Planting date, cultivar maturity, and environment effects on soybean yield and crop stage

Selecting soybean (Glycine max (L.) Merr.) planting date and maturity group are important agronomic decisions that are often affected by unfavorable weather. The objective of this study was to quantify how the selection of maturity groups and later than optimal planting dates effected soybean seed yield and crop development over time across Iowa, US. Field experiments were conducted at seven locations between 2014 and 2016 for a total of 21 environments. Cultivar maturities varied by location (ranging from 2.2 to 2.5 MG) and planting dates were scheduled for 20-day intervals from early May to early July. Studied planting date and maturity group combinations that resulted in grain yields ranging from 0.27 to 7.54 Mg ha-1. Analyses showed that the main effect of maturity group had little (3.28 to 4.30 Mg ha -1) to no effect on grain yield at 4 of 7 sites while the main effect of planting date was significant (p \u3c 0.001) at all sites. The interaction of planting date and cultivar maturity was not significance. With delayed planting dates, the length of the non-grain filling (VE-R3) and grain filling (R3-R7) period were shortened by up to 15-20 days, resulting in less radiation, smaller growing degree day accumulation, and lower yields. Across northern Iowa, there was a critical radiation accumulation of 946 MJ whereas the critical radiation accumulation (1074 MJ) was much higher across southern Iowa. These results show that yield potential would be maximized by planting before May 20 using a cultivar maturity group that is well-adapted to specific location or geography. To maximize yield, planting soybean earlier in the growing season was a better management practice than maturity selection, and the duration of the grain filling period was critical in determining potential yield each growing season

Water availability, root depths and 2017 crop yields

During 2016 and 2017, June-July precipitation was below normal in many parts of Iowa creating midseason concerns about potential yield loss due to water stress. However, these concerns were not realized. In contrast, 2016 and 2017 crop yields over-performed yields obtained in many years with average of above average June-July precipitation. In Iowa, deep root systems, high soil water storage capacity, and shallow water tables are common explanations for high yields in years with below normal precipitation. How deep can roots grow? How much does groundwater contribute to the yields? To answer these questions and more, the Forecast and Assessment of Cropping sysTemS (FACTS) project was established in 201

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

Planting date, cultivar maturity, and environment effects on soybean yield and crop stage

Selecting soybean (Glycine max (L.) Merr.) planting date and maturity group are important agronomic decisions that are often affected by unfavorable weather. The objective of this study was to quantify how the selection of maturity groups and later than optimal planting dates effected soybean seed yield and crop development over time across Iowa, US. Field experiments were conducted at seven locations between 2014 and 2016 for a total of 21 environments. Cultivar maturities varied by location (ranging from 2.2 to 2.5 MG) and planting dates were scheduled for 20-day intervals from early May to early July. Studied planting date and maturity group combinations that resulted in grain yields ranging from 0.27 to 7.54 Mg ha-1. Analyses showed that the main effect of maturity group had little (3.28 to 4.30 Mg ha -1) to no effect on grain yield at 4 of 7 sites while the main effect of planting date was significant (p < 0.001) at all sites. The interaction of planting date and cultivar maturity was not significance. With delayed planting dates, the length of the non-grain filling (VE-R3) and grain filling (R3-R7) period were shortened by up to 15-20 days, resulting in less radiation, smaller growing degree day accumulation, and lower yields. Across northern Iowa, there was a critical radiation accumulation of 946 MJ whereas the critical radiation accumulation (1074 MJ) was much higher across southern Iowa. These results show that yield potential would be maximized by planting before May 20 using a cultivar maturity group that is well-adapted to specific location or geography. To maximize yield, planting soybean earlier in the growing season was a better management practice than maturity selection, and the duration of the grain filling period was critical in determining potential yield each growing season.</p

Forecasting and Assessment of Plant Growth, Soil Water-Nitrogen, and Grain Yield for Central Iowa

In 2017, the Forecast and Assessment of Cropping sysTemS (FACTS) project was replicated to achieve the objective of forecasting and evaluating in-season soil-crop dynamics. This concept was initiated to help farmers and agronomists make in-season management decisions, in addition to identifying the management practices that could have been changed to improve grain yields, net profits, and also reduce environmental impacts.</p

Late Soybean Planting Options

This spring's weather conditions may be slowing down corn planting but soybean planting has not yet been impacted. As of May 5, soybean planting progress is estimated at 8% compared to 11% for the 5-year average (USDA-NASS, 2019). However, because of recent rains and corn planting delays there is concern that soybean planting will soon fall behind. In this article, we discuss the soybean yield potential and maturity selection considerations as planting progresses into late May and possibly June.</p

Soybean yield and crop stage response to planting date and cultivar maturity in Iowa, USA

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, US. Field experiments were conducted in seven locations between 2014 and 2016. Cultivar maturities ranged from 2.2 to 3.7 MG and planting dates targeted for 20-day 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 4 of 7 sites while planting date was significant at all sites (pThis is a manuscript of an article published as Kessler, A., S. V. Archontoulis, and M. A. Licht. "Soybean yield and crop stage response to planting date and cultivar maturity in Iowa, USA." Agronomy Journal (2020). doi: 10.1002/agj2.20053. Posted with permission.</p

Water availability, root depths and 2017 crop yields

During 2016 and 2017, June-July precipitation was below normal in many parts of Iowa creating midseason concerns about potential yield loss due to water stress. However, these concerns were not realized. In contrast, 2016 and 2017 crop yields over-performed yields obtained in many years with average of above average June-July precipitation. In Iowa, deep root systems, high soil water storage capacity, and shallow water tables are common explanations for high yields in years with below normal precipitation. How deep can roots grow? How much does groundwater contribute to the yields? To answer these questions and more, the Forecast and Assessment of Cropping sysTemS (FACTS) project was established in 2015</p

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.This article is published as Archontoulis, Sotirios V., Michael J. Castellano, Mark A. Licht, Virginia Nichols, Mitch Baum, Isaiah Huber, Rafael Martinez‐Feria et al. "Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt." Crop Science (2020). doi: 10.1002/csc2.20039.</p