The Nashua agronomic, water quality, and economic dataset

This paper describes a dataset relating management to nitrogen (N) loading and crop yields from 1990 to 2003 on 36, 0.4 ha (1 ac) individually tile-drained plots on the Northeast Research and Demonstration Farm near Nashua, Iowa, United States. The field-measured data were used to calibrate the Root Zone Water Quality Model (RZWQM), and the results were summarized in a special issue ofGeoderma (Ahuja and Hatfield 2007). With a comprehensive, long-term measured dataset and a model that simulates many of the components of the agricultural system, one can begin to understand the effects of management practices on N loading, crop yields, and net income to the farmers. Other researchers can use this dataset to assess the effects of management on similar tile-drained systems occurring some distance from Nashua, under alternative climates and soils, with other management systems, or with simulation models using different process representations. By integrating the understanding developed at Nashua with datasets from other highly monitored sites and other sources, progress can be made in addressing problems related to excessive N fluxes in the Mississippi Basin. An example 30-year RZWQM simulation of 18 management systems implies that significant management changes are needed to meet the goal of reducing N loads to the Gulf of Mexico by 45%. This paper and the associated datasets are intended to be used in conjunction with the analyses and process descriptions presented in the Geoderma special issue. The datasets and additional explanatory materials are available for download at http://apps.tucson.ars.ag.gov/nashua

RZWQM simulated effects of crop rotation, tillage, and controlled drainage on crop yield and nitrate-N loss in drain flow

Accurate simulation of agricultural management effects on N loss in tile drainage is vitally important for understanding hypoxia in the Gulf of Mexico. An experimental study was initiated in 1978 at Nashua, Iowa of the USA to study long-term effects of tillage, crop rotation, and N management practices on subsurface drainage flow and associated N losses. The Root Zone Water Quality Model (RZWQM) was applied to evaluate various management effects in several previous studies. In this study, the simulation results were further analyzed for management effects (tillage, crop rotation, and controlled drainage) on crop production and N loss in drain flow. RZWQM simulated the observed increase in N concentration in drain flow with increasing tillage intensity from NT (no-till) to RT (ridge till) to CP (chisel plow) and to MP (moldboard plow). It also adequately simulated tillage effects on yearly drain flow and yearly N loss in drain flow. However, the model failed to simulate lower corn and soybean yields under NT than under MP, CP, and RT. On the other hand, RZWQM adequately simulated lower yearly drain flow and lower flow-weighted N concentration in drain flow under CS (corn–soybean) and SC (soybean–corn) than under CC (continuous corn). The model adequately simulated higher corn yield under CS and SC than under CC. Applying the newly suggested N management practice for the Midwest of controlled drainage, the model simulated a 30% reduction in drain flow and a 29% decrease in N losses in drain flow under controlled drainage (CD) compared to free drainage (FD). With most of the simulations in reasonably close agreement with observations, we concluded that RZWQM is a promising tool for quantifying the relative effects of tillage, crop rotation, and controlled drainage on N loss in drainage flow. Further improvements on simulated management effects on crop yield and N mineralization are needed, however

Sensitivity of tile drainage flow and crop yield on measured and calibrated soil hydraulic properties

Process-based agricultural system models require detailed description of soil hydraulic properties that are usually not available. The objectives of this study were to evaluate the sensitivity of model simulation results to variability in measured soil hydraulic properties and to compare simulation results using measured and default soil parameters. To do so, we measured soil water retention curves and saturated soil hydraulic conductivity (Ksat) from intact soil cores taken from a long-term experimental field near Nashua, Iowa for the Kenyon–Clyde–Floyd–Readlyn soil association. The soil water retention curves could be well described using the pore size distribution index (λ). Measured λ values from undisturbed soil cores ranged from 0.04 to 0.12 and the measured Ksat values ranged from 1.8 to 14.5 cm/h. These hydraulic properties were then used to calibrate the Root Zone Water Quality Model (RZWQM) for simulating soil water content, water table, tile drain flow, and crop yield (corn and soybean) by optimizing the lateral Ksat(LKsat) and hydraulic gradient (HG) for subsurface lateral flow. The measured soil parameters provided better simulations of soil water storage, water table, and N loss in tile flow than using the default soil parameters based on soil texture classes in RZWQM. Sensitivity analyses were conducted for λ, Ksat, saturated soil water content (θs) or drainable porosity, LKsat, and HG using the Latin Hypercubic Sampling (LHS) and for LKsat and HG also using a single variable analysis. Results of sensitivity analyses showed that RZWQM-simulated yield and biomass were not sensitive to soil hydraulic properties. Simulated tile flow and N losses in tile flow were not sensitive to λ and Ksat either, but they were sensitive to LKsat and HG. Further sensitivity analyses using a single variable showed that LKsat in the tile layer was a more sensitive parameter compared to LKsat in other soil layers, and HG was the most sensitive parameter for tile flow under the experimental soil and weather conditions

Continuum of Risk Analysis Methods to Assess Tillage System Sustainability at the Experimental Plot Level

This study applied a broad continuum of risk analysis methods including mean-variance and coefficient of variation (CV) statistical criteria, second-degree stochastic dominance (SSD), stochastic dominance with respect to a function (SDRF), and stochastic efficiency with respect to a function (SERF) for comparing income-risk efficiency sustainability of conventional and reduced tillage systems. Fourteen years (1990–2003) of economic budget data derived from 35 treatments on 36 experimental plots under corn (Zea mays L.) and soybean (Glycine maxL.) at the Iowa State University Northeast Research Station near Nashua, IA, USA were used. In addition to the other analyses, a visually-based Stoplight or “probability of target value” procedure was employed for displaying gross margin and net return probability distribution information. Mean-variance and CV analysis of the economic measures alone provided somewhat contradictive and inconclusive sustainability rankings, i.e., corn/soybean gross margin and net return showed that different tillage system alternatives were the highest ranked depending on the criterion and type of crop. Stochastic dominance analysis results were similar for SSD and SDRF in that both the conventional and reduced tillage system alternatives were highly ranked depending on the type of crop and tillage system. For the SERF analysis, results were dependent on the type of crop and level of risk aversion. The conventional tillage system was preferred for both corn and soybean for the Stoplight analysis. The results of this study are unique in that they highlight the potential of both traditional stochastic dominance and SERF methods for distinguishing economically sustainable choices between different tillage systems across a range of risk aversion. This study also indicates that the SERF risk analysis method appears to be a useful and easily understood tool to assist farm managers, experimental researchers, and potentially policy makers and advisers on problems involving agricultural risk and sustainability

Decision support for nitrogen management in tile-drained agriculture

Farmers will adopt alternative management systems to improve water quality more readily if they understand how those management alternatives affect the release of contaminants, crop yields, and ultimately, their net income. We propose a method to address these issues by integrating observed data from field experiments, a comprehensive simulation model, review by local experts, and application through a decision support system by technically trained conservationists. An example for reducing nitrogen loading from tile-drained corn and soybean production in Iowa demonstrates the approach. Fourteen years of observed data from 30 research plots on the Northeast Research and Demonstration Farm near Nashua, Iowa were used to calibrate the Root Zone Water Quality Model (RZWQM) to simulate the effects of 35 management systems on crop yields and nitrogen (N) loadings into tile drains. The EconDocs tool was used for an economic analysis of the management effects. An Expert Panel reviews the simulations and the long term average annual management effects. Those management effects, as well as the daily values of variables that describe the crop growth and nitrogen loading in tile flow processes, are put into a database. As part of the conservation planning process, Conservationists and farmers would use the database inside a decision support system to select management systems that meet the farmers\u27 goals and reduce water quality problems

Simulating management effects on crop production, tile drainage, and water quality using RZWQM–DSSAT

The objective of this study was to explore if more crop-specific plant growth modules can improve simulations of crop yields, and N in tile flow under different management practices compared with a generic plant growth module. We calibrated and evaluated the Root Zone Water Quality Model (RZWQM) with the Decision Support for Agrotechnology Transfer (DSSAT v3.5) plant growth modules (RZWQM–DSSAT) for simulating tillage (NT — no till, RT — ridge till, CP — chisel plow, and MP — moldboard plow), crop rotation {CC — continuous corn, and CS — corn (Zea mays L.)–soybean [Glycine max (L.) Merr.]}, and nitrogen (N) (SA — single application at preplant, and LSNT — late spring soil N test based application) and manure (SM — fall injected swine manure) management effects on crop production and water quality. Data from 1978 to 2003 from a water quality experiment near Nashua (Nashua experiments), Iowa, USA, were used. The model was calibrated using data from one treatment plot and validated for the rest of the plots. Simulated management effects on annual N loading in tile flow were agreeable with measured effects in 85%, 99%, 88%, and 78% of the cases for tillage, crop rotation (CS vs. CC), N application timing (SA vs. LSNT), and swine manure applications (SM vs. SA), respectively. On average, the LSNT plots were simulated to have 359 kg ha− 1 higher corn yield compared to SA, when the observed increase was 812 kg ha− 1. Grain yield simulations were not sensitive to differences between RT and NT, between SM and SA treatments, and between CS and CC. We conclude that considering the uncertainties of basic input data, processes in the field, and lack of site specific weather data, the results obtained with this RZWQM–DSSAT hybrid model were not much better than the results obtained earlier with the generic crop growth module

RZWQM simulation of long-term crop production, water and nitrogen balances in Northeast Iowa

Agricultural system models are tools to represent and understand major processes and their interactions in agricultural systems. We used the Root Zone Water Quality Model (RZWQM) with 26 years of data from a study near Nashua, IA to evaluate year to year crop yield, water, and N balances. The model was calibrated using data from one 0.4 ha plot and evaluated by comparing simulated values with data from 29 of the 36 plots at the same research site (six were excluded). The dataset contains measured tile flow that varied considerably from plot to plot so we calibrated total tile flow amount by adjusting a lateral hydraulic gradient term for subsurface lateral flow below tiles for each plot. Keeping all other soil and plant parameters constant, RZWQM correctly simulated year to year variations in tile flow (r2 = 0.74) and N loading in tile flow (r2 = 0.71). Yearly crop yield variation was simulated with less satisfaction (r2 = 0.52 for corn and r2 = 0.37 for soybean) although the average yields were reasonably simulated. Root mean square errors (RMSE) for simulated soil water storage, water table, and annual tile flow were 3.0, 22.1, and 5.6 cm, respectively. These values were close to the average RMSE for the measured data between replicates (3.0, 22.4, and 5.7 cm, respectively). RMSE values for simulated annual N loading and residual soil N were 16.8 and 47.0 kg N ha−1, respectively, which were much higher than the average RMSE for measurements among replicates (7.8 and 38.8 kg N ha−1, respectively). The high RMSE for N simulation might be caused by high simulation errors in plant N uptake. Simulated corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] yields had high RMSE (1386 and 674 kg ha−1) with coefficient of variations (CV) of 0.19 and 0.25, respectively. Further improvements were needed for better simulating plant N uptake and yield, but overall, results for annual tile flow and annual N loading in tile flow were acceptable

Evaluating and predicting agricultural management effects under tile drainage using modified APSIM

An accurate and management sensitive simulation model for tile-drained Midwestern soils is needed to optimize the use of agricultural management practices (e.g., winter cover crops) to reduce nitrate leaching without adversely affecting corn yield. Our objectives were to enhance the Agricultural Production Systems Simulator (APSIM) for tile drainage, test the modified model for several management scenarios, and then predict nitrate leaching with and without winter wheat cover crop. Twelve years of data (1990–2001) from northeast Iowa were used for model testing. Management scenarios included continuous corn and corn–soybean rotations with single or split N applications. For 38 of 44 observations, yearly drain flow was simulated within 50 mm of observed for low drainage (\u3c 100 mm) or within 30% of observed for high drain flow. Corn yield was simulated within 1500 kg/ha for 12 of 24 observations. For 30 of 45 observations yearly nitrate-N loss in tile drains was simulated within 10 kg N/ha for low nitrate-N loss (\u3c 20 kg N/ha) or within 30% of observed for high nitrate-N loss. Several of the poor yield and nitrate-N loss predictions appear related to poor N-uptake simulations. The model accurately predicted greater corn yield under split application (140–190 kg N/ha) compared to single 110 kg N/ha application and higher drainage and nitrate-N loss under continuous corn compared to corn/soybean rotations. A winter wheat cover crop was predicted to reduce nitrate-N loss 38% (341 vs. 537 kg N/ha with and without cover) under 41-years of corn-soybean rotations and 150 kg N/ha applied to corn. These results suggest that the modified APSIM model is a promising tool to help estimate the relative effect of alternative management practices under fluctuating high water tables