Predicting soil carbon changes in switchgrass grown on marginal lands under climate change and adaptation strategies

The United States Great Lakes Region (USGLR) is a critical geographic area for future bioenergy production. Switchgrass (Panicum virgatum) is widely considered a carbon (C)‐neutral or C‐negative bioenergy production system, but projected increases in air temperature and precipitation due to climate change might substantially alter soil organic C (SOC) dynamics and storage in soils. This study examined long‐term SOC changes in switchgrass grown on marginal land in the USGLR under current and projected climate, predicted using a process‐based model (Systems Approach to Land‐Use Sustainability) extensively calibrated with a wealth of plant and soil measurements at nine experimental sites. Simulations indicate that these soils are likely a net C sink under switchgrass (average gain 0.87 Mg C ha^?1 year^?1), although substantial variation in the rate of SOC accumulation was predicted (range: 0.2–1.3 Mg C ha^?1 year^?1). Principal component analysis revealed that the predicted intersite variability in SOC sequestration was related in part to differences in climatic characteristics, and to a lesser extent, to heterogeneous soils. Although climate change impacts on switchgrass plant growth were predicted to be small (4%–6% decrease on average), the increased soil respiration was predicted to partially negate SOC accumulations down to 70% below historical rates in the most extreme scenarios. Increasing N fertilizer rate and decreasing harvest intensity both had modest SOC sequestration benefits under projected climate, whereas introducing genotypes better adapted to the longer growing seasons was a much more effective strategy. Best‐performing adaptation scenarios were able to offset >60% of the climate change impacts, leading to SOC sequestration 0.7 Mg C ha^?1 year^?1 under projected climate. On average, this was 0.3 Mg C ha^?1 year^?1 more C sequestered than the no adaptation baseline. These findings provide crucial knowledge needed to guide policy and operational management for maximizing SOC sequestration of future bioenergy production on marginal lands in the USGLR.     

Spatial evaluation of switchgrass productivity under historical and future climate scenarios in Michigan

Switchgrass (Panicum virgatum) productivity on marginal and fertile lands has not been thoroughly evaluated in a systematic manner that includes soil–crop–weather–management interactions and to quantify the risk of failure or success in growing the crop. We used the Systems Approach to Land Use Sustainability (SALUS) model to identify areas with low risk of failing to having more than 8000 kg ha^?1 yr^?1 switchgrass aboveground net primary productivity (ANPP) under rainfed and unfertilized conditions. In addition, we diagnosed constraining factors for switchgrass growth, and tested the effect of nitrogen fertilizer application on plant productivity across Michigan for 30 years under three climate scenarios (baseline climate in 1981–2010, future climate with emissions using RCP 2.6 and RCP 6.0). We determined that <16% of land in Michigan may have at least 8 Mg ha^?1 yr^?1 ANPP under rainfed and unfertilized management with a low risk of failure. Of the productive low‐risk land, about 25% was marginal land, with more than 80% of which was affected by limited water availability due to low soil water‐holding capacity and shallow depth. About 80% of the marginal land was N limited under baseline conditions, but that percentage decreased to 58.5% and 42.1% under RCP 2.6 and RCP 6.0 climate scenarios, respectively, partly due to shorter growing season, smaller plants and less N demand. We also found that the majority of Michigan's land could have high switchgrass ANPP and low risk of failure with no more than 60 kgN ha^?1 fertilizer input. We believe that the methodology used in this study works at different spatial scales, as well as for other biofuel crops.     

Uncertain effectiveness of Miscanthus bioenergy expansion for climate change mitigation explored using land surface,agronomic and integrated assessment models

Large-scale bioenergy plays a key role in climate change mitigation scenarios, but its efficacy is uncertain. This study aims to quantify that uncertainty by contrasting the results of three different types of models under the same mitigation scenario (RCP2.6-SSP2), consistent with a 2°C temperature target. This analysis focuses on a single bioenergy feedstock, Miscanthus × giganteus, and contrasts projections for its yields and environmental effects from an integrated assessment model (IMAGE), a land surface and dynamic global vegetation model tailored to Miscanthus bioenergy (JULES) and a bioenergy crop model (MiscanFor). Under the present climate, JULES, IMAGE and MiscanFor capture the observed magnitude and variability in Miscanthus yields across Europe; yet in the tropics JULES and IMAGE predict high yields, whereas MiscanFor predicts widespread drought-related diebacks. 2040–2049 projections show there is a rapid scale up of over 200 Mha bioenergy cropping area in the tropics. Resulting biomass yield ranges from 12 (MiscanFor) to 39 (JULES) Gt dry matter over that decade. Change in soil carbon ranges from +0.7 Pg C (MiscanFor) to −2.8 Pg C (JULES), depending on preceding land cover and soil carbon.2090–99 projections show large-scale biomass energy with carbon capture and storage (BECCS) is projected in Europe. The models agree that <2°C global warming will increase yields in the higher latitudes, but drought stress in the Mediterranean region could produce low yields (MiscanFor), and significant losses of soil carbon (JULES and IMAGE). These results highlight the uncertainty in rapidly scaling-up biomass energy supply, especially in dry tropical climates and in regions where future climate change could result in drier conditions. This has important policy implications—because prominently used scenarios to limit warming to ‘well below 2°C’ (including the one explored here) depend upon its effectiveness.     

Bivalve aquaculture‐environment interactions in the context of climate change

Coastal embayments are at risk of impacts by climate change drivers such as ocean warming, sea level rise and alteration in precipitation regimes. The response of the ecosystem to these drivers is highly dependent on their magnitude of change, but also on physical characteristics such as bay morphology and river discharge, which play key roles in water residence time and hence estuarine functioning. These considerations are especially relevant for bivalve aquaculture sites, where the cultured biomass can alter ecosystem dynamics. The combination of climate change, physical and aquaculture drivers can result in synergistic/antagonistic and nonlinear processes. A spatially explicit model was constructed to explore effects of the physical environment (bay geomorphic type, freshwater inputs), climate change drivers (sea level, temperature, precipitation) and aquaculture (bivalve species, stock) on ecosystem functioning. A factorial design led to 336 scenarios (48 hydrodynamic × 7 management). Model outcomes suggest that the physical environment controls estuarine functioning given its influence on primary productivity (bottom‐up control dominated by riverine nutrients) and horizontal advection with the open ocean (dominated by bay geomorphic type). The intensity of bivalve aquaculture ultimately determines the bivalve–phytoplankton trophic interaction, which can range from a bottom‐up control triggered by ammonia excretion to a top‐down control via feeding. Results also suggest that temperature is the strongest climate change driver due to its influence on the metabolism of poikilothermic organisms (e.g. zooplankton and bivalves), which ultimately causes a concomitant increase of top‐down pressure on phytoplankton. Given the different thermal tolerance of cultured species, temperature is also critical to sort winners from losers, benefiting Crassostrea virginica over Mytilus edulis under the specific conditions tested in this numerical exercise. In general, it is predicted that bays with large rivers and high exchange with the open ocean will be more resilient under climate change when bivalve aquaculture is present.     

Land management and climate change determine second‐generation bioenergy potential of the US Northern Great Plains

Bioenergy with carbon capture and storage (BECCS) has been proposed as a potential climate mitigation strategy raising concerns over trade‐offs with existing ecosystem services. We evaluate the feasibility of BECCS in the Upper Missouri River Basin (UMRB), a landscape with diverse land use, ownership, and bioenergy potential. We develop land‐use change scenarios and a switchgrass (Panicum virgatum L.) crop functional type to use in a land‐surface model to simulate second‐generation bioenergy production. By the end of this century, average annual switchgrass production over the UMRB ranges from 60 to 210 Tg dry mass/year and is dependent on the Representative Concentration Pathway for greenhouse gas emissions and on land‐use change assumptions. Under our simple phase‐in assumptions this results in a cumulative total production of 2,000–6,000 Tg C over the study period with the upper estimates only possible in the absence of climate change. Switchgrass yields decreased as average CO₂ concentrations and temperatures increased, suggesting the effect of elevated atmospheric CO₂ was small because of its C4 photosynthetic pathway. By the end of the 21st century, the potential energy stored annually in harvested switchgrass averaged between 1 and 4 EJ/year assuming perfect conversion efficiency, or an annual electrical generation capacity of 7,000–28,000 MW assuming current bioenergy efficiency rates. Trade‐offs between bioenergy and ecosystem services were identified, including cumulative direct losses of 1,000–2,600 Tg C stored in natural ecosystems from land‐use change by 2090. Total cumulative losses of ecosystem carbon stocks were higher than the potential ~300 Tg C in fossil fuel emissions from the single largest power plant in the region over the same time period, and equivalent to potential carbon removal from the atmosphere from using biofuels grown in the same region. Numerous trade‐offs from BECCS expansion in the UMRB must be balanced against the potential benefits of a carbon‐negative energy system.     

A socio‐ecological model for predicting impacts of land‐use and climate change on regional plant diversity in the Austrian Alps

Climate and land‐use change jointly affect the future of biodiversity. Yet, biodiversity scenarios have so far concentrated on climatic effects because forecasts of land use are rarely available at appropriate spatial and thematic scales. Agent‐based models (ABMs) represent a potentially powerful but little explored tool for establishing thematically and spatially fine‐grained land‐use scenarios. Here, we use an ABM parameterized for 1,329 agents, mostly farmers, in a Central European model region, and simulate the changes to land‐use patterns resulting from their response to three scenarios of changing socio‐economic conditions and three scenarios of climate change until the mid of the century. Subsequently, we use species distribution models to, first, analyse relationships between the realized niches of 832 plant species and climatic gradients or land‐use types, respectively, and, second, to project consequent changes in potential regional ranges of these species as triggered by changes in both the altered land‐use patterns and the changing climate. We find that both drivers determine the realized niches of the studied plants, with land use having a stronger effect than any single climatic variable in the model. Nevertheless, the plants' future distributions appear much more responsive to climate than to land‐use changes because alternative future socio‐economic backgrounds have only modest impact on land‐use decisions in the model region. However, relative effects of climate and land‐use changes on biodiversity may differ drastically in other regions, especially where landscapes are still dominated by natural or semi‐natural habitat. We conclude that agent‐based modelling of land use is able to provide scenarios at scales relevant to individual species distribution and suggest that coupling ABMs with models of species' range change should be intensified to provide more realistic biodiversity forecasts.     

Synergistic effects of climate and land‐use change influence broad‐scale avian population declines

Climate and land‐use changes are expected to be the primary drivers of future global biodiversity loss. Although theory suggests that these factors impact species synergistically, past studies have either focused on only one in isolation or have substituted space for time, which often results in confounding between drivers. Tests of synergistic effects require congruent time series on animal populations, climate change and land‐use change replicated across landscapes that span the gradient of correlations between the drivers of change. Using a unique time series of high‐resolution climate (measured as temperature and precipitation) and land‐use change (measured as forest change) data, we show that these drivers of global change act synergistically to influence forest bird population declines over 29 years in the Pacific Northwest of the United States. Nearly half of the species examined had declined over this time. Populations declined most in response to loss of early seral and mature forest, with responses to loss of early seral forest amplified in landscapes that had warmed over time. In addition, birds declined more in response to loss of mature forest in areas that had dried over time. Climate change did not appear to impact populations in landscapes with limited habitat loss, except when those landscapes were initially warmer than the average landscape. Our results provide some of the first empirical evidence of synergistic effects of climate and land‐use change on animal population dynamics, suggesting accelerated loss of biodiversity in areas under pressure from multiple global change drivers. Furthermore, our findings suggest strong spatial variability in the impacts of climate change and highlight the need for future studies to evaluate multiple drivers simultaneously to avoid potential misattribution of effects.     

Large‐scale impact of climate change vs. land‐use change on future biome shifts in Latin America

Climate change and land‐use change are two major drivers of biome shifts causing habitat and biodiversity loss. What is missing is a continental‐scale future projection of the estimated relative impacts of both drivers on biome shifts over the course of this century. Here, we provide such a projection for the biodiverse region of Latin America under four socio‐economic development scenarios. We find that across all scenarios 5–6% of the total area will undergo biome shifts that can be attributed to climate change until 2099. The relative impact of climate change on biome shifts may overtake land‐use change even under an optimistic climate scenario, if land‐use expansion is halted by the mid‐century. We suggest that constraining land‐use change and preserving the remaining natural vegetation early during this century creates opportunities to mitigate climate‐change impacts during the second half of this century. Our results may guide the evaluation of socio‐economic scenarios in terms of their potential for biome conservation under global change.     

Spatial heterogeneity in climate change effects decouples the long‐term dynamics of wild reindeer populations in the high Arctic

The ‘Moran effect’ predicts that dynamics of populations of a species are synchronized over similar distances as their environmental drivers. Strong population synchrony reduces species viability, but spatial heterogeneity in density dependence, the environment, or its ecological responses may decouple dynamics in space, preventing extinctions. How such heterogeneity buffers impacts of global change on large‐scale population dynamics is not well studied. Here, we show that spatially autocorrelated fluctuations in annual winter weather synchronize wild reindeer dynamics across high‐Arctic Svalbard, while, paradoxically, spatial variation in winter climate trends contribute to diverging local population trajectories. Warmer summers have improved the carrying capacity and apparently led to increased total reindeer abundance. However, fluctuations in population size seem mainly driven by negative effects of stochastic winter rain‐on‐snow (ROS) events causing icing, with strongest effects at high densities. Count data for 10 reindeer populations 8–324 km apart suggested that density‐dependent ROS effects contributed to synchrony in population dynamics, mainly through spatially autocorrelated mortality. By comparing one coastal and one ‘continental’ reindeer population over four decades, we show that locally contrasting abundance trends can arise from spatial differences in climate change and responses to weather. The coastal population experienced a larger increase in ROS, and a stronger density‐dependent ROS effect on population growth rates, than the continental population. In contrast, the latter experienced stronger summer warming and showed the strongest positive response to summer temperatures. Accordingly, contrasting net effects of a recent climate regime shift—with increased ROS and harsher winters, yet higher summer temperatures and improved carrying capacity—led to negative and positive abundance trends in the coastal and continental population respectively. Thus, synchronized population fluctuations by climatic drivers can be buffered by spatial heterogeneity in the same drivers, as well as in the ecological responses, averaging out climate change effects at larger spatial scales.     

Effects of climate change and management on net climate impacts of production and utilization of energy biomass in Norway spruce with stable age‐class distribution

We studied the effects of climate change and forest management scenarios on net climate impacts (radiative forcing) of production and utilization of energy biomass, in a Norway spruce forest area over an 80‐year simulation period in Finnish boreal conditions. A stable age‐class distribution was used in model‐based analyses to identify purely the management effects under the current and changing climate (SRES B1 and A2 scenarios). The radiative forcing was calculated based on an integrated use of forest ecosystem model simulations and a life cycle assessment (LCA) tool. In this work, forest‐based energy was used to substitute coal, and current forest management (baseline management) was used as a reference management. In alternative management scenarios, the stocking was maintained 20% higher in thinning compared to the baseline management, and nitrogen fertilization was applied. Intensity of energy biomass harvest (e.g. logging residues, coarse roots and stumps) was varied in the final felling of the stands at the age of 80 years. Also, the economic profitability (NPV, 3% interest rate) of integrated production of timber and energy biomass was calculated for each management scenario. Our results showed that compared to the baseline management, climate benefits could be increased by maintaining higher stocking in thinning over rotation, using nitrogen fertilization and harvesting logging residues, stumps and coarse roots in the final felling. Under the gradually changing climate (in both SRES B1 and A2), the climate benefits were lower compared to the current climate. Trade‐offs between NPV and net climate impacts also existed.     

Ecosystem‐based management of coral reefs under climate change

Coral reefs provide food and livelihoods for hundreds of millions of people as well as harbour some of the highest regions of biodiversity in the ocean. However, overexploitation, land‐use change and other local anthropogenic threats to coral reefs have left many degraded. Additionally, coral reefs are faced with the dual emerging threats of ocean warming and acidification due to rising CO₂ emissions, with dire predictions that they will not survive the century. This review evaluates the impacts of climate change on coral reef organisms, communities and ecosystems, focusing on the interactions between climate change factors and local anthropogenic stressors. It then explores the shortcomings of existing management and the move towards ecosystem‐based management and resilience thinking, before highlighting the need for climate change‐ready marine protected areas (MPAs), reduction in local anthropogenic stressors, novel approaches such as human‐assisted evolution and the importance of sustainable socialecological systems. It concludes that designation of climate change‐ready MPAs, integrated with other management strategies involving stakeholders and participation at multiple scales such as marine spatial planning, will be required to maximise coral reef resilience under climate change. However, efforts to reduce carbon emissions are critical if the long‐term efficacy of local management actions is to be maintained and coral reefs are to survive.     

Effects of climate change on productivity of cereals and legumes; model evaluation of observed year-to-year variability of the CO2 response

The effect of elevated [CO₂] on the productivity of spring wheat, winter wheat and faba bean was studied in experiments in climatized crop enclosures in the Wageningen Rhizolab in 1991–93. Simulation models for crop growth were used to explore possible causes for the observed differences in the CO₂ response. Measurements of the canopy gas exchange (CO₂ and water vapour) were made continuously from emergence until harvest. At an external [CO₂] of 700 μmol mol^?1 Maximum Canopy CO₂ Exchange Rate (CCERmax) at canopy closure was stimulated by 51% for spring wheat and by 71% for faba bean. At the end of the growing season, above ground biomass increase at 700 μmol mol^?1 was 58% (faba bean), 35% (spring wheat) and 19% (winter wheat) and the harvest index did not change. For model exploration, weather data sets for the period 1975-88 and 1991–93 were used, assuming adequate water supply and [CO₂] at 350 and 700 μmol mol^?1. For spring wheat the simulated responses (35–50%) were at the upper end of the experimental results. In agreement with experiments, simulations showed smaller responses for winter wheat and larger responses for faba bean. Further model explorations showed that this differential effect in the CO₂ response may not be primarily due to fundamental physiological differences between the crops, but may be at least partly due to differences in the daily air temperatures during comparable stages of growth of these crops. Simulations also showed that variations between years in CO₂ response can be largely explained by differences in weather conditions (especially temperature) between growing seasons.     

Time‐lag effects of global vegetation responses to climate change

Climate conditions significantly affect vegetation growth in terrestrial ecosystems. Due to the spatial heterogeneity of ecosystems, the vegetation responses to climate vary considerably with the diverse spatial patterns and the time‐lag effects, which are the most important mechanism of climate–vegetation interactive effects. Extensive studies focused on large‐scale vegetation–climate interactions use the simultaneous meteorological and vegetation indicators to develop models; however, the time‐lag effects are less considered, which tends to increase uncertainty. In this study, we aim to quantitatively determine the time‐lag effects of global vegetation responses to different climatic factors using the GIMMS3g NDVI time series and the CRU temperature, precipitation, and solar radiation datasets. First, this study analyzed the time‐lag effects of global vegetation responses to different climatic factors. Then, a multiple linear regression model and partial correlation model were established to statistically analyze the roles of different climatic factors on vegetation responses, from which the primary climate‐driving factors for different vegetation types were determined. The results showed that (i) both the time‐lag effects of the vegetation responses and the major climate‐driving factors that significantly affect vegetation growth varied significantly at the global scale, which was related to the diverse vegetation and climate characteristics; (ii) regarding the time‐lag effects, the climatic factors explained 64% variation of the global vegetation growth, which was 11% relatively higher than the model ignoring the time‐lag effects; (iii) for the area with a significant change trend (for the period 1982–2008) in the global GIMMS3g NDVI (P < 0.05), the primary driving factor was temperature; and (iv) at the regional scale, the variation in vegetation growth was also related to human activities and natural disturbances. Considering the time‐lag effects is quite important for better predicting and evaluating the vegetation dynamics under the background of global climate change.     

气候变化对蒙古扁桃适宜分布范围和空间格局的影响

为模拟、预测气候变化对孑遗、濒危植物蒙古扁桃(Amygdalus mongolica)潜在分布的影响, 利用最大熵(MAXENT)模型模拟、预测、对比、分析、揭示蒙古扁桃在最大冰期(CCSM及MIROC模型)、历史气候(1961-1990年)及未来气候(2020年、2050年和2080年, 政府间气候变化专门委员会排放情景特别报告的A2A情景)条件下的适宜分布范围和空间格局的变化。结果表明: (1)蒙古扁桃在历史气候条件下的潜在分布区集中在蒙古的南戈壁省及东戈壁省, 我国内蒙古巴彦淖尔市、阿拉善左旗、鄂尔多斯市、锡林郭勒盟西部, 河西走廊中部及东部, 宁夏北部及陕西北部, 以及河北北部的部分地区; (2)与历史气候条件下的潜在分布相比, 蒙古扁桃在最大冰期CCSM气候情景下的分布经历了明显的、大范围的向南迁移和范围缩小; (3)未来A2A气候情景下, 其潜在分布范围表现出在2020年明显扩大, 在2050年减小, 到2080年又略有增大的趋势。分布格局表现出不断向我国河北及内蒙古东部, 蒙古东部、北部及西部大幅度扩散、迁移的趋势。     

Implications of CO2 fertilization for future climate change in a coupled climate–carbon model

The terrestrial carbon cycle plays a critical role in determining levels of atmospheric CO₂ that result from anthropogenic carbon emissions. Elevated atmospheric CO₂ is thought to stimulate terrestrial carbon uptake, through the process of CO₂ fertilization of vegetation productivity. This negative carbon cycle feedback results in reduced atmospheric CO₂ growth, and has likely accounted for a substantial portion of the historical terrestrial carbon sink. However, the future strength of CO₂ fertilization in response to continued carbon emissions and atmospheric CO₂ rise is highly uncertain. In this paper, the ramifications of CO₂ fertilization in simulations of future climate change are explored, using an intermediate complexity coupled climate–carbon model. It is shown that the absence of future CO₂ fertilization results in substantially higher future CO₂ levels in the atmosphere, as this removes the dominant contributor to future terrestrial carbon uptake in the model. As a result, climate changes are larger, though the radiative effect of higher CO₂ on surface temperatures in the model is offset by about 30% due to reduced positive dynamic vegetation feedbacks; that is, the removal of CO₂ fertilization results in less vegetation expansion in the model, which would otherwise constitute an important positive surface albedo‐temperature feedback. However, the effect of larger climate changes has other important implications for the carbon cycle – notably to further weaken remaining carbon sinks in the model. As a result, positive climate–carbon cycle feedbacks are larger when CO₂ fertilization is absent. This creates an interesting synergism of terrestrial carbon cycle feedbacks, whereby positive (climate–carbon cycle) feedbacks are amplified when a negative (CO₂ fertilization) feedback is removed.     

Contribution of crop model structure,parameters and climate projections to uncertainty in climate change impact assessments

Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple‐ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple‐ensemble probabilistic assessment, the median of simulated yield change was ?4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981–2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple‐ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources.     

Productivity, 15N dynamics and water use efficiency in low‐ and high‐input switchgrass systems

Sustainable and environmentally benign switchgrass production systems need to be developed for switchgrass to become a large‐scale dedicated energy crop. An experiment was conducted in California from 2009 to 2011 to determine the sustainability of low‐ and high‐input irrigated switchgrass systems as a function of yield, irrigation requirement, crop N removal, N translocation from aboveground (AG) to belowground (BG) biomass during senescence, and fertilizer ¹⁵N recovery (FNR) in the AG and BG biomass (0–300 cm), and soil (0–300 cm). The low‐input system consisted of a single‐harvest (mid‐fall) irrigated until flowering (early summer), while the high‐input system consisted of a two‐harvest system (early summer and mid‐fall) irrigated throughout the growing season. Three N fertilization rates (0, 100, and 200 kg N ha^?1 yr^?1) were applied as subtreatments in a single application in the spring of each year. A single pulse of ¹⁵N enriched fertilizer was applied in the first year of the study to micro‐plots within the 100 kg N ha^?1 subplots. Average yields across years under optimal N rates (100 and 200 kg ha^?1 yr^?1 for low‐ and high‐input systems, respectively) were 20.7 and 24.8 Mg ha^?1. However, the low input (372 ha mm) required 47% less irrigation than the high‐input system (705 ha mm) and achieved higher irrigation use efficiency. In addition, the low‐input system had 46% lower crop N removal, 53% higher N stored in BG biomass, and a positive N balance, presumably due to 49% of ¹⁵N translocation from AG to BG biomass during senescence. Furthermore, at the end of 3 years, the low‐input system had lower fertilizer ¹⁵N removed by harvest (26%) and higher FNR remaining in the system in BG biomass plus soil (31%) than the high‐input system (45% and 21%, respectively). Based on these findings, low‐input systems are more sustainable than high‐input systems in irrigated Mediterranean climates.