Daniel W. Barker

Understanding the DayCent model

The ability of biogeochemical ecosystem models to represent agro-ecosystems depends on their correct integration with field observations. We report simultaneous calibration of 67 DayCent model parameters using multiple observation types through inverse modeling using the PEST parameter estimation software. Parameter estimation reduced the total sum of weighted squared residuals by 56% and improved model fit to crop productivity, soil carbon, volumetric soil water content, soil temperature, N2O, and soil NO 3 - compared to the default simulation. Inverse modeling substantially reduced predictive model error relative to the default model for all model predictions, except for soil NO 3 - and NH 4 + . Post-processing analyses provided insights into parameter-observation relationships based on parameter correlations, sensitivity and identifiability. Inverse modeling tools are shown to be a powerful way to systematize and accelerate the process of biogeochemical model interrogation, improving our understanding of model function and the underlying ecosystem biogeochemical processes that they represent. Several DayCent submodels were calibrated simultaneously using inverse modeling.Parameter estimation reduced DayCent total sum of weighted squared residuals by 56%.Soil temperature and water content are highly informative in DayCent calibration.Parameter estimation is an efficient way to calibrate soil biogeochemical models.Post-estimation analyses provide unique insights into model structure and function.

Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation.

Improved prediction of optimal N fertilizer rates for corn (Zea mays L.) can reduce N losses and increase profits. We tested the ability of the Agricultural Production Systems sIMulator (APSIM) to simulate corn and soybean (Glycine max L.) yields, the economic optimum N rate (EONR) using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn) and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha-1) applied to corn. Our objectives were to: (a) quantify model prediction accuracy before and after calibration, and report calibration steps; (b) compare crop model-based techniques in estimating optimal N rate for corn; and (c) utilize the calibrated model to explain factors causing year to year variability in yield and optimal N. Results indicated that the model simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration), which provided strong evidence that important soil and crop processes were accounted for in the model. The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration). For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-year mean differences in EONR’s were within the historical N rate error range (40–50 kg N ha-1). However, for accurate year-by-year simulation of EONR the calibrated version should be used. Model analysis revealed that higher EONR values in years with above normal spring precipitation were caused by an exponential increase in N loss (denitrification and leaching) with precipitation. We concluded that long-term experimental data were valuable in testing and refining APSIM predictions. The model can be used as a tool to assist N management guidelines in the US Midwest and we identified five avenues on how the model can add value toward agronomic, economic, and environmental sustainability.

Does nitrogen fertilizer application rate to corn affect nitrous oxide emissions from the rotated soybean crop

Little information exists on the potential for N fertilizer application to corn ( L.) to affect NO emissions during subsequent unfertilized crops in a rotation. To determine if N fertilizer application to corn affects NO emissions during subsequent crops in rotation, we measured NO emissions for 3 yr (2011-2013) in an Iowa, corn-soybean [ (L.) Merr.] rotation with three N fertilizer rates applied to corn (0 kg N ha, the recommended rate of 135 kg N ha, and a high rate of 225 kg N ha); soybean received no N fertilizer. We further investigated the potential for a winter cereal rye ( L.) cover crop to interact with N fertilizer rate to affect NO emissions from both crops. The cover crop did not consistently affect NO emissions. Across all years and irrespective of cover crop, N fertilizer application above the recommended rate resulted in a 16% increase in mean NO flux rate during the corn phase of the rotation. In 2 of the 3 yr, N fertilizer application to corn (0-225 kg N ha) did not affect mean NO flux rates from the subsequent unfertilized soybean crop. However, in 1 yr after a drought, mean NO flux rates from the soybean crops that received 135 and 225 kg N ha N application in the corn year were 35 and 70% higher than those from the soybean crop that received no N application in the corn year. Our results are consistent with previous studies demonstrating that cover crop effects on NO emissions are not easily generalizable. When N fertilizer affects NO emissions during a subsequent unfertilized crop, it will be important to determine if total fertilizer-induced NO emissions are altered or only spread across a greater period of time.

A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate

Historically crop models have been used to evaluate crop yield responses to nitrogen (N) rates after harvest when it is too late for the farmers to make in-season adjustments. We hypothesize that the use of a crop model as an in-season forecast tool will improve current N decision-making. To explore this, we used the Agricultural Production Systems sIMulator (APSIM) calibrated with long-term experimental data for central Iowa, USA (16-years in continuous corn and 15-years in soybean-corn rotation) combined with actual weather data up to a specific crop stage and historical weather data thereafter. The objectives were to: (1) evaluate the accuracy and uncertainty of corn yield and economic optimum N rate (EONR) predictions at four forecast times (planting time, 6th and 12th leaf, and silking phenological stages); (2) determine whether the use of analogous historical weather years based on precipitation and temperature patterns as opposed to using a 35-year dataset could improve the accuracy of the forecast; and (3) quantify the value added by the crop model in predicting annual EONR and yields using the site-mean EONR and the yield at the EONR to benchmark predicted values. Results indicated that the mean corn yield predictions at planting time (R2 = 0.77) using 35-years of historical weather was close to the observed and predicted yield at maturity (R2 = 0.81). Across all forecasting times, the EONR predictions were more accurate in corn-corn than soybean-corn rotation (relative root mean square error, RRMSE, of 25 vs. 45%, respectively). At planting time, the APSIM model predicted the direction of optimum N rates (above, below or at average site-mean EONR) in 62% of the cases examined (n = 31) with an average error range of ±38 kg N ha−1 (22% of the average N rate). Across all forecast times, prediction error of EONR was about three times higher than yield predictions. The use of the 35-year weather record was better than using selected historical weather years to forecast (RRMSE was on average 3% lower). Overall, the proposed approach of using the crop model as a forecasting tool could improve year-to-year predictability of corn yields and optimum N rates. Further improvements in modeling and set-up protocols are needed toward more accurate forecast, especially for extreme weather years with the most significant economic and environmental cost.

Seasonal and Rotational Influences on Corn Nitrogen Fertilization in South Central Iowa

This project was designed to study the nitrogen (N) fertilization needs in continuous corn (CC) and corn rotated with soybean (CS) as influenced by location and climate. Multiple rates of N fertilizer were spring-applied, with the intent to measure yield response to N within each rotation on a yearly basis for multiple years at multiple sites across Iowa.