{"paper_id":"49dc88d3-212a-4ba4-9489-7fba984d94fa","body_text":"Predicted yield and soil organic carbon changes in grassland, arable, woodland, and agroforestry systems under climate change in a cool temperate Atlantic climate | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predicted yield and soil organic carbon changes in grassland, arable, woodland, and agroforestry systems under climate change in a cool temperate Atlantic climate Michail L. Giannitsopoulos, Paul J. Burgess, Anil R. Graves, Rodrigo J. Olave, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4473355/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract This study predicts the effects of climate change on crop yields, timber volumes and soil organic carbon in grassland, arable, ash woodland, poplar plantation, and silvopastoral and silvoarable systems in Northern Ireland. We modified a version of the biophysical Yield-SAFE agroforestry model that includes a RothC soil carbon module and also the effect of atmospheric CO 2 fertilisation. The model was calibrated using existing field measurements and weather data from 1989 to 2021. The effect of two future climate scenarios were modelled, based on two representative concentration pathways (RCP 4.5 and RCP 8.5) for 2020–2060 and 2060–2100. The study revealed that the impact of future climate scenarios on grass and arable yields, and tree growth were positive with the effect of CO 2 fertilisation more than offsetting a generally negative effect of increased temperatures and drought stress on yields. The predicted land equivalent ratio (LER) remained relatively constant between the baseline and the future climate scenarios for the silvopastoral system (1.08 to 1.11). The corresponding values for the silvoarable system were 0.87–0.92 based on arable and timber yields alone, or 1.11–1.17 if grass yields were included. In the silvopastoral system, but not the silvoarable system, the model suggested that climate change would benefit tree growth relative to the understorey crop. Greater losses of soil organic carbon were predicted under barley-only (1.02–1.18 t C ha − 1 yr − 1 ) than grassland (0.48–0.55 t C ha − 1 yr − 1 ), with relatively small differences between the baseline and climate scenarios. However, the analysis indicated that these losses could be mitigated by planting trees, but this effect was not immediate as soil organic matter would continue to decline for the first 10 years until the trees were well-established. The model was also used to examine the effect of different tree densities on the trade-offs between timber volume and understorey crop yields. Biomass model crop resilience timber tree sequestration RCP RothC Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The interactions between land use and climate change are complex. Agriculture and changes in land use are an important source of greenhouse gases, and changes in climate and carbon dioxide concentrations have implications for crop, grass, and tree growth. Globally about 22% of greenhouse gas emissions are associated with agriculture, and land use and land-use change including deforestation (IPCC 2023 ). In the EU, for period 2016–2018, land use, land use change and forestry (LULUCF) and agriculture were associated with an annual average net source of about 118 Mt CO 2e , derived from annual average agricultural emissions of about 386 Mt CO 2e (European Environment Agency 2023 ) and an annual average sink from LULUCF of 268 Mt CO 2e (European Commission 2018 ). In the United Kingdom (UK) in 2019, agriculture emitted 46 Mt CO 2 e and LULUCF was associated with another 6 Mt CO 2 e being down from a total of 18 Mt CO 2 e in 1990 (BEIS 2021). Agroforestry or “farming with trees” is one method that farmers can use to mitigate against and adapt to the impact of climate change. The European Commission ( 2013 ) define agroforestry as a land use system in which trees are grown in combination with agriculture on the same land. Silvopasture, the combination of trees with grazing animals, is the main agroforestry system in Europe, whilst silvoarable, the integration of trees with arable crops, is present on much smaller areas (den Herder et al., 2017 ; Rubio-Delgado et al., 2023 ). A large and growing body of literature has investigated the benefits of integrating trees in agricultural land. These include agricultural outputs such as cereals and livestock, and outputs derived directly from the tree component such as fruit, nuts, timber and wood fuel (Reed et al., 2017 ; Wiebe et al., 2022 ). There can also be enhanced ecosystem services from integrating trees into agricultural systems such as carbon sequestration, regulation of runoff, and biodiversity enhancement (Giannitsopoulos et al., 2020; Torralba et al., 2016 ; Medinski et al., 2015 ). Agroforestry systems can offer production benefits per unit area of land compared to growing trees on separate areas of land from pasture or crop production. This is because the trees and the crops or pasture can be complementary in terms of the capture of solar radiation and water (Cannell et al., 1996 ). For example, when establishing widely spaced trees, an interrow arable crop can make effective use of the solar radiation and water not intercepted by the trees (Burgess et al., 2005 ; Ivezic et al., 2021 ). Hence the combined yields of timber and arable crops within an agroforestry system are typically greater than when trees and crops are grown separately. Trees can also moderate microclimatic extremes, providing more stable environmental conditions for understory species such as avoiding heat stress (Arenas-Corraliza et al., 2018 ). Since the pre-industrial period, land surface air temperature has risen nearly twice as much as the global average temperature (land and ocean) (IPCC, 2023 ). Additionally, increases in frequency and intensity of weather extremes are adversely impacting terrestrial ecosystems and the services they provide (Seneviratne et al., 2021 ). A system which can absorb perturbations, bounce back, or adapt whilst still retaining the same functions can be defined as “resilient” (Viñals et al., 2023 ). Combinations of trees and annual crops have been reported to enhance agro-ecosystem resilience to extreme weather, contribute to soil and water conservation and improve the carbon stock and sequestration potential (Kay et al., 2019 ; Kumar et al., 2020 ). Although they are simplified simulations of reality, models can be used to investigate the interactions of crops, trees and the environment (Burgess et al., 2019 ). They can guide decision-making and help land managers and policy makers to identify potential challenges related to, for instance, climate change and climate variability, and possible ways to address the effects. Even though there are agroforestry biophysical simulation models of varying complexity, the application of these models to climate adaptation is lacking (Farrell et al., 2023 ). Hence this study aims to examine the effects of current and future climates on crop yields, timber volumes, and soil organic carbon under grassland, arable, woodland, and agroforestry systems by carrying out simulations using a biophysical agroforestry model based on Yield-SAFE (van der Werf et al., 2007 ; Burgess et al., 2023 ). The modelled predictions of tree and crop growth under current weather conditions were validated by using data from two long term experimental sites in Northern Ireland. The model was then used to run virtual experiments to explore the effect of climate change, and the effect of different tree densities. 2. Method The workflow consisted of (i) developing the Yield-SAFE model to account for increased CO 2 atmospheric contents, (ii) compiling the measured data from two experiments, (iii) preparing unbiased weather data and climate scenarios, (iv) using the data to calibrate the Yield-SAFE model, (v) modelling climate scenarios, and (vi) undertaking virtual experiments for adaptive agroforestry management. 2.1. Yield-SAFE model and improvements The biophysical Yield-SAFE model was originally developed to predict the growth and yields of poplar silvoarable systems in England (van der Werf et al., 2007 ). The model works on a daily time step and describes the growth of trees and crops in response to temperature, the capture of solar radiation by tree, grass or crop canopies, and the competition for water between trees, grass and/or crops (Palma et al., 2016 ). We followed the recommended procedure of calibrating the model for a monoculture tree system and a monoculture crop or grass system, before running it for the selected agroforestry systems. A version of Yield-SAFE v2 model is available online (Burgess et al., 2023 ). However, the version used for this paper was enhanced so that crop water use responded to the daily vapour pressure deficit and the trees modified the temperature and wind (Palma et al., 2016 ). In addition, soil carbon changes to a depth of 23 cm were modelled using the Rothamsted Research soil carbon model (RothC). The predicted decomposition rate of soil organic fractions within the RothC model are dependent on the temperature, moisture and soil cover. To determine the effect of climate change on tree and crop yields, we modified Yield-SAFE to include the effect of increases in atmospheric carbon dioxide (CO 2 ) on the radiation use efficiency of the trees, grass, and crops. This was included by applying a multiplier to the radiation use efficiency as the atmospheric carbon dioxide concentration increased from 360 ppm (multiplier 1), to 720 ppm (multiplier 1.25), up to a maximum 28% benefit at a CO 2 concentration of 800 ppm (Supplementary material - Figure S1 ; Prooter et al., 2022 ; Jägermeyr et al., 2021 ; Rodriguez et al., 1999 ). 2.2. Site description and agroforestry experiments The study sites providing the experimental data are located at the Agri-Food and Biosciences Institute (AFBI) research centre (54°23'53.1\"N 6°36'41.6\"W) in Loughgall, County Armagh, Northern Ireland, UK at an altitude of 30 m above sea level. Daily weather observations taken at the sites between 2003 and 2015 indicated that the mean annual temperature at the site was 8.5°C and the mean annual rainfall 810 mm. In 1989, a silvopastoral experiment was established comprising three plots planted at a spacing of 5 m x 5 m (400 stems ha-1) and three woodland (2500 stems ha-1; 2 m x 2 m) plots planted with ash trees (Fraxinus excelsior L.), and three permanent grassland plots (Fornara et al. 2018 ; Table 1 , Fig. 1a) with a dominant ryegrass sward. All silvopastoral and grassland plots received an annual fertilizer application of between 120 and 150 kg N ha − 1 . Sheep grazing typically takes place in the pasture and silvopastoral system from April to October each year with a stocking rate of 12 ewes per hectare (1.2 livestock units; Fornara et al., 2018 ). The soil at this site is classified as brown earth on red limestone till with clay contents between 30 and 45% (Cruickshank, 1997 ; Fornara et al., 2018 ), and analyses between 1990 and 1999 indicated a pH of 6.4 to 6.7 for all plots. Thinning regimes resulted in an ash density of 265, 170 and 128 trees ha − 1 in 2004, 2009 and 2023 respectively, whilst the woodland system was thinned and pruned in 2009 and 2021 to create a residual density of 1100 and 708 trees ha − 1 respectively (Table 1 ). Ten years later in 1999, a silvoarable system was established with four poplar hybrids on a different grassland field approx. 200m from the silvopastoral site (Fig. 1b). The four poplar ( Populus ) clones were initially Beaupré, Boelare, Hoogvorst, and Hassendans. However, due to poor establishment Boelare and Hassadans were substituted in 2000 with Trichobel, and Gibecq hybrids. The poplars were planted as unrooted sets 5 m apart within rows and 14 m row spacings to give a 12 m crop alley (a density of 142 stems ha − 1 ; Table 1 ). The soil has been classified as a brown earth on red limestone till, with pH ranging from 5.69 to 6.21 within the site. The experiment was set as a randomised block design in four blocks with a split-split block treatment structure. An annual crop of spring barley (Riviera) was planted in the alleys for the first ten years until 2009 and was then followed by a pasture mix of ryegrass and red and white clover. Two forms of mulching within the rows were adopted, continuous polythene and 1.5m square mulch mats with intercrop rows sown with grass/clover. In 2013, as part of a study on tree crop interactions on alley coppice (Lunny, 2017 ), a willow crop was established for a 4-year cycle as an understorey crop and since then the land has been cultivated as a permanent grassland sward. Table 1 Initial planted tree densities, thinning year and resulting tree densities in 2023 for the silvopasture and silvoarable agroforestry and woodland systems at Loughgall, Northern Ireland (For SP, thinning operation was carried out between Dec 2009 and April 2010) System Initial tree density (trees ha − 1 ) Thinning year Resulting density (trees ha − 1 ) Silvopasture (SP) 400 2004 265 2009 170 2023 128 Woodland (WD) 2500 2009 1100 2021 708 Silvoarable 142 - 142 2.3. Data preparation 2.3.1. Weather data The Yield-SAFE model requires daily weather data. The modelling of the yields in the baseline period was a two-stage process. Firstly, the model was calibrated using data from planting to 2021 (Supplementary material - Table S1 ). Secondly the yields over a 40-year period were determined using predicted weather for the first 40 years after planting. The weather for the calibration period to 2021 was firstly based on on-site weather data collected from AFBI for the period 2003–2015 (Supplementary material -Table S2). However, where there were gaps in the data, the weather station records were supplemented with data from the E-OBS Europe-wide gridded observational dataset (Cornes et al., 2018 ). Temperature, relative humidity, wind speed, rainfall, and radiation data were taken from E-OBS, which is available on a 0.1° resolution (approximately 9 km), and evaporation data from ERA5 (2.5° resolution), the fifth-generation global atmospheric reanalysis of European Centre for Medium-Range Weather Forecasts (ECMWF), covering the period from January 1950 to present (Hurbasch et al., 2020). Day-to-day weather for the period from 2021 to 2029 were taken from the Regional Atmospheric Climate Model (RACMO) developed by the Koninklijk Nederlands Meteorologisch Instituut (van Meijgaard et al., 2008 ; 2012 ). RACMO performs favourably with other regional climate models that contribute to the European Coordinated Regional Downscaling Experiment (EURO-CORDEX; Jacob et al., 2014 ; Vautard et al., 2021 ). The required daily data were accessed using CliPick, a webtool designed to facilitate the selection of climate change data for applications in forestry and agriculture (Palma, 2017 ). Using the complete observational dataset (i.e. station observations supplemented by E-OBS/ERA5) as a reference, quantile mapping (Cannon 2015) was applied to all RACMO data to correct systematic model biases. 2.3.2. Soil, grass, crop, and tree data The initial organic matter content of the soil in 1989 was assumed to be 8.2% at 0–10 cm and 5.4% at 10–20 cm, giving an average of 6.8%, for the treatments related to the silvopastoral system (Fornara et al., 2018 ) and 5.0% (0–20 cm) for those related to the silvoarable system. For this study, the soil bulk densities in the pasture, silvopasture and woodland systems were assumed to be the same at 1.02 g cm − 3 , whilst a bulk density of 1.33 g cm − 3 was assumed for the treatments related to the silvoarable experiment (Fornara et al., 2018 ; Supplementary material - Table S7). It was also assumed that the sheep returned 1.17 kg C ha − 1 day − 1 . Data on grass and tree growth in the grass monoculture, silvopastoral system, and ash woodland control were obtained directly from measurements taken in the experimental site. Tree and crop yields in the silvoarable system were also sourced from the AFBI Loughgall site. However, because there was no dense stand of poplar at the experimental site, data on the growth of unthinned poplar at a density of 3 m x 3 m (1093 trees ha − 1 ) was derived from yield-class tables provided by Christie ( 1994 ). Because the yield-class table assumes some tree mortality, a constant tree density of 900 trees per hectare was assumed in the model. Yield estimates for monoculture crops of spring-planted barley of 5.3–5.9 t ha − 1 were derived from Mercer ( 2006 ) and Irish Farmers Journal (2021). Timber volumes were calculated from field measurements of tree height and diameter at breast height, and it was assumed that carbon formed 50% of the biomass. A land equivalent ratio ( LER ) was calculated to determine whether it was agronomically more efficient to combine two or more crops in the same piece of land, compared to monocultures (Eq. 1 ). In the equation, Ytree AF and Ycrop AF are the tree and crop yields in the agroforestry system, and Y tree Mono and Ycrop Mono are the tree and crop yields in the monoculture systems. The parameters used and assumptions made in Yield-SAFE for crops, trees and soil are provided in Supplementary material (Table S4; Table S5; Table S6). $$LER= \\frac{{Ytree}_{AF}}{{Ytree}_{Mono}}+\\frac{{Ycrop}_{AF}}{{Ycrop}_{Mono}}$$ 1 2.4. Future climate scenarios Future climate scenarios are simulated by global and regional climate models following a set of representative concentration pathways (RCPs) (Moss et al., 2010 ) that specify concentrations of greenhouse gases that will lead to a given increase in total radiative forcing by 2100, relative to pre-industrial levels. Four pathways have been defined by the Intergovernmental Panel on Climate Change (IPCC), RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5, that cover a range of global temperature change through the remainder of the century (van Vuuren et al., 2011 ). For example, the RCP 4.5 pathway represents 4.5 W m − 2 of extra warming by 2100. The RCPs map directly to the Shared Socioeconomic Pathways (SSPs) used in the IPCC 6th Assessment Report (IPCC 2023 ). For this study, the effect of climate change on tree, crop and agroforestry yields were examined using the “intermediate” RCP 4.5 scenario and the “high” RCP 8.5 emissions scenario. Assuming a tree rotation of 40 years, the scenarios were split into two periods of 2020–2060 and 2060–2100. In addition to the changes in climate, we included estimates of the changes in atmospheric CO 2 up to 2100 for both RCP 4.5 and RCP 8.5 scenarios, using data presented by Meinshausen et al. ( 2020 ). The value of CO 2 was estimated to reach 603 ppm in 2100 in the RCP 4.5 scenario, and 1142 ppm in 2100 in the RCP 8.5 scenario (Supplementary material - Figure S4). As mentioned in Section 2.1 however, a cut-off point of 800 ppm was assumed in Yield-SAFE for the fertilisation effect on plants. The general effect of the climate change scenarios was to increase maximum air temperatures by between 1.2 and 2.8°C and minimum air temperatures by between 1.2 and 3.0°C (Table 2 ). The change in annual rainfall was predicted to range from a decrease of 16 mm to an increase of 84 mm. It was predicted that the mean level of solar radiation would decrease by 0.2–0.5 MJ m − 2 d − 1 , but higher temperatures were predicted to increase mean daily evaporation rates. A detailed summary is provided in Supplementary material - Table S8. Mean atmospheric CO 2 concentration was assumed to be 340 ppm for the period 1960–2000, rising to a mean of 574 ppm and 879 ppm in RCP 4.5 and RCP 8.5 respectively for 2060–2100 (Table 2 ). Table 2 Predicted mean annual rainfall, maximum and minimum temperatures, daily evaporation and solar radiation, and CO 2 concentration predicted for a baseline period (1960–2000) and for 2020–2060 and 2060–2100 for two representative concentration pathways (RCP) Baseline RCP 4.5 RCP 8.5 (1960– 2000) (2020–2060) (2060–2100) (2020–2060) (2060–2100) Annual rainfall (mm) 861 845 930 891 945 Mean maximum temperature (°C) 13.0 14.2 14.6 14.2 15.8 Mean minimum temperature (°C) 5.1 6.3 6.8 6.6 8.0 Daily evaporation (mm) 1.49 1.57 1.58 1.57 1.58 Solar radiation (MJ m − 2 d − 1 ) 9.3 9.2 9.1 9.0 8.8 Carbon dioxide concentration (ppm) 340 480 574 515 879 2.5. Virtual experiments Within the high radiative forcing climate scenario RCP 8.5 and for the period 2060–2100, it was examined whether by decreasing or increasing the original tree densities (Table 1 ), had an effect on crop yields of the silvoarable and silvopastoral experiments. The range of tree densities tested were 0, 50, 142, 300, 400, and 600 trees per hectare. The thinning regime for the silvopastoral tree density simulations, was of the same proportion to the original (Supplementary material - Table S3). 3. Results The results first cover the calibration of the Yield-SAFE model for the crop, grass, and tree yields using the baseline weather data. Following calibration of the model, the model was then used to determine the effect of future climate scenarios and tree densities on crop, grass, and tree growth and changes in soil organic carbon. 3.1. Calibration of the pasture, silvopastoral and woodland systems The mean modelled monoculture pasture yield with Yield-SAFE of 9.1 t DM ha − 1 was similar to the measured pasture yields of 9.3 t DM ha − 1 in 1995, 1998 and 2000. In the woodland with a tree density of 2500 trees ha − 1 , the measured timber yield of 104 m 3 ha − 1 in year 15 was similar to the modelled value of 98 m 3 ha − 1 (Supplementary material -Figure S2). In the silvopastoral system at a tree density of 400 trees ha − 1 , the measured timber volume in year 15 of 39 m 3 ha − 1 was close to the modelled value of 36 m 3 ha − 1 (Supplementary material -Figure S3). The mean measured grass yield in 1995, 1998 and 2000 was 6.6 t DM ha − 1 whilst the model predicted a mean dry matter yield of 7.3 t DM ha − 1 for the same years. As trees matured, grass yields were predicted to decline starting with a dry matter yield of 10.2 t ha − 1 in the first year and decreasing to about 1.5 t ha − 1 after 30 years (Supplementary material – Figure S5). 3.2. Calibration of the arable, poplar plantation and silvoarable systems The mean monoculture spring barley yield predicted by Yield-SAFE (5.8 t ha − 1 ) was similar to the mean spring barley yield reported for Northern Ireland (5.3–5.9 t ha − 1 ; Mercer 2006 ; Irish Farmers Journal 2021). For the poplar woodland system, the modelled timber yield of 233 m 3 ha − 1 with Yield-SAFE was similar to the yield of 260 m 3 ha − 1 in year 20 described in yield profiles of poplar with a yield class of 6 as described by Christie ( 1994 ) (Supplementary material -Figure S3). In the silvoarable system, the measured timber volume reached 163 m 3 ha − 1 (as averaged across the four cultivars) at 22 years, compared to a modelled value of 169 m 3 ha − 1 in year 22 (Supplementary material -Table S9). With the silvoarable system, the modelled spring barley yield of 6.3 t ha − 1 in year 1 was higher than the measured value of 3.9 t ha − 1 . However, the modelled value of 4.8 t ha − 1 was similar to the measured value of 4.3 t ha − 1 in year 3. With time, and as trees developed, the modelled grass yields in the silvoarable system were predicted to decline from 5.0 t ha − 1 in year 11 to 2.5 t ha − 1 in year 20 (Supplementary material – Figure S6). 3.3. Effect of climate change on yields of pasture, woodland and silvopastoral system The calibrated model was then used to determine the effect of climate change. In the monoculture grassland plots, Yield-SAFE predicted that climate change and the increase in CO 2 would result in higher grass yields in future years, with the mean grass yield increasing from 9.6 t ha − 1 to 11.0-12.7 t ha − 1 (Table 3 ; Fig. 2). The model also predicted that the total timber volume from the woodland system at 40 years would increase from 428 m 3 ha − 1 to 457–513 m 3 ha − 1 (Table 3 ). Within the silvopastoral system, the predicted timber after 40 years increased from 271 m 3 ha − 1 to 300–365 m 3 ha − 1 , with the increase due to the assumed fertilisation effect of the carbon dioxide (Table 3 ). Within the silvopastoral system, there was also a predicted increase in the understorey grass yield from 4.3 t ha − 1 to 4.7-5.0 t ha − 1 . Hence the net effect was that the predicted increase in timber growth was greater than the increase in grass growth as demonstrated by the declining proportion of the land equivalent ratio derived from the grass component (Table 3 ). Table 3 Predicted average annual grass yield (t ha -1 yr -1 ) and timber yield (m 3 ha -1 ) in the monoculture systems and within the silvopastoral system for the baseline period and in two time steps of 40 years each (with and without the carbon dioxide fertilisation effect), and the predicted land equivalent ratio Scenario Time step CO 2 Grass Woodland Silvopastoral Predicted land effect yield (40 years) standing+ harvested timber (at year 40) grass yield (40 years) standing+ harvested timber (at year 40) equivalent ratio (grass + tree) Baseline 1989- No 9.2 416 4.2 261 0.46 + 0.63 = 1.08 2029 Yes 9.6 428 4.3 271 0.45 + 0.63 = 1.08 RCP 4.5 2020- No 9.5 420 4.4 267 0.46 + 0.64 = 1.10 2060 Yes 11.2 461 4.7 305 0.42 + 0.66 = 1.08 2060- No 8.9 412 4.3 257 0.48 + 0.62 = 1.11 2100 Yes 12.0 487 5.0 325 0.42 + 0.67 = 1.08 RCP 8.5 2020- No 8.9 405 4.3 252 0.48 + 0.62 = 1.11 2060 Yes 11.0 457 4.7 300 0.43 + 0.66 = 1.08 2060- No 7.7 397 4.0 248 0.52 + 0.62 = 1.14 2100 Yes 12.7 513 5.0 365 0.39 + 0.71 = 1.11 3.4. Effect of climate change on yields of arable, poplar plantation and silvoarable system For the treatments related to the silvoarable system, the Yield-SAFE model predicted that climate change and the increase in CO 2 would result in higher yields in a monoculture arable system, with the mean arable yield increasing from 6.2 t ha − 1 to 6.4–7.3 t ha − 1 (Table 4 . ; Fig. 3). The model also predicted that the total timber volume from the poplar plantation at year 40 would increase from 429 m 3 ha − 1 to 429–473 m 3 ha − 1 (Table 4 ). Within the silvoarable system, the predicted harvested timber after 40 years increased from 297 m 3 ha − 1 to 298–324 m 3 ha − 1 . There was also a predicted increase in the alley crop arable yield from 4.9 t ha − 1 to 4.8–6.5 t ha − 1 with the largest increase observed in 2060–2100. If the land equivalent ratio is calculated on the basis of the crop (barley) and tree yields only, then the silvoarable system resulted in a land equivalence ratio of less than 1.0. If the grass yield was also included, then a higher LER would have been derived (Supplementary material - Table S11). Table 4 Predicted average annual crop yield (t ha -1 yr -1 ) and timber yield (m 3 ha -1 ) in the monoculture systems and within the silvoarable system with the carbon dioxide fertilisation effect, and the predicted land equivalent ratio (silvoarable crop yield is per unit system) Scenario Time step Barley only yield (40 years) Poplar only yield (at year 40) Silvoarable poplar (at year 40) Silvo- arable crop (10 years) LER (crop + tree) Baseline 1999–2039 6.2 429 297 4.9 0.20 + 0.69 = 0.89 RCP 4.5 2020–2060 6.6 429 298 5.5 0.21 + 0.69 = 0.90 2060–2100 7.3 473 324 6.5 0.22 + 0.68 = 0.90 RCP 8.5 2020–2060 6.4 459 314 4.8 0.19 + 0.68 = 0.87 2060–2100 7.0 462 323 6.5 0.23 + 0.69 = 0.92 3.5. Predicted effect of land use and climate change on soil organic carbon In terms of the changes in soil organic carbon, the Yield-SAFE model predicted a mean net decrease in the soil organic carbon content across 40 years of 0.48 t ha − 1 yr − 1 within the monoculture grass system under the baseline climate (Table 5 , Fig. 4a). Table 5 Predicted effect of climate change and carbon dioxide fertilisation on the mean annual change in soil organic carbon in the pasture, woodland and silvopastoral systems (assuming the same bulk density of 1.02 Mg m -3 ) Scenario Time step Mean soil organic carbon change (t C ha − 1 yr − 1 ) Grass Woodland Silvopasture Baseline 1989–2029 -0.48 0.90 0.07 RCP 4.5 2020–2060 -0.43 1.09 0.16 2060–2100 -0.46 0.98 0.10 RCP 8.5 2020–2060 -0.44 0.90 0.09 2060–2100 -0.55 0.90 0.03 In contrast to the grassland system, the soil organic carbon storage in the woodland system was predicted to decline in the initial years but then increase from year 11 to 40 after tree planting, with a mean increase of 0.9–1.09 t C ha − 1 yr − 1 over the 40 years (Table 5 ; Fig. 4c). The soil organic carbon in the silvopastoral system was predicted to show an intermediate effect with a decrease predicted in the initial 13 years, a stable period until year 20, and an increase from year 20 to year 40 (Fig. 4b). In general, the four future climate scenarios resulted in similar changes and temporal profiles in the predicted soil organic carbon content as the baseline system. The greatest increases in soil organic carbon (or smallest decreases) occurred within the RCP4.5 2020–2060 scenario, and the smallest increases or greatest decreases occurred in the RCP 8.5 2060–2100 scenario (Table 5 ; Fig. 4a). In the systems related to the silvoarable experiment, the Yield-SAFE model predicted an average annual decline in the soil organic carbon in the barley only system of 1.0-1.1 t C ha − 1 yr − 1 over 40 years in the baseline climate. A monoculture system (without trees) of barley followed by grass was predicted to result in a decline of 0.51 t C ha − 1 yr − 1 (Table 6 ) and similar to that observed for the monoculture grass system (Table 5 ). The poplar system predicted an increase of soil organic carbon of 0.19 t C ha − 1 yr − 1 , and the silvoarable system was predicted to result in an intermediate loss of 0.29 t C ha − 1 yr − 1 . Table 6 Predicted effect of climate change and carbon dioxide fertilisation on the mean annual change in soil organic carbon in the monocrop barley, monoculture barley followed by grass, poplar plantation, and silvoarable systems (Spring barley was present in the field in the initial 10-years, followed by 30-years of grass) Scenario Time step Mean soil organic carbon change (t C ha − 1 yr − 1 ) Barley-only Arable Poplar plantation Silvoarable Baseline 1999–2039 -1.02 -0.51 0.19 -0.29 RCP 4.5 2020–2060 -1.02 -0.48 0.22 -0.27 2060–2100 -1.07 -0.50 0.20 -0.29 RCP 8.5 2020–2060 -1.05 -0.50 0.20 -0.29 2060–2100 -1.18 -0.58 0.05 -0.39 There was a distinct temporal pattern to the loss of soil organic carbon. For the arable and the silvoarable system, the soil organic carbon tended to decline when there was barley crop, before stabilising when grass was established (Fig. 5a, b). In the poplar plantation, although the soil organic carbon declined for the first 10 years, it then increased from year 10 to 40 after tree planting (Fig. 5c). Again, the predicted effects of climate on the soil organic carbon were broadly similar for the four future climate scenarios. However. the lowest increase or greatest decrease in soil organic carbon occurred in the RCP 8.5 (2060–2100) scenario (Fig. 5b). 3.6. Virtual experiments with tree densities The final set of results relate to the effect of different tree densities with the RCP 8.5 scenario for 2060–2100. Within the silvopastoral experiments, the greatest grass yield was predicted with no trees (Fig. 6a), and the greatest timber volumes were predicted with high tree densities (Fig. 6c). When trees were integrated with the grass, then whilst the initial grass yields were similar to the monoculture grass, the grass yields eventually declined as the trees grew in size and captured more resources (Fig. 6a). For example, reducing the initial silvopastoral tree density of 400 trees ha − 1 to 300 trees ha − 1 enhanced the mean grass yield over 40 years by 16% (Fig. 6a; approximately 0.8 t ha − 1 yr − 1 ), but reduced the total timber volume by 26 m 3 ha − 1 (Fig. 6c). Although timber volume per hectare declined with the reduced tree density, the predicted timber volume per tree was predicted to be 0.35 m 3 tree − 1 greater in year 40 after planting (Supplementary material - Figure S7a). Furthermore, reducing trees from 400 to only 50 per hectare, were predicted to give approximately the same grass yields as the pasture (no trees; Fig. 6a). In the silvoarable experiment, the highest crop yields were derived with no trees (Fig. 6b) and the highest timber volume per hectare was derived from the highest tree density (Fig. 6d). Increasing the tree density from 50 to 142 trees ha − 1 led to similar understorey crop yields in the first 4–5 years, but the mean crop yield across 40 years decreased by 3.9 t ha − 1 yr − 1 (Fig. 6b), and the predicted timber volume in the final year increased from 178 to 322 to m 3 ha − 1 . In a similar way, increasing the tree density from 142 to 300 stems ha − 1 reduced the mean crop yield by 1.9 t ha − 1 yr − 1 and increased timber volume by 78 m 3 ha − 1 at year 40 (Fig. 6b and 6d). For RCP 8.5 (2060–2100), the land equivalent ratio (including crops, trees, and grass) of 1.19 for the silvoarable system at 50 trees ha − 1 (Supplementary material - Table S12) was similar to the value of 1.17 for the same system at a density of 142 trees ha − 1 (Supplementary material - Table S11). Although the effect was marginal, the land equivalence ratios for RCP 8.5 (1.17–1.19) at these two tree densities was greater than those predicted for the baseline climate (1.14) and those (1.09–1.14) for the two RCP4.5 scenarios and the RCP 8.5 (2020–2060) scenarios (Supplementary material -Table S11; Table S12). In the silvopasture, at a tree density of 400 trees ha − 1 , the model predicted a decrease in soil organic carbon in the first 15 years after planting, followed by an increase in the next 25 years (Fig. 6e). Reducing the density from 400 to 142 trees ha − 1 led to a smaller reduction in soil organic carbon in the first 10 years, but soil organic carbon continued to steadily decline. By contrast within the silvoarable-related treatments, although there was difference in the soil organic carbon between systems with and without trees, the difference between the different tree densities was relatively small. In both the silvopastoral and silvoarable systems, moving from no trees to 50 trees ha − 1 increased the predicted soil organic carbon by 9.6 and 9.1 t C ha − 1 respectively across 40 years (Fig. 6e and f). 4. Discussion 4.1. Collating weather data and calibration Predicting the effect of climate change on grass, crop, tree, and agroforestry yields in a systematic way is a complicated process. It requires multiple steps including the derivation of current weather data, experimental data of tree and crop growth, and the parameterisation and calibration of models for the baseline situation. The prediction of the effect of climate change then involved the derivation and modification of future climate datasets, and the inclusion of carbon dioxide fertilisation effects in a biophysical model. 4.2. Climate change and food and fibre production The study indicates that the effect of climate change on grass, crop, and tree yields in the temperate Atlantic climate of Northern Ireland is a balance between a generally negative effect of increased temperatures and associated drought stress on yields and the positive effect of carbon dioxide fertilisation. Zhao et al. ( 2017 ) in China and Webber et al. ( 2018 ) applying crop models on a spatial grid across Europe also reported that the adverse impacts of warming and drought are counterbalanced by CO 2 fertilisation for crops such as wheat and maize showing some regional yield increases. Xiao et al. ( 2018 ) also reported the tension between the negative effects of warming temperature and decreasing rainfall on wheat yield and the fertilizing effect of increased CO 2 concentrations in the atmosphere under both RCP 4.5 and RCP 8.5 scenarios at the beginning of the twenty-first century in China. The Yield-SAFE model predicted that climate change and carbon dioxide fertilisation would increase woodland timber production by 20% in the RCP 8.5 scenario to 513 m 3 ha − 1 , compared to 428 m 3 ha − 1 in the baseline scenario. This is broadly similar to an increase of 21–29% in net primary productivity predicted for forests in Germany in the RCP 8.5 scenario by Sperlich et al. ( 2020 ). Gutsch et al. ( 2018 ) also predicted that timber production would increase with climate change in Germany, and Favero et al. ( 2022 ) reported a 10% increase in forest productivity by 2100 in Latin America under the RCP 8.5 scenario. Tree growth in cool regions is likely to benefit from warmer and longer growing seasons (Kellomäki et al., 2018 ). AlRahahleh et al. ( 2018 ) also estimated higher forest growth in Finland under future climates in a model that included the effect of carbon dioxide concentrations. Norby et al. ( 2010 ) showed that elevated CO 2 experiments in Tennessee in the United States, resulted in significant enhancements (24%) of trees net primary productivity during the initial 6 years, however from year 7 onwards the enhancement reduced to 9% due to soil nitrogen limitations. A similar observation was reported by Broadmeadow and Jackson ( 2000 ) in a factorial experiment on one year old tree seedlings in Britain, where elevated CO 2 , increased growth of Pinus sylvestris L., Quercus petraea (Matt.) Liebl. and Fraxinus excelsior L, by 20% in year one. However, a longer-term lack of nitrogen meant that there was no enhanced growth in year three for a nitrogen demanding species like ash. We note that the Yield-SAFE model does not account for nitrogen limitations for plant growth (van der Werf et al. 2007 ). In addition, our model does not account for the effect of changes in temperature on pests and diseases. For example, van Niekerk et al. ( 2022 ) predicted a decline of wood production with warmer climates in Europe, which they related to an increase in the decay potential of fungi and the range extension for certain wood degrading termite species. 4.3. Agroforestry and food and fibre production The Yield-SAFE model predicted that a silvopastoral system would result in declines in grass yields relative to a grass monoculture and declines in timber yields relative to a woodland system (Table 3 ). This is consistent with Ehret et al. ( 2015 ), who in a 2-year artificial shade experiment, showed for example reductions of 70% in grass species herbage production when shade reached 80% in Lower Saxony, Germany. The model predicted lower grass yields in the silvopastoral system after year 8 or 9 when compared to the pasture system due to competition from the trees for light and water (Supplementary material - Figure S5). This reduction in grass yields will also reduce the density of livestock that can be supported below the trees. However, the trees can provide other benefits such as moderating high temperatures in summer, resulting in fewer stress days for livestock, and this may offset the effect of reduced grass production (Palma et al., 2016 ). The analysis revealed that the predicted land equivalent ratio (LER) would range from 1.08 to 1.11 for grass and ash timber (Table 3 ), meaning that monocultures require 8–11% more land than the silvopastoral system to obtain similar relative yields. By contrast, the land equivalent ratios for the silvoarable system in terms of arable crop and poplar timber were below one (Table 4 . ), meaning that the agroforestry system was less productive than the two monoculture systems. This is primarily because the understorey of the Loughgall silvoarable system comprised 10-years spring barley followed by grass, and the grass yield was not considered. In the LER calculations, if grass was also included then the LER would range from 1.11 to 1.17 (Supplementary material - Table S12). Seserman et al. ( 2018 ) in Saxony in Germany also reported a LER lower than 1 for a cereal-poplar agroforestry system. In such analyses, the choice of the default forestry system can be critical. For example, a silvoarable system may have a LER of 1.22–1.45 if the default tree system is widely spaced (Graves et al., 2007 ), but less than 1.12 (Graves et al., 2010 ) if the default tree system is densely spaced. 4.4. Changes in soil carbon The study showed that the model predicted declines in soil carbon in the arable, silvoarable and pasture systems, and increases in the silvopastoral and two woodland systems. Xu et al. ( 2011 ) also using RothC predicted a decline in soil organic carbon (SOC) on grassland in Ireland of between 2 and 6% in future climates compared to a baseline. The Yield-SAFE model predicted SOC increases for the silvopastoral system ranging from 0.16 t C ha − 1 yr − 1 for RCP 4.5 (2020–2060) to 0.03 t C ha − 1 yr − 1 for RCP 8.5 (2060–2100). These values are similar to reported increases in SOC in forest soils of 0.12 t C ha − 1 yr − 1 in Finland and 0.35 t C ha − 1 yr − 1 in France (Rantakari et al., 2012 ; Jonard et al., 2017 ). In previous research, Upson et al. ( 2016 ) measured a decline in soil organic carbon during the first 14 years when trees were planted on grassland. The inclusion of the RothC within Yield-SAFE provides an explanation for this in that whilst soil organic carbon may decline in the first 15 years from tree planting, it may then recover (Supplementary material - Table S10; Fig. 4b). Such an analysis illustrates the potential strength of using a biophysical model to account for temporal changes. Ashwood et al. ( 2019 ) in a study focused on woodlands in the UK also found that, whereas the levels of soil carbon under pasture and young woodland were similar, the soil carbon content in the woodlands increased with time. Pardon et al. ( 2017 ) also highlighted the potential of middle-aged to mature tree rows to increase soil organic carbon stocks for the agricultural crop in agroforestry systems. With the Yield-SAFE model, the predicted increase in the soil organic carbon of the ash woodland system of 0.90–1.09 t C ha − 1 yr − 1 was substantially greater than in the poplar plantation system of 0.05–0.22 C ha − 1 yr − 1 (Fig. 7 ). Although the timber volumes of the ash and the poplar were similar after 40 years (428–429 m 3 ha − 1 ), the ash wood would have a higher wood density than the poplar, and hence the biomass accumulation of the ash woodland would be greater and this will lead to greater cycling of biomass carbon to the soil. The model predicted that the RCP 4.5 2020–2060 and 2060–2100 and RCP 8.5 2020–2060 climate scenarios would marginally increase soil organic carbon in the ash woodland (Table 5 ) and the poplar plantation (Table 6 ), compared to the baseline. This could be explained by the greater biomass production and recycling of biomass within the system. As temperatures increases, retaining soil organic carbon becomes more difficult, and hence there is potentially a greater role for trees to help maintain or increase soil organic carbon. It has been reported that soil organic carbon decomposition rates may be lower in agroforestry systems than in arable and grassland systems due to the maintenance of high levels of moisture, reduced soil evaporation and cooler soil temperatures (Falloon et al., 2011 ; Das et al., 2019 ). Within the RothC module in Yield-SAFE, we predicted greater soil organic carbon decomposition rates in the grass monocrop than under the silvopasture for the RCP 8.5 2060–2100 scenario (Supplementary material - Figure S8). Likewise, in the poplar plantation, the predicted soil organic carbon decomposition rates were lower under the RCP8.5 (2020–2060), than in RCP 8.5 (2060–2100) (data not shown) causing the lowest soil organic carbon increase in the latter (Fig. 7 ). Furthermore, maintaining higher levels of soil organic matter can also be useful to increase climate-resilience as soils with high organic contents can also maintain higher water contents, reducing the impact of prolonged droughts (IPCC 2019 ). 4.5. Management interventions One advantage of developing and using calibrated models is that it is possible to investigate management interventions that could affect tree growth, grass and crop yields, and soil carbon levels. For example, within the models we included the effect of regular tree pruning to create high-value knot-free timber. This in turn will affect the value of the timber and the solar radiation reaching the understorey grass and arable crops. It would be possible to use the model developed to examine the effect of different pruning regimes on tree growth and grass and crop yields alongside different initial tree densities and thinning regimes. The choice of initial tree density is an important choice when planting agroforestry systems, and it can be affected by whether the priority is tree growth or the crop (Isaac and Borden 2019 ). Low tree densities result in greater solar radiation availability for the understory crop when compared to high densities. As tree density increases, competition for resources like nutrients, water, and light can result in a substantial decrease in crop yields (Pardon et al., 2018 ; Ivezic et al., 2021 ; Honfy et al., 2023 ). In our simulations, the understorey yield decreased significantly when tree density increased from 50 trees ha − 1 to 400 trees ha − 1 , but then remained almost the same as tree density was further increased (Fig. 6a and b). Timber volume per tree, on the other hand, increased by more than double for the silvopasture (Fig. 6c) and 50% for the silvoarable (Fig. 6d) when the original tree stand of both systems reduced to 50 trees ha − 1 . In some situations, the priority may not be tree or crop yield, but for example biodiversity benefits which may be achievable at relatively low tree densities (Edo et al., 2023). 4.6. Limitations of the study In this study, CO 2 fertilisation was accounted for, in the biophysical simulations after a simplified inclusion in Yield-SAFE. However, the mechanisms linking atmospheric CO 2 fertilisation to biomass accumulation and evapotranspiration are still not well understood (Morison and Lawlor 1999; Deryng et al., 2016 ; Sperlich et al., 2020 ). There is also a question as to whether short-term enhancement of growth from CO 2 fertilisation can continue over long time periods (Norby et al., 2010 ). As discussed, low nitrogen availability can constrain CO 2 fertilisation effects (Piao et al., 2013 ; Li et al., 2022 ), and this has not been considered in Yield-SAFE. A counter argument is that nutrients limitations to tree growth on high quality agricultural land is often considered to be low. Furthermore, in this study it is assumed that there are no growth limitations due to the potential higher risk of pests or diseases associated with a changing climate. With respect to the climate model and RCP emissions scenarios chosen, it is important to note the following points. RACMO was chosen for this analysis based on (i) its strong performance in simulating the meteorological variables required by Yield-SAFE, and (ii) the ease of its accessibility via the CliPick web portal, which facilitates its application for further study. We note that each model has inherent errors and biases and the use of multiple models is encouraged in studies seeking to assess differences between climate scenarios. Future work will have the opportunity to utilise the next generation of EURO-CORDEX regional climate, simulations for which are currently in progress. Additionally, we note that, while the appropriateness of RCP 8.5 as the most likely scenario for the future has been questioned (Hausfather and Peters 2020 ), RCP 8.5 has been shown to be a closer match to historical (2005–2020) and anticipated future CO 2 emissions than any of the alternative RCPs (Schwalm et al., 2020 ). 5. Conclusions To our knowledge, this is the first study to validate a biophysical model for arable and grassland, and mature woodland, silvopastoral and silvoarable systems at the same site in northern Europe as well as using the calibrated model to predict the effect on yield and soil carbon for the contrasting land-use systems for two future emissions scenarios based on the IPCC’s Representative Concentration Pathways RCP 4.5 and RCP 8.5 for 2020–2060 and 2060–2100. This combination of modelling alongside the use of calibration data from long-term experimental sites creates a powerful combination to investigate the effect on future climate scenarios. The capacity to model daily changes in soil organic carbon over long time periods also provided new insights into the changes in soil organic carbon when planting trees on grassland and cropland. Although integrating trees on cropland and grassland resulted in higher soil organic carbon at 40 years, the positive effects only became apparent once the tree had been established for at least 10 years. Furthermore, by running virtual experiments farming systems including agroforestry can be optimized to adapt to future weather extremes. The modelled results suggest that the land equivalence ratio of agroforestry systems is relatively resilient to changes to climate change, as an increased capacity of one component to capture light and water resources is offset by a decline in the resources available to another component. Within the silvopastoral system, the effect of integrating 50 trees per hectare, compared to no trees, on grass yields appeared to be relatively low. The study also highlights the importance of including the atmospheric CO 2 fertilisation effect on plant growth when predicting tree, crop, water, and soil responses to climate change. Within each system, management interventions such as thinning and pruning can also moderate the climate impacts on yield. Declarations Supplementary information The online version contains supplementary material available at Funding We acknowledge support of the European Union’s Horizon 2020 research and innovation programme for the AGROMIX research project (AGROforestry and MIXed farming systems; grant agreement 862993). Data availability statement The datasets generated or analyzed during the current study are available in Cranfield University repository (Link to be included later) Authors contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by MLG, RJO, and JME. The first draft of the manuscript was written by MLG and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. 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Sci, 114(35), 9326–9331, https://doi.org/10.1073/pnas.1701762114 Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 24 Jun, 2024 Reviewers invited by journal 22 Jun, 2024 Editor invited by journal 18 Jun, 2024 Editor assigned by journal 03 Jun, 2024 First submitted to journal 24 May, 2024 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4473355\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":317682553,\"identity\":\"f4800c65-60e6-4df0-a260-eed0dde5d2be\",\"order_by\":0,\"name\":\"Michail L. 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12:12:47\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2057967,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSilvopastoral (a) and silvoarable (b) experiments at Loughgall in Northern Ireland\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4473355/v1/8c56674512277ea6cb272782.png\"},{\"id\":60157089,\"identity\":\"a9276a48-d8ff-4205-8736-5c455c7cd845\",\"added_by\":\"auto\",\"created_at\":\"2024-07-12 12:12:47\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":84047,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePredicted grass yields in a) the monoculture grassland and b) silvopastoral systems, and timber volume in the c) silvopastoral and (d) woodland systems for the baseline and RCP 4.5 and RCP 8.5 scenarios in 2020-2060 and 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m\\u003csup\\u003e-3\\u003c/sup\\u003e)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4473355/v1/d6cf76db20d9cb131218c7d7.png\"},{\"id\":60157092,\"identity\":\"c4d262c7-d649-4f0f-bd94-ce74cf4dc4df\",\"added_by\":\"auto\",\"created_at\":\"2024-07-12 12:12:47\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":55606,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSoil organic carbon (0-23 cm) as simulated for the baseline and RCP 4.5 and RCP 8.5 scenarios across 40 years in the a) arable (10 years arable + 30 years grassland), b) silvoarable system and c) poplar plantation system\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4473355/v1/b52a30050a941e22179ae8c1.png\"},{\"id\":60157338,\"identity\":\"8e8f7c92-0ac0-4bf2-9abb-281c86242209\",\"added_by\":\"auto\",\"created_at\":\"2024-07-12 12:20:47\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":95798,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eEffect of six tree densities ranging from 0 to 600 trees ha\\u003csup\\u003e-1\\u003c/sup\\u003e on a) the predicted understorey grass yields in the silvopasture system and on b) the predicted understory crop (barley and grass) yields in the silvoarable system, the standing timber volume per hectare c) in the silvopasture and d) in the silvoarable system, and predicted response of soil organic carbon (SOC) in the e) silvopasture and f) silvoarable system, as simulated for the late RCP 8.5 scenario across 40 years (2060 – 2100) [original silvopasture tree density: 400 t ha\\u003csup\\u003e-1\\u003c/sup\\u003e; original silvoarable tree density: 142 trees ha\\u003csup\\u003e-1\\u003c/sup\\u003e]\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4473355/v1/74181af623c7e67cab9c5bd7.png\"},{\"id\":60157865,\"identity\":\"b3aa373e-6223-43a7-8ffa-0736dd29815d\",\"added_by\":\"auto\",\"created_at\":\"2024-07-12 12:28:49\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":3336386,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4473355/v1/646d6d92-3257-4e91-8154-2bcfd8092cf8.pdf\"},{\"id\":60157095,\"identity\":\"ec55f2db-4753-4c04-9ed7-3cc581570f9d\",\"added_by\":\"auto\",\"created_at\":\"2024-07-12 12:12:47\",\"extension\":\"docx\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":386503,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Supplementarymaterial.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4473355/v1/ac2c0ab2ffef3fcf302841db.docx\"}],\"financialInterests\":\"\",\"formattedTitle\":\"Predicted yield and soil organic carbon changes in grassland, arable, woodland, and agroforestry systems under climate change in a cool temperate Atlantic climate\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eThe interactions between land use and climate change are complex. Agriculture and changes in land use are an important source of greenhouse gases, and changes in climate and carbon dioxide concentrations have implications for crop, grass, and tree growth. Globally about 22% of greenhouse gas emissions are associated with agriculture, and land use and land-use change including deforestation (IPCC \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). In the EU, for period 2016\\u0026ndash;2018, land use, land use change and forestry (LULUCF) and agriculture were associated with an annual average net source of about 118 Mt CO\\u003csub\\u003e2e\\u003c/sub\\u003e, derived from annual average agricultural emissions of about 386 Mt CO\\u003csub\\u003e2e\\u003c/sub\\u003e (European Environment Agency \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e) and an annual average sink from LULUCF of 268 Mt CO\\u003csub\\u003e2e\\u003c/sub\\u003e (European Commission \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). In the United Kingdom (UK) in 2019, agriculture emitted 46 Mt CO\\u003csub\\u003e2\\u003c/sub\\u003ee and LULUCF was associated with another 6 Mt CO\\u003csub\\u003e2\\u003c/sub\\u003ee being down from a total of 18 Mt CO\\u003csub\\u003e2\\u003c/sub\\u003ee in 1990 (BEIS 2021).\\u003c/p\\u003e \\u003cp\\u003eAgroforestry or \\u0026ldquo;farming with trees\\u0026rdquo; is one method that farmers can use to mitigate against and adapt to the impact of climate change. The European Commission (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e) define agroforestry as a land use system in which trees are grown in combination with agriculture on the same land. Silvopasture, the combination of trees with grazing animals, is the main agroforestry system in Europe, whilst silvoarable, the integration of trees with arable crops, is present on much smaller areas (den Herder et al., \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Rubio-Delgado et al., \\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eA large and growing body of literature has investigated the benefits of integrating trees in agricultural land. These include agricultural outputs such as cereals and livestock, and outputs derived directly from the tree component such as fruit, nuts, timber and wood fuel (Reed et al., \\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Wiebe et al., \\u003cspan citationid=\\\"CR85\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). There can also be enhanced ecosystem services from integrating trees into agricultural systems such as carbon sequestration, regulation of runoff, and biodiversity enhancement (Giannitsopoulos et al., 2020; Torralba et al., \\u003cspan citationid=\\\"CR74\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Medinski et al., \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eAgroforestry systems can offer production benefits per unit area of land compared to growing trees on separate areas of land from pasture or crop production. This is because the trees and the crops or pasture can be complementary in terms of the capture of solar radiation and water (Cannell et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e1996\\u003c/span\\u003e). For example, when establishing widely spaced trees, an interrow arable crop can make effective use of the solar radiation and water not intercepted by the trees (Burgess et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e; Ivezic et al., \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Hence the combined yields of timber and arable crops within an agroforestry system are typically greater than when trees and crops are grown separately. Trees can also moderate microclimatic extremes, providing more stable environmental conditions for understory species such as avoiding heat stress (Arenas-Corraliza et al., \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eSince the pre-industrial period, land surface air temperature has risen nearly twice as much as the global average temperature (land and ocean) (IPCC, \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Additionally, increases in frequency and intensity of weather extremes are adversely impacting terrestrial ecosystems and the services they provide (Seneviratne et al., \\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). A system which can absorb perturbations, bounce back, or adapt whilst still retaining the same functions can be defined as \\u0026ldquo;resilient\\u0026rdquo; (Vi\\u0026ntilde;als et al., \\u003cspan citationid=\\\"CR83\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Combinations of trees and annual crops have been reported to enhance agro-ecosystem resilience to extreme weather, contribute to soil and water conservation and improve the carbon stock and sequestration potential (Kay et al., \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Kumar et al., \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eAlthough they are simplified simulations of reality, models can be used to investigate the interactions of crops, trees and the environment (Burgess et al., \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). They can guide decision-making and help land managers and policy makers to identify potential challenges related to, for instance, climate change and climate variability, and possible ways to address the effects. Even though there are agroforestry biophysical simulation models of varying complexity, the application of these models to climate adaptation is lacking (Farrell et al., \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Hence this study aims to examine the effects of current and future climates on crop yields, timber volumes, and soil organic carbon under grassland, arable, woodland, and agroforestry systems by carrying out simulations using a biophysical agroforestry model based on Yield-SAFE (van der Werf et al., \\u003cspan citationid=\\\"CR77\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e; Burgess et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). The modelled predictions of tree and crop growth under current weather conditions were validated by using data from two long term experimental sites in Northern Ireland. The model was then used to run virtual experiments to explore the effect of climate change, and the effect of different tree densities.\\u003c/p\\u003e\"},{\"header\":\"2. Method\",\"content\":\"\\u003cp\\u003eThe workflow consisted of (i) developing the Yield-SAFE model to account for increased CO\\u003csub\\u003e2\\u003c/sub\\u003e atmospheric contents, (ii) compiling the measured data from two experiments, (iii) preparing unbiased weather data and climate scenarios, (iv) using the data to calibrate the Yield-SAFE model, (v) modelling climate scenarios, and (vi) undertaking virtual experiments for adaptive agroforestry management.\\u003c/p\\u003e\\n\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.1. Yield-SAFE model and improvements\\u003c/h2\\u003e\\n \\u003cp\\u003eThe biophysical Yield-SAFE model was originally developed to predict the growth and yields of poplar silvoarable systems in England (van der Werf et al., \\u003cspan class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e). The model works on a daily time step and describes the growth of trees and crops in response to temperature, the capture of solar radiation by tree, grass or crop canopies, and the competition for water between trees, grass and/or crops (Palma et al., \\u003cspan class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). We followed the recommended procedure of calibrating the model for a monoculture tree system and a monoculture crop or grass system, before running it for the selected agroforestry systems.\\u003c/p\\u003e\\n \\u003cp\\u003eA version of Yield-SAFE v2 model is available online (Burgess et al., \\u003cspan class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). However, the version used for this paper was enhanced so that crop water use responded to the daily vapour pressure deficit and the trees modified the temperature and wind (Palma et al., \\u003cspan class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). In addition, soil carbon changes to a depth of 23 cm were modelled using the Rothamsted Research soil carbon model (RothC). The predicted decomposition rate of soil organic fractions within the RothC model are dependent on the temperature, moisture and soil cover.\\u003c/p\\u003e\\n \\u003cp\\u003eTo determine the effect of climate change on tree and crop yields, we modified Yield-SAFE to include the effect of increases in atmospheric carbon dioxide (CO\\u003csub\\u003e2\\u003c/sub\\u003e) on the radiation use efficiency of the trees, grass, and crops. This was included by applying a multiplier to the radiation use efficiency as the atmospheric carbon dioxide concentration increased from 360 ppm (multiplier 1), to 720 ppm (multiplier 1.25), up to a maximum 28% benefit at a CO\\u003csub\\u003e2\\u003c/sub\\u003e concentration of 800 ppm (Supplementary material - Figure \\u003cspan class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e; Prooter et al., \\u003cspan class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; J\\u0026auml;germeyr et al., \\u003cspan class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Rodriguez et al., \\u003cspan class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.2. Site description and agroforestry experiments\\u003c/h2\\u003e\\n \\u003cp\\u003eThe study sites providing the experimental data are located at the Agri-Food and Biosciences Institute (AFBI) research centre (54\\u0026deg;23\\u0026apos;53.1\\u0026quot;N 6\\u0026deg;36\\u0026apos;41.6\\u0026quot;W) in Loughgall, County Armagh, Northern Ireland, UK at an altitude of 30 m above sea level. Daily weather observations taken at the sites between 2003 and 2015 indicated that the mean annual temperature at the site was 8.5\\u0026deg;C and the mean annual rainfall 810 mm.\\u003c/p\\u003e\\n \\u003cp\\u003eIn 1989, a silvopastoral experiment was established comprising three plots planted at a spacing of 5 m x 5 m (400 stems ha-1) and three woodland (2500 stems ha-1; 2 m x 2 m) plots planted with ash trees (Fraxinus excelsior L.), and three permanent grassland plots (Fornara et al. \\u003cspan class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Table \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, Fig.\\u0026nbsp;1a) with a dominant ryegrass sward. All silvopastoral and grassland plots received an annual fertilizer application of between 120 and 150 kg N ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e. Sheep grazing typically takes place in the pasture and silvopastoral system from April to October each year with a stocking rate of 12 ewes per hectare (1.2 livestock units; Fornara et al., \\u003cspan class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). The soil at this site is classified as brown earth on red limestone till with clay contents between 30 and 45% (Cruickshank, \\u003cspan class=\\\"CitationRef\\\"\\u003e1997\\u003c/span\\u003e; Fornara et al., \\u003cspan class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e), and analyses between 1990 and 1999 indicated a pH of 6.4 to 6.7 for all plots. Thinning regimes resulted in an ash density of 265, 170 and 128 trees ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e in 2004, 2009 and 2023 respectively, whilst the woodland system was thinned and pruned in 2009 and 2021 to create a residual density of 1100 and 708 trees ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e respectively (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\n \\u003cp\\u003eTen years later in 1999, a silvoarable system was established with four poplar hybrids on a different grassland field approx. 200m from the silvopastoral site (Fig. 1b). The four poplar (\\u003cem\\u003ePopulus\\u003c/em\\u003e) clones were initially Beaupr\\u0026eacute;, Boelare, Hoogvorst, and Hassendans. However, due to poor establishment Boelare and Hassadans were substituted in 2000 with Trichobel, and Gibecq hybrids. The poplars were planted as unrooted sets 5 m apart within rows and 14 m row spacings to give a 12 m crop alley (a density of 142 stems ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e; Table \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). The soil has been classified as a brown earth on red limestone till, with pH ranging from 5.69 to 6.21 within the site. The experiment was set as a randomised block design in four blocks with a split-split block treatment structure. An annual crop of spring barley (Riviera) was planted in the alleys for the first ten years until 2009 and was then followed by a pasture mix of ryegrass and red and white clover. Two forms of mulching within the rows were adopted, continuous polythene and 1.5m square mulch mats with intercrop rows sown with grass/clover. In 2013, as part of a study on tree crop interactions on alley coppice (Lunny, \\u003cspan class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e), a willow crop was established for a 4-year cycle as an understorey crop and since then the land has been cultivated as a permanent grassland sward.\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003c/p\\u003e\\u0026nbsp;\\u003ctable id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eInitial planted tree densities, thinning year and resulting tree densities in 2023 for the silvopasture and silvoarable agroforestry and woodland systems at Loughgall, Northern Ireland (For SP, thinning operation was carried out between Dec 2009 and April 2010)\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSystem\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eInitial tree density\\u003c/p\\u003e\\n \\u003cp\\u003e(trees ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eThinning year\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eResulting density\\u003c/p\\u003e\\n \\u003cp\\u003e(trees ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\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\\u003eSilvopasture (SP)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e400\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2004\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e265\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2009\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e170\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2023\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e128\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWoodland (WD)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2500\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2009\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2021\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e708\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSilvoarable\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e142\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e142\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003cp\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.3. Data preparation\\u003c/h2\\u003e\\n \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section3\\\"\\u003e\\n \\u003ch2\\u003e2.3.1. Weather data\\u003c/h2\\u003e\\n \\u003cp\\u003eThe Yield-SAFE model requires daily weather data. The modelling of the yields in the baseline period was a two-stage process. Firstly, the model was calibrated using data from planting to 2021 (Supplementary material - Table \\u003cspan class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). Secondly the yields over a 40-year period were determined using predicted weather for the first 40 years after planting.\\u003c/p\\u003e\\n \\u003cp\\u003eThe weather for the calibration period to 2021 was firstly based on on-site weather data collected from AFBI for the period 2003\\u0026ndash;2015 (Supplementary material -Table S2). However, where there were gaps in the data, the weather station records were supplemented with data from the E-OBS Europe-wide gridded observational dataset (Cornes et al., \\u003cspan class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). Temperature, relative humidity, wind speed, rainfall, and radiation data were taken from E-OBS, which is available on a 0.1\\u0026deg; resolution (approximately 9 km), and evaporation data from ERA5 (2.5\\u0026deg; resolution), the fifth-generation global atmospheric reanalysis of European Centre for Medium-Range Weather Forecasts (ECMWF), covering the period from January 1950 to present (Hurbasch et al., 2020).\\u003c/p\\u003e\\n \\u003cp\\u003eDay-to-day weather for the period from 2021 to 2029 were taken from the Regional Atmospheric Climate Model (RACMO) developed by the Koninklijk Nederlands Meteorologisch Instituut (van Meijgaard et al., \\u003cspan class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e; \\u003cspan class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e). RACMO performs favourably with other regional climate models that contribute to the European Coordinated Regional Downscaling Experiment (EURO-CORDEX; Jacob et al., \\u003cspan class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Vautard et al., \\u003cspan class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). The required daily data were accessed using CliPick, a webtool designed to facilitate the selection of climate change data for applications in forestry and agriculture (Palma, \\u003cspan class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Using the complete observational dataset (i.e. station observations supplemented by E-OBS/ERA5) as a reference, quantile mapping (Cannon 2015) was applied to all RACMO data to correct systematic model biases.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section3\\\"\\u003e\\n \\u003ch2\\u003e2.3.2. Soil, grass, crop, and tree data\\u003c/h2\\u003e\\n \\u003cp\\u003eThe initial organic matter content of the soil in 1989 was assumed to be 8.2% at 0\\u0026ndash;10 cm and 5.4% at 10\\u0026ndash;20 cm, giving an average of 6.8%, for the treatments related to the silvopastoral system (Fornara et al., \\u003cspan class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e) and 5.0% (0\\u0026ndash;20 cm) for those related to the silvoarable system. For this study, the soil bulk densities in the pasture, silvopasture and woodland systems were assumed to be the same at 1.02 g cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;3\\u003c/sup\\u003e, whilst a bulk density of 1.33 g cm\\u003csup\\u003e\\u0026minus;\\u0026thinsp;3\\u003c/sup\\u003e was assumed for the treatments related to the silvoarable experiment (Fornara et al., \\u003cspan class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Supplementary material - Table S7). It was also assumed that the sheep returned 1.17 kg C ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e day\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e.\\u003c/p\\u003e\\n \\u003cp\\u003eData on grass and tree growth in the grass monoculture, silvopastoral system, and ash woodland control were obtained directly from measurements taken in the experimental site. Tree and crop yields in the silvoarable system were also sourced from the AFBI Loughgall site. However, because there was no dense stand of poplar at the experimental site, data on the growth of unthinned poplar at a density of 3 m x 3 m (1093 trees ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) was derived from yield-class tables provided by Christie (\\u003cspan class=\\\"CitationRef\\\"\\u003e1994\\u003c/span\\u003e). Because the yield-class table assumes some tree mortality, a constant tree density of 900 trees per hectare was assumed in the model. Yield estimates for monoculture crops of spring-planted barley of 5.3\\u0026ndash;5.9 t ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e were derived from Mercer (\\u003cspan class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e) and Irish Farmers Journal (2021).\\u003c/p\\u003e\\n \\u003cp\\u003eTimber volumes were calculated from field measurements of tree height and diameter at breast height, and it was assumed that carbon formed 50% of the biomass. A land equivalent ratio (\\u003cem\\u003eLER\\u003c/em\\u003e) was calculated to determine whether it was agronomically more efficient to combine two or more crops in the same piece of land, compared to monocultures (Eq. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). In the equation, \\u003cem\\u003eYtree\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003eAF\\u003c/em\\u003e\\u003c/sub\\u003e and \\u003cem\\u003eYcrop\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003eAF\\u003c/em\\u003e\\u003c/sub\\u003e are the tree and crop yields in the agroforestry system, and \\u003cem\\u003eY\\u003c/em\\u003etree\\u003csub\\u003eMono\\u003c/sub\\u003e and \\u003cem\\u003eYcrop\\u003c/em\\u003e\\u003csub\\u003eMono\\u003c/sub\\u003e are the tree and crop yields in the monoculture systems. The parameters used and assumptions made in Yield-SAFE for crops, trees and soil are provided in Supplementary material (Table S4; Table S5; Table S6).\\u003c/p\\u003e\\n \\u003cdiv id=\\\"Equ1\\\" class=\\\"Equation\\\"\\u003e\\n \\u003cdiv class=\\\"mathdisplay\\\" id=\\\"FileID_Equ1\\\" name=\\\"EquationSource\\\"\\u003e$$LER= \\\\frac{{Ytree}_{AF}}{{Ytree}_{Mono}}+\\\\frac{{Ycrop}_{AF}}{{Ycrop}_{Mono}}$$\\u003c/div\\u003e\\n \\u003cdiv class=\\\"EquationNumber\\\"\\u003e1\\u003c/div\\u003e\\n \\u003c/div\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.4. Future climate scenarios\\u003c/h2\\u003e\\n \\u003cp\\u003eFuture climate scenarios are simulated by global and regional climate models following a set of representative concentration pathways (RCPs) (Moss et al., \\u003cspan class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e) that specify concentrations of greenhouse gases that will lead to a given increase in total radiative forcing by 2100, relative to pre-industrial levels. Four pathways have been defined by the Intergovernmental Panel on Climate Change (IPCC), RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5, that cover a range of global temperature change through the remainder of the century (van Vuuren et al., \\u003cspan class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). For example, the RCP 4.5 pathway represents 4.5 W m\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e of extra warming by 2100. The RCPs map directly to the Shared Socioeconomic Pathways (SSPs) used in the IPCC 6th Assessment Report (IPCC \\u003cspan class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e\\n \\u003cp\\u003eFor this study, the effect of climate change on tree, crop and agroforestry yields were examined using the \\u0026ldquo;intermediate\\u0026rdquo; RCP 4.5 scenario and the \\u0026ldquo;high\\u0026rdquo; RCP 8.5 emissions scenario. Assuming a tree rotation of 40 years, the scenarios were split into two periods of 2020\\u0026ndash;2060 and 2060\\u0026ndash;2100. In addition to the changes in climate, we included estimates of the changes in atmospheric CO\\u003csub\\u003e2\\u003c/sub\\u003e up to 2100 for both RCP 4.5 and RCP 8.5 scenarios, using data presented by Meinshausen et al. (\\u003cspan class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). The value of CO\\u003csub\\u003e2\\u003c/sub\\u003e was estimated to reach 603 ppm in 2100 in the RCP 4.5 scenario, and 1142 ppm in 2100 in the RCP 8.5 scenario (Supplementary material - Figure S4). As mentioned in Section 2.1 however, a cut-off point of 800 ppm was assumed in Yield-SAFE for the fertilisation effect on plants.\\u003c/p\\u003e\\n \\u003cp\\u003eThe general effect of the climate change scenarios was to increase maximum air temperatures by between 1.2 and 2.8\\u0026deg;C and minimum air temperatures by between 1.2 and 3.0\\u0026deg;C (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The change in annual rainfall was predicted to range from a decrease of 16 mm to an increase of 84 mm. It was predicted that the mean level of solar radiation would decrease by 0.2\\u0026ndash;0.5 MJ m\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e d\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, but higher temperatures were predicted to increase mean daily evaporation rates. A detailed summary is provided in Supplementary material - Table S8. Mean atmospheric CO\\u003csub\\u003e2\\u003c/sub\\u003e concentration was assumed to be 340 ppm for the period 1960\\u0026ndash;2000, rising to a mean of 574 ppm and 879 ppm in RCP 4.5 and RCP 8.5 respectively for 2060\\u0026ndash;2100 (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003c/p\\u003e\\u0026nbsp;\\u003ctable id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003ePredicted mean annual rainfall, maximum and minimum temperatures, daily evaporation and solar radiation, and CO\\u003csub\\u003e2\\u003c/sub\\u003e concentration predicted for a baseline period (1960\\u0026ndash;2000) and for 2020\\u0026ndash;2060 and 2060\\u0026ndash;2100 for two representative concentration pathways (RCP)\\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 align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBaseline\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eRCP 4.5\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eRCP 8.5\\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\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e(1960\\u0026ndash;\\u003c/p\\u003e\\n \\u003cp\\u003e2000)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e(2020\\u0026ndash;2060)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e(2060\\u0026ndash;2100)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e(2020\\u0026ndash;2060)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e(2060\\u0026ndash;2100)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAnnual rainfall (mm)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e861\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e845\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e930\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e891\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e945\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMean maximum temperature (\\u0026deg;C)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e13.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e14.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e14.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e14.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e15.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMean minimum temperature (\\u0026deg;C)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDaily evaporation (mm)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.49\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.57\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.58\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.57\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.58\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSolar radiation (MJ m\\u003csup\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/sup\\u003e d\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCarbon dioxide concentration (ppm)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e340\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e480\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e574\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e515\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e879\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003cp\\u003e\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.5. Virtual experiments\\u003c/h2\\u003e\\n \\u003cp\\u003eWithin the high radiative forcing climate scenario RCP 8.5 and for the period 2060\\u0026ndash;2100, it was examined whether by decreasing or increasing the original tree densities (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e), had an effect on crop yields of the silvoarable and silvopastoral experiments. The range of tree densities tested were 0, 50, 142, 300, 400, and 600 trees per hectare. The thinning regime for the silvopastoral tree density simulations, was of the same proportion to the original (Supplementary material - Table S3).\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cp\\u003eThe results first cover the calibration of the Yield-SAFE model for the crop, grass, and tree yields using the baseline weather data. Following calibration of the model, the model was then used to determine the effect of future climate scenarios and tree densities on crop, grass, and tree growth and changes in soil organic carbon.\\u003c/p\\u003e\\n\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.1. Calibration of the pasture, silvopastoral and woodland systems\\u003c/h2\\u003e\\n \\u003cp\\u003eThe mean modelled monoculture pasture yield with Yield-SAFE of 9.1 t DM ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e was similar to the measured pasture yields of 9.3 t DM ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e in 1995, 1998 and 2000. In the woodland with a tree density of 2500 trees ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, the measured timber yield of 104 m\\u003csup\\u003e3\\u003c/sup\\u003e ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e in year 15 was similar to the modelled value of 98 m\\u003csup\\u003e3\\u003c/sup\\u003e ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (Supplementary material -Figure S2). In the silvopastoral system at a tree density of 400 trees ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, the measured timber volume in year 15 of 39 m\\u003csup\\u003e3\\u003c/sup\\u003e ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e was close to the modelled value of 36 m\\u003csup\\u003e3\\u003c/sup\\u003e ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (Supplementary material -Figure S3). The mean measured grass yield in 1995, 1998 and 2000 was 6.6 t DM ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e whilst the model predicted a mean dry matter yield of 7.3 t DM ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e for the same years. As trees matured, grass yields were predicted to decline starting with a dry matter yield of 10.2 t ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e in the first year and decreasing to about 1.5 t ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e after 30 years (Supplementary material \\u0026ndash; Figure S5).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.2. Calibration of the arable, poplar plantation and silvoarable systems\\u003c/h2\\u003e\\n \\u003cp\\u003eThe mean monoculture spring barley yield predicted by Yield-SAFE (5.8 t ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) was similar to the mean spring barley yield reported for Northern Ireland (5.3\\u0026ndash;5.9 t ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e; Mercer \\u003cspan class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e; Irish Farmers Journal 2021). For the poplar woodland system, the modelled timber yield of 233 m\\u003csup\\u003e3\\u003c/sup\\u003e ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e with Yield-SAFE was similar to the yield of 260 m\\u003csup\\u003e3\\u003c/sup\\u003e ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e in year 20 described in yield profiles of poplar with a yield class of 6 as described by Christie (\\u003cspan class=\\\"CitationRef\\\"\\u003e1994\\u003c/span\\u003e) (Supplementary material -Figure S3). In the silvoarable system, the measured timber volume reached 163 m\\u003csup\\u003e3\\u003c/sup\\u003e ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (as averaged across the four cultivars) at 22 years, compared to a modelled value of 169 m\\u003csup\\u003e3\\u003c/sup\\u003e ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e in year 22 (Supplementary material -Table S9). With the silvoarable system, the modelled spring barley yield of 6.3 t ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e in year 1 was higher than the measured value of 3.9 t ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e. However, the modelled value of 4.8 t ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e was similar to the measured value of 4.3 t ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e in year 3. With time, and as trees developed, the modelled grass yields in the silvoarable system were predicted to decline from 5.0 t ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e in year 11 to 2.5 t ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e in year 20 (Supplementary material \\u0026ndash; Figure S6).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.3. Effect of climate change on yields of pasture, woodland and silvopastoral system\\u003c/h2\\u003e\\n \\u003cp\\u003eThe calibrated model was then used to determine the effect of climate change. In the monoculture grassland plots, Yield-SAFE predicted that climate change and the increase in CO\\u003csub\\u003e2\\u003c/sub\\u003e would result in higher grass yields in future years, with the mean grass yield increasing from 9.6 t ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e to 11.0-12.7 t ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e; Fig.\\u0026nbsp;2). The model also predicted that the total timber volume from the woodland system at 40 years would increase from 428 m\\u003csup\\u003e3\\u003c/sup\\u003e ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e to 457\\u0026ndash;513 m\\u003csup\\u003e3\\u003c/sup\\u003e ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e\\n \\u003cp\\u003eWithin the silvopastoral system, the predicted timber after 40 years increased from 271 m\\u003csup\\u003e3\\u003c/sup\\u003e ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e to 300\\u0026ndash;365 m\\u003csup\\u003e3\\u003c/sup\\u003e ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, with the increase due to the assumed fertilisation effect of the carbon dioxide (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Within the silvopastoral system, there was also a predicted increase in the understorey grass yield from 4.3 t ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e to 4.7-5.0 t ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e. Hence the net effect was that the predicted increase in timber growth was greater than the increase in grass growth as demonstrated by the declining proportion of the land equivalent ratio derived from the grass component (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003c/p\\u003e\\u0026nbsp;\\u003ctable id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003ePredicted average annual grass yield (t ha\\u003csup\\u003e-1\\u003c/sup\\u003e yr\\u003csup\\u003e-1\\u003c/sup\\u003e) and timber yield (m\\u003csup\\u003e3\\u003c/sup\\u003e ha\\u003csup\\u003e-1\\u003c/sup\\u003e) in the monoculture systems and within the silvopastoral system for the baseline period and in two time steps of 40 years each (with and without the carbon dioxide fertilisation effect), and the predicted land equivalent ratio\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eScenario\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eTime\\u003c/p\\u003e\\n \\u003cp\\u003estep\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCO\\u003csub\\u003e2\\u003c/sub\\u003e\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGrass\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWoodland\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eSilvopastoral\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePredicted land\\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\\u003eeffect\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eyield\\u003c/p\\u003e\\n \\u003cp\\u003e(40 years)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003estanding+\\u003c/p\\u003e\\n \\u003cp\\u003eharvested timber\\u003c/p\\u003e\\n \\u003cp\\u003e(at year 40)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003egrass\\u003c/p\\u003e\\n \\u003cp\\u003eyield\\u003c/p\\u003e\\n \\u003cp\\u003e(40\\u003c/p\\u003e\\n \\u003cp\\u003eyears)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003estanding+\\u003c/p\\u003e\\n \\u003cp\\u003eharvested timber\\u003c/p\\u003e\\n \\u003cp\\u003e(at year 40)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eequivalent ratio (grass\\u0026thinsp;+\\u0026thinsp;tree)\\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\\u003eBaseline\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1989-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e9.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e416\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e261\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.46\\u0026thinsp;+\\u0026thinsp;0.63\\u0026thinsp;=\\u0026thinsp;1.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2029\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e9.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e428\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e271\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.45\\u0026thinsp;+\\u0026thinsp;0.63\\u0026thinsp;=\\u0026thinsp;1.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRCP 4.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2020-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e9.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e420\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e267\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.46\\u0026thinsp;+\\u0026thinsp;0.64\\u0026thinsp;=\\u0026thinsp;1.10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2060\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e11.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e461\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e305\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.42\\u0026thinsp;+\\u0026thinsp;0.66\\u0026thinsp;=\\u0026thinsp;1.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2060-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e8.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e412\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e257\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.48\\u0026thinsp;+\\u0026thinsp;0.62\\u0026thinsp;=\\u0026thinsp;1.11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e12.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e487\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e325\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.42\\u0026thinsp;+\\u0026thinsp;0.67\\u0026thinsp;=\\u0026thinsp;1.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRCP 8.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2020-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e8.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e405\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e252\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.48\\u0026thinsp;+\\u0026thinsp;0.62\\u0026thinsp;=\\u0026thinsp;1.11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2060\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e11.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e457\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e300\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.43\\u0026thinsp;+\\u0026thinsp;0.66\\u0026thinsp;=\\u0026thinsp;1.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2060-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e7.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e397\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e248\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.52\\u0026thinsp;+\\u0026thinsp;0.62\\u0026thinsp;=\\u0026thinsp;1.14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e12.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e513\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e365\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.39\\u0026thinsp;+\\u0026thinsp;0.71\\u0026thinsp;=\\u0026thinsp;1.11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003cp\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.4. Effect of climate change on yields of arable, poplar plantation and silvoarable system\\u003c/h2\\u003e\\n \\u003cp\\u003eFor the treatments related to the silvoarable system, the Yield-SAFE model predicted that climate change and the increase in CO\\u003csub\\u003e2\\u003c/sub\\u003e would result in higher yields in a monoculture arable system, with the mean arable yield increasing from 6.2 t ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e to 6.4\\u0026ndash;7.3 t ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e. ; Fig.\\u0026nbsp;3). The model also predicted that the total timber volume from the poplar plantation at year 40 would increase from 429 m\\u003csup\\u003e3\\u003c/sup\\u003e ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e to 429\\u0026ndash;473 m\\u003csup\\u003e3\\u003c/sup\\u003e ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). Within the silvoarable system, the predicted harvested timber after 40 years increased from 297 m\\u003csup\\u003e3\\u003c/sup\\u003e ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e to 298\\u0026ndash;324 m\\u003csup\\u003e3\\u003c/sup\\u003e ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e.\\u003c/p\\u003e\\n \\u003cp\\u003eThere was also a predicted increase in the alley crop arable yield from 4.9 t ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e to 4.8\\u0026ndash;6.5 t ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e with the largest increase observed in 2060\\u0026ndash;2100. If the land equivalent ratio is calculated on the basis of the crop (barley) and tree yields only, then the silvoarable system resulted in a land equivalence ratio of less than 1.0. If the grass yield was also included, then a higher LER would have been derived (Supplementary material - Table S11).\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003c/p\\u003e\\u0026nbsp;\\u003ctable id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003ePredicted average annual crop yield (t ha\\u003csup\\u003e-1\\u003c/sup\\u003e yr\\u003csup\\u003e-1\\u003c/sup\\u003e) and timber yield (m\\u003csup\\u003e3\\u003c/sup\\u003e ha\\u003csup\\u003e-1\\u003c/sup\\u003e) in the monoculture systems and within the silvoarable system with the carbon dioxide fertilisation effect, and the predicted land equivalent ratio (silvoarable crop yield is per unit system)\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eScenario\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTime\\u003c/p\\u003e\\n \\u003cp\\u003estep\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBarley only\\u003c/p\\u003e\\n \\u003cp\\u003eyield\\u003c/p\\u003e\\n \\u003cp\\u003e(40 years)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePoplar only yield\\u003c/p\\u003e\\n \\u003cp\\u003e(at year 40)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSilvoarable poplar\\u003c/p\\u003e\\n \\u003cp\\u003e(at year 40)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSilvo-\\u003c/p\\u003e\\n \\u003cp\\u003earable crop\\u003c/p\\u003e\\n \\u003cp\\u003e(10 years)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLER\\u003c/p\\u003e\\n \\u003cp\\u003e(crop\\u0026thinsp;+\\u0026thinsp;tree)\\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\\u003eBaseline\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1999\\u0026ndash;2039\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e6.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e429\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e297\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.20\\u0026thinsp;+\\u0026thinsp;0.69\\u0026thinsp;=\\u0026thinsp;0.89\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRCP 4.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2020\\u0026ndash;2060\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e6.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e429\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e298\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.21\\u0026thinsp;+\\u0026thinsp;0.69\\u0026thinsp;=\\u0026thinsp;0.90\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2060\\u0026ndash;2100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e7.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e473\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e324\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e6.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.22\\u0026thinsp;+\\u0026thinsp;0.68\\u0026thinsp;=\\u0026thinsp;0.90\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRCP 8.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2020\\u0026ndash;2060\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e6.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e459\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e314\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e4.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.19\\u0026thinsp;+\\u0026thinsp;0.68\\u0026thinsp;=\\u0026thinsp;0.87\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2060\\u0026ndash;2100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e7.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e462\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e323\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e6.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.23\\u0026thinsp;+\\u0026thinsp;0.69\\u0026thinsp;=\\u0026thinsp;0.92\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003cp\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.5. Predicted effect of land use and climate change on soil organic carbon\\u003c/h2\\u003e\\n \\u003cp\\u003eIn terms of the changes in soil organic carbon, the Yield-SAFE model predicted a mean net decrease in the soil organic carbon content across 40 years of 0.48 t ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e yr\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e within the monoculture grass system under the baseline climate (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e, Fig.\\u0026nbsp;4a).\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003c/p\\u003e\\u0026nbsp;\\u003ctable id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003ePredicted effect of climate change and carbon dioxide fertilisation on the mean annual \\u003cem\\u003echange\\u003c/em\\u003e in soil organic carbon in the pasture, woodland and silvopastoral systems (assuming the same bulk density of 1.02 Mg m\\u003csup\\u003e-3\\u003c/sup\\u003e)\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eScenario\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTime\\u003c/p\\u003e\\n \\u003cp\\u003estep\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003eMean soil organic carbon change\\u003c/p\\u003e\\n \\u003cp\\u003e(t C ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e yr\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\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\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGrass\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWoodland\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSilvopasture\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBaseline\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1989\\u0026ndash;2029\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.48\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.90\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRCP 4.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2020\\u0026ndash;2060\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.43\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2060\\u0026ndash;2100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.46\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.98\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRCP 8.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2020\\u0026ndash;2060\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.44\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.90\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2060\\u0026ndash;2100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.55\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.90\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003cp\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eIn contrast to the grassland system, the soil organic carbon storage in the woodland system was predicted to decline in the initial years but then increase from year 11 to 40 after tree planting, with a mean increase of 0.9\\u0026ndash;1.09 t C ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e yr\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e over the 40 years (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e; Fig.\\u0026nbsp;4c). The soil organic carbon in the silvopastoral system was predicted to show an intermediate effect with a decrease predicted in the initial 13 years, a stable period until year 20, and an increase from year 20 to year 40 (Fig.\\u0026nbsp;4b).\\u003c/p\\u003e\\n \\u003cp\\u003eIn general, the four future climate scenarios resulted in similar changes and temporal profiles in the predicted soil organic carbon content as the baseline system. The greatest increases in soil organic carbon (or smallest decreases) occurred within the RCP4.5 2020\\u0026ndash;2060 scenario, and the smallest increases or greatest decreases occurred in the RCP 8.5 2060\\u0026ndash;2100 scenario (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e; Fig.\\u0026nbsp;4a).\\u003c/p\\u003e\\n \\u003cp\\u003eIn the systems related to the silvoarable experiment, the Yield-SAFE model predicted an average annual decline in the soil organic carbon in the barley only system of 1.0-1.1 t C ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e yr\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e over 40 years in the baseline climate. A monoculture system (without trees) of barley followed by grass was predicted to result in a decline of 0.51 t C ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e yr\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e) and similar to that observed for the monoculture grass system (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). The poplar system predicted an increase of soil organic carbon of 0.19 t C ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e yr\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, and the silvoarable system was predicted to result in an intermediate loss of 0.29 t C ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e yr\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e.\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003c/p\\u003e\\u0026nbsp;\\u003ctable id=\\\"Tab6\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 6\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003ePredicted effect of climate change and carbon dioxide fertilisation on the mean annual \\u003cem\\u003echange\\u003c/em\\u003e in soil organic carbon in the monocrop barley, monoculture barley followed by grass, poplar plantation, and silvoarable systems (Spring barley was present in the field in the initial 10-years, followed by 30-years of grass)\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eScenario\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTime\\u003c/p\\u003e\\n \\u003cp\\u003estep\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"4\\\"\\u003e\\n \\u003cp\\u003eMean soil organic carbon change\\u003c/p\\u003e\\n \\u003cp\\u003e(t C ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e yr\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\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\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBarley-only\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eArable\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePoplar plantation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSilvoarable\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBaseline\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1999\\u0026ndash;2039\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-1.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.51\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRCP 4.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2020\\u0026ndash;2060\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-1.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.48\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.22\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.27\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2060\\u0026ndash;2100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-1.07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.50\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRCP 8.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2020\\u0026ndash;2060\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-1.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.50\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2060\\u0026ndash;2100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-1.18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.58\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.39\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003cp\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eThere was a distinct temporal pattern to the loss of soil organic carbon. For the arable and the silvoarable system, the soil organic carbon tended to decline when there was barley crop, before stabilising when grass was established (Fig. 5a, b). In the poplar plantation, although the soil organic carbon declined for the first 10 years, it then increased from year 10 to 40 after tree planting (Fig. 5c).\\u003c/p\\u003e\\n \\u003cp\\u003eAgain, the predicted effects of climate on the soil organic carbon were broadly similar for the four future climate scenarios. However. the lowest increase or greatest decrease in soil organic carbon occurred in the RCP 8.5 (2060\\u0026ndash;2100) scenario (Fig. 5b).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.6. Virtual experiments with tree densities\\u003c/h2\\u003e\\n \\u003cp\\u003eThe final set of results relate to the effect of different tree densities with the RCP 8.5 scenario for 2060\\u0026ndash;2100. Within the silvopastoral experiments, the greatest grass yield was predicted with no trees (Fig.\\u0026nbsp;6a), and the greatest timber volumes were predicted with high tree densities (Fig.\\u0026nbsp;6c). When trees were integrated with the grass, then whilst the initial grass yields were similar to the monoculture grass, the grass yields eventually declined as the trees grew in size and captured more resources (Fig.\\u0026nbsp;6a).\\u003c/p\\u003e\\n \\u003cp\\u003eFor example, reducing the initial silvopastoral tree density of 400 trees ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e to 300 trees ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e enhanced the mean grass yield over 40 years by 16% (Fig. 6a; approximately 0.8 t ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e yr\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e), but reduced the total timber volume by 26 m\\u003csup\\u003e3\\u003c/sup\\u003e ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (Fig. 6c). Although timber volume per hectare declined with the reduced tree density, the predicted timber volume per tree was predicted to be 0.35 m\\u003csup\\u003e3\\u003c/sup\\u003e tree\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e greater in year 40 after planting (Supplementary material - Figure S7a). Furthermore, reducing trees from 400 to only 50 per hectare, were predicted to give approximately the same grass yields as the pasture (no trees; Fig. 6a).\\u003c/p\\u003e\\n \\u003cp\\u003eIn the silvoarable experiment, the highest crop yields were derived with no trees (Fig.\\u0026nbsp;6b) and the highest timber volume per hectare was derived from the highest tree density (Fig.\\u0026nbsp;6d). Increasing the tree density from 50 to 142 trees ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e led to similar understorey crop yields in the first 4\\u0026ndash;5 years, but the mean crop yield across 40 years decreased by 3.9 t ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e yr\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (Fig. 6b), and the predicted timber volume in the final year increased from 178 to 322 to m\\u003csup\\u003e3\\u003c/sup\\u003e ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e. In a similar way, increasing the tree density from 142 to 300 stems ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e reduced the mean crop yield by 1.9 t ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e yr\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e and increased timber volume by 78 m\\u003csup\\u003e3\\u003c/sup\\u003e ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e at year 40 (Fig. 6b and 6d).\\u003c/p\\u003e\\n \\u003cp\\u003eFor RCP 8.5 (2060\\u0026ndash;2100), the land equivalent ratio (including crops, trees, and grass) of 1.19 for the silvoarable system at 50 trees ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (Supplementary material - Table S12) was similar to the value of 1.17 for the same system at a density of 142 trees ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (Supplementary material - Table S11). Although the effect was marginal, the land equivalence ratios for RCP 8.5 (1.17\\u0026ndash;1.19) at these two tree densities was greater than those predicted for the baseline climate (1.14) and those (1.09\\u0026ndash;1.14) for the two RCP4.5 scenarios and the RCP 8.5 (2020\\u0026ndash;2060) scenarios (Supplementary material -Table S11; Table S12).\\u003c/p\\u003e\\n \\u003cp\\u003eIn the silvopasture, at a tree density of 400 trees ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, the model predicted a decrease in soil organic carbon in the first 15 years after planting, followed by an increase in the next 25 years (Fig.\\u0026nbsp;6e). Reducing the density from 400 to 142 trees ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e led to a smaller reduction in soil organic carbon in the first 10 years, but soil organic carbon continued to steadily decline. By contrast within the silvoarable-related treatments, although there was difference in the soil organic carbon between systems with and without trees, the difference between the different tree densities was relatively small. In both the silvopastoral and silvoarable systems, moving from no trees to 50 trees ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e increased the predicted soil organic carbon by 9.6 and 9.1 t C ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e respectively across 40 years (Fig. 6e and f).\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.1. Collating weather data and calibration\\u003c/h2\\u003e \\u003cp\\u003ePredicting the effect of climate change on grass, crop, tree, and agroforestry yields in a systematic way is a complicated process. It requires multiple steps including the derivation of current weather data, experimental data of tree and crop growth, and the parameterisation and calibration of models for the baseline situation. The prediction of the effect of climate change then involved the derivation and modification of future climate datasets, and the inclusion of carbon dioxide fertilisation effects in a biophysical model.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.2. Climate change and food and fibre production\\u003c/h2\\u003e \\u003cp\\u003eThe study indicates that the effect of climate change on grass, crop, and tree yields in the temperate Atlantic climate of Northern Ireland is a balance between a generally negative effect of increased temperatures and associated drought stress on yields and the positive effect of carbon dioxide fertilisation. Zhao et al. (\\u003cspan citationid=\\\"CR88\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e) in China and Webber et al. (\\u003cspan citationid=\\\"CR84\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e) applying crop models on a spatial grid across Europe also reported that the adverse impacts of warming and drought are counterbalanced by CO\\u003csub\\u003e2\\u003c/sub\\u003e fertilisation for crops such as wheat and maize showing some regional yield increases. Xiao et al. (\\u003cspan citationid=\\\"CR86\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e) also reported the tension between the negative effects of warming temperature and decreasing rainfall on wheat yield and the fertilizing effect of increased CO\\u003csub\\u003e2\\u003c/sub\\u003e concentrations in the atmosphere under both RCP 4.5 and RCP 8.5 scenarios at the beginning of the twenty-first century in China.\\u003c/p\\u003e \\u003cp\\u003eThe Yield-SAFE model predicted that climate change and carbon dioxide fertilisation would increase woodland timber production by 20% in the RCP 8.5 scenario to 513 m\\u003csup\\u003e3\\u003c/sup\\u003e ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, compared to 428 m\\u003csup\\u003e3\\u003c/sup\\u003e ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e in the baseline scenario. This is broadly similar to an increase of 21\\u0026ndash;29% in net primary productivity predicted for forests in Germany in the RCP 8.5 scenario by Sperlich et al. (\\u003cspan citationid=\\\"CR73\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Gutsch et al. (\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e) also predicted that timber production would increase with climate change in Germany, and Favero et al. (\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) reported a 10% increase in forest productivity by 2100 in Latin America under the RCP 8.5 scenario. Tree growth in cool regions is likely to benefit from warmer and longer growing seasons (Kellom\\u0026auml;ki et al., \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). AlRahahleh et al. (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e) also estimated higher forest growth in Finland under future climates in a model that included the effect of carbon dioxide concentrations. Norby et al. (\\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e) showed that elevated CO\\u003csub\\u003e2\\u003c/sub\\u003e experiments in Tennessee in the United States, resulted in significant enhancements (24%) of trees net primary productivity during the initial 6 years, however from year 7 onwards the enhancement reduced to 9% due to soil nitrogen limitations. A similar observation was reported by Broadmeadow and Jackson (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e) in a factorial experiment on one year old tree seedlings in Britain, where elevated CO\\u003csub\\u003e2\\u003c/sub\\u003e, increased growth of \\u003cem\\u003ePinus sylvestris\\u003c/em\\u003e L., \\u003cem\\u003eQuercus petraea (Matt.)\\u003c/em\\u003e Liebl. and \\u003cem\\u003eFraxinus excelsior\\u003c/em\\u003e L, by 20% in year one. However, a longer-term lack of nitrogen meant that there was no enhanced growth in year three for a nitrogen demanding species like ash. We note that the Yield-SAFE model does not account for nitrogen limitations for plant growth (van der Werf et al. \\u003cspan citationid=\\\"CR77\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e). In addition, our model does not account for the effect of changes in temperature on pests and diseases. For example, van Niekerk et al. (\\u003cspan citationid=\\\"CR80\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) predicted a decline of wood production with warmer climates in Europe, which they related to an increase in the decay potential of fungi and the range extension for certain wood degrading termite species.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.3. Agroforestry and food and fibre production\\u003c/h2\\u003e \\u003cp\\u003eThe Yield-SAFE model predicted that a silvopastoral system would result in declines in grass yields relative to a grass monoculture and declines in timber yields relative to a woodland system (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). This is consistent with Ehret et al. (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e), who in a 2-year artificial shade experiment, showed for example reductions of 70% in grass species herbage production when shade reached 80% in Lower Saxony, Germany. The model predicted lower grass yields in the silvopastoral system after year 8 or 9 when compared to the pasture system due to competition from the trees for light and water (Supplementary material - Figure S5). This reduction in grass yields will also reduce the density of livestock that can be supported below the trees. However, the trees can provide other benefits such as moderating high temperatures in summer, resulting in fewer stress days for livestock, and this may offset the effect of reduced grass production (Palma et al., \\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe analysis revealed that the predicted land equivalent ratio (LER) would range from 1.08 to 1.11 for grass and ash timber (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e), meaning that monocultures require 8\\u0026ndash;11% more land than the silvopastoral system to obtain similar relative yields. By contrast, the land equivalent ratios for the silvoarable system in terms of arable crop and poplar timber were below one (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e. ), meaning that the agroforestry system was less productive than the two monoculture systems. This is primarily because the understorey of the Loughgall silvoarable system comprised 10-years spring barley followed by grass, and the grass yield was not considered. In the LER calculations, if grass was also included then the LER would range from 1.11 to 1.17 (Supplementary material - Table S12). Seserman et al. (\\u003cspan citationid=\\\"CR72\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e) in Saxony in Germany also reported a LER lower than 1 for a cereal-poplar agroforestry system. In such analyses, the choice of the default forestry system can be critical. For example, a silvoarable system may have a LER of 1.22\\u0026ndash;1.45 if the default tree system is widely spaced (Graves et al., \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e), but less than 1.12 (Graves et al., \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e) if the default tree system is densely spaced.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.4. Changes in soil carbon\\u003c/h2\\u003e \\u003cp\\u003eThe study showed that the model predicted declines in soil carbon in the arable, silvoarable and pasture systems, and increases in the silvopastoral and two woodland systems. Xu et al. (\\u003cspan citationid=\\\"CR87\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e) also using RothC predicted a decline in soil organic carbon (SOC) on grassland in Ireland of between 2 and 6% in future climates compared to a baseline.\\u003c/p\\u003e \\u003cp\\u003eThe Yield-SAFE model predicted SOC increases for the silvopastoral system ranging from 0.16 t C ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e yr\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e for RCP 4.5 (2020\\u0026ndash;2060) to 0.03 t C ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e yr\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e for RCP 8.5 (2060\\u0026ndash;2100). These values are similar to reported increases in SOC in forest soils of 0.12 t C ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e yr\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e in Finland and 0.35 t C ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e yr\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e in France (Rantakari et al., \\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Jonard et al., \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). In previous research, Upson et al. (\\u003cspan citationid=\\\"CR76\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e) measured a decline in soil organic carbon during the first 14 years when trees were planted on grassland. The inclusion of the RothC within Yield-SAFE provides an explanation for this in that whilst soil organic carbon may decline in the first 15 years from tree planting, it may then recover (Supplementary material - Table S10; Fig.\\u0026nbsp;4b). Such an analysis illustrates the potential strength of using a biophysical model to account for temporal changes. Ashwood et al. (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e) in a study focused on woodlands in the UK also found that, whereas the levels of soil carbon under pasture and young woodland were similar, the soil carbon content in the woodlands increased with time. Pardon et al. (\\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e) also highlighted the potential of middle-aged to mature tree rows to increase soil organic carbon stocks for the agricultural crop in agroforestry systems.\\u003c/p\\u003e \\u003cp\\u003eWith the Yield-SAFE model, the predicted increase in the soil organic carbon of the ash woodland system of 0.90\\u0026ndash;1.09 t C ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e yr\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e was substantially greater than in the poplar plantation system of 0.05\\u0026ndash;0.22 C ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e yr\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e). Although the timber volumes of the ash and the poplar were similar after 40 years (428\\u0026ndash;429 m\\u003csup\\u003e3\\u003c/sup\\u003e ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e), the ash wood would have a higher wood density than the poplar, and hence the biomass accumulation of the ash woodland would be greater and this will lead to greater cycling of biomass carbon to the soil.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe model predicted that the RCP 4.5 2020\\u0026ndash;2060 and 2060\\u0026ndash;2100 and RCP 8.5 2020\\u0026ndash;2060 climate scenarios would marginally increase soil organic carbon in the ash woodland (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e) and the poplar plantation (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e), compared to the baseline. This could be explained by the greater biomass production and recycling of biomass within the system.\\u003c/p\\u003e \\u003cp\\u003eAs temperatures increases, retaining soil organic carbon becomes more difficult, and hence there is potentially a greater role for trees to help maintain or increase soil organic carbon. It has been reported that soil organic carbon decomposition rates may be lower in agroforestry systems than in arable and grassland systems due to the maintenance of high levels of moisture, reduced soil evaporation and cooler soil temperatures (Falloon et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Das et al., \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Within the RothC module in Yield-SAFE, we predicted greater soil organic carbon decomposition rates in the grass monocrop than under the silvopasture for the RCP 8.5 2060\\u0026ndash;2100 scenario (Supplementary material - Figure S8). Likewise, in the poplar plantation, the predicted soil organic carbon decomposition rates were lower under the RCP8.5 (2020\\u0026ndash;2060), than in RCP 8.5 (2060\\u0026ndash;2100) (data not shown) causing the lowest soil organic carbon increase in the latter (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e). Furthermore, maintaining higher levels of soil organic matter can also be useful to increase climate-resilience as soils with high organic contents can also maintain higher water contents, reducing the impact of prolonged droughts (IPCC \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec22\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.5. Management interventions\\u003c/h2\\u003e \\u003cp\\u003eOne advantage of developing and using calibrated models is that it is possible to investigate management interventions that could affect tree growth, grass and crop yields, and soil carbon levels. For example, within the models we included the effect of regular tree pruning to create high-value knot-free timber. This in turn will affect the value of the timber and the solar radiation reaching the understorey grass and arable crops. It would be possible to use the model developed to examine the effect of different pruning regimes on tree growth and grass and crop yields alongside different initial tree densities and thinning regimes.\\u003c/p\\u003e \\u003cp\\u003eThe choice of initial tree density is an important choice when planting agroforestry systems, and it can be affected by whether the priority is tree growth or the crop (Isaac and Borden \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Low tree densities result in greater solar radiation availability for the understory crop when compared to high densities. As tree density increases, competition for resources like nutrients, water, and light can result in a substantial decrease in crop yields (Pardon et al., \\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Ivezic et al., \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Honfy et al., \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). In our simulations, the understorey yield decreased significantly when tree density increased from 50 trees ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e to 400 trees ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, but then remained almost the same as tree density was further increased (Fig.\\u0026nbsp;6a and b). Timber volume per tree, on the other hand, increased by more than double for the silvopasture (Fig.\\u0026nbsp;6c) and 50% for the silvoarable (Fig.\\u0026nbsp;6d) when the original tree stand of both systems reduced to 50 trees ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e. In some situations, the priority may not be tree or crop yield, but for example biodiversity benefits which may be achievable at relatively low tree densities (Edo et al., 2023).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec23\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.6. Limitations of the study\\u003c/h2\\u003e \\u003cp\\u003eIn this study, CO\\u003csub\\u003e2\\u003c/sub\\u003e fertilisation was accounted for, in the biophysical simulations after a simplified inclusion in Yield-SAFE. However, the mechanisms linking atmospheric CO\\u003csub\\u003e2\\u003c/sub\\u003e fertilisation to biomass accumulation and evapotranspiration are still not well understood (Morison and Lawlor 1999; Deryng et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Sperlich et al., \\u003cspan citationid=\\\"CR73\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). There is also a question as to whether short-term enhancement of growth from CO\\u003csub\\u003e2\\u003c/sub\\u003e fertilisation can continue over long time periods (Norby et al., \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e). As discussed, low nitrogen availability can constrain CO\\u003csub\\u003e2\\u003c/sub\\u003e fertilisation effects (Piao et al., \\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; Li et al., \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), and this has not been considered in Yield-SAFE. A counter argument is that nutrients limitations to tree growth on high quality agricultural land is often considered to be low. Furthermore, in this study it is assumed that there are no growth limitations due to the potential higher risk of pests or diseases associated with a changing climate.\\u003c/p\\u003e \\u003cp\\u003eWith respect to the climate model and RCP emissions scenarios chosen, it is important to note the following points. RACMO was chosen for this analysis based on (i) its strong performance in simulating the meteorological variables required by Yield-SAFE, and (ii) the ease of its accessibility via the CliPick web portal, which facilitates its application for further study. We note that each model has inherent errors and biases and the use of multiple models is encouraged in studies seeking to assess differences between climate scenarios. Future work will have the opportunity to utilise the next generation of EURO-CORDEX regional climate, simulations for which are currently in progress. Additionally, we note that, while the appropriateness of RCP 8.5 as the most likely scenario for the future has been questioned (Hausfather and Peters \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e), RCP 8.5 has been shown to be a closer match to historical (2005\\u0026ndash;2020) and anticipated future CO\\u003csub\\u003e2\\u003c/sub\\u003e emissions than any of the alternative RCPs (Schwalm et al., \\u003cspan citationid=\\\"CR70\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"5. Conclusions\",\"content\":\"\\u003cp\\u003eTo our knowledge, this is the first study to validate a biophysical model for arable and grassland, and mature woodland, silvopastoral and silvoarable systems at the same site in northern Europe as well as using the calibrated model to predict the effect on yield and soil carbon for the contrasting land-use systems for two future emissions scenarios based on the IPCC\\u0026rsquo;s Representative Concentration Pathways RCP 4.5 and RCP 8.5 for 2020\\u0026ndash;2060 and 2060\\u0026ndash;2100. This combination of modelling alongside the use of calibration data from long-term experimental sites creates a powerful combination to investigate the effect on future climate scenarios. The capacity to model daily changes in soil organic carbon over long time periods also provided new insights into the changes in soil organic carbon when planting trees on grassland and cropland. Although integrating trees on cropland and grassland resulted in higher soil organic carbon at 40 years, the positive effects only became apparent once the tree had been established for at least 10 years. Furthermore, by running virtual experiments farming systems including agroforestry can be optimized to adapt to future weather extremes. The modelled results suggest that the land equivalence ratio of agroforestry systems is relatively resilient to changes to climate change, as an increased capacity of one component to capture light and water resources is offset by a decline in the resources available to another component. Within the silvopastoral system, the effect of integrating 50 trees per hectare, compared to no trees, on grass yields appeared to be relatively low. The study also highlights the importance of including the atmospheric CO\\u003csub\\u003e2\\u003c/sub\\u003e fertilisation effect on plant growth when predicting tree, crop, water, and soil responses to climate change. Within each system, management interventions such as thinning and pruning can also moderate the climate impacts on yield.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eSupplementary information\\u003c/p\\u003e\\n\\u003cp\\u003eThe online version contains supplementary material available at\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eFunding\\u003c/p\\u003e\\n\\u003cp\\u003eWe acknowledge support of the European Union\\u0026rsquo;s Horizon 2020 research and innovation programme for the AGROMIX research project (AGROforestry and MIXed farming systems; grant agreement 862993).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eData availability statement\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets generated or analyzed during the current study are available in Cranfield University repository (Link to be included later)\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eAuthors contribution\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by MLG, RJO, and JME. The first draft of the manuscript was written by MLG and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003eAcknowledgements\\u003c/p\\u003e\\n\\u003cp\\u003eWe acknowledge the support of AFBI (Agri-Food and Biosciences Institute) and DAERA (Department of Agriculture, Environment and Rural Affairs) in establishing and maintaining the silvopastoral and silvorable experiments at Loughgall. \\u0026nbsp;ERA5 is made available by the Copernicus Climate Change Service (C3S) at European Centre for Medium-Range Weather Forecasts (ECMWF).\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAllen R, Pereira L, Raes D, Smith M (1998) Crop Evaporation, in: Crop Evapotranspiration \\u0026ndash; Guidelines for Computing Crop Water Requirements \\u0026ndash; FAO Irrigation and Drainage Paper 56. FAO, Rome\\u003c/li\\u003e\\n\\u003cli\\u003eALRahahleh L, Kilpel\\u0026auml;inen A, Ikonen VP et al. 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Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 1817\\u0026ndash;1927, https://doi.org/10.1017/9781009325844.015\\u003c/li\\u003e\\n\\u003cli\\u003eBEIS (Department for Business, Energy and Industrial Strategy) (2021) Final UK greenhouse gas emissions national statistics: 1990 to 2019. https://www.gov.uk/government/statistics/final-uk-greenhouse-gas-emissions-national-statistics-1990-to-2019\\u003c/li\\u003e\\n\\u003cli\\u003eB\\u0026ouml;hm C, Kanzler M, Freese D. (2014) Wind speed reductions as influenced by woody hedgerows grown for biomass in short rotation alley cropping systems in Germany. 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July 2023. 22 pp. https://cord.cranfield.ac.uk/articles/software/Yield-SAFE_v2_-_Biophysical_model_for_tree_and_crop_yields_in_agroforestry/24250549\\u003c/li\\u003e\\n\\u003cli\\u003eCannell MGR, Van Noordwijk M, Ong CK (1996).The central agroforestry hypothesis: the trees must acquire resources that the crop would not otherwise acquire. \\u003cem\\u003eAgroforest Syst\\u003c/em\\u003e 34, 27\\u0026ndash;31 (1996). https://doi.org/10.1007/BF00129630\\u003c/li\\u003e\\n\\u003cli\\u003eCannon AJ, Sobie SR and Murdock TQ (2015) Bias correction of simulated precipitation by quantile mapping: How well do methods preserve relative changes in quantiles and extremes? \\u003cem\\u003eJournal of Climate\\u003c/em\\u003e, 28:6938-6959. https://doi\\u003cu\\u003e:10.1175/JCLI-D-14-00754.1\\u003c/u\\u003e \\u003c/li\\u003e\\n\\u003cli\\u003eChristie JM (1994) Provisional yield tables for poplar in Britain. Forestry Commission. Technical Paper 6. https://cdn.forestresearch.gov.uk/1994/09/fctp006.pdf [Accessed on 27 January 2023]\\u003c/li\\u003e\\n\\u003cli\\u003eColeman K, Jenkinson D (2014) RothC - A Model for the Turnover of Carbon in Soil - Model description and users guide. Rothamsted Research, Harpenden, UK. https://doi.org/10.1007/978-3-642-61094-3_17\\u003c/li\\u003e\\n\\u003cli\\u003eCornes R, van der Schrier G, van den Besselaar EJM, Jones P (2018) An Ensemble Version of the E-OBS Temperature and Precipitation Datasets, J. Geophys. Res. Atmos, 123.\\u003c/li\\u003e\\n\\u003cli\\u003eCruickshank JG (1997) Soil and environment: Northern Ireland Agricultural and Environment Science Division, DANI and the Agricultural Environmental Science Department, The Queen\\u0026rsquo;s University Belfast. p. 77 \\u003c/li\\u003e\\n\\u003cli\\u003eCurtis PG, Slay CM, Harris NL, Tyukavina A, Hansen MC (2018) Classifying drivers of global forest loss. Science 361, 1108\\u0026ndash;1111.\\u003c/li\\u003e\\n\\u003cli\\u003eDas S, Richards BK, Hanley KL, Krounbi L, Walter MF, Walter MT, Steenhuis TS, Lehmann J (2019) Lower mineralizability of soil carbon with higher legacy soil moisture, Soil Biology and Biochemistry, 130, 94-104, https://doi.org/10.1016/j.soilbio.2018.12.006\\u003c/li\\u003e\\n\\u003cli\\u003eden Herder M, Moreno G, Mosquera-Losada RM, Palma JHN, Sidiropoulou A, Santiago Freijanes JJS, et al. (2017) Current extent and stratification of agroforestry in the European Union. Agric. Ecosyst. Environ. 241, 121\\u0026ndash;132. https://doi.org/10.1016/j.agee.2017.03.005\\u003c/li\\u003e\\n\\u003cli\\u003eDeryng D, Elliott J, Folberth C, M\\u0026uuml;ller C, et al. (2016) Regional disparities in the beneficial effects of rising CO2 concentrations on crop water productivity. Nat Clim Change 6:786\\u0026ndash;790. https://doi.org/10.1038/nclimate2995\\u003c/li\\u003e\\n\\u003cli\\u003eEhret M, Gra\\u0026szlig; R, Wachendorf M (2015) The effect of shade and shade material on white clover/perennial ryegrass mixtures for temperate agroforestry systems. Agroforest Syst 89, 557\\u0026ndash;570. https://doi.org/10.1007/s10457-015-9791-0\\u003c/li\\u003e\\n\\u003cli\\u003eEsche L, Schneider M, Milz J et al. (2023) The role of shade tree pruning in cocoa agroforestry systems: agronomic and economic benefits. Agroforest Syst 97, 175\\u0026ndash;185. https://doi.org/10.1007/s10457-022-00796-x\\u003c/li\\u003e\\n\\u003cli\\u003eEuropean Commission (2013) Regulation (EU) No 1305/2013 of the European Parliament and of the Council of 17 December 2013 on support for rural development by the European Agricultural Fund for Rural Development (EAFRD) and repealing Council Regulation (EC) No 1698/2005\\u003c/li\\u003e\\n\\u003cli\\u003eEuropean Commission (2018) Regulation (EU) 2018/841 of the European Parliament and of the Council of 30 May 2018 on the inclusion of greenhouse gas emissions and removals from land use, land use change and forestry in the 2030 climate and energy framework, and amending Regulation (EU) No 525/2013 and Decision No 529/2013/EU. http://data.europa.eu/eli/reg/2018/841/2023-05-11\\u003c/li\\u003e\\n\\u003cli\\u003eEuropean Environment Agency (2023) Greenhouse gas emissions from agriculture in Europe. https://www.eea.europa.eu/en/analysis/indicators/greenhouse-gas-emissions-from-agriculture\\u003c/li\\u003e\\n\\u003cli\\u003eFalloon P, Jones CD, Ades M, Paul K (2011) Direct soil moisture controls of future global soil carbon changes: An important source of uncertainty. Global Biogeochem. Cycles, 25, GB3010, https://doi.org/10.1029/2010GB003938\\u003c/li\\u003e\\n\\u003cli\\u003eFarrell AD, Derying D, Neufeldt H (2023) Modelling adaptation and transformative adaptation in cropping systems: recent advances and future directions. Current Opinion in Environmental Sustainability, 61, 101265, ISSN 1877-3435, https://doi.org/10.1016/j.cosust.2023.101265\\u003c/li\\u003e\\n\\u003cli\\u003eFavero A, Sohngen B, Parker Hamilton W (2022) Climate change and timber in Latin America: Will the forestry sector flourish under climate change? Forest Policy and Economics, 135, 102657, https://doi.org/10.1016/j.forpol.2021.102657\\u003c/li\\u003e\\n\\u003cli\\u003eFeng Y, Zeng Z, Searchinger TD et al. (2022) Doubling of annual forest carbon loss over the tropics during the early twenty-first century. 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Ecological Engineering 29: 434-449. https://doi.org/10.1016/j.ecoleng.2006.09.018\\u003c/li\\u003e\\n\\u003cli\\u003eGraves AR, Burgess PJ, Palma J, Keesman K, van der Werf W, Dupraz C, van Keulen H, Herzog F, Mayus M (2010) Implementation and calibration of the parameter-sparse Yield-SAFE model to predict production and land equivalent ratio in mixed tree and crop systems under two contrasting production situations in Europe. Ecological Modelling 221: 1744-1756. https://doi.org/10.1016/j.ecolmodel.2010.03.008\\u003c/li\\u003e\\n\\u003cli\\u003eGraves AR, Burgess PJ, Liagre F, Terreaux JP, Borrel T, Dupraz C, Palma J, Herzog F (2011) Farm-SAFE: the process of developing a plot- and farm-scale model of arable, forestry, and silvoarable economics. 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(2011) Modeling the change in soil organic carbon of grassland in response to climate change: Effects of measured versus modelled carbon pools for initializing the Rothamsted Carbon model. Agriculture, Ecosystems \\u0026amp; Environment, 140:3-4, 372-381, https://doi.org/10.1016/j.agee.2010.12.018\\u003c/li\\u003e\\n\\u003cli\\u003eZhao C, Piao S, Wang X, et al. (2017) Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl. Acad. Sci, 114(35), 9326\\u0026ndash;9331, https://doi.org/10.1073/pnas.1701762114\\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\":true,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"agronomy-for-sustainable-development\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"ASDE\",\"sideBox\":\"Learn more about [Agronomy for Sustainable Development](https://www.springer.com/journal/13593)\",\"snPcode\":\"13593\",\"submissionUrl\":\"https://www2.cloud.editorialmanager.com/asde/default2.aspx\",\"title\":\"Agronomy for Sustainable Development\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Biomass, model, crop, resilience, timber, tree, sequestration, RCP, RothC\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4473355/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4473355/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThis study predicts the effects of climate change on crop yields, timber volumes and soil organic carbon in grassland, arable, ash woodland, poplar plantation, and silvopastoral and silvoarable systems in Northern Ireland. We modified a version of the biophysical Yield-SAFE agroforestry model that includes a RothC soil carbon module and also the effect of atmospheric CO\\u003csub\\u003e2\\u003c/sub\\u003e fertilisation. The model was calibrated using existing field measurements and weather data from 1989 to 2021. The effect of two future climate scenarios were modelled, based on two representative concentration pathways (RCP 4.5 and RCP 8.5) for 2020\\u0026ndash;2060 and 2060\\u0026ndash;2100. The study revealed that the impact of future climate scenarios on grass and arable yields, and tree growth were positive with the effect of CO\\u003csub\\u003e2\\u003c/sub\\u003e fertilisation more than offsetting a generally negative effect of increased temperatures and drought stress on yields. The predicted land equivalent ratio (LER) remained relatively constant between the baseline and the future climate scenarios for the silvopastoral system (1.08 to 1.11). The corresponding values for the silvoarable system were 0.87\\u0026ndash;0.92 based on arable and timber yields alone, or 1.11\\u0026ndash;1.17 if grass yields were included. In the silvopastoral system, but not the silvoarable system, the model suggested that climate change would benefit tree growth relative to the understorey crop. Greater losses of soil organic carbon were predicted under barley-only (1.02\\u0026ndash;1.18 t C ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e yr\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e) than grassland (0.48\\u0026ndash;0.55 t C ha\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e yr\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e), with relatively small differences between the baseline and climate scenarios. However, the analysis indicated that these losses could be mitigated by planting trees, but this effect was not immediate as soil organic matter would continue to decline for the first 10 years until the trees were well-established. The model was also used to examine the effect of different tree densities on the trade-offs between timber volume and understorey crop yields.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Predicted yield and soil organic carbon changes in grassland, arable, woodland, and agroforestry systems under climate change in a cool temperate Atlantic climate\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-07-12 12:12:42\",\"doi\":\"10.21203/rs.3.rs-4473355/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"reviewerAgreed\",\"content\":\"\",\"date\":\"2024-06-24T05:53:47+00:00\",\"index\":0,\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-06-22T14:25:29+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"Agronomy for Sustainable Development\",\"date\":\"2024-06-18T08:55:42+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-06-03T08:35:02+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Agronomy for Sustainable Development\",\"date\":\"2024-05-24T11:28:09+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"agronomy-for-sustainable-development\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"ASDE\",\"sideBox\":\"Learn more about [Agronomy for Sustainable Development](https://www.springer.com/journal/13593)\",\"snPcode\":\"13593\",\"submissionUrl\":\"https://www2.cloud.editorialmanager.com/asde/default2.aspx\",\"title\":\"Agronomy for Sustainable Development\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"e480823f-d6f3-4489-ba7f-c2543cf5300f\",\"owner\":[],\"postedDate\":\"July 12th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-03-27T16:02:57+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-07-12 12:12:42\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4473355\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4473355\",\"identity\":\"rs-4473355\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}