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Nitrogen fixation coefficient
The nitrogen fixation coefficient in the SWAT+ plant database is 0.5 for legumes and 0.0 for non-legumes.
Plant/landcover type
perennial
Perennial plants
warm_annual
Warm-season annuals
cold_annual
Cold-season annuals
Fall-planted land covers will go dormant when daylength is less than the threshold daylength.
Harvest index for optimal growth conditions
The harvest index defines the fraction of the aboveground biomass that is removed in a harvest operation. This value defines the fraction of plant biomass that is “lost” from the system and unavailable for conversion to residue and subsequent decomposition. For crops where the harvested portion of the plant is aboveground, the harvest index is always a fraction less than 1. For crops where the harvested portion is belowground, the harvest index may be greater than 1. Two harvest indices are provided in the database, the harvest index for optimal growing conditions and the harvest index under highly stressed growing conditions ().
To determine the harvest index, the plant biomass removed during the harvest operation is dried at least 2 days at 65°C and weighed. The total aboveground plant biomass in the field should also be dried and weighed. The harvest index is then calculated by dividing the weight of the harvested portion of the plant biomass by the weight of the total aboveground plant biomass. Plants will need to be grown in two different plots where optimal climatic conditions and stressed conditions are produced to obtain values for both harvest indices.
Maximum potential leaf area index
The variable lai_pot is one of six parameters used to quantify leaf area development of a plant species during the growing season. The figure below illustrates the relationship of the database parameters to the leaf area development modeled by SWAT+.
The values for lai_pot in the plant growth database are based on average plant densities in dryland (rainfed) agriculture. The parameter may need to be adjusted for drought-prone regions where planting densities are much smaller or irrigated conditions where densities are much greater.
To identify the leaf area development parameters, record the leaf area index and number of accumulated heat units for the plant species throughout the growing season and then plot the results. For best results, several years worth of field data should be collected. At the very minimum, data for two years is recommended. It is important that the plants undergo no water or nutrient stress during the years in which data is collected.
The leaf area index incorporates information about the plant density, so field experiments should either be set up to reproduce actual plant densities or the maximum LAI value for the plant determined from field experiments should be adjusted to reflect plant densities desired in the simulation. Maximum LAI values in the default database correspond to plant densities associated with rainfed agriculture.
The leaf area index is calculated by dividing the green leaf area by the land area. Because the entire plant must be harvested to determine the leaf area, the field experiment needs to be designed to include enough plants to accommodate all leaf area measurements made during the year.
Although measuring leaf area can be laborious for large samples, there is no intrinsic difficulty in the process. The most common method is to obtain an electronic scanner and feed the harvested green leaves and stems into the scanner. Older methods for estimating leaf area include tracing of the leaves (or weighed subsamples) onto paper, the use of planimeters, the punch disk method of Watson (1958) and the linear dimension method of Duncan and Hesketh (1968).
Chapter 5:1 in the Theoretical Documentation reviews the methodology used to calculate accumulated heat units for a plant at different times of the year as well as determination of the fraction of total, or potential, heat units that is required for the plant database.
Watson (1958)
Duncan and Hesketh (1968)

Fraction of the plant growing season or the total potential heat units corresponding to the 2nd point on optimal leaf area development curve
Please see the explanation given for parameter to obtain additional information about this parameter and methods used to measure it.
Fraction of the maximum leaf area index corresponding to the 2nd point on optimal leaf area development curve
Please see the explanation given for parameter to obtain additional information about this parameter and methods used to measure it.
Maximum canopy height
Maximum canopy height is a straightforward measurement. The canopy height of non-stressed plants should be recorded at intervals throughout the growing season. The maximum value recorded is used in the database.
Fraction of growing season when leaf area begins to decline
Please see the explanation given for parameter to obtain additional information about this parameter and methods used to measure it.
Optimal temperature for plant growth
Both optimal and base temperatures are very stable for cultivars within a species.
Optimal temperature for plant growth is difficult to measure directly. Looking at the figure below, one might be tempted to select the temperature corresponding to the peak of the plot as the optimal temperature. This would not be correct. The peak of the plot defines the optimal temperature for leaf development—not for plant growth.
If an optimal temperature cannot be obtained through a review of literature, use the optimal temperature listed for a plant already in the database with similar growth habits.
Review of temperatures for many different plants have provided generic values for base and optimal temperatures as a function of growing season. In situations, where temperature information is unavailable, these values may be used. For warm season plants, the generic base temperature is ~8ºC and the generic optimal temperature is ~25ºC. For cool season plants, the generic base temperature is ~0ºC and the generic optimal temperature is ~13ºC.

Normal fraction of phosphorus in yield
In addition to the amount of plant biomass removed in the yield, SWAT+ needs to know the amount of nitrogen and phosphorus removed in the yield. The harvested portion of the plant biomass is sent to a testing laboratory to determine the fraction of nitrogen and phosphorus in the biomass.
This value is estimated on a dry weight basis.
Normal fraction of nitrogen in plant biomass at 50% maturity
In order to calculate the plant nutrient demand throughout a plant’s growing cycle, SWAT+ needs to know the fraction of nutrient in the total plant biomass (on a dry weight basis) at different stages of crop growth. Six variables in the plant database provide this information: , frac_n_50, , , , and . Plant samples are analyzed for nitrogen and phosphorus content at three times during the growing season: shortly after emergence, near the middle of the season, and at maturity. Ideally, the plant samples tested for nutrient content should include the roots as well as the aboveground biomass. Differences in partitioning of nutrients to roots and shoots can cause erroneous conclusions when comparing productivity among species if only the aboveground biomass is measured.
Normal fraction of phosphorus in plant at maturity
In order to calculate the plant nutrient demand throughout a plant’s growing cycle, SWAT+ needs to know the fraction of nutrient in the total plant biomass (on a dry weight basis) at different stages of crop growth. Six variables in the plant database provide this information: , , , , , and frac_p_mat. Plant samples are analyzed for nitrogen and phosphorus content at three times during the growing season: shortly after emergence, near the middle of the season, and at maturity. Ideally, the plant samples tested for nutrient content should include the roots as well as the aboveground biomass. Differences in partitioning of nutrients to roots and shoots can cause erroneous conclusions when comparing productivity among species if only the aboveground biomass is measured.
Harvest index that represents the lowest harvest index expected due to water stress
The value between 0.0 and that represents the lowest harvest index expected due to water stress.
The harvest index defines the fraction of the aboveground biomass that is removed in a harvest operation. This value defines the fraction of plant biomass that is “lost” from the system and unavailable for conversion to residue and subsequent decomposition. For crops where the harvested portion of the plant is aboveground, the harvest index is always a fraction less than 1. For crops where the harvested portion is belowground, the harvest index may be greater than 1. Two harvest indices are provided in the database, the harvest index for optimal growing conditions () and the harvest index under highly stressed growing conditions.
To determine the harvest index, the plant biomass removed during the harvest operation is dried at least 2 days at 65°C and weighed. The total aboveground plant biomass in the field should also be dried and weighed. The harvest index is then calculated by dividing the weight of the harvested portion of the plant biomass by the weight of the total aboveground plant biomass. Plants will need to be grown in two different plots where optimal climatic conditions and stressed conditions are produced to obtain values for both harvest indices.
Normal fraction of nitrogen in plant biomass at maturity
In order to calculate the plant nutrient demand throughout a plant’s growing cycle, SWAT+ needs to know the fraction of nutrient in the total plant biomass (on a dry weight basis) at different stages of crop growth. Six variables in the plant database provide this information: , , frac_n_mat, , , and . Plant samples are analyzed for nitrogen and phosphorus content at three times during the growing season: shortly after emergence, near the middle of the season, and at maturity. Ideally, the plant samples tested for nutrient content should include the roots as well as the aboveground biomass. Differences in partitioning of nutrients to roots and shoots can cause erroneous conclusions when comparing productivity among species if only the aboveground biomass is measured.
Normal fraction of phosphorus in plant at 50% maturity
In order to calculate the plant nutrient demand throughout a plant’s growing cycle, SWAT+ needs to know the fraction of nutrient in the total plant biomass (on a dry weight basis) at different stages of crop growth. Six variables in the plant database provide this information: , , , , frac_p_50, and . Plant samples are analyzed for nitrogen and phosphorus content at three times during the growing season: shortly after emergence, near the middle of the season, and at maturity. Ideally, the plant samples tested for nutrient content should include the roots as well as the aboveground biomass. Differences in partitioning of nutrients to roots and shoots can cause erroneous conclusions when comparing productivity among species if only the aboveground biomass is measured.
Normal fraction of nitrogen in plant biomass at emergence
In order to calculate the plant nutrient demand throughout a plant’s growing cycle, SWAT+ needs to know the fraction of nutrient in the total plant biomass (on a dry weight basis) at different stages of crop growth. Six variables in the plant database provide this information: frac_n_em, , , , , and . Plant samples are analyzed for nitrogen and phosphorus content at three times during the growing season: shortly after emergence, near the middle of the season, and at maturity. Ideally, the plant samples tested for nutrient content should include the roots as well as the aboveground biomass. Differences in partitioning of nutrients to roots and shoots can cause erroneous conclusions when comparing productivity among species if only the aboveground biomass is measured.
Normal fraction of phosphorus in plant biomass at emergence
In order to calculate the plant nutrient demand throughout a plant’s growing cycle, SWAT+ needs to know the fraction of nutrient in the total plant biomass (on a dry weight basis) at different stages of crop growth. Six variables in the plant database provide this information: , , , frac_p_em, , and . Plant samples are analyzed for nitrogen and phosphorus content at three times during the growing season: shortly after emergence, near the middle of the season, and at maturity. Ideally, the plant samples tested for nutrient content should include the roots as well as the aboveground biomass. Differences in partitioning of nutrients to roots and shoots can cause erroneous conclusions when comparing productivity among species if only the aboveground biomass is measured.
Normal fraction of nitrogen in yield
In addition to the amount of plant biomass removed in the yield, SWAT+ needs to know the amount of nitrogen and phosphorus removed in the yield. The harvested portion of the plant biomass is sent to a testing laboratory to determine the fraction of nitrogen and phosphorus in the biomass.
This value is estimated on a dry weight basis.
Fraction of maximum stomatal conductance corresponding to the second point on the stomatal conductance curve
The first point on the stomatal conductance curve is comprised of a vapor pressure deficit of 1 kPa and a fraction of maximum stomatal conductance equal to 1.00.
As with radiation-use efficiency, stomatal conductance is sensitive to vapor pressure deficit. Stockle et al. (1992) compiled a short list of stomatal conductance response to vapor pressure deficit for a few plant species. Due to the paucity of data, default values for the second point on the stomatal conductance vs. vapor pressure deficit curve are used for all plant species in the database.
The fraction of maximum stomatal conductance (frac_stcon) is set to 0.75 and the vapor pressure deficit corresponding to the fraction given by vpd is set to 4.00 kPa. If the user has actual data, they should use those values, otherwise the default values are adequate.
Stockle et al. (1992)
Plant residue decomposition coefficient
The plant residue decomposition coefficient is the fraction of residue that will decompose in a day assuming optimal moisture, temperature, C:N ratio, and C:P ratio.
A default value of 0.05 is used for all plant species in the database, but users may vary decomposition by plant species.
Rate of decline in radiation use efficiency per unit increase in vapor pressure deficit
Stockle and Kiniry (1990) first noticed a relationship between RUE and vapor pressure deficit and were able to explain a large portion of within-species variability in RUE values for sorghum and corn by plotting RUE values as a function of average daily vapor pressure deficit values. Since this first article, a number of other studies have been conducted that support the dependence of RUE on vapor pressure deficit. However, there is still some debate in the scientific community on the validity of this relationship. If the user does not wish to simulate a change in RUE with vapor pressure deficit, the variable ru_vpd can be set to 0.0 for the plant.
To define the impact of vapor pressure deficit on RUE, vapor pressure deficit values must be recorded during the growing seasons that RUE determinations are being made. It is important that the plants are exposed to no other stress than vapor pressure deficit, i.e. plant growth should not be limited by lack of soil water and nutrients.
Vapor pressure deficits can be calculated from relative humidity (see Chapter 1:2 in Theoretical Documentation) or from daily maximum and minimum temperatures using the technique of Diaz and Campbell (1988) as described by Stockle and Kiniry (1990). The change in RUE with vapor pressure deficit is determined by fitting a linear regression for RUE as a function of vapor pressure deficit. The figure below shows a plot of RUE as a function of vapor pressure deficit for grain sorghum.
From the figure, the rate of decline in radiation-use efficiency per unit increase in vapor pressure deficit, Δruedcl, for sorghum is 8.4x10-1 g*MJ-1*kPa-1. When RUE is adjusted for vapor pressure deficit, the model assumes the RUE value reported for is the radiation-use efficiency at a vapor pressure deficit of 1 kPa.
The value of ru_vpd varies among species, but a value of 6 to 8 is suggested as an approximation for most plants.
Campbell (1988)
Stockle & Kiniry (1990)

Biomass-energy ratio corresponding to the 2nd point on the radiation use efficiency curve
The 1st point on the radiation use efficiency curve is comprised of the ambient CO2 concentration, 330 μL CO2/L air, and the biomass-energy ratio reported for .
Please read the explanation for parameters and to obtain additional information about this parameter and methods used to measure it.
Minimum LAI during dormant period
This variable pertains to perennials and trees only. It is not used for other types of plants.
Please see the explanation given for parameter to obtain additional information about this parameter and methods used to measure it.
Fraction of biomass accumulated each year that is converted to residue during dormancy
This variable pertains to trees only. It is not used for other types of plants. It governs the amount of biomass that falls off the tree and is converted to residue when the plant goes dormant in the winter.
Years to maturity
This variable pertains to trees only. It is not used for other types of plants.
Perennial leaf turnover rate with maximum stress
Plant population corresponding to the 1st point on the population LAI curve
Fraction of field capacity to initiate growth of tropical plants during monsoon season
Biomass-energy ratio
The biomass-energy ration or radiation-use efficiency (RUE) is the amount of dry biomass produced per unit intercepted solar radiation. It is assumed to be independent of the plant’s growth stage. The variable bm_e represents the potential or unstressed growth rate (including roots) per unit of intercepted photosynthetically active radiation.
This parameter can greatly change the rate of growth, incidence of stress during the season, and the resultant yield. It should be one of the last to be adjusted. Adjustments should be based on research results. Care should be taken to make adjustments based only on data with no drought, nutrient, or temperature stress.
The following overview of the methodology used to measure RUE was summarized from Kiniry et al. (1998) and Kiniry et al. (1999).
To calculate RUE, the amount of photosynthetically active radiation (PAR) intercepted and the mass of aboveground biomass is measured several times throughout a plant’s growing season. The frequency of the measurements taken will vary, but in general 4 to 7 measurements per growing season are considered to be adequate. As with leaf area determinations, the measurements should be performed on non-stressed plants. Intercepted radiation is measured with a light meter. Whole spectrum and PAR sensors are available and calculations of RUE will be performed differently depending on the sensor used. A brief discussion of the difference between whole spectrum and PAR sensors and the difference in calculations is given in Kiniry (1999). The use of a PAR sensor in RUE studies is strongly encouraged.
When measuring radiation, three to five sets of measurements are taken rapidly for each plant plot. A set of measurements consists of 10 measurements above the leaf canopy, 10 below, and 10 more above. The light measurements should be taken between 10:00 am and 2:00 pm local time. The measurements above and below the leaf canopy are averaged and the fraction of intercepted PAR is calculated for the day from the two values. Daily estimates of the fraction of intercepted PAR are determined by linearly interpolating the measured values. The fraction of intercepted PAR is converted to an amount of intercepted PAR using daily values of incident total solar radiation measured with a standard weather station. To convert total incident radiation to total incident PAR, the daily solar radiation values are multiplied by the percent of total radiation that has a wavelength between 400 and 700 mm. This percent usually falls in the range 45 to 55% and is a function of cloud cover. 50% is considered to be a default value. Once daily intercepted PAR values are determined, the total amount of PAR intercepted by the plant is calculated for each date on which biomass was harvested. This is calculated by summing daily intercepted PAR values from the date of seedling emergence to the date of biomass harvest.
To determine biomass production, aboveground biomass is harvested from a known area of land within the plot. The plant material should be dried at least 2 days at 65°C and then weighed.
RUE is determined by fitting a linear regression for aboveground biomass as a function of intercepted PAR. The slope of the line is the RUE. The figure below shows the plots of aboveground biomass and summed intercepted photosynthetically active radiation for Eastern gamagrass. Note that the units for RUE values in the graph, as well as values typically reported in literature, are different from those used by SWAT+. To obtain the value used in SWAT+, multiply by 10.
Kiniry et al. (1998)
Kiniry (1999)
Kiniry et al. (1999)
Aeration stress factor

Phenology trigger
Fraction of the maximum leaf area index corresponding to the 1st point on optimal leaf area development curve
Please see the explanation given for parameter to obtain additional information about this parameter and methods used to measure it.
Days to maturity
The PHU program was incorporated directly into SWAT+. The heat units to maturity were changed to days to maturity (days_mat). The concept of heat units to maturity was developed for annual crops and we use heat units for the entire growing season for native perennials and native annuals. By inputting days to maturity, we can include different crop varieties as defined by length of growing season (e.g., 120-, 110-, 100- and 90-day varieties for corn). The heat units to maturity calculation in the model first computes base zero heat units for the entire year and assumes a planting date when heat units exceed 0.15 * base zero. Then, the model calculates heat units from planting date through the days to maturity using the crop's base temperature as specified by tmp_base. If the maximum days for a crop are input, e.g., 120 days for corn, and the growing season is less than 120 days, the model essentially sums heat units for the entire growing season which represents (and estimates) the maximum days to maturity.
The algorithm currently uses monthly weather generator parameters but could be modified to alternatively use daily temperature inputs. The model provides heat unit estimates in both the northern and southern hemispheres.
There are several advantages to incorporating the heat unit program into SWAT+ including:
It eliminates the need for running an external program when developing inputs,
allows input of a commonly understood variable (days) instead of a variable that is not commonly known at every location (heat units),
allows the model to calculate heat units for native perennials and annuals that are location dependent,
a database (plants.plt) can be maintained and supported that includes different crop varieties, and
by inputting the maximum growing season for a crop, the model will calculate appropriate heat units for that crop anywhere in the northern or southern hemisphere.
Fraction of the plant growing season or the total potential heat units corresponding to the 1st point on optimal leaf area development curve
Please see the explanation given for parameter lai_pot to obtain additional information about this parameter and methods used to measure it.
Exponent that governs LAI decline rate
Maximum rooting depth
To determine maximum rooting depth, plant samples need to be grown on soils without an impermeable layer. Once the plants have reached maturity, soil cores are taken for the entire depth of the soil. Each 0.25-meter increment is washed and the live plant material collected. Live roots can be differentiated from dead roots by the fact that live roots are whiter and more elastic and have an intact cortex. The deepest increment of the soil core in which live roots are found defines the maximum rooting depth.
The plant growth database file stores information required to simulate plant growth by plant species.
The plant growth database distributed with SWAT+ includes parameters for most of the common plant species. If you need to model a land use or plant not included in the database, please feel free to contact the SWAT+ development team for assistance in determining plant parameters.
Name of the plant/landcover
The name of the plant/landcover is a primary key referenced by plt_name in plant.ini. All names in the plants.plt database must be unique.
The names in the plant database are also used by QSWAT+ to link the grid codes in land use/land cover maps to SWAT+ land use types.
agrc
Generic agricultural land
agrl
Agricultural land with row crops
agrr
Close-grown agricultural land
alfa
Alfalfa
almd
Almonds
appl
Apple
aspn
Aspen
aspr
Asparagus
bana
Banana
barl
Barley
barl100
Barley - 100-day variety
barl105
Barley - 105-day variety
bbls
Little bluestem
berm
Bermudagrass
blug
Kentucky bluegrass
bocu
???
broc
Broccoli
brom
Meadow bromegrass
bros
Smooth bromegrass
bsvg
Barren or sparsely vegetated land
cabg
Cabbage
cana
Spring canola Argentina
cang
Canary Grass
canp
Spring canola Poland
cant
Cantaloupe
cash
Cashews
cauf
Cauliflower
cedr
Cedar
celr
Celery
clva
Alsike clover
clvr
Red clover
clvs
Sweet clover
cngr
Canada grass
cocb
Cockle burr
coco
Coconut
coct
Cocoa
coff
Coffee
cont
???
corn
Corn
corn100
Corn - 100-day variety
corn110
Corn - 110-day variety
corn120
Corn - 120-day variety
corn50
Corn - 50-day variety
corn90
Corn - 90-day variety
cotp
Cotton
cotp135
Cotton - 135-day variety
cotp145
Cotton - 145-day variety
cotp155
Cotton - 155-day variety
cotp180
Cotton - 180-day variety
cots
Cotton
cots135
Cotton - 135-day variety
cots145
Cotton - 145-day variety
cots155
Cotton - 155-day variety
cots180
Cotton - 180-day variety
crdy
Dryland cropland and pasture
crgr
Cropland/grassland mosaic
crir
Irrigated cropland and pasture
crrt
Carrot
crwo
Cropland/woodland mosaic
csil
Corn silage
csil100
Corn silage - 100-day variety
csil110
Corn silage - 110-day variety
csil120
Corn silage - 120-day variety
csil90
Corn silage - 90-day variety
cucm
Cucumber
cwgr
Crested wheatgrass
cwps
Cowpeas
deil
???
dwht
Durum wheat
dwht110
Durum wheat - 110-day variety
dwht120
Durum wheat - 120-day variety
egam
Eastern Gamagrass
eggp
Eggplant
fesc
Tall Fescue
flax
Flax
fodb
Deciduous broadleaf forest
fodn
Deciduous needleleaf forest
foeb
Evergreen broadleaf forest
foen
Evergreen needleleaf forest
fomi
Mixed forest
fpea
Field peas
frsd
Deciduous forest
frsd_suhf
Deciduous forest - subtropical humid
frsd_sums
Deciduous forest - subtropical mountain systems
frsd_sust
Deciduous forest - subtropical steppe
frsd_tecf
Deciduous forest - temperate continental
frsd_tems
Deciduous forest - temperate mountain systems
frsd_teof
Deciduous forest - temperate oceanic
frsd_test
Deciduous forest - temperate steppe
frse
Evergreen forest
frse_sudrf
Evergreen forest - subtropical desert
frse_suds
Evergreen forest - subtropical dry
frse_suhf
Evergreen forest - subtropical humid
frse_sums
Evergreen forest - subtropical mountain systems
frse_sust
Evergreen forest - subtropical steppe
frse_tecf
Evergreen forest - temperate continental
frse_teds
Evergreen forest - temperate desert
frse_tems
Evergreen forest - temperate mountain systems
frse_teof
Evergreen forest - temperate oceanic
frse_test
Evergreen forest - temperate steppe
frst
Mixed forest
frst_suhf
Mixed forest - subtropical humid
frst_sums
Mixed forest - subtropical mountain systems
frst_sust
Mixed forest - subtropical steppe
frst_tecf
Mixed forest - temperate continental
frst_tems
Mixed forest - temperate mountain systems
frst_teof
Mixed forest - temperate oceanic
frst_test
Mixed forest - temperate steppe
grap
Grape
grar
Red grape
gras
Grassland
grbn
Green beans
grsg
Grain sorghum
grsg100
Grain sorghum - 100-day variety
grsg105
Grain sorghum - 105-day variety
grsg110
Grain sorghum - 110-day variety
grsg95
Grain sorghum - 95-day variety
hay
Hay
hmel
Honeydew melon
indn
Indianagrass
jhgr
Johnsongrass
ldgp
Lodgepole pine
lent
Lentils
lett
Head lettuce
lima
Lima beans
mapl
Maple
mesq
Honey mesquite
migs
Mixed grassland/shrubland
mint
Mint
mixc
Mixed dryland/irrigated cropland
mung
Mung beans
oak
Oak
oats
Oats
oats110
Oats - 110-day variety
oats120
Oats - 120-day variety
oilp
Oil palm
oliv
Olives
onio
Onions
oran
Oranges
orcd
Orchard
papa
Papaya
part
Parthenium
past
Pasture
peas
Garden peas
pepp
Pepper
pepr
Bell pepper
pine
Pine
pinp
Pineapple
plan
Plaintains
pmil
Pearl millet
pmil100
Pearl millet - 100-day variety
pmil105
Pearl millet - 105-day variety
pmil110
Pearl millet - 110-day variety
pmil95
Pearl millet - 95-day variety
pnut
Peanut
popl
Poplar
popy
Poppy
pota
Potato
ptbn
Pinto beans
radi
Radish
rice
Rice
rice120
Rice - 120-day variety
rice140
Rice - 140-day variety
rice160
Rice - 160-day variety
rice180
Rice - 180-day variety
rngb
Range brush
rngb_sudrf
Range brush - subtropical dry forest
rngb_suds
Range brush - subtropical desert
rngb_suhf
Range brush - subtropical humid forest
rngb_sums
Range brush - subtropical mountain systems
rngb_sust
Range brush - subtropical steppe
rngb_tecf
Range brush - temperate continental forest
rngb_teds
Range brush - temperate desert
rngb_tems
Range brush - temperate mountain systems
rngb_teof
Range brush - temperate oceanic forest
rngb_test
Range brush - temperate steppe
rnge
Range grasses
rnge_sudrf
Range grasses - subtropical dry forest
rnge_suds
Range grasses - subtropical desert
rnge_suhf
Range grasses - subtropical humid forest
rnge_sums
Range grasses - subtropical mountain systems
rnge_sust
Range grasses - subtropical steppe
rnge_tecf
Range grasses - temperate continental forest
rnge_teds
Range grasses - temperate desert
rnge_tems
Range grasses - temperate mountain systems
rnge_teof
Range grasses - temperate oceanic forest
rnge_test
Range grasses - temperate steppe
rubr
Rubber
rye
Rye
rye90
Rye - 90-day variety
ryea
Altai wildrye
ryeg
Italian annual ryegrass
ryer
Russian wildrye
saaz
???
sava
Savanna
scrn
Sweetcorn
scsc
???
sept
Septic area
sesb
Sesban
sgbt
Sugarbeet
sghy
Sorghum hay
shrb
Shrubland
side
Sideoats grama
soct
???
sont
???
sonu
???
sosa
???
soyb
Soybeans
soyb100
Soybeans - 100-day variety
soyb105
Soybeans - 105-day variety
soyb110
Soybeans - 110-day variety
soyb115
Soybeans - 115-day variety
soyb120
Soybeans - 120-day variety
spas
Summer pasture
spin
Spinach
spot
Sweet potato
strw
Strawberry
sugc
Sugarcane
sunf
Sunflower
sunf100
Sunflower - 100-day variety
sunf110
Sunflower - 110-day variety
sunf90
Sunflower - 90-day variety
swch
Alamo switchgrass
swgr
Slender wheatgrass
swht
Spring wheat
swht110
Spring wheat - 110-day variety
swht120
Spring wheat - 120-day variety
swrn
Southwestern US ???
teff
Teff
timo
Timothy grass
tobc
Tobacco
toma
Tomato
tral
???
trit
Triticale
tubg
Bare ground tundra
tuhb
Herbaceous tundra
tumi
Mixed tundra
tuwo
Wooded tundra
urbn_cool
Cool-season grass in urban areas
urbn_warm
Warm-season grass in urban areas
waln
Walnut
wbar
Winter barley
wehb
Herbaceous wetland
wetf
Forested wetland
wetl
Wetland
wetn
Non-forested wetland
wewo
Wooded wetland
will
Willow
wmel
Watermelon
wpas
Winter pasture
wspr
White spruce
wwgr
Western wheatgrass
wwht
Winter wheat
wwht150
Winter wheat - 150-day variety
wwht160
Winter wheat - 160-day variety
wwht170
Winter wheat - 170-day variety
Minimum value of the USLE C factor for water erosion
The minimum C factor can be estimated from a known average annual C factor using the following equation (Arnold and Williams, 1995):
CUSLE,mn=1.463∗ln[CUSLE,aa]+0.1034
where CUSLE,mn is the minimum C factor for the land cover and CUSLE,aa is the average annual C factor for the land cover.
Arnold and Williams (1995)
Maximum forest biomass
This variable pertains to trees only. It is not used for other types of plants. The maximum biomass for a mature forest stand generally falls in the range of 30-50 metric tons/ha.
Vapor pressure deficit corresponding to the second point on the stomatal conductance curve
The first point on the stomatal conductance curve is comprised of a vapor pressure deficit of 1 kPa and a fraction of maximum stomatal conductance equal to 1.00.
As with radiation-use efficiency, stomatal conductance is sensitive to vapor pressure deficit. Stockle et al. (1992) compiled a short list of stomatal conductance response to vapor pressure deficit for a few plant species. Due to the paucity of data, default values for the second point on the stomatal conductance vs. vapor pressure deficit curve are used for all plant species in the database.
The fraction of maximum stomatal conductance (frac_stcon) is set to 0.75 and the vapor pressure deficit corresponding to the fraction given by vpd is set to 4.00 kPa. If the user has actual data, they should use those values, otherwise the default values are adequate.
Stockle et al. (1992)
Maximum stomatal conductance at high solar radiation and low vapor pressure deficit
Stomatal conductance of water vapor is used in the Penman-Monteith calculations of maximum plant evapotranspiration. The plant database contains three variables pertaining to stomatal conductance that are required only if the Penman-Monteith equation is used to model evapotranspiration: maximum stomatal conductance and two variables that define the impact of vapor pressure deficit on stomatal conductance (frac_stcon, vpd).
Körner et al. (1979) defines maximum leaf diffusive conductance as the largest value of conductance observed in fully developed leaves of well-watered plants under optimal climatic conditions, natural outdoor CO2 concentrations and sufficient nutrient supply. Leaf diffusive conductance of water vapor cannot be measured directly but can be calculated from measurements of transpiration under known climatic conditions. Various different methods are used to determine diffusive conductance: transpiration measurements in photosynthesis cuvettes, energy balance measurements or weighing experiments, ventilated diffusion porometers, and non-ventilated porometers. Körner (1977) measured diffusive conductance using a ventilated diffusion porometer.
To obtain maximum leaf conductance values, leaf conductance is determined between sunrise and late morning until a clear decline or no further increase is observed. Depending on phenology, measurements are taken on at least three bright days in late spring and summer, preferably just after a rainy period. The means of maximum leaf conductance of 5 to 10 samples each day are averaged, yielding the maximum diffusive conductance for the species. Due to the variation of the location of stomata on plant leaves for different plant species, conductance values should be calculated for the total leaf surface area.
Körner (1977)
Körner et al. (1979)
Root to shoot ratio at the beginning of the growing season
Above-ground biomass that dies off at dormancy
Fraction of the maximum leaf area index corresponding to the 1st point on the leaf area development curve
Plant population corresponding to the 2nd point on the population LAI curve
n/a
n/a
Nitrogen fixation coefficient
real
none
0.0
n/a
Days to maturity
real
days
110.0
0.0-300.0
Biomass-energy ratio
real
(kg/ha/(MJ/m^2)
15.0
10.0-90.0
Harvest index for optimal growth conditions
real
(kg/ha)/(kg/ha)
0.76
0.01-1.25
Maximum potential leaf area index
real
none
5.0
0.50-10.0
Fraction of the growing season heat units corresponding to the 1st point on optimal leaf area development curve
real
fraction
0.05
0.0-1.0
Fraction of the maximum leaf area index corresponding to the 1st point on optimal leaf area development curve
real
fraction
0.05
0.0-1.0
Fraction of the growing season heat units corresponding to the 2nd point on optimal leaf area development curve
real
fraction
0.40
0.0-1.0
Fraction of the maximum leaf area index corresponding to the 2nd point on optimal leaf area development curve
real
fraction
0.95
0.0-1.0
Fraction of growing season when leaf area begins to decline
real
fraction
0.99
0.2-1.0
Exponent that governs the LAI decline rate
real
n/a
1.0
Maximum canopy height
real
m
6.0
0.1-20.0
Maximum rooting depth
real
m
3.50
0.0-3.0
Optimal temperature for plant growth
real
deg c
30.0
11.0-38.0
Minimum temperature for plant growth
real
deg c
10.0
0.0-18.0
Normal fraction of N in yield
real
kg N/kg yield
0.0015
0.0015-0.075
Normal fraction of P in yield
real
kg P/kg yield
0.0003
0.0015-0.0075
Normal fraction of N in plant biomass at emergence
real
kg N/kg biomass
0.006
0.004-0.07
Normal fraction of N in plant biomass at 50% maturity
real
kg N/kg biomass
0.002
0.002-0.05
Normal fraction of N in plant biomass at maturity
real
kg N/kg biomass
0.0015
0.001-0.27
Normal fraction pf P in plant biomass at emergence
real
kg P/kg biomass
0.0007
0.0005-0.01
Normal fraction of P in plant at 50% maturity
real
kg P/kg biomass
0.0004
0.0002-0.007
Normal fraction of P in plant at maturity
real
kg P/kg biomass
0.0003
0.0003-0.0004
Harvest index that represents the lowest harvest index expected due to water stress
real
(kg/ha)/(kg/ha)
0.01
-0.2-1.1
Minimum value of the USLE C factor for water erosion
real
none
0.001
0.001-0.50
Maximum stomatal conductance
real
m/s
0.002
0.0-0.50
Vapor pressure deficit corresponding to the 2nd point on the stomatal conductance curve
real
kPa
4.0
1.5-6.0
Fraction of maximum stomatal conductance corresponding to the 2nd point on the stomatal conductance curve
read
fraction
0.75
0.0-1.0
Rate of decline in radiation use efficiency per unit increase in vapor pressure deficit
real
none
8.0
0.0-50.0
Elevated CO2 atmospheric concentration corresponding the 2nd point on the radiation use efficiency curve
real
μL CO2/L air
660.0
300.0-1000.0
Biomass-energy ratio corresponding to the 2nd point on the radiation use efficiency curve
real
(kg/ha)/(MJ/m^2)
16.0
5.0-100.0
Plant residue decomposition coefficient
real
none
0.05
0.01-0.099
Minimum LAI during dormant period
real
m^2/m^2
0.75
0.00-0.99
Fraction of biomass accumulated each year
real
fraction
0.30
0.0-1.0
Years to maturity
integer
years
10
0-100
Maximum forest biomass
real
metric tons/ha
1000.0
0.0-5000.0
Light extinction coefficient
real
none
0.65
0.0-2.0
Perennial leaf turnover rate with minimum stress
real
12.0
Perennial leaf turnover rate with maximum stress
real
3.0
Above-ground biomass that dies off at dormancy
real
fraction
1.0
0.0-1.0
Root to shoot ratio at the beginning of the growing season
real
fraction
0.
Root to shoot ratio at the end of the growing season
real
fraction
0.
Plant population corresponding to the 1st point on the population LAI curve
real
plants/m^2
0.
Fraction of the maximum leaf area index corresponding to the 1st point on the leaf area development curve
real
fraction
0.
0.0-1.0
Plant population corresponding to the 2nd point on the population LAI curve
real
plants/m^2
0.0
Fraction of the maximum leaf area index corresponding to the 2nd point on the leaf area development curve
real
fraction
0.0
0.0-1.0
Fraction of field capacity to initiate growth of tropical plants during monsoon season
real
fraction
0.5
0.0-1.0
Aeration stress factor
real
0.0
Residue factor for percent cover equation
real
0.0
Residue factor for surface cover (C factor) equation
real
0.0
Name of the plant/landcover
string
n/a
n/a
n/a
Plant/landcover type
string
n/a
n/a
n/a
Phenology trigger
string
n/a
Minimum temperature for plant growth
SWAT+ uses the base temperature to calculate the number of heat units accrued every day. The minimum or base temperature for plant growth varies with growth stage of the plant. However, this variation is ignored by the model - SWAT+ uses the same base temperature throughout the growing season.
Base temperature is measured by growing plants in growth chambers at several different temperatures. The rate of leaf tip appearance as a function of temperature is plotted. Extrapolating the line to the leaf tip appearance rate of 0.0 leaves/day gives the base or minimum temperature for plant growth. The figure below plots data for corn. Note that the line intersects the x-axis at 8°C.
Elevated CO2 atmospheric concentration corresponding the 2nd point on the radiation use efficiency curve
The 1st point on the radiation use efficiency curve is comprised of the ambient CO2 concentration, 330 μL CO2/L air, and the biomass-energy ratio reported for bm_e.
In order to assess the impact of climate change on agricultural productivity, SWAT+ incorporates equations that adjust RUE for elevated atmospheric CO2 concentrations. Values must be entered for co2_hi and bm_e_hi in the plant database whether or not the user plans to simulate climate change.
For simulations in which elevated CO2 levels are not modeled, co2_hi should be set to some number greater than 330 ppmv and should be set to some number greater than .
To obtain radiation-use efficiency values at elevated CO2 levels for plant species not currently in the database, plants should be established in growth chambers set up in the field or laboratory where CO2 levels can be controlled. RUE values are determined using the same methodology described in the explanation of .
Light extinction coefficient
This coefficient is used to calculate the amount of intercepted photosynthetically active radiation.
Differences in canopy structure for a species are described by the number of leaves present (leaf area index) and the leaf orientation. Leaf orientation has a significant impact on light interception and consequently on radiation-use efficiency. More erect leaf types spread the incoming light over a greater leaf area, decreasing the average light intensity intercepted by individual leaves (see figure below). A reduction in light intensity interception by an individual leaf favors a more complete conversion of total canopy-intercepted light energy into biomass.
Using the light extinction coefficient value (kℓ) in the Beer-Lambert formula to quantify efficiency of light interception per unit leaf area index, more erect leaf types have a smaller kℓ.
To calculate the light extinction coefficient, the amount of photosynthetically active radiation (PAR) intercepted and the mass of aboveground biomass (LAI) is measured several times throughout a plant’s growing season using the methodology described in the previous sections. The light extinction coefficient is then calculated using the Beer-Lambert equation:
PARTPAR=(1−exp(−kl⋅LAI))
or
kl=−ln(PARTPAR)∗LAI1
where TPAR is the transmitted photosynthetically active radiation, and PAR is the incoming photosynthetically active radiation.


Perennial leaf turnover rate with minimum stress
Residue factor for surface cover (C factor) equation
Residue factor for percent cover equation
Root to shoot ratio at the end of the growing season
Fraction of the maximum leaf area index corresponding to the 2nd point on the leaf area development curve