The effect of year-to-year variability on planting date and relative maturity selection for maize

Current maize (Zea mays L.) planting date recommendations have not been updated in the state of Iowa since 2001. A state that produced 68.8 million tons of maize on 5.5 million hectares in 2016. It is imperative that this information be regularly updated as both climate and maize hybrid selection are constantly changing. We analyzed maize yield and phenology, from a multi-location, year, relative maturity (RM), and planting date (PD) experiment carried out in Iowa, US. The dataset was used to calibrate a site-specific model (Agricultural Production System sIMulation, APSIM) and extrapolate APSIM results across Iowa, using a region scale model (parallel System for Integrating Impact Models and Sectors, pSIMS). Our objectives were to determine the combination of PD and RM to maximize maize grain yield by environment and to explain the risk associated with the use of full season RM when planting dates are delayed beyond the optimum PD. Additionally, the impact of climate change effects on optimum PD and RM selection by location were examined. Field scale analysis found slight grain yield differences between full and short season RM on a given PD with yield maximized when planting occurred at or before May 5th. However, running a regional scale model over 36 years, we determined that a static recommendation of optimum PD is not suitable as large variation exists between locations within the state and between years. The coefficient of variation (CV) was 20% and 68% for the optimum PD within and between years respectively. Furthermore, the PD window, or time frame around the optimum PD to achieve 98% of maximum yield, across years was heavily influenced by latitude and RM selection. Overall, this study brings new results to assist decision making regarding PD and RM across Iowa

Corn planting decisions: What’s changed and what’s the same?

Corn planting is arguably the most important field operation. Wide sweeping decisions from hybrid selection, row spacing, and seeding rates to planter adjustments, seed depth, and field conditions all come together the day planting occurs. It is a perfect storm where all these decisions align to achieve ideal stand establishment and set the stage for high yield potential

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

The effect of year-to-year variability on planting date and relative maturity selection for maize

Current maize (Zea mays L.) planting date recommendations have not been updated in the state of Iowa since 2001. A state that produced 68.8 million tons of maize on 5.5 million hectares in 2016. It is imperative that this information be regularly updated as both climate and maize hybrid selection are constantly changing. We analyzed maize yield and phenology, from a multi-location, year, relative maturity (RM), and planting date (PD) experiment carried out in Iowa, US. The dataset was used to calibrate a site-specific model (Agricultural Production System sIMulation, APSIM) and extrapolate APSIM results across Iowa, using a region scale model (parallel System for Integrating Impact Models and Sectors, pSIMS). Our objectives were to determine the combination of PD and RM to maximize maize grain yield by environment and to explain the risk associated with the use of full season RM when planting dates are delayed beyond the optimum PD. Additionally, the impact of climate change effects on optimum PD and RM selection by location were examined. Field scale analysis found slight grain yield differences between full and short season RM on a given PD with yield maximized when planting occurred at or before May 5th. However, running a regional scale model over 36 years, we determined that a static recommendation of optimum PD is not suitable as large variation exists between locations within the state and between years. The coefficient of variation (CV) was 20% and 68% for the optimum PD within and between years respectively. Furthermore, the PD window, or time frame around the optimum PD to achieve 98% of maximum yield, across years was heavily influenced by latitude and RM selection. Overall, this study brings new results to assist decision making regarding PD and RM across Iowa.</p

Forecasting and Assessment of Cropping Systems in Northwest Iowa

In 2017, the Forecasting and Assessment of Cropping sysTemS (FACTS) project continued with the objective of forecasting in-season soil water-nitrogen dynamics, in-season plant growth, and end-of-season grain yields. This concept was initiated to help farmers and agronomists make in-season management decisions, plus look back on the growing season to see what management practices could have been changed to improve grain yields and net profits, but also reduce nitrogen loss.</p

Impacts of climate change on the optimum planting date of different maize cultivars in the central US Corn Belt

Planting date and cultivar selection are major factors in determining the yield potential of any crop and in any region. However, there is a knowledge gap in how climate scenarios affect these choices. To explore this gap, we performed a regional scale analysis (11 planting dates x 8 cultivars x 281 fields x 36 weather years x 6 climate scenarios) using the APSIM model and pSIMS software for Iowa, the leading US maize (Zea mays L.) producing state. Our objectives were to determine how the optimum planting date (optPD) changes with weather scenarios and cultivars and the potential economic implications of planting outside the optimum windows. Results indicated that the mean optPD corresponds to the US Department of Agriculture, National Agriculture Statistics Service (USDA-NASS) 18.4% planting progress (April 28th) in Iowa. The optPD was found to be advancing by –0.13 d yr-1 from 1980 to 2015. A 1oC increase in mean temperature increased the length of the growing season by 10 days while the optPD changed by –2 to + 6 days, depending on cultivar. Under a more realistic scenario of increasing the minimum temperature by 0.5oC, decreasing the maximum temperature by 0.5oC, increasing spring rainfall by 10% and decreasing summer rainfall by 10%, the optPD only changed by –2 days compared to current trends, however, yield increased by 6.6%. Analysis of historical USDA-NASS planting durations indicated that on average, the planting duration (1% to 99% statewide reported planting progress) is 44 days, while it can be as low as 21 days in years with favorable weather. A simple economic analysis illustrated a potential revenue loss up to $340 million per year by planting maize outside the optimum window. We conclude that future investments in planting technologies to accelerate planting, especially in challenging weather years, as well as improved optPD x cultivar recommendations to farmers, will provide economic benefits and buffer climate variability.This is a manuscript of an article published as Baum, Mitch E., Mark A. Licht, Isaiah Huber, and Sotirios V. Archontoulis. "Impacts of climate change on the optimum planting date of different maize cultivars in the central US Corn Belt." European Journal of Agronomy 119 (2020): 126101. doi:10.1016/j.eja.2020.126101. Posted with permission. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License

Corn planting decisions: What’s changed and what’s the same?

Corn planting is arguably the most important field operation. Wide sweeping decisions from hybrid selection, row spacing, and seeding rates to planter adjustments, seed depth, and field conditions all come together the day planting occurs. It is a perfect storm where all these decisions align to achieve ideal stand establishment and set the stage for high yield potential.</p