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EPIC 0810 User Manual - EPIC & APEX Models

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1. ENVIRONMENTAL POLICY INTEGRATED CLIMATE MODEL USER S MANUAL VERSION 0810 SEPTEMBER 2015 EPIC ENVIRONMENTAL POLICY INTEGRATED CLIMATE EPIC Development Team Dr Tom Gerik Co project leader quality control and beta testing Dr Jimmy Williams Author of EPIC Steve Dagitz Visual Basic programming Melanie Magre Database maintenance beta testing guide development Avery Meinardus EPIC programming support Evelyn Steglich Model validation website maintenance guide development Robin Taylor EPIC 0810 User Manual revision Blackland Research and Extension Center Texas A amp M AgriLife 720 East Blackland Road Temple Texas EPIC Evaporation and Transpiration Rain ke Snow Z j Subsurface Chemicals Flow gt Below Root Zone Disclaimer Warning copyright law and international treaties protect this computer program Unauthorized reproduction or distribution of this program or any portion of it may result in severe civil and criminal penalties and will be prosecuted to the full extent of the law Information presented is based upon best estimates available at the time prepared The Texas A amp M University System makes no warranty expressed or implied or assumes any legal liability or responsibility for the accuracy completeness or usefulness of any information iv Model Objectives Assess the effect of soil erosion on productivity Predict the effects of management decisions on s
2. 86 To validate erosion adjust PARM 46 for a more accurate simulation of MUST MUSS Increasing PARM 46 increases the effect of crop residue and therefore reduces erosion 87 Pesticide Fate The GLEAMS Model GLEAMS Leonard et al 1987 technology for simulating pesticide transport by runoff percolate soil evaporation and sediment was added to EPIC Pesticides may be applied at any time and rate to plant foliage or below the soil surface at any depth When the pesticide is applied there is a loss to the atmosphere Thus the amount that reaches the ground or plants is expressed by the equation PAPE PAPR PAEF where PAPE is the effective amount of pesticide applied in kg ha PAPR is the actual amount applied in kg ha and PAEF is an application efficiency factor To determine how much pesticide reaches the ground the amount of ground cover provided by plants is estimated with the equation GC 1 0 erfe 1 33 LAI 2 2 0 where GC is the fraction of the ground that is covered by plants LAI is the leaf area index Therefore the pesticide application is partitioned between plants and soil surface with the equations FP GC PAPE GP PAPE ER where FP is the amount of pesticide that is intercepted by plants GP is the amount that reaches the ground Pesticide that remains on the plant foliage can be washed off by rain storms It is assumed that the fraction of pesticide that is potentially dislodgeable is washed
3. 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 Coefficient used in allocating root growth between two functions 0 0 root growth exponential distribution of depth 1 0 root growth function of water use values between 0 0 and 1 0 weight the two functions Range 0 0 1 0 Exponential coefficient in root growth distribution by depth function Range 5 10 Volatilization nitrification partitioning coefficient Fraction of process allocated to volatilization Range 0 05 0 5 Runoff amount to delay pest application mm pesticide is not applied on days with runoff greater than PARM 58 Range 0 0 25 0 Soil water value to delay tillage tillage delayed when PDSW FCSW gt PARM 59 PDSW Plow depth soil water content FCSW Field capacity soil water content Range 0 0 1 0 Exponential coefficient in EPIC soil erosion C factor equation relates C factor to soil cover by flat and standing residue and growing biomass Range 0 5 2 0 Weighting factor for estimating soil evaporation at 0 total compensation of water deficit is allowed between soil layers At 1 0 no compensation is allowed 0 lt PARM 61 lt 1 0 gives partial compensation Range 0 0 1 0 Exponential coefficient regulates upward N movement by evaporation Increasing PARM 62 increases upward N movement Range 0 2 2 0 Upper limit of N concentration in percolating water ppm Range 100 10000 Upper limi
4. Curve number Precipitation Runoff Time of concentration of the watershed Peak runoff rate Rainfall duration Maximum rainfall of duration tc total storm rainfall Maximum 0 5 hour rainfall total storm rainfall DPS Daily Pesticide Variable Y M D RT PAPL PSRO PLCH PSSF PSED PDGF PDGS PFOL PSOL PDRN Q SSF PRK ROCONC I Description Year Month Day Pesticide number Pesticide applied Pesticide in runoff Pesticide in percolate from root zone Pesticide in subsurface flow Pesticide transported by sediment Pesticide degradation from foliage Pesticide degradation from soil Pesticide on the plant foliage Pesticide present in soil Pesticide in drainage system outflow Surface runoff Total subsurface flow Percolation Pesticide concentration in runoff DWC Daily Water Cycle Variable Description 70 Units Units g ha g ha g ha g ha g ha g ha g ha g ha g ha g ha ppb Units PRCP PET ET EP Q SSF PRK QDRN IRGA QIN RZSW WTBL GWST Year Month Day Precipitation Potential evapotranspiration Evapotranspiration Plant evaporation Runoff Subsurface flow Percolation Soluble nitrogen from drainage system Irrigation water Inflow for water table Root zone soil water Water table Groundwater storage DWT Daily Soil Water In Control Section And 0 5m Soil Table Variable Description Y Year sequence Y Year Month D Day SWI SW2 TMP S
5. Nitrogen loss in percolate 64 Units T ha T ha T ha Units T ha T ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha MNP YP QAP PRKP LIME OCPD TOC APBC TAP TNO3 Phosphorus mineralized Phosphorus loss in sediment Labile phosphorus loss in runoff Phosphorus loss in percolate Lime applied Organic carbon in plow layer depth set by PARM 16 Organic carbon in soil profile Labile phosphorus content in plow layer Total labile p in soil profile Total nitrate in soil profile ACN Annual Soil Organic Carbon amp Nitrogen Table Variable DEPTH BD33 SAND SILT CLAY ROCK WLS WLM WLSL WLSC WLMC WLSLC WLSLNC WBMC WHSC WHPC WOC WLSN WLMN WBMN WHSN WHPN WON Description Depth of layer Bulk density at 33 kPa Sand Silt Clay Rock Structural litter Metabolic litter Lignin content of structural litter Carbon content of structural litter Carbon content of metabolic litter Carbon content of lignin of structural litter Nitrogen content of lignin of structural litter Carbon content of biomass Carbon content of slow humus Carbon content of passive humus Organic carbon concentration Nitrogen content of structural litter Nitrogen content of metabolic litter Nitrogen content of biomass Nitrogen content of slow humus Nitrogen content of passive humus Organic nitrogen concentration 65 kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha
6. WPM10810 dat amp filename wpl n nanas 26 Wind Files WINDO810 dat amp Glenome wmd 29 How to Prepare Weather Input Files cccccccesccsssceesceeseeeseeeseeeseecseecsaecsaecaeceaeceaeesaeeseeeseeeeeseseseeeseeesaes 31 Operation Schedule Files OPSC0810 dat amp filename ops asas 33 Crop File ECROPOSIO Cat ise 32831 na usta pusu Su asas aaa hayaya eege 39 Tillage File lt TILEOS EE EE 46 Fertilizer File FERTOS TO dort 49 Pesticide Pile GPESTOST0 EE 50 Multi Run File OMILRNOS TO dort 51 Parameter File PARM0S810 dort 52 Print File PRNTOSI O 480 EEN 59 Bt Uer 78 How to Validate Crop Y16148 uu aan anal iaia aa u aaa Babu as 82 How to Validate Runoff Sediment Losses amp Sediment Losses a 84 Pesticide Fate The GLEAMS Model 88 References 28 n l EE E L a u EE pina Seel 91 Overview EPIC is a process based computer model that simulates the physico chemical processes that occur in soil and water under agricultural management It is designed to simulate a field farm or small watershed that is homogenous with respect to climate soil land use and topography termed a hydrologic land use unit HLU The area modeled may be of any size consistent with required HLU resolution EPIC operates solely in time there is no explicitly spatial component Output from the model includes files giving the water
7. rainfall days only NO for N day interval during growing season 21 24 NGN ID number of weather variables input Precip 1 Temp 2 SolarRad 3 WindSpd 4 RelHum 5 If any variables are input rain must be included Thus it is not necessary to specify ID 1 unless rain is the only input variable Examples NGN 1 inputs rain NGN 23 inputs rain temp and RAD NGN 2345 inputs all 5 variables If MLRNOS10 dat is activated with NBYR gt 0 then NGN must equal 0 for measured weather to be actually simulated 25 28 IGN Number of times random number generator cycles before simulations starts 29 32 IGSO Determines day weather generator stops generating daily weather 0 for normal operation of weather model N duplicate weather in a given year up to date N N for a rewind of weather after N years 15 33 36 37 40 41 44 45 48 49 52 53 56 57 60 61 64 65 68 69 72 73 76 77 80 LPYR IET ISCN ITYP ISTA IHUS NDUM NVCN INFL MASP LBP NSTP A V V 366 will simulate entire year etc 0 if leap year is considered 1 if leap year is ignored Potential evapotranspiration PET method code 0 or 1 for Penman Monteith usually for windy conditions 2 for Penman 3 for Priestly Taylor 4 for Hargreaves 5 for Baier Robertson 0 for stochastic curve number estimator 0 for rigid curve number estimator 0 for modified rational EQ peak rate estimate 0 for SCS TR55 Peak Rat
8. 0 10 0 Number years of maximum monthly 0 5 h rainfall record Coefficient 0 1 governing wet dry probabilities given number of days of rain blank if unknown or if W D probabilities are input Parameter used to modify exponential rainfall amount distribution blank if unknown or if standard deviation amp skewness are input Field length if wind erosion is to be considered km Field width if wind erosion is to be considered km Description Clockwise angle of field length from north if wind erosion is to be considered Standing dead crop residue Power parameter of modified exponential distribution of wind speed if wind erosion is to be considered Soil particle diameter in micron if wind erosion is to be considered Wind erosion adjustment factor Irrigation trigger 3 options 1 Plant water stress factor 0 1 2 Soil water tension in top 200 mm gt 1 kpa 3 Plant available water deficit in root zone mm Runoff volume volume irrigation water applied blank if IRR 0 Maximum annual irrigation volume allowed mm Minimum single application volume allowed mm 18 73 80 Line 5 Column 1 8 9 16 17 24 25 32 33 40 41 48 49 56 57 64 65 72 73 80 Line 6 Column 1 8 9 16 17 24 25 32 33 40 41 48 41 48 ARMX Variable BFTO FNP FMX DRT FDSO PECO VLGN COWW DDLG SOLQ Variable GZLM FFED DZ DRV RSTO RFPO BUS 1 Maximum single application volume allowed Descr
9. JEJE 9 lloS uo J u e AA S epoL Jo pays jnduioo epol juaweheueyy sues luluo l 1 U1E AA WO Japan Aug s epoL lej u o eyeq DESu 3 z jeu keq yu wau wejbeiq MOL ldg Cs conveniently accomplished using published crop yield data Definitions EPIC Projects Scenarios amp Runs A project is a study designed to model and explore an idea or concept regarding the impact of agricultural management practice s geography location and or topography or climate on crop yield environmental impact and or economics of the agricultural enterprise It will involve the manipulation of one or more variables e g presence or absence of a management practice or constant versus increasing atmospheric CO Each model execution with a defined set of input data is a scenario A scenario may be run standalone or as a member of a batch run A scenario is therefore a single specific model configuration within a project or study which will typically consist of one or more runs of one or more scenarios The following examples illustrate the flexibility of EPIC to simulate the environmental impact of agriculture An EPIC project may involve the same crop and land management scenario applied to several separate parcels of land a field farm or small watershed each with different soil and or weather input in a series of runs An EPIC project may involve a variety of management scenarios applied in a series
10. Various components from CREAMS Knisel 1980 and SWRRB Williams et al 1985 were used in developing EPIC and the GLEAMS Leonard et al 1987 pesticide model used to estimate runoff leaching sediment transport and decay was added later Sabbagh et al 1991 EPIC was used to respond to the soil conservation questions raised by the 1985 National Resource Conserv ation Act Putman et al 1988 Since then the model has been expanded and refined to allow simulation of many processes important in agricultural management Sharpley and Williams 1990 Williams 1995 The computational unit or HLU homogeneous land use unit is an area homogeneous for soil aspect and slope weather and management practice The size of the HLU depends on the desired resolution and precision The drainage area or HLU considered by EPIC is generally a field size area up to about 100 ha where weather soils and management systems are assumed to be homogeneous The major components in EPIC are weather simulation hydrology erosion sedimentation nutrient cycling pesticide fate crop growth soil temperature tillage economics and plant environment control Although EPIC operates on a daily time step the optional Green amp Ampt 1911 infiltration equation simulates rainfall excess rates at shorter time intervals 0 1 h The model is capable of simulating thousands of years if necessary The model offers options for simulating several other processes five pote
11. WP OTROARIVd OImsiap 1ep 0180SS2LL QMey p Iep 0 809S4O THeTSp WP OTSOTIOS DOT TEE ays ad JU IUO 1IS zptr 1 T SON Tolga ATED sop akanmg OEP ep 0180 LAA Ee enung PAA a 09 TYAS o pamo AUO MSU IUP yao eeg pure dye See F omg IOA PULA JURIS J001 SST IIe IqR ams SL UONE Toupee DD N Dia ATUJUICUI JMUT ADHUH d HOI Japan lqe los SUD Sp ADUTA Ze te JSO5 Term ODE ADULTI uru Jad au auo g ny Joquiny SOUS JO SET Plats dodo Tenure Ase SUDHU LVG NITSO ILd3I metap POTENS IOBA IOS Ap ap erun Jndjno pug Apep usp 20011 angel APD eJI ios AIPULLLMS UIS ona SIM NP Gg los Alp UOP gan SO NM MULE IOS PRUUL UOR SOND JSO9 Gutts RUWUME oos mggpuuru REALS dow lt PP SSD auiren uewdoI AUU WAU auen emjpredttt los App dp ommemcu zi los BUUS JOS AULA TEUUUE UUE Anam apronsed ATUOUT sdt arnan Ditsch Anton SJUT Jiminy aponsed App sdp owens ABoqoIpAY Amep Alp awouuny ueurmums Jenuup PAV UNS ont pd ENUE UIDE ADUH Indjno pippupjs jo away ura sad gz eat mdmo afale uonesade Sees Qtnejep ep OLSOLSAd e ET S GIS ania pf pue QEP POO TILL nit POTEA OND p sn 20 0 SWEAR aly uPA S L4 III saureu ai Ip At Soigtaoseug IVa He Imuej p POISOANIA mg iep 0I801IN4AA sunt Jo dnom Jo Apnys amua Jo yueysuos mujop EP OTSOALIS are yey sisjowesed SUYAS OI Joo IVT TNOODOIdA FANPN YS IA 01802143 T ANBA sayy mdu Master File EPICFILE dat The use
12. each sub file or for individual irrigation applications the runoff ratio may be entered on the line of the irrigation operation in each ops file having irrigated crops NOTE if automatic irrigation has been selected with a value 0 0 in line 7 NIRR of each sub file that is irrigated irrigation runoff will be significantly lower than when using rigid applications of the amounts indicated in the ops files v What type of runoff is in error Q SSF QRF QDRN or RTF If Q and or QDRN are in error follow the next twelve steps If SSF QRF and RTF are in error go to the next item First check land use curve number values Correct runoff sediment losses by checking the accuracy of estimated curve numbers that dictate runoff sediment losses This may be done by checking the land use number in line 2 LUN of each ops file If multiple crop rotations are used simulated runoff sediment losses accuracy will be enhanced if LUN is revised at planting and harvest of each crop by entering a value on the appropriate operation line NOTE Land use numbers may be substituted with curve numbers Second check the saturated conductivity values for soils Correct runoff sediment losses by checking the accuracy of the saturated conductivity values of each soil in the sol files Third check hydrologic soil group values Correct runoff sediment losses by checking the accuracy of the hydrologic soil group in line 2 HSG in each of the sol f
13. energy ratio WA increases yields through biomass changes and therefore both grain and forage yields increase 83 How to Validate Runoff Sediment Losses amp Sediment Losses USER NOTE OF CAUTION If a multiple run has been executed denoted by a value greater than zero in col 4 in MLRNO810 dat and the pre run results are of no interest then open out and find TOTAL WATER BALANCE The applicable simulation results follow this section beginning with a new EPIC descriptive title Likewise use only the second set of results given in man asa asw wss msw etc files TO CHECK THE ACCURACY OF SIMULATED RUNOFF SEDIMENT LOSSES AND SEDIMENT LOSSES FOR THE WATERSHED OUTLET open the asw file for the yearly simulated losses and consult your EPIC0810 manual for the definitions of the column headings If QTW values for the years being validated are unacceptable usually YW will also be in error follow the instructions below First check land use values Correct runoff sediment losses by checking the accuracy of estimated curve numbers that dictate runoff sediment losses This may be done by checking the land use number in line 2 LUN of each ops file If multiple crop rotations are used simulated runoff sediment losses accuracy will be enhanced if LUN is revised at planting and harvest of each crop by entering a value on the appropriate operation line Second check hydrologic soil group values Correct runoff sediment lo
14. erosion routines Incorporation of nitrification volatilization component Improved water table dynamics routine Incorporation of RUSLE water erosion equation Renard 1997 Improved snowmelt runoff and erosion component Purveen et al 1997 Improved EPIC wind erosion model WESS Potter et al 1998 Incorporation of Baier Robertson PET routine Roloff et al 1998 Incorporation of Green amp Ampt infiltration function Williams et al 2000 Enhanced carbon cycling routine that is based on the Century model Izaurralde et al 2004 approach Incorporation of a potassium K cycling routine experimental de Barros et al 2004 A key output provided by EPIC is crop yield predictions Studies in the U S and abroad have specifically tested the accuracy of EPIC crop growth and yield predictions A comprehensive test of the crop growth submodel comparing simulated barley corn rice soybean sunflower and wheat yields with published values found average predicted yields were within 7 of the average measured yields Williams et al 1989 Calibration and validation of an EPIC implementation is frequently most Flow Diagram EPIC Logic amp Operations UO dora nduioo s luguuuinns yndjno A dot uo 1e A ju uij BeuelA JO pug JO pol aindwo5 J EAA 9 IIOS uo juowabeueyy JO pays ajndwog JB EN 9 lloS uo do19 jo pays nduioo painb y se Ajyjuoy Jo pue Ae indino dote pue o juowebeueyy Addy
15. exchangeable and fixed k pools Range 0 00001 0 0005 Exponential coefficient in RUSLE C residue factor equation used in estimating the residue effect Range 0 01 0 5 Maximum depth for biological mixing m Range 0 1 0 3 Biological mixing efficiency simulates mixing in top soil by earth worms etc Range 0 1 0 5 Exponential coefficient in RUSLE C live plant factor equation used in estimating the effect of growing plants Range 0 01 0 2 Lower limit nitrate concentration maintains soil nitrate concentration at or above PARM 27 Range 0 10 Acceptable plant N stress level used to estimate annual N application rate as part of the automatic fertilizer scheme Range 0 1 K pool flow coefficient regulates flow between soluble and exchangeable K pools Range 0 001 0 02 Denitrification soil water threshold fraction of field capacity soil water storage to trigger denitrification Range 0 9 1 1 Furrow irrigation sediment routing exponent Exponent of water velocity function for estimating potential sediment concentration Range 1 5 Minimum C factor value in EPIC soil erosion equation Range 0 0001 0 8 Puddling saturated conductivity mm h simulates puddling in rice paddies by setting second soil layer saturated conductivity to a low value Range 0 00001 0 1 55 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53
16. irrigation operations are used Revise the global irrigation runoff ratio in line 8 of each sub file or for individual irrigation applications the runoff ratio may be entered on the line of the irrigation operation in each ops file having irrigated crops NOTE if automatic irrigation has been selected with a value 0 0 in line 7 NIRR of each sub file that is irrigated irrigation runoff will be significantly lower than when using rigid applications of the amounts indicated in the ops files Tenth revise the land uses To check the accuracy of the land use by major land use category such as forest grass and crops open the out file and find LAND USE SUMMARY This listing provides the proportionate breakdown of the watershed into the land uses by crop or other use NOTE Since runoff and erosion are highly correlated with cropland and its land condition straight row contoured contoured and terraced carefully verify the proportion of each crop in the watershed in this listing v To check another runoff component RTF Open EPICCONT dat and determine the value of RFPO on line 4 fourth variable If this is 0 0 change it to 0 01 or higher until you have validated RTF v To check other runoff components SSF and QRF Open each sol file and determine the value for each layer of HCL line 23 If this is 0 0 change it to 0 1 or higher until SSF and or QRF are validated v After validating runoff check MUST or MUSS for accuracy
17. kg ha Units T m kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha ACO Annual Cost Variable Description Units Y Year M Month D Day OP Tillage operation CROP Crop name MT Fertilizer or pesticide number HC Operation code EQ Equipment number TR Tractor number COTL Cost of tillage operation ha COOP Operation cost ha MTCO Cost of fertilizer or pesticide operation kg MASS Mass of fertilizer or pesticide applied kg ha ACY Annual Crop Yield Variable Description Units Y Year RT Fertilizer ID CPNM Crop name YLDG Grain yield T ha YLDF Forage yield T ha BIOM Biomass T ha YLN Nitrogen used by crop kg ha YLP Phosphorus used by crop kg ha FTN Nitrogen applied kg ha FTP Phosphorus applied kg ha IRGA Irrigation volume applied mm IRDL Irrigation water lost in delivery system mm WUEF Water use efficiency crop yield growing season ET kg mm GSET Growing season et mm mm CAW Crop available water soil water at planting growing season mm rainfall runoff CRF Growing season rainfall mm COM Growing season runoff mm COST Costof production ha 66 COOP RYLG RYLF PSTF WS NS PS KS TS AS SS PPOP IPLD IGMD IHVD Operating cost Return for grain yield Return for forage yield Pest damage factor fraction of yield remaining after pest damage Water stress days N
18. of 24 kg day 12 kg day consumed and an equivalent amount trampled Fraction of soil compacted tire width tillage width Fraction plant population reduced by operation Carbon emission kg ha Not used in EPIC Not used in EPIC Full tillage operation name this is optional and not read 48 Fertilizer File FERT0810 dat The fertilizer database FERT0810 dat includes most common fertilizers and or other nutrient materials used in agricultural management There are 12 parameters used to describe each fertilizers properties Those parameters are all listed in a single line in FERT0810 dat file which includes the following data elements Each Line Column Variable Description 1 5 FTNO Fertilizer reference number 7 14 FTNM Fertilizer name abbreviation 15 22 FN Mineral N fraction 23 30 FP Mineral P fraction 31 38 FK Mineral K fraction 39 46 FNO Organic N fraction 47 54 FPO Organic P fraction 55 62 FNH3 Ammonia N fraction 63 70 FOC Organic C fraction 71 78 FSLT Salt fraction 79 86 FCST Fertilizer cost kg 87 94 FCEM Carbon emission per unit fertilizer kg kg 96 NAME Full name of fertilizer this is optional and not read 49 Pesticide File PEST0810 dab The fertilizer database PESTO810 dat includes most common pesticides used in agricultural management There are 9 parameters used to describe each fertilizer s properties Those parameters are all listed in a single line in PESTO81
19. split biomass between above ground and roots RWPC1 is the partitioning fraction at emergence and RWPC2 is partitioning fraction at maturity Between those two points there is a linear extrapolation Heat Units required for Germination degree days This delays germination from the planting date or the date at which the temperature of soil layer 2 exceeds TG Plant Population Crops amp Grass 1st Point Plant Population for crops grass etc except trees or plants requiring more than 1 m2 plant 1st point on population curve The number to the left of the decimal is the number of plants and the number to right is the fraction of maximum leaf area at the population Plant population is expressed as plants per square meter If trees the population is expressed as plants per hectare and the second plant population point is placed in the SMR1 position and the first point placed in the SMR2 position The first point should be the higher population Thus PPLP1 SMR1 lt PPLP2 SMR2 PLANTS M 2 PPLP1 SMR1 gt PPLP2 SMR2 PLANTS HA Plant Population Crops amp Grass 2nd Point The number to the left of the decimal is the number of plants and the number to right is the fraction of maximum leaf area at the population Plant population is expressed as plants per square meter If trees the population is expressed as plants per hectare and the second plant population point is placed in the SMR1 position and the first point placed in the SMR2 position The f
20. t ha ER is the enrichment ratio concentration of pesticide in the sediment divided by the pesticide concentration in the top 10 mm of soil computed with equation 157 The pesticide concentration in the soil is calculated by substituting 214 into 217 and solving for PSYC PSYC KD GP 0 01 ST 0 1 KD BD Soil layers with low storage volumes have high leaching potentials not only because percolation is greater but also because storage volume displacement is greater higher concentration Pesticides with low KD values and high solubility are transported rapidly with water Conversely high KD value pesticides are adsorbed to soil particles and travel largely with sediment 90 References Adams RM Houston LL McCarl BA Tiscarefio M Matus J amp Weiher RF 2003 The benefits to Mexican agriculture of an El Ni o Southern Oscillation ENSO early warning system Agric Forest Meteorol 115 183 194 Apezteguia HP Izaurralde RC amp Sereno R 2002 Simulation of soil organic matter dynamics as affected by land use and agricultural practices in semiarid Cordoba Argentina Agron Abstr Benson VW Potter KN Bogusch HC Goss D amp Williams JR 1992 Nitrogen leaching sensitivity to evapotranspiration and soil water storage estimates in EPIC J Soil Water Cons 47 334 337 Bouniols A Cabelguenne M Jones CA Chalamet A Charpenteau JL amp Marty JR 1991 Simulation of soybean nitrogen nutrition for a silty clay soil in s
21. to check 1 PET is not reasonable Try another PET eq that may be more appropriate for the site Hargreaves is the most robust and can be adjusted by varying the coefficient PARM 23 0 0023 0 0032 or the exponential PARM 34 0 5 0 6 in PARM0810 DAT Penman Monteith is generally considered the most accurate but is sensitive to wind speed which is subject to measurement errors It can also be adjusted through the stomatal conductance coefficient PARM 1 1 0 2 0 in PARM0810 DAT The Baier Robertson equation developed in Canada is a good choice in cold climates ET is not reasonable Crop growing season may be incorrect check planting and harvest dates and potential heat units crg ops Also check harvest time each year in txbell out for the value of HUSC look for CORN YLD HUSC should normally range from 1 to 1 2 If HUSC is lt 1 PHU is too large or harvest date is too early If HUSC is gt 1 2 PHU is too small or harvest date is too late For many annual crops the value of HUSC should be set to 1 2 using an early harvest date crg ops Harvest can t occur until the input harvest date and then only after the accumulated heat units have reached the input HUSC value Forage crops may be grazed too closely or cut too often to allow leaf area to develop properly for normal plant water use Check Runoff equations NRCS curve number equation The CN equation varies with soil water EPIC has four different methods of linking CN and soi
22. txt file to create the generated monthly weather file wp1 31 Run EPIC Weather Program Put the historical daily weather input file under the weather program directory Before starting to run the weather generating program WXGN3020 exe one needs to set up WXGNRUN dat file This can be done by putting the actual daily weather file name dly on the first line in WSONRUN dat file if only one weather data set needs to be generated In the event of several weather data sets need to be generated by WXGN23020 exe each individual actual daily weather data set name has to be listed in WXGNRUN dat file By doing so the WXGN3020 exe will read all the daily weather files listed n WXGNRUN dat and generate all the monthly weather files When WXGNRUN dat is set up one can execute the weather generation program by typing WXGN3020 under the appropriate driver path prompt where both actual daily weather and weather generating program are stored Then press ENTER key The weather program will start to run until it is finished When it is finished it produces three files DLY an actual daily weather file OUT and INP files In which only INP file is needed for EPIC simulation To be consistent this INP file should be renamed as WP1 The WP1 file will be listed in the weather list file WPM10810 dat For the content of WP1 file please refer to the next section of WPM10810 dat 32 Operation Schedule Files OPSC0810 dat amp filename o
23. 0 dat file which includes the following data elements Each Line Column Variable Description 1 5 PSTNO Pesticide reference number 7 22 PSTN Pesticide name abbreviation 23 38 PSOL Pesticide solubility in ppm 39 54 PHLS Pesticide half life in soil in days 55 70 PHLF Pesticide half life in foliage in days 71 86 PWOF Pesticide wash off fraction 87 102 PKOC Pesticide organic C absorption coefficient 103 118 PCST Pesticide cost kg 119 134 PCEM Carbon emission per unit pesticide kg kg 136 NAME Full name of pesticide this is optional and not read 50 Multi Run File WL RNOST0 dat An EPIC study may involve the analysis of consecutive weather seeds on wind and water erosion without reloading the model That can be easily done with the multi run option in EPIC The simulation continues until a zero NBYR is encountered Line 1 et seq Column Variable Description 1 4 NBYR Number of years for second through the last simulation 5 8 11 0 for normal erosion of soil profile for static soil profile erosion control practice factor 9 12 12 Output code for annual watershed output for annual output for annual with soil table for monthly output for monthly with soil table for monthly with soil table at harvest for N days interval for soil table only n day interval for soil table only during growing season N day interval I x D A S Q Q H ra for N day interval during growing season 13 16 N2 We
24. 2 for original EPIC denitrification method N amp P plant uptake concentration code 0 for Smith curve 0 for S curve 0 for original EPIC oxygen depth function 0 for Amen Kamanian carbon clay function 0 for reading data from working directory 1 for reading from WEATDATA directory 2 for reading from working directory plus 3 other directories 0 for reading saturated conductivity in soil file 0 for computing saturated conductivity with Rawls method 0 for using input latitudes for subareas 0 for equivalent latitude based on azimuth orientation of land slope 17 68 72 73 76 77 80 Line 3 Column 1 8 9 16 17 24 25 32 33 40 41 48 49 56 57 64 65 72 73 80 Line 4 Column 1 8 9 16 17 24 25 32 33 40 41 48 49 56 57 64 65 72 IPAT ISCI NDM Variable RFNO CO20 CNO30 CSLT PSTX YWI BTA EXPK FL FW Variable ANG0 STDO UXP DIAM ACW BIR EFI VIMX ARMN 0 turns off auto P application 0 for auto P application 0 for new SCI equations 0 for original EPIC SCI equations 0 for no metal simulation 0 for metal simulation Description Average concentration of nitrogen in rainfall ppm CO concentration in atmosphere ppm Concentration of NO in irrigation water ppm Concentration of salt in irrigation water ppm Pest damage scaling factor 0 0 10 0 0 shuts off pest damage function 0 0 damage function can be regulated from very mild 0 05 0 10 to very severe 1
25. 3 If the amount of moisture in the plow layer is not equal to or greater than Parm 11 germination will not occur Setting this parm to a negative number such as 100 essentially turns this parm off and the seed will germinate regardless of moisture amount in the soil Range 10 30 Soil evaporation coefficient governs rate of soil evaporation from top 0 2 m of soil Range 1 5 2 5 Hargreaves PET equation exponent Original value 0 5 Modified to 0 6 to increase PET Range 0 5 0 6 Nitrate leaching ratio Ratio of nitrate concentration in surface runoff to nitrate concentration in percolate Range 0 1 1 Runoff CN Residue Adjustment Parameter Increases runoff for crop residue RSD lt 1 0 t ha and decreases for RSD gt 1 0 t ha Range 0 0 0 3 Plow layer depth m used to track soluble phosphorus concentration or weight organic carbon and soil water content Crack flow coefficient Fraction inflow partitioned to vertical crack or pipe flow Range 0 0 5 Pesticide leaching ratio Ratio of pesticide concentration in surface runoff to pesticide concentration in percolation Range 0 1 1 Fraction of maturity at spring growth initiation allows fall growing crops to reset heat unit index to a value greater than 0 when passing through the minimum temperature month Range 0 1 KOC for carbon loss in water and sediment KD KOC C Range 500 1500 K pool flow coefficient regulates flow between
26. 32 497 511 Williams JR Richardson JW amp Griggs RH 1992 The weather factor incorporating weather variance into computer simulation Weed Technol 6 731 735 Williams JR Arnold JG amp Srinivasan R 2000 The APEX model BRC Report No 00 06 Temple TX Texas Agric Expt Station Texas Agric Exten Service Texas A amp M Univ Zhao J Kurkalova LA amp Kling CL 2004 Alternative green payment policies when multiple benefits 93 matter Agric Resour Econ Rev 33 148 158 94
27. 4 days before actual harvest is expected to occur This is recommended so that the date of the operation will be met before the heat units are met If the date is set too late and the heat units are met before the date of the operation is met the crop will continue to grow longer than expected which can affect yield EPIC first checks to see that the date of the operation has been met then it checks to see if the fraction of heat units has been met as defined below Date Heat Units Action Date is met Heat unit fraction Operation will not occur until heat not met units requirement is met Date isnot Het unit fraction Operation will occur as soon as date is met met met Note Excess GDUs will accumulate causing the operation to occur later in the growing cycle than expected Date ismet Heat unit fraction Operation will occur immediately met 36 Heat unit scheduling can also be used to adjust operations to the weather temperatures from year to year If heat units are not scheduled set to 0 operations will occur on the date as scheduled in the operation schedule They will occur on the same date every year the crop is grown Heat unit scheduling operations which occur from planting to harvest are based on the heat units set at planting Operations which occur before planting are based on the total annual heat units which are calculated by the model For some grain crops an in field dry down period is allowed It is expressed as a frac
28. 54 Soluble P runoff exponent modified GLEAMS method makes soluble P runoff concentration a nonlinear function of organic P concentration in soil layer 1 Range 1 1 5 Water stress weighting coefficient at 0 plant water stress is strictly a function of soil water content at 1 plant water stress is strictly a function actual ET divided by potential ET 0 lt PARM 35 lt 1 considers both approaches Range 0 0 1 0 Furrow irrigation base sediment concentration T m potential sediment concentration when flow velocity 1 m s Range 0 01 0 2 Pest kill scaling factor scales pesticide kill effectiveness to magnitude of pest growth index Range 100 10000 Hargreaves PET equation coefficient original value 0 0023 modified to 0 0032 to increase PET Range 0 0023 0 0032 Auto N fertilizer scaling factor sets initial annual crop N use considering WA amp BN3 Range 50 500 Crop growth climatic factor adjustment c mm ratio of average annual precipitation temperature PARM 40 0 0 recommended or irrigation gt 0 sets CLF 1 Range 40 100 Soil evaporation plant cover factor Reduces effect of plant cover as related to LAI in regulating soil evaporation Range 0 00 0 5 NRCS curve number index coefficient regulates the effect of PET in driving the NRCS curve number retention parameter Range 0 5 1 5 Upward movement of soluble P by evaporation coefficient Range 1 0 20 0 Ratio of sol
29. 6 140 147 148 155 156 163 164 171 172 179 180 187 188 195 196 203 EMX RR TLD RHT RIN DKH DKI IHC A V Mixing efficiency 0 1 The mixing efficiency of the operation EMX is the fraction of materials crop residue and nutrients that is mixed uniformly in the plow depth of the implement Suggested values for EMX random roughness RR tillage depth TLD ridge height RHT and ridge interval RIN are given in V 1 However since these values may vary with soils and management modifications may be needed Random surface roughness created by tillage operation mm Tillage depth in mm Also used as the lower limit of grazing height mm 0 Indicates depth is below the surface 0 Indicates above ground cutting height Ridge height mm Ridge interval m Height of furrow dikes ignored if no dikes mm Distance between furrow dikes ignored if no dikes m Operation Code 1 kill crop 2 harvest without kill 3 harvest once during simulation without kill 4 5 Plant in rows 6 Plant with drill 7 apply pesticide 8 irrigate 9 fertilize 10 bagging amp ties cotton 11 ginning 12 hauling 13 drying 14 burn 15 puddle 16 destroy puddle 17 build furrow dikes 18 destroy furrow dikes 19 start grazing 20 stop grazing 21 Scrape manure from pens 22 auto mow 23 place plastic cover 24 remove plastic cover 47 204 211 212 219 220 227 228 235 236 243 244 251 252 259 264 HE ORHI FR
30. ATA and examine organic N P and C C N should be near 10 N P should be near 8 The mineralization rate can be increased by decreasing the number of years of cultivation at the beginning of simulation sol line 3 Check N leaching in the last table AVERAGE ANNUAL DATA under QNO3 If large values relative to annual N fertilizer are found go to SUMMARY TABLE and look at PRKN and PRK High percolation values PRK may result from low ET or runoff low soil plant available water storage FC WP or high saturated conductivity values PRK is sensitive to the user choice to use manual irrigation applications of rigid amounts 81 How to Validate Crop Yields USER NOTE OF CAUTION If a multiple run has been executed denoted by a value greater than zero in col 4 in MLRN0810 DA T and the pre run results are of no Interest then open out and go to or find TOTAL WATER BALANCE The applicable simulation results follow this section beginning with a new epic descriptive title Likewise use only the second set of results given in man asa asw wss msw etc files First check the accuracy of soil depths if specific simulated yields are low To determine if soil depth and the important related water holding capacity is curtailing a specific crop yield open the acy file where both grain and forage yields are listed by crop Data entry errors in the depth of soil data can be checked by opening the appropriate sol file and referring to th
31. CL Lateral hydraulic conductivity mm h BIU 21 WPO Initial organic P concentration g T BIU 22 EXCK Exchangeable K concentration g T 23 ECND Electrical condition mmho cm 24 STFR Fraction of storage interacting with NO leaching BIU 25 ST Initial soil water storage fraction of field capacity 26 CPRV Fraction inflow partitioned to vertical crack or pipe flow BIU 24 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 CPRH WLS WLM WLSL WLSC WLMC WLSLC WLSLNC WBMC WHSC WHPC WLSN WLMN WBMN WHSN WHPN OBC Fraction inflow partitioned to horizontal crack or pipe flow BIU Structural litter kg ha Metabolic litter kg ha Lignin content of structural litter kg ha BIU Carbon content of structural litter kg ha BIU Carbon content of metabolic litter kg ha BIU Carbon content of lignin of structural litter kg ha BIU N content of lignin of structural litter kg ha BIU Carbon content of biomass kg ha BIU Carbon content of slow humus kg ha BIU Carbon content of passive humus kg ha BIU N content of structural litter kg ha BIU N content of metabolic litter kg ha BIU N content of biomass kg ha BIU N content of slow humus kg ha BIU N content of passive humus kg ha BIU Observed carbon content at end of simulation used only in sot T ha Variables in BOLD are required all others can be estimated by EPIC 25 Monthly Weather Files WPM10810 dat amp filename wp Monthly weather statis
32. CP FPOP CFEM EFI STIR NAME 25 stop drainage system flow 26 Resume drainage system flow Harvest efficiency 0 1 As a harvest operation IHC 2 0 this is the ratio of crop yield removed from the field to total crop yield Besides its normal function harvest efficiency can be used in simulating grazing HE approx equal to 0 1 or growing green manure crops HE 0 0 Pesticide application efficiency if Operation Code IHC 7 Overrides simulated Harvest Index HD if 0 lt ORHI lt 1 Near optimal harvest index values HI are contained in the crop parameters database As the crop grows these values may be adjusted for water stress For some crops like hay the harvest index is not affected by water stress and should maintain the original value Thus the harvest index override ORHI is used to give a constant harvest index Another important feature of ORHI is the provision for two different types of harvest of the same crop For example the seed could be removed from a crop and the later the straw could be baled The water stress adjusted HI is appropriate for the seed harvest but probably not for baling the straw Thus two separate harvest machines are required The second harvester sets ORHI approx equal to 0 9 to override the adjusted HI used in the first harvest 1 Grazing rate kg head day Values greater than 1 are kg ha of biomass removed per head per day by grazing For example one adult cow or beef consumes the equivalent
33. N Annualsummary 9 SOT Ending soil table 10 DTP Daily soil temperature 11 MCM Monthly cropman 12 DCS Daily crop stress 13 SCO Summary operation cost 14 ACN Annual soil organic C amp N table 15 DCN Daily soil organic C amp N table 16 SCN Organic C amp N summary table 17 DGN Daily general table 18 DWT Daily soil water in control section and 0 5m soil table 19 ACY Annual crop yield 20 ACO Annual cost 21 DSL Daily soil table 22 MWC Monthly water cycle amp N cycle 23 ABR Annual biomass root weight 24 ATG Annual tree growth 25 MSW Monthly output to SWAT 26 APS Annual pesticide 27 DWC Daily water cycle 28 DS 29 R84 30 APP 31 RTS 32 DBG 33 MBG 34 ABG 35 DSV 60 Table 3 List of Output Variables the User can choose from Name 1 TMX 2 TMN 3 RAD 4 PRCP 5 SNOF 6 SNOM 7 WSPD 8 RHUM 9 VPD 10 PET 11 ET 12 PEP 13 EP 14 Q 15 CN 16 SSF 17 PRK 18 QDRN 19 IRGA 20 QIN 21 TLGE 22 TLGW 23 TLGQ 24 TLGF 25 LGIR 26 LGMI 27 LGMO 28 EI 29 CVF 30 USLE 31 MUSL 32 AOF 33 MUSS 34 MUST Description Maximum temperature Minimum temperature Solar radiation Precipitation Snow fall Snow melt Wind velocity Relative humidity Vapor pressure deficit Potential evaporation Evapotranspiration Potential transpiration Transpiration Annual surface runoff SCS runoff curve number Lateral subsurface flow Percolation below t
34. PIC model results at Valdivia Chile Agric Syst Parton WJ Ojima DS Cole CV amp Schimel DS 1994 A general model for soil organic matter dynamics senstivity to litter chemistry texture and management Pp 147 167 in Quantitative modeling of soil forming processes SSSA Spec Public No 39 SSSA Madison WI Potter KN amp Williams JR 1994 Predicting daily mean temperatures in the EPIC simulation model Agron J 86 1006 1011 Potter KN Williams JR Larney FJ amp Bullock MS 1998 Evaluation of EPIC s wind erosion submodel using data from southern Alberta Can J Soil Sci 78 485 492 Purveen H Izaurralde RC Chanasyk DS Williams JR amp Grant RF 1997 Evaluation of EPIC s snowmelt and water erosion submodels using data from the Peace River region of Alberta Can J Soil Sci 77 41 50 Renard KG 1997 Predicting soil erosion by water a guide to conservation planning with the revised universal loss soil equation RUSLE USDA ARS Washington DC 92 Roloff G de Jong R Zentner RP Campbell CA amp Benson V W 1998 Estimating spring wheat yield variability with EPIC Can J Plant Sci 78 541 549 Sabbagh GJ Geleta S Elliott RL Williams JR amp Griggs RH 1991 Modification of EPIC to simulate pesticide activities EPIC PST Trans ASAE 34 1683 1692 Sabbagh GJ Norris PE Geleta S Bernado DJ Elliott RL Mapp HP amp Stone JF 1992 Environmental and economic impacts of pesticide and irrigation p
35. RP1 8 SCRP2 8 Determines the plant stress caused by N or P deficiency X of optimal n or P content present in plant 52 SCRP1 9 SCRP1 10 SCRP1 11 SCRP1 12 SCRP1 13 SCRP1 14 SCRP1 15 SCRP1 16 SCRP1 17 SCRP1 18 SCRP1 19 SCRP1 20 SCRP1 21 SCRP1 22 SCRP1 23 SCRP1 24 SCRP1 25 SCRP1 26 SCRP1 27 SCRP1 28 SCRP1 29 SCRP2 9 SCRP2 10 SCRP2 11 SCRP2 12 SCRP2 13 SCRP2 14 SCRP2 15 SCRP2 16 SCRP2 17 SCRP2 18 SCRP2 19 SCRP2 20 SCRP2 21 SCRP2 22 SCRP2 23 SCRP2 24 SCRP2 25 SCRP2 26 SCRP2 27 SCRP2 28 SCRP2 29 Calculates the pest damage factor as a function of temperature and relative humidity considering thresholds for 30 day rainfall and above ground plant material X sum of Product of daily average temperature and relative humidity Calculates the effect of water stress on harvest index as a Function of plant water use X plant water use as a of Potential plant water use during critical period Estimates plant water stress as a function of plant available Water stored X soil water stored divided by total Plant available water storage FC WP Governs N volatilization as a function of soil depth X Depth at the center of a soil layer mm Calculates wind erosion vegetative cover factor as a function of above ground plant material X vegetative equivalent C1 BIOM C2 STD C3 RSD where Cl C2 amp C3 are coefficients BIOM is above gr
36. Table 3 Right justified 4 spaces each 20 per line 6 JC Output variable ID concentration variables Select up to 4 variables from Table 3 Right justified 4 spaces each 20 per line 7 8 KS Output variable ID monthly state variables Select up to 40 variables from this list input number Right justified 4 spaces each 20 per line Enter 1 to omit all accumulated variables 9 10 KD Output variable ID daily output variables Select up to 40 variables by number from Table 3 Right justified 4 spaces each 20 per row 11 12 KY Annual output variable ID accumulated and average values Select up to 40 variables by number from Table 3 Right justified 4 spaces each 20 per row Enter 1 to omit all accumulated variables 13 14 KFS Monthly variables for Flipsim economic analysis 15 16 KF 0 gives no output gt 0 gives output for selected files there are 35 possible output files These lines have 20 right justified variables of 4 spaces each For a desired file enter a 1 in the appropriate variable space For example 10000000100000010001 prints files 1 9 16 and 20 from Table 2 File names are runname where runname refers to run ASTN and is the filename extension 59 Table 2 Output Files File Name Description 1 OUT Standard output file 2 ACM Annualcropman file 3 SUM Average annual summary 4 DHY Daily hydrology 5 DPS Daily pesticide 6 MES Monthly flipsim 7 MPS Monthly pesticide 8 AN
37. USLE MUSS and MUST usually give similar results and are appropriate for estimating sediment yield from small watersheds up to about 250 km The USLE is an erosion equation that is useful in studies like assessing the effect of erosion on productivity Slope length and steepness factor Both USLE and RUSLE equations are available RUSLE is preferred for steep slopes gt 20 Crop growth In out go to AVE ANNUAL CROP YLD and AVE STRESS DAYS The stress days reveal the stresses that are constraining crop growth Root growth stresses of bulk density BD or aluminum saturation ALSAT can reduce crop yields 80 greatly Go to SOIL PHYSICAL DATA and check for unreasonably high BD Go to SOIL CHEMICAL DATA and check for high aluminum saturation values gt 90 caused by low pH lt 5 BD can be lowered by deep tillage or simply corrected if the data are erroneous Aluminum saturation can be lowered by applying lime or by correcting erroneous pH data Water stress is the most common constraint to crop growth Excessive PET or runoff estimates are major causes Plant available water is another important limitation that causes water stress Erroneous estimates of plant available water occur when field capacity or wilting point are incorrect Soil water storage is particularly important in dry climates Nitrogen and Phosphorus stress is caused by low mineralization rates inadequate fertilizer or excessive leaching of N Go to SOIL CHEMICAL D
38. al unstressed growth rate including roots per unit of intercepted photosynthetically active radiation This parameter should be one of the last to be adjusted Adjustments should be based on research results This parameter can greatly change the rate of growth incidence of stress during the season and the resultant yield Care should be taken to make adjustments in the parameter only based on data with no drought nutrient or temperature stress 19 26 HI Harvest index This crop parameter should be based experimental data where crop stresses have been minimized to allow the crop to attain its potential EPIC adjusts HI as water stress occurs from near flowering to maturity 27 34 TOPC Optimal temperature for plant growth G TB and TG are very stable for cultivars within a species They should not be changed once they are determined for a species Varietal or maturity type differences are accounted for by different sums of thermal units 35 42 TBSC Minimum temperature for plant growth C TB and TG are very stable for cultivars within a species They should not be changed once they are determined for a species Varietal or maturity type differences are accounted for by different sums of thermal units 43 50 DMLA Maximum potential leaf area index The parameters in the CROP8 90 dat data set are based on the highest expected plant densities for crops not expected to have water stress DMLA is internally adjusted for drought prone re
39. alized for a site 0 for EPIC enrichment ratio method 1 for GLEAMS enrichment ratio method 0 for traditional EPIC radiation to biomass conversion 0 for new experimental water use to biomass 0 applies lime 1 does not apply lime 0 uses RUSLE C factor for all erosion equations 0 uses EPIC C factor for all erosion equations except RUSLE 0 field capacity wilting point estimate Rawls dynamic method 1 field capacity wilting point estimate Baumer dynamic method 2 field capacity wilting point input Rawls dynamic method 3 field capacity wilting point input Baumer dynamic method 4 field capacity wilting point estimate Rawls static method 5 field capacity wilting point estimate Baumer static methold 6 field capacity wilting point static input 7 field capacity wilting point nearest neighbor dynamic method 8 field capacity wilting point nearest neighbor static method 9 field capacity wilting point Norfleet dynamic method 10 field capacity wilting point Norfleet static method 0 for normal runs with daily weather input 0 for continuous daily weather from run to run no rewind 0 for constant atmospheric CO 1 for dynamic atmospheric CO 2 for inputting atmospheric CO2 0 for reading data from working directory 0 for reading from WEATDATA directory 0 Normal run no southern oscillation 0 Day of year when southern oscillation correction to stop 0 for Cesar Izaurralde denitrification method 1 for Armen Kemanian denitrification method
40. ameters that will be held constant for the entire study e g number of years of simulation period of simulation output print specification weather generator options etc This file cannot be renamed but can be edited EPICRUN dat file includes one row of data for each scenario Each row of data assigns a unique run number to the scenario and specifies which site weather station soil and tillage operation schedule files will be used Scenarios are listed one to a line a run is terminated when a blank line or EOF is reached Two weather files may be specified the weather and wind weather files If the regular weather and wind station identification parameters are zero EPIC will use the latitude and longitude data from the filename sit file and choose the closest weather and wind stations listed in the WPM1MO dat and WINDMO dat files respectively This file cannot be renamed but can be edited EPIC looks in the site catalog file SITEO810 dat or the catalog named in EPICFILE dat for the site number referenced in EPICRUN dat and obtains the name of the file containing the site specific data The site specific file is used to describe each Hydrologic Landuse Unit HLU which is homogenous with respect to climate soil landuse and topography The site may be of any size consistent with required HLU resolution Site files filename sit describe each site latitude longitude elevation area etc A project may involve several site
41. amp terraced Poor 63 73 80 83 18 j i Good 51 67 76 80 19 Pasture or range lt 50 ground cover or heavily grazed Poor 68 79 86 89 20 50 75 ground cover amp not heavily grazed Fair 49 69 79 84 21 gt 75 g round cover amp lightly grazed Good 39 61 74 80 22 As above amp Contoured Poor 47 67 81 88 23 R Fair 25 59 75 83 24 u Good 6 35 70 79 25 Meadow continuous grass not grazed mown for hay Good 30 58 71 78 26 Woods Small trees and brush heavy grazing amp regular burning Poor 45 66 77 83 27 Woods grazed not burned some litter covers soil Fair 36 60 73 79 28 Woods not grazed litter amp brush cover soil Good 25 55 70 77 29 Farmsteads 59 74 82 86 30 Roads dirt 72 82 87 89 31 hard surface 74 84 90 92 32 Sugarcane 39 61 74 80 33 Bermuda grass 49 69 79 84 34 Impervious Pavement urban area 98 98 98 98 35 1 National Engineering Handbook USDA Soil Conservation Service 1972 2 Close drilled or broadcast 3 Including rights of way 38 Crop File CROP0810 dat The crops database CROP0810 dat includes over 100 crops including trees and other perennials There are 59 parameters used to describe each crops growth characteristics Those parameters are all listed in a single line in CROP0810 dat file which includes the following data elements Each Line Column Variable Description 1 5 CNUM Crop reference number 7 10 CPNM Crop name abbreviation 11 18 WA Biomass Energy Ratio CO 330ppm This is the potenti
42. an annual minimum day length PARM 6 Range 0 1 NOTE This parm can cause problems at sites close to the equator where day length variation is very small Nitrogen fixation is limited by soil water or nitrate content or by crop growth stage At 0 fixation meets crop n uptake demand A combination of the 2 fixation estimates is obtained by setting 0 lt PARM 7 lt 1 Range 0 1 Soluble P runoff coefficient 0 1 m t P concentration in sediment divided by that of the water Range 10 20 Pest damage moisture threshold mm previous 30 day rainfall minus runoff Range 25 150 One of several parameters to regulate pest insect amp disease growth see also parm 10 PSTX in the control file PST in the crop file amp SCRP 9 Pest damage cover threshold t ha crop residue above ground biomass This is the amount of cover required for pests to begin to grow Range 1 10 Setting parm 10 at a large number 50 will result in little or no pest growth because it will be impossible to reach such high levels of cover One of several parameters used to regulate pest growth See also parm 9 PSTX in the control file PST in the crop file and SCRP 9 54 11 12 13 14 15 16 17 18 19 21 22 23 24 25 26 27 28 29 30 31 32 33 Moisture required for seed germination mm soil water stored minus wilting point storage in the plow depth plow layer depth parm 4
43. ather ID number concatenated from following Precipitation 2 Temperature max amp min 3 Solar radiation 4 Wind speed 5 Relative humidity If any variables are input precipitation must be included Therefore it is not necessary to specify N2 1 unless precipitation is the only input variable 51 Parameter File PAR M0810 dat The PARM0810 dat file plays a very sensitive part in EPIC because many coefficients of equations are maintained in that file The equation coefficients should not be changed without first consulting the model developer This file contains definitions of S curve and miscellaneous parameters used in EPIC0810 S Curves An S shaped curve is used to describe the behavior of many processes in EPIC The Y axis is scaled from 0 1 to express the effect of a range in the X axis variable on the process being simulated The S curve may be described adequately by two points contained in this file It is convenient to represent the X and Y coordinates of the two points with two numbers contained in this file The numbers are split by EPIC the X value is left of the decimal and the Y value is right of the decimal The two points are contained in an array called SCRP To illustrate the procedure consider the two SCRP values in the first line of the PARMO0810 dat file 90 05 99 95 SCRP 1 1 90 05 SCRP 1 2 99 95 When split we have X1 90 Y1 0 05 X2 99 Y2 0 95 EPIC uses these two points to solve the expone
44. ave been made to the EPIC model up to 2004 Several spin off versions have been developed for region or task specific applications e g the AUSCANE model created to simulate Australian sugar cane production Jones et al 1989 Table 1 Developmental History of EPIC from Gassman et al 2004 Modified component or input data Reference Original model used for RCA in 1985 Williams et al 1984 Improved and expanded crop growth sub model Williams et al 1989 Enhanced root growth functions Jones et al 1991 Improved nitrogen fixation routine for legume crops that calculates fixation Bouniols et al 1991 as a function of soil water soil nitrogen amp crop physiological stage Incorporation of pesticide routines from GLEAMS model Sabbagh et al 1991 Improved crop growth parameters for sunflower Kiniry et al 1992 Incorporation of CO amp vapor pressure effects on radiation use efficiency Stockle et al 1992a leaf resistance and transpiration of crops Incorporation of functions that allow two or more crops to be grown Kiniry et al 1992 simultaneously Improved soil temperature component Potter amp Williams 1994 Improved crop growth parameters for cereal oilseed and forage crops Kiniry et al 1995 grown in the northern Great Plains of North America Improved and expanded weather generator component Williams 1995 Incorporation of NRCS TR 55 peak runoff rate component Incorporation of MUSS MUST amp MUSI water
45. biomass C pool Final biomass C pool Initial total C pool Final total C pool Change in total C pool Observed total C pool final 76 kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha Units K kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha HSNO HSNF HPNO HPNF LSNO LSNF LMNO LMNF BMNO BMNF WONO WONF DWON C NO C NF I Initial slow humus N pool Final slow humus N pool Initial passive humus N pool Final passive humus N pool Initial structural litter N pool Final structural litter N pool Initial metabolic litter N pool Final metabolic litter N pool Initial biomass N pool Final biomass N pool Initial total N pool Final total N pool Change in total N pool Initial C N ratio Final C N ratio SCO Summary Operation Cost Variable Y M D OP CROP MT HC EQ TR COTL COOP MTCO MASS Description Year Month Day Tillage operation Crop name Fertilizer or pesticide number Operation code Equipment number Tractor number Cost of tillage operation Operation cost Cost of fertilizer or pesticide operation Mass of fertilizer or pesticide applied kg ha 77 kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha Units ha ha ha kg ha Output Analyzer Failed runs 1 Soil data soD Missing essential data Layer depths out of order Cu
46. crops Precise data for field application is subject to microclimate variation across the landscape Current parameters are reasonable estimates However they are more likely to understate frost damage than to overstate frost damage First point on frost damage curve Two points on the frost damage curve Numbers before decimal are the minimum temperatures degrees C and numbers after decimal are the fraction of biomass lost each day the specified minimum temperature occurs NOTE 10 20 means 20 percent of the biomass is lost each day a temperature of 10C is reached The negative sign on degrees is added by EPIC since no frost damage is assumed to occur above 0 degrees C These two parameters should be based on a combination of research results and observation Precise data for field application is subJect to microclimate variation across the landscape Current parameters are reasonable estimates However they are more likely to understate frost damage than to overstate frost damage Second point on frost damage curve Two points on the frost damage curve Numbers before decimal are the minimum temperatures C and numbers after decimal are the fraction of biomass lost each day the specified minimum temperature occurs NOTE 10 20 means 20 percent of the biomass is lost each day a temperature of 10C is reached The negative sign on degrees is added by EPIC since no frost damage is assumed to occur above 0 degrees C These two parameters
47. e accumulated depth m of the last soil layer Second check the accuracy of the heat units from planting to harvest After completing a run if automatic heat unit scheduling is not selected in EPICCONT dat line 1 THUS open the out file and find TOTAL WATER BALANCE scroll down a few lines to the beginning of the appropriate simulation to SA ID Scroll down until a HARV operation is found This is a list of harvest operations in year 1 for each subarea Scroll to the right to HUSC for each crop harvested If any HUSC values for a crop are outside the range of 0 9 to 1 1 scroll down to check following years If all years are outside the range check both the planting above the harvest operations and the harvest date for accuracy If they are accurate to the best of your knowledge then open the appropriate ops file s which contains the specific crop for which the heat units need adjusted If HUSC in the out file is less than 1 0 decrease the heat units at the planting operation and if greater than 1 0 increase the heat units If automatic heat unit scheduling is selected in EPICCONT dat line 1 IHUS open the out file and follow the same procedure as above except instead of changing the heat units change either the plant or harvest date to result in a more optimum HUSC approx 1 0 in the out file for the HARV operation Third check the plant population for accuracy If a crop yield is too low check the plant population i
48. e daily weather from the long term averages in the wind and weather files Daily weather data are solar radiation mJ m or Langley maximum and minimum temperatures C precipitation mm relative humidity fraction or dew point temperature gt 1 C and wind speed averaged over the month m s Monthly climate data are mean and standard deviation of maximum air temperature C mean and standard deviation of minimum air temperature C mean mm standard deviation mm and skewness of precipitation the probability of wet day after dry day and the probability of a wet day after wet day number days of rain per month maximum half hour rainfall mm mean solar radiation MJ m or Langley mean relative humidity fraction and mean wind speed m s Monthly wind data are average monthly wind speed m s and of time the wind is from the 16 cardinal points starting with North N NNE NE ENE E ESE SE SSE S SSW SW WSW W WNW NW NNW EPIC looks in the daily weather file catalog WLST0810 dat for the numbered daily weather station file referenced in EPICRUN dat Daily weather files have the form filename dly and contain the date and the 6 weather variables listed above The weather catalog WLST0810 dat and the weather file can be renamed and edited EPIC looks in the monthly weather file catalog WPM10810 dat for the numbered monthly weather station file referenced in EPICRUN dat Monthly weather files have t
49. e estimate 1 for type rainfall pattern 2 for type 1A rainfall pattern 3 for type 2 rainfall pattern 4 for type 3 rainfall pattern 0 for normal erosion of soil profile 1 for static soil profile 0 for normal operation 1 for automatic heat unit schedule PHU must be input at planting in operations schedule file Not used 0 variable daily CN with depth soil water weighting 1 variable daily CN without depth weighting 2 variable daily CN linear CN SW no depth weighting 3 non varying CN CN2 used for all storms 4 variable daily CN SMI soil moisture index 0 for CN estimate of Q 1 for Green amp Ampt estimate of Q rainfall exponential distribution peak rain fall rate simulated 2 for G amp A Q rainfall exponential distribution peak rainfall input 3 for G amp A Q rainfall uniformly distribution peak rainfall input 0 for mass only no pesticide in OUT 0 for mass only pesticides in OUT 0 for pesticide amp nutrient output in mass and concentration 0 for soluble P runoff estimate using GLEAMS pesticide approach 0 for modified nonlinear approach real time day of year 16 Line 2 Column 1 4 5 8 9 12 13 16 17 20 21 24 25 28 29 32 33 36 37 40 44 48 49 52 52 56 57 60 61 64 65 68 Variable IGMX IERT ICG LMS ICF ISW IRW ICO2 IDUM ICOR IDN NUPC 10X IDIO ISAT IAZM V V V V V V Description times generator seeds are initi
50. eld Biomass Root weight Leaf area index Standing dead crop residue Description Year Month Day Table with the following variable lines and 11 across consisting of 10 soil layers and a total Depth Soil water Soil temperature Crop residue CO loss Net mineralization 68 kg ha g ha g ha g ha g ha g ha g ha g ha g ha Units T ha T ha T ha T ha Units Ze T ha kg ha kg ha DCS Daily Crop Stress Variable Y M D RT CPNM WS NS PS KS TS AS SS Description Year Month Day The following variables are repeated 4 times Crop name Water stress factor Nitrogen stress factor Phosphorus stress factor Potassium stress factor Temperature stress factor Aeration stress factor Salinity stress factor DGN Daily General Output Variable Y M D PDSW TMX TMN RAD PRCP TNO3 WNO3 PKRZ S03 HUI BIOM YLDF UNO3 Description Year Month Day Plow depth soil water content Maximum temperature Minimum temperature Solar radiation Precipitation Total nitrate present in soil profile Nitrate content Initial labile P concentration Nitrate in lateral subsurface flow Heat unit index Biomass Forage yield nitrogen uptake by the crop 69 Units Units C C mJ m kg ha kg ha g ha kg ha T ha T ha kg ha DHY Daily Hydrology Variable Y M D CN PRCP Q TC QP DUR ALTC AL5 Description Year Month Day
51. er in order to obtain a distribution of soil erosion This file defines the options for selecting different consecutive weather runs without reloading the inputs The multi run control file MLRN0810 dat can be renamed and edited EPIC Version 0810 is a compiled Fortran program with very specific format and file structure requirements Description of the input files and definitions of the input variables follows 13 Run File EPICRUN dab When EPIC is executed each row in the EPICRUN dat file is read to determine the configuration of the scenario to be run one row per scenario A blank line or EOF terminates execution definitions of old scenarios can be kept at the end of the file if preceded by a blank line Each Line blank line or EOF terminates run Column 1 8 9 12 13 16 17 20 21 24 25 28 29 32 33 36 Variable ASTN ISIT IWP1 IWP5 IWND INPS IOPS IWTH Description Run name and or provides a unique ID for each run so that output files are not overwritten Site must be one of the sites listed in the file SITEO810 dat Monthly weather station must be one of the stations listed in WPM10810 dat if left blank EPIC will use the latitude and longitude given in the site file filename sit to choose a station Monthly weather station must be one of the stations listed inWPM50810 dat if left blank EPIC will use the latitude and longitude given in the site file filename sit to choo
52. ess than PARM 76 Range 100 1500 Coefficient regulating p flux between labile and active pool RMN PARM 77 WPML ISLI WPMA ISL RTO Range 0 0001 0 001 57 78 79 80 81 82 83 Coefficient regulating p flux between active and stable pool ROC PARM 78 BK ISL 4 WPMA ISL WPMS ISL Range 0 0001 0 001 Weighting factor for locating appropriate weather stations 1 gives strictly distance 0 gives strictly elevation Recommended value 0 9 Range 0 0 1 0 Partitions N gt and N20 N2 fraction of denitrification in original EPIC denitrification function Range 0 1 0 9 Weights the effect of TMX TMN and RAD on soil temperature Large values reduce the effect of TMX TMN and RAD relative to TX Range 5 0 20 0 Damping depth adjustment for soil temperature Regulates soil temperature change with depth Range 0 0 2 0 Runoff volume adjustment for direct link NVCN 0 Inversely related to runoff Used like PARM 42 in CN index method NVCN 4 Range 0 1 2 0 58 Print File PRNT0810 dat The file PRNT0810 DAT controls printing of output see also IPD in EPICCONT DAT The user can select output variables from the following lists The simulated output and summary files are numerous and some output variables are repeated in several files see KFL below Line Variable Description 1 5 KA Output variable ID accumulated and average values Select up to 100 variables by number from
53. eters used to describe each tillage operation and those parameters are all listed in a single line in TILL0810 dat file which Includes the following data elements Each Line Column Variable 1 5 TNUM 7 14 TIL 16 19 PCD 20 27 PRIC 28 35 XLP 36 43 HRY 44 51 HRL 52 59 PWR 60 67 WDT 68 75 SPD 76 83 RC1 84 91 RC2 92 99 XLB 100 107 FCM 108 115 RFV1 116 123 RFV2 124 131 EFM 132 139 RTI Description Equipment number for reference purposes only Operations are accessed by their sequential location in the file For example an operation number 9 will access the ninth operation regardless of the setting of this variable Tillage operation name abbreviation Power code POWE the machine with its own engine for power used to pull other machinery or equipment e g a tractor SELF the machine has its own engine for power but it does the operation by itself e g a combine NON the machine or equipment has no engine for power and it must be pulled by other machinery with engine power IRRI irrigation equipment CUST customized equipment Purchase price exception custom cost ha Initial list price in current Annual use hours Life of equip hours Power of unit kW Width of pass m Operating speed kph Repair cost coefficient 1 Repair cost coefficient 2 Lubricant factor Fuel consumption multiplier Remaining farm value PARM 1 Remaining farm value PARM 2 Machine efficiency Annual real interest rate 4
54. ferent crop and land management practices a function reflected its original name Erosion Productivity Impact Calculator The development of the field scale EPIC model was initiated in 1981 to support assessments of soil erosion impact on soil productivity for soil climate and cropping practices representative on a broad spectrum of U S agricultural production regions The first major application of EPIC was a national analysis performed in support of the 1985 Resources Conservation Act RCA assessment The model has continuously evolved since that time and has been used in a wide range of field regional and national studies both in the U S and in other countries The range of EPIC applications has also expanded greatly over that time including studies of Irrigation Climate change effects on crop yields Nutrient cycling and nutrient loss Wind and water erosion Soil carbon sequestration Economic and environmental Comprehensive regional assessments Modeling pesticide fate The EPIC acronym now stands for Environmental Policy Integrated Climate to reflect the greater diversity of problems that the model is currently applied to EPIC has continued to evolve and to be applied to an ever increasing range of scenarios since the 1985 RCA analysis Some applications have focused specifically on testing EPIC components Enhancements to facilitate the needs of various users continue to be made Table lists examples of modifications that h
55. gions as planting densities are much smaller in these areas unless irrigation is used 39 51 58 59 66 67 74 75 82 DLAI DLAP1 DLAP2 RLAD Fraction of growing season when leaf area declines The fraction of the growing season in heat units in divided by the total heat units accumulated between planting and crop maturity If the date at which leaf area normally declines is known one of the options in EPIC can be used to estimate the fraction of heat units accumulated A multi run EPIC simulation is setup with IGSD equal to 366 A one year simulation followed by a one year multi run will produce a multi run simulation which has average heat units per month and the total heat units to maturity The harvest date kill operations should be set to the crop maturity date The estimated heat units at maximum leaf area can then be divided by the heat units at maturity to estimate the fraction of the growing season at which leaf area index start to decline First point on optimal leaf area development curve Two points on optimal nonstress leaf area development curve Numbers before decimal are of growing season Numbers after decimal are fractions of maximum potential LAI Research results or observations on the of maximum leaf area at two points in the development of leaf area can be used in conjunction with an EPIC simulation like that described for DLAI The results of the one year multi run will establish the cumulative hea
56. ha Mean 0 5 hour rain mean storm amount Maybeleft blank or zero if unknown 14 OBSL Ave monthly solar radiation 3 options mJ m or Langley Average monthly solar radiation May be input in mJ m or LY Special note if you intend to use daily weather files Entering MJ M3 here indicates you will be reading mJ m Entering LY here indicates you will be reading Langleys mJ m 0 0419 LY May be left blank or zero if unknown 15 RH Monthly average relative humidity fraction 3 options 26 1 Average Monthly relative humidity Fraction e g 0 75 2 Average Monthly dew point temp E 3 Blanks or zeros if unknown NOTE May be left zero unless a PENMAN equation is used to estimate potential evaporation see variable IET 16 UAVO Average monthly wind speed m s The WPM50810 dat file has the same format 27 Daily Weather Files WLST0810 dat amp filename dly Daily weather statistics of a single weather station are maintained in filename dly This file must be listed in the database file WLST0810 dat or user defined name with a unique reference number which corresponds to the variable IWTH in the run file EPICRUN dat filename dly includes the following data elements Line 1 et seq Column Variable Description Units 3 6 YEAR 7 10 MNTH 11 14 DAY 15 20 SRAD Solar radiation mJ m or Langleys 21 26 TMAX Maximum temperatures C 26 32 TMIN Minimum temperatures C 33 38 PRCP Prec
57. he form filename wpm and contain the 13 weather variables listed above The weather catalog WPM10810 dat and the weather file can be renamed and edited EPIC looks in the monthly wind file catalog WIND0810 dat for the numbered monthly wind station file referenced in EPICRUN dat Monthly wind station files have the form filename wnd and contain monthly average wind run and the 16 cardinal points wind rose The wind catalog WIND0810 dat and the wind file can be renamed and edited 11 WPM50810 WIDX0810 Operation Schedules Crops Tillage Fertilizers Pesticides EPIC looks in an alternate catalog of monthly weather stations for use with the southern oscillation coefficients in WIDX0810 dat Monthly weather files have the form filename wp5 and contain 13 weather variables filename wp5 files have the same structure as filename wpm which may be referenced in WPM50810 dat This feature is experimental and should be validated if used EPIC reads a file containing coefficients for adjusting monthly averages according to the phase of the southern oscillation if this correction is requested This feature is experimental and should be validated if used EPIC looks in the operation schedule catalog file OPSC0810 dat or the catalog named in EPICFILE dat for the operation schedule number referenced in EPICRUN dat and obtains the name of the file containing the required operation schedule The operations file named filename ops
58. he root zone Flow from a drainage system Irrigation water applied Inflow to the root zone from the water table Lagoon evaporation Water wash to lagoon Runoff to lagoon Lagoon overflow Irrigation water from a lagoon Manure input to lagoon Manure output from lagoon Rainfall energy factor Average water erosion crop management factor Soil loss from water erosion using USLE Soil loss from water erosion using MUSLE Soil loss from water erosion using Onstad Foster Soil erosion water Soil loss from water erosion using modified MUSLE 61 Units T ha T ha T ha T ha T ha 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 RUS2 RUSL RUSC WEI RHTT RRUF RGRF YW YON QNO3 SSFN PRKN NMN GMN DN NFIX NITR AVOL DRNN YP QAP MNP PRKP ER FNO FNO3 FNH3 FPO FPL FSK FCO LIME TMP SW10 SLTI SLTQ Soil loss from water erosion using RUSLE2 Soil erosion by water estimated with RUSLE Soil erosion by water estimated with Modified RUSLE Wind erosion soil erodibility factor Ridge Height Random roughness of soil Wind erosion ridge roughness factor Soil erosion by wind Nitrogen transported from area in sediment Nitrogen in runoff Mineral Nitrogen lost in the horizontal movement of water in the soil Mineral Nitrogen loss in percolate Nitrogen mineralized from stable organic matter Nitrogen mineralized Nitrogen loss b
59. he same as the simulation period Second in general the historical daily weather data are primarily used to generate monthly weather data which then are used to generate EPIC weather input data The format for historical daily weather data is explained below Linel Weather file name Line2 Number of the years in the actual daily weather data col 1 4 followed by the beginning year For example 131981 means that there are 13 years of weather data beginning with year of 1981 Line3 From this line forward every line includes nine variables These nine variables are Column Variable 1 6 Year 7 10 Month 11 14 Day 15 20 Solar Radiation 21 26 Maximum temperature 27 32 Minimum temperature 33 38 Precipitation 39 44 Relative humidity 45 50 Wind velocity After completing the following steps to develop the WPM10810 dat file if any daily record of maximum temperature minimum temperature or precipitation are missing enter 9999 0 in the missing field s of the record s EPIC will generate the missing record automatically when using measured weather in a simulation NOTE DO NOT USE 9999 0 FOR ANY RECORD BEFORE DEVELOPING THE WPM10810 dat BELOW Format of Daily Weather Input Files The easiest way to build a historical daily weather input file is to enter the data in an Excel spreadsheet and then save it as prn file and rename the prn file to a txt file The included EPIC weather program WXGN3020 exe will read this
60. iles This value should be consistent with the sand silt and the residual clay Fourth check upland and channel hydrology values Correct runoff sediment losses by checking the hydrology of the subareas Open the out file and find HYDROLOGIC DATA which describes the channel and upland hydrology of each subarea Note check the accuracy of each subarea upland and channel slopes Fifth check monthly and annual rainfall values Correct runoff sediment losses by checking the simulated annual rainfall for the years being validated 85 in the aws file To determine the monthly average rainfall for the years simulated open the wss file and again go to the second set of results to find the row with PRCPI Sixth check the accuracy of the erosion control practice factor Correct runoff sediment losses by checking the accuracy of the erosion control practice factor in line 9 PEC of each sub file Seventh check the choice of water erosion equation For watershed analyses open EPICCONT dat line 5 DRV where sediment losses need to be indicated with the recommended choices of 3 MUSS or 0 MUST Eighth revise the method of calculating the daily adjusted curve numbers Revise the method of calculating daily adjusted curve numbers in line 2 of each sub file Usually 4 or 0 are recommended The choice made for a run can be checked by opening out and finding VARIABLE CN Nineth revise the irrigation runoff ratios if
61. ions may be added by appending a new record with unique reference number to TILLO8 10 dat Fertilizer properties are maintained in the database FERT0810 dat The database includes both organic and inorganic nutrient components in 8 fields plus name and cost Some commercial fertilizers have potassium in the mix but EPIC does not utilize K20 in the simulated nutrient uptake yield relationship The fertilizer database FERT0810 dat can be renamed and edited New fertilizers may be added by appending a new record with unique reference number to FERTO810 dat Pesticide properties are maintained in the database PESTO810 dat Properties include solubility half life and carbon absorption coefficient Database includes most common pesticides used in the USA during the past 20 years The pesticides database PESTO810 dat can be renamed and edited New pesticides may be added by appending a new record with unique reference number to PEST0810 dat 12 Print Includes the control data for printing selected output variables in the sections of the EPIC0810 out file and 19 other summary files The print definition file PRNT0810 dat can be renamed and edited Parameter Includes numerous model parameters The parameter file PARM0810 dat can be renamed but should not be edited without first consulting the developers Multi Run There are circumstances in which a number of runs of the same scenario must be executed for example with different generated weath
62. ipitation Mm 39 44 RHUM Relative humidity Fraction 45 50 WIND Wind speed m s 28 Wind Files WIND0810 dat amp filename wnd Monthly wind statistics of a single wind weather station are maintained in filename wnd This file must be listed in the database file WIND0810 dat or user defined name with a unique reference number which corresponds to the variable IWND in the run file EPICRUN dat filename ops includes the following data elements Lines 1 amp 2 Title amp Description Line 3 et seq Each line has 12 variables in 6 columns one for each month January December Line Variable Description 3 WVL Average monthly wind speed m s UAVM Average monthly wind speed m s required to simulate wind erosion ACW gt 0 and potential ET if Penman or Penman Montheith equation are used Wind speed is measured at a 10m height To convert 2m height wind speed to a 10m height equivalent multiply the 2m height speed by 1 3 Required to simulate wind erosion ACW gt 0 See ACW LINE23 Also required if Penman or Penman Monteith equations are used to calculate potential ET See IET Line4 DIR1 Monthly wind from North Ignored if wind erosion is not estimated DIR2 Monthly wind from North North East Ignored if wind erosion is not estimated 6 DIR3 Monthly wind from North East Ignored if wind erosion is not estimated 7 DIR4 Monthly wind from East North East Ignored if wind erosion
63. iption Auto fertilizer trigger 2 options 1 plant N stress factor 0 1 2 soil N concentration in root zone Fertilizer application variable 2 meanings 1 application rate auto fixed g T kg ha 2 manure input to lagoon kg cow day Maximum annual N fertililzer application for a crop Time required for drainage system to reduce plant stress Furrow dike safety factor 0 1 Conservation practice factor 0 0 eliminates water erosion Lagoon volume ratio normal maximum Lagoon input from wash water Time to reduce lagoon storage from maximum to normal Ratio liquid total manure applied Description Above ground plant material grazing limit Fraction of time herd is in feeding area Layer thickness for solution of gas diffusion differential equation Specifies water erosion driving equation 0 MUST Modified MUSLE theoretical equation 1 AOF Onstad Foster 2 USLE Universal Soil Loss Equation 3 MUSS Small Watershed MUSLE 4 MUSL Modified USLE 5 MUSI MUSLE with input parameters see BUS 1 6 RUSLE Revised Universal Loss Equation 7 RUSL2 Modified RUSLE Base stocking rate Return flow deep percolation kg ha days days T ha ha head YSD 6 BUS 1 QD BUS 2 QP BUS 3 WSA BUS 4 KCPLS 19 49 56 57 64 65 72 Line 7 Column 1 8 9 16 17 24 25 32 33 40 BUS 2 BUS 3 BUS 4 Variable COIR COL FULP WAGE CSTZ Input for MUSI equation parameter 1 MUSI input parame
64. irst point should be the higher population Thus PPLP1 SMR1 lt PPLP2 SMR2 PLANTS M 2 PPLP1 SMR1 gt PPLP2 SMR2 PLANTS HA For example in corn PPLP1 30 43 and PPLP2 50 71 which mean 30 plants per square meter and 43 of maximum leaf area in 1st point on population curve and 50 plants per square meter and 71 of maximum leaf area in 2na point on population curve in corn production Since PPI PI is less than PPLP2 it shows the population density of crop instead of tree However for pine tree PPLP1 1000 95 and PPLP2 100 10 While the numbers before and after decimal have the same explanations as given for corn it tells the population density of tree instead of crop because here PPLP1 is greater than PPLP2 Plant population for crops and grass 2na point Plant population for trees 1s point 44 403 410 411 418 419 426 427 434 435 442 443 450 451 458 459 466 468 STX1 STX2 BLG1 BLG2 WUB FTO FLT CCEM NAME Yield decreases salinity increase T ha mmho cm Salinity threshold mmho cm Lignin fraction in plant at 50 maturity Lignin fraction in plant at maturity Water use conversion to biomass T mm Fraction turnout for cotton Fraction lint for cotton Carbon emission seed weight kg kg Full name of crop this is optional and not read 45 Tillage File TIL L0810 dat The tillage operations database TILL0810 dat includes most common field management activities in agricultural land use There are 31 param
65. is not estimated DIRS Monthly wind from East Ignored if wind erosion is not estimated DIR6 Monthly wind from East South East Ignored if wind erosion is not estimated 10 DIR7 Monthly wind from South East Ignored if wind erosion is not estimated 11 DIR8 Monthly wind from South South East Ignored if wind erosion is not estimated 12 DIR9 Monthly wind from South Ignored if wind erosion is not estimated 13 DIR10 Monthly wind from South South West Ignored if wind erosion is not estimated 29 14 15 16 17 18 19 DIR11 DIR12 DIR13 DIR14 DIR15 DIR16 Monthly wind from South West Ignored if wind erosion is not estimated Monthly wind from West South West Ignored if wind erosion is not estimated Monthly wind from West Ignored if wind erosion is not estimated Monthly wind from West North West Ignored if wind erosion is not estimated Monthly wind from North West Ignored if wind erosion is not estimated Monthly wind from North North West Ignored if wind erosion is not estimated NOTE EPIC considers 16 wind directions which are crucial for estimates of wind erosion and dust distribution and air quality from feedlots 30 How to Prepare Weather Input Files Historical daily weather data can be used in two ways First these data can be directly used in EPIC simulation when the length of historical daily weather is t
66. itrogen stress days Phosphorus stress days Potassium stress days Temperature stress days Aeration stress days Salinity stress factor Plant population Planting date Germination date Harvest date ANN Annual Water Summary Variable RUN YR AP15 PRCP Q MUST MUSI SSF PRK YOC Description Year Labile p concentration in top soil to a depth set by PARM 16 Precipitation mm Runoff mm Water erosion MUST t ha Water erosion MUSI t ha Subsurface flow mm Percolation mm Carbon loss with sediment kg ha APS Annual Pesticide Variable YR YR Q SSF PRK QDRN Description Year Year sequence Runoff Subsurface flow Percolation Drain tile flow Sediment yield 67 ha ha ha d yr d yr d yr d yr d yr d yr plants m Units ppm T ha T ha kg ha Units YOC PSTN PAPL PSRO PLCH PSSF PDGF PDGS PDRN CMX4D I Carbon loss with sediment Variables repeated 10 times Pesticide name Pesticide applied Pesticide in runoff Pesticide in percolate from root zone Pesticide in subsurface flow Pesticide degradation from foliage Pesticide degradation from soil Pesticide in drainage system outflow Pesticide 4 day runoff ATG Annual Tree Growth Variable Y Y CROP YLD BIOM RWT LAI STD DCN Daily Soil Organic Carbon amp Nitrogen Table Variable Y M D SW TEMP RSD CLOSS NETMN Description Year Year sequence Crop name Yi
67. l 79 water plus a constant CN option The methods are 1 Variable daily CN nonlinear CN SW with depth soil water weighting Variable daily CN nonlinear CN SW no depth weighting Variable daily CN linear CN SW no depth weighting Non Varying CN CN2 used for all storms E Et at Variable Daily CN SMI Soil Moisture Index Generally the soil moisture index 5 is the most robust and reliable because it is not sensitive to errors in soil data This method is adjustable using PARM 42 PARM0810 DAT PARM 42 usually is in the range 0 5 2 0 small values reduce runoff The nonlinear forms 1 2 also perform very well in many situations The constant CN method 4 is a good choice when soil water is not a dominant factor Green and Ampt infiltration equation 6 The G amp A equation is available for use in special cases where CN is not performing well The three variations of G amp A are 1 Rainfall intensity is simulated with a double exponential distribution and peak rainfall rate is simulated independently 2 Same as 1 except peak rainfall rate is input 3 Rainfall intensity is uniformly distributed and peak rainfall rate is input useful in rainfall simulator studies Erosion sedimentation problems 1 Runoff must be realistic 2 Crop growth must be realistic to provide proper cover and residue 3 Tillage must mix residue with soil properly 4 Erosion equations The USLE and five modifications are available M
68. listed in the catalog file contains the schedule of management events for the HLU in the field farm or small watershed study It describes the unique landuse operations such as crops and crop rotations with typical tillage operations ponds or reservoir farmstead with or without lagoon etc for the HLU over a defined period The events defined in the selected filename ops are repeated until the simulation terminates after NBYR years Schedules may be combined to create a new cropping system The operations catalog OPSC0810 dat and the operations files can be renamed and edited New schedules may be added by appending a new record with unique reference number to OPSC0810 dat Crops are maintained in a database CROP0810 dat This file contains data crop characteristics in 56 fields containing parameters describing the crop and its growth characteristics The crops database CROP0810 dat can be renamed and edited New plants may be added by appending a new record with unique reference number to CROP0810 dat Tillage operations are maintained in the database TILL0810 dat This file includes the operations e g sowing fertilizing harvesting etc and the equipment used in the operation An operation therefore may have several entries one for each of several pieces of machinery designed to execute the operation e g different kinds of planter sprayer or harvester The tillage database TILLO810 dat can be renamed and edited New tillage operat
69. low rate through the layer in mm hour The total amount of pesticide contained in the soil layer is the sum of adsorbed and mobile phases GP 0 01 PSQC ST 0 1 PSYC BD where ST is the amount of water stored in the soil layer in mm PSYC is the concentration of adsorbed pesticide in g t BD is the soil bulk density in t m 3 The ratio of the concentration of pesticide adsorbed to the concentration of pesticide in the water has been estimated for various pesticides Leonard et al 1987 and is expressed by the equation KD PSYC PSQC where KD is the portioning constant in m 3 t The value of KD is computed from the equation KD KOC OC where KOC is the linear adsorption coefficient for organic carbon OC is the fraction of organic carbon in the soil layer Substituting equation 214 into equation 213 gives GP 0 01 PSQC ST 0 1 PSQC KD BD Solving equation 216 for PSQC gives PSQC GP 0 01 ST 0 1 KD BD Substituting PSQC from equation 217 into equation 212 yields dGP dt GP q 0 01 ST 0 1 KD BD Rearranging equation 218 and integrating gives the equation expressing the amount of pesticide as a function of the amount of water flowing through the zone GP GPo exp QT 0 01 ST 0 1 KD BD where GPo is the initial amount of pesticide in the soil layer in kg ha GP is the amount that remains after the amount of flow QT passes through the zone ST is the initial water storage in mm To obtain the amount of pes
70. mber in the right of the decimal Leaf area index decline rate parameter Leaf area index decline rate parameter estimated LAI decline between DLAI and harvest 1 0 is linear gt 1 accelerates decline lt 1 retards decline rate Values range from 0 to 10 40 83 90 91 98 99 106 107 114 115 122 123 130 131 138 139 146 147 154 155 162 163 170 RBMD ALT GSI CAF SDW HMX RDMX WAC2 CNY CPY CKY Biomass energy ratio decline rate parameter Biomass energy ratio decline rate parameter for late in the cropping season This crop parameter functions like the RLAD above for values ranging from 0 10 It reduces the efficiency of conversion of intercepted photosynthetically active radiation to biomass due to production of high energy products like seeds and or translocation of N from leaves to seeds Index of crop tolerance to aluminum saturation 1 5 1 sensitive 5 tolerant Maximum Stomatal Conductance m s The crop parameter GSI is the maximum stomatal conductance m s at high solar radiation and low vapor pressure deficit Korner et al 1979 reported maximum stomatal conductance values for 246 species and cultivars Critical aeration factor Fraction of soil porosity where poor aeration starts limiting plant growth This is set at 0 85 for most crops with rice being the major exception with a value of 1 0 Seeding rate Kg ha Normal planting rate Note this does not change the plant
71. n Year Month Precipitation Potential evapotranspiration Evapotranspiration Plant evaporation Runoff Subsurface flow Percolation Soluble nitrogen from drainage system Inflow for water table Root zone soil water Water table Groundwater storage Nitrogen loss with sediment Nitrate lost in runoff Nitrogen in subsurface flow Nitrogen in percolate Denitrification Nitrogen volatilization Change in organic carbon caused by soil respiration Nitrogen fixation Organic n fertilizer Nitrogen fertilizer nitrate Nitrogen fertilizer ammonia Nitrogen uptake by crop Nitrogen in crop yield Crop name Yield Total nitrogen fertilizer applied OUT Standard Output File Variable TMX Description Max temperature 73 Units kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha T ha kg ha Unit G TMN RAD PRCP SNOF SNOM WSPD RHUM VPD PET ET PEP EP CN SSF PRK QDRN IRGA QIN TLGE TLGW TLGQ TLGF LGIR LGMI LGMO El CVF USLE MUSL AOF MUSS MUST MUSI RUSL RUSC WKI RHTT Min temperature Solar radiation Rainfall Snowfall Snowmelt Wind Speed Relative Humidity Vapor Pres Deficit Potential ET Evapotranspiration Potential plant evaporation Plant evaporation Runoff SCS Curve Number Subsurface Flow Percolation Drain Tile Flow Irrigation Inflow for watertable Lagoon evaporation Water wash to lagoon Runoff to lagoon Lagoo
72. n from residue decay C leached from soil profile C lost with runoff Carbon loss with sediment 75 T ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha C kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha YEFK QSK SSK VSK SLTV IRDL HMN RNAD NIMO FALF K lost with sediment K lost with runoff K lost with lateral subsurface flow K leached from soil profile Salt leached from soil profile Irrigation water lost in delivery system Change in organic C caused by soil respiration N content of plant residue added to soil Immobilized N Leaf fall from plant to soil surface SCN Summary Soil Organic Carbon amp Nitrogen Table 15 soil layers going across plus a total for the following variable lines Variable Z SWF TEMP SWTF TLEF SPDM RSDC RSPC RNMN DNO3 HSCO HSCF HPCO HPCF LSCO LSCF LMCO LMCF BMCO BMCF WOCO WOCF DW0C OBCF Description Soil depth m Soil water factor Soil temperature Combined soil water and temp factor Tillage factor N supply demand Carbon input in residue Carbon respiration from residue Net N mineralization Initial slow humus C pool Final slow humus C pool Initial passive humus C pool Final passive humus C pool Initial structural litter C pool Final structural litter C pool Initial metabolic litter C pool Final metabolic litter C pool Initial
73. n overflow Irrigation volume from a lagoon Manure input to lagoon Manure output from lagoon Rainfall energy MUSLE crop cover factor Water erosion USLE Water erosion MUSL Onstad Foster MUSLE Water erosion MUSS Water erosion MUST Water erosion MUSI RUSLE soil loss estimate RUSLE crop cover factor NO3 loss in runoff Ridge Height 74 T ha Thha T ha T ha T ha T ha T ha T ha kg ha RRUF RGRF YW YON QNO3 SSFN PRKN NMN GMN DN NFIX NITR AVOL DRNN YP MNP PRKP ER FNO FNO3 FNH3 FPO FPL FSK FCO LIME TMP SW10 SLTI SLTQ SLTS SLTF RSDC RSPC CLCH CQV YOC Surface Random Roughness Wind erosion ridge roughness factor Wind erosion N loss with sediment Nitrate loss in surface runoff N in subsurface flow N leaching Humus mineralization N mineralized Denitrification Nitrogen fixation Nitrification N volatilization Nitrogen in drain tile flow P loss with sediment Labile P loss in runoff P mineralized P in percolation Enrichment Ratio Organic N fertilizer N fertilizer nitrate N fertilizer ammonia Organic P fertilizer Labile P fertilizer Soluble K fertilizer rate Organic C content of fertilizer Lime applied Soil temperature in 2nd layer Soil water in top layer Salt content of irrigation application Salt content of runoff Salt content of lateral subsurface flow Salt content of fertilizer application Carbon content of crop residue Carbon respiratio
74. n the ops file Correct to the best of your knowledge Increasing Decreasing it will increase lower the simulated yield Increasing plant population usually increases yield but not always sometimes in very dry climates lower populations produce more yield Fourth check plant stress levels if a crop yield is low To determine the cause of stress to biomass and root development from lack of water nutrients bulk density excessive aluminum toxicity or insufficient air for biomass or roots open the out file and find TOTAL WATER BALANCE and then find AVE ANNUAL CROP YLD DATA If the crop of interest is not in the first listing scroll down to subsequent listings Then scroll to the right of the screen and view the stress days for the crop If a large number of days of N stress are observed for example open the ops file s that contains the stressed crop s and add more N fertilizer continue to do the 82 same for the crop s with P stress and if Irrigation is being applied manually and water stress days are high add more irrigations if appropriate In contrast if air stress days are high in either roots or biomass reduce irrigation applications Aluminum toxicity stress is usually a soil condition treated by adding lime automatically applied if selected in the sub file line 7 If soil bulk density causes root stress check all sol file s for errors in the bulk density data entries for each subarea that produces the affected cro
75. ng by the fraction of dry matter to total yield Fraction of phosphorus in yield g g Normal fraction of P in yield Estimated by same procedure as CNY above Fraction of K in yield g g 41 171 178 179 186 187 194 195 202 203 210 211 218 219 226 227 234 235 242 243 250 251 258 259 266 267 274 275 282 283 290 291 298 299 306 307 314 315 322 WSYF PST CSTS PRYG PRYF WCY BN1 BN2 BN3 BP1 BP2 BP3 BK1 BK2 BK3 BW1 BW2 BW3 IDC Lower limit of harvest index Fraction between 0 and HI value that represents the lowest harvest index expected due to water stress A few crops can have slight increases in harvest index ie the sugar content is higher in somewhat stressed sugar crops Pest damage factor insects weeds disease Fraction of yield remaining after damage Usually set at 0 60 EPIC has an adjustment process that is function of moisture temperature and residue This presently is a reasonable estimate but future versions may include more detailed procedures You may wish to adjust the parameter in geographic areas known to have large amounts of damage from pests Seed cost kg Price for yield T Price for forage yield T Fraction water in yield Nitrogen uptake parameter N fraction in plant at emergence Normal fraction of N in crop biomass at emergence This parameter is based on research results published in the literature for this o
76. nic carbon changes in CRP land and a long term crop rotation trial with EPIC Ecol Model 91 Jones CA Dyke PT Williams JR Kiniry JR Benson VW amp Griggs RH 1991 EPIC an operational model for evaluation of agricultural sustainability Agric Syst 37 341 350 Jones CA Wegner MK Russell JS McLeod IM amp Williams JR 1989 AUSCANE Simulation of Australian sugarcane with EPIC Commonwealth Scientific and Industrial Research Organization Brisbane Australia Kiniry JR Blanchet R Williams JR Texier V Jones CA amp Cabelguenne M 1992 Sunflower simulation using the EPIC and ALMANAC models Field Crop Res 30 403 423 Kiniry JR Major DJ Izaurralde RC Williams JR Gassman PW Morrison M Bergentine R amp Zentner RP 1995 EPIC model parameters for cereal oilseed and forage crops in the northern Great Plains region Can J Plant Sci 75 679 688 Korner CH Scheel JA amp Bauer H 1979 Maximum leaf diffusive conductance in vascular plants Photosynthetica 13 1 45 82 Legler DM Bryant KJ amp O Brien JJ 1999 Impact of ENSO related climate anomalies on crop yields in the U S Climatic Change 42 351 375 Leonard RA Knisel WG amp Still DA 1987 GLEAMS Groundwater loading effects of agricultural management systems Trans ASAE 30 1403 1418 Meza FJ amp Wilks DS 2004 Use of seasonal forecasts of sea surface temperature anomalies for potato fertilization management Theoretical study considering E
77. ntained in a separate soil file named filename sol This file must be listed in the database file SOILO810 dat or user defined name with a unique reference number which corresponds to the variable JNPS in the run file EPICRUN dat filename sol includes the following data elements Linel Title amp Description Line2 Column Variable Description 1 8 SALB Soil albedo 9 16 HSG Soil hydrologic group 1 A 2 B 3 C 4 D 17 24 FFC Initial soil water content fraction of field capacity BIU 25 32 WTMN Min depth to water table m BIU 33 40 WTMX Max depth to water table m BIU 41 48 WTBL Initial water table height m BIU 49 56 GWST Groundwater storage mm BIU 57 64 GWMX Maximum groundwater storage mm BIU 65 72 RFTO Groundwater residence time days BIU 73 80 RFPK Return flow return flow deep percolation BIU Line3 Column Variable Description 1 8 TSLA Maximum number of soil layers after splitting 3 15 0 no splitting occurs initially 9 16 XIDP Soil weathering code 0 for calcareous and non calcareous soils without weathering information for non CaCO slightly weathered 2 for non CaCO moderately weathered 3 for non CaCO highly weathered 4 input PSP or active stable mineral P kg ha 17 24 RTNO Number of years of cultivation at start of simulation BIU 25 32 XIDK 1 for kaolinitic soil group 2 for mixed soil group 3 for smectitic soil group 33 40 ZQT Minim
78. ntial equation for two parameters that guarantee the curve originates at zero passes through the two given points and Y approaches 1 0 as X increases beyond the second point The form of the equation is Y X X exp B1 B2 X where B1 and B2 are the EPIC determined parameters S Curve parameter definitions 2 fields of 8 columns 30 lines Point 1 Point 2 Description SCRP1 1 SCRP2 1 Expresses the effect of soil course fragment content on N 1 2 plant root growth restriction X course fragment SCRP1 2 SCRP2 2 Governs soil evaporation as a function of soil depth X soil depth mm SCRP1 3 SCRP2 3 Drives harvest index development as a function of crop Maturity X of growing season SCRP1 4 SCRP2 4 NRCS runoff curve number soil water relationship Exception to normal S curve procedure soil water fractions taken from SCRP 30 N to match with CN2 and CN3 average and wet condition runoff curve numbers SCRP1 5 SCRP2 5 Estimates soil cover factor used in simulating soil temperature X total above ground plant material dead and Alive SCRP1 6 SCRP2 6 Settles after tillage soil bulk density to normal value as a Function of rainfall amount soil texture and soil depth X rainfall mm adjusted for soil texture and depth SCRP1 7 SCRP2 7 Determines the root growth aeration stress factor as a function Of soil water content and the critical aeration factor For the crop X soil water critical aeration factor SC
79. ntial evapotran spiration equations six erosion sediment yield equations two peak runoff rate equations etc EPIC can be used to compare management systems and their effects on nitrogen phosphorus carbon pesticides and sediment The management components that can be changed are crop rotations tillage operations irrigation scheduling drainage furrow diking liming grazing tree pruning thinning and harvest manure handling and nutrient and pesticide application rates and timing Commercial fertilizer or manure may be applied at any rate and depth on specified dates or automatically Water quality in terms of nitrogen ammonium nitrate and organic phosphorus soluble and adsorbed mineral and organic and pesticide concentrations may be estimated at the edge of the field EPIC is a console application written in Fortan that reads and writes text files Two convenient graphical interfaces are available for assembling inputs and interpreting outputs are WinEPIC and iEPIC EPIC D v lopment Tegm uay apna EEN Ee dee Edel El EE de EE iv RICH EE iv ere We E Hi Executive Summary ta QA ENEE eet edd deed ii ELE i uereg na hnknin DEE arable chee eH a aa ere eee haha asa qa 1 EPIGC RE EE H Master File EPICFIDE dat sa a ua eet REEL Seege AER H Run Pile EPIGRUON OL iiss eee deeg Bieden 14 Control File EPIC COND dat eer deed 15 Site File STTEOS10 dat amp Dlenome ei 21 Soil Files SOIL0810 dat amp filename sot 23 Weather Files
80. nutrient and pesticide flux in the HLU at time scales from daily to annual The growth of crop plants is simulated depending on the availability of nutrients and water and subject to ambient temperature and sunlight The crop and land management methods used by growers can be simulated in considerable detail The model can be subdivided into nine separate components defined as weather hydrology erosion nutrients soil temperature plant growth plant environment control tillage and economic budgets Williams 1990 It is a field scale model that is designed to simulate drainage areas that are characterized by homogeneous weather soil landscape crop rotation and management system parameters It operates on a continuous basis using a daily time step and can perform long term simulations for hundreds and even thousands of years A wide range of crop rotations and other vegetative systems can be simulated with the generic crop growth routine used in EPIC An extensive array of tillage systems and other management practices can also be simulated with the model Seven options are provided to simulate water erosion and five options are available to simulate potential evapotranspiration PET Detailed discussions of the EPIC components and functions are given in Williams et al 1984 Williams 1990 Sharply amp Williams 1990 and Williams 1995 Brief History of EPIC The original function of EPIC was to estimate soil erosion by water under dif
81. of runs to the same parcel of land having the same soil and weather files An EPIC project may be created for a virtual or real parcel of land subjected to the same scenario management practices soil and weather kept constant while the geographic characteristics latitude longitude altitude slope or aspect of the site are varied in a series of runs EPIC Applications Irrigation studies Yield estimates by EPIC simulations of irrigation experiments in California Minnesota Oklahoma Texas Virginia Ontario and Quebec agreed well with the observed yields of a wide range of crops reviewed in Gassman et al 2004 Climate change effects on crop yields EPIC simulates the effects of changes in CO concentrations and vapor pressure deficit on crop growth and yield via radiation use efficiency leaf resistance and transpiration Assessments of potential CO and climate change impacts on crop yields of corn wheat and soybean cropping systems in the central U S predicted increases in yield in response to increased CO and variable changes in yield in response to changing temperature and precipitation Stockle et al 1992a b The impact of tropical Pacific El Ni o Southern Oscillation ENSO phenomena on crop yields has been assessed using EPIC Izaurralde et al 1999 Legler et al 1999 Adams et al 2003 and the effect of sea surface temperature anomalies SSTA on potato fertilization management has been investigated in Chile Meza am
82. off the plants once a threshold rainfall amount is exceeded The model uses a threshold value of 2 5 mm and potential washoff fractions for various pesticides have been estimated Leonard et al 1987 The appropriate equations for computing washoff are WO WOF FP RFV gt 2 5 mm WO 0 0 RFV lt 2 5 mm where WO is the amount of pesticide washed off the plants by a rainstorm of RFV mm WOF is the washoff fraction for the particular pesticide Washed off pesticide is added to GP and subtracted from FP Pesticide on the plants and in the soil is lost from the system based on the decay equations GP GPo exp 0 693 HLS FP FPo exp 0 693 HLP where GPo and GP are the initial and final amounts of pesticide on the ground FPo and FP are the initial and final amounts of pesticide on the plants HLS is the half life for pesticide in the soil in days HLP is the half life of the foliar residue in days 88 Values of HLP and HLS have been established for various pesticides Leonard et al 1987 Another way that pesticide can be lost is through leaching The GLEAMS leaching component is used here with slight modification The change is the amount of pesticide contained in a soil layer is expressed as a function of time concentration and amount of flow from the layer using the equation dGP dt PSQC q where GP is the amount of pesticide in the soil layer at time t PSQC is the pesticide concentration in the water in g t q is the water f
83. oil water nutrient and pesticide movements Predict the combined impact of changes to soil water and nutrient flux and pesticide fate on water quality and crop yields for areas with homogeneous soils and management Model Operation Daily time step Long term simulations 1 4 000 years Soil weather tillage and crop parameter data supplied with model Soil profile can be divided into ten layers Choice of actual weather or weather generated from long term averages Homogeneous areas up to large fields or small watersheds Model Components Weather Soil temperature Evapotranspiration Snow melt Surface runoff Return flow Percolation Lateral subsurface flow Water erosion Wind erosion Nitrogen leaching N amp P loss in runoff Organic N amp P transport N amp P immobilization N amp P mineralization Denitrification by sediment and uptake Mineral P cycling N fixation Tillage practices Crop rotations Crop growth amp yield for Plant environment Fertilization Pesticide fate amp over 100 crops control transport Liming Drainage Irrigation Furrow diking Feed yards Lagoons Waste management Economic accounting Model Applications 1985 RCA analysis 1988 Drought assessment Soil loss tolerance tool Australian sugarcane model AUSCANE Pine tree growth simulator Global climate change analysis Farm level planning Drought impacts on residue cover Nutrient and pesticide movement estimates for alternative farming systems for wa
84. oil temperature at 0 5 meters MCM Monthly Cropman Variable Description Y Year M Month RT CPNM Crop name WS Water stress factor NS Nitrogen stress factor PS Phosphorus stress factor KS Potassium stress factor TS Temperature stress factor 71 Units C Units AS SS RZSW PRCP ET Q PRK SSF Aeration stress factor Salinity stress factor Root zone soil water Precipitation Evapotranspiration Runoff Percolation Subsurface flow MS Monthly Flipsim Variable Y M RT PRCP PET ET EP Q PRK SSF QDRN IRGA QIN RZSW WTBL GWST Description Year Month Precipitation mm Potential evapotranspiration mm Evapotranspiration mm Plant evaporation mm Runoff mm Percolation mm Subsurface flow mm Soluble nitrogen from drainage system kg ha Irrigation water mm Inflow for water table mm Root zone soil water mm Water table mm Groundwater storage mm MSW Monthly Output To Swat Variable Y M Q Y YN YP QN QP Description Year Month Runoff Sediment lost Nitrogen lost in sediment Phosphorus lost in sediment Nitrogen lost in runoff Phosphorus lost in runoff 72 Units Units T ha kg ha kg ha kg ha kg ha MWC Monthly Water amp Nitrogen Cycle Variable Y M PRCP PET ET EP Q SSF PRK QDRN QIN RZSW WTBL GWST RNO3 YON QNO3 SSFN PRKN DN AVOL HMN NFIX FNO FNO3 FNH3 UNO3 YLN CPMN YLD TOTN Descriptio
85. on organic matter stabilization are also modeled Simulations of sites in Nebraska Kansas Texas and Alberta showed EPIC satisfactorily replicated the soil carbon dynamics over a range of environmental conditions and cropping vegetation and management systems Izaurralde et al 2004 EPIC performed robustly for simulations of deforested conditions cropping systems and native vegetation in Argentina Apezteguia et al 2002 Soil organic carbon SOC values estimated in an EPIC simulation of a conservation tillage compared favorably with measured SOC rates Zhao et al 2004 Economic and environmental studies EPIC tracks production costs and crop income for input to economic models The FLIPSIM whole farm economic model has been coupled with EPIC to perform economic analyses of irrigated agriculture in Texas Ellis et al 1993 Gray et al 1997 Other examples of economic analyses using EPIC are given in Table 4 of Gassman et al 2004 Comprehensive regional assessments EPIC has been used in a number of studies to evaluate the impacts of cropping systems management practices and environmental conditions on multiple environmental indicators Studies have focused on evaluating specific agricultural policy options including those conducted by the USDA Natural Resources Conservation Service NRCS The first application of EPIC by the NRCS was to evaluate the potential loss in cropland productivity into the future for the 2 Resources Conserva
86. onditions inhibit pest growth 34 46 53 54 61 62 69 70 77 OPV3 OPV4 OPV5 OPV6 Automatic Irrigation Trigger This is the same irrigation trigger function as in the control file The control file value can be overridden by setting the trigger value in the operation schedule Leaving OP V3 0 no modifications will be made to the irrigation trigger as set in the control file To trigger automatic irrigation the water stress factor is set 0 Manual irrigation or model uses BIR set in control file EPICCONT dat 0 1 0 Plant water stress factor 1 BIR equals the fraction of plant water stress allowed 1 0 Does not allow water stress lt 0 0 Plant available water deficit in root zone number is in mm and must be negative gt 1 0 Soil water tension in top 200mm Absolute number is in kilopascals 1000 Sets water deficit high enough that only manual irrigations will occur This effectively turns auto irrigation off NOTE e When using a BIR based on anything other than plant water stress 0 1 be aware that irrigation will be applied outside of the growing season if the soil water deficit or soil water tension reaches BIR This will reduce the amount of water available for irrigation during the growing season e Once the trigger has been set within a operation schedule it will remain in effect until changed within the operation schedule If the schedule is used in rotation with other schedule
87. ot zone Soil erosion by water estimated with Modified MUSLE Irrigation distribution loss Nitrogen mineralized from stable organic matter Leaf fall Loss of dinitrogen gas Fuel use Nitrous oxide loss Surface flux of O2 Surface flux of CO2 Carbon emission Carbon loss from burning crop residue or forest Nitrogen loss from burning crop residue or forest Soluble Phosphorus in subsurface flow Soluble Phosphorus loss through drainage system 63 kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha T ha kg ha kg ha kg ha L ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha Output File Variable Definitions ABR Annual Biomass Root Weight Variable Description Y Year Y Year sequence M Month D Day CROP Crop name BIOM Biomass RWT Root weight in layer Repeated 10 times for 10 soil layers at depth in mm TOT Total root weight ACM Annual Cropman Variable Description Y Year RT Rotation number PRCP Precipitation ET Potential evapotranspiration ET Evapotranspiration Q Runoff SSF Subsurface flow PRK Percolation CVF MUSLE crop cover factor MUSS Water erosion YW Wind erosion GMN N mineralized NMN Humus mineralization NFIX Nitrogen fixation NITR Nitrification AVOL Nitrogen volatilization DN Denitrification YON Nitrogen loss with sediment QNO3 Nitrate loss in surface runoff SSFN Nitrogen in subsurface flow PRKN
88. ound biomass STD is standing dead plant residue and RSD is flat residue Calculates soil temperature factor used in regulating microbial Processes X soil temperature deg C Expresses plant population effect on epic water erosion cover factor X plant population plants m Increases snow melt as a function of time since the last snow fall X time since the last snowfall d Estimates the snow cover factor as a function of snow present X snow present mm water Expresses soil temperature effect on erosion of frozen soils X temperature of second soil layer deg C Drives water table between maximum and minimum limits as a function of ground water storage X of maximum ground water storage Simulates oxygen content of soil as a function of depth Used in microbial processes of residue decay X depth to center of each soil layer m Governs plant water stress as a function of soil water tension X gravimetric osmotic tension Not used Estimates fraction plant ground cover as a function of LAI X LAI Simulates oxygen content of soil as a function of C and clay Used in microbial processes of residue decay X F C clay Regulates denitrification as a function of soil water content X ST FC PO FC Estimates plant ground cover as a function of standing Live biomass X standing live biomass T ha Not used Not used Not used 53 SCRP1 30 SCRP2 30 Exception to normal S Curve procedu
89. outhern France Field Crops Res 26 19 34 de Barros I Williams JR amp Gaiser T 2004 Modeling soil nutrient limitations to crop production in semiarid NE of Brazil with a modified EPIC version I changes in the source code of the model Ecol Model 178 441 456 Ellis JR Lacewell RD Moore J amp Richardson JW 1993 Preferred irrigation strategies in light of declining government support J Prod Agric 6 112 11 Gassman PW Williams JR Benson VW Izaurralde RC Hauck LM Jones CA Atwood JD Kiniry JR Flowers JD 2004 Historical Development and Applications of the EPIC and APEX models ASAE Ottowa Conf Proc Paper 042097 pp 31 Gray AW Harman WL Richardson JW Weise AF Regier GC Zimmel PT amp Lansford VD 1997 Economic and financial viability of residue management an application to the Texas High Plains J Prod Agric 10 175 183 Green WH amp Ampt GA 1911 Studies on soil physics 1 Flow of air and water through soils J Agric Sci 4 1 24 Harman WL Wang E amp Williams JR 2004 Reducing atrazine losses water quality implications of alternative runoff control practices J Environ Qual 33 7 12 Izaurralde RC Rosenberg NJ Brown RA Legler DM Tiscarefio Lopez M amp Srinivasan R 1999 Modeled effects of moderate and strong Los Ni os on crop productivity in North America Agric Forest Meteor 94 259 268 Izaurralde RC Williams JR McGill WB amp Rosenberg NJ 2004 Modeling soil orga
90. p Also check PARM 2 the original value is 1 15 but may need increasing to 1 5 for many cases to reduce bulk density stress Setting PARM 2 to 2 0 eliminates all root stresses Fifth check the leaf area index MXLA To determine if the leaf area setting is inadequate for optimum yields of a crop open out and find CROP PARAMETERS Scroll down to a row indicating MXLA for the value of a low yielding crop and compare it with the value DMLA in line 1 of the CROP0810 dat file for the appropriate crop In the Crop Parameters table each row with the same parameter name a different subarea If the two leaf area indices are near equal and the crop yield is low increase the index value in CROP0810 dat DMLA is set at the maximum LAI that the crop can obtain under ideal conditions so it seldom needs increasing MXLA the adjusted DMLA based on plant population can be increased by increasing population Sixth revise the Harvest Index and Biomass Energy Ratios If after the first five checks are completed and crop yields remain inaccurate some basic crop parameters can be revised as a last resort Normally these parameters are not to be revised being accurate for crops in the U S They may need to be revised slightly for international use In CROP0810 dat the harvest index HI relates to the grain yield only as a ratio of the above ground biomass The higher the ratio the more grain yield reported for a given level of biomass Similarly the biomass to
91. p Wilks 2004 Nutrient cycling and nutrient loss studies Validation studies show that EPIC satisfactorily simulates measured soil nitrogen N and or crop N uptake levels and leached N below the root zone or in tile flow are generally accurately predicted See Tables 2 amp 3 in Gassman et al 2004 Sensitivity analyses shows that EPIC N leaching estimates can be very sensitive to choice of evapotranspiration routine soil moisture estimates curve number precipitation solar radiation and soil bulk density Roloff et al 1998c Benson et al 1992 Wind and water erosion studies Several water erosion models are implemented in EPIC Universal Soil Loss Equation USLE Onstad Foster AOF version of USLE Modified USLE MUSLE amp RUSLE and three MUSLE variants MUST MUSS amp MUSI These models differ primarily in how the energy component is modeled Williams et al 1983 1984 Williams 1995 The wind erosion model is the Wind Erosion Stochastic Simulator WESS Potter et al 1998 Numerous EPIC applications have been performed for soil erosion see Gassman et al 2004 for example applications including validation and scenario studies Soil carbon sequestration Based on concepts used in the Century model Parton et al 1994 EPIC simulates carbon and nitrogen compounds stored in and converted between biomass slow and passive soil pools Carbon leaching from surface litter to deeper soil layers and the effect of soil texture
92. population It only impacts seed cost and start crop biomass Maximum crop height in m Maximum root depth in m This effects soil moisture extraction CO Concentration Resulting WA value Split Variable In EPIC radiation use efficiency is sensitive to atmospheric CO2 concentration WAC2 is an S curve parameter used to describe the effect of CO2 concentration on the crop parameter WA The value on the left of the decimal is a value of CO2 concentration higher that ambient i e 450 or 660 ul l The value on the right of the decimal is the corresponding value WA This elevated value of WA can be estimated from experimental data on short term crop growth at elevated CO levels Calculate the ratio of crop growth rate at elevated CO to crop growth at approximately 330 ul l 1 CO2 Multiply that ratio by the value of WA at 330 ul 1 1 to obtain the value on the right of the decimal Typical values of the ratio are 1 1 to 1 2 1 15 used in crop8190 for crops with the C4 photosynthetic pathway and 1 3 to 1 4 1 35 used in crop8190 for C3 crops Kimball B A 1983 Carbon dioxide and agricultural yield an assemblage and analysis of 770 prior observations Water Conservation Laboratory Report 14 USDA ARS Phoenix Arizona Fraction of nitrogen in yield g g Normal fraction N in yield This was estimated from Morrison s Feeds and Feeding and other data sources plant nutrition The percentage N in Morrison was adjusted to a dry weight by dividi
93. ps Data of field operation schedules are maintained in a separate file named filename ops This file must be listed in the database file OPSC0810 data or user defined name with a unique reference number which corresponds to the variable IOPS in the run file EPICRUN dat filename ops includes the following data elements Linel Title amp Description Line2 Column Variable 1 4 LUN 5 8 IAUI Description Land use number from NRCS Land Use Hydrologic Soil Group Table Refer to the column labeled Land User Number in the table on Page 33 This number along with the hydrologic soil group is used to determine the curve number Range 1 35 Auto irrigation apply irrigation operation from TILL0810 dat Range 1 oo If auto irrigation is used this irrigation operation found in the TILLO810 dat file will be used to apply irrigation water If none is specified the default is operation 500 Line3 et seq one line per operation Column Variable 1 3 TYEAR 4 6 MON 7 9 DAY 10 14 CODE 15 19 TRAC 20 24 CRP 25 29 XMTU Description Year of operation Range 1 N Month of operation Range 1 12 Day of operation Range 1 31 NOTE e tis recommended not to schedule something for 29 February Tillage ID number Refers to the ID number that is given to each tillage operation or piece of equipment in TILLO8 10 dat Tractor ID number Refers to the ID number given to each tractor in TILLO810 dat NOTE e This ma
94. r a similar crop Nitrogen uptake parameter N fraction in plant at 0 5 maturity Normal fraction of N in crop biomass at mid season Same as BN1 Nitrogen uptake parameter N fraction in plant at maturity Normal fraction of N in crop biomass at maturity Same as BN1 Phosphorus uptake parameter P fraction in plant at emergence Normal fraction of P in crop biomass at emergence Same as BN1 Phosphorus uptake parameter P fraction in plant at 0 5 maturity Normal fraction of P in crop biomass at mid season Same as BN1 Phosphorus uptake parameter P fraction in plant at maturity Normal fraction of P in crop biomass at maturity Same as BN1 K uptake at emergence K uptake at 0 5 maturity K uptake at maturity Wind erosion factor for standing live biomass Based on the Manhattan wind erosion equations for this crop or a similar crop used in the Manhattan wind erosion equations Wind erosion factor for standing dead crop residue Same as BW1 Wind erosion factor for flat residue Same as BWI Crop category number 1 Warm season annual legume 2 Cold season annual legume 3 Perennial legume 4 Warm season annual 42 323 330 331 338 339 346 347 354 FRST1 FRST2 WAVP VPTH 5 Cold season annual 6 Perennial 7 Evergreen tree 8 Deciduous tree 9 Cotton 10 N fixing tree NOTE Other crop parameters TB TG FRS1 FRS2 also differentiate between cold and warm climate
95. r must specify the file names to be associated with internal EPIC file references in the EPICFILE dat file as shown here in Table 2 As one example of how some of these files are referenced consider the problem of where the analyst desires to change management after a long period i e 25 years of one system followed by 25 years of another system Instead of specifying 50 years of tillage operations in an OPSC file the same effect can be achieved with two runs The first run will use the first OPSC file and the desired soil file The second run will use the second OPSC file but for the soil will be linked by a soil identification number in the EPICRUN dat and FSOIL to the EPIC0001 SOT file which is the final soil table from the first run The final soil table written by an EPIC run has the identical format to the soil input data files Table 2 Input data file names are defined in EPICFILE dat file Internal File Reference FSITE FWPM1 FWPM5 FWIND FWIDX FCROP FTILL FPEST FFERT FSOIL FOPSC FTR55 FPARM FMLRN FPRNT FCMOD FWLST Default File Name dat SITEOS810 WPM10810 WPM50810 WINDOS10 WIDX0810 CROPO810 TILLOS 10 PESTO810 FERTO810 SOILO810 OPSC0810 TR550810 PARM0810 MLRN0810 PRNTO810 CMOD0810 WLST0810 Description Catalog of site files available for the project Catalog of weather stations with monthly weather data Alternate weather station catalog used with FWIDX Catalog of weather station
96. ractices EPICPST simulation J Prod Agric 5 312 317 Sharpley AN amp Williams JR Eds 1990 EPIC erosion productivity impact calculator 1 model documentation USSDA Tech Bull 1768 Washington DC Stockle CO Williams JR Jones CA amp Rosenberg NJ 1992a A method for estimating the direct and climatic effects of rising atmospheric carbon dioxide on growth and yield of crops I Modification of the EPIC model for climate change analysis Agric Syst 38 225 238 Stockle CO Williams JR Rosenberg NJ amp Jones CA 1992b A method for estimating the direct and climatic effects of rising atmospheric carbon dioxide on growth and yield of crops II Sensitivity analysis at three sites in the Midwestern USA Agric Syst 38 239 256 Williams JR 1990 The erosion productivity impact calculator EPIC model A case history Phil Trans R Soc Lond 329 421 428 Williams JR Renard KG amp Dyke PT 1983 EPIC a new method for assessing erosion s effect on soil productivity J Soil and Water Cons 38 381 383 Williams JR Jones CA amp Dyke PT 1984 A modeling approach to determining the relationship between erosion and soil productivity Trans ASAE 27 129 144 Williams JR 1995 The EPIC Model Pp 909 1000 in Computer Models of Watershed Hydrology Ed Singh VP Water Resources Publications Highlands Ranch CO Williams JR Jones CA Kiniry JR amp Spanel DA 1989 The EPIC crop growth model Trans ASAE
97. re sets soil water contents coinciding with CN2 and CN3 X1 soil water content as of field capacity wilting point X2 soil water content as of saturation field capacity NOTE THIS PARAMETER DOES NOT FOLLOW THE SAME X Y FORMAT AS THE OTHER PARAMETERS IN THIS CASE Y IS ALWAYS 0 EXAMPLE X1 45 00 this indicates that CN2 is 45 of the volume between field capacity and wilting point 0 45 FC WP WP X2 10 00 this indicates that CN3 is 10 of the volume between saturation and field capacity 0 10 SAT FC FC Parameter Definitions 10 fields of 8 columns 11 lines PARM 1 10 Definition Units and or Range Crop canopy PET factor used to adjust crop canopy resistance in the Penman Monteith PET equation Range 1 2 Root growth soil strength Normally 1 15 lt PARM 2 lt 1 2 Set to 1 5 to minimize soil strength constraint on root growth PARM 2 gt 2 eliminates all root growth stress Range 2 Water stress harvest index 0 1 sets fraction of growing season when water stress starts reducing harvest index Range 0 1 Denitrification rate constant limits daily denitrification loss from each soil layer Range 0 1 5 Soil water lower limit of water content in the top 0 5 m soil depth expressed as a fraction of the wilting point water content Range 0 1 Winter dormancy h causes dormancy in winter grown crops Growth does not occur when day length is less th
98. rve number input instead of hydrologic soil group number line 2 2 Operation schedule ops Land use number not input line 2 Format problems data in wrong columns Dates not in sequence 3 When daily weather is input Incorrect format Problems that may or may not cause failed run 1 Soil data Inconsistent data Bulk density texture Texture plant available water Organic C N P 2 Operation Schedule No kill after harvest of annual crop Problems that cause near 0 crop yield 1 CO 0 2 When daily weather is input Monthly and daily solar radiation units don t match 3 Plant population 0 was not input at planting in ops General problems 78 Working files don t match those contained in EPICFILE dat For example you are working with CROP0810 dat and EPICFILE dat contains USERCROP dat When daily weather is input The date must be input on the first line year month day format is 2X 314 The beginning simulation date in EPICCONT dat must be equal or greater than the one appearing on line one of the weather file wth Completed runs examine out files Select monthly output in EPICCONT dat IPD 3 Preliminary investigation Check nutrient and water balances for each run look for BALANCE They should be near 0 Check water balance for the entire watershed TOTAL WATER BALANCE Check average annual surface runoff water yield and sediment and nutrient Runoff problems things
99. s typically fields but could be a larger area Sites fields may contain buffers and filter strips etc The site catalog SITEO810 dat and the site files can be renamed and edited 10 Soils Weather WLST0810 WPM10810 WIND0810 EPIC looks in the soil catalog file SOIL0810 dat or the catalog named in EPICFILE dat for the soil number referenced in EPICRUN dat and obtains the name of the file containing the soil specific data The soil specific file named filename sol listed in the catalog file contains data describing the soil profile and the individual horizons The study may involve several different soils for the farm or watershed analysis and are selected for use in the subarea file The soil catalog SOLL OS TO dor and the soil files can be renamed and edited Weather and wind data files are listed in three catalogs WLST0810 dat WPM10810 dat amp WINDO810 dat for daily weather monthly climate averages and average monthly wind roses respectively EPICRUN dat defines the run specific catalog entries to be used The daily catalog points to files containing daily weather data and the monthly catalogs point to individual files containing long term climate and wind averages typically 30 years Databases of averages at U S weather stations are included with the program If no weather or wind file is specified in EPICRUN dat EPIC will find the closest station given the latitude and longitude given in SITEO8010 da and generat
100. s the trigger will stay as set even into the next schedule When setting the irrigation trigger within an operation schedule it is wise to set the irrigation trigger to 1000 mm at the end of the schedule so that when the operation schedule is used in rotation with another non automatically irrigated crop the second crop is not influenced by the irrigation trigger Proportion of irrigation water applied lost to runoff vol vo l Setting the runoff fraction EFI within the operation schedule overrides the EFI set within the control file The irrigation runoff ratio specifies the fraction of each irrigation application that is lost to runoff Soluble nutrient loss through runoff applies Changes in soil slope do not affect this amount dynamically Range 0 1 Plant population at planting plants m for small plants plants ha for larger plants with densities lt im e g trees NOTE e EPIC does not simulate tillering In crops such as wheat and sugarcane which produce higher numbers of yielding tillers compared to the number of seeds or shoots planted the plant population must be estimated based on the final yield producing tiller number Range 0 500 Maximum annual N fertilizer applied to a crop 0 or blank does not change FMX EPICCONT dat 35 78 85 OPV7 gt 0 sets new FMX for planting only In the control file FMX was set to limit the amount of fertilizer that could be applied on an annual basis regardless of
101. s with monthly wind data Southern oscillation coefficients file Database of crop parameters Database of field operations amp machines Database of pesticide properties Database of fertilizer properties Catalog of soil data files Catalog of available operation schedules Data for stochastic runoff estimation Contains equation parameters to be used for the run Sets up a multi run application Controls printing of output Database of crop prices for economic analysis Catalog of weather stations with daily weather data Execution of Runs EPIC0810 is a compiled Fortran program It may be run from the command line or via a dedicated interface such as WinEPIC or EPIC When run from the command line the directory containing the EPIC0810 exe must contain all the input files A set of three files controls the flow and scope of an EPIC simulation EPICFILE dat lists the run specific data files and renames them if required EPICCONT dat controls the run length various run options and defaults for the project EPICRUN dat lists the site specific data files and initiates a run of one or more scenarios These files may be edited but not renamed all other files may be renamed with the new names defined in EPICFILE dat Table 1 Files Definition Project Constants Runs Sites EPICFILE dat file provide EPIC with the names of the data files This file cannot be renamed but can be edited EPICCONT dat file contains par
102. se a station if southern oscillation option XX XX is chosen Monthly wind Station must be one of the stations listed inWINDO810 dat if left blank EPIC will use the latitude and longitude given in the site file filename sit to choose a station Soil must be one of the soils listed in SOILOS10 dat Operations Schedule must be one of the schedules listed in OPSC0810 dat Daily weather station H must be one of the stations listed inWLST0810 dat if left blank EPIC will use the monthly weather station listed in IWP1 or will use the latitude and longitude given in the site file filename sit to choose a station 14 Control File EPICCONT dat EPICCONT DAT includes a variety of data parameters that will be held constant for all of the scenarios to be run from EPICRUN dat EPICCONT DAT includes the following data elements Line 1 Column Variable Description 1 4 NBYRO 5 8 IYRO 9 12 IMOO 13 16 IDAO Day of month simulation begins 17 19 NIPD I Number of years of simulation Beginning year of simulation Month simulation begins II N the printout interval i e annually monthly daily enter a 5 if interval is every 5 days months or year 20 IPD Controls printing NI for annual printout N2 for annual with soil table N3 for monthly N4 for monthly with soil table N5 for monthly with soil table at harvest N for N day interval N7 for soil table only n day interval N8 for N day interval
103. should be based on a combination of research results and observation Parm relating vapor pressure deficit to WA In EPIC radiation use efficiency RUE is sensitive to vapor pressure deficit VPD As VPD increases RUE decreases The crop parameter WAVP is the rate of the decline in RUE per unit increase in VPD The value of WAVP varies among species but a value of 6 to 8 is suggested as an approximation for most crops Threshold VPD SPA F 1 In EPIC leaf conductance is insensitive to VPD until VPD calculated hourly exceeds the threshold value VPTH usually 0 5 to 1 0 kPa 43 355 362 363 370 371 378 379 386 387 394 395 402 VPD2 RWPC1 RWPC2 GMHU PPLP1 PPLP2 VPD value KPA F2 1 In EPIC leaf conductance declines linearly as VPD increases above VPTH VPD2 is a double parameter in which the number on the left of the decimal is some value of VPD above VPTH e g 4 0 and the number of the right of the decimal is the corresponding fraction of the maximum leaf conductance at the value of VPD e g 0 7 Fraction of root weight at emergence Partitioning parameters to split biomass between above ground and roots RWPC1 is the partitioning fraction at emergence and RWPC2 is partitioning fraction at maturity Between those two points there is a linear interpolation of the partitioning fraction relative to accumulative heat units Fraction of root weight at maturity Partitioning parameters to
104. sses by checking the accuracy of the hydrologic soil group in line 2 HSG in each of the sol files Third check upland and chanel hydrology values Correct runoff sediment losses by checking the hydrology of the subareas Open the out file and find HYDROLOGIC DATA which describes the channel and upland hydrology of each subarea Note check the accuracy of each subarea upland and channel slopes Fourth check monthly and annual rainfall values Correct runoff sediment losses by checking the simulated monthly and annual rainfall for the years being validated in the wss file Fifth check the saturated conductivity values for soils Correct runoff sediment losses by checking the accuracy of the saturated conductivity values of each soil Sixth check the accuracy of the erosion control practice factor Correct runoff sediment losses by checking the accuracy of the erosion control practice factor in line 9 PEC of each ops file Seventh check the choice of water erosion equation 84 For watershed analyses sediment losses need to be indicated with the recommended choices of 3 MUSS or 0 MUST Eighth revise the method of calculating the daily adjusted curve numbers Revise the method of calculating daily adjusted curve numbers in line 2 of each sub file Usually 4 or 0 are recommended Nineth revise the irrigation runoff ratios if irrigation operations are used Revise the global irrigation runoff ratio in line 8 of
105. t of nitrification volatilization as a fraction of NH present Range 0 0 1 0 Reduces NRCS runoff CN retention PARM for frozen soil fraction of S frozen soil Reduce to increase runoff from frozen soils Range 0 05 0 5 Converts standing dead residue to flat residue Daily fall rate as a fraction of standing live STL Range 0 0001 0 05 Wind erosion threshold wind speed Normal value 6 0 Range 4 0 10 0 N fixation upper limit kg ha d Traditional value 20 0 Range 1 0 30 0 Heat unit adjustment at harvest replaces setting back to 0 0 or to a fraction set by harvest index Range 0 0 1 0 Power of change in day length component of LAI growth equation Causes faster growth in spring and slower growth in fall Traditional value 3 0 Range 1 0 10 RUSLE 2 transport capacity parameter Regulates deposition as a function of particle size and flow rate Range 0 001 0 1 RUSLE 2 Threshold transport capacity coefficient Adjusts threshold flow rate slope steepness Range 1 0 10 0 Upper limit of curve number retention parameter S SUL PARM 73 S1 allows CN to go below CN1 Range 1 0 2 0 Penman Monteith adjustment factor adjusts PM PET estimates Range 0 5 1 5 Runoff CN residue adjustment parameter Increases runoff for RSD lt 1 0 t ha decreases for RSD gt 1 0 Range 0 0 0 3 Harvest index adjustment for fruit and nut trees Reduces yield when crop available soil water is l
106. t units by month from planting to maturity Then calculate percent of cumulative heat units by dividing estimated cumulative heat units for each of the two dates where you ve estimated percent of Max LAI by the average annual heat units shown on the bottom of the crop parameter set at the beginning of the EPIC run NOTE The percent of heat units for first monthly estimate is the number on the left of the decimal for DLAP1 and the estimated percent of the Max LAI is the number in the right of the decimal Second point on optimal leaf area development curve Two points on optimal nonstress leaf area development curve Numbers before decimal are of growing season Numbers after decimal are fractions of maximum potential LAI Research results or observations on the of maximum leaf area at two points in the development of leaf area can be used in conjunction with an EPIC simulation like that described for DLAI The results of the one year multi run will establish the cumulative heat units by month from planting to maturity Then calculate percent of cumulative heat units by dividing estimated cumulative heat units for each of the two dates where you ve estimated percent of Max LAI by the average annual heat units shown on the bottom of the crop parameter set at the beginning of the EPIC run NOTE The percent of heat units for second date estimate is the number on the left of the decimal for DLAP2 and the estimated percent of the Max LAI is the nu
107. ter 2 MUSI input parameter 3 MUSI input parameter 4 Description Cost of irrigation water Cost of lime Cost of fuel Labor cost Miscellaneous costs 20 m T gal ha ha Site File S77E0810 dat amp filename sit A study may involve several sites fields farms or watersheds described and saved in filename sit This file must be listed in the database file SITE0810 dat or user defined name with a unique reference number corresponding to the variable ISIT in the run file EPICRUN dat filename sit includes following data elements Linel1 3 TITLE Description Lined Column Variable Description 1 8 XLAT Latitude decimal degrees 9 16 XLOG Longitude ve for West of Greenwich decimal degrees 17 24 ELEV Elevation m 25 32 APM Peak rate El adjustment factor BIU 33 40 CO2X CO concentration in atmosphere ppm gt Ooverrides CO input in EPICCONT dat ppm 41 48 CNO3X Concentration of NO in irrigation water ppm gt O overrides CNO input in EPICCONT dat ppm 49 56 RFNX Average concentration of N in rainfall ppm gt O overrides N gt input in EPICCONT dat ppm 56 64 X1 Not used 65 72 X2 Not used 73 80 SNOO Water content of snow on ground at start of simulation mm 81 88 AZM Azimuth orientation of land slope degrees clockwise from North LineS Column Variable Description 1 8 WSA HLU field farm or watershed area ha 9 16 CHL Mainstream channel length km BIU 17 24 CHS Mains
108. ter quality analysis Users NRCS Temple and other locations Universities Iowa State Texas A amp M University of Missouri Washington State and others INRA Toulouse France Other Countries Australia Syria Jordan Canada Germany Taiwan over of the world USDA ARS and other research and extension agencies iii Executive Summary The Environmental Policy Integrated Climate EPIC model was developed for use in field manage ment several fields may be simulated to comprise a whole farm Originally called Erosion Productivity Impact Calculator EPIC was constructed to evaluate the effect of various land management strategies on soil erosion Later developments extended EPIC s scope to encompass aspects of agricultural sustainability including wind sheet and channel erosion water supply and quality soil quality plant competition weather pests and economics Management capabilities include irrigation drainage furrow diking buffer strips terraces waterways fertilization manure management lagoons reservoirs crop rotation and selection pesticide application grazing and tillage Besides these farm management functions EPIC can be used to evaluate the effects of global climate CO change design environment ally safe economic landfills designing biomass energy production systems and other applications EPIC was developed in the early 1980 s to assess the effect of erosion on productivity Williams et al 1984
109. the number of crops grown within a year Refer to FMX page 17 for further information The maximum annual amount of nitrogen fertilizer can also be set here in the operation schedule and can be set per crop so that each crop has a specified amount of nitrogen fertilizer available to it This is especially important when automatically applying fertilizer NOTE If this variable is set either in the control file or in the operation schedule and manual fertilization is applied the model will only apply up to this maximum amount regardless of the amount specified in the manual fertilization operation Time of operation as fraction of growing season This is also referred to as heat unit scheduling Heat unit scheduling can be used to schedule operations at a particular stage of growth For example irrigation could be scheduled at 0 25 0 5 and 0 75 which might represent varying stages of crop growth Irrigation would then be applied at 25 50 and 75 of the potential heat units set at planting Enter earliest possible Month amp Day in JX 2 amp JX 3 NOTE When setting up an operation using heat unit scheduling it is best to enter earliest possible Month and day JX 2 amp JX 3 that the operation could occur on because in order for the operation to occur the date of the operation as well as the number of heat units scheduled must be met This is especially true for harvest operations It is recommended that the harvest date be set 10 1
110. ticide leached by the amount of water QT GP is subtracted from GPo using 89 the equation PSTL GPo 1 0 exp QT 0 01 ST 0 1 KD BD where PSTL is the amount of pesticide leached by QT The average concentration during the percolation of QT is PSTC PSTL QT Since percolation usually starts before runoff the vertical flow concentration is usually higher than that of the horizontal The relative concentrations may be user specified with the parameter p24 P24 PCH PCV where P24 is a parameter ranging from near 0 0 to 1 0 usually 0 5 PCH is the horizontal concentration PCV is the vertical concentration PSTL is partitioned into vertical and horizontal components using the equation PSTL PCV QV PCH QH Substituting equation 222 into equation 223 and solving for PCV gives PCV PSTL QV P24 QH PCH P24 PCV Amounts of PSTL contained in runoff lateral flow quick return flow and horizontal pipe flow are estimated as the products of the flow component and PCH Percolation and vertical pipe flow loads are estimated similarly using PCV The total amount of pesticide lost in the runoff is estimated by adding the soluble fraction computed with equations 220 224 to the amount adsorbed to the sediment Pesticide yield from the adsorbed phase is computed with an enrichment ratio approach PSTY 0 001 PSYC ER where PSTY is the pesticide yield adsorbed to the sediment in kg ha Y is the sediment yield in
111. tics of a single weather station are maintained in filename wp1 This file must be listed in the database file WPM10810 dat or user defined name with a unique reference number which corresponds to the variable IWP1 in the run file EPICRUN dat filename ops includes the following data elements Lines 1 amp 2 Title amp Description Line 3 et seq Each line has 14 variables in 12 columns one for each month January December Line Variable Description 3 OBMX Average monthly maximum air temperature C 4 OBMN Average monthly minimum air temperature C 5 SDTMX Monthly average standard deviation of daily maximum temperature C 6 SDTMN Monthly average standard deviation of daily minimum temperature C 7 RMO Average monthly precipitation mm 8 RST2 Monthly standard deviation of daily precipitation mm May be left zero if unknown or daily rainfall is input 9 RST3 Monthly skew coefficient for daily precipitation May be left zero if unknown or daily rainfall is input 10 PRW1 Monthly probability of wet day after dry day May be left zero if unknown or daily rainfall is input 11 PRW2 Monthly probability of wet day after wet day May be left zero if unknown or daily rainfall is input 12 DAYP Average number days of rain per month days May be left zero if rainfall is generated and wet dry probabilities are input 13 WI Monthly max 0 5h rainfall 3 options mm Monthly maximum 5 hour rainfall mm for period in Y WI Alp
112. tion Act evaluation Other examples of Comprehensive regional assessments using EPIC are given in Table 5 of Gassman et al 2004 Modeling pesticide fate Leonard et al s 1987 GLEAMS pesticide fate model is incorporated into EPIC Sabbagh et al 1991 it has been tested for pesticide movement and losses by Williams et al 1992 and Sabbagh et al 1992 and used to estimate the impact of atrazine loss on water quality Harman et al 2004 EPIC Data Structure For a given study a Run Definition file specifies which site soil weather and schedule files are to be used for each scenario in a run For a given study the major data elements to be developed by a user include descriptions of sites soils field operation schedules weather and the constant data An overview of the files and data flow is given in Figure 1 and the file structure and linkage are briefly discussed below uonuls Joya Jad att Suo da PUPUA don lttDu JLT pue Jequinu suoneyg Iaez JO Isr nejep JEP OISOIIN AA UOTIS lan puray Jad ag 200 puw trpu t q PUM BLIDUD IEF pue Jaquinu sote tip AA PULM JO 1SUT Q nej p JEP 01800K1IA Thos Jod AY au jos umt gug jos atubuarisy pue Joquinn sog Jo Ier Qynepsp 1YeP 0180 IOS elnpauos uormg4 do sad am ouo sdo smu ri sdo utpu n q pue Jaquiny s np auo uodo Jo st Qepsp FP OL80ISATO Gnejep POLSON A IN Gqneyep I p p0IS0LN21d ngjop
113. tion of the total heat units set at planting In most cases the dry down period is 10 to 15 of the total heat units If a dry down period is required heat unit schedule the harvest operation to occur at 1 10 1 15 or another appropriate fraction In the case of forage harvesting the forage is actually harvested well before the crop reaches full maturity In this case heat unit schedule the forage harvest to 0 55 or another appropriate fraction Minimum USLE C Factor Moisture content of grain required for harvest 86 93 OPV8 94 101 OPV9 NOTE Variables LYR OPV1 amp OPV2 are context dependent i e they have different meanings and variable names depending on the type of operation 37 Runoff Curve Numbers for Hydrologic Soil cover complexes Land use Cover Treatment or Hydrologic Hydrologic Soil Group Land Use Practice Condition A B C D Number Fallow Straight row 77 86 91 94 1 Row crops Straight row Poor 72 81 88 91 2 u ss Good 67 78 85 89 3 Contoured Poor 70 79 84 88 4 u Good 65 75 82 86 5 i Contoured amp terraced Poor 66 74 80 82 6 S s Good 62 71 78 81 7 Small grain Straight row Poor 65 76 84 88 8 u Good 63 75 83 87 9 u Contoured Poor 63 74 82 85 10 i Good 61 73 81 84 11 S Contoured amp terraced Poor 61 72 79 82 12 u Good 59 70 78 81 13 Close seeded Straight row Poor 66 77 85 89 14 Legumes2 or Good 58 72 81 85 15 rotation meadow Contoured Poor 64 75 83 85 16 u Good 55 69 78 83 17 Contoured
114. tream channel slope m m BIU 25 32 CHD Channel depth m 33 40 CHN Manning s N for channel BIU 41 48 SN Surface N for channel BIU 49 56 UPSL Upland slope length m 57 64 UPS Upland slope steepness m m 21 65 72 73 80 Line6 Column 1 4 5 8 9 12 13 16 17 20 21 24 25 28 29 32 33 36 PEC DTG Variable IRR IRI IFA IFD IDRO IDFO MNU IMW IDFP Conservation practice factor 0 0 eliminates water erosion Time interval for gas diffusion equations h Description Input value created from two digits N followed by values defined below N 0 applies volume defined by ARMX N 1 applies input or ARMX NO for dryland areas NI from sprinkler irrigation N2 for furrow irrigation N3 for irrigation with fertilizer added N4 for irrigation from lagoon N5 for drip irrigation N day application interval for automatic irrigation Minimum fertilizer application interval blank for user specified 0 without furrow dikes 1 with furrow dikes 0 No drainage Depth of drainage system mm Fertilizer for auto fertilizer amp fertigation blank is elemental N 0 automatic dry manure application without trigger Minimum interval between automatic mow Fertilizer number for automatic P application blank is elemental P BIU leave blank if the parameter value is unknown it will be estimated by EPIC from other data 22 Soil Files SOILO810 dat amp filename sol Data for each soil is mai
115. uble C concentration in runoff to percolate Range 0 1 1 0 Coefficient in century equation allocating slow to passive humus original value 0 003 Range 0 001 0 05 Auto fertilizer weighting factor 0 0 sets N application average annual N in crop yield 1 0 uses N stress function to set N application The two methods are weighted with Parm 46 for values between 0 0 and 1 0 Range 0 0 1 0 Century slow humus transformation rate D 1 original value 0 000548 Range 0 00041 0 00068 Century passive humus transformation rate D 1 original value 0 000012 Range 0 0000082 0 000015 Fraction of above ground plant material burned Burning operation destroys specified fraction of above ground biomass and standing and flat residue Range 0 1 Technology annual rate coefficient Linear adjustment to harvest index base year 2000 Set to 0 For level technology Increase to increase technology effect on crop yield Range 0 0 0 01 Coefficient in oxygen equation used in modifying microbial activity with soil depth See also SCRP 20 Range 0 8 0 95 Exponential coefficient in equation expressing tillage effect on residue decay rate Range 5 15 Coefficient in oxygen equation used in modifying microbial activity with soil depth 0 8 0 95 Exponential coefficient in potential water use root growth distribution equation Range 2 5 7 5 56 55 56 57 58 59 60 61
116. um thickness of maximum layer m splitting stops when ZQT is reached 23 41 48 ZF Minimum profile thickness stops simulation if reached m 49 56 ZTK Minimum layer thickness for beginning simulation layer splitting model splits first layer with thickness greater than ZTK if none exists the thickest layer is split m 57 64 FBM Fraction of organic carbon in biomass pool 0 03 0 05 65 72 FHP Fraction of organic carbon in passive pool 0 3 0 7 73 80 XCC Code written automatically for sot not user input Line4 et seq One column of data per soil layer up to 10 layers fields of 8 columns Line Variable Description 1 Z Depth to bottom of layer m 2 BD Bulk Density T m 3 U Soil water content at wilting point 1500 KPA m m BIU 4 FC Water content at field capacity 33 KPA m m BIU 5 SAN Sand content 6 SIL Silt content 7 WN Initial organic N Concentration g T BIU 8 PH Soil pH 9 SMB Sum of bases cmol kg BIU 10 WOC Organic carbon concentration 11 CAC Calcium carbonate content of soil BIU 12 CEC Cation exchange capacity cmol kg BIU 13 ROK Coarse fragment content by volume BIU 14 CNDS Initial NO concentration g T BIU 15 PKRZ Initial labile P concentration g T BIU 16 RSD Initial crop residue T ha BIU 17 BDD Bulk density oven dry T m 18 PSP 1 Phosphorus sorption ratio gt 1 Active amp stable mineral P kg ha 19 SATC Saturated conductivity mm h 20 H
117. umulate PHUs from year to year to simulate the maturity of the tree Application volume in mm for irrigation Range 1 5000 Fertilizer application rate in kg ha For variable rate set equal to 0 Range 0 500 Pesticide application rate in kg ha Range 0 500 Stocking rate for grazing in ha head On a Start Grazing operation this variable is used to set the stocking rate in number of hectares animal Using this feature the user can change the number of animals in the herd at any point in time simulating buying selling of animals Range 0 200 Two 2 condition SCS Runoff Curve number or Land Use number optional The land use number set previously can be overridden at this point if an operation has caused the land condition to change Range 1 35 Fraction of pests controlled by pesticide application This factor is used to control pest populations by applying pesticides It only applies to insects and diseases Weeds are handled through intercropping Range 0 1 NOTE e If this factor is set to 0 99 99 of the pests will be killed After each treatment the population will begin to regrow based on several parameters set in the Control file PSTX Crop file PST and Parm file parms 9 amp 10 e Currently the model is set so that very minimal damage is caused by pests and therefore does not reduce yield Pest growth is dependent on temperature and humidity Warm and wet conditions favor pest growth while dry and cool c
118. y be omitted if economic analysis is not required Crop ID number Refers to the crop ID number given to each crop as listed in CROPOS810 dat Time from planting to maturity in Years for tree crops only Time from planting to harvest in Years for tree crops at planting only This refers to the time to complete maturity of the tree full life of the tree No potential heat units are entered for trees This value is calculated from XMTU Range 5 300 33 30 37 38 45 LYR OPVI OPV2 Time from planting to harvest in years if JX 4 is a harvest operation for trees proportion of full maturity Range 5 100 Pesticide ID number from PEST0810 dat for pesticide application only Fertilizer ID number from FERT0810 dat for fertilizer application only Potential heat units PHU from germination required by the plant to reach maturity Total number of heat units or growing degree days needed to bring the plant from emergence to physiological maturity Used in determining the growth curve Enter 0 if unknown Range 1 5000 NOTE e For trees no PHU are entered They are calculated from XMTU For crops other than trees PHU are accumulated annually and reset to 0 at the end of the year Trees are a special case in which PHUs continue to accumulate from year to year Deciduous trees are also a special case within trees in which PHUs are calculated annually similar to non tree crops in order to simulate leaf drop as well as acc
119. y denitrification Nitrogen fixed by leguminous crops Nitrification Nitrogen volatilization Soluble Nitrogen in drainage outflow Phosphorus loss with sediment Phosphorus in runoff Phosphorus mineralized Phosphorus loss in percolate Enrichment ratio Organic Nitrogen fertilizer manure Nitrate Nitrogen fertilizer Ammonium Nitrogen fertilizer Organic Phosphorus fertilizer manure Mineral Phosphorus fertilizer labile Potassium fertilizer applied Organic Carbon fraction in fertilizer Limestone applied CaCO3 equivalent Temperature in second soil layer Ratio soil water wilting point in top 10mm Salt in irrigation water Salt in runoff 62 T ha T ha T ha T ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha kg ha T ha C kg ha kg ha 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 SLTS SLTF RSDC RSPC CLCH CQV YOC YEFK QSK SSK VSK SLTV MUSI IRDL HMN RNAD NIMO FALF DN2 RLSF REK FULU DN20 FO2 FCO2 CFEM BURC BURN NPPC SSFP DRNP Salt in lateral subsurface flow Salt in fertilizer Carbon contained in crop residue CO2 respiration Soluble Carbon leached Carbon in runoff Carbon loss with sediment Soluble Potassium in surface runoff Potassium in subsurface flow Potassium in percolate Salt percolated out of ro

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