Lajpat R. Ahuja

Advances and challenges in predicting agricultural management effects on soil hydraulic properties

Agricultural management practices can significantly affect soil hydraulic properties and processes in space and time. These responses are coupled with the processes of infiltration, runoff, erosion, chemical movement, and crop growth. It is essential to quantify and predict management effects on soil properties in order to model their consequent effects on production and the environment. We present work done thus far on this topic area along with the challenges that lie ahead. The effects of tillage and reconsolidation, wheel-track soil compaction, crop residue management, macropore development and management interactions with natural sources of variability, such as topography, are addressed. Whether explicitly or implicitly, the available field studies include interactions between treatments, such as tillage, crop rotation and residue management. Controlled equipment traffic has been shown to have significant effects on soil compaction and related hydraulic properties in some soils and climates, but in others, landscape and temporal variability overwhelm any effects of wheel tracks. New research results on wheel-track effects in Colorado are highlighted along with initial attempts to predict their effects on hydraulic properties. The greatest challenge for the future is improved process-based prediction using a systems approach to include tightly coupled process interactions in space and time.

Assimilation of surface soil moisture to estimate profile soil water content

Abstract The use of surface soil water content data as additional input for the Root Zone Water Quality Model in modeling profile soil water content was investigated at four field sites in the Little Washita River Experimental Watershed in south central Oklahoma, coincident with the Southern Great Plains 1997 (SGP97) Hydrology Experiment. Modeled soil water profile estimates were compared to field measurements made periodically during the same time period using a field calibrated time-domain reflectometry (TDR) system. The model was first run in the normal mode with inputs of initial conditions and upper boundary conditions of measured rainfall intensities and daily mean meteorological variables that determined evapotranspiration (ET). Soil hydraulic properties used in the model were estimated from limited soils data information, since in practical terms this is usually the case. Moreover, in our earlier study even the complete description of hydraulic properties based on laboratory and field measurements did not improve the results over average profile estimates using only limited input data. The model runs were then repeated with the daily simulated soil water content in the surface 0–5 cm layer being replaced by 0–5 cm measured soil water content. This process of forcing measured surface water content as additional model input is called direct insertion data assimilation. The simulated profile soil water contents, with and without data assimilation, were compared with TDR-measured profiles to a depth of 60 cm. Gravimetric surface soil water content was measured during SGP97 from June 18 to July 16, 1997 and used as a surrogate for remotely sensed surface moisture data. Data assimilation of surface soil moisture improved model estimates to a depth of 30 cm at all sites. Of particular significance, with data assimilation, model estimates more closely matched the measured dynamic fluctuations of soil moisture in the top 30 cm in response to rainfall events. There was no significant improvement in soil water estimates below the 30 cm depth. This may indicate that data assimilation of surface soil moisture tends to compensate for any errors in model simulations emanating from: (1) errors in the measurement of rainfall intensities or in using 5-min averaged rainfall intensities as done here; (2) errors in using daily average values of meteorological variables that determine ET in a daily ET model; (3) errors in determining hydraulic properties of the surface soil by either laboratory methods or more simple techniques; (4) errors due to the spatial variability of soil hydraulic properties not properly represented in the model.

A software engineering perspective on environmental modeling framework design: The Object Modeling System

The environmental modeling community has historically been concerned with the proliferation of models and the effort associated with collective model development tasks (e.g., code generation, data transformation, etc.). Environmental modeling frameworks (EMFs) have been developed to address this problem, but much work remains before EMFs are adopted as mainstream modeling tools. Environmental model development requires both scientific understanding of environmental phenomena and software developer proficiency. EMFs support the modeling process through streamlining model code development, allowing seamless access to data, and supporting data analysis and visualization. EMFs also support aggregation of model components into functional units, component interaction and communication, temporal-spatial stepping, scaling of spatial data, multi-threading/multi-processor support, and cross-language interoperability. Some EMFs additionally focus on high-performance computing and are tailored for particular modeling domains such as ecosystem, socio-economic, or climate change research. The Object Modeling System Version 3 (OMS3) EMF employs new advances in software framework design to better support the environmental model development process. This paper discusses key EMF design goals/constraints and addresses software engineering aspects that have made OMS3 framework development efficacious and its application practical, as demonstrated by leveraging software engineering efforts outside of the modeling community and lessons learned from over a decade of EMF development. Software engineering approaches employed in OMS3 are highlighted including a non-invasive lightweight framework design supporting component-based model development, use of implicit parallelism in system design, use of domain specific language design patterns, and cloud-based support for computational scalability. The key advancements in EMF design presented herein may be applicable and beneficial for other EMF developers seeking to better support environmental model development through improved framework design.

Agricultural system models in field research and technology transfer

Whole System Integration and Modeling - Essential to Agricultural Science and Technology in the 21st Century, L. R. Ahuja, L. Ma, and T. A. Howell Forage-Livestock Models for the Australian Livestock Industry, J.R. Donnelly, R. J. Simpson, L. Salmon, A.D. Moore, M. Freer, and H. Dove Applications of Cotton Simulation Model, GOSSYM, for Crop Management, Economic and Policy Decisions, K. R. Reddy, V. G. Kakani, J. M. McKinion, and D. N. Baker Experience with On-Farm Applications of GLYCIM/GUICS, D. Timlin, Y. Pachepsky, F. Whisler, and V. Reddy Benefits of Models in Research and Decision Support: The IBSNAT Experience, G. Y.Tsuji, A. duToit, A. Jintrawet, J. W. Jones, W. T. Bowen, R. M. Ogoshi, and G. Uehara Decision Support Tools for Improved Resource Management and Agricultural Sustainability, U. Singh, P. W. Wilkens, W. E. Baethgen, and T. S. Bontkes An Evaluation of RZWQM, CROPGRO, and CERES-Maize for Responses to Water Stress in the Central Great Plains of the U.S., L. Ma, D. C. Nielson, L. R. Ahuja, J. R. Kiniry, J. D. Hanson, and G. Hoogenboom The Co-evolution of the Agricultural Production Systems Simulator (APSIM) and its use in Australian Dryland Cropping Research and Farm Management Intervention, R. L. McCown, B. A. Keating, P. S. Carberry, Z. Hochman, and D. Hargreaves Applications of Crop Growth Models in the Semi-Arid Regions, M. V. K. Sivakumar and A. F. Glinni Applications of Models with Different Spatial Scale, J. R. Kiniry, J. G. Arnold, and Y. Xie Modeling Crop Growth and Nitrogen Dynamics for Advisory Purposes Regarding Spatial Variability, K.C. Kersebaum, K. Lorenz, H. I. Reuter, and O. Wendroth Addressing Spatial Variability in Crop Model Applications, E. J. Sadler, E. M. Barnes, W.D. Batchelor, J. Paz, and A. Irmak Topographic Analysis, Scaling, and Models to Evaluate Spatial/Temporal Variability of Landscape Processes and Management, L. R. Ahuja, T. R. Green, R. H. Erskine, L. Ma, J. C. Ascough, G. H. Dunn, and M. J. Scaffer Parameterization of Agricultural System Models: Current Approaches and Future Needs, L. R. Ahuja and L. Ma The Object Modeling System, O. David, S. L. Markstrom, K. W. Rojas, L. R. Ahuja, and I. W.. Schneider Future Research to Fill Knowledge Gaps, J. L. Hatfield and B. A. Kimball

Characteristics of Macropore Transport Studied with the ARS Root Zone Water Quality Model

The ARS Root Zone Water Quality Model components dealing with preferential water and chemical transport are presented and used to study macropore flow and transport in a silty clay loam soil. Macroporosity of the soil was assumed to be 0.05% by volume, half of which was continuous and the rest discontinuous. Two rainfall sequences with two initial soil water contents, evaporation versus transpiration, macropore radius ranging from 1.0 to 0.125 mm, and three different chemicals were evaluated. Over a five-week period, weekly rainfall of 25.4 mm in one hour, with soil water redistribution and evaporation or transpiration occurring between storms, generated no macropore flow when the soil was initially dry (–1500 kPa). A slight amount of macropore flow was generated under the same rainfall when the soil was initially wet (–33 kPa). Doubling the weekly rainfall amount and intensity generated macropore flow varying between 30 to 50% of rainfall depending on initial and boundary conditions. Chemicals transported with this flow were 0.05 to 8% of the surface-applied amount, depending on conditions and type of chemical. A moderately adsorbed chemical (Atrazine) was the most susceptible to macropore transport, followed in order by a strongly adsorbed chemical (Prometryn), and a mobile chemical (Nitrate). The flow entering the macropores was partially absorbed by soil at progressively deeper depths; it increased the water content of the root zone, and created a tail of low concentrations in the soil chemical content distributions. The macropore size had very little effect on macropore flow and transport, but the smallest size pores retarded the downward chemical movement by wall adsorption a little more than the largest size pores. Surface evaporation decreased macropore flow, soil water contents, and downward chemical movement, but increased chemical content of the macropore flow. Transpiration, on the other hand, decreased both macropore flow and its chemical content. Thus, this modeling study gives very useful insights into the macropore flow behavior that are very difficult to obtain experimentally, and which will be useful in characterizing macropore flow in the field.

COMPARISON OF METHODS TO ESTIMATE SOIL WATER CHARACTERISTICS FROM SOIL TEXTURE, BULK DENSITY, AND LIMITED DATA

Four approaches used to estimate the soil water characteristic (soil water content-matric potential relationship) were compared on a data set based on 366 cores of Bernow soil (Glossic Paleudalf). Regression equations based on soil texture and bulk density provided poorer estimates of soil water content, with large errors at some matric potentials, compared with other approaches examined. Regression model results were improved when one measured value of soil water content (−1500 kPa) was included as a variable in the equations, and greatly improved when two (−33 and −1500 kPa) measured values were included. A simple log-log interpolation/extrapolation approach, based on two measured values at −33 and −1500 kPa, provided results similar to the regression model with two known values. The similar-media scaling approach, utilizing one measured value at −33 kPa, displayed results similar to the log-log method, but the error was slightly higher. Estimates with the one-parameter model of Gregson, Hector and McGowan (GHM), based on one known value (−33 kPa), was similar to the log-log interpolation/extrapolation when a required generalized slope-intercept relation was calculated for the soils in the study; and the error was slightly higher when using the generalized relationship found by GHM for their data. We conclude that the models which incorporated even one known value of soil water content-matric potential relationship were much better than those based on soil texture and bulk density alone. The simple log-log interpolation/extrapolation and the one-parameter GHM model provided the best estimates of soil water content. The scaling method estimates were only slightly worse than the GHM model estimates. The soil survey data often contain at least one value of the water characteristic. These one point methods should, therefore, be the methods of choice.

Root Zone Water Quality Model sensitivity analysis using Monte Carlo simulation.

Performing a sensitivity analysis for a mathematical simulation model is helpful in identifying key model parameters and simulation errors resulting from parameter uncertainty. The Root Zone Water Quality Model (RZWQM) has been evaluated for many years, however, detailed sensitivity analyses of the model to various agricultural management systems and their representative input parameters are lacking. This study presents results of RZWQM output response sensitivity to selected model input parameters. Baseline values for the parameters were measured for an experiment on a manured corn field in eastern Colorado. Four groups of model input parameters (saturated hydraulic conductivity, organic matter/nitrogen cycling, plant growth, and irrigation water/manure application rates) were selected and three model output responses (plant nitrogen uptake, silage yield, and nitrate leaching) were used to quantify RZWQM sensitivity to selected model input parameters. A modified Monte Carlo sampling method (Latin Hypercube Sampling) was used to obtain parameter sets for model realizations. The model parameter sets were then analyzed separately using linear regression analysis. In general, RZWQM output responses were most sensitive to plant growth input parameters and manure application rates. The plant nitrogen uptake and silage yield model output responses were less sensitive to nitrogen cycling and irrigation rate input parameters than those observed in previous field experiments. This finding may warrant further study on the effects of water and nitrogen stresses on crop growth. Finally, the results showed that model output responses were more sensitive to the average saturated hydraulic conductivity of the entire soil profile than to the saturated hydraulic conductivity of individual soil layers.

Orthogonal collocation and alternating-direction procedures for unsaturated flow problems

Abstract The alternating-direction collocation method has recently been developed for general parabolic equations. In order to test the applicability of the procedure to highly nonlinear problems, an alternating-direction collocation algorithm is developed to simulate two-dimensional flow in unsaturated porous media. The algorithm employs an alternating-direction solution procedure within the framework of a modified Picard iteration scheme. Numerical behaviour of the new procedure is compared to the behaviour of a standard two-dimensional collocation formulation. The new method is also tested on several infiltration problems of practical interest, including a layered and sloping soil. Results demonstrate the method to be accurate and highly mass conservative. The algorithm also produces significant savings in both execution time and storage.

Root Zone Water Quality Model (RZWQM2): Model Use, Calibration, and Validation

The Root Zone Water Quality Model (RZWQM2) has been used widely for simulating agricultural management effects on crop production and soil and water quality. Although it is a one-dimensional model, it has many desirable features for the modeling community. This article outlines the principles of calibrating the model component by component with one or more datasets and validating the model with independent datasets. Users should consult the RZWQM2 user manual distributed along with the model and a more detailed protocol on how to calibrate RZWQM2 provided in a book chapter. Two case studies (or examples) are included in this article. One is from an irrigated maize study in Colorado to illustrate the use of field and laboratory measured soil hydraulic properties on simulated soil water and crop production. It also demonstrates the interaction between soil and plant parameters in simulated plant responses to water stresses. The other is from a maize-soybean rotation study in Iowa to show a manual calibration of the model for crop yield, soil water, and N leaching in tile-drained soils. Although the commonly used trial-and-error calibration method works well for experienced users, as shown in the second example, an automated calibration procedure is more objective, as shown in the first example. Furthermore, the incorporation of the Parameter Estimation Software (PEST) into RZWQM2 made the calibration of the model more efficient than a grid (ordered) search of model parameters. In addition, PEST provides sensitivity and uncertainty analyses that should help users in selecting the right parameters to calibrate.

Prediction of NO3-N losses with subsurface drainage water from manured and UAN-fertilized plots using GLEAMS

Excessive application of swine manure to a field over long durations can increase nitrate-nitrogen (NO 3 -N) leaching as a result of accumulation of more nutrients in the root zone than the subsequent crops may need. The objective of this study was to use the GLEAMS (V.2.1) model to compare measured versus simulated effects of swine manure application with urea-ammonium-nitrate (UAN) on subsurface drain water quality from beneath long-term corn (Zea mays L.) and soybean (Glycine max L.) plots. Four years (1993-1996) of field data from an Iowa site were used for model calibration and validation. The SCS curve number and effective rooting depth were adjusted to minimize the difference between simulated percolation below the root zone and measured subsurface drain flows. Model predictions of percolation water below the root zone followed the pattern of measured drain flow data, giving an average difference of 10%, and –5% between predicted and measured values for manured and UAN-fertilized plots, respectively, for four years from 1993 to 1996. Model simulations for overall NO 3 -N losses with percolation water were comparable to measured NO 3 -N losses with subsurface drain water giving an average difference of 20% for manured plots. The model overpredicted NO 3 -N losses, particularly for soybean on plots, which received manure in the previous year. Predicted NO 3 -N losses with subsurface drainage from fertilized plots were much lower than measured values with an average difference of –32%. The best fit line with zero intercept showed correlation coefficients of 0.73 and 0.66 between monthly predicted and measured NO 3 -N losses with subsurface drain flows for manured and UAN-fertilized plots for four years from 1993 to 1996, respectively. The results of the study show that the N-transformation processes and the associated rate factors based on soil temperature and soil water levels may need to be refined for consistent simulation of NO 3 -N losses with subsurface drainage water when fertilized with either swine manure or UAN for corn production.