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Nitrate (NO3) leaching from agriculture represents the primary source of groundwater contamination and freshwater ecosystem degradation. At the field level, NO3 leaching is highly variable due to interactions among soil, weather and crop management factors, but the relative effects of these drivers have not been quantified on a global scale. Using a global database of 82 field studies in temperate rainfed cereal crops with 961 observations, our objectives were to (a) quantify the relative importance of environmental and management variables to identify key leverage points for NO3 mitigation and (b) determine associated changes in crop productivity and potential tradeoffs for high and low NO3 loss scenarios. Machine learning algorithms (XGboost) and feature importance analysis showed that the amount and intensity of rainfall explained the most variability in NO3 leaching (up to 24 kg N ha-1), followed by nitrogen (N) fertilizer rate and crop N removal. In contrast, other soil and management variables such as soil texture, crop type, tillage and N source, timing and placement had less importance. To reduce N losses from global agriculture under changing weather and climatic conditions, these results highlight the need for better targeting and increased adoption of science-based, locally adapted management practices for improving N use efficiency. Future policy discussions should support this transition through different instruments while also promoting more advanced weather prediction analytics, especially in areas susceptible to extreme climatic variation.

Growing public concern over agricultural nitrogen (N) pollution is now reflected in consumers’ food choices and shareholders’ resolutions, causing rapid changes in global food supply chains. Nitrate (NO3) leaching represents the primary N source for groundwater contamination and freshwater ecosystem degradation. However, simplified science-based indicators are still lacking to facilitate improved N management practices at the farm-level. We conducted a global analysis of published field studies to evaluate N balance (N inputs minus N outputs) as a robust predictor for NO3 losses. Using 82 studies (1110 observations) for rainfed cereal crops in temperate regions, we 1) quantified the response of NO3 losses to changes in N balance for major rainfed cereal crops while accounting for environmental and management variables; and 2) assessed the feasibility of improving water quality through lower N balance under different scenarios using the case study of maize (Zea mays L.) data from the US Corn Belt. Observations were grouped in studies from the US and non-US regions. Results show that NO3 losses increased exponentially as N balance increases for both the US and non-US regions, though they were 60% higher in the US at a given N balance. Scenario analysis revealed that reducing the N rate from the agronomic optimum to the lower point within the economic optimum N rate range decreased NO3 losses by 13% without impacting economic returns. The case study for maize showed that improvements in N use efficiency that increase grain yield at a given fertilizer rate can substantially reduce N balance and mitigate NO3 losses. This study provides an evidence-based foundation for food supply chain companies to mitigate global NO3 pollution, specifically by using the generalized relationships presented here to track progress in NO3 leaching mitigation. To further resolve uncertainties and improve region-specific estimates for NO3 losses, we propose a tiered monitoring and assessment framework similar to the IPCC (Intergovernmental Panel on Climate Change) methodology for N2O emissions, widely implemented in science and used for policy.

Introduction: Advances in farming technologies and practices have helped farmers to increase crop yields, but continued production increases - while needed to feed a growing global population - are being hindered by climate change and other environmental challenges. Kansas, a globally important region for wheat production, is already experiencing threats to staple crop production. This study explores one possible alternative future for Kansas crop production in which farmers could, by crop switching, continue to grow nutrient-rich crops while responding to hotter temperatures and increasing water demand. Methods: We used a combination of climate and crop modeling with simple water budgets to identify optimal crop mixes under anticipated climatic and water constraints. Interviews with Kansas farmers helped identify feasible crop switching options: sorghum instead of corn (maize), winter rye and winter oats instead of winter wheat, and millet instead of soybeans. Results: Our analysis suggests that a sizeable proportion of current Kansas cropland would need to shift to these alternative crops by 2050 to meet anticipated water constraints and produce equivalent nutritional value under projected climate conditions. Alternative crops could increase from 16% of Kansas' crop area in 2021 to 43% of the area in 2050, resulting in a reduction in 2050 crop water demand of 12% relative to that of the current cropmix. This crop water demand reduction would be concentrated in parts of the state that will experience the greatest change in water needs between today and mid-century due to changing climate conditions. Discussion: Our analysis shows that, by changing (diversifying) the mix of crops grown, it is biophysically possible for crop production in Kansas to be both sustainable and resilient under future climate conditions. However, achieving a more climate-resilient crop mix on the ground, in Kansas and elsewhere, will require major shifts in the broader agricultural system. Food companies, agricultural lenders, and policymakers can play a key role in enabling farmers to adapt cropping systems in the face of climate and environmental challenges. [ABSTRACT FROM AUTHOR]

Continued large-scale public investment in declining ecosystems depends on demonstrations of "success". While the public conception of "success" often focuses on restoration to a pre-disturbance condition, the scientific community is more likely to measure success in terms of improved ecosystem health. Using a combination of literature review, workshops and expert solicitation we propose a generalized framework to improve ecosystem health in highly altered river basins by reducing ecosystem stressors, enhancing ecosystem processes and increasing ecosystem resilience. We illustrate the use of this framework in the Mississippi-Atchafalaya River Basin (MARB) of the central United States (U.S.), by (i) identifying key stressors related to human activities, and (ii) creating a conceptual ecosystem model relating those stressors to effects on ecosystem structure and processes. As a result of our analysis, we identify a set of landscape-level indicators of ecosystem health, emphasizing leading indicators of stressor removal (e.g., reduced anthropogenic nutrient inputs), increased ecosystem function (e.g., increased water storage in the landscape) and increased resilience (e.g., changes in the percentage of perennial vegetative cover). We suggest that by including these indicators, along with lagging indicators such as direct measurements of water quality, stakeholders will be better able to assess the effectiveness of management actions. For example, if both leading and lagging indicators show improvement over time, then management actions are on track to attain desired ecosystem condition. If, however, leading indicators are not improving or even declining, then fundamental challenges to ecosystem health remain to be addressed and failure to address these will ultimately lead to declines in lagging indicators such as water quality. Although our model and indicators are specific to the MARB, we believe that the generalized framework and the process of model and indicator development will be valuable in an array of altered river basins., Competing Interests: Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.