John E. Sawyer

Effect of Nitrogen Fertilizer Application on Growing Season Soil Carbon Dioxide Emission in a Corn–Soybean Rotation

Nitrogen application can have a significant effect on soil carbon (C) pools, plant biomass production, and microbial biomass C processing. The focus of this study was to investigate the short-term effect of N fertilization on soil CO(2) emission and microbial biomass C. The study was conducted from 2001 to 2003 at four field sites in Iowa representing major soil associations and with a corn (Zea mays L.)-soybean (Glycine max L. Merr.) rotation. The experimental design was a randomized complete block with four replications of four N rates (0, 90, 180, and 225 kg ha(-1)). In the corn year, season-long cumulative soil CO(2) emission was greatest with the zero N application. There was no effect of N applied in the prior year on CO(2) emission in the soybean year, except at one of three sites, where greater applied N decreased CO(2) emission. Soil microbial biomass C (MBC) and net mineralization in soil collected during the corn year was not significantly increased with increase in N rate in two out of three sites. At all sites, soil CO(2) emission from aerobically incubated soil showed a more consistent declining trend with increase in N rate than found in the field. Nitrogen fertilization of corn reduced the soil CO(2) emission rate and seasonal cumulative loss in two out of three sites, and increased MBC at only one site with the highest N rate. Nitrogen application resulted in a reduction of both emission rate and season-long cumulative emission of CO(2)-C from soil.

A long-term nitrogen fertilizer gradient has little effect on soil organic matter in a high-intensity maize production system

Global maize production alters an enormous soil organic C (SOC) stock, ultimately affecting greenhouse gas concentrations and the capacity of agroecosystems to buffer climate variability. Inorganic N fertilizer is perhaps the most important factor affecting SOC within maize-based systems due to its effects on crop residue production and SOC mineralization. Using a continuous maize cropping system with a 13 year N fertilizer gradient (0-269 kg N ha(-1) yr(-1)) that created a large range in crop residue inputs (3.60-9.94 Mg dry matter ha(-1) yr(-1)), we provide the first agronomic assessment of long-term N fertilizer effects on SOC with direct reference to N rates that are empirically determined to be insufficient, optimum, and excessive. Across the N fertilizer gradient, SOC in physico-chemically protected pools was not affected by N fertilizer rate or residue inputs. However, unprotected particulate organic matter (POM) fractions increased with residue inputs. Although N fertilizer was negatively linearly correlated with POM C/N ratios, the slope of this relationship decreased from the least decomposed POM pools (coarse POM) to the most decomposed POM pools (fine intra-aggregate POM). Moreover, C/N ratios of protected pools did not vary across N rates, suggesting little effect of N fertilizer on soil organic matter (SOM) after decomposition of POM. Comparing a N rate within 4% of agronomic optimum (208 kg N ha(-1) yr(-1)) and an excessive N rate (269 kg N ha(-1) yr(-1)), there were no differences between SOC amount, SOM C/N ratios, or microbial biomass and composition. These data suggest that excessive N fertilizer had little effect on SOM and they complement agronomic assessments of environmental N losses, that demonstrate N2 O and NO3 emissions exponentially increase when agronomic optimum N is surpassed.

Drought impact on crop production and the soil environment: 2012 experiences from Iowa

Enormous challenges were presented by the 2012 drought. Poor water availability and high temperatures resulted in significant stress during critical phases of corn (Zea mays L.) and soybean (Glycine max L.) development. These stress factors lead to management challenges with insects, diseases, and reduced nutrient availability and uptake by plants. The drought triggered soil changes, particularly in conventional tillage systems, such as increased fracturing, crusting, and deterioration of soil structure and aggregation. All this reinforced the need for soil conservation planning, especially its necessary role in buffering against unpredictable conditions and the impacts of dry and wet events on production and soil quality. In 2011, the USDAs National Drought Mitigation Center reported that 43% of Iowa experienced moderate-drought conditions and nearly 10% experienced severe-drought conditions. In 2012, 100% of Iowa experienced severe-drought conditions, while 65% experienced extreme-drought conditions by October. This article addresses several effects of drought on soil and crop production and lessons learned that will help develop appropriate drought mitigation strategies for future soil and crop management practices. The 2012 drought created unfavorable soil conditions for plant development and growth and changes in soil structure in many areas in the Midwest. These changes in soil structure included fracturing…

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.

Nitrogen Application Timing, Forms, and Additives

First paragraph: Wet, poorly drained soils throughout North America and Europe are often artificially drained with subsurface tile systems to remove excess (gravitational) water from the upper 1 to 1.2 m soil profile. Improved crop production that often results from drainage is in large part due to better physical conditions for field operations and a deeper unrestricted root zone for greater crop rooting, nutrient uptake, and yields. Removal of excess water by drainage lessens the potential for anaerobic conditions and consequently reduces the potential for nitrate to be lost from the soil profile by the process of denitrification. The combination of greater soil organic matter N mineralization with increased aerobic soil conditions, less N lost via denitrification, and increased transport of subsurface water results in higher nitrate concentrations in the receiving surface water bodies. Watersheds containing similar production systems and soils without subsurface drainage generate lower nitrate concentrations because anaerobic conditions exist more frequently. Under anaerobic conditions, denitrification predominates, resulting in nitrate losses as N gas to the atmosphere as well as economic losses to the farmer because of reduced available N.

What does it take to detect a change in soil carbon stock? A regional comparison of minimum detectable difference and experiment duration in the north central United States

Variability in soil organic carbon (SOC) results from natural and human processes interacting across time and space, and leads to large variation in the minimum difference in SOC that can be detected with a particular experimental design. Here we report a unique comparison of minimum detectable differences (MDDs) in SOC, and the estimated times required to observe those MDDs across the north central United States, calculated for the two most common SOC experiments: (1) a comparison between two treatments, e.g., moldboard plow (MP) and no-tillage (NT), using a randomized complete block design experiment; and (2) a comparison of changes in SOC over time for a particular treatment, e.g., NT, using a randomized complete block design experiment with time as an additional factor. We estimated the duration of the two experiment types required to achieve MDD through simulation of SOC dynamics. Data for the study came from 13 experimental sites located in Iowa, Illinois, Ohio, Michigan, Wisconsin, Missouri, and Minnesota. Soil organic carbon, bulk density, and texture were measured at four soil depths. Minimum detectable differences were calculated with probability of Type I error of 0.05 and probability of Type II error of 0.15. The MDDs in SOC were highly variable across the region and increased with soil depth. At 0 to 10 cm (0 to 3.9 in) soil depth, MDDs with five replications ranged from 1.04 g C kg−1 (0.017 oz C lb−1; 6%) to 7.15 g C kg−1 (0.114 oz C lb−1; 31%) for comparison of two treatments; and from 0.46 g C kg−1 (0.007 oz C lb−1; 3%) to 3.12 g C kg−1 (0.050 oz C lb−1; 13%) for SOC change over time. Large differences were also predicted in the experiment duration required to detect a difference in SOC between MP and NT (from 8 to >100 years with five replications), or a change in SOC over time under NT management (from 11 to 71 years with five replications). At most locations, the time required to detect a change in SOC under NT was shorter than the time required to detect a difference between MP and NT. Minimum detectable difference and experiment duration decreased with the number of replications and were correlated with SOC variability and soil texture of the experimental sites, i.e., they tended to be lower in fine textured soils. Experiment duration was also reduced by increased crop productivity and the amount of residue left on the soil. The relationships and methods described here enable the design of experiments with high power of detecting differences and changes in SOC and enhance our understanding of how management practices influence SOC storage.

Soil water during the drought of 2012 as affected by rye cover crops in fields in Iowa and Indiana

The drought of 2012 provides a unique opportunity to evaluate the effects of cover crop on soil moisture under relatively extreme conditions. The objective of this study was to quantify potential differences in soil moisture due to the presence of a rye (Secale cerale L.) cover crop in a corn (Zea mays L.)–soybean (Glycine max L.) rotation at various locations in the Midwestern United States during the drought of 2012. Soil volumetric water content (θ) and soil water storage (SWS) were monitored at three sites in Iowa and Indiana. Daily measurements of soil θ were taken at 10, 20, 40, and 60 cm (3.9, 7.9, 15.7, and 23.6 in) soil depths, and SWS was estimated to an 80 cm (31.5 in) depth. Soil water during the drought of 2012 was affected by a rye cover crop in comparison to without a rye cover crop for one (i.e., located in Iowa) of the three sites monitored. At the Iowa site, soil θ was on average 0.041 and 0.033 cm3 cm−3 (0.041 and 0.033 in3 in−3) greater at the 10 and 20 cm (3.9 and 7.8 in) depths, respectively, following termination of a rye cover crop than crops without a rye cover crop. Thus, during the 2012 drought, the use of a rye cover crop as compared to without a rye cover crop in a corn–soybean rotation did not significantly lower soil water conditions. The use of a cover crop either had no impact or significantly increased soil water conservation.

Maximum soil organic carbon storage in Midwest U.S. cropping systems when crops are optimally nitrogen-fertilized

Nitrogen fertilization is critical to optimize short-term crop yield, but its long-term effect on soil organic C (SOC) is uncertain. Here, we clarify the impact of N fertilization on SOC in typical maize-based (Zea mays L.) Midwest U.S. cropping systems by accounting for site-to-site variability in maize yield response to N fertilization. Within continuous maize and maize-soybean [Glycine max (L.) Merr.] systems at four Iowa locations, we evaluated changes in surface SOC over 14 to 16 years across a range of N fertilizer rates empirically determined to be insufficient, optimum, or excessive for maximum maize yield. Soil organic C balances were negative where no N was applied but neutral (maize-soybean) or positive (continuous maize) at the agronomic optimum N rate (AONR). For continuous maize, the rate of SOC storage increased with increasing N rate, reaching a maximum at the AONR and decreasing above the AONR. Greater SOC storage in the optimally fertilized continuous maize system than in the optimally fertilized maize-soybean system was attributed to greater crop residue production and greater SOC storage efficiency in the continuous maize system. Mean annual crop residue production at the AONR was 22% greater in the continuous maize system than in the maize-soybean system and the rate of SOC storage per unit residue C input was 58% greater in the monocrop system. Our results demonstrate that agronomic optimum N fertilization is critical to maintain or increase SOC of Midwest U.S. cropland.

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.

Standardized research protocols enable transdisciplinary research of climate variation impacts in corn production systems

The important questions about agriculture, climate, and sustainability have become increasingly complex and require a coordinated, multifaceted approach for developing new knowledge and understanding. A multistate, transdisciplinary project was begun in 2011 to study the potential for both mitigation and adaptation of corn-based cropping systems to climate variations. The team is measuring the baseline as well as change of the systems carbon (C), nitrogen (N), and water footprints, crop productivity, and pest pressure in response to existing and novel production practices. Nine states and 11 institutions are participating in the project, necessitating a well thought out approach to coordinating field data collection procedures at 35 research sites. In addition, the collected data must be brought together in a way that can be stored and used by persons not originally involved in the data collection, necessitating robust procedures for linking metadata with the data and clearly delineated rules for use and publication of data from the overall project. In order to improve the ability to compare data across sites and begin to make inferences about soil and cropping system responses to climate across the region, detailed research protocols were developed to standardize the types of measurements taken and the specific details such as depth, time, method, numbers of samples, and minimum data set required from each site. This process required significant time, debate, and commitment of all the investigators involved with field data collection and was also informed by the data needed to run the simulation models and life cycle analyses. Although individual research teams are collecting additional measurements beyond those stated in the standardized protocols, the written protocols are used by the team for the base measurements to be compared across the region. A centralized database was constructed to meet the needs of current researchers on this project as well as for future use for data synthesis and modeling for agricultural, ecosystem, and climate sciences.