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nfix_co

Nitrogen fixation coefficient

The nitrogen fixation coefficient in the SWAT+ plant database is 0.5 for legumes and 0.0 for non-legumes.

plnt_typ

Plant/landcover type

Option
Description

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.

harv_idx

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.

harv_idx_ws

lai_pot

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.

hashtag
References

Watson (1958)

Duncan and Hesketh (1968)

frac_hu2

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.

lai_pot

lai_max2

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.

lai_pot

can_ht_max

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.

hu_lai_decl

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.

lai_pot

tmp_opt

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.

frac_p_yld

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.

frac_n_50

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.

frac_n_em
frac_n_mat
frac_p_em
frac_p_50
frac_p_mat

frac_p_mat

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.

frac_n_em
frac_n_50
frac_n_mat
frac_p_em
frac_p_50

harv_idx_ws

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.

harv_idx
harv_idx

frac_n_mat

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.

frac_p_50

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.

frac_n_em
frac_n_50
frac_p_em
frac_p_50
frac_p_mat
frac_n_em
frac_n_50
frac_n_mat
frac_p_em
frac_p_mat

frac_n_em

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.

frac_p_em

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.

frac_n_50
frac_n_mat
frac_p_em
frac_p_50
frac_p_mat
frac_n_em
frac_n_50
frac_n_mat
frac_p_50
frac_p_mat

frac_n_yld

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.

frac_stcon

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.

circle-info

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.

hashtag
References

Stockle et al. (1992)

plnt_decomp

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.

circle-info

A default value of 0.05 is used for all plant species in the database, but users may vary decomposition by plant species.

ru_vpd

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.

circle-info

The value of ru_vpd varies among species, but a value of 6 to 8 is suggested as an approximation for most plants.

hashtag
References

Campbell (1988)

Stockle & Kiniry (1990)

bm_e

bm_e_hi

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.

bm_e
co2_hi
bm_e

lai_min

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.

lai_pot

bm_tree_acc

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.

yrs_mat

Years to maturity

This variable pertains to trees only. It is not used for other types of plants.

leaf_tov_max

Perennial leaf turnover rate with maximum stress

plnt_pop1

Plant population corresponding to the 1st point on the population LAI curve

frac_sw_gro

Fraction of field capacity to initiate growth of tropical plants during monsoon season

bm_e

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.

circle-info

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.

hashtag
References

Kiniry et al. (1998)

Kiniry (1999)

Kiniry et al. (1999)

aeration

Aeration stress factor

gro_trig

Phenology trigger

lai_max1

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.

lai_pot

days_mat

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:

  1. It eliminates the need for running an external program when developing inputs,

  2. allows input of a commonly understood variable (days) instead of a variable that is not commonly known at every location (heat units),

  3. allows the model to calculate heat units for native perennials and annuals that are location dependent,

  4. a database (plants.plt) can be maintained and supported that includes different crop varieties, and

  5. 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.

frac_hu1

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.

dlai_rate

Exponent that governs LAI decline rate

rt_dp_max

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.

plants.plt

The plant growth database file stores information required to simulate plant growth by plant species.

circle-info

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.

Field
Description
Type
Unit
Default
Range

name (plants.plt)

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.

Name
Description

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

usle_c_min

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]+0.1034C_{USLE,mn} = 1.463 * ln⁡[C_{USLE,aa}] + 0.1034CUSLE,mn​=1.463∗ln⁡[CUSLE,aa​]+0.1034

where CUSLE,mnC_{USLE,mn} CUSLE,mn​ is the minimum C factor for the land cover and CUSLE,aaC_{USLE,aa} CUSLE,aa​ is the average annual C factor for the land cover.

hashtag
References

Arnold and Williams (1995)

bm_tree_max

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.

vpd

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.

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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.

hashtag
References

Stockle et al. (1992)

stcon_max

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.

hashtag
References

Körner (1977)

Körner et al. (1979)

rt_st_beg

Root to shoot ratio at the beginning of the growing season

bm_dieoff

Above-ground biomass that dies off at dormancy

frac_lai1

Fraction of the maximum leaf area index corresponding to the 1st point on the leaf area development curve

plnt_pop2

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

Name of the plant/landcover

​string

​n/a

n/a

n/a

plnt_typ

Plant/landcover type

​string

n/a

n/a

n/a

gro_trig

Phenology trigger

string

n/a

tmp_base

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.

co2_hi

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.

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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 .

ext_co

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:

bm_e_hi
bm_e
bm_e
nfix_co
days_mat
bm_e
harv_idx
lai_pot
frac_hu1
lai_max1
frac_hu2
lai_max2
hu_lai_decl
dlai_rate
can_ht_max
rt_dp_max
tmp_opt
tmp_base
frac_n_yld
frac_p_yld
frac_n_em
frac_n_50
frac_n_mat
frac_p_em
frac_p_50
frac_p_mat
harv_idx_ws
usle_c_min
stcon_max
vpd
frac_stcon
ru_vpd
co2_hi
bm_e_hi
plnt_decomp
lai_min
bm_tree_acc
yrs_mat
bm_tree_max
ext_co
leaf_tov_min
leaf_tov_max
bm_dieoff
rt_st_beg
rt_st_end
plnt_pop1
frac_lai1
plnt_pop2
frac_lai2
frac_sw_gro
aeration
rsd_pctcov
rsd_covfac

TPARPAR=(1−exp⁡(−kl⋅LAI))\frac{TPAR}{PAR}=(1-exp⁡(-k_l⋅LAI))PARTPAR​=(1−exp⁡(−kl​⋅LAI))

or

kl=−ln⁡(TPARPAR)∗1LAIk_l=-ln⁡(\frac{TPAR}{PAR}) * \frac{1}{LAI}kl​=−ln⁡(PARTPAR​)∗LAI1​

where TPAR is the transmitted photosynthetically active radiation, and PAR is the incoming photosynthetically active radiation.

leaf_tov_min

Perennial leaf turnover rate with minimum stress

rsd_covfac

Residue factor for surface cover (C factor) equation

rsd_pctcov

Residue factor for percent cover equation

rt_st_end

Root to shoot ratio at the end of the growing season

frac_lai2

Fraction of the maximum leaf area index corresponding to the 2nd point on the leaf area development curve