Daily Erosion Project: Daily estimates of soil erosion and water runoff

Soil erosion and water runoff drive water quality degradation and are liabilities to crop production, yet their magnitude is neither quantified nor inventoried for US agricultural areas. This project’s goals are to: (1) estimate soil erosion and surface runoff across the Upper Midwest as contributors to soil and water degradation and (2) inventory these quantities for the next several years

Project and Research Management: Integrating Systems, Data, and People in Multidisciplinary Work (Vol. 5)

This technical report summarizes the experiential and technical knowledge in project and research management from the Sustainable Corn Coordinated Agricultural Project team. The management infrastructure, processes, outcomes, lessons learned, and insights presented in this report will be particularly relevant to directors and managers of other large teams

Extreme weather‐year sequences have nonadditive effects on environmental nitrogen losses

The frequency and intensity of extreme weather years, characterized by abnormal precipitation and temperature, are increasing. In isolation, these years have disproportionately large effects on environmental N losses. However, multi-year sequences of extreme weather years (e.g., wet-dry vs. dry-wet) and annual crop rotation (legume-cereal vs. cereal-legume) may interact to affect cumulative N losses across the complete crop rotation sequence. We calibrated and validated the DAYCENT model with a comprehensive set of biogeophysical measurements from a maizesoybean rotation managed at three different N fertilizer inputs with and without a winter cereal rye cover crop in Iowa, USA. Our objectives were to determine: i) how two-year sequences of extreme weather years interact with annual crop rotation sequence to affect two-year cumulative N losses, and ii) if the inclusion of a winter cover crop between corn and soybean and N fertilizer management mitigate the effect of extreme weather on N losses. Using historical weather data (1951-2013), we created nine two-year weather scenarios with all possible combinations of the hottest and driest (‘dry’), coolest and wettest (‘wet’), and average (‘normal’) weather years. We analyzed the effects of these scenarios following a period of relatively normal weather. Compared to the normal-normal two-year weather scenario, two-year extreme weather scenarios affected two-year cumulative NO3- leaching (range: -28 to +295%) more than N2O emissions (range: -54 to +21%). Moreover, the two-year weather scenarios had non-additive effects on N losses: although dry weather decreased NO3- leaching in isolation, two-year cumulative NO3- losses from the dry-wet scenario were 89% greater than the normal-normal scenario. Cover crops reduced the effect of extreme weather on NO3- leaching, but not N2O emissions. As the frequency of extreme weather events is expected to increase, understanding of interactions between crop rotation and interannual weather patterns can be used to mitigate the effect of extreme weather on environmental N losses

Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt

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

An interactive severe weather activity to motivate student learning

An interactive Web-based severe weather activity has been developed at Iowa State University with broad applications to motivate student learning. The exercise uses an extensive archive of weather data emphasizing warm-season severe convective events and cold-season winter storms. Several variations of the activity have been developed based upon the meteorological background of students. The flexible design of the activity may allow for its use in K-12 settings, or as a significant training tool for weather forecasters outside the classroom.This article is from Bulletin of the American Meteorological Society 81 (2000): 2205, doi: 2.3.CO;2" target="_blank">10.1175/1520-0477(2000)0812.3.CO;2. Posted with permission.</p