SD. The rest of the cultivars in the three experimental conditions studied had lower and similar WUEi values. The exceptions in the control samples were M and MS. CJ was the most efficient cultivar in WUEi under MD. These results suggest that the most water-use-efficient cultivars are not the most drought-tolerant. It seems that there are adaptations of the most sensitive cultivars to use the remaining water in their cells during drought in a more efficient way, avoiding compromising the photosynthetic apparatus, thereby preventing irreparable damage. Figure 7 c, which represents the relationship between the amount of CO2 fixed by the plant and the amount of CO2 available within the leaf (A/ci), indicates a clear downward trend in all cultivars with the increasing water deficit. This is evident, since, although it is assumed that the amount of intercellular CO2 in the leaves is practically the same in control and drought stress samples, the water potential is much lower due to the lack of water inside the cells, producing a significantly lower assimilation of CO2 compared to the control plants, further decreasing with the increasing lack of irrigation. Exceptions occurred in F and K, where A/ci was higher in MD than in the control plants, although in SD they adopted the same behaviour as the rest. Another interesting parameter was A/Qabs (Fig. 7 d), which represents the relationship between the amount of CO2 fixed by the plant and the amount of light absorbed by it, or photon flux density. This ratio yielded more homogeneous values than the former in the three experimental conditions for several cultivars (AE, AO, C, CH, E, and S2). In other cultivars, it increased with drought (CJ, K, F, H and P), indicating that none of such cultivars were very tolerant to drought. The cause of this increase could be the amount of light absorbed in these cultivars, which was very low especially in SD. Proline and malondialdehyde quantification Osmotic adjustment has been shown to be an effective component of stress tolerance. The role that proline plays in the defence of the plant against abiotic stress is well known. Marjanossadat et al. (2022) have recently carried out a review in which they describe its role as an osmolyte, which is a signalling molecule, inducing the synthesis of abscisic acid (Vandana et al., 2020 ), thereby acting as a potent antioxidant. It seems to be synthesised mainly in roots and transported via phloem through the plant to the leaves and stems, thus a higher concentration of proline in the plant helps it to defend itself against water stress (Sadeghipour et al., 2020; Qirat et al., 2018 ). The exogenous application of proline improves the ability of the plant to avoid the degradation of photosynthetic pigments, as well as lipid peroxidation (Alkahtani et al., 2021 ; Elewa et al., 2017 ; Farooq et al., 2017 ; Ali et al., 2018). Proline improves the antioxidant activity of many enzymes, acting as a protein stabilizer and a ROS scavenger (Dar et al., 2016 ; Adejumo et al., 2021 ). While different studies report proline accumulation in drought, heat stress does not result in proline accumulation in tobacco and arabidopsis plants (Rizhsky et al., 2004 ; Dobra et al., 2010 ; Lv et al., 2011 ). During exposure to multiple stressors (drought and heat stress), proline does not seem to accumulate, while it does under drought alone (Rizhsky et al., 2004 ; Sofo et al., 2004 ). Our results show that this amino acid accumulates and synthesises significantly in the resistance phase of the olive tree against drought, that is, during MD (Fig. 8 ), and decreases significantly and drastically during the SD phase. This occurs as described in most of the cultivars studied, except in E, F, H and P, which curiously resisted the lack of irrigation during the maximum number of days that the experiment lasted (42 days). In these four cases, the amount of proline inside the cells increased (E, H and P) or remained constant (F) during SD. Therefore, these results corroborate the defensive role of this osmolyte in plants stressed by the lack of water, showing greater accumulation with longer time and more drought-tolerant cultivars. In the case of MDA content, the results were less homogeneous than the previous ones (Fig. 9 ). To date, an increase in MDA in a plant subjected to water stress indicated the existence of a greater amount of lipid peroxidation in its cells; therefore, these results indicate that there was greater damage in said plant. However, during the biotic stress produced in beans by Garipova et al. ( 2022 ), there was a simultaneous increase in MDA and proline in the most resistant plants. It seems that the MDA content decreased with the inoculation of the pathogen. In the last years, the work of Morales et al. (2019) suggests that the role of these compounds as damagers or protectors relies on the enzymatic activity of aldehyde dehydrogenases (ALDHs). Under environmental stress or developmental signals, MDA is produced from the lipid peroxidation of polyunsaturated fatty acids as a consequence of ROS attack or the activation of lipoxygenases. MDA increases during the progressive increment of drought stress (Sofo et al., 2004 ; Ahmadipour et al., 2018 ). When aldehyde levels increase, protein carbonylation occurs, and this may result in defence signalling (Ahmadipour et al., 2018 ; Morales et al., 2019). In this work, most of the cultivars did not present large differences in MDA content during the two drought conditions tested with respect to the control samples. There are exceptions such as P and AO (two cultivars with great tolerance to drought) during MD, where MDA increased up to 2 µmolar. Another cultivar that increased its concentration of MDA in SD was F, which is also quite tolerant to water deficit. Therefore, we could agree with Morales et al. (2019), who indicate that an increase in MDA could trigger a signalling pathway to induce plant defence against water stress, taking into account that, in some resistant cultivars, there was a considerable amount of MDA in control conditions. 4. Conclusions In this work, an in-depth study of 14 olive cultivars subjected to two experimental conditions of drought, MD and SD, was carried out to determine their greater or lesser tolerance against the lack of water. Photosynthetic data were collected at the saturation irradiances of each cultivar, observing that the CO 2 assimilation capacity was highly variable among them, both under ad libitum irrigation conditions and with water deficit. Some striking results are worth highlighting. The stomatal conductance and transpiration data indicate a positive correlation in most of the cultivars with the assimilation produced. Regarding the ISat, as well as Qabs or photon flux density, they appeared to have highly variable values among the cultivars. Curiously, it should be noted that the plants with the highest Isat, and therefore the greatest light absorption, were not always the ones with the greatest photosynthesis. Such is the case of K, which, despite having a very high Isat, presented an intermediate assimilation with respect to that obtained by the rest of the cultivars. Another example is P, which, despite having an intermediate Isat, showed the greatest capacity to fix CO 2 . The photochemical efficiency of PSII in the dark (Fv/Fm) and under a light acclimation status (Fv'/Fm’) was quite stable in the three experimental conditions and in all the cultivars. The same was observed with the quantum yield of PSII (PhiPS2). However, the electron transfer rate (ETR) throughout the photosynthetic apparatus varied among the cultivars. This indicates that, although the PSII seems to function in a very stable way in all the cultivars, possible differences in the PSI, which also participates in this electron transfer, may be the cause of these cultivars presenting a different ETR and, therefore, generating different amounts of energy in the form of ATP and NADPH. Thus, a question arises: Is PSI the macro complex that is most affected in the photosynthetic apparatus when plants are subjected to drought conditions? Finally, the cultivars with the most efficient use of water were not the most tolerant. There could be adaptations at the cellular or molecular level in the most sensitive cultivars to use the remaining water in their cells in a more efficient way, thereby protecting the photosynthetic apparatus and thus avoiding irreparable damage. Table 2 classifies the 14 cultivars studied into four groups according to their tolerance to drought: 1) tolerant, the most resistant to the lack of water, which, after 42 days without irrigation, were little affected and, in some cases, were in perfect condition; 2) moderately tolerant, which, despite their resistance, in some of the analyses carried out, they appeared to have some sensitivity to the lack of water; 3) moderately sensitive, which resisted between 5 and 6 weeks with no irrigation, without reaching the last day of the experiment, and showed clear signs of wilting (even some of the individuals subjected to drought completely withered); and 4) sensitive, which resisted less than 4–5 weeks without irrigation (several individuals withered during the treatment time). Table 2 Classification of the 14 olive cultivars according to the tolerance to drought Tolerant Moderately tolerant Moderately Sensitive Sensitive Martina Cornicabra Sikitita2 Arbosana Frantoio Chemlali Sikitita1 Picual Manzanilla de Sevilla Arbequina Hojiblanca Cornezuelo de Jaén Koroneiki Empeltre Some authors have recently suggested that plants develop a "stress memory" and hold on to past events without altering their genetic constitution, although they show changes in their epigenome, transcriptome, proteome and metabolome under repetitive drought stress (Kambona et al., 2023 ; Liu et al., 2022 , Shadukhan et al., 2022). Several reviews address this issue, indicating that new opportunities are opening up to work on cultivars that are increasingly resilient to climate change. However, further research is required to better understand the triggers that produce this "stress memory" and the compound(s) in the signalling chain that could set off these results. Declarations The authors declare that there is no conflict of interest. Financial interests: This work was supported by the European Project H2020 “Mobilization of Olive GenRes through pre-breeding activities to face the future challenges and development of an intelligent interface to ensure friendly information availability for end users”. GEN4OLIVE. Call SFS-28-2018-2019-2020 and topic Genetic resources and pre-breeding communities. Authors Elena Illana Rico (EIR) and Genoveva Carmen Martos de la Fuente (GCMF) received honoraria from the University of Jaén and H2020 Gen4Olive project, respectively, to work in the laboratory as technical professionals of research support. Author AOM did not receive research funding from Gen4Olive. Author AMFO received travel support, honoraria and funding research to manage this work from the H2020 Gen4Olive project. Authors contribution: All authors contributed to this study. Conception and design were performed by Ana Maria Fernández Ocaña (AMFO). Material preparation, data collection and analysis were performed by AMFO, EIR, GCMF and Ainhoa Ortega Morillas (AOM). Statistical analyses were performed by GCMF and AMFO. The first draft of the manuscript was written by AMFO and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgements: The authors thank Cordoplant S.L. nurseries for the great help provided with the supply of certified specimens of the 14 olive cultivars with which we have worked in this article. Technical and human support provided by Centro de Instrumentación Científico-Técnica (CICT)- Servicios Centrales de Apoyo a la Investigación (SCAI)- Universidad de Jaén (UJA, MICINN, Junta de Andalucía, FEDER) is gratefully acknowledged. References Adejumo, S. A., Oniosun, B., Akpoilih, O. A., Adeseko, A., & Arowo, D. O. (2021). Anatomical changes, osmolytes accumulation and distribution in the native plants growing on Pb-contaminated sites. 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Supplementary Files ESM1.xlsx Cite Share Download PDF Status: Published Journal Publication published 04 Nov, 2023 Read the published version in Photosynthesis Research → Version 1 posted Editorial decision: Major revision 27 Aug, 2023 Reviews received at journal 23 Aug, 2023 Reviews received at journal 09 Jul, 2023 Reviewers agreed at journal 29 Jun, 2023 Reviewers invited by journal 27 Jun, 2023 Editor assigned by journal 23 Jun, 2023 Submission checks completed at journal 13 Jun, 2023 First submitted to journal 13 Jun, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3057192","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":212482067,"identity":"9288da4f-0475-43bf-b2bb-e594da1c1f37","order_by":0,"name":"Elena Illana Rico","email":"","orcid":"","institution":"Universidad de Jaén","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Elena","middleName":"Illana","lastName":"Rico","suffix":""},{"id":212482068,"identity":"e762ddaf-5c4f-4af3-9282-8314f622db9e","order_by":1,"name":"Genoveva Carmen Martos Fuente","email":"","orcid":"","institution":"Universidad de Jaén","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Genoveva","middleName":"Carmen Martos","lastName":"Fuente","suffix":""},{"id":212482069,"identity":"24c6dc0a-0860-44fb-95aa-6cd2dd568e3b","order_by":2,"name":"Ainhoa Ortega Morillas","email":"","orcid":"","institution":"Universidad de Jaén","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ainhoa","middleName":"Ortega","lastName":"Morillas","suffix":""},{"id":212482070,"identity":"c2994ccf-3cf2-42d2-9210-9e9a154de7af","order_by":3,"name":"Ana Maria Fernández Ocaña","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYBADOQaGAyRqMSZdS2ID0Ur5px0++OHnDrv0DQcPP3xcwGBjT1CLxO20ZMneM8m5Gw4cMzaewZBGhHW3c8wYeNuYgVoOmEnzMBxOIKhDHqiF8W9bfbrBgePff/Mw/CfsMAOgFmbetsMJBgfOmDHzMBxgJOgwQ6BfpGXbjhvOPHCmWJrHIJmwX+RuJx/8+LatWp7vxvGNn3kq7Ag7DAEkDoDcSYIGYAwRdNEoGAWjYBSMVAAA42U+EbuARFMAAAAASUVORK5CYII=","orcid":"","institution":"Universidad de Jaén","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Maria Fernández","lastName":"Ocaña","suffix":""}],"badges":[],"createdAt":"2023-06-13 09:14:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3057192/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3057192/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11120-023-01052-8","type":"published","date":"2023-11-04T15:01:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":39258518,"identity":"ad830fd8-2386-4fe1-a499-b4897f6bc439","added_by":"auto","created_at":"2023-06-28 20:11:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":64289,"visible":true,"origin":"","legend":"\u003cp\u003eChlorophyll concentration comparison between treatments (control, MD and SD samples) in the 14 cultivars studied. For each cultivar, the significant differences between the experimental conditions are indicated by letter codes. Significant changes are identified with different letters (a, b, c) (p\u0026lt;0.01); the treatments with the same letter are homogeneous.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3057192/v1/2e0267a13d76d0da7576325e.png"},{"id":39258517,"identity":"d2023b71-5809-4fb5-98d7-fd15696835fc","added_by":"auto","created_at":"2023-06-28 20:11:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":55143,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of water decrease in the pots throughout the days after irrigation\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3057192/v1/8d27f6106f4aeba333f273fd.png"},{"id":39258525,"identity":"1382ee62-1746-4deb-bd57-1f8f054e9c36","added_by":"auto","created_at":"2023-06-28 20:11:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":442886,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Dendrogram resulting from the similarity analysis by Euclidean distances. (B) Relationship between the different groups of cultivars based on their leaf area and size.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3057192/v1/5faacb7b8c6df0a1aa58f7a4.png"},{"id":39258522,"identity":"67ece5c7-8062-418f-97c7-34624b4bfeef","added_by":"auto","created_at":"2023-06-28 20:11:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":146142,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Photosynthesis rate (CO\u003csub\u003e2 \u003c/sub\u003eassimilation rate; A, µmol CO\u003csub\u003e2\u003c/sub\u003e m⁻² s⁻¹).\u0026nbsp;(b) Leaf transpiration rate (E, mol H\u003csub\u003e2\u003c/sub\u003eO m⁻² s⁻¹). (c) Stomatal conductance to water vapour (a measure of the degree of stomatal opening; gsw, mol H\u003csub\u003e2\u003c/sub\u003eO m⁻² s⁻¹) and (d) leaf-incident photosynthetic photon flux density (PPFD), in which the assimilation rate is maximal (Saturation Irradiances; Isat, µmol photons m⁻² s ⁻¹). Error bars represent standard deviation (Controls, n=6; Water deficit treatment, n=9).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3057192/v1/ea8449bab885c91475c7fe9f.png"},{"id":39259348,"identity":"b3f677e5-a465-4965-86f9-a5652ba70471","added_by":"auto","created_at":"2023-06-28 20:19:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":138527,"visible":true,"origin":"","legend":"\u003cp\u003e(a) The maximum potential quantum efficiency of Photosystem II when a plant is dark-adapted (Fv/Fm, Normalised ratio created by dividing the variable fluorescence by maximal fluorescence). (b) Photochemical efficiency of PS II under a light acclimation status (Fv’/Fm’). (c) Photochemical quenching (qP, (Fm’-Fs)/(Fm’-Fo’)). (d) Non-photochemical quenching (NPQ, ((Fm-Fm’)/Fm’)). Error bars represent standard deviation (Controls, n=6; Water deficit treatment, n=9).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3057192/v1/82d41b6c6cd16f9816119e3f.png"},{"id":39258520,"identity":"bd170d8d-1fa9-409d-b184-ed6615df6191","added_by":"auto","created_at":"2023-06-28 20:11:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":133659,"visible":true,"origin":"","legend":"\u003cp\u003e(a) PhiPS2 is the quantum yield of PSII calculated from fluorescence ((1-Fs/Fm’)), (b) Qabs is the photosynthetic photon flux density (PPFD) absorbed by the leaf (Qabs, µmol photons m⁻² s ⁻¹), (c) Ci is the intercellular CO\u003csub\u003e2 \u003c/sub\u003econcentration in the leaves (Ci, µmol mol⁻¹), and (d) ETR is the electron transport rate at PSI (µmol electrons m⁻² s⁻¹) (d). Error bars represent standard deviation (Controls, n=6; Water deficit treatment, n=9).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3057192/v1/f79c01aaf3c2faa9b2801997.png"},{"id":39258526,"identity":"0a2cb8f6-8362-4cbb-bead-246011514388","added_by":"auto","created_at":"2023-06-28 20:11:52","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":105584,"visible":true,"origin":"","legend":"\u003cp\u003e(a) WUE: Intrinsic water use efficiency in leaves (A/gsw); µmol CO\u003csub\u003e2 \u003c/sub\u003eproduced by mol H\u003csub\u003e2\u003c/sub\u003eO entering the leaf through the stomata. (b) WUEi: Instantaneous water use efficiency in leaves (A/E); µmol CO\u003csub\u003e2\u003c/sub\u003e by mol H\u003csub\u003e2\u003c/sub\u003eO transpired. (c) A/Ci ratio. (d) A/Qabs ratio. Error bars represent standard deviation (Controls, n=6; Water deficit treatment, n=9)\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3057192/v1/4087675b06b31bc75d8e6c22.png"},{"id":39258519,"identity":"6b6793b2-ce4a-4eee-8614-2f1a04deb809","added_by":"auto","created_at":"2023-06-28 20:11:52","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":36278,"visible":true,"origin":"","legend":"\u003cp\u003eProline content in the three experimental conditions studied\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3057192/v1/341153745046bfc30282a8d4.png"},{"id":39258523,"identity":"201013ae-22a6-434f-882d-784cce09da97","added_by":"auto","created_at":"2023-06-28 20:11:52","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":45132,"visible":true,"origin":"","legend":"\u003cp\u003eMalondialdehyde (MDA) content in the three experimental conditions studied expressed per gram of tissue\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-3057192/v1/180d70476fe03ecbb3e67739.png"},{"id":45943169,"identity":"f181e9d1-cd3d-4328-addf-a64b694a7594","added_by":"auto","created_at":"2023-11-06 15:08:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1334011,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3057192/v1/78350ff6-1c11-4c14-9248-d5d8584a62a7.pdf"},{"id":39258524,"identity":"1b8c8875-a5d9-4d37-8c8e-63242d267656","added_by":"auto","created_at":"2023-06-28 20:11:52","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":153334,"visible":true,"origin":"","legend":"","description":"","filename":"ESM1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3057192/v1/6f2a24a355ee47ed67b09ab0.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Physiological and biochemical study of the drought tolerance of 14 main olive cultivars in the Mediterranean Basin","fulltext":[{"header":"Key message","content":"\u003cp\u003eClassification of 14 olive cultivars of great economic importance according to their drought tolerance, based on the analysis of numerous physiological, photosynthetic and biochemical parameters.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eClimate change is strongly related to crop productivity, both directly and indirectly. Its consequences on the different environmental factors have caused important changes, such as temperature and precipitation variability, changes in the distribution of plant diseases, different seasonal patterns, impacts on the soil and the seas, desertification, etc., which reduce crop yield by up to 70%. The scarcity of water, resulting from the progressive increase in temperatures, is one of the most worrying problems in agricultural science. At a physiological, morphological and biochemical level, plants have undergone changes in their metabolism that have affected their turgor, growth, Rubisco activity, chlorophyll content, photosystems and the functionality of the latter. Numerous plants synthesise and accumulate compatible solutes or osmolytes, also known as osmoprotectants, which are small and highly soluble non-toxic molecules that protect the cells against adverse conditions and stabilise the proteins and nucleic acids and membranes, producing the osmotic equilibrium. Research in metabolomics and transcriptomics reports that plants overexpressing osmolyte biosynthesis or metabolic genes showed enhanced stress tolerance (Nahar et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWater stress affects the correct development of the physiological and metabolic processes of the plant, responding to the appearance of a stress situation according to a sequential model that includes four phases (Shabala, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e): 1) alarm phase, in which the plant detects environmental changes and prepares itself to combat stress; 2) resistance phase: the plant sets off mechanisms or synthesises osmolytes that increase its tolerance; 3) depletion phase, in which the individual is not able to generate more resistance or withstand stress; and 4) regeneration phase, if the stress has finished and the plant can recover from the damage sustained.\u003c/p\u003e \u003cp\u003eThe olive tree (Olea europaea L.) has been historically considered as a tree endowed with great rusticity and resistance to adverse conditions. However, the great changes that are currently occurring in the different climatic factors, such as large increases in temperatures, prolonged periods of drought combined with irregular and abundant punctual rains, produce damage to the crops and alterations in the growth and development of the plantations, including olive crops. The most affected growth phase of the olive grove is flowering, which is very susceptible to temperature, and it is currently taking place ealier than usual in the spring months. Consequently, the stage of development and maturation of the fruit also occurs earlier, before the end of summer. The high temperatures of recent years in the spring months cause the blossom to fall, leading to less productive crops with poor fruit quality and low yields (Malhi et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In Andalusia, two varieties represent 80% of olive crops: Picual (60%) and Hojiblanca (20%), which are the most productive and versatile. However, many other varieties are identified and characterised as more adapted to certain local climates, soil types, or difficult terrain (Barbuzano, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere is a strikingly small number of publications about olive trees and the drought tolerance of different cultivars using physiological, photosynthetic and spectrophotometric studies. Bacelar et al. (2007) published a review of the effects of drought on photosynthesis, transpiration, stomatic conductance and water use efficiency of several Portuguese olive cultivars. A study was carried out on two cultivars, i.e., Arbequina and Manzanilla de Sevilla, during the pre-flowering and flowering periods; according to the results of these authors, it seems that both cultivars behaved similarly against drought (Pierantozzi et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Other studies provide information on the morphological changes suffered by two cultivars, Chemlali and Meski, with lack of water (Tangu, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and on the variation in the phenolic content of Meski under water stress (Mechri et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Another work related to drought analysed the tolerance of four cultivars (two Persian cultivars: Fishomi and Dezful and two Greek cultivars: Amigdalolia and Conservolia) to water deficit. The higher drought tolerance of these plants was related to the higher concentration of soluble carbohydrates, proline, potassium, and calcium in their leaves (Karimi et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). There are also enzymatic analyses of proteins related to oxidative stress in Koroneiki grown under different foliar treatments and subjected to water deficit (Denaxa et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). According to the work of Bacelar et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), cultivars native to dry regions, such as Cobrancosa, Manzanilla and Negrinha, have a greater capacity to acclimate to drought conditions than cultivars originating in regions with a more temperate climate, like Arbequina and Blanqueta. Faraloni et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) measured chlorophyll fluorescence on detached olive leaves subjected to dehydration in vitro in 24 cultivars. Boughalleb et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) compared two cultivars against drought, i.e., Zalmati and Chemlali, concluding that the latter variety is much more resistant than Zalmati. Boussadia et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) measured photosynthetic gas exchange and fluorescence parameters in Koroneiki and Meski to obtain their tolerance to drought. According to Mousavi et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Koroneiki is a drought-sensitive cultivar.\u003c/p\u003e \u003cp\u003eThe objective of this work was the physiological and biochemical characterisation of 14 olive cultivars, which were exposed to water deficit for six weeks, without any irrigation: Arbequina (AE), Arbosana (AO), Chemlali (CH), Cornezuelo de Ja\u0026eacute;n (CJ), Cornicabra (C), Empeltre (E), Frantoio (F), Hojiblanca (H), Koroneiki (K), Martina (M), Manzanilla de Sevilla (MS), Picual (P), Sikitita 1 (S1) and Sikitita 2 (S2). We also evaluated the degree of tolerance and adaptation of each of the selected cultivars to drought, through the measurement of their response to oxidative stress with photosynthetic and ecophysiological tests over time.\u003c/p\u003e \u003cp\u003eOn the one hand, photosynthetic parameters of each cultivar were measured, including chlorophyll content, soil moisture, leaf area, saturation irradiance, maximal photosynthetic activity, stomatal conductance, transpiration, PSII efficiency, photochemical and non-photochemical quenching, water use efficiency and instantaneous water use efficiency. Furthermore, the degree of tolerance and adaptation of each selected cultivar to drought was evaluated, using spectrophotometric tests to quantify proline and malondialdehyde, in order to determine which of the analysed cultivars are the most tolerant or sensitive to drought.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Plant material\u003c/h2\u003e \u003cp\u003eThe cultivars selected for the study are some of the most important worldwide due to the large extensions of cultivation that they occupy in the Mediterranean basin. These cultivars are certified by the World Olive Germplasm Bank of C\u0026oacute;rdoba (WOGBC), and they are cultivated and provided by Cordoplant Viveros: Picual, Arbequina, Hojiblanca, Manzanilla de Sevilla, Empeltre, Cornicabra, Cornezuelo de Ja\u0026eacute;n, Arbosana, Sikitita 1, Sikitita 2, Martina, Koroneiki, Chemlali and Frantoio.\u003c/p\u003e \u003cp\u003eThe experiment was carried out in an Aralab growth chamber, model 12000PHL-LED, regulated by a FITOLOG 9000 control software incorporated into the installation. Environmental conditions: temperature (21\u0026ordm;C/18\u0026ordm;C, day/night), relative humidity (50%), photoperiod (16 h light/8 h dark), irradiance (165 \u0026micro;mol\u0026middot;m\u003csup\u003e2\u003c/sup\u003e\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and CO\u003csub\u003e2\u003c/sub\u003e concentration (400 ppm).\u003c/p\u003e \u003cp\u003eIrrigation was applied on alternate days by sprinkler only to control samples (six samples per cultivar). The individuals subjected to drought did not receive any irrigation for at least 28 days (in the case of the most drought-sensitive cultivars) and up to 42 days (in the case of the most drought-resistant cultivars). Samples from Koroneiki and Empeltre were taken after 28 days of drought; Hojiblanca after 30 days; Cornezuelo de Ja\u0026eacute;n, Picual, and Arbequina after 35 days; and finally, the rest of the cultivars endured the absence of water until harvested after 42 days.\u003c/p\u003e \u003cp\u003eLeaves of control samples (5\u0026ndash;6 leaves/cultivar) were collected, as well as leaves in two different times of drought: moderate drought (MD), after 14 days without receiving any irrigation, and in the last day of severe drought (SD) before their irrigation, which was after 28 days in all cultivars and up to 42 days in the most resistant cultivars. All the samples were collected from at least three different samples and frozen in liquid nitrogen. Subsequently, the plant material was ground using a ball mill, always in a liquid nitrogen environment to avoid thawing, and was stored in an ultra-freezer at -80\u0026ordm;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Photosynthetic parameters determination\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Chlorophyll content and soil humidity\u003c/h2\u003e \u003cp\u003eTo measure the chlorophyll content of the leaves, an M-100 Apogee Instrument was used. The device uses equations that allow it to measure any plant species (Parry et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Chlorophyll content per leaf surface (\u0026micro;mol/m\u003csup\u003e2\u003c/sup\u003e) was quantified each week by performing 10 measurements to obtain the mean in each case. The quantification was made in triplicate in three different samples per week.\u003c/p\u003e \u003cp\u003eTo ensure that the plants were experiencing increasing drought over time, soil moisture was measured with a multi-probe using a sensor for temperature, humidity and electrical conductivity of the soil, verifying the percentage of water in the soil during the course of the experiment in drought.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Leaf area, photosynthetic parameters and water use efficiency\u003c/h2\u003e \u003cp\u003eA scanner was used to measure the leaf area, collecting ​​six leaves from three random control individuals in each cultivar, employing the Image J software, thus obtaining an average for each variety.\u003c/p\u003e \u003cp\u003eTo generate the light-response data, experiments were conducted to measure photosynthesis\u0026ndash;light curves in triplicate, weekly, on the mature, fully expanded leaves of the 14 previously mentioned olive cultivars from November 2021 to March 2022. A LI-6800 Portable Photosynthesis System (68H-581003/68C-571003) with a fluorometer was used to this end. Light response data were gathered using nine different incident light levels: 2000, 1500, 1000, 750, 500, 250, 100, 50 and 0 \u0026micro;mol m\u0026thinsp;\u0026minus;\u0026thinsp;2 s\u0026thinsp;\u0026minus;\u0026thinsp;1. Leaf temperature was selected at 21\u0026deg;C and CO2 flux at 400 ppm. Relative humidity was 60\u0026ndash;65% during the quantum yield experiments. Data were collected at 0.5 Hz for the full light response curves. The auto-programmes were set to run for 60 to 180 s at a given light level before moving to the next light level. At each light level, the final 40 s of data were used as the representative steady-state data population that was subsequently analysed. Previously, steady-state modulated chlorophyll fluorescence was determined in the leaves of three different plants of each cultivar since the beginning of the experiment. Leaves were dark-adapted overnight to obtain F0 (minimum fluorescence), Fm (maximal fluorescence dark-adapted), and A Dark (respiration of the plant in the dark), and then we calculated Fv (variable fluorescence, equivalent to Fm-F0 dark-adapted) and Fv/Fm (maximum quantum yield of ΦPSII dark-adapted). With the light response curves, we calculated the saturation irradiance and, at this point, we obtained the maximal fluorescence light-adapted (Fm\u0026rsquo;), variable fluorescence Fv\u0026acute; (equivalent to Fm\u0026acute;-F0) and the quantum yield of PSII electron transport (ΦPSII), equivalent to (F\u0026acute;m -F)/F\u0026acute;m. Data on maximal photosynthesis (A, \u0026micro;mol CO2 m-2 s-1) at the specific saturation irradiance of each cultivar in each experimental condition (control, MD and SD), intracellular CO2 concentration content (Ci, ppm), stomatal conductance (gsw, mol H2O m-2 s-1) and transpiration rate (E, mol H2O m-2 s-1) were gathered. Moreover, LiCor 6800 calculated the electron transport radius (ETR), quantity of light absorbed (Qabs), photochemical quenching (qP) (i.e., the percentage of light used by the photosystems to produce energy), and NPQ (non-photochemical quenching or percentage of light heat dissipated by carotenoids).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Proline and malondialdehyde quantification\u003c/h2\u003e \u003cp\u003eAnalysis of proline concentration was performed following the protocol of Sofo et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) modified. To 0.1 g of the ground plant tissue, 2 ml of 3% sulfosalicylic acid was added. Subsequently, the samples were taken to a thermostatic bath at 100\u0026ordm;C for 20 minutes and centrifuged at 2500 rpm for 7 minutes at room temperature. The supernatant was obtained and two 300 \u0026micro;l aliquots were collected from it, to which 300 \u0026micro;l of milli-Q water and 2 ml of the reaction mixture were added. The samples were incubated at 100\u0026deg;C for 25 minutes and subsequently cooled down on ice. Finally, 4 ml of toluene was added, and the samples were stirred and allowed to stand for 15 minutes until the phases were differentiated, selecting the upper phase to measure absorbance at 520 nm. The spectrophotometric determination of proline was performed with the extrapolation of the data on a standard curve generated from a 10mM solution of proline.\u003c/p\u003e \u003cp\u003eMDA was measured according to the protocol of Lozano-Rodr\u0026iacute;guez, E. et al. (1997), with some modifications. The TCA-TBA-HCl reagent was prepared extemporaneously, consisting of 15% (w/v) trichloroacetic acid (TCA), 0.37% (w/v) 2-thiobarbituric acid (TBA), 2.4% (v/v) concentrated hydrochloric acid (HCl) and 0.01% (w/v) butyl hydroxytoluene (BHT). To 0.1 g of the plant tissue of each sample, 1.5 ml of the TCA-TBA-HCl reagent was added and incubated in a thermostated bath at 95\u0026deg;C for 30 minutes. The samples were quickly cooled in a container with ice and, subsequently, they were centrifuged at 12,000 g and 4\u0026ordm;C for 15 minutes, collecting the supernatant, where the absorbance at 532 nm and 600 nm was measured. The amount of MDA in each sample was calculated using the Lambert-Beer equation, using the following equation: A = ɛ \u0026middot; c \u0026middot; l, where A corresponds to the difference of the absorbances, ɛ is the molar extinction coefficient (1.56\u0026middot;105 M-1\u0026middot;cm-1), c is the concentration of malondialdehyde, and, finally, l corresponds to the distance in cm that the light from the spectrophotometer travels when passing through the sample (1 cm). All samples were measured in triplicate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical analyses\u003c/h2\u003e \u003cp\u003eAll data were processed using the statistical programme IBM\u0026reg; SPSS\u0026reg; Statistics v.27. The data were treated and preprocessed before the statistical analysis, from the photosynthesis-light curves of each cultivar. The data selected were those obtained by the Li-Cor 6800 in each parameter at their saturating irradiance, which was different in each cultivar. We worked with datasets grouped into two intervals: in the case of MD situations, the data obtained at 7, 14 and 21 days were used, and in SD, the data obtained at 21, 28 and 35 days were used.\u003c/p\u003e \u003cp\u003eThe ANOVA comparison test was applied. To identify the significance between the drought treatments in each cultivar, a post hoc multiple-comparison test was performed, assuming equal variances and using Bonferroni\u0026rsquo;s and Tukey\u0026rsquo;s HSD tests.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cp\u003eThis is a physiological, photosynthetic and biochemical study of the drought tolerance of 14 olive cultivars widely represented in the olive groves of the Mediterranean basin. The plants were subjected to a total absence of irrigation for a maximum of 42 days. Not all cultivars withstood the extreme degree of drought: some varieties had to be irrigated at 28 days, such as Empeltre and Koroneiki; Hojiblanca was irrigated at 30 days; Cornezuelo de Ja\u0026eacute;n, Picual, and Arbequina were irrigated at 35 days; and, finally, Arbosana, Chemlali, Cornicabra, Frantoio, Martina, Manzanilla de Sevilla, Sikitita 1 and Sikitita 2 withstood the absence of water until they were harvested at 42 days. This ability to withstand water deficit already provides valuable information that helps to deduce which varieties are more tolerant to drought and which are more sensitive. Once the samples were collected and frozen until they were processed, significant results were obtained for almost all the parameters measured, which allowed us to make decisions about their drought tolerance.\u003c/p\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1. Quantification of chlorophylls and humidity of the soil\u003c/h2\u003e\n\u003cp\u003eChlorophyll measurements were made in the 14 olive cultivars mentioned throughout the 6 weeks of the experiment. The measurements were made one day per week, thereby obtaining data at 0, 7, 14, 21, 28, 35 and 42 days after irrigation. Chlorophyll levels are negatively affected by the passage of time, decreasing progressively with water deficit (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Some cultivars such as Cornicabra (56.9%), Empeltre (55.6%) and Hojiblanca (52%) reflect a loss of photosynthetic pigments in the leaf at the end of the experiment of more than 50% with respect to the initial values, while decreasing in the rest of the cultivars, although less drastically (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eChlorophyll concentration (\u0026micro;mol/m2) of each cultivar and % decrease of pigments in severe drought with respect to the control samples.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eChlorophyll amount over time\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModerate Drought\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSevere Drought\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAE\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e513.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e412.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e322.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAO\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e738.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e638.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e448.43\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCH\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e315.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e748.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e593.77\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCJ\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e357.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e323.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e286.48\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e885.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e368.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e382.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e658.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e431.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e292.57\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e615.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e470.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e362.03\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e364.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e248.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e174.95\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eK\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e602.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e501.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e420.22\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMS\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e250.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e293.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e230.86\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e844.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e732.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e581.52\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e450.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e415.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e295.18\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eS1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e622.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e497.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e316.45\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eS2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e420.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e453.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e365.38\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eA two-way ANOVA was performed to determine whether water deficit (control vs. moderate/severe drought) and cultivar type (AE, AO, CH, CJ, C, E, F, H, K, MS, M, P, S1, S2) have a significant effect (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) on chlorophyll concentration. The results revealed that both water deficit (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and cultivar type (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had a statistically significant effect on chlorophyll concentration. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e represents the concentration of chlorophyll in each cultivar grouped by treatments (Control, MD and SD). In the statistical analysis, the majority of the cultivars present statistical differences between the three treatments, with some exceptions: C has homogeneous data in all the experimental conditions; in the case of CJ and P, only the conditions of SD present significant differences compared to the rest; MS is different in MD; and S2 only presents differences between MD and SD. These data are in line with those published by Bacelar et al. (2007) and Brito et al. (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) about the progressive decrease in pigments suffered by the olive tree under water stress.\u003c/p\u003e\n\u003cp\u003eMeasurements of soil humidity throughout the 6 weeks of the experiment were carried out using a multiprobe to corroborate the degree of water deficit suffered by all cultivars. It was found that the % of water reached almost the limit of the permanent wilting index in the 14 cultivars, being less than 5% in all cases in the last days of the experiment. In general, it was possible to verify that this is a characteristic of the soil and not of the plant, since the behaviour was very similar in all the cultivars (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2. Leaf area, photosynthetic parameters and water use efficiency\u003c/h2\u003e\n\u003cp\u003eThe measurement of the leaf area (LA) for each cultivar was evaluated from six leaves collected from three control individuals per cultivar studied, in order to ascertain the leaf area under normal \"ad libitum\" irrigation conditions and determine whether a greater surface area of the leaves involves a greater amount of pigments per leaf, and thus greater photosynthesis, transpiration and chlorophyll content. Comparison by Euclidean analysis was made to calculate the similarity index between cultivars, obtaining a dendrogram with notable differences in their leaf area and identifying the existence of six clusters (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e): Cluster A (Sikitita 2, Martina, Koroneiki and Arbequina); Cluster B (Sikitita 1, Frantoio and Chemlali); Cluster C (Arbosana, Empeltre and Picual); Cluster D (Hojiblanca and Conezuelo de Ja\u0026eacute;n); Cluster E (Cornicabra); and, finally, Cluster F (Manzanilla de Sevilla). The cluster with the highest LA is C, which includes values in the range of 5.04\u0026ndash;5.79 cm\u003csup\u003e2\u003c/sup\u003e, followed by clusters A (4.36\u0026ndash;5.37 cm\u003csup\u003e2\u003c/sup\u003e), D (3.98\u0026ndash;4.4 cm\u003csup\u003e2\u003c/sup\u003e), B (3.15\u0026ndash;4.06 cm\u003csup\u003e2\u003c/sup\u003e) and E (3.98 cm\u003csup\u003e2\u003c/sup\u003e), with group F showing the lowest LA (2.57cm\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003eUsing the Li-Cor 6800 photosynthetic analyser, measurements were made in triplicate from the cultivars at their saturation irradiance (Isat) calculated. Data on photosynthesis (A), transpiration (E), stomatal conductance of water (gsw), and Isat are represented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Regarding the assimilation rate of CO\u003csub\u003e2\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea), a generalized behaviour of a sudden decrease in assimilation was observed in all cultivars as the water deficit increased, being very low when the plants were in SD. The majority of the cultivars showed significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) between the three conditions, except for CH, MS, M and S2, where only a significant change was observed in control, MD, SD and control, respectively, with respect to the rest of conditions. The most active cultivars were CH, E, H, AE and P, with activities greater than 10 \u0026micro;moles of CO\u003csub\u003e2\u003c/sub\u003e fixed per m\u003csup\u003e2\u003c/sup\u003e s under control conditions. These varieties also had a high rate of transpiration and stomatal conductance under ad libitum water conditions. In the case of F, K and MS, assimilation was much lower, with values of less than 5 \u0026micro;moles of CO\u003csub\u003e2\u003c/sub\u003e fixed per m\u003csup\u003e2\u003c/sup\u003e s. However, these last three cultivars behaved in the same way in MD, increasing their photosynthesis considerably, which did not occur in the rest of the cultivars. In SD, the least active cultivars were P, E, F and CH, which showed practically no photosynthesis, with C and S2 being the most active cultivars in this condition. Transpiration rate (E) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb) was similar to assimilation (A). Cultivar M, with a high rate of transpiration, and S2 in control conditions are two of the most tolerant cultivars. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec represents stomatic conductance. In this parameter, all the cultivars present significant differences in all the treatments. Photosynthesis is directly related to transpiration and stomatal conductance, thus, in general, the plants with higher assimilation of CO\u003csub\u003e2\u003c/sub\u003e are also the cultivars with the best transpiration and stomatal conductance. As the lack of water in the plants increased, these three parameters decreased considerably. These results match those described by Brito et al. (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) in a review of the effects of drought on the olive tree. M and S2 stand out as two of the cultivars with the highest transpiration and stomatal conductance, which, at the same time, appear to be very tolerant to drought. Finally, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ed displays the saturation irradiance, in which assimilation (A) is maximal for each experimental condition (control, MD and SD). In this case, the majority of the cultivars had significant differences between the three treatments, except for AO, MS and S2. In these three cultivars, there were no significant differences in the Isat between the degrees of drought. The varieties with the highest Isat in the control samples were CH, K and H, which exceeded 1200 \u0026micro;moles m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003es\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Although CH is a very drought-tolerant cultivar, neither H nor K are. In the case of K, it is a very sensitive cultivar with one of the highest Isat, although its assimilation is quite low compared with the rest of the cultivars. In general, the high irradiance that most olive cultivars need (above 800 \u0026micro;moles m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003es\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in 9 of the 14 cultivars in control conditions) indicates the high resistance of this species to drought and their acclimation to the Mediterranean climate. This Isat decreases sharply with the lack of water. In this way, the highest Isat does not reach 500 \u0026micro;moles m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003es\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e when the plants suffer from moderate water deficit, except in M and C, which are two of the most drought-tolerant cultivars. In SD, the Isat is below 400 \u0026micro;moles m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003es\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for all the cultivars.\u003c/p\u003e\n\u003cp\u003eOther important photosynthetic parameters were also calculated. The Fv/Fm ratio is the value of the maximum photochemical efficiency of the dark-adapted PSII, that is, when all the cofactors that constitute the PSII are in a reduced state. After the first pulse of light, which induces the activation of all the photosynthetic processes and when these reach a steady state, the Fs value is obtained, which represents the baseline fluorescence value when the photosynthetic material is adapted to the light. At this time, the application of a saturating light pulse results in the maximum emission of fluorescence in the presence of light (Fm'). Baseline fluorescence in a dark-adapted state is called Fo, and baseline fluorescence in a light-adapted state is Fo'. Thus, variable fluorescence (Fv) is Fm-Fo and the maximal fluorescence in a light-adapted state is Fm', with Fv\u0026rsquo;/Fm\u0026rsquo; being the value of the excitation capture efficiency by open PSII reaction centres in the light-adapted state (Fv'/Fm'), which is calculated as (Fm'-Fo')/Fm', also called photosynthetic efficiency.\u003c/p\u003e\n\u003cp\u003eFluorescence quenching has a photochemical and a non-photochemical contribution. Photochemical (qP) quenching is due to the light-induced activation of enzymes involved in carbon cycling and stomata opening, and non-photochemical (NPQ) quenching is due to an increase in the yields of carbon heat dissipation. Non-photochemical quenching is a process used by plants and algae to protect themselves from excess light, and it consists in the deactivation of excited chlorophyll (Chl*) molecules by internal conversion to the ground state (heat dissipation). This deactivation competes with two other photochemical processes: the emission of fluorescence from (Chl*) and the transfer of electrons in the photosynthetic process. Non-photochemical quenching reduces the formation of excited chlorophyll triplets and consequently decreases the formation of reactive oxygen species.\u003c/p\u003e\n\u003cp\u003eIn Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea, Fv/Fm has homogeneous values in most of the cultivars (8 of the 14). Contrarily, CJ, E and P showed significant differences in all the treatments. The rest of the varieties (F, K and S2) presented a variable behaviour. With respect to Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb, Fv\u0026rsquo;/Fm\u0026rsquo; represent the photosynthetic efficiency of the cultivars. This parameter is always lower than Fv/Fm. In the most tolerant cultivars (CH, C, M and S2), there were no significant differences in Fv\u0026rsquo;/Fm\u0026rsquo; with the increase of drought, presenting very homogeneous data. K, which is a sensitive cultivar, showed homogeneous photosynthetic efficiency along the period of water deficit. The rest of the cultivars obtained heterogeneous data in the three conditions, showing a progressive decrease. The most photosynthetically efficient cultivars in both MD and SD conditions were F and CJ, which are cultivars with a low assimilation value. These two cultivars obtained the highest qP in MD and SD (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec). However, it can be observed that almost all cultivars had good photosynthetic efficiency in general, demonstrating, once again, that the olive tree is very well adapted to water deficit, and thus to the Mediterranean climate.\u003c/p\u003e\n\u003cp\u003ePhotochemical quenching (qP) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec) distinguished two groups: those in which the differences were significant in all conditions (AE, CJ, C, E, F, H, M and P), and those in which the control was significantly different from the two drought conditions (CH, K). In AO, MD was a different condition, and in S1, SD was significantly different from the other two conditions. Note that qP increased with the lack of water. This utilisation of light to produce energy in the light reactions of photosynthesis and to activate enzymes involved in the Calvin cycle and its increase with water deficit indicates that the % of incident light on the plant was used differently in control plants compared to water-stressed plants. It should be taken into account that the amount of light absorbed by the plant decreased in water deficit (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb), thus the lower amount of effective light reaching the plant was used to produce energy through the light reactions of photosynthesis in most of the cultivars, and therefore it is not dissipated as heat. On the contrary, as expected, the trend of NPQ with water deficit showed a progressive decrease (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ed). There are two sets of data: AO, CJ, E, F, H, P and S1 were heterogeneous in all situations, decreasing with water deficit, whereas the rest of the cultivars showed no differences in NPQ with the degree of drought.\u003c/p\u003e\n\u003cp\u003eFigure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea shows PhiPS2, which is calculated as (1-Fs)/Fm\u0026rsquo; and represents the effective quantum efficiency of the PSII to produce the photolysis of the water and the oxidation of plastoquinone. It obtained very homogeneous values in the cultivars for the three experimental conditions, except for P and E in the SD, where this parameter decreased to values below 0.6. Regarding the intercellular concentration of CO\u003csub\u003e2\u003c/sub\u003e in the leaf (Ci) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ec), the cultivars showed very similar values in the three experimental conditions, with a slight tendency to increase progressively with the lack of water, except for AO and E, where the results were homogeneous. The exception is C, where Ci diminished with MD and SD. P and F in SD showed a significantly high concentration of CO\u003csub\u003e2\u003c/sub\u003e in their leaves, although the Qabs in both cultivars were practically null under SD conditions. However, Brito et al. (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) describe a decrease in the intercellular concentration of CO\u003csub\u003e2\u003c/sub\u003e in the leaves as the drought increases. Qabs is Photon Plux Fotonic Density absorbed by the leaf, which decreased sharply with the water deficit in the majority of the cultivars. It is important to highlight that the plants that absorbed the highest light quantity in the control samples (CH, H and K) were not the ones that had the greatest CO\u003csub\u003e2\u003c/sub\u003e assimilation or the most efficient photosynthesis. For example, M, with a relatively low Qabs, was quite photosynthetically efficient. CJ, whose Qabs is very low under MD and SD conditions, presented the highest Fv`/Fm\u0026acute; ratio among all the cultivars studied under these two experimental conditions. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ed is the ETR or photosynthetic electron transport rate at PS1, whose pattern is similar to that of Qabs in the cultivars. C and P were the cultivars with the best ETR in MD and SD.\u003c/p\u003e\n\u003cp\u003eFrom the assimilation and transpiration data, instantaneous water use efficiency (WUEi) was calculated. The relationship between assimilation and stomatic conductance is the intrinsic water use efficiency (WUE). Both parameters were calculated throughout time (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). The objective was to verify those varieties which, in drought, showed a better use of water. In Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea, the most efficient cultivars in SD are P, CJ, C and CH, with WUE values greater than 150. This behaviour is maintained in MD for CJ and F. Curiously, CJ was one of the most drought-sensitive cultivars, although, as can be observed, it was also one of the most efficient in the use of water. The cultivars that showed the worse efficiency in control conditions were H, M and S2, which are three of the most drought-tolerant cultivars. Brito et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) indicate that WUE typically increases with MD. In this work, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea shows an increasing tendency in all cultivars for MD. In control conditions, the cultivars with the greatest WUE were CH and MS. In the case of WUEi (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eb), P remained significantly the most efficient cultivar in the use of water during SD. The rest of the cultivars in the three experimental conditions studied had lower and similar WUEi values. The exceptions in the control samples were M and MS. CJ was the most efficient cultivar in WUEi under MD. These results suggest that the most water-use-efficient cultivars are not the most drought-tolerant. It seems that there are adaptations of the most sensitive cultivars to use the remaining water in their cells during drought in a more efficient way, avoiding compromising the photosynthetic apparatus, thereby preventing irreparable damage. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ec, which represents the relationship between the amount of CO2 fixed by the plant and the amount of CO2 available within the leaf (A/ci), indicates a clear downward trend in all cultivars with the increasing water deficit. This is evident, since, although it is assumed that the amount of intercellular CO2 in the leaves is practically the same in control and drought stress samples, the water potential is much lower due to the lack of water inside the cells, producing a significantly lower assimilation of CO2 compared to the control plants, further decreasing with the increasing lack of irrigation. Exceptions occurred in F and K, where A/ci was higher in MD than in the control plants, although in SD they adopted the same behaviour as the rest. Another interesting parameter was A/Qabs (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ed), which represents the relationship between the amount of CO2 fixed by the plant and the amount of light absorbed by it, or photon flux density. This ratio yielded more homogeneous values than the former in the three experimental conditions for several cultivars (AE, AO, C, CH, E, and S2). In other cultivars, it increased with drought (CJ, K, F, H and P), indicating that none of such cultivars were very tolerant to drought. The cause of this increase could be the amount of light absorbed in these cultivars, which was very low especially in SD.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eProline and malondialdehyde quantification\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOsmotic adjustment has been shown to be an effective component of stress tolerance. The role that proline plays in the defence of the plant against abiotic stress is well known. Marjanossadat et al. (2022) have recently carried out a review in which they describe its role as an osmolyte, which is a signalling molecule, inducing the synthesis of abscisic acid (Vandana et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), thereby acting as a potent antioxidant. It seems to be synthesised mainly in roots and transported via phloem through the plant to the leaves and stems, thus a higher concentration of proline in the plant helps it to defend itself against water stress (Sadeghipour et al., 2020; Qirat et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). The exogenous application of proline improves the ability of the plant to avoid the degradation of photosynthetic pigments, as well as lipid peroxidation (Alkahtani et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Elewa et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Farooq et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ali et al., 2018). Proline improves the antioxidant activity of many enzymes, acting as a protein stabilizer and a ROS scavenger (Dar et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Adejumo et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). While different studies report proline accumulation in drought, heat stress does not result in proline accumulation in tobacco and arabidopsis plants (Rizhsky et al., \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e; Dobra et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lv et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eDuring exposure to multiple stressors (drought and heat stress), proline does not seem to accumulate, while it does under drought alone (Rizhsky et al., \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e; Sofo et al., \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e). Our results show that this amino acid accumulates and synthesises significantly in the resistance phase of the olive tree against drought, that is, during MD (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e), and decreases significantly and drastically during the SD phase. This occurs as described in most of the cultivars studied, except in E, F, H and P, which curiously resisted the lack of irrigation during the maximum number of days that the experiment lasted (42 days). In these four cases, the amount of proline inside the cells increased (E, H and P) or remained constant (F) during SD. Therefore, these results corroborate the defensive role of this osmolyte in plants stressed by the lack of water, showing greater accumulation with longer time and more drought-tolerant cultivars.\u003c/p\u003e\n\u003cp\u003eIn the case of MDA content, the results were less homogeneous than the previous ones (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e). To date, an increase in MDA in a plant subjected to water stress indicated the existence of a greater amount of lipid peroxidation in its cells; therefore, these results indicate that there was greater damage in said plant. However, during the biotic stress produced in beans by Garipova et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), there was a simultaneous increase in MDA and proline in the most resistant plants. It seems that the MDA content decreased with the inoculation of the pathogen. In the last years, the work of Morales et al. (2019) suggests that the role of these compounds as damagers or protectors relies on the enzymatic activity of aldehyde dehydrogenases (ALDHs). Under environmental stress or developmental signals, MDA is produced from the lipid peroxidation of polyunsaturated fatty acids as a consequence of ROS attack or the activation of lipoxygenases. MDA increases during the progressive increment of drought stress (Sofo et al., \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e; Ahmadipour et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). When aldehyde levels increase, protein carbonylation occurs, and this may result in defence signalling (Ahmadipour et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Morales et al., 2019). In this work, most of the cultivars did not present large differences in MDA content during the two drought conditions tested with respect to the control samples. There are exceptions such as P and AO (two cultivars with great tolerance to drought) during MD, where MDA increased up to 2 \u0026micro;molar. Another cultivar that increased its concentration of MDA in SD was F, which is also quite tolerant to water deficit. Therefore, we could agree with Morales et al. (2019), who indicate that an increase in MDA could trigger a signalling pathway to induce plant defence against water stress, taking into account that, in some resistant cultivars, there was a considerable amount of MDA in control conditions.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eIn this work, an in-depth study of 14 olive cultivars subjected to two experimental conditions of drought, MD and SD, was carried out to determine their greater or lesser tolerance against the lack of water. Photosynthetic data were collected at the saturation irradiances of each cultivar, observing that the CO\u003csub\u003e2\u003c/sub\u003e assimilation capacity was highly variable among them, both under \u003cem\u003ead libitum\u003c/em\u003e irrigation conditions and with water deficit. Some striking results are worth highlighting. The stomatal conductance and transpiration data indicate a positive correlation in most of the cultivars with the assimilation produced. Regarding the ISat, as well as Qabs or photon flux density, they appeared to have highly variable values among the cultivars. Curiously, it should be noted that the plants with the highest Isat, and therefore the greatest light absorption, were not always the ones with the greatest photosynthesis. Such is the case of K, which, despite having a very high Isat, presented an intermediate assimilation with respect to that obtained by the rest of the cultivars. Another example is P, which, despite having an intermediate Isat, showed the greatest capacity to fix CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003eThe photochemical efficiency of PSII in the dark (Fv/Fm) and under a light acclimation status (Fv'/Fm\u0026rsquo;) was quite stable in the three experimental conditions and in all the cultivars. The same was observed with the quantum yield of PSII (PhiPS2). However, the electron transfer rate (ETR) throughout the photosynthetic apparatus varied among the cultivars. This indicates that, although the PSII seems to function in a very stable way in all the cultivars, possible differences in the PSI, which also participates in this electron transfer, may be the cause of these cultivars presenting a different ETR and, therefore, generating different amounts of energy in the form of ATP and NADPH. Thus, a question arises: Is PSI the macro complex that is most affected in the photosynthetic apparatus when plants are subjected to drought conditions?\u003c/p\u003e\n\u003cp\u003eFinally, the cultivars with the most efficient use of water were not the most tolerant. There could be adaptations at the cellular or molecular level in the most sensitive cultivars to use the remaining water in their cells in a more efficient way, thereby protecting the photosynthetic apparatus and thus avoiding irreparable damage.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e classifies the 14 cultivars studied into four groups according to their tolerance to drought: 1) tolerant, the most resistant to the lack of water, which, after 42 days without irrigation, were little affected and, in some cases, were in perfect condition; 2) moderately tolerant, which, despite their resistance, in some of the analyses carried out, they appeared to have some sensitivity to the lack of water; 3) moderately sensitive, which resisted between 5 and 6 weeks with no irrigation, without reaching the last day of the experiment, and showed clear signs of wilting (even some of the individuals subjected to drought completely withered); and 4) sensitive, which resisted less than 4\u0026ndash;5 weeks without irrigation (several individuals withered during the treatment time).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eClassification of the 14 olive cultivars according to the tolerance to drought\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTolerant\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModerately\u003c/p\u003e\n\u003cp\u003etolerant\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModerately\u003c/p\u003e\n\u003cp\u003eSensitive\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSensitive\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMartina\u003c/p\u003e\n\u003cp\u003eCornicabra\u003c/p\u003e\n\u003cp\u003eSikitita2\u003c/p\u003e\n\u003cp\u003eArbosana\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrantoio\u003c/p\u003e\n\u003cp\u003eChemlali\u003c/p\u003e\n\u003cp\u003eSikitita1\u003c/p\u003e\n\u003cp\u003ePicual\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eManzanilla de Sevilla\u003c/p\u003e\n\u003cp\u003eArbequina\u003c/p\u003e\n\u003cp\u003eHojiblanca\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCornezuelo de Ja\u0026eacute;n\u003c/p\u003e\n\u003cp\u003eKoroneiki\u003c/p\u003e\n\u003cp\u003eEmpeltre\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eSome authors have recently suggested that plants develop a \"stress memory\" and hold on to past events without altering their genetic constitution, although they show changes in their epigenome, transcriptome, proteome and metabolome under repetitive drought stress (Kambona et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Liu et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e, Shadukhan et al., 2022). Several reviews address this issue, indicating that new opportunities are opening up to work on cultivars that are increasingly resilient to climate change. However, further research is required to better understand the triggers that produce this \"stress memory\" and the compound(s) in the signalling chain that could set off these results.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFinancial interests:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the European Project H2020 \u0026ldquo;Mobilization of Olive GenRes through pre-breeding activities to face the future challenges and development of an intelligent interface to ensure friendly information availability for end users\u0026rdquo;. GEN4OLIVE. Call SFS-28-2018-2019-2020 and topic Genetic resources and pre-breeding communities.\u003c/p\u003e\n\u003cp\u003eAuthors Elena Illana Rico (EIR) and Genoveva Carmen Martos de la Fuente (GCMF) received honoraria from the University of Ja\u0026eacute;n and H2020 Gen4Olive project, respectively, to work in the laboratory as technical professionals of research support. Author AOM did not receive research funding from Gen4Olive. Author AMFO received travel support, honoraria and funding research to manage this work from the H2020 Gen4Olive project.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors contribution:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to this study. Conception and design were performed by Ana Maria Fern\u0026aacute;ndez Oca\u0026ntilde;a (AMFO). Material preparation, data collection and analysis were performed by AMFO, EIR, GCMF and Ainhoa Ortega Morillas (AOM). Statistical analyses were performed by GCMF and AMFO. The first draft of the manuscript was written by AMFO and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Cordoplant S.L. nurseries for the great help provided with the supply of certified specimens of the 14 olive cultivars with which we have worked in this article. Technical and human support provided by Centro de Instrumentaci\u0026oacute;n Cient\u0026iacute;fico-T\u0026eacute;cnica (CICT)- Servicios Centrales de Apoyo a la Investigaci\u0026oacute;n (SCAI)- Universidad de Ja\u0026eacute;n (UJA, MICINN, Junta de Andaluc\u0026iacute;a, FEDER) is gratefully acknowledged.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdejumo, S. A., Oniosun, B., Akpoilih, O. A., Adeseko, A., \u0026amp; Arowo, D. O. (2021). 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Academic Press.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"photosynthesis-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pres","sideBox":"Learn more about [Photosynthesis Research](http://link.springer.com/journal/11120)","snPcode":"11120","submissionUrl":"https://submission.nature.com/new-submission/11120/3","title":"Photosynthesis Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"water stress, olive cultivars, drought tolerance, photosynthesis, physiological study","lastPublishedDoi":"10.21203/rs.3.rs-3057192/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3057192/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA complete study of 14 olive cultivars of great economic importance was carried out. These cultivars are Arbequina, Arbosana, Chemlali, Cornicabra, Cornezuelo de Ja\u0026eacute;n, Empeltre, Frantoio, Hojiblanca, Koroneiki, Manzanilla de Sevilla, Martina, Picual, Sikitita1 and Sikitita 2. All of them are certified by the World Olive Germplasm Bank of C\u0026oacute;rdoba (Spain). They are predominant cultivars in the olive groves of different locations throughout the Mediterranean basin, and they were subjected to total water deficit for a minimum of 14 days and a maximum of 42 days in the present study. Data such as chlorophyll content, soil moisture and specific leaf area were gathered. Photosynthetic parameters measured at the respective saturation irradiance of each cultivar were also analysed: assimilation rate, transpiration, stomatal conductance, photosynthetic efficiency, photochemical and non-photochemical quenching, photonic flux density, electron transference ratio, efficient use of water and amount of proline and malondialdehyde as indicators of oxidative stress. In addition to the control, two different experimental conditions were analysed: moderate drought, after 14 days of lack of irrigation, and severe drought, after 28 to 42 days of total absence of irrigation, depending on the tolerance of each cultivar. Based on the results, the cultivars were characterised and divided into four groups according to their drought tolerance: tolerant, moderately tolerant, moderately sensitive and sensitive to drought. This work represents the first contribution of drought tolerance of a considerable number of olive cultivars, with all of them being subjected to the same criteria and experimental conditions for their classification.\u003c/p\u003e","manuscriptTitle":"Physiological and biochemical study of the drought tolerance of 14 main olive cultivars in the Mediterranean Basin","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-28 20:11:47","doi":"10.21203/rs.3.rs-3057192/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-08-27T14:40:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-08-23T11:08:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-07-09T14:42:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"06ca3bf0-16a0-4f26-9ec2-82b67f4accd6","date":"2023-06-29T19:20:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-06-27T22:53:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-06-23T07:20:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-06-13T10:47:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Photosynthesis Research","date":"2023-06-13T09:11:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"photosynthesis-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pres","sideBox":"Learn more about [Photosynthesis Research](http://link.springer.com/journal/11120)","snPcode":"11120","submissionUrl":"https://submission.nature.com/new-submission/11120/3","title":"Photosynthesis Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8bcfb7a7-1b58-4676-8dd3-44246cebd63b","owner":[],"postedDate":"June 28th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-11-06T15:06:11+00:00","versionOfRecord":{"articleIdentity":"rs-3057192","link":"https://doi.org/10.1007/s11120-023-01052-8","journal":{"identity":"photosynthesis-research","isVorOnly":false,"title":"Photosynthesis Research"},"publishedOn":"2023-11-04 15:01:36","publishedOnDateReadable":"November 4th, 2023"},"versionCreatedAt":"2023-06-28 20:11:47","video":"","vorDoi":"10.1007/s11120-023-01052-8","vorDoiUrl":"https://doi.org/10.1007/s11120-023-01052-8","workflowStages":[]},"version":"v1","identity":"rs-3057192","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3057192","identity":"rs-3057192","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}