RCA III
Fate and Transport of Nitrogen
What Models Can and Cannot Do
Working Paper No. 11
M. J. Shaffer
USDA, Agricultural Research Service
Great Plains Systems Research Unit
Fort Collins, Colorado
September 1995
Contents
Introduction
General types of applications and associated models
Availability of data for use in nitrogen models
Variability of field data needed to drive and test
nitrogen models
Capabilities of soil root zone models to predict
soil nitrogen status and potential nitrate leaching
Potential impacts on groundwater quality
Capabilities of soil root zone models to help design
best management practices for N applications
Capabilities of groundwater nitrate-N models
Limitations of nitrogen models
Conclusions
References
Introduction
What a Simulation Model Is and Isn't
Over the past several decades, attempts have been made to develop integrated
theories (i.e., models) of the carbon and nitrogen cycle in soil-crop-aquifer
systems. These models represent approximations to (i.e., simulations of)
the actual processes, process interactions, and matter and energy exchanges
that take place in the real world. The amount of detail contained in simulation
models varies widely, depending on the needs and objectives of the projects
under which they were developed.
How Decision Support Systems, Expert Systems, and Simulation Models Differ
Decision support systems (DSS's) provide their users with integrated tools
to help them make better management decisions. Generally, these tools may
include databases, simulation models, and expert systems. An important feature
here is that making the final decision is left up to the user.
Expert systems attempt to capture the knowledge and decision-making logic
of experts in a limited subject area and to place this capability into a
computer program capable of making decisions similar to those that would
have been reached by the original expert.
Simulation models attempt to approximate real-world processes and their
interactions at the mechanistic level. They are extremely important components
of decision support systems, and some expert systems may also contain simulation
components. Simulation models usually contain logical relationships derived
from the subject knowledge base and also may include expert systems.
The overlap and interrelationships of DSS's, expert systems, and simulation
models can be quite involved, depending on a particular application. However,
examination of the primary purpose of the system or model can be used to
reveal whether it is a DSS, expert system, or simulation model.
Interrelationships between Modeling and Classical Field and Laboratory
Research
A common misconception frequently stated in the literature is that classical
field and laboratory research provides the basic knowledge, while modeling
packages this knowledge for use by the end user. The reality of the situation,
however, is somewhat different. This is particularly true of simulation
models but also applies to DSS's and expert systems. Modeling generally
involves integration of subsystems. Extremely important subsystem interactions
cannot be studied or tested outside the context of an integrated model.
This means that classical research that isolates processes for study has
a difficult or impossible task in making real progress in areas where multiple
process interactions are involved--such as soil carbon and nitrogen transformations
and plant environmental stress--unless an integrated model is developed.
Classical field research alone has not been able to adequately address these
badly needed research areas. Evidently, modeling and classical research
methods need to be combined at all stages of the research process where
whole systems are involved.
Additional Model Benefits
Once a suitable model (or models) has been developed and tested, long-term
simulation studies and interpolation of results between field research stations
are possible. In addition, knowledge gaps concerning whole systems and subsystem
interactions can be identified for further study. Education is another area
where models can play a significant role. For example, students can use
models to learn how systems respond to environmental and managerial inputs
and which parameters and state variables are the most important.
General Types of Applications and Associated
Models
Point and Field-scale Simulations
These model applications are used to address local impacts of various management,
soil, and climate scenarios. Individual fields, research plots, or soils
within a field are the spatial units of interest here. In general, most
soil nitrogen models have been designed to address this range of field scales.
Geographical Information System (GIS) technology can be used to spatially
reference individual soil simulation analyses within a field and provide
a link to "farming by soil" methods.
Farm-scale and Ranch-scale Analyses
Multiple fields and management enterprises are considered simultaneously.
Models designed to make fertilizer recommendations or predict nitrate-N
leaching at the point and field scale may also have application at the whole-farm
or whole-ranch scale by aggregating results obtained for fields or smaller
areas through the use of spatially referenced databases and GIS technology.
Regional or Basinwide Analyses
This approach combines GIS, remote sensing, and simulation technology to
address large-scale spatial and temporal impacts of management, soil, and
cllimate. The models being used here either are field-scale models that
have been adapted for use at these larger scales or are large-scale, 2-
or 3-dimensional models designed primarily for surface runoff calculations
with some provision for subsurface flows.
Soil, Aquifer, and Combined Soil-Aquifer Models
Models also can be grouped into (1) those that address the crop root zone,
(2) specific aquifer models, and (3) combined approaches that take an integrated
look at the soil-aquifer system. Most nitrogen models are limited to the
crop root zone, but some consider N transport and limited N-fate processes
in aquifers, and a few models look at the combined effects of the soil root
zone, deep vadose zone, and aquifer.
Examples of Nitrogen Models and Associated Scales
Soil nitrogen models have been developed at various levels of resolution
and for various purposes (Hansen et al. 1994). Probably the most common
type comprises the fertilizer recommendation models developed by the individual
State Experiment Stations and by agribusiness. These models generally are
based on results obtained from field trials and may use crop types and yield
goals, soil NO3-N tests, leaf tissue and chlorophyll
meter tests, soil organic matter levels, manure and legume credits, and
other information sources to help calculate soil nitrogen budgets and make
fertilizer recommendations to producers. Nitrogen models of this type are
normally applied at the field scale and are limited to the crop root zone.
Another significant group of soil nitrogen models has been developed that
can make assessments of nitrate-N leaching below the crop root zone as a
function of soils, climate, and management. Examples include EPIC, Williams
et al. (1984); GLEAMS, Knisel (1993); NLEAP, Shaffer et al. (1991); NTRM,
Shaffer and Larson (1987); LEACHM-N, Wagenet and Hutson (1989); CENTURY,
Wetherell et al. (1993); and RZWQM, USDA(ARS (1992). These models include
soil process mechanisms at varying degrees of complexity for computing soil
water and nitrogen budgets, and transport of nitrate-N through and out of
the root zone. These models were initially developed for use at the point
and field scales, but some of them, such as NLEAP, CENTURY, and LEACHM-N,
have also been applied at the farm and regional scales through the use of
GIS and related techniques (Wylie et al. 1994; Burke et al. 1989; Bleecker
et al. 1990).
Another group of models has been designed primarily to estimate transport
of nitrogen and other chemicals in surface runoff. These include general
models such as CREAMS (Knisel 1980) and more detailed 2-dimensional models
such as AGNPS (Young et al. 1989) and SWRRB (Williams and Nicks 1985). Other
models such as EPIC, GLEAMS, NLEAP, NTRM, RZWQM, and LEACHM have 1-dimensional
surface runoff components that include soil nitrogen.
Availability of Data for Use in Nitrogen
Models
On-site Data and Their Relative Importance
Quantitative or semiquantitative model predictions of site-specific plant-available
soil nitrogen, soil nitrate-N leached from the root zone, gaseous losses
of N, soil carbon levels, and residual soil nitrate-N require local data
on soil physical, chemical, and biological properties. For example, local
measurements of soil properties such as plant-available water-holding capacity,
percentage of soil organic matter (SOM), fraction of SOM in the fast mineralization
pool, initial soil water content, and initial soil nitrate-N are needed
to make site-specific predictions of nitrate-N leached and residual soil
nitrate-N in field research plots and farm fields.
USDA Natural Resources Conservation Service (NRCS) Soil Databases
Typical NRCS soil databases used in nitrogen modeling include the SOILS
5/6 and the Pedon databases. These databases contain information on soil
properties such as texture, drainage class, hydrologic group, bulk density,
pH, plant-available water-holding capacity, percentage of organic matter,
and percentage of coarse fragments. In addition, the Pedon database contains
more detailed information on soil properties such as water retention relationships
and soil chemistry.
NRCS databases such as the 1:24,000 SURRGO and the 1:250,000 STATSGO provide
georeferenced data on soils for use in GIS and modeling applications. Soil
property attribute types available in the SOILS 5/6 database are also generally
available in the STATSGO and SURRGO databases.
The STATSGO database is available for the entire United States. However,
the SURRGO database is under development and has been completed in only
a limited number of States and localities. The relative usefulness of these
databases in nitrogen models depends on the objectives and required resolution
of a particular study. For example, the 1:24,000 SURRGO database has a rasterized
resolution of about 28 m on the ground, while the STATSGO resolution is
about 290 m. Also, generalization of local State soil survey data has been
done in the national SOILS 5/6 database and in the SURRGO and STATSGO databases.
Application of soil nitrogen models to specific fields may require remapping
of the fields with an order-1 survey and accompanying soil sampling. Model
applications at larger scales may be able to make use of existing SURRGO
and STATSGO databases.
National Climate Databases
The National Climate Data Center (NCDC) database contains historical weather
records at numerous stations across the United States. Daily data are available
for precipitation, air temperature, pan evaporation, and snow. The database
is available on CD-ROM from commercial companies, and the NRCS has its own
version of the database on its computer system.
Local Soil and Climate Databases from Research Plots
Detailed soil and climate databases are frequently collected by researchers
working on field plots. These data represent the most detailed information
available for use in nitrogen models. Access to this information is through
the individual research scientists.
Management Databases
These databases represent summaries of management practices commonly practiced
in different regions of the country. They are really summaries of management
systems that include cropping practices, tillage and fertilizer methods,
irrigation practices, pest control, erosion and leaching control methods,
and others.
Model-specific Databases
Various models often have supporting databases. For example, the NLEAP model
has regional databases for soil and climate information. These databases
are designed to function with the specific model or models but may also
have other applications.
Variability of Field Data Needed to Drive
and Test Nitrogen Models
Model Output No Better Than Field Measurements
In general, the accuracy of model predictions cannot exceed the accuracy
of the input data used in the analysis. This is particularly true of the
more sensitive state variables. Also, in comparisons of model predictions
with observed field data, the model cannot be tested beyond the accuracy
of the field measurements.
Spatial and Temporal Variability in the Field
Field variability associated with measurements of soil residual nitrate-N
and nitrate-N leached is known to be quite high. The reasons for this are
numerous and include the complex, interrelated processes associated with
the carbon and nitrogen cycles, the spatial variability of the soil and
the management practices, and temporal variability of management as well
as state variables such as temperature and precipitation.
Capabilities of Soil Root Zone Models to
Predict Soil Nitrogen Status and Potential Nitrate Leaching
Status of Crop Residue, Manure, and Other Organic Amendment Pools
Many simulation models such as RZWQM, NTRM, NLEAP and CENTURY track the
nitrogen and carbon contents of residue additions during the decay process.
This information is useful in determining the stage of decay, potential
contributions to and immobilization from the soil mineral N pool, contributions
of carbon and nitrogen to the soil organic matter (humus) pools, and production
of CO2.
Status of Soil Humus Pools (Fast and Slow)
The size of these pools helps determine how much soil organic N is potentially
available for mineralization in a given year. Models such as CENTURY, RZWQM,
NLEAP and others are designed to track the carbon and nitrogen contents
of these pools over seasonal as well as longer time periods. This can provide
valuable information relative to trends in the readily mineralizable (No)
nitrogen pool, in the more stable nitrogen pools, and in the soil organic
carbon levels.
Nitrogen Uptake by Crops
Soil nitrogen models have the capability of estimating the amount of
mineral nitrogen (NH4-N and NO3-N)
available for crop uptake. This information can be used in conjunction with
a crop growth model or curve to estimate N uptake by the crop.
Gaseous Losses of Nitrogen
Soil nitrogen models have the capability of estimating soil gaseous
losses from denitrification (N2 and N2O)
and ammonia (NH3) volatilization (Hansen et al.
1994; Shaffer et al. 1991; Shaffer et al. 1992). Some soil models can also
estimate fluxes of carbon dioxide (CO2). These
capabilities have implications relative to studies involving greenhouse
gases. Soil models can make predictions of gas fluxes for a variety of soil,
climate, and management conditions that are difficult or too costly and
time consuming to measure in the field.
Status of Nitrate-N Available for Leaching (NAL) and Residual Soil Nitrate-N
NAL is defined as the mass of soil nitrate-N in the root zone that is available
for leaching after sources and sinks other than nitrate-N leaching have
been considered. Residual soil nitrate-N is NAL minus nitrate-N leached
from the root zone. NAL and residual soil nitrate-N in the root zone are
valuable indicators of potentially leachable nitrate-N as well as important
components of soil fertility status. Soil nitrogen models include these
components as part of their nitrogen budget calculations. In particular,
the models are capable of tracking these components over time during the
growing season and the off-season periods (Shaffer et al. 1994).
Nitrate-N Leached
Nitrate-N leached from the crop root zone is estimated in most soil nitrogen
models by combining an estimate of nitrate-N dissolved in the soil pore
water with estimates of soil water flux. The effects of dispersion and diffusion
are accounted for by the introduction of appropriate coefficients into the
solute transport equations. The effects of soil macropores on nitrate-N
leaching are included in some of the research level models such as RZWQM,
USDA-ARS (1992).
Potential Impacts on Groundwater Quality
Studies have shown that the mass of nitrate-N leached from the crop root
zone often is positively correlated with nitrate-N concentrations in shallow
underlying groundwater aquifers (Wylie et al. 1994). Nitrate-N leaching
models can be used in conjunction with appropriate cropping system, soil,
and climate data to make long-term estimates of annual nitrate-N leaching
across broad geographical areas (Shaffer et al. 1993; Shaffer et al. 1994b;
Wylie et al. 1994). Geographical Information System (GIS) maps of simulated
nitrate-N leached can be used to help identify potential leaching hot-spot
areas across an agricultural landscape. For example, a shallow alluvial
aquifer along the South Platte River near Greeley, Colorado, was evaluated
by Shaffer and Wylie (1994). Results for irrigated agriculture in the region
showed that long-term steady-state predictions of the NLEAP model nitrate-N
leached (NL) index were correlated with nitrate-N concentrations in the
underlying shallow aquifer (figure 1).
** Missing Image **
Capabilities of Soil Root Zone Models to Help
Design Best Management Practices for N Applications
Models can rapidly make long-term analyses as opposed to expensive and time-consuming
field experiments. A range of potential best management practices (BMP's)
can be evaluated using models, and the most promising ones can be field
tested. Model results can be used in conjunction with field demonstration
sites to help producers develop BMP's for their farms.
Examples of BMP Studies Using Models
Nitrogen models can be used to help determine management strategies that
reduce leaching of nitrate-N while maintaining crop yields. For example,
NLEAP simulations were used to determine the periods during the year when
nitrate-N leaching is most likely to occur for sites in Ohio, Colorado,
and North Dakota (Shaffer et al. 1994). This type of information is extremely
valuable from the standpoint of strategic planning of nitrogen fertilizer
applications and other N management techniques.
In another example (figure 2), NLEAP was used to simulate nitrate-N leaching
under a sandy loam and a loam soil for furrow, surge, and sprinkler irrigation
(Shaffer et al. 1994b). This type of model application provides a rapid
method of determining relative potential leaching under alternative management
scenarios.
** Missing Image **
Capabilities of Groundwater Nitrate-N Models
Groundwater models simulate water and solute transport, but processes such
as denitrification and N uptake by riparian vegetation are not well quantified.
These models are capable of simulating solute mixing and transport effects
within the aquifer. Losses in nitrate-N are simulated using empirical degradation
coefficients determined by calibration. Model examples include USGS-2D-Transport/MOC
(Konikow and Gredehoeft 1978) and MODFLOW (McDonald and Harbaugh 1988).
A major input to nutrient simulations in shallow aquifers often is nitrate-N
leached from the root zone.
Limitations of Nitrogen Models
Input Data Limitations on Model Applications
Most model applications are limited by the availability of input data. For
example, high-resolution field simulations (i.e., a few meters) of soil
nitrogen status and nitrate-N leaching are generally limited to field research
plots or fields where appropriate soil, climate, and management data are
available. Simulations involving larger areas such as whole farms, drainage
basins, and regions are limited to predictions of trends in a qualitative
and/or relative sense. For example, would higher or lower leaching be expected
under a given set of conditions as opposed to others? In such large-scale
situations, values for nitrate-N leached or residual soil nitrate-N cannot
be predicted at specific locations.
Use of Root Zone Models to Predict Aquifer Nitrate-N Concentrations
Root zone nitrogen models predict mass of nitrate-N leached and nitrate-N
concentrations in the leachate. They do not, however, account for processes
in the deep vadose zone and aquifer that can modify nitrate-N concentrations
in a shallow underlying aquifer. For example, denitrification, dilution
and mixing in the aquifer, aquifer sideflows, N uptake by deep-rooted riparian
vegetation, travel times through the deep vadose zone, and other factors
can make significant contributions to nitrate-N concentrations measured
in the aquifer. Root zone leachate volumes and nitrate-N concentrations
must be considered in conjunction with other factors in the deep vadose
zone and aquifer before predictions can be made of nitrate-N concentrations
in an associated shallow aquifer.
Quantification of Deep Vadose Zone and Aquifer Processes
Methods do not yet exist to adequately quantify certain processes such as
denitrifica-tion in the deep vadose zone and aquifer, or N uptake from a
shallow water table by deep-rooted vegetation.
Limitations in Testing and Evaluation of BMP's
Potential best management practice (BMP) benefits to nitrate-N leached cannot
be distinguished better than the resolution of the model and its associated
input data. For example, studies have shown that existing nitrate-N leaching
models applied using feasible levels of research plot and farm-field level
input data have a predictive resolution of about 20 to 50 lb N/ac/yr for
residual soil nitrate-N at the end of the growing season and for annual
soil nitrate-N leached below the root zone (Radke et al. 1991; Shaffer et
al. 1991; Khakural and Robert 1993; Follett et al. 1994; Shaffer et al.
1994b; Hoffner and Crookston 1994). This means that BMP's for farm fields
that are expected to alter nitrate-N leaching less than about 50 lb N/ac/yr
probably cannot be tested using a simulation model.
Conclusions
Numerous models are available that calculate nitrogen budgets and simulate
soil nitrogen processes within the crop root zone. These models have been
shown to be useful in making fertilizer recommendations, in estimating leaching
of nitrate-N below the crop root zone, in helping to design BMP's for efficient
use of soil nitrogen inputs, and in estimating and maintaining soil carbon
levels. Nitrogen models have also been developed that are useful in estimating
agricultural loading of N to surface streams and water bodies. Nitrogen
modeling associated with the deep vadose zone and shallow aquifers has been
limited primarily to conservative routing and dispersion of nitrate-N without
adequate consideration of source-sink processes within those regions.
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