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 Abbreviations: APSIM, Agricultural Production Systems sIMulator; RRMSE, relative root mean square error.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. one-fourth of the variation in maize yield. End of season model evaluations indicated that the model simulated well crop phenology (R 2 = 0.88), root depth (R 2 = 0.83), biomass production (R 2 = 0.93), grain yield (R 2 = 0.90), plant N uptake (R 2 = 0.87), soil moisture (R 2 = 0.42), soil temperature (R 2 = 0.93), soil nitrate (R 2 = 0.77), and water table depth (R 2 = 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. Neil Huth from CSIRO for their support with the APSIM model, Iowa State University students () for assistance with data collection and managing the field experiments. We also thank the APSIM Initiative for making the software publicly available and for ensuring software quality. ORCIDSotirios V. Archontoulis https://orcid.org/0000-0001-7595-8107 Mark A. Licht https://orcid.org/0000-0001-6640-7856 Kendall R. Lamkey

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In the U.S. Corn Belt, annual croplands are the primary source of nitrate loading to waterways. Long periods of fallow cause most nitrate loss, but there is extreme interannual variability in the magnitude of nitrate loss due to weather. Using mean annual (2001–2018) flow‐weighted nitrate‐N concentration (FWNC; mg NO3––N L–1), load (kg NO3––N), and yield (kg NO3––N ha–1 cropland) for 29 watersheds, our objectives were (a) to quantify the magnitude and interannual variability of 5‐yr moving average FWNC, load, and yield; (2) to estimate the probability of measuring 41% reductions in nitrate loss after isolating the effect of weather on nitrate loss by quantifying the interannual variability of nitrate loss in watersheds where there was no trend in 5‐yr moving average nitrate loss (Iowa targets a 41% nitrate loss reduction from croplands); and (c) to identify factors that, in the absence of long‐term trends in nitrate loss, best explain the interannual variability in nitrate loss. Averaged across all watersheds, the mean probability of measuring a statistically significant 41% reduction in FWNC across 15 yr, should it occur, was 96%. However, the probabilities of measuring 41% reductions in nitrate load and yield were only 44 and 32%. Across watersheds, soil organic matter, tile drainage, interannual variability of precipitation, and watershed area accounted for interannual variability in these nitrate loss indices. Our results have important implications for setting realistic timelines to measure nitrate loss reductions against the background of interannual weather variation and can help to target monitoring intensity across diverse watersheds.