- Appendix 1. CENTURY Reprints
- Appendix 2. Definitions for CENTURY Parameters
- Appendix 2. Definitions for CENTURY Parameters
- Appendix 2.1. Crop parameters (crop.100)
- Appendix 2.2. Cultivation parameters (cult.100)
- Appendix 2.3. Fertilization parameters (fert.100)
- Appendix 2.4. Fire parameters (fire.100)
- Appendix 2.5. Fixed parameters (fix.100)
- Appendix 2.6. Grazing parameters (graz.100)
- Appendix 2.7. Harvest parameters (harv.100)
- Appendix 2.8. Irrigation parameters (irri.100)
- Appendix 2.9. Organic matter addition parameters (omad.100)
- Appendix 2.10. Tree parameters (tree.100)
- Appendix 2.11. Tree removal parameters (trem.100)
- Appendix 2.12. Site specific parameters (<site>.100)
- Appendix 2.13. Output variables
- Appendix 2.2. Cultivation parameters (cult.100)
- Appendix 3. Sample Weather File and Atmospheric C14 Label
File
- Appendix 3.1. Sample Weather File for Weld County,
Colorado
- Appendix 3.2. Sample Atmospheric C14 Label File
- Appendix 3.2. Sample Atmospheric C14 Label File
- Appendix 4. Flow Diagrams for the Subroutines and Model Output
Variables
- Figure 2-1
- Figure 3-1
- Figure 3-2
- Figure 3-3
- Figure 3-4
- Figure 3-5
- Figure 3-6
- Figure 3-7
- Figure 3-8a
- Figure 3-8b
- Figure 3-9
- Figure 3-10
- Figure 3-11
- Figure 3-12
- Figure 3-13
- Figure 3-14
- Figure 3-15
- Figure 3-16
- Figure 3-17
- Figure 3-18
- Figure 3-19
- Figure 3-20
- Figure 3-21
- Figure 3-22
- Figure 3-23
- Figure 3-24
- Figure 3-1
- Appendix 5. CENTURY Data Files
- Appendix 5.1. CROP.100 Parameter Values
- Appendix 5.2. CULT.100 Parameter Values
- Appendix 5.3. FERT.100 Parameter Values
- Appendix 5.4. FIRE.100 Parameter Values
- Appendix 5.5. FIX.100 Parameter Values
- Appendix 5.6. GRAZ.100 Parameter Values
- Appendix 5.7. HARV.100 Parameter Values
- Appendix 5.8. IRRI.100 Parameter Values
- Appendix 5.9. OMAD.100 Parameter Values
- Appendix 5.10. TREE.100 Parameter Values
- Appendix 5.11. TREM.100 Parameter Values
- Appendix 5.12. <SITE.100> Parameter Values
- Appendix 5.2. CULT.100 Parameter Values
- Appendix 6. Addendum
CENTURY Soil Orgainc Matter Model Environment
Report any problems with this document via email
century@nrel.colostate.edu
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CENTURY Agroecosystem Version 4.0 was especially developed to deal with a wide range of cropping system rotations and tillage practices for system analysis of the effects of management and global change on productivity and sustainability of agroecosystems. Version 4.0 integrates the effects of climate and soil driving variables and agricultural management to simulate carbon, nitrogen, and water dynamics in the soil-plant system. Simulation of complex agricultural management systems including crop rotations, tillage practices, fertilization, irrigation, grazing, and harvest methods is now possible in this enhanced release of the model.
The CENTURY model is a general FORTRAN model of the plant-soil ecosystem that has been used to represent carbon and nutrient dynamics for different types of ecosystems (grasslands, forest, crops, and savannas). A brief description of the model structure and scientific basis for the model is included in this manual. Aspects of the current version are discussed in Metherell (1992). A more detailed description of the earlier development of the CENTURY model is contained in Parton et al. (1987), Parton et al. (1988), and Sanford et al. (1991).
The model is available on either the PC or UNIX platforms. The PC version is designed to work with the VIEW run time output module of "TIME-ZERO™: the integrated modeling environment." which allows the user to run the model and then generate graphic output analysis. Also available is a stand-alone PC version which produces ASCII text files and does not provide any graphics capabilities. The UNIX version is also stand-alone (with no graphics) and can be run on Sun, Hewlett-Packard, and IBM platforms. These platforms are suggested for batch processing of large numbers of sites.
This document will describe how to use the CENTURY model and the two utility programs which assist the user in creating the input files needed for CENTURY. Section two describes the components of the CENTURY environment and gives the installation instructions. Section three gives a brief description of the scientific basis for the model, with reference to actual variable names where applicable. The fourth section explains the variable parameterization program, FILE100. The fifth section gives instructions on how to use EVENT100, the scheduling utility. Section six explains how to run the CENTURY model for each of the available versions. Section seven describes a specific CENTURY scenario. Finally, section eight lists in bibliographical form the literature cited in the manual.
Note that the CENTURY model output names for the state variables and flows are shown in the figures (output names are shown in standard type under the state names shown in bold). Some of the output variables are not available in PC CENTURY. The exact definitions of these output variables are found in the *.def data files and are available through the FILE100 program. When running the model it is quite useful to have copies of flow diagram figures since they indicate the names of the output variables for the different submodels.
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2. CENTURY ENVIRONMENT
2.1. Overview of the CENTURY Environment
The program "CENTURYM" is a Fortran representation of the CENTURY SOM model which was developed by Parton et al. (1987). It simulates C, N, P, and S dynamics through an annual cycle to centuries and millennia. A grassland/crop, forest or savanna system may be selected as a producer submodel with the flexibility of specifying potential primary production curves which represent the site-specific plant community. While running the simulation, the program writes files which interface with the runtime output module of TIME- ZERO™ (called VIEW in this document). The use of this runtime module allows you to specify which variables are to be plotted or printed. Alternatively, the stand-alone version on either the PC or UNIX platforms creates a binary file and an ASCII list of selected variables can be created using the LIST100 utility.
The CENTURY environment (Figure 2-1) consists of the CENTURY model, which uses the VIEW output program, and two utilities. The FILE100 program assists the user in creating and updating any of the twelve data files used by CENTURY. The EVENT100 program creates the scheduling file which contains the agricultural plants and events that are to occur during the simulation.
The CENTURY model obtains input values through twelve data files. Each file contains a certain subset of variables; for example, the cult.100 file contains the values related to cultivation. Within each file there may be multiple options in which the variables are defined for multiple variations of the event. For example, within the cult.100 file, there may be several cultivation options defined such as plowing or rod-weeder. For each option, the variables are defined to simulate that particular option. Each data input file is named with a ".100" extension to designate it as a CENTURY file. These files can be updated and new options created through the FILE100 program.
The timing variables and schedule of when events are to occur during the simulation is maintained in the schedule file, named with a ".sch" extension. This file can be created and updated through the EVENT100 program.
First, the CENTURY environment must be installed on the computer to be used (see Section 2.5). Then, follow these steps to work through each facet of the environment:
- 1.
- Use the FILE100 program to update values or
create new options in any of the .100 data files
(Section 4).
- 2.
- Use the EVENT100 program to establish the
simulation time and to schedule events to occur during the simulation
(Section 5).
- 3.
- Run the CENTURY model. Refer to Section 6 for
specific instructions based on the PC or UNIX platform to be used.
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The CENTURY Environment consists of these files:
century.bat batch file used to run CENTURY and VIEW
centurym.exe the CENTURY executable model
centurym.tab table file generated by TIME-ZERO™ to handle I/O
centurym.dat master list of all variables used in CENTURY, not to be
modified by the user
temp.sav file required by VIEW
centuryx.exe the stand-alone CENTURY executable model
fix.100 file with fixed parameters primarily relating to organic matter
decomposition and not normally adjusted between runs
<site>.100 site-specific parameters such as precipitation, soil texture, and
the initial conditions for soil organic matter;
the name of this file is provided by the user
crop.100 crop options file
cult.100 cultivation options file
fert.100 fertilization options file
fire.100 fire options file
graz.100 grazing options file
harv.100 harvest options file
irri.100 irrigation options file
omad.100 organic matter addition options file
tree.100 tree options file
trem.100 tree removal options file
*.def for each *.100 file, there is a corresponding ".def" file which
contains the definitions of each parameter needed for
each option; the format of these ASCII files should
not be modified by the user
sample.wth sample weather file
c14data sample 14C data file
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2.3. Units of Major Parameters
Time step: one month (1/12 year or .083333 year) Minimum time: year Soil Organic Matter: grams C, N, P, or S per meter square Plant Material: grams C, N, P, or S per meter square Mineral pools: grams N, P, or S per meter square Temperature: degrees Centigrade Precipitation: centimeters per month
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2.4. Hardware Requirements for PC Version
The CENTURY Model plus the VIEW module from TIME-ZERO™ requires an IBM-PC or compatible with at least 512K of RAM. A graphics adapter (CGA, EGA, VGA, or Hercules monographic) is recommended. The model files supplied on the diskettes require approximately 220 kilobytes of disk space. The VIEW files require 394 kilobytes of disk space. An output file (CENTURYM.PLT) with data saved monthly for 100 years or annually for 1200 years requires 1-2 Mb of disk space.
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2.5. Installation of PC Version
The package contains 2 diskettes. Disk 1 is labeled VIEW, and contains the VIEW
module from TIME-ZERO™. Disk 2, labeled CENTURY, contains the CENTURY
environment files.
The CENTURY model files may be installed in any directory you wish. The
CENTURY diskettes contain an installation program. To run the installation program,
1. Insert Disk 1 into an appropriate drive (For example, drive A).
2. Change directories to the drive you chose:
A:
3. At the A:\ prompt enter,
INSTALLC C:\path ... \CENTURY
where path is the directory path to the location where you want to install
the model.
4. Follow the directions as they appear on the screen.
The installation procedure will create all the necessary directories and copy all files to the
appropriate directory.
During the installation of VIEW, you will be required to select a printer for the
screen dump utility. When the menu of printers is shown, select a printer using the
cursor keys, then press the TAB key and select the port to which the printer is connected.
Press RETURN when you are done.
There are two ways to set up the directory path so that the VIEW module can be
found by the CENTURY.BAT program.
1. Update the PATH statement in the AUTOEXEC.BAT file to include the
VIEW directory. This has the advantage that the path does not need to be
temporarily updated every time CENTURY is run.
OR
2. Allow the CENTURY.BAT file to temporarily update the PATH. If this
method is chosen, the batch file will need some environment space. If you
get an "OUT OF ENVIRONMENT SPACE" error message while running
CENTURY, modify your CONFIG.SYS file to provide additional
environment space. A typical entry to expand the environment space would
be,
SHELL=COMMAND.COM /P /E:512
where 512 is the number of bytes to be reserved for the environment. There
should also be at least 20 file handles reserved by the CONFIG.SYS. To
reserve 20 file handles, put the statement,
FILES=20
in the CONFIG.SYS. Be sure to CHECK THE DOS MANUAL for
instructions. Some versions of DOS prior to 3.2 used paragraphs (16
bytes/paragraph) as the argument in SHELL. If you try to reserve too much
space, DOS will ignore your /E: argument.
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2.6. Version 4.0 Upgrade Information
Minor upgrades to version 4.0 will be available free of charge via anonymous
ftp. These versions will be numbered 4.x and a text file will be included to
describe the changes.
Ftp to "ftp.nrel.colostate.edu", using "anonymous" as the name and your full
login name (e-mail address) as the password.
Change to the pub/century4.0 directory by typing "cd pub/century4.0".
You can get a listing of the contents of the directory by typing "dir".
Retrieve files by using the "get filename" command.
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3. CENTURY MODEL DESCRIPTION
3.1. Introduction
The CENTURY model simulates the long-term dynamics of Carbon (C), Nitrogen (N), Phosphorus (P), and Sulfur (S) for different Plant-Soil Systems. The model can simulate the dynamics of grassland systems, agricultural crop systems, forest systems, and savanna systems. The grassland/crop and forest systems, have different plant production submodels which are linked to a common soil organic matter submodel. The savanna model uses the grassland/crop and forest subsystems and allows for the two subsystems to interact through shading effects and nitrogen competition. The soil organic matter submodel simulates the flow of C, N, P, and S through plant litter and the different inorganic and organic pools in the soil. The model runs using a monthly time step and the major input variables for the model include:
(1) monthly average maximum and minimum air temperature,
(2) monthly precipitation,
(3) lignin content of plant material,
(4) plant N, P, and S content,
(5) soil texture,
(6) atmospheric and soil N inputs, and
(7) initial soil C, N, P, and S levels.
The input variables are available for most natural and agricultural
ecosystems and can generally be estimated from existing literature. Most of
the parameters that control the flow of C in the system are in the
fix.100 file.
The user can choose to run the model considering only C and N dynamics
(NELEM=1) or C, N, and P (NELEM=2) or
C, N, P, and S (NELEM=3).
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3.2. Soil Organic Matter Submodel
The SOM submodel is based on multiple compartments for SOM and is similar to
other models of SOM dynamics (Jenkinson and Rayner,
1977; Jenkinson, 1990;
van Veen and Paul, 1981). The pools and flows of C
are illustrated in Figure 3-1. The model includes three
soil organic matter pools (active, slow and passive) with different potential
decomposition rates, above and belowground litter pools and a surface microbial
pool which is associated with decomposing surface litter.
Above and belowground plant residues and organic animal excreta are partitioned
into structural (STRUCC(*)) and metabolic
(METABC(*)) pools as a function of the lignin to N ratio
in the residue. With increases in the ratio, more of the residue is
partitioned to the structural pools which have much slower decay rates than the
metabolic pools. The structural pools contain all of the plant lignin
(STRLIG(*)).
The decomposition of both plant residues and SOM are assumed to be microbially
mediated with an associated loss of CO2 (RESP(*)) as a
result of microbial respiration. The loss of CO2 on decomposition of the
active pool increases with increasing soil sand content. Decomposition
products flow into a surface microbe pool (SOM1C(1)) or
one of three SOM pools, each characterized by different maximum decomposition
rates. The potential decomposition rate is reduced by multiplicative functions
(DEFAC) of soil moisture and soil temperature and may be
increased as an effect of cultivation (CLTEFF(*),
cult.100). Average monthly soil temperature near the
soil surface (STEMP) is the input for the temperature
function while the moisture function uses the ratio of stored soil water (0-30
cm depth, AVH2O(3)) plus current month's precipitation
(RAIN) to potential evapotranspiration
(PET). The decomposition rate of the structural material
(STRUCC(*)) is a function of the fraction of the
structural material that is lignin. The lignin fraction of the plant material
does not go through the surface microbe (SOM1C(1)) or
active pools (SOM1C(2)) but is assumed to go directly to
the slow C pool (SOM2C) as the structural plant material
decomposes.
The active pool (SOM1C(2)) represents soil microbes and
microbial products (total active pool is ~2 to 3 times the live microbial
biomass level) and has a turnover time of months to a few years depending on
the environment and sand content. The soil texture influences the turnover
rate of the active soil SOM (higher rates for sandy soils) and the efficiency
of stabilizing active SOM into slow SOM (higher stabilization rates for clay
soils). The surface microbial pool (SOM1C(1)) turnover
rate is independent of soil texture, and it transfers material directly into
the slow SOM pool (SOM2C). The slow pool includes
resistant plant material derived from the structural pool and soil-stabilized
microbial products derived from the active and surface microbe pools. It has a
turnover time of 20 to 50 years. The passive pool (SOM3C)
is very resistant to decomposition and includes physically and chemically
stabilized SOM and has a turnover time of 400 to 2000 years. The proportions
of the decomposition products which enter the passive pool from the slow and
active pools increase with increasing soil clay content.
A fraction of the products from the decomposition of the active pool is lost as
leached organic matter (STREAM(5)). Leaching of organic
matter is a function of the decay rate for active SOM, and the clay content of
the soil (less loss for clay soils) and only occurs if there is drainage of
water below the 30 cm soil depth (leaching loss increases with increasing water
flow up to a critical level - OMLECH(3),
fix.100).
Anaerobic conditions (high soil water content) cause decomposition to decrease.
The soil drainage factor (DRAIN,
<site>.100) allows a soil to have differing
degrees of wetness (e.g., DRAIN=1 for well drained sandy
soils and DRAIN=0 for a poorly drained clay soil).
A detailed description of the structure of an earlier version of the model and
the way in which model parameters were estimated is found in
Parton et al. (1987) (see
Appendix 1).
The model has N, P, and S pools analogous to all of the C pools. Each SOM pool
has an allowable range of C to element ratios based on the conceptual model of
McGill and Cole (1981). Reflecting the concept that
N is stabilized in direct association with C, C to N ratios are constrained
within narrow ranges, while the ester bonds of P and S allow C to P and C to S
ratios to vary widely. The ratios in the structural pool are fixed at high
values, while the ratio in the metabolic pool is allowed to float in concert
with the nutrient content of the plant residues. The actual ratios for
material entering each SOM pool are linear functions of the quantities of each
element in the labile inorganic mineral pools in the surface soil layers
(MINERL(1,*)). Low nutrient levels in the labile pools
result in high C to element ratios in the various SOM pools. The N, P, and S
flows between SOM pools are related to the C flows. The quantity of each
element flowing out of a particular pool equals the product of the C flow and
the element to C ratio of the pool. Mineralization or immobilization of N, P,
and S occurs as is necessary to maintain the ratios discussed above. Thus,
mineralization of N, P, and S occurs as C is lost in the form of CO2 and as C
flows from pools with low ratios, such as the active pool, to those with higher
ratios, such as the slow pool. Immobilization occurs when C flows from pools
with high ratios, such as the structural pool, to those with lower ratios, such
as the active pool. The decomposition rate is reduced if the quantity of any
element is insufficient to meet the immobilization demand.
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3.3. Water Budget, Leaching and Soil Temperature
The CENTURY model includes a simplified water budget
model which calculates monthly evaporation (EVAP) and
transpiration (TRAN) water loss, water content of the soil
layers (ASMOS(*)), snow water content
(SNOW), and saturated flow of water between soil layers
(Figure 3-2). If the average air temperature
(TAVE) is less than 0 C monthly precipitation
(RAIN) occurs as snow. Sublimation and evaporation of
water from the snow pack occurs at a rate equal to the potential
evapotranspiration rate (PET). Snow melt occurs if the
average air temperature is greater than 0 C and is a linear function of the
average air temperature.
The potential evapotranspiration rate
(PET) is calculated as a function of the average monthly
maximum (TMX2M(*)) and minimum
(TMN2M(*)) air temperature using the equations developed
by Linacre (1977) and may be modified by a user
specified multiplier (FWLOSS(4),
fix.100). Bare soil water loss is a function of
standing dead and litter biomass (lower for high biomass levels),
rainfall and PET. Interception water
loss is a function of aboveground biomass (increases with biomass level),
rainfall and PET. Potential
transpiration water loss (PTTR) is a function of the live
leaf biomass and PET. Interception and bare soil water
losses are calculated as fractions of the monthly precipitation and are
subtracted from the total monthly precipitation, with the remainder of the
water added to the soil.
Water is distributed to the different layers by adding the water to the top
layer (0-15 cm, ASMOS(1)) and then draining excess water
(water above field capacity) to the next layer. Transpiration water loss
(TRAN) occurs after the water was added to the soil. Water
loss occurs first as interception, followed by bare soil evaporation and
transpiration (the sum does not exceed the PET rate). The
maximum monthly evapotranspiration water loss rate is equal to
PET.
Depending on the value of SWFLAG
(<site>.100), the field capacity
(AFIEL(*), <site>.100) and
wilting point (AWILT(*),
<site>.100) for the different soil layers can
optionally be input from the <site>.100 file or
calculated as a function of the bulk density (BULKD,
<site>.100), soil texture
(SAND, SILT, CLAY,
<site>.100), and organic matter content
(SOMSC) using a choice of equations developed by
Gupta and Larson (1979) or
Rawls et al. (1982). The number of soil layers
(NLAYER, <site>.100) is an
input variable in the model. 15 cm increments were used for each layer up to
the 60 cm soil depth and 30 cm increments below the 60 cm depth
(LAYER = 4 has this structure: 0-15,15-30,30-45,45-60,
and NLAYER = 6 has this structure: 0-15,15-30,30-45,
45-60,60-90,90-120). Water leached below the last soil layer is not available
for evapotranspiration and is a measure of interflow, runoff or leaching losses
from the soil profile. Water going below the profile can be lost as storm flow
(STORMF, <site>.100 -
fraction lost as fast stream flow) or leached into the subsoil where it can
accumulate or move into the stream flow (STREAM(1)) at a
specified rate (BASEF,
<site>.100 - fraction per month of subsoil H2O
going into stream flow). The model can simulate watershed stream flow by
adjusting STORMF and BASEF.
Leaching of labile mineral N, (NO3 + NH4), P, and S pools occurs when there is
saturated water flow between soil layers. The fraction of the mineral pool
that flows from the upper layer to the lower layer is a function of the sand
content (increasing with increasing sand content -
FLEACH(1) and FLEACH(2),
fix.100) and the amount of water that flows between
layers (linear function up to a maximum value - MINLCH,
fix.100 cm per month).
FLEACH(3), FLEACH(4) and
FLEACH(5) (fix.100) control
inorganic N, P, and S leaching respectively. Monthly watershed losses of H2O
(STREAM(1)), inorganic N, P, and S
(STREAM(2), STREAM(3) and
STREAM(4)), and organic C, N, P, and S
(STREAM(5), STREAM(6),
STREAM(7), and STREAM(8)) are
simulated by the model.
Average monthly soil temperature near the soil surface
(STEMP) is calculated using equations developed by
Parton (1984). These equations calculate maximum
soil temperature as a function of the maximum air temperature and the canopy
biomass (lower for high biomass) while the minimum soil temperature is a
function of the minimum air temperature and canopy biomass (higher for higher
biomass). The actual soil temperature (STEMP) used for
decomposition and plant growth rate functions is the average of the minimum and
maximum soil temperatures.
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The N submodel (Figure 3-3) has the same structure as
the soil C model. The N flows follow the C flows (Figure
3-3, N flows between organic pools not shown can be seen in
Figure 3-1) and are equal to the product of the carbon
flow and the N:C ratio of the state variable that receives the carbon. The C:N
ratio of the structural pools (150) remains fixed while the N contents of the
metabolic pools vary as a function of the N content of the incoming plant
residue. The C:N ratio of newly formed surface microbial biomass is a function
of the N content of the material being decomposed (increases for low N
content). The C:N ratios of organic matter entering each of the three soil
pools vary as linear functions of the size of the mineral N pool. As mineral N
in the surface soil layer increases from 0 to 2 g N / m2, the C:N ratios
decrease from 15 to 3 for the active pool, from 20 to 12 for the slow pool and
from 10 to 7 for the passive pool. The C:N ratio for slow material formed from
surface microbial biomass is a function of C:N ratio of the surface microbe pool.
The N associated with carbon lost in respiration (30% to 80% of the carbon flow
is respired) is assumed to be mineralized. Given the C:N ratio of the state
variables and the microbial respiration loss for each flow, decomposition of
metabolic residue, active, slow, and passive pools generally result in net
mineralization of N, while decomposition of structural residue immobilizes
N.
The model uses simple equations to represent N inputs due to atmospheric
deposition and soil and plant N fixation. Atmospheric N inputs
(EPNFA(*), <site>.100) are a
linear function of annual precipitation (PRCANN). The
model has the option (NSNFIX) of calculating soil N
fixation rates as a function of the mineral N to labile P ratio (high fixation
with lower ratios) or as a linear function (EPNFS(*),
<site>.100) of annual precipitation. Symbiotic
plant N fixation (SNFXAC,
crop.100) is assumed to occur only when there is
insufficient mineral N to satisfy the plant N requirement, having taken into
account all possible growth reductions including P or S deficiency. Symbiotic
N fixation can occur up to a maximum level of g N fixed per g C fixed
(SNFXMX, crop.100) specified for
each crop type and is hence related to the plant growth rate. The model also
includes fertilizer N inputs and N inputs through organic matter additions (see
parameters in the fert.def and
omad.def files, Appendix 2).
The losses of N due to leaching of NO3 are related to soil texture and the
amount of water moving through the soil profile (see water
flow submodel description, Section 3.3). Losses
accumulate in the layer below the last soil layer
(MINERL (NLAYER+1,1)) or are lost in the stream
flow (STREAM(2)). Loss of organic N
(STREAM(6) occurs with the leaching of organic matter.
Gaseous losses of N compounds associated with mineralization /nitrification
(VOLGMA), denitrification (VOLEXA),
volatilization from maturing crops or senescing grassland
(VOLPLA) are calculated. Losses due to crop removal,
burning, transfer of N in animal excreta, and soil erosion are also accounted
for.
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The P submodel (Figure 3-4) has the same general
structure as the N submodel. The major difference is that there are five
mineral P pools (labile P (PLABIL), sorbed P, strongly
sorbed P (SECNDY(2)), parent P
(PARENT(2), and occluded P
(OCCLUD)). The phosphorus submodel
(Figure 3-4) has been revised to give a better
representation of phosphorus sorption. Because CENTURY uses a relatively long
timestep (¼ month for the soil nutrient submodel) and soil solution very
rapidly equilibriates with the labile fraction of adsorbed P
(Cole et al., 1977) it is not appropriate to use soil
solution P for the available nutrient pool. Instead, a labile P pool
(PLABIL) has been defined, equivalent to resin
extractable P, which is in equilibrium with a sorbed P pool
(Figure 3-5). The equilibrium between the labile and
sorbed P pools is recalculated after any P additions or removals from the soil.
The sum of labile P and sorbed P are represented by the state variable
MINERL(1,2). Plant uptake, immobilization and leaching
of P (if allowed) are controlled by the size of the labile P pool. The
fraction of labile P that is available for plant uptake varies from 0.4 to 0.8
as a function (FAVAIL(*)) of the mineral N pool size
(higher fractions for high mineral N levels). As more P is removed through
plant and soil microbial uptake, larger amounts become immobilized in organic
matter.
The equilibrium relationship between labile P and sorbed P is defined in terms
of two parameters, sorption affinity (PSLSRB,
<site>.100) and sorption maximum
(SORPMX, <site>.100). The
sorption affinity parameter controls the fraction of the labile plus sorbed
pools which is in the labile pool at low levels of P in these pools. The
sorption maximum is the maximum amount of P which can be in the sorbed P pool.
The sorption maximum controls the curvature of the relationship between labile
P and the sum of the labile and sorbed P pools.
The sorbed P is in dynamic equilibrium (PSECMN(2),
PMNSEC(2), fix.100) with a more
strongly sorbed P pool (SECNDY(2)) which may in turn
lose P (PSECOC, fix.100) to an
occluded P pool (OCCLUD). Phosphorus can enter the
cycling P pools by weathering of parent material P
(PARENT(2)), which is typically apatite. The rate of
weathering (PPARMN(2), fix.100)
can be a function of soil texture (TEXEPP(*),
fix.100) (higher for fine textured soils). The rate of
these P flows are all multiplied by the same moisture and temperature functions
(DEFAC) that are used for organic matter
decomposition.
The organic part of the P submodel operates in the same way that the N submodel
works; C:P ratios of organic fractions are fixed for the structural P pool
(500) and vary as a function of the labile P pool
(PLABIL) for the active (30-80), slow (90-200), and
passive (20-200) SOM pools. C:P ratios of newly formed surface microbes are
functions of the P content of the material decomposing, and the C:P ratio of
slow material formed from the surface microbes is a function of the C:P ratio
of surface microbes. The flows for the organic P pools are calculated in
exactly the same way as organic N flow.
Phosphorus losses from the system occur as result of leaching of labile P
(MINERL(NLAYER+1,2) - P losses accumulate in
the soil layer below the last layer) and organic P compounds
(STREAM(7)), soil erosion, crop removal, grazing, and
burning P losses. P additions come from P fertilizer and organic matter
additions (see parameters in the fert.def and
omad.def file).
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The structure of the sulfur submodel (Figure 3-6) is similar to the P submodel. The only major difference is that the S model does not include occluded or sorbed pools. The main source of S in most soils is the weathering of primary minerals. Secondary S is formed as a result of adsorption of S on clay minerals. Organisms in the soil and plant roots take up S from soil solution (MINERL(layer,3) and start the formation of organic S compounds. The organic component of the S model operates in the same way as the organic N and P submodels with the C:S ratio of the structural pool being fixed (500) while the C:S ratios for the active (20-80), slow (90-200) and passive (20-200) pools vary as a function of the labile S pool (MINERL(1,3)). C:S ratios for surface microbes are calculated in the same way as the C:N and C:P ratios. The C:S ratios for the organic components are specified in the file fix.100 (see Appendix 2). The organic S flows are calculated in the same manner as the organic N and P flows while the inorganic S flows are functions of specified rate parameters (PPARMN(3), PSECMN(3), PMNSEC(3), fix.100) and the moisture and temperature functions that are used for organic matter decomposition (DEFAC). The model allows for S fertilization, addition of organic S material (see parameters in the fert.def and omad.def files, Appendix 2), atmospheric deposition (SATMOS(*), <site>.100), S in irrigation water (SIRRI, <site>.100), and accounts for S losses due to crop removal, grazing, leaching of organic S compounds (STREAM(8)), erosion of SOM, and fire. The S submodel has not been as well tested as the N and P submodels. Parton et al. (1988), Metherell (1992), and Metherell et al. (1993a) describe interactions of S with C, N, and P. The S model could be set up to simulate K dynamics instead of S dynamics if K is a limiting factor in particular soils.
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3.7. Plant Production Submodels
The CENTURY model is set up to simulate the dynamics of
grasslands, agricultural crops,
forests, and savanna (tree-grass)
systems. The grassland/crop production model simulates
plant production for different herbaceous crops and plant communities (e.g.
warm or cool season grasslands, wheat and corn). Grassland/crop options are
selected from the crop.100 file. Existing crop options
may be altered to suit particular varieties or environments or new options
created using the FILE100 program. Harvest, grazing,
fire and cultivation can all directly effect aboveground biomass, while grazing
and fire may also impact root to shoot ratios and nutrient content. The
forest model simulates the growth of deciduous or
evergreen forests in juvenile and mature phases. Fire, large scale
disturbances (e.g. hurricanes), and tree harvest practices may impact forest
production. The savanna system is simulated as a
tree-grass system, essentially using the existing tree
and grassland/crop submodels with the two subsystems
interacting through shading effects and nitrogen competition.
Both plant production models assume that the monthly maximum plant production
is controlled by moisture and temperature and that maximum plant production
rates are decreased if there are insufficient nutrient supplies (the most
limiting nutrient constrains production). The fraction of the mineralized
pools that are available for plant growth is a function of the root biomass
with the fraction of nutrients available for uptake increasing exponentially as
live root biomass increases from 20 to 300 gm-2. Most forest or grassland/crop
systems are limited by nutrient availability and generally respond to the
addition of N and P. The savanna model modifies maximum
grass production by a shade modifier that is a function of tree leaf biomass
and canopy cover. Additional nutrient constraints on plant production due to
nutrient allocation between trees and grasses decrease maximum production rates
for the grasses.
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3.7.1. Grassland/Crop Submodel
The model can simulate a wide variety of crops and grasslands by altering a
number of crop specific parameters (see Appendix 2 for the
crop.100 parameters). CENTURY is not designed to be a
plant production model and some parameters may have to be calibrated for
specific environments.
The plant production model (Figure 3-7) has pools for
live shoots and roots, and standing dead plant material. Potential production
(g C / m2 / month) is a function of a genetic maximum defined for each crop
(PRDX(1), crop.100) and 0-1
scalars depending on soil temperature, moisture status, shading by dead
vegetation, and seedling growth.
The maximum potential production of a crop, unlimited by temperature, moisture
or nutrient stresses, is primarily determined by the level of
photosynthetically active radiation, the maximum net assimilation rate of
photosynthesis, the efficiency of conversion of carbohydrate into plant
constituents, and the maintenance respiration rate
(van Heemst, 1986). Thus, the parameter for
maximum potential production (PRDX(1)) has both genetic
and environmental components. However, in CENTURY, the seasonal distribution
of production is primarily controlled by the temperature response function
rather than the seasonal variation in photosynthetically active radiation, so
the maximum potential production parameter should reflect aboveground crop
production in optimal summer conditions. This parameter will frequently be
used to calibrate the predicted crop production for different environments,
species, and varieties. In the CENTURY model formulation the potential
production is based on aboveground production, therefore root-shoot allocation
must also be taken into account. The value used should be set according to
estimates of potential crop production. In general, C4 species have higher
potential growth rates than C3 species because of higher maximum net
assimilation rates (van Heemst, 1986). The range
of potential production from 200 to 580 kg DM / ha / day corresponds to 240 to
700 g C / m2 / month.
The growth of most plant species exhibits a response curve to root temperature
which is sigmoidal up to an optimum temperature, has a band of optimum
temperatures over which there is relatively little effect on growth, and a
rapid decline above the optimum (Cooper, 1973).
Plant growth rates will depend on the combined temperature response of
photosynthesis and respiration. For most temperate species the lower limit at
which the rate of development is perceptible is between zero and 5 C.
Development increases in rate up to an optimum of 20 to 25 C and then
declines to an upper limiting temperature between 30 and 35 C. For tropical
species the base, optimum and maximum temperatures are approximately 10 higher
(Monteith, 1981). In the CENTURY model the
temperature response curve can be parameterized for each crop using a
generalized Poisson density function (PPDF(1...4),
crop.100) as shown in Figure
3-8.
The moisture status effect reduces growth when
The slope of the linear relationship is dependent on the available soil water
holding capacity, which varies with soil texture (Figure
3-9). This effect of soil texture has been observed in field data
(Sala et al., 1988) and accounts for the "reverse
texture effect" (Noy-Meir, 1973), in which the
greater infiltration rate and hence lower bare soil evaporation rate in coarser
textured soils results in higher production in arid environments.
NLAYPG (<site>.100) is the
number of soil layers that control plant growth (e.g. 0-60 cm depth for
NLAYPG=4 and 0-45 cm depth for
NLAYPG=3) and can be less than or equal to the total
number of soil layers.
The shading effect on potential growth rate is a response surface dependent on
the amounts of live and dead vegetation. This function, which was originally
developed for the tall grass prairie, was found to be too restrictive for
no-till cropping systems. Therefore, the magnitude of the effect has been
greatly reduced for crops by increasing the value of BIOK5
(crop.100).
A scaling factor for crops growing from seedlings
(PLTMRF, FULCAN,
crop.100) reflects the partial interception of light
with less than a full canopy present (Figure 3-10).
This factor takes effect after a PLTM (planting month)
command in EVENT100, but not after a
FRST (first month of growth) command.
Root growth is proportional to potential shoot growth, but the allocation of
carbon to root growth can be made a function of time since planting
(FRTC(1...3)) (Figure 3-11) to
reflect the dominance of root growth in seedling cereal crops or the initial
dominance of shoot growth in root crops. To account for winter dormancy the
root - shoot ratio does not change in months when soil temperature is below 2
C (RTDTMP, crop.100). In an
alternative formulation (FRTC(1) = 0.0) developed for Great
Plains grasslands, the root-shoot ratio is controlled by annual precipitation
(Parton et al., 1987) as shown in
Figure 3-12.
The actual production is limited to that achievable with the currently
available nutrient supply with plant nutrient concentrations constrained
between upper and lower limits set separately for shoots and roots. Invoking
Liebig's Law of the Minimum, the most limiting nutrient
(ELIMIT) constrains production
(RELYLD). The limits of nutrient content for shoot
growth are a function of plant biomass in order to reflect the changing
nutrient content with plant age (Figure 3-13). The user
specifies the effect of live shoot biomass on maximum and minimum nutrient
content (BIOMAX, PRAMN(*,*),
PRAMX(*,*), crop.100). This
formulation does cause some anomalies when growth is limited by nutrients, as a
nutrient limited crop can have a higher nutrient concentration than an
unlimited crop of the same age with greater biomass. The limits on nutrient
content of roots are a function of annual precipitation
(PRBMN(*,*), PRBMX(*,*),
crop.100). CENTURY also incorporates a function to
restrict nutrient availability in relation to root biomass (RTIMP;
Figure 3-14). For legume crops the potential rate of
symbiotic nitrogen fixation is specified in terms of grams N fixed per gram C
fixed (SNFXMX, crop.100). It is
assumed that plant available soil N will be preferentially used by the crop.
All other potential limitations to growth, including P and S supply, are taken
into account before calculating symbiotic N2 fixation.
Fertilizer addition can be either fixed amounts (FERAMT,
fert.100) or calculated automatically according to the
crop requirements. The automatic option (AUFERT,
fert.100) can be set to maintain crop growth at a
particular fraction of potential production with the minimum nutrient
concentration or to maintain maximum production with plant nutrient
concentrations at a nominated level between the minimum and maximum for that
growth stage.
At harvest, grain is removed from the system and live shoots can either be
removed or transferred to standing dead and surface residue. For grain crops a
harvest index is calculated based on a genetic maximum
(HIMAX, crop.100) and moisture
stress (HIWSF, crop.100) in the
months corresponding to anthesis and grain fill
(HIMON(1,2), crop.100) as shown in
Figure 3-15. Moisture stress is calculated as the ratio
of actual to potential transpiration in these months. The fractions of
aboveground N, P, and S partitioned to the grain are crop-specific constants
(EFRGRN(*), crop.100) modified by
the square root of the moisture stress term, resulting in higher grain nutrient
concentrations when moisture stress reduces the harvest index. At harvest a
proportion of the aboveground nitrogen is lost to volatilization
(VLOSSP, crop.100). The crop
harvest routine also allows for the harvest of roots, hay crops or straw
removal after a grain crop (see harv.100; Appendix 2).
The crop may be killed at harvest, as for cereal grain crops, or a fraction of
roots and shoots may be unaffected by harvest operations and growth may
continue.
The crop model allows for the death of shoots and roots during the growing
season. Shoot and root death are functions of available soil water in the
whole profile and the plant root zone respectively (Figure
3-16). Both are multiplied by crop specific maximum death rates
(FSDETH(1), RDR,
crop.100). Shoot death rates may be further increased
(FSDETH(3)) due to shading if the live biomass is
greater than a critical level (FSDETH(4)). Root death
is only allowed to occur when roots are physiologically active, defined by soil
temperature being greater than 2 C (RTDTMP,
crop.100). In months nominated as senescence months
the shoot death rate is set to a fixed fraction of live biomass
(FSDETH(2)). Standing dead material is transferred to
surface litter at a crop specific relative fall rate
(FALLRT, crop.100).
Plant lignin contents (FLIGNI(*,*),
crop.100) are specified for shoots and roots, and may
be constants or a linear function of annual precipitation
(Parton et al., 1992). They should reflect the
lignin content of senescent plant material.
The effects of grazing and fire on plant production are represented in the
model by using data from Holland et al. (1992) and
Ojima et al. (1990). The major impact of fire is to
increase the root to shoot ratio (FRTSH,
fire.100), increase the C:N ratio of live shoots and
roots (FNUE(*), fire.100), remove
vegetation and return nutrients during the years when fire occurs
(Ojima et al. 1990). Grazing removes vegetation,
returns nutrients to the soil, alters the root to shoot ratio, and increases
the N content of live shoots and roots (Holland et al.
1992). The model has three options (GRZEFF = 0, 1,
2) for dealing with the impact of grazing on the system. For option 1
(GRZEFF=0) there are no direct impacts of grazing on
plant production except for the removal of vegetation and return of nutrients
by the animals. Option 2 (GRZEFF=1) is referred to as
the lightly grazed effect (Holland et al., 1992)
and includes a constant root:shoot ratio (not changing with grazing) and a
linear decrease in potential plant production with increasing grazing
intensity. Option 3 (GRZEFF=2) is referred to as the
heavy grazed (Holland et al., 1992) option and
includes a complex grazing optimization curve where aboveground plant
production is increased for moderate grazing and decreasing sharply for heavy
grazing levels (<40% removed per month). The root:shoot ratio is constant
for low to moderate grazing levels and decreases rapidly for heavy grazing
levels. In all three options the nutrient content of new shoot will increase
in relation to the residual biomass (PRAMN(*,*),
PRAMX(*,*), BIOMAX,
crop.100).
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The forest plant production model (Figure 3-17)
divides the tree into leaves, fine roots, fine branches, large wood, and coarse
roots with carbon and nutrients allocated to the different plant parts using a
fixed allocation scheme. Maximum monthly gross production is calculated as the
product of maximum gross production rate (PRDX(2),
tree.100), moisture, soil temperature and live
leaf-area-index terms. The effect of moisture and temperature on potential
productions are the same functions used for the monthly grassland model
(Figures 3-8 and 3-9), while the
effect of live leaf-area-index on production is shown in
Figure 3-18. Plant respiration is calculated as a
function of wood N content and temperature using an equation developed by
Ryan (1991) and subtracted from the gross production
rate in order to get the net potential production rate. The net potential
production rate is not allowed to exceed the tree specific maximum net
production rate (PRDX(3) times the other limiting
factors). The model assumes that only the sapwood part of the tree respires C
and the sapwood fraction of aboveground large wood biomass is calculated using
the relationship shown in Figure 3-19. The same sapwood
fraction is used for coarse woody roots (Ryan, 1991).
The leaf biomass is not allowed to exceed a maximum value that is a function of
the live wood biomass (Figure 3-20). This function
specifies the effect of tree allometry and structure on maximum leaf area and
is potentially different for different species. Some of the important forest
specific parameters include the maximum gross and net production rates
(PRDX(2), PRDX(3),
tree.100), the leaf area index to wood biomass
relationship parameters (MAXLAI,
KLAI, tree.100), the sapwood to
large wood C ratio parameter (SAPK,
tree.100), and the allocation of C into different plant
parts (FCFRAC(1-5,1-2),
tree.100).
The model has two carbon allocation patterns for young and mature forests and
can represent either deciduous forests or forests that grow continuously. With
a continuous growth or evergreen forest the death of the live leaves is
specified as a function of month (LEAFDR(1-12),
tree.100), while with a deciduous forest the leaf death
rate is very high at the senescence month. For deciduous forest the leaf
growth rate is also much higher during the first month of leaf growth. Dead
leaves and fine roots are transferred to the surface and root residue pools
and are then allocated into structural and metabolic pools. Dead fine branch,
large wood, and coarse root pools receive dead wood material from the live
fine branch, large wood, and coarse root pools respectively. Each dead wood
pool has a specific decay rate. The dead wood pools decay in the same way
that the structural residue pool decomposes with lignin going to the slow SOM
pool and the non-lignin fraction going to surface microbes or active SOM
pool (above- or belowground material). The decay rates of the dead wood pools
are also reduced by the temperature and moisture decomposition functions,
and include CO2 losses.
A forest removal event, which is defined in the trem.100
file, can simulate the impact of different forest harvest practices, fires, and
the effect of large scale disturbances such as hurricanes. For each
disturbance or harvest event, the fraction of each live plant part lost and the
fraction of material that is returned to the soil system is specified (see
trem.def Appendix 2). Death of fine and coarse roots
are also considered in the removal event along with the removal of dead wood.
Another feature is that the nutrient concentration of live leaves that go into
surface residue can be elevated above the dead leaf nutrient concentration
(e.g. simulating the effect of adding live leaves to surface residue as a
result of hurricane disturbance) by specifying the return nutrient fraction of
the leaves to be greater than one (RETF(1,*),
trem.100).
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The savanna model is a coupled tree-grass system and uses the
forest and grassland/crop submodels
already described. The fundamental difference in the savanna submodel is the
manner in which total system production is obtained. Total system production
is the sum of forest and grass production. Potential maximum production of
forest is computed in the manner described above.
Grassland/crop production is modified to include the effect of tree canopy
cover on grassland/crop production. A shade modifier is calculated as a
function of the canopy cover and leaf biomass (Figure
3-21) and is multiplied by the normal grassland/crop production equation
(see Grassland/Crop Submodel, Section
3.7.1). Increasing canopy cover and leaf biomass reduces the potential
grass production. Removal of grass or forest is accomplished independently
with the FIRE and TREM commands
in EVENT100, so that user can specify fire intensity
and frequency as desired. Fire removal parameters for grassland/crop
vegetation are specified in fire.100, while forest fire
parameters are specified in trem.100. In this manner,
a grass fire can occur at a higher intensity and/or frequency than fires
affecting forest combustion losses. In the present model, fire does not
influence tree distribution and establishment.
Nitrogen competition is the other major interaction between the forest and
grass systems. The interaction is controlled by the amount of tree basal area,
total nitrogen available, and site potential for plant production. The
fraction of N available for tree uptake is calculated as a function of tree
basal area (m2 ha-1) and available mineral N using the function shown in
Figure 3-22. The fraction of N uptake by grass is one
minus the forest fraction and if grass N uptake did not consume all of the N
allocated to it, this amount is added to the pool of N which is available to
the trees. Two important site-specific parameters for the savanna model are
the site potential parameter (SITPOT,
tree.100) and the basal area conversion factor
(BASFCT, tree.100) which
calculates tree basal area as a function of large wood C level.
SITPOT controls how fast trees can dominate grasslands
with lower numbers (1200 vs. 2400) leading to quicker dominance by trees.
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Fertilizer addition can be either fixed amounts
(FERAMT(*), fert.100) or
calculated automatically (AUFERT <> 0.0,
fert.100) according to the crop requirements. The
automatic option can be set to maintain crop growth at a particular fraction of
potential production with the minimum nutrient concentration (0.0 <
AUFERT <= 1.0) or to maintain maximum production with
plant nutrient concentrations at a nominated level between the minimum and
maximum for that growth stage (1.0 < AUFERT
<= 2.0).
Organic matter additions are specified in omad.100.
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Irrigation amounts can be either fixed amounts (IRRAMT, irri.100) or automatically set (AUIRRI, irri.100) according to the soil moisture status. Automatic irrigations are scheduled if the available water stored in the plant root zone falls below a nominated fraction of the available water holding capacity (FAWHC, irri.100). The amount of water applied by the automatic option allows for the addition of a nominated amount of water (IRRAUT, irri.100) or for irrigation up to field capacity or up to field capacity plus an allowance for potential evapotranspiration.
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Cultivation options allow for the transfer of defined fractions (CULTRA(*), cult.100) of shoots, roots, standing dead and surface litter into standing dead, surface and soil litter pools as is appropriate. Thus the model can simulate a variety of conventional cultivation methods, such as plowing or sweep tillage, thinning operations or herbicide application. Each cultivation option also has parameters (CLTEFF(*) cult.100) for the multiplicative effect of soil disturbance by cultivation on organic matter decomposition rates for the structural, active, slow and passive pools. The values for these parameters range from 1.0 to about 1.6 with the actual value dependant on the degree of soil stirring and disruption caused by each implement.
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The grazing options can be parameterized to remove defined fractions of aboveground live (FLGREM, graz.100) and standing dead (FDGREM, graz.100) plant material each month. The fractional returns of C (GFCRET, graz.100), N, P, and S (GRET(*), graz.100) are specified, having allowed for losses in animal carcasses and milk, transfer of dung and urine off the area being simulated, volatile losses of N from dung and urine patches, and leaching of N and S under urine patches. The proportion of N, P, and S returned in organic forms (FECF(*), graz.100) is also specified as is the lignin content of the feces (FECLIG, graz.100). As discussed above in Section 3.7.1, grazing can have variable effects on plant production (GRZEFF, graz.100).
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The effect of different intensities of fire in herbaceous vegetation can be parameterized by specifying the fractions of live shoots (FLFREM, fire.100), standing dead (FDFREM(1), fire.100) and surface litter (FDFREM(2), fire.100) removed by a fire along with the return of N, P, and S in inorganic forms. As discussed above in Section 3.7.1, fire can also affect plant growth.
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3.13. Labeled C Simulation (14C and 13C)
The CENTURY model can simulate labeling by either 14C or 13C.
C labeling is specified in the .sch schedule file,
created by EVENT100. The 14C simulations act as a
labeled tracer from atmospheric sources or added organic matter
(ASTLBL, omad.100). The
c14data file contains a record of atmospheric 14C
concentrations which are used by the model to label new plant material, which
then flows through the other organic matter pools. A sample
c14data data file is included on the CENTURY
diskette.
Simulations using the option for 13C give a constant label to plant material
based on the value of DEL13C in the crop.100 and
tree.100 files. This option will primarily be of use
to follow the change in stable isotope signal when there has been a switch from
C3 to C4 vegetation or vice-versa. Fractionation of the stable carbon isotopes
is included in the model as discussed below.
The 13C/12C ratio in soil organic matter remains close to the ratio in the
original vegetation, but fractionation during decomposition of the plant
residues and soil organic matter can produce significant changes in the ratio.
The magnitude and direction of the change in the ratio may vary with time
and the prevailing environmental conditions (Stout and
Rafter, 1978; Stout et al., 1981).
13C/12C ratios are expressed relative to a standard as delta 13C values, where
The standard is carbonate from Pee Dee belemnite limestone and units are per
mille (‰). Atmospheric CO2, plant material, and soil organic matter are
depleted in 13C relative to the standard and therefore have negative delta 13C
values. The more depleted in 13C a material is, the more negative the delta
13C value will be.
Stout et al. (1981) identified four points in the
biological carbon cycle where major fractionation of carbon isotopes occurs.
The first takes place during photosynthesis with plant tissue being depleted in
13C relative to atmospheric CO2. Of considerable interest is the difference in
delta 13C between plants with different photosynthesis pathways
(Bender, 1971; Smith and
Epstein, 1971). The C3 plants, with the Calvin pathway, have low delta 13C
values (-24 to -34‰), while the C4 plants, with the Hatch and Slack pathway,
have high delta 13C values (-6 to -19‰). This difference in stable carbon
isotope signature can be used as a tracer for in situ labelling of soil organic
matter when the dominant vegetation type has changed from C3 to C4 species or
vice-versa (Cerri et al., 1985;
Schwartz et al., 1986;
Balesdent et al., 1987;
Balesdent et al., 1988;
Martin et al., 1990;
Balesdent and Balabane, 1992). The CENTURY model
has been modified to partition carbon production by plants to the two isotope
pools on the basis of a delta 13C value nominated in
the crop.100 file for each grassland or crop type.
The second major biological fractionation occurs in the synthesis of the major
cell components (Stout et al., 1981). The data of
Benner et al. (1987) for a variety of vascular
plants showed that cellulose and hemicellulose were typically enriched in 13C
by 1 to 2 ‰ relative to whole plant material while lignin was depleted by 2 to
6‰. They observed a greater depletion of 13C in grass lignins than in wood
lignins, which they attributed to different amino acid precursors. In the
CENTURY model this fractionation in the partitioning of plant material (shoots
and roots from crops and grasses, and leaves and fine roots from trees) to the
structural and metabolic pools is accounted for as all of the plant lignin is
assumed to enter the structural pool. The 13C depletion of lignin relative to
the whole plant 13C signature can be altered (DLIGDF,
fix.100). Because all dead wood and large tree roots
enter dead wood pools, which are analogous to the structural pool, there was no
need to account for 13C fractionation in wood lignin.
The third major biological fractionation of carbon noted by
Stout et al. (1981) is associated with animal
consumption of plant material, with animal tissues being depleted in 13C
relative to the plant material on which they feed. This is not accounted for
this in the model because the important comparison for the CENTURY model is
between delta 13C levels in feces and plant material.
The fourth major biological fractionation of carbon takes place during
microbial metabolism (Stout et al., 1981).
Macko and Estep (1984) examined the isotopic
composition of an aerobic, heterotrophic bacteria growing on a variety of amino
acid substrates. With most of substrates the bacterial cells were enriched in
13C relative to the amino acid. They suggested that the CO2 respired during
the Krebs cycle would be isotopically depleted in 13C. However, in an
anaerobic environment methane evolved is very depleted in 13C relative to the
organic substrate, but the CO2 evolved is enriched (Games
and Hayes, 1976). The net effect on the residual organic matter would
depend on the relative size of the fluxes. Environmental effects on
fractionation are also reflected in different patterns of stable isotope
distribution in soil profiles (Stout and Rafter,
1978). In well-drained mineral soils delta 13C values increase slightly
with depth and soil age, which is consistent with respired CO2 being slightly
depleted in 13C. In organic soils where decomposition is inhibited the delta
13C values decrease with depth. This could be due to the loss of readily
decomposable plant fractions, such as sugars and proteins, with an accumulation
of lignin, lipids and waxes in the residual plant material, resulting in
depletion of 13C relative to the original plant material
(Stout et al., 1981). In other soils, with
intermediate levels of drainage and organic matter accumulation, there may be
no change in delta 13C values with depth indicating a balance between
fractionation due to respiration and accumulation of the depleted plant
fractions. All decomposition flows in the CENTURY model are assumed to be the
result of microbial activity and have an associated loss of CO2. Fractionation
of the carbon isotopes in the loss of CO2 is allowed for
(DRESP, fix.100). The coefficient
for isotope discrimination was calibrated to give a slight increase in the
delta 13C value for the total soil organic matter relative to the
vegetation.
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The model was also enhanced to include the effects of documented changes in
atmospheric CO2 and thus predict the effects on crop production. The direct
effects of an increase in atmospheric CO2 concentration on soil processes will
be insignificant because the CO2 concentration in the soil atmosphere is
already greatly elevated. However, the indirect effects on SOM mediated
through effects on plant processes could be substantial and must be accounted
for in simulations of the effect of global change on SOM
(Long, 1991). Net primary production, litter
quality, and transpiration are all likely to be affected. Increases in
atmospheric CO2 concentration have increased plant production of a wide variety
of species by an average of 33% (Kimball, 1983).
Generally, the plant dry matter response to increasing rates of CO2 can be
approximated with a logarithmic response function
(Gifford, 1979;
Goudriaan, 1992):
where NPPE and NPP0 refer to net primary production in enriched and control CO2
environments respectively. Beta is an empirical parameter which ranges between
0 and approximately 0.7.
The response to CO2 is not simply due to the removal of a single limiting
factor (Sinclair, 1992), but results from a
hierarchy of effects (Acock, 1990).
First, increasing CO2 has a direct effect on C availability by stimulating
photosynthesis and reducing photorespiration. There is a very important
difference between C3 species, such as wheat, and C4 species, such as corn, in
this response. At present day CO2 concentrations around 350 umol / mol, C4
plants have higher rates of photosynthesis than C3 species. However, net
photosynthesis in corn does not increase much beyond 400 umol CO2 / mol, while
wheat responds to CO2 levels up to 800 umol / mol (Akita
and Moss, 1973). The growth response to CO2 is usually lower in C4 species
than in C3 species (Wong, 1979;
Rogers et al., 1983;
Morrison and Gifford, 1984b;
Cure and Acock, 1986). With wheat, a growth response
to elevated CO2 is almost invariably obtained (Kimball,
1983; Cure and Acock, 1986). Corn sometimes shows
no response to CO2 (Hocking and Meyer, 1991b). In
a field study with elevated CO2 in open top chambers, in which corn growth was
increased by about 40%, there was no effect on net photosynthesis per unit leaf
area (Rogers et al., 1983). Summarizing a number of
experiments, Cure and Acock (1986) found average
biomass responses of 31, 9, and 9% for wheat, corn and sorghum respectively.
The main reason for responses to CO2 in C4 species is due to improved water use
efficiency as discussed below.
The second effect of increased CO2 concentrations is a decrease in stomatal
conductance (Moss et al., 1961;
Akita and Moss, 1973; Wong,
1979; Rogers et al., 1983;
Morrison and Gifford, 1984a) at high CO2
concentrations, which reduces the transpiration rate per unit leaf area.
Reduced transpiration will also increase the leaf temperature which can further
increase photosynthesis (Acock, 1990). The effect on
stomatal conductance and transpiration is observed in both C3 and C4 species.
Over a range of species Morrison and Gifford
(1984a) found that stomatal conductance was reduced by 36% while
transpiration was reduced by 21%, the difference being attributed to the higher
leaf temperatures. Similar average values of 34% and 23% for stomatal
conductance and transpiration respectively were found in the literature survey
of Cure and Acock (1986). Both an increase in
photosynthesis and a decrease in transpiration result in an increase in the
plant's water use efficiency.
The third major effect of increased CO2 is a decrease in the plant N
concentration in C3 species (Schmitt and Edwards,
1981; Hocking and Meyer, 1991b). Clearly with
a fixed nutrient supply, an increase in C assimilation is likely to result in
lower plant nutrient concentrations due to a dilution effect, but this is not
the only effect. Hocking and Meyer (1991a)
clearly demonstrated that the critical plant N concentration for 90% maximum
yield is decreased under elevated CO2. However, CO2 had little effect on the
relationship between relative yield and the external N concentration. A
practical implication of this is that similar fertilizer application rates will
still allow near maximum yields under a high CO2 environment, but that more
fertilizer may be required to maintain similar grain protein concentrations
(Hocking and Meyer, 1991b). Physiologically, an
increase in N use efficiency in C3 species with elevated CO2 has been related
to decreased concentrations of the enzyme ribulose 1,5-bisphosphate carboxylase
(Schmitt and Edwards, 1981) which catalyses the
initial carboxylation reaction in C3 species and accounts for a large
proportion of the leaf protein.
A fourth effect of increased CO2 on plant growth which affects SOM levels is an
increase in root growth. Most studies with elevated CO2 with grain crops in
which root growth has been measured show very little or no effect on the root
to shoot ratio (Cure and Acock, 1986).
The above effects can be taken into account in CENTURY model simulations of
global change effects by selecting the enriched CO2 option in
EVENT100. This option can be implemented with either
a constant CO2 concentration or with a linear ramp with annual increments
from an initial concentration to a final concentration; the parameters
CO2RMP, CO2PPM(1), and
CO2PPM(2) are found in the
fix.100 file. The various effects of CO2 described
above are controlled by functions of the CO2 concentration and crop or tree
specific parameters in crop.100 and
tree.100. Parameter values are set using reference
concentrations of 350 and 700 ppm CO2 for ambient and doubled CO2
respectively.
The impact on maximum potential monthly production is described by a
transformation of Equation 3 given above in order that
the relative production for doubled CO2 can be set for each crop (CO2IPR(*),
crop.100, tree.100). The
effect on potential transpiration rate also uses this equation with the
fraction to which the transpiration will be reduced with a doubling of
atmospheric CO2 set (CO2ITR(*), crop.100,
tree.100). (See Figure 3-24.)
The effect of elevated CO2 on carbon to element ratios is similarly modelled
with the effect of doubled CO2 on the minimum and maximum ratios for N, P, and
S, in the shoots of grasses and crops and in the leaves of trees set
(CO2ICE(*,*,*), crop.100,
tree.100). The effect of CO2 on the allocation of C
to roots is set by (CO2IRS(*), crop.100,
tree.100) which specifies the proportional increase
in the root to shoot ratio at doubled CO2. A linear relationship of this
effect with CO2 concentration is assumed.
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3.15. Soil Incubation (Microcosms)
The model can be set up to simulate litter bag decomposition and soil
incubations at constant temperature and soil moisture. The incubation
option will simulate the dynamics of soil organic matter and surface or buried
litter under constant soil temperature and soil water conditions. Changes
in carbon levels and nutrient mineralization can be simulated for laboratory
incubations using this option. The soil temperature
(MCTEMP, .sch schedule file) is the only abiotic input
parameter; it is specified in the schedule file. To simulate a litter bag
simulation you would specify the initial litter level
(CLITTR, <site>.100) and
C:N, C:P and C:S ratio of the litter (RCELIT,
<site>.100). The lignin content of the litter bag
(FLIGNI, crop.100) would be
specified for either above- or belowground material depending on the placement
of the bag. Incubation of the soil occurs in a similar manner by initializing
all of the soil variables. Some of the options include fertilization,
cultivations (mixing of the soil) and the addition of new labeled or unlabeled
plant material during the incubations. Plant growth does not occur during the incubation.
Microcosm simulation is specified in the .sch
schedule file, created by EVENT100.
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CENTURY uses monthly precipitation (PRECIP
<site>.100) and mean monthly minimum and maximum
temperatures (TMN2M, TMX2M,
<site>.100). For each block in the simulation,
EVENT100 allows the user to choose between four
options for weather data. The first option uses the mean
values for each month in every year of the block simulation. The second option
uses the mean monthly temperature values in every year and
stochastically generates precipitation from a skewed
distribution (Nicks, 1974). If skewness parameters
are unavailable, a truncated normal distribution is used but this will increase
the overall mean precipitation when the coefficient of variation for
precipitation is high. The third option reads the monthly values for
precipitation, minimum and maximum air temperature from the
start of a weather data file, while the fourth option will
continue reading from the same file without
rewinding.
If a monthly value is missing from an actual weather file, it should be set
equal to the value "-99.99" within the file. When reading in this missing
value flag, CENTURY will replace the flag as follows:
for a minimum or maximum temperature, the mean monthly value (TMN2M or
TMX2M) from the <site>.100 file will be used.
for a precipitation value, the skewed distribution value will be calculated if
possible (if PRCSKW is not zero). Otherwise, the monthly mean (PRECIP)
will be used.
FILE100 can automatically analyze a CENTURY model
weather file with monthly precipitation and temperatures and place the
parameters for climate statistics in a <site>.100
file.
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Because CENTURY uses a monthly timestep and incorporates both continuous events such as crop growth and decomposition, and discrete events such as fertilizer addition, cultivation and harvest, it is necessary to set a priority order for calls to the model's subroutines (Figure 3-23). This is also necessary because the combined effect of subroutines on the changes in pool sizes can be large relative to the amount present and negative overflows would otherwise be a problem. Furthermore, because of the importance of nutrient availability to immobilization in organic matter, and the limitation that immobilization can place on the rate of organic matter decomposition, the decomposition and soil nutrient routines have a timestep of one quarter of a month.
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Most of the internal parameters in CENTURY were determined by fitting the
model to long-term soil decomposition experiments (1 to 5 year) where different
types of plant material were added to soils with a number of soil textures
(Parton et al., 1987). Other more general databases
(Parton et al., 1988; Parton
et al., 1989) were used to parameterize the P and S submodels and flows for
the formation of passive SOM. Many of the parameters such as the plant
nutrient content and lignin content were determined using a linear equation
where the slope and intercept were the input parameters. Work in the Great
Plains suggested that lignin and N content changed as a linear function of
annual precipitation. To specify constant values for these parameters, set the
slope parameter (FLIGNI(2,*),
crop.100) equal to zero and set the intercept
(FLIGNI(1,*), crop.100) equal to
the desired value for the parameter.
The model includes a method for estimating steady state soil C and N levels in
grassland systems which was developed for the U.S. Great Plains. If
IVAUTO (<site>.100) is set
to 1, the model will estimate initial soil C and N levels for the different
soil fractions based on the mean annual temperature, annual precipitation and
soil texture of grassland (Burke et al., 1989).
IVAUTO = 2 uses the cultivated fields equations to
estimate these levels. The soil P and S levels are quite different depending
on soil parent material and need to be estimated with site-specific
data.
One of the most difficult parts of initializing the model is
estimating the C, N, P, and S levels for the different soil fractions.
However, substantial progress has been made recently in estimating the size of
the soil fractions. The active soil fraction includes the live soil microbes
and microbial products. This fraction can be estimated by using the microbial
fumigation technique (Jenkinson and Powlson,
1976; Jenkinson et al.,1976;
Jenkinson and Rayner 1977) to estimate the live
microbial biomass and then doubling the live microbial biomass to account for
the microbial products (active SOM = 2 to 3 times the live microbial biomass).
In most soils the active soil fraction is approximately 2 to 4% of the total
soil C. The slow SOM fraction is made up of lignin derived plant material and
stabilized microbial products. This fraction makes up approximately 55% of the
total SOM. Recent developments in SOM fractionation
(Elliott and Cambardella, 1992) suggest that 40% of
the total SOM in grasslands is lignin-derived plant material (referred to as
POM (partial organic matter) in the paper). Comparison of the size of the slow
pool from C simulations with measurements of SOM indicate that the slow pool is
approximately 1.6 times the amount of POM (Metherell
et al., 1993b).
Unfortunately there is not a good technique for estimating the size of the
stabilized microbial products pool; however, it is estimated that it is
approximately 10 to 20% of the soil. The passive SOM generally makes up 30 to
40% of the total SOM and will have a higher value for high clay content soils.
The best estimate of the N content of these fractions are that the slow
fraction has a C:N ratio of 15 to 20, the active SOM has a C:N ratio of 8 to
12, while the passive SOM has a C:N ratio of 7 to 10. Clay soils have lower
C:N ratios while silty soils have higher C:N ratios for the passive SOM. These
approximations seem to work well for a large number of different soils.
The C:P and C:S ratios are not as predictable and are functions of the initial
soil parent material and degree of soil weathering. The same general rules
apply for C:P and C:S ratios with the active SOM having relatively low ratios
(50-100), the slow SOM the highest C:P and C:S ratios (100-300), while the
passive C:P and C:S ratios are fairly low (40-120). These values are
appropriate for the relatively unweathered soils in the U.S. Great Plains.
More weathered tropical soils have much higher C:P and C:S ratios that can be
as high as 800. To use the P and S submodels, determine the organic P and S
levels and it would be preferable to run full P fractionation of the soil (see
citations in Hedley et al., 1982). The C:N ratio
and relative size approximations are incorporated into the model when the Burke
equations are used (IVAUTO=1,
<site>.100) to estimate initial SOM pools. For
cultivated soils it is generally assumed that the size of the slow pool is
lower because of cultivation (40 to 50% of the total SOM) while the size of the
passive pool is increased (45 to 50%).
The model has been parameterized to simulate soil organic matter dynamics in
the top 20 cm of the soil. The model does not simulate organic matter
in the deeper soil layers and increasing the soil depth parameter
(EDEPTH, fix.100) does not have
much impact on the model. EDEPTH is only used to
calculate C, N and P loss when erosion occurs. To simulate a deeper soil depth
(i.e., 0-30 or 0-40 cm depth) the soil organic matter pools must be initialized
appropriately. As a general rule deeper soil depths have older soil carbon
dates (Jenkinson et al., 1992) and lower
decomposition rates (lower temperature at deeper depths). Thus, it would be
assumed that the fraction of total SOM in the passive SOM would be greater.
The major change for initializing the model for deep soil depths is adjusting
the fraction of SOM in the different pools (more C in passive SOM). The
initial soil C levels should reflect the observed soil C levels over that depth
and the decomposition rates should be decreased for all of the SOM pools
(DEC3, DEC4, DEC5).
To increase the soil depth from 20 cm to 30 cm, the decomposition rates should
be decreased by 15%. The other adjustment would be to increase the rate of
formation of passive SOM; the recommended way is to increase the flow of C from
active and slow SOM to passive SOM (PS1S3 and
PS2S3, fix.100). For example,
increasing the coefficients in PS2S3 and
PS1S3 will increase the amount of passive SOM formed from
slow SOM and active SOM.
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4. PARAMETERIZATION THROUGH FILE100
4.1. Introduction
The FILE100 program is designed to help the user create new options or change
values in existing options in any of the .100 data files used with EVENT100 and
CENTURY. This utility also provides parameter definitions, units, and valid values or
ranges. The instructions given below apply to both the PC and UNIX versions.
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The program begins with a numbered list of the .100 files, and asks the user to
enter the number of the file he wishes to work with:
File Updating Utility
Enter the number of the file you wish to update:
0. quit
1. crop.100
2. cult.100
3. fert.100
4. fire.100
5. fix.100
6. graz.100
7. harv.100
8. irri.100
9. omad.100
10. tree.100
11. trem.100
12. <site>.100
13. weather statistics
Enter selection:
Within that .100 file, the user may take any of five actions, as shown by the next menu:
What action would you like to take:
0. Return to main menu
1. Review all options
2. Add a new option
3. Change an option
4. Delete an option
5. Compare options
Enter selection:
Reviewing a file will list the abbreviations and descriptions found in the file.
Adding an option will allow the user to choose an existing option to copy, and then allow
the user to enter a new abbreviation and new values for the new option. Changing an
option will allow the user to change the abbreviation or any of the values associated with
that option. Deleting an option will completely remove the option from the .100 file.
Comparing shows the differences between options in the .100 file. Each of these actions is
described in more detail in the following sections.
Entering a "q" or "quit" at any point will return the user to the next highest menu.
[Previous Topic] [Next Topic] [Table of Contents]
"Review all options" will print a list on the screen of the options found in that .100
file by listing each option's abbreviation and corresponding descriptions. After reviewing,
the user may choose any of the five actions, or return to the main menu to choose another
.100 file. Note that reviewing automatically causes the file to be re-formatted to the
specifications needed by the PC version of CENTURY.
[Previous Topic] [Next Topic] [Table of Contents]
The user may choose to add a new option to the file. After entering 1, for adding,
the program will display each option already existing in the file and ask if the user would
like to begin with that option:
Current option is W1 Wheat-type-one
Is this an option you wish to start with?
A response of "Y" or "y" will cause the program to copy this option to begin the
addition phase. If no option is responded to with a yes answer, the
program will return to
the previous menu of five actions. Once an affirmative response has been given, the user
will be asked for a new abbreviation and description:
Enter a new abbreviation:
The abbreviation must be unique to that file and no more than 5 characters; if a
duplicate is entered, the user will be asked to enter another abbreviation.
Enter a new description:
The description may not be longer than 65 characters.
Then, for each value in that option, the program will display the value which the
original option had for that parameter and ask the user for a new value:
Commands: D F H L Q <new value> <return>
Name: PRDX(1) Previous value: 300
Enter response:
The user may enter any of these possible responses, as shown on the Command
line:
see the definition of that parameter ............. enter D
find a specific parameter in that option ......... enter F
see a help message ............................... enter H
list the next 12 parameters ...................... enter L
quit, retaining the old values for
this and the remaining parameters
in this option .............................. enter Q
take the old value ............................... enter <return>
enter a new value ................................ enter a new value
The command and previous value lines will continue to be shown until the user
enters Q, to quit, or until the end of the option is reached.
[Previous Topic] [Next Topic] [Table of Contents]
The user may change values of parameters within an existing option. After
entering 2, for changing, the program will display each option which exists in the file and
ask if the user would like to change that option:
Current option is W1 Wheat-type-one
Is this an option you wish to change
A response of "Y" or "y" will cause the program to move on to the change phase. If
no option is responded to with a yes answer, the program will return to the previous
menu of five actions. Once an affirmative response has been given, the user will be asked
for a new abbreviation and description:
Enter a new abbreviation or a <return>
to use the existing abbreviation:
A new abbreviation must be unique to that file and no more than 5 characters; if a
duplicate is entered, the user will be asked to enter another abbreviation.
Enter a new description or a <return>
to use the existing description:
The description may not be longer than 65 characters.
Then, for each value in that option, the program will display the existing value
for that parameter and ask the user for a new value:
Commands: D F H L Q <new value> <return>
Name: PRDX(1) Previous value: 300
Enter response:
The user may enter any of these possible responses, as shown on the Command
line:
see the definition of that parameter ............. enter D
find a specific parameter in that option ......... enter F
see a help message ............................... enter H
list the next 12 parameters ...................... enter L
quit, retaining the old values for
this and the remaining parameters
in this option .............................. enter Q
take the old value ............................... enter <return>
enter a new value ................................ enter a new value
The command and previous value lines will continue to be shown until the user
enters Q, to quit, or until the end of the file is reached. Finally, the user is asked if
changes made should be saved:
Do you want to save the changes made?
If this is answered with "y" or "Y", the changed values will be saved. Otherwise,
the changes will be lost.
[Previous Topic] [Next Topic] [Table of Contents]
4.6. Changing the <site>.100 File
Making changes to the <site>.100 file is different in that the parameters in
this file are subdivided for easier review. After selecting <site>.100 from the main menu,
enter the name of the site file without the .100 extension. The user may name a new
<site>.100 file to save these changes to:
Enter a new site filename to save changes
to or a <return> to save to (original filename).100:
The program will then display the existing abbreviation and description and allows
the user to provide new ones:
Enter a new abbreviation or a <return>
to use the existing abbreviation:
Enter an abbreviation of no more than 5 characters.
Enter a new description or a <return>
to use the existing description:
The description may not be longer than 65 characters.
The next menu will show the subheadings within the file:
Which subheading do you want to work with?
0. Return to main menu
1. Climate parameters
2. Site and control parameters
3. External nutrient input parameters
4. Organic matter initial parameters
5. Forest organic matter initial parameters
6. Mineral initial parameters
7. Water initial parameters
Enter selection:
Entering a response of 1 through 7 will cause the first parameter shown to be from
that subheading. The program then continues as with the regular Change function.
For each value in that subheading, the program will display the value which the
original had for that parameter and ask the user for a new value:
Commands: D F H L Q <new value> <return>
Name: PRDX(1) Previous value: 300
Enter response:
The user may enter any of these possible responses, as shown on the Command
line:
see the definition of that parameter ............. enter D
find a specific parameter in that option ......... enter F
see a help message ............................... enter H
list the next 12 parameters ...................... enter L
quit, retaining the old values for
this and the remaining parameters
in this option .............................. enter Q
take the old value ............................... enter <return>
enter a new value ................................ enter a new value
The command and previous value lines will continue to be shown until the user
enters Q, to quit, or until the end of the subheading is reached.
After selecting choice 0, Return to the main menu, from the subheadings menu, the
user is asked if the changes made should be saved:
Do you want to save the changes made?
If this is answered with "y" or "Y", the changed values will be saved. Otherwise,
the changes will be lost.
[Previous Topic] [Next Topic] [Table of Contents]
The user may choose to delete one or more options from that .100 file. After
entering 4, for delete, each abbreviation and description of each option found is shown:
Current option is W1 Wheat-type-one
Is this an option you wish to delete?
If the user responds with a "Y" or "y", a double check is made to insure that no
error was made:
Are you sure yo