Garden with Insight v1.0 Help: Auto Operations
The plant environment control component provides mechanisms for applying irrigation water, fertilizer,
lime, and pesticide or for simulating grazing or drainage systems.
Drainage
Drainage via underground drainage systems is treated as a modification of the natural lateral
subsurface flow of the area. Drainage is simulated by indicating which soil layer contains the drainage
system and the time required for the drainage system to reduce plant stress. The drainage time in days
replaces the travel time in equation 44 for the layer containing the system.
Irrigation
The EPIC user has the option to simulate dryland or irrigated agricultural areas. Sprinkler or furrow
irrigation may be simulated and the applications may be scheduled by the user or automatically. As
implied, the user scheduled option allows application dates and rates to be inputted. With the automatic
option, the model decides when and how much water to apply.
Required inputs for the automatic version include a signal to trigger applications (the three trigger choices
include: plant water stress level (01), plow layer soil water tension in kPa, or root zone soil water deficit
in mm), the maximum volume applied to each crop in mm, the runoff fraction, minimum and maximum
single application volumes in mm, and the minimum time interval between applications in days.
Two modes of application, rigid and flexible, are available.
Rigid mode:
1. User schedule  the exact input volumes are applied
on specified dates.
2. Automatic option  maximum single application volumes
are applied when triggered.
Flexible mode:
1. User schedule  the application volume is the minimum
of the specified volume, the maximum single application
volume, and the volume required to fill the root zone
to field capacity.
2. Automatic option  the application volume is the minimum
of the maximum single application volume and the volume
required to fill the root zone to field capacity.
Also, irrigation does not occur when the application volume derived from the appropriate mode and
options (except for rigid, userscheduled) is less than the input minimum single application volume.
The application mode (rigid or flexible) is fixed for the entire crop rotation. However, the trigger value
and criterion (plant water stress level, soil water tension, or root zone water deficit) and the runoff fraction
may be changed at any time during the operation schedule. Also, a combination of user and automatic
scheduling is permitted.
Fertilization
Fertilizer application is similar to irrigation  scheduling may be input or automatic and rigid and
flexible modes are available. Required inputs for the automatic version include a trigger (plant N stress
level (01)), maximum annual N applied to a crop in kg/ha, and minimum time between applications in
days. Automatic fertilizing at planting is also optional.
Rigid mode:
1. User schedule  The exact input rates of N and P are
applied at specified depths on scheduled dates.
2. Automatic option  a fraction of the annual maximum
N rate for the crop is applied when triggered. The
application fraction and the maximum rate are inputs.
Also P is applied at a rate necessary to bring the plow
layer (0.2 m) P concentration to a level specified at the
start of a simulation. All automatic applications are
placed in the second soil layer.
Flexible mode:
1. User schedule  the model samples N and P concentration
in the root zone and compares with user preset N and P
concentrations. Applications occur on schedule at specified
depths and rates if the root zone N and P concentrations do
not exceed user standards, otherwise, applications are
delayed until N and P concentrations are depleted below
user standards.
2. Automatic option  the N application rate is the difference
between the preset rate (application fraction times the
maximum annual rate) and the root zone N content. The P
application strategy is the same as in the rigid mode.
Other features and limitations include only mineral N (in NO3 form) and P may be applied automatically.
Organic N and P and ammonia are applied by user scheduling. The maximum annual N application for a
crop can be changed at planting. A combination of user and automatic scheduling is permitted. Automatic
applications occur only when N is the active crop growth constraint even though the trigger value is
reached. Thus, the annual N and P application rates vary according to the crop's needs, the soil's ability to
supply those needs, and the magnitude of the N stress relative to water and temperature stresses.
Liming
EPIC simulates the use of lime to neutralize toxic levels of Al and/or to raise soil pH to near
optimum levels. Different algorithms are used to estimate lime requirements of "highly
weathered" soils (Oxisols, Ultisols, Quartzipsamments, Ultic subgroups of Alfisols, and Dystric
suborders of Inceptisols) (Sharpley et al., 1985) and other soils. The highly weathered soils have large
amounts of variablecharge clays. Moderate amounts of lime are required to increase their pH to about 5.5
and convert extractable Al to more inactive forms. However, the pH of these soils is highly buffered above
pH 5.5, and very large amounts of lime are required to raise the pH to near 7.0. As a aResult, soils with
variable charge clays are usually limed only to reduce Al saturation to acceptable levels.
The Al saturation of each soil layer is estimated with the equations (Jones, 1984) [Equation 330] and
[Equation 331] where ALS is the Al saturation of soil layer l in percent calculated as KCLextractable Al
divided by effective cation exchange capacity (ECEC), BSA is the base saturation calculated from cation
exchange capacity (CEC) determined by the NH4OAc (pH = 7.0) method in percent, C is the organic
carbon content in percent, and PH is the soil pH.
Equation 330, 331
ALS = 154.2  1.017 * BSA  3.173 * C  14.23 * PH, if PH <= 5.6
ALS = 0.0, if PH > 5.6
Code:
same but added bounds of 095 if ph <= 5.6
Variables:
ALS = AluminumSaturation_pct
BSA = baseSaturation_pct
C = organicC_pct
PH = soilpH
For highly weathered soils, the lime required to neutralize toxic Al in the plow layer is estimated with the
equation [Equation 332] where RLA is the lime required to neutralize Al in t/ha, ECEC is the effective
cation exchange capcity in cmol(p+)/kg, BD is the soil bulk density in t/m3, and PD is the plow depth in
m. (These are not by layer.)
Equation 332
RLA = 0.1 * ALS * ECEC * PD * BD
Code:
deltaBSA = 0.1 * ALS * ECEC
SWT = PD * BD
RLA = deltaBSA * SWT
so it is the same
Variables:
RLA = LimeToNeutralizeAlForHighlyWeatheredSoil_kgPha
ALS = aluminumSaturation_pct
ECEC = effectiveCEC_cmolpplusPkg
deltaBSA = changeInBaseSaturationToOffsetAlSat_pct
SWT = totalSoilWeightInMaxTillageDepth_tPha
PD = plowDepth_m
BD = bulkDensity_tPm3
ECEC is calculated as SMB/ALS (Soil Survey Staff, 1982), where SMB in cmol/kg is the sum of the bases
extracted by NH4OAc (pH = 7.0).
The constant 0.1 (in equation 332) converts cmol(p+)/kg extractable aluminum to equivalent CaCO3 in
t/ha, assuming 2 cmol(p+) CaCO3 are required to completely neutralize 1 cmol(p+) extractable Al
(Kamprath, 1970). At the end of each year, enough lime is applied to meet the lime requirement (RLA) if
RLA >= 1 t/ha. If RLA < 1 t/ha no lime is applied. When lime is applied, the plow layer pH is raised
to 5.4 and ALS is reduced to 0.
For EPIC, soil acidification and decreasing base saturation are caused by addition of fertilizer N and
symbiotic N fixation by legumes. All fertilizer N is assumd to derived from anhydrous ammonia, urea,
ammonium nitrate, or mixtures of these with equivalent acidifying effects. The CaCO3 equivalent of
fertilizer or fixed N is assumed to be 1.8 kg CaCO3/kg N (Pesek et al., 1971). This is within the range of
variation reported by Pierre et al. (1971) for fertilized corn and by Nyatsanga and Pierre (1973) and Jarvis
and Robson (1983) for legumes.
At the end of each year of simulation, the plow layer pH is reduced to reflect the change in base saturation
caused by N fertilizer and N fixation. The change in base saturation is computed with the equation
[Equation 333] where FN is the amount of N fertilizer added during the year in kg/ha and WFX is the
amount of N fixation by legumes in kg/ha.
Equation 333
deltaBSA = 0.036 * (FN + WFX) / (PD * BD * CEC)
Code:
deltaBSA = 0.036 * (FN + WFX) / SWT
SWT = PD * BD, but CEC term is in next equation (PH)
Variables:
deltaBSA = ChangeInBaseSaturationByNAdded_frn
FN = nFertilizerAdded_kgPha
WFX = nFixation_kgPha
SWT = totalSoilWeightInMaxTillageDepth_kgPha
PD = plowDepth_m
BD = bulkDensity_tPm3
CEC = cationExchangeCapacity_cmolPkg
The PH value is reduced by using the equation [Equation 334] where the constant 0.5 approximates the
slope of the relationship between pH and deltaBSA for several soils when the values of BSA are between
60 and 90 (Peech, 1965).
Equation 334
PH = PH(o)  0.05 * deltaBSA
Code:
PH = PH(o)  0.05 * 100 * deltaBSA / CEC
(here is the CEC term from the deltaBSA equation)
Variables:
PH = soilpH = SoilpHAfterChangeFromNAdded
deltaBSA = changeInBaseSaturationByNAdded_pct
CEC = cationExchangeCapacity_cmolPkg
For other soils, the lime requirement is the amount of time needed to raise soil pH to 6.5 according to the
equation [Equation 335] where deltaBSA is the change in base saturation needed to raise soil pH to 6.5.
The constant 0.05 converts deltaBSA in percent to equivalent CaCO3 in t/ha, assuming that applied
CaCO3 reacts with equivalent unsaturated CEC.
Equation 335
RLA = 0.05 * PD * BD * CEC * deltaBSA
Code:
RLA = 0.05 * SW * CEC * deltaBSA
PD * BD = SW, so this is the same
Variables:
RLA = LimeFor6p5PHForNonHighlyWeatheredSoil_kgPha
PD = plowDepth_m
BD = bulkDensity_tPm3
SW = totalSoilWeightInMaxTillageDepth_kgPha
CEC = cationExchangeCapacity_cmolPkg
deltaBSA = changeInBaseSaturationToRaisePHTo6p5_pct
The deltaBSA is estimated with the relation [Equation 336].
Equation 336
deltaBSA = min((6.5  PH) / 0.023, 90  BSA)
Code:
same
Variables:
deltaBSA = ChangeInBaseSaturationToRaisePHTo6p5_pct
PH = soilpH
BSA = baseSaturation_pct
For soils that are not highly weathered, lime application is simulated if at the end of the year, RLA >
2.0 t/ha. When lime is applied, pH is changed to 6.5, base saturation is increased by deltaBSA, and ALS is
set to 0.
This new equation, derived from equations 336 and 335, estimates the new pH value when a given
amount of lime is added. The derivation is as follows.
deltaBSA = min((6.5  pH) / 0.023, 90  BSA) to reach a pH of 6.5
RLA = 0.01 * SWT * ECEC * deltaBSA
so
deltaBSA = RLA / (0.05 * SWT * ECEC)
substituting equation 336 for deltaBSA and ignoring the minimum,
(newpH  pH) / 0.023 = RLA / (0.05 * SWT * ECEC)
now solving for newpH,
newpH = 0.023 * RLA / (0.05 * SWT * ECEC) + pH
Pests
Note: We did not include the pest part of EPIC in this version of Garden with Insight.
The three pests considered by EPIC are insects, weeds, and plant diseases. The effects of all three pests are
expressed in the EPIC pest factor. Crop yields are estimated at harvest as the product of simulated yield
and pest factor. The pest factor ranges from 0.0 to 1.0  1.0 means no pest damage and 0.0 means total
crop destruction by pests. The pest factor is simulated daily as a function of temperature, moisture, and
ground cover [Equation 337] where PSTI is the accumulated pest index for day k, T(mn) is the minimum
temperature for day i in degrees C, RFS is the accumulated rainfall for 30 days preceding day i in mm,
RFS(T) is the threshold 30day rainfall amount in mm, CV is the ground cover (live biomass and crop
residue) on day i in t/ha, and CV(T) is the threshold cover value in t/ha. When T(mn) is less than 0.0, the
pest index is reduced using the equation [Equation 338]. Thus, the pest index grows rapidly during warm
moist periods with adequate ground cover and is reduced by cold temperatures. This general pest index is
an attempt to account for major differences in pest problems related to climate variability.
Equation 338
if RFS > RFS(T) and CV > CV(T) and T(mn,i) > 0.0
PSTI = (sum with i from 1 to k of) T(mn,i) * (1.0 + RFS  RFS(T))
if T(mn,i) < 0.0
PSTI(k) = PSTI(k1) + T(mn)
Code:
if RFS > RFS(T) and CV > CV(T) and T(mn,i) > 0.0
PSTI(k) = PSTI(k1) + T(mn,i) * (1.0 + (RFS  RFS(T)) / 100)
otherwise the same
Variables:
PSTI = PestPopulationIndex
T(mn,i) = minTempForDay_degC
RFS = previousThirtyDaysRainfall_mm (computed every day)
RFS(T) = thresholdThirtyDayRainfallForPests_mm
CV = aboveGroundBiomassAndResidue_tPha
CV(T) = thresholdBiomassAndResidueForPests_tPha
The pest index is reduced using the equation [Equation 339] where PSTE is the pesticide kill fraction
ranging from near 0.0 to near 1.0. Thus, if the kill fraction approaches 1.0, the pest index is reduced
nearly 1000 units.
Equation 339
PSTI(k) = PSTI(k1)  1000 * PSTE
Code:
same
Variables:
deltaPSTI = PestFactorReductionFromPesticide
PSTE = killFractionForPesticide_frn
At harvest, the pest factor is computed from the pest index using the equation [Equation 340] where PSTF
is the pest factor used to adjust crop yield, PSTM is the minimum pest factor value for a crop, and k is
time since last harvest in days.
Equation 340
PSTI* = PSTI / k
PSTF = 1.0  (1.0  PSTM) * (PSTI* / (PSTI* + exp(2.7  0.499 * PSTI*)))
Code:
Variables:
PSTF = PestFactor_frn
PSTM = minPestWeedDiseaseFactor_frn
PSTI = pestPopulationIndex
PSTI* = pestPopulationIndex / daysSinceLastHarvest
k = daysSinceLastHarvest
Furrow Diking
Furrow diking is the practice of building small temporary dikes across furrows to conserve water for
crop production. Since they reduce runoff, they may also aid in erosion control. The EPIC furrow diking
model allows construction of dikes for any ridge spacing and at any interval down the furrows. Dikes may
be constructed or destroyed mechanically on any day of the year. If estimated runoff for a particular event
(rainfall) exceeds the dike storage volume (average for all furrows in the field), overtopping occurs and all
of the estimated runoff is lost. If not, all of the rainfall infiltrates and is available for plant use. When
runoff destroys the dikes, the model rebuilds them automatically. Rainstorms that do not overtop the dikes
cause settling and, thus, reduce storage volume. Settling is estimated with the equation [Equation 341]
where H(o) is the dike height before settling, H is the dike height after settling, and Y is the USLE
estimate of soil loss (sediment yield) in t/ha. Ridge height is also reduced with the settling function
contained in equation 341. The dikes are automatically rebuilt when H/H(o) < 0.7.
Equation 341
H = H(o) * exp(0.1 * Y)
Code:
much different.
Variables:
H = DikeHeightAfterSettlingDueToRain_mm
Y = totalErosion_tPha
Dike volume
The dike storage volume is estimated by assuming that the furrow and the dike are triangular and that the
dike side slopes are 2:1. Given the dike and ridge heights, the dike interval, and the slope down the
furrow, the volume can be calculated directly.
There are two possible dike configurations that require slightly different solutions.
Normal case  lower slope
Normally, the dike interval is relatively short (13 m) and the slope along the furrow is relatively flat
(<1.0%). When the dike is full, water extends from the top of the downslope dike up the furrow to a
point above the toe (bottom) of the upslope dike. The volume is calculated by using crosssectional areas at
the toes of the two dikes. This approach computes the volume in three parts (1. between the top and the
toe of the downslope dike, 2. between the toes of the two dikes, and 3. between the toe and the waterline
on the upslope dike). Beginning at the centerline of the downslope dike, the volume equations are
DV(I) = 1/2 * H * D(2) * W(2) (Equation 342)
DV(II) = 1/4 * (DI  4 * H) * (D(2) * W(2) + D(3) * W(3)) (Equation 343)
DV(III) = 1/4 * (XD  DI + 2 * H) * D(3) * W(3) (Equation 344)
where DV is the dike volume between cross sections in m3, H is the dike height in m, D is the water depth
in m, W is the water surface width in m, DI is the dike interval in m, XD is the distance from the center of
the downslope dike to the waterline on the upslope dike in m, and subscripts 2 and 3 refer to cross sections
2 and 3. Cross section 2 is at the toe of the downslope dike and cross section 3 is at the toe of the upslope
dike.
Water depth is calculated with the equations
D(2) = H  2 * S * H (Equation 345)
D(3) = H  S * (DI  2 * H) (Equation 346)
where S is the slope in m/m along the furrow (which I think is the same as the land surface slope).
Water surface width is a function of depth and ridge spacing, RS in mm
W = RS * D / H (Equation 347)
The distance XD is computed with the equations
XD = DI  2 * (H  DZ) (Equation 348)
DZ = H  S * XD (Equation 349)
where DZ is the water line eleveation on the upslope dike. The constant 2 in equation 348 comes from the
assumed 2:1 dike upslopes.
Simultaneous solution of equations 348 and 349 yields
XD = DI / (1 + 2 S) (Equation 350)
Substituting D, W, and XD into equations 342, 343, and 344 and summing gives
DV = 1/4 * RS / H * (H2 * (12S)2 * (DI  2H) + (H  S(DI  2H))2
* (DI / (1+2S)  2H)) (Equation 351)
Equation 351 is divided by the total surface area of a furrow dike to convert volume from m3 to
mm [Equation 352].
DV = DikeVolumeForLowSlope_mm
DI = dikeInterval_m
H = dikeHeight_m
S = slopeSteepness_mPm
More unusual case  higher slope
In the simpler and more unusual dike configuration, the upslope waterline does not extend to the toe of
the upslope dike. Only one cross section is involved and the volume is computed into two parts. Equation
342 is used to calculated the most downslope volume, and the upslope volume is calculated with the
equation
DV(2) = 1/4 * D(2) * W(2) * H * (1/S  2) (Equation 353)
Adding equations 342 and 353, substituting D and W, and converting from m3 to mm gives [Equation
354].
Equation 354
DV = 250 / (DI * H)
* (sqr(H) * sqr(1  2 * S)
+ sqr(H  S * (DI  2 * H))
* (DI / (1 + 2 * S)  2 * H))
DV(2) = 250 * sqr(H) * sqr(1  2 * S) / (S * DI)
Code:
DV = 250 * 1000 * FDSF / (DI * RH)
* (sqr(H) * sqr(1  2 * S)
* (DI  2 * H)
+ sqr(H  S * (DI  2 * H))
* (0.5 * DI / (S + 0.5)  2 * H)
only difference here is extra term and extra factor (mentioned in publication)
DV(2) = 250 * 1000 * FDSF / (RH * DI) * H / S * sqr(H) * sqr(1  2 * S)
Variables:
DV = DikeVolumeForHighSlope_mm
DI = dikeInterval_m
H = dikeHeight_mm
RH = ridgeHeight_mm
S = slopeSteepness_mPm
FDSF = fractDikeVolAvailForWaterStorage_frn
Thus, the average dike volume of a field is estimated with equation 352 or 354 as dictated by slope and
dike height and interval. However, no field is exactly uniform in slope; dike and ridge heights vary, and
furrow and dike side slopes may not be triangular. Therefore, the model provides a user controlled dike
efficiency factor to allow for varying conditions across a field. The dike efficiency factor also provides for
conservative or optimistic dike system design.
Grazing
Note: We did not include the grazing part of EPIC in this version of Garden with Insight.
Livestock grazing is simulated as a daily harvest operation. Users specify daily grazing rate in kg/ha,
minimum grazing height in mm, harvest efficiency, and date grazing begins and ends. Harvest efficiency
is used to estimate the fraction of grazed plant material used by animals  not returned as manure, etc.
Any number of grazing periods may occur during a year and the grazing schedule may vary from year to
year within a rotation. Grazing ceases when forage height is reduced to the userspecified cutoff value and
resumes automatically when new growth exceeds the cutoff height if the grazing period has not expired.
Economics
Note: We did not include the economics part of EPIC in this version of Garden with Insight.
The economic component of EPIC is more accurately represented as a crop budget and accounting
subsystem. The algorithms keep track of the costs of producing and marketing the crops. Costs (and
income) are divided into two groups: those costs which do not vary with yield and those that do. These
groups will be addressed in turn. All cost registers are cleared at harvest. All operations after harvest are
charged to the next crop in the cropping sequence.
Tillage and (preharvest) machine operation costs are assumed to be independent of yield. These operation
costs must be calculated outside of EPIC and are inputted as one variable into the tillage file. This cost cell
contains all costs associated with the single operation or activity (e.g., a chiseling activity includes fuel,
labor, depreciation, repair, interest, etc., for both the tractor and the chisel). A budget generator program
like the Micro Budge Management System (MBMS) (McGrann et al., 1986) is convenient for making
these calculations. This is an updated interaction program developed from the Enterprise Budget
Calculator (Kletke, 1979). The MBMS is more compatible with EPIC in that it has output capabilities to
itemize cost by machine operation. This information (when converted to metric units) can be input
directly into the equiment file in EPIC. Farm overhead, land rent, and other fixed costs can be charged to
the crop by first creating null operations in the equipment file with machine number and cost information
only and then triggering the cost in EPIC with a null activity. Government payments can be credited by
using negative cost entries in the same way.
Costs which are yield and management dependent are entered into EPIC in two regions of the input data.
Seed costs, seeding rates, and crop prices are entered in the crop parameter file for each crop code. Seed
costs are calculated as the product of seeding rate and cost per kilogram. Amendment costs are calculated
similarly. The amendments include elemental N and P, irrigation water, and lime. Total cost per hectare
is based on the product of crop yield and net crop price. Net crop price is the market price minus the
harvest, hauling, and other processing costs which are yield dependent. The net price must be determined
outside EPIC.
When valid cost figures are entered into these EPIC input cells, the model will return annual cost and
returns by crop. EPIC budget information is valuable not only for profit analyses but also risk analyses,
since the annual distributions of profits and costs can be captured. Risk analysis capability greatly
enhances the analytical value of EPIC for economic studies.
The greatest value of EPIC to economic analysis is not its internal economic accounting, but the stream of
physical outputs on daily, monthly, annual, or multi year periods that can be input into economic models,
budget generators, and risk analysis systems. EPIC estimates crop yields, movement of nutrients and
pesticides, and water and sediment yields. Changes in inputs necessary to respond to changes in
managment, soil quantity and quality, climate (i.e., global warming), droughts etc., are also estimated.
These outputs become inputs into economic and natural resource models facilitating comprehensive
analyses of alternative policies and programs.
You can import EPIC data files into Garden with Insight with much caution  see Importing EPIC data files.
