Search for: All records

Reactive nitrogen (Nr) is an essential nutrient to life on earth, but its mismanagement in waste has emerged as a major problem in water pollution to our ecosystems, causing severe eutrophication and health concerns. Sustainably recovering Nr [such as nitrate (NO3−)–N] and converting it into ammonia (NH3) could mitigate the environmental impacts of Nr, while reducing the NH3 demand from the carbon-intensive Haber–Bosch process. In this work, high-performance NO3−-to-NH3 conversion was achieved in a scalable, versatile, and cost-effective membrane-free alkaline electrolyzer (MFAEL): a remarkable NH3partial current density of 4.22 ± 0.25 A cm−2 with a faradaic efficiency of 84.5 ± 4.9%. The unique configuration of MFAEL allows for the continuous production of pure NH3-based chemicals (NH3 solution and solid NH4HCO3) without the need for additional separation procedures. A comprehensive techno-economic analysis (TEA) revealed the economic competitiveness of upcycling waste N from dilute sources by combining NO3− reduction in MFAEL and a low-energy cost electrodialysis process for efficient NO3− concentration. In addition, pairing NO3− reduction with the oxidation of organic Nr compounds in MFAEL enables the convergent transformation of N–O and C–N bonds into NH3 as the sole N-containing product. Such an electricity-driven process offers an economically viable solution to the growing trend of regional and seasonal Nr buildup and increasing demand for sustainable NH3 with a reduced carbon footprint. 

Nitrogen (N) is an essential but generally limiting nutrient for biological systems. Development of the Haber-Bosch industrial process for ammonia synthesis helped to relieve N limitation of agricultural production, fueling the Green Revolution and reducing hunger. However, the massive use of industrial N fertilizer has doubled the N moving through the global N cycle with dramatic environmental consequences that threaten planetary health. Thus, there is an urgent need to reduce losses of reactive N from agriculture, while ensuring sufficient N inputs for food security. Here we review current knowledge related to N use efficiency (NUE) in agriculture and identify research opportunities in the areas of agronomy, plant breeding, biological N fixation (BNF), soil N cycling, and modeling to achieve responsible, sustainable use of N in agriculture. Amongst these opportunities, improved agricultural practices that synchronize crop N demand with soil N availability are low-hanging fruit. Crop breeding that targets root and shoot physiological processes will likely increase N uptake and utilization of soil N, while breeding for BNF effectiveness in legumes will enhance overall system NUE. Likewise, engineering of novel N-fixing symbioses in non-legumes could reduce the need for chemical fertilizers in agroecosystems but is a much longer-term goal. The use of simulation modeling to conceptualize the complex, interwoven processes that affect agroecosystem NUE, along with multi-objective optimization, will also accelerate NUE gains. 

We used the Agricultural Production Systems sIMulator (APSIM) to predict and explain maize and soybean yields, phenology, and soil water and nitrogen (N) dynamics during the growing season in Iowa, USA. Historical, current and forecasted weather data were used to drive simulations, which were released in public four weeks after planting. In this paper, we (1) describe the methodology used to perform forecasts; (2) evaluate model prediction accuracy against data collected from 10 locations over four years; and (3) identify inputs that are key in forecasting yields and soil N dynamics. We found that the predicted median yield at planting was a very good indicator of end‐of‐season yields (relative root mean square error [RRMSE] of ∼20%). For reference, the prediction at maturity, when all the weather was known, had a RRMSE of 14%. The good prediction at planting time was explained by the existence of shallow water tables, which decreased model sensitivity to unknown summer precipitation by 50–64%. Model initial conditions and management information accounted for one‐fourth of the variation in maize yield. End of season model evaluations indicated that the model simulated well crop phenology (R² = 0.88), root depth (R² = 0.83), biomass production (R² = 0.93), grain yield (R² = 0.90), plant N uptake (R² = 0.87), soil moisture (R² = 0.42), soil temperature (R² = 0.93), soil nitrate (R² = 0.77), and water table depth (R² = 0.41). We concluded that model set‐up by the user (e.g. inclusion of water table), initial conditions, and early season measurements are very important for accurate predictions of soil water, N and crop yields in this environment.