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 (0-1), 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, user-scheduled) 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 (0-1)), 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 variable-charge 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 KCL-extractable 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 0-95 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 30-day 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(k-1) + T(mn)
Code:
if RFS > RFS(T) and CV > CV(T) and T(mn,i) > 0.0
PSTI(k) = PSTI(k-1) + 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(k-1) - 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 (1-3 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 cross-sectional 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 * (1-2S)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 user-specified 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.