Irrigation is the largest sector of human water use and an important option for increasing crop production and reducing drought impacts. However, the potential for irrigation to contribute to global crop yields remains uncertain. Here, we quantify this contribution for wheat and maize at global scale by developing a Bayesian framework integrating empirical estimates and gridded global crop models on new maps of the relative difference between attainable rainfed and irrigated yield (ΔY). At global scale, ΔY is 34 ± 9% for wheat and 22 ± 13% for maize, with large spatial differences driven more by patterns of precipitation than that of evaporative demand. Comparing irrigation demands with renewable water supply, we find 30–47% of contemporary rainfed agriculture of wheat and maize cannot achieve yield gap closure utilizing current river discharge, unless more water diversion projects are set in place, putting into question the potential of irrigation to mitigate climate change impacts.

The impact of human water management on river discharge is increasingly viewed as a missing process in Earth system modeling. Models which have attempted to include it are generally at coarse resolution and uncoupled to the atmosphere. We propose to describe human water management at high spatial resolution using ORganizing Carbon and Hydrology In Dynamic EcosystEms concept of hydrological‐transfer‐units in the routing parametrization. Irrigated areas are linked to river abstraction points using a minimization process. The directed graphs of river flows and adduction network for irrigation is transposed to propagate water demands upstream. Reservoirs and dams are placed along the graph to balance supply and demands in the four chosen water value classes. Dam regulation is assumed to maximize demand satisfaction and dampen floods while respecting the properties of the infrastructure. The developed human water management module is applied to the Yellow River where irrigation and dam regulation are known to have a strong impact. Results show that the impact of human water management is strongly heterogeneous over space. It propagates along the river channels and can be mitigated by the confluence of tributaries. Moreover, the human impact has a strong seasonality due to time varying irrigation demands and the response of dam regulation. A number of uncertainties still remain and affect the simulated river discharge. Nevertheless, the representation of human water management improves the model's behavior in terms of magnitude and intra‐annual variations of river discharge, and offers the opportunity to implement anthropogenic processes in the water cycle of Earth System Models.

The streamflow of the Yellow River (YR) is strongly affected by human activities like irrigation and dam operation. Many attribution studies have focused on the long-term trends of streamflows, yet the contributions of these anthropogenic factors to streamflow fluctuations have not been well quantified with fully mechanistic models. This study aims to (1) demonstrate whether the mechanistic global land surface model ORCHIDEE (ORganizing Carbon and Hydrology in Dynamic EcosystEms) is able to simulate the streamflows of this complex rivers with human activities using a generic parameterization for human activities and (2) preliminarily quantify the roles of irrigation and dam operation in monthly streamflow fluctuations of the YR from 1982 to 2014 with a newly developed irrigation module and an offline dam operation model. Validations with observed streamflows near the outlet of the YR demonstrated that model performances improved notably with incrementally considering irrigation (mean square error (MSE) decreased by 56.9 %) and dam operation (MSE decreased by another 30.5 %). Irrigation withdrawals were found to substantially reduce the river streamflows by approximately $\mathrm{242.8}\pm \mathrm{27.8}\times {\mathrm{10}}^{\mathrm{8}}$ m³ yr⁻¹ in line with independent census data ($\mathrm{231.4}\pm \mathrm{31.6}\times {\mathrm{10}}^{\mathrm{8}}$ m³ yr⁻¹). Dam operation does not change the mean streamflows in our model, but it impacts streamflow seasonality, more than the seasonal change of precipitation. By only considering generic operation schemes, our dam model is able to reproduce the water storage changes of the two large reservoirs, LongYangXia and LiuJiaXia (correlation coefficient of ∼ 0.9). Moreover, other commonly neglected factors, such as the large operation contribution from multiple medium/small reservoirs, the dominance of large irrigation districts for streamflows (e.g., the Hetao Plateau), and special management policies during extreme years, are highlighted in this study. Related processes should be integrated into models to better project future YR water resources under climate change and optimize adaption strategies.

World agriculture needs to find the right balance to cope with the trilemma between feeding a growing population, reducing its impact on biodiversity and minimizing greenhouse gas (GHG) emissions. In this paper, we evaluate a broad range of scenarios that achieve 4.3 GtCO_2,eq/year GHG mitigation in the Agriculture, Forestry and Other Land-Use (AFOLU) sector by 2100. Scenarios include varying mixes of three GHG mitigation policies: second-generation biofuel production, dietary change and reforestation of pasture. We find that focusing mitigation on a single policy can lead to positive results for a single indicator of food security or biodiversity conservation, but with significant negative side effects on others. A balanced portfolio of all three mitigation policies, while not optimal for any single criterion, minimizes trade-offs by avoiding large negative effects on food security and biodiversity conservation. At the regional scale, the trade-off seen globally between biodiversity and food security is nuanced by different regional contexts.

Agriculture is the main driver of the rapid collapse of biodiversity, upon which all life on Earth, including agricultural production, depends. As we face the challenge of feeding a growing human population under a changing climate regime, the pressure on biodiversity is expected to further intensify. While the potential to expand and improve natural habitats for biodiversity conservation has been widely explored in large-scale scenarios of agricultural systems, the critical role of agricultural landscapes’ management on halting the loss of biodiversity remains unexplored at this scale. We argue that, to achieve an effective conservation of biodiversity (both natural and agricultural), the combined multivariate effects of agriculture on biodiversity must be accounted for, including its surface area as well as its management. Based on a literature review, we identified the main biodiversity pressures stemming from agriculture: land-use change, contribution to climate change, water withdrawal, pesticide pollution, nutrient (nitrogen and phosphorus) pollution, and landscape and farm-scale simplification (of croplands and pastures). For each one, we proposed a critical boundary, based on reviews of studies covering a range of taxa, biodiversity metrics, and biomes, below or above which negative impacts on biodiversity are minimized or positive effects arise. Implemented simultaneously, the identified boundaries would integrate biodiversity conservation within and across farmlands and minimize agriculture’s far-reaching impacts on biodiversity. We present a framework called “agricultural boundaries for biodiversity” that will allow to explore the potential of developing agricultural systems that effectively reconcile food production and biodiversity conservation at large scales.