Highlights d We offer a Sustainable Agriculture Matrix to track performance of countries worldwide d Priority areas for improving agricultural sustainability depend on development stage d Analysis of trade-offs and synergies among indicators can inform national policies

Global use of reactive nitrogen (N) has increased over the past century to meet growing food and biofuel demand, while contributing to substantial environmental impacts. Addressing continued N management challenges requires anticipating pathways of future N use. Several studies in the scientific literature have projected future N inputs for crop production under a Business-As-Usual (BAU) scenario. However, it remains unclear how using yield response functions to characterize a given level of technology and management practices (TMP) will alter the projections when using a consistent dataset. In this study, to project N inputs to 2050, we developed and tested three approaches, namely “Same NUE”, “Same TMP”, and “Improving TMP”. We found the approach that considers diminishing returns in yield response functions (“Same TMP”) resulted in 268 Tg N yr-1 of N inputs, which was 61 and 48 Tg N yr-1 higher than when keeping NUE at the current level with and without considering changes in crop mix, respectively. If TMP continue to evolve at the pace of past five decades, projected N inputs reduce to 204 Tg N yr-1, a value that is still 59 Tg N yr-1 higher than the inputs in the baseline year 2006. Overall, our results suggest that assuming a constant NUE may be too optimistic in projecting N inputs, and the full range of projection assumptions need to be carefully explored when investigating future N budgets.

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Extreme weather poses a major challenge to global food security by causing sharp drops in crop yield and supply. International crop trade can potentially alleviate such challenge by reallocating crop commodities. However, the influence of extreme weather stress and synchronous crop yield anomalies on trade linkages among countries remains unexplored. Here we use the international wheat trade network, develop two network-based covariates (i.e., difference in extreme weather stress and short-term synchrony of yield fluctuations between countries), and test specialized statistical and machine-learning methods. We find that countries with larger differences in extreme weather stress and synchronous yield variations tend to be trade partners and with higher trade volumes, even after controlling for factors conventionally implemented in international trade models (e.g., production level and trade agreement). These findings highlight the need to improve the current international trade network by considering the patterns of extreme weather stress and yield synchrony among countries.

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