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

Soybean [Glycine max (L.) Merr.] planting date and maturity group are important agronomic decisions. This study quantified how maturity group selection and later than optimal planting dates affected grain yield and crop development across Iowa, USA. Field experiments were conducted in seven locations between 2014 and 2016. Cultivar maturities ranged from 2.2 to 3.7 maturity group and planting dates targeted for 20‐d intervals from early May to early July. Soybean grain yield ranged from 0.27 to 7.54 Mg ha−1. Cultivar maturity had little to no effect on grain yield at four of seven sites while planting date was significant at all sites (p < .001) and the planting date and cultivar maturity interaction was not significant. As planting date was delayed, the VE‐R3 and R3‐R7 periods were each shortened by up to 15–20 d. The shorter growing period resulted in less radiation and growing degree day accumulation. An exponential‐plateau relationship between relative yield and GDD was evident for the VE‐R3 phase, with a plateau at 700°C‐d. A linear relationship between yield and GDD was evident from R3‐R7, suggesting greater yield with more accumulated GDD. The opposite relationships were found for photoperiod which had a linear relationship for the VE‐R3 and curvilinear for the R3‐R7 phases. These results showed that yield potential would be maximized by planting before 20 May. We concluded that planting earlier in the spring was a better management practice than maturity selection to maximize yield and the R3‐R7 period duration was critical in determining potential yield.

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