Impact of climate change on pasture quality in Mediterranean dehesas subjected to different grazing histories

Vol.: (0123456789) 1 3 Plant Soil (2023) 488:465–483 https://doi.org/10.1007/s11104-023-05986-9 RESEARCH ARTICLE Impact ofclimate change onpasture quality inMediterranean dehesas subjected todifferent grazing histories MariaDoloresHidalgo‑Galvez · LuisMatías· JesúsCambrollé· EduardoGutiérrez· IgnacioManuelPérez‑Ramos Received: 19 September 2022 / Accepted: 10 March 2023 / Published online: 21 March 2023 © The Author(s) 2023 Abstract Background and aims Forecasted climate change and overgrazing are threatening the sustainability of dehesas, human-managed ecosystems where pastures, livestock and scattered trees coexist. Pasture quality is particularly sensitive to these global-change drivers, but there are still many gaps to broaden knowledge about the interactive effects of both factors on it. In addition, scattered trees might play a relevant role in maintaining high levels of pasture quality under future scenarios of higher aridity, but its role remains largely unexplored. Methods We designed a field manipulative experiment of rainfall exclusion and increased temperature aimed to evaluate the impact of forecasted climate on pasture quality under different historical grazing intensities. To test the potential buffering effect of trees, experimental plots were installed equally in two habitat types: under trees and open grassland. Results Warming reduced the nutrient concentration of pasture, while drought increased it. Tree canopy improved soil fertility, which translated into an increase in pasture quality. Livestock exclusion and high grazing intensity caused a decrease in pasture quality, whereas moderate grazing intensity exerted positive effects on it. Finally, warming beneath tree canopy negatively affected the P concentration of pasture, specifically in the site subjected to moderate grazing intensity. Conclusion Our findings suggest that communities subjected to moderate grazing are more sensitive to climate change from a nutritional standpoint, likely because this management type provides high levels of P to the soil. In addition, we highlight the essential role of trees in agroforestry ecosystems to maintain high values of nutritional quality of pasture. Keywords Buffer effect· Grasslands· Nutrient concentration· Rainfall exclusion· Warming Responsible Editor: Wen-Hao Zhang. Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s11104- 023- 05986-9. M.D.Hidalgo-Galvez(*)· E.Gutiérrez· I.M.Pérez-Ramos(*) Institute ofNatural Resources andAgrobiology ofSevilla (IRNAS-CSIC), 10 Reina Mercedes avenue, 41012Seville, Spain e-mail: mdhidalgogalv[email protected] I. M. Pérez-Ramos e-mail: [email protected] L.Matías· J.Cambrollé Department ofPlant Biology andEcology, Faculty ofBiology, University ofSeville, 6 Reina Mercedes avenue, 41012Seville, Spain I.M.Pérez-Ramos Seville, Spain 466 Plant Soil (2023) 488:465–483 1 3 Vol:. (1234567890) Introduction Climate change models predict a temperature increase of 2–3 °C by the end of the XXI century for the Mediterranean area. Likewise, longer time periods of drought combined with more intense rainy events are forecasted for this region (IPCC 2021). In addition, overgrazing constitutes another major threat for the sustainability and functioning of terrestrial ecosystems (Fleischner 1994; Vázquez and Simberloff 2003), since herbivores reduce plant cover and may cause serious problems of soil degradation. Both stress sources could cause diverse effects on different ecosystem processes, including those that strongly depend on soil nutrients, such as net primary productivity, fiber content or carbon sequestration, among others (Hidalgo-Galvez et al. 2022; Sternberg and Yakir 2015; Yuan and Chen 2015). Warming can induce contrasted effects on plant nutrient balance and their use efficiency depending on the region, the intensity and duration of temperature increase, the target species and the duration of the growing season (Polley etal. 2013; Thornton etal. 2009). In general, a temperature increase (within an optimal range) alters microbial activity and decomposition rates of organic matter, stimulates root growth and increases leaf transpiration rate, commonly leading to an increase in plant uptake of soil water and nutrients (Pregitzer and King 2005; Viciedo et al. 2021). However, in water-limited ecosystems, the effect of warming is commonly negative since high temperatures tend to increase water deficit that triggers stomatal closure, therefore reducing water diffusion pathway in leaves (Abbate et al. 2004) and the uptake of water-soluble nutrients (Brouder and Volenec 2008; Cramer et al. 2009). In addition, an increase in temperature enhances the use of photosynthetic nitrogen (Dwyer etal. 2007) and decreases the phosphorus content of pasture due to a dilution effect caused by a more accelerated plant growth (Martinez etal. 2014; Reich and Oleksyn 2004). Plant acclimation to drought is complex and depends on different factors, such as the species characteristics or the duration of water restriction (Farooq et al. 2009). Drought alters plant associations with soil microorganisms, which are essential for nutrient uptake strategies (Pérez-Ramos etal. 2021; Schimel etal. 2007). Under moderate water stress, plant maturation can be delayed, thus nutrient values of pasture remain higher for a longer period (Coblentz et al. 2000). However, a more intense drought usually translates into reductions in plant cover, litter decomposition rates and nutrient mineralization (Homet et al. 2021; Jiao et al. 2016). This plant cover reduction increases the risk of soil nutrient loss due to leaching or erosion, which implies a depletion of nutrients in the medium to short term (Delgado-Baquerizo etal. 2013; Matías etal. 2011). However, most of previous studies have evaluated the independent effects of both sources of climate stress (temperature increase and drought) on pasture quality, while the impact of their interaction remains poorly known, particularly in semi-arid ecosystems. The impact of climate change on nutrient cycling usually follows a strong spatial variation (Chang etal. 2021). An example of heterogeneous ecosystem is the Mediterranean dehesa, which is composed of a large layer of herbaceous species typical of grasslands that coexist with scattered trees (López-Díaz etal. 2015; Moreno 2008). Pasture growing beneath tree canopy usually has higher nutrient content than those located in open areas. This higher nutrient concentration is probably due to the higher soil fertility of this habitat coming from the nutrient transport from distant areas through tree lateral roots (Sileshi 2016; Tiedemann and Klemmedson 1973), tree litter decomposition (Aponte etal. 2012; Ludwig etal. 2008) and animal droppings coming from that graze and refuge under the tree cover (Serrano etal. 2018; Tucker et al. 2008). In addition, trees reduce solar radiation (Pezzopane et al. 2010; Siles et al. 2010) and air temperature (Rahman etal. 2017; Siles etal. 2010), leading to a stabilization of the microclimate, a decrease in the variability of CO2 flux (De Carvalho Gomes etal. 2016) and a reduction of evapotranspiration (Lin 2007). All these evidences suggest that trees could mitigate the impact of climate change on pasture growth and quality (Bayala et al. 2014; Sida et al. 2018). Microclimate conditions beneath tree canopies directly affect plant physiological processes, which delays the vegetative development and maintains higher metabolic levels for a longer period, which could increase pasture quality in this habitat type (Jackson and Ash 1998; Sousa etal. 2010). However, to our knowledge, no experimental studies to date have assessed under field conditions the 467 Plant Soil (2023) 488:465–483 1 3 Vol.: (0123456789) potential buffering role of scattered trees in attenuating the impact of on-going aridity increase on pasture quality. Additionally to climate change, changes in land use such as those derived from different livestock management constitutes other of the main factors modifying soil properties (Medina-Roldán etal. 2012; Panayiotou etal. 2017) and, therefore, pasture production (Geng etal. 2012; He etal. 2010). The impact of grazing varies depending on its intensity (Piñeiro etal. 2010; Zhou etal. 2017). On the one hand, a moderate grazing pressure increases the amount of organic material through animal droppings (Sitters and Venterink 2018; Wang etal. 2018) and increases soil nutrient concentrations (Han et al. 2008; Tessema etal. 2011), which generally translates into an increase in the nutritional value of pasture (McCarthy etal. 2013; Miao etal. 2015). Moreover, livestock droppings accelerate litter mineralization rates and provide a source of readily available nutrients for plants and soil microorganisms (López-Mársico etal. 2015; Schrama etal. 2013). On the other hand, a higher grazing intensity might reduce drastically plant cover (Eldridge etal. 2016, 2017; Pulido etal. 2018), decrease plant growth rate (Bilotta etal. 2007; Smith 1979) and alter some physical soil properties (Bilotta etal. 2007; Carrero-González etal. 2012). Although the impact of climate change and grazing intensity on pasture quality has been analyzed separately in previous studies, the interactive effects of both stress sources remain largely untested, particularly in Mediterranean ecosystems. Regarding this issue, Maestre etal. (2022) highlight the importance of taking into account the interactions between grazing and local abiotic and biotic factors when assessing ecosystem services in drylands. Here, we are interested in evaluating whether plant communities historically harboring high grazing pressure are more sensitive to climate change from a nutritional point of view or, conversely, these communities have developed efficient strategies that make them more resistant to those environmental changes forecasted by climate change models. In the Iberian Peninsula, between 3.5 and 4 million hectares are dedicated to a type of ecosystem called dehesa in Spain or montado in Portugal (Olea and San Miguel-Ayanz 2006). Due to its economic importance and its potential susceptibility to changes in climate (Moreno and Pulido 2009), we carried out a field experiment for two years with different scenarios of temperature and rainfall. The aim of this study was to evaluate how pasture quality could be influenced by the temperature increase and reduced rainfall predicted by climate change models, as well as to analyze whether scattered trees could modulate these potential climate-induced changes in dehesas. In addition, we also tested if the impact of both climatic stressors could vary depending on the historical grazing pressure of the site. We raise several questions: (i) What are the effects of climate change (temperature increase and rainfall reduction) on pasture quality?; (ii) Can tree canopy alter pasture quality and buffer the impact of climate change on this ecosystem property? (iii) What is the influence of management history on pasture quality?; (iv) Is the impact of climate change more pronounced when plant communities have been historically subjected to higher grazing intensity? Results of this study will provide new insights into the interactive effects of two global change drivers threatening Mediterranean dehesas on pasture quality with the aim of designing action plans that mitigate the negative consequences of both stress sources on this ecosystem property. Material andmethods Experimental design This study was carried out in southwestern Spain (38°22′50.64”N, 4°45′27.69”W). The climate of the study area is continental-Mediterranean, with cold, wet winters and hot, dry summers. The annual precipitation is 416mm/year and the mean annual temperature is 15.4°C. January is the coldest and July the warmest month (5.9°C and 26.9°C on average, respectively; IFAPA weather station, Hinojosa del Duque; data from 2010 to 2020, https:// www. junta deand alucia. es/ agr ic ultur aypes ca/ ifapa/ r iaweb/ web/ estac ion/ 14/ 102). The studied area is characterized by a dense herbaceous layer (≥ 80%), with a mean species richness of 9.6 ± 0.3 species/m2 and it is dominated by native herbaceous species such as Sinapis alba L., Avena sterilis L., Erodium moschatum L. or Hordeum murinum L. subsp. leporium (Link) Acang. Scattered trees [Quercus ilex subsp. ballota (Desf.) Samp.] are frequent, with a mean density of 14.5 ± 1.3 trees/ha, by occupying approximately 20% of the total 468 Plant Soil (2023) 488:465–483 1 3 Vol:. (1234567890) cover. Soils are usually shallow, reaching a maximum depth of 50cm. Their pH is acid, varying between 6.2 and 6.4 and its texture is loamy-sandy. For this study, three neighboring dehesas with similar characteristics in terms of vegetation, tree density, slope, orientation, and soil texture and depth were selected (more details in Pérez-Ramos etal. 2021). Specifically, the distance across sites was 4.3km. They were all located in the same valley in a very flat area (difference in elevation between highest and lowest site is 13m, namely 685, 672 and 680 m.a.s.l.) with no hills or major elevations between them. Moreover, differences in rainfall across sites are minimal. These dehesas were subjected to different grazing histories, from high livestock pressure (0.85LU/ha), moderate grazing intensity (0.64LU/ha), to livestock exclusion. Livestock was mainly composed of Iberian sheep and pigs; however, to avoid possible damages by them we fenced the experimental plots and we analyzed the legacy effects of the management history of each dehesa. In September 2016, 36 sampling plots of 4 × 6m were installed at a minimum distance of 20 m. They were equally and randomly distributed in the three study dehesas and in the two selected habitats (half of them under tree canopy and the other half in open grassland, more than 10 m from the edge of trees). In each plot, we simulated three climatic scenarios mimicking the predicted changes for the period 2040–2070 in the Mediterranean area (IPCC 2021). To reproduce the forecasted temperature increase (‘warming’ treatment), four open top chambers (OTCs, Marion et al. 1997) were placed per plot. They consist of hexagonal pyramids made up of 40 × 50 × 32cm inclined panels of methacrylate without UV filter (Faberplast, Madrid) to avoid modifying the light spectrum and allow the transmission of wavelengths between 280 and 750 nm. They increase the interior temperature between 2 and 3°C (more details in Pérez-Ramos etal. 2021). Following the design of Matías et al. (2012), we built rainfall exclusion shelters (2.5 × 2.5 × 1.5m) using 6 methacrylate gutters (11cm wide, at a distance from each other of 36cm and inclined at an angle of 20°) in order to implement the ‘drought’ treatment. These rainfall exclusion structures do not modify the frequency of precipitation events, but reduce a third of the total precipitation (Yahdjian and Sala 2002). Two of the four OTCs installed in each plot were placed under these rainfall exclusion shelters with the aim of analyzing the impact of temperature increase and drought simultaneously (‘warming + drought’ treatment). Finally, an experimental unit called ‘control’ (2.5 × 2.5m) was delimited and exposed to the current natural conditions of temperature and precipitation. Thus, the experimental design resulted in a total of 144 experimental units (3 dehesas × 2 habitats × 4 climatic treatments × 6 replicates). Environmental characterization of the experimental plots Chemical properties ofthesoil Soil fertility was assessed in 2017 and 2019 by taking samples from the first 10cm of the soil at four different locations within each of the 144 experimental units using a 3cm diameter auger. In the laboratory, samples were air-dried and sieved. The fraction smaller than 2 mm was analyzed in order to evaluate nine chemical properties (Sparks 1996): pH (measured in a ratio 1:2.5 soil:water), total organic carbon (by oxidation of organic matter with H2SO4 and K2Cr2O7 and determined by spectrophotemetry), organic matter [expressed as total organic carbon corrected with a coefficient assuming that the organic matter contains humic acids with 58% carbon (100/58 = 1.724, Van Bemmelen factor)], available phosphorus (by spectrophotometry), NH4+ and NO3− assimilable content (extracted with 1 KCl and determined by spectrophotemetry, and available Ca2+, Mg2+ and K+ (extracted with 1N NH4CH3CO2 and determined by atomic absorption spectroscopy). Microclimate Tree cover Tree cover was estimated in each of the 144 experimental units by measuring plant area index (PAI), which includes both leaves and branches. Hemispherical photographs of the canopy were taken in spring 2018, before sunrise or after sunset using a horizontally leveled digital camera (Coolpix 4500; Nikon, Tokyo, Japan) positioned 0.5m aboveground and using a ‘fisheye’ lens with a wide field (FCE8; Nikon). Images were analyzed using Hemiview Canopy Analysis software version 2.1 (1999; Delta-TDe- vices Ltd., Cambridge, UK). 469 Plant Soil (2023) 488:465–483 1 3 Vol.: (0123456789) Thermal stress index In 24 randomly chosen experimental units, air temperature was hourly registered at ground level with a resolution of 0.1 °C using HOBOMX2201 data loggers (Onset Computer, Bourne, MA, USA). Plants experience a reduction in biomass production caused by a rapid decline in photosynthesis when the temperature exceeds 30°C (Benavides etal. 2009; Fahad etal. 2017; Saini and Aspinall 1982). Moreover, seed sterility appears and root development is prevented (Ferris et al. 1998; Huang etal. 2012; Saini and Aspinall 1982) if temperatures overpass this threshold. For these reasons, it is considered that plants suffer stress above 30°C and cumulated thermal stress was calculated for mean temperature during January–July (that is, the duration of pasture growth in the study area) using the following equation (Eq.1): where Tmean is the daily mean temperature, and monitoring days represent the number of days of the period sampled. Water stress index In 72 randomly chosen experimental units, soil volumetric water content (% VWC) was periodically (i.e. weekly in spring and monthly in the rest of the year) measured up to 40cm deep at intervals of 10 cm using a PR2 humidity probe (Delta-T Devices Ltd., Cambridge, UK). For the purpose of determining the amount of water available for plants, the total transpirable soil water (% TTSW) was calculated for the average of the four depths (Eq.2): where VWCmax and VWCmin represent the maximum and minimum soil volumetric water content (%) for each experimental unit during January–July period, respectively. This parameter represents the ability of plant communities to uptake water under particular soil conditions. It is based on the observed SWC dynamics and assumes that the minimum SWC in summer reflects the limit of plant water uptake. According to Barkaoui et al. (2017), plants suffer water stress when the TTSW is below 30% (that is, when their accessible soil water reserve is almost empty). Thus, water stress index was calculated (1) Thermal stress =Accumulated temperature ( Tmean −30 ) Monitoring days( January − July ) (2) TTSW =VWCmax −VWCmin during January–July using the following formula (Eq.3): where VWCi is the soil volumetric water content in each monitoring data, and VWCstress threshold shows the result of the sum of 30% TTSW and VWCmin. Pasture quality At the end of the vegetative cycle of 2017 and 2019, aboveground biomass produced in each of the 144 experimental units was collected using 50 × 50 cm quadrats. The collected samples were cleaned of nonherbaceous material, oven-dried at 70°C for 48hours and finely ground (< 2mm) in an IKA mill. Pasture quality was determined through chemical analyses of nutrient concentration using the plant biomass previously collected. Using an Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES), a multielemental inorganic analysis (P, Ca2+, Mg2+ and K+) was carried out in the Analysis Service of the Institute of Natural Resources and Agrobiology of Seville (IRNAS-CSIC), thanks to an ICP-OES VARIAN 720-ES spectrophotometer with electronic nebulizer. C and N concentration was determined at the Zaidin Experimental Station (EEZ-CSIC, Granada) using a LecoTruSpec C N elemental analyzer. Statistical analyses A Principal Component Analysis (PCA, hereafter) was carried out with soil chemical variables (pH, organic matter, carbon, ammonium, nitrate, phosphorus, calcium, magnesium and potassium) to analyze the correlations between them and identify the main axes of edaphic variation among the 144 experimental units. Factor 1 resulting from PCA (which we call ‘soil fertility’; more details in the Results section) was included as a fixed factor in the statistical analyses explained below. Using Linear Mixed Models (LMMs, hereafter) we tested the individual and combined effects of the main factors considered in this study (year, habitat type, climatic treatment, management history and soil fertility) on the main nutrients of pasture (carbon, nitrogen, (3) Water stress index = VWC stress threshold −VWCi TTSW x 0, 3 470 Plant Soil (2023) 488:465–483 1 3 Vol:. (1234567890) phosphorus, calcium, magnesium and potassium). Response variables were transformed when necessary to fulfil the normality and homoscedasticity criteria. Experimental plots were included as a random factor in the model and coded to affect only intercepts (not slopes). A ‘complete model’ was built based on the combination of all factors. In the cases where we detected a significant effect of any factor or its interaction with other(s), Tukey’s post-hoc test was performed to identify those homogeneous subsets of means that did not differ from each other. In addition, through bivariate linear regressions, we evaluated the relationship between stress indices (thermal and hydric) and the different pasture nutrients, as well as between pasture and soil nutrients. Statistical analyses were performed in R v.4.0.0 (R Development Core Team 2020) using the following packages: tidyverse (for data manipulation and visualization), nortest (for normality calculation), car and pbkrtest (for testing the estimated parameters, p value), lmerTest, ModelMetrics, and mgcv (for running linear mixed models), and ggplot2 and ggpubr (for graphical representation). Results Descriptive analysis of soil nutrients and their spatial distribution in the experimental plots Results of the Principal Component Analysis (PCA) for the nine soil variables analyzed are shown in Fig. 1a. Factor 1 represented most of the variability (36.41%) and was related to the concentration of P, OM, C, Ca2+ and K+ in the soil. Factor 2, with a variability of 17.82%, was related to pH, Ca2+ and Mg2+ in the soil (Fig.1a). TableS1 shows the details of these relationships. In a second PCA using the averaged values of both sampling years for the nine edaphic variables quantified in this study, we observed that throughout Factor 1 (with 56.53% of variability) experimental units were distributed according to the habitat type, with those plots beneath tree canopies being located at the positive side of the axis and those installed in open grasslands at the negative side. This result indicates that plots located beneath tree canopies showed higher values for most of the soil nutrients analyzed (i.e. higher fertility). Throughout Factor 2 (with 18.03% of variability), experimental units were differentiated according to the management history, with plots subjected to moderate grazing intensity being located at the negative side of the axis and the remaining plots (subjected to null or high grazing intensity) at the positive side (Fig.1b). Specifically, this means that soils subjected to moderate grazing intensity were less acid and more fertile in terms of Ca2+ and Mg2+ concentration. Factors influencing pasture quality Results from the linear mixed models showed that all the pasture nutrients considered in our study (excepting carbon) were significantly affected by management history and habitat type (Table 1). However, only plant P and Ca2+ were influenced by climatic treatments. It is worth noting the interaction between management history and climatic treatment, which affected plant N, P and K+. Although the interaction of both stress sources had a significant effect on the concentration of these three pasture elements, only plant P showed significant differences between different climatic treatments when a Tukey’s post-hoc test was applied (Fig.S1). Specifically, plant P was negatively affected by warming, but this effect was only evident beneath tree canopies in the site subjected to moderate grazing intensity. Effects ofclimate change onpasture quality Plant P and Ca2+ were the only pasture elements influenced by climatic treatments. Plant P was significantly lower in the treatments that simulated a temperature increase (i.e. warming and warming + drought) compared to those located outside the OTCs (i.e. control and drought). Compared to warming, drought treatment induced higher P concentration in the pasture (Fig.2a). Plant Ca2+ was significantly lower in warming than in drought treatment (Fig.2b). Intermediate values of this element were observed in the other two climatic treatments. Effects oftree canopy onpasture quality Overall, pasture quality was strongly influenced by habitat type (Table 1), appearing higher values of 471 Plant Soil (2023) 488:465–483 1 3 Vol.: (0123456789) plant nutrients beneath tree canopies (TableS2). The largest differences between habitats appeared for N, which increased 33.8% under tree canopy in comparison with open grasslands. In both habitat types, C was the most important element present in the pasture and P the scarcest (TableS2). Effect ofthermal andwater stress onpasture quality Results from our linear regressions showed that pasture quality responded significantly to both sources of stress, with slight differences depending on the habitat type. Specifically, plant N increased with Fig. 1 Graphical representation of the first and second axis from the Principal Component Analysis (PCA). Soil nutrients (C, P, NO3−, NH4+, Ca2+, Mg2+ and K+), organic matter (OM) and soil pH for the 144 experimental units are represented over the two sampling years (2017 and 2019; panel a). Projection of the 24 combinations resulting from the three factors considered in this study (management history, habitat type and climatic treatment) along the plane defined by the two main PCA dimensions. Average values of the two sampling years (2017 and 2019) are represented. Abbreviations = H: high grazing intensity (in red); M: moderate grazing intensity (in green); E: livestock exclusion (in blue); T: under tree (with light colours); O: open grassland (with dark colours); C: control; D: drought; W: warming, WD: warming + drought (panel b) 472 Plant Soil (2023) 488:465–483 1 3 Vol:. (1234567890) increasing thermal stress (marginally significant in open grassland) (Fig. 3a). However, plant K+ showed the opposite pattern in both habitat types (Fig.3b). Regarding water stress, an increase on it caused an increase in plant C (significant in open grassland) and plant K+ (in both habitat types; Fig.3c-e). However, water stress caused a decrease in plant N (Fig. 3d). Table S3 shows differences in plant nutrients according to the habitat types. Figure3 show s that the two habitat types differed in thermal stress (lower under trees than in open grasslands) but not in water stress. Effects ofmanagement history onpasture quality Management history influenced all pasture nutrients except C concentration. On the one hand, livestock exclusion reduced the nutrient concentration of some Table 1 Results from the linear mixed models to evaluate the influence of management history, habitat type, climatic treatment, year and soil fertility on plant C, N, P, Ca2+, Mg2+ and K+. Only those factors (and their interactions) that exerted a significant effect on the response variables are included in the table Response variable Factors with significant effect SS DF F p Plant carbon Year 1.83 1 5.29 0.02 Plant nitrogen Management history 0.40 2 4.61 0.02 Habitat type 1.60 1 36.68 <0.001 Year 6.31 1 144.22 <0.001 Soil fertility 0.38 1 8.69 0.004 Management history × Climatic treatment 0.60 6 2.28 0.04 Management history × Year 0.75 2 8.56 <0.001 Habitat type × Year 0.40 1 9.25 0.003 Management history × Climatic treatment × Year 0.67 6 2.57 0.02 Plant phosphorus Management history 0.25 2 3.35 0.05 Climatic treatment 1.49 3 13.06 <0.001 Soil fertility 0.37 1 9.65 0.002 Management history × Climatic treatment 0.57 6 2.49 0.02 Management history × Year 0.41 2 5.36 0.01 Management history × Habitat type × Climatic treatment 0.88 6 3.88 0.001 Management history × Climatic treatment × Year 0.86 6 3.78 0.002 Plant calcium Management history 3.21 2 20.33 <0.001 Habitat type 0.52 1 6.62 0.01 Climatic treatment 0.94 3 3.96 0.01 Management history × Habitat type 0.96 2 6.08 0.01 Management history × Year 2.83 2 17.88 <0.001 Habitat type × Year 0.82 1 10.39 0.001 Plant magnesium Management history 0.01 2 3.76 0.03 Habitat type 0.01 1 4.76 0.03 Year 0.03 1 21.80 <0.001 Management history × Year 0.03 2 9.96 <0.001 Habitat type × Year 0.02 1 13.21 <0.001 Plant potassium Management history 0.13 2 4.67 0.02 Habitat type 0.18 1 12.98 0.001 Year 0.55 1 38.79 <0.001 Soil fertility 0.14 1 9.95 0.002 Management history × Climatic treatment 0.24 6 2.82 0.01 Management history × Year 0.50 2 17.75 <0.001 473 Plant Soil (2023) 488:465–483 1 3 Vol.: (0123456789) elements, appearing lower plant N (Fig.4a) and Ca2+ (Fig.4c) in those plots. Pastures subjected to moderate grazing intensity were significantly richer in P (Fig.4b) and Mg2+ (Fig.4d). Finally, plant K+ differed when comparing the dehesas historically subjected to grazing, with higher values in those pastures subjected to high grazing intensity (Fig.4e). Effects ofsoil fertility onpasture quality Soil N, P, Ca2+, Mg2+ and K+ influenced significantly and positively the concentration of them in the pasture (Fig. 5a-e). In addition, the same pattern was also observed between plant N and NO3− in the soil (Fig.5f). Finally, a higher percentage of organic matter in the soil enhanced the concentration of plant C and N (Fig.5g-h). Effects ofsampling year onpasture quality Plant C, N, Mg2+ and K+ differed strongly for the two sampling years. A significantly higher concentration of N was observed in the pasture collected in 2017 (1.31% in 2017 vs. 0.94% in 2019; p < 0.001; R2 = 0.18). However, the pastures collected in 2019 presented a significantly higher concentration of Mg2+ (0.17% in 2019 vs. 0.16% in 2017; p = 0.002; R2 = 0.03) and K+ (1.26% in 2019 vs. 1.10% in 2017; p < 0.001; R2 = 0.04). Discussion Results from this study show how temperature increase, rainfall reduction and management history affect pasture quality, and highlight the important role that scattered trees could play in Mediterranean dehesas under future scenarios of higher aridity. What are the effects of climate change on pasture quality? This study shows that forecasted climate change could modify pasture quality in Mediterranean Fig. 2 Effects of the climatic treatments on plant P (panel a) and Ca2+ (panel b) (in %). Two sampling years (2017 and 2019) are represented. Different letters denote significant differences between climatic treatments after a Tukey post-hoc test 480 Plant Soil (2023) 488:465–483 1 3 Vol:. 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