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1. Graphs Text Files Notice that in year 21 or 1999 the available forage in the third quarter fell to 300 kg ha This type of information can be used to easily identify forage availability issues that affect animal well being and be an early indicator of the onset of drought There are many other functions in PHYGROW which can accumulate values on a specified interval or from a given start date average values over a specified time interval sum values view a subset of values review data statistics of the selected parameter and copy cut paste data between graphs VI Exporting data for further analysis The data generated in Phygrow can be exported and used in other applications such as Excel for further analysis For instance in the graphs window select the Leaf Growth graph Select Graphs g x xplantc nc Grazer Stem Litter Consumption xplantc nc Heat Units xplantc nc Leaf Death plantc nc Leaf Growth xplantc nc Leaf Litter Decomposition xplantc nc Leaf Turnover xplantc nc Leaf Water Storage xplantc nc Leaf to Stem Drip Click the OK button 36 Die v1 8 1 Fri Sep 26 2003 Help Model Status Fue ean e Accumulate row CSV Brazil Leaf Growth 40 eee tom Stat Axonopus purpusii Done Phygrow CSV Brazil Average SCH Average from Start E Melochia simplex Constrain Data A Hropogon spp View Subset I Rit Ari Hifldre LI Panicum laxu T Statis
2. 059 Loamy sand 368 437 506 060 125 190 019 055 091 Sandy loam 351 453 555 126 207 288 031 095 159 Loam 375 463 551 195 270 345 069 117 165 Silt loam 420 501 582 258 330 402 078 133 188 Silt 490 510 550 260 280 300 060 090 120 Sandy clay loam 332 398 464 186 255 324 085 148 211 Clay loam 409 464 519 250 318 386 115 197 279 Silty clay loam 418 471 024 304 366 428 138 208 278 Sandy clay 370 430 490 245 339 433 162 239 316 Silty clay 425 479 533 332 387 442 193 250 307 Low and High values are 1 standard deviation about the mean Rawls et al 1982 does not list values for the silt category so the values given here for silt are from Lane and Stone 1983 44 Table 6 Dry matter to radiation ratios for crop forage and rangeland species Derived from Table A 5 in the Soil Water Assessment Tool User s Manual 2001 Dry Matter to Radiation Ratio Common name g MJ Corn 3 9 Eastern gamagrass 2 1 Grain Sorghum 3 35 Sugarcane 2 5 Spring wheat 3 5 Winter wheat 3 Oats 3 5 Rice 2 2 Pearl millet 3 5 Timothy 3 5 Smooth bromegrass 3 5 Meadow bromegrass 3 5 Tall fescue 3 Kentucky bluegrass 1 8 Crested wheatgrass 3 5 Western wheatgrass 3 5 Slender wheatgrass 3 5
3. 0 to 60 precision 0 01 Base Temperature This parameter defines the minimum temperature at which a species can grow Below this temperature plant growth will cease in the model Information on this parameter can be gathered from published literature for the species or from online databases such as ECOCROP http ecocrop fao org If published values are not available Appendix A table 7 can be used as a general reference The plant growth curve in PHYGROW is sensitive to this number as it defines the starting point of the growth curve code variable name baseTemp units Celsius range 10 to 60 precision 0 01 Optimum Temperature This parameter defines the optimum temperature at which a species will grow Above or below this temperature plant growth will incrementally decline as the base temperature or the suppression temperature are approached Information on this parameter can be gathered from published literature for the species or from online databases such as ECOCROP http ecocrop fao org If published values are not available Appendix A table 7 can be used as a general reference The plant growth curve in PHYGROW is sensitive to this number as it defines the point where maximum growth under water non limiting conditions would occur Changes in this parameter will lead to shifts in the growth curve code variable name optTemp units Celsius range 10 to 60 precision 0 01 Left Side of Temperature Curve
4. SPUR Percolation in soil layer k on day i PR is a function of soil water content field capacity and upper soil water storage limit for the soil type in layer k SWik Soil water content cm in layer k on day i FC Field capacity soil water content cm of soil layer k PRix Percolation in soil layer k on day i If SW ik lt FC then PRix 0 If SW ik gt FC then SHC Saturated hydraulic conductivity cm h of soil layer k UL x Upper limit of soil water storage cm in layer k v Control parameter for hydraulic conductivity v 2 655 logio FC UE SPUR equation 18 57 HCix Hydraulic conductivity cm h through soil layer k on day i HC ix SHC k SW ix UL x SPUR equation 17 Tik Travel time h through soil layer k on day i Tik SW ix FC HCix SPUR equation 16 PRik SWix FCx 1 674 SPUR equation 15 2 4 6 Matrix flow Soil moisture below field capacity flows between adjacent soil layers in the direction of positive soil moisture gradients In PHYGROW the routine used to estimate matrix flow is a modification of the inter layer water movement routine used in CREAMS Smith and Williams 1980 and represents a crude analogy to Darcy s law In PHYGROW matrix flow MF is a function of volumetric soil moisture Swi expressed as percent by volume of soil instead of cm of water per total depth of soil volumetric soil moisture at saturation SW0 and soil layer thickness TH
5. This parameter defines the left curve shape for parameterization of a Poisson Density Function curve to simulate temperature effect on growth Values for the left side of the curve can range from 1 to 5 A value of 1 makes the curve side more spread out A value of 5 makes the curve side thin and skewed toward the midpoint Appendix table 7 provides several examples of this number for several species code variable name leftTempCurve units none range 0 to 5 precision 0 1 Right Side of Temperature Curve This parameter defines the right curve shape for parameterization of a Poisson Density Function curve to simulate temperature effect on growth Values for the left side of the curve can range from 1 to 5 A value of 1 makes the curve side more spread out A value of 5 makes the curve side thin and skewed toward the midpoint Appendix table 7 provides several examples of this number for several species code variable name rightTempCurve units none range 0 to 10 precision 0 1 Leaf Green to Dead Rate Leaf green to dead rate is the percentage of the total green leaf biomass that is transferred to standing dead leaf biomass on a daily basis when leaves are actively growing In field situations this can be determined from leaf mapping or leaf marking techniques followed over a period of time It can also be estimated from scientific studies where litter fall and leaf turnover have been measured In PHYGROW this is an extr
6. 0 RTCILTC v y TRTE Century 2003 4 8 12 16 20 24 28 32 36 40 H TEMPERATURE C Figure 3 2 1 Relative production using Poisson distribution curve 68 3 3 Effective Green Leaf Area Index Most plant growth models do not have a strong feedback mechanism for reflecting the impact of defoliation on regrowth of leaf tissue that remains after grazing and the subsequent development of new leaf tissue from remaining meristems The following formulas are designed to provide differential impact on leaf regrowth rates by allowing defoliation to influence regrowth and turnover at different rates by stage of growth The percent of green leaf tissue removed on day i is a primary driving variable EGLAI Effective Green Leaf Area Index or the amount of stand green leaf area relative to the ground area under the canopy of the plant LAI Leaf Area Index or the amount of standing leaf area relative to the ground area under the canopy of the plant EXP _ Plant x s maximum expression MinL Minimum LAI for plant x that allows green tissue to be produced even when grazing exceeds the growth rate MinL 0 1 EXP LAI 100 GLC Green Leaf Consumption of grazer j on day i PG i 1 Plant Growth of species x on day i 1 yesterday PLT Plant Leaf Turnover for species x on day i 1 PGi Plant Growth of species x on day i GEI Physical effect of grazing on plant species x on day i CLyi Accumulated amount of live cur
7. 1982 Estimation of soil water properties Trans ASAE 25 1316 1328 Soil Conservation Service currently Natural Resources Conservation Service 1985 National Engineering Handbook Section 4 Hydrology Washington DC USDA NRCS 1986 Urban Hydrology for Small Watersheds 210 VI TR 55 2nd Edition 50 PHYGROW TECHNICAL DOCUMENTATION Chapter 1 Introduction to PHYGROW PHYGROW is a point based daily time step algorithmic or computation engine that models above ground plant growth forage consumption and hydrological processes The model was first coded in 1990 and has undergone many enhancements since that time The model s original computation algorithms are a mixture of formulas adapted from other plant growth models CREAMS GLEAMS EPIC WEPP SPUR CENTURY ERHYM Il as well as biological relationship from grass tiller level research and dietary selection conducted at Texas A amp M University The model is capable of simulating growth of multiple species of plants subject to selective grazing by multiple animals on a soil with multiple layers for indefinite periods of time The model is designed to be integrated with a wide variety of weather databases vegetation databases and stocking rule databases and provide output for a wide variety of data sources and formats including all relational databases netcdf file formats commas separated and tab delimited file formats and linkages to other models and the internet using
8. CN gt A e 0 006729 100 CN 2 EPIC equation 2 5 slp Average slope of watershed user input CN Moisture condition II CN adjusted for slope CN CN3 CN 2 1 0 2 0 e 3 8S 3 CN EPIC equation 2 3 CN Moisture condition CN USDA SCS Hydrology Handbook 1972 CN CNg 20 100 CN s 100 CN s 999 200800100 cnsh EPIC equation 2 4 rx Maximum value of retention factor rx 1 000 CN1 10 2 54 SPUR equation 8 adjusted for units in cm UL x Upper moisture storage capacity cm of soil layer k ri Soil moisture retention parameter on day i P ry 1 0 gt Wx SWi UL vil SPUR equation 10 lac Initial abstraction coefficient for SCS curve number USDA SCS Hydrology Handbook general value 0 2 if AT gt 0 and if EP gt IAc st then Ri Runoff cm on day i Ri EP lAc r EP 1 IAcr SPUR equation 7 if AT gt 0 and if EP lt IAc r then Ri 0 Effective runoff Ri Runoff cm on day i ER Effective runoff cm on day i SSC Above ground water storage capacity cm 59 Soil input Surface Water Storage if Rij lt SSC then ER 0 if Ri gt SSC then ER Rij SSC Available Soil Water The model fills each soil layer to its upper moisture storage capacity before starting to fill the next lower layer in the following way ifk 1 Elik EP ER ifk gt 1 Elik EP ER ULk SW ul PRik s if SW ix gt UL x SWik U
9. If the species drops leaves due to photoperiod put the day length of the to Grow time when the plant drops it leaves Maximum H20 Live Woody Use a default of 180 Maximum H20 Live Current Use a default of 200 Years Growth CYG 48 Appendix C Parameterization Guidelines for the Grazer Section Name Comments Grazer Proportion of Herd Stock density Forage demand Intake of dry matter kg hd d Preference Name of animal kind e g cow sheep goat This can be classes of animals as well eg cows steers heifers Standard animal science text books and wildlife biology texts can provide the naming conventions The length of the production cycle that is to be specified for the grazers typically 365 days one year unless there is some long migration pattern or grazing scheme that is greater than 365 days Typical days of the year that producers will make decisions on herd density by grazer Extension literature is the most common public source of information for this variable other than interviews of selected producers This value is typically reflected as a head ha value and is normally found by listening sessions with producers extension bulletins or research articles published for the area in question This value is typically reflected as a head ha value and is normally found by listening sessions with producers extension bulletins or research articles published for the area in question This value is t
10. The maximum value of the SCS condition curve number for the parameterized soil given the cover type and hydrologic condition The SCS Runoff Curve Number CN method is used to determine water runoff in the model This method is described in detail in NEH 4 SCS 1985 The major factors that determine CN are the hydrologic soil group HSG cover type treatment hydrologic condition and antecedent runoff condition See Appendix A tables 1 and 2 for suggested curve numbers for rangeland cover types for various hydrological groups and hydrological conditions code variable name scsc units none range 0 to 150 precision 1 13 Min SCS Condition Curve Number The minimum value of the SCS condition curve number for the parameterized soil given the cover type and hydrologic condition The SCS Runoff Curve Number CN method is used to determine water runoff in the model This method is described in detail in NEH 4 SCS 1985 The major factors that determine CN are the hydrologic soil group HSG cover type treatment hydrologic condition and antecedent runoff condition See Appendix A tables 1 and 2 for suggested curve numbers for rangeland cover types for various hydrological groups and hydrological conditions code variable name minscsc units none range 0 to 150 precision 1 Bottom type This parameter defines the conditions below the bottom most layer of parameterized soil The following codes are used to define the co
11. also be estimated from scientific studies where litterfall and stem turnover have been measured In PHYGROW this is a sensitive variable This parameter defines the daily loss of woody material from the canopy Values that are too high can lead to too much stem litter biomass and values that are too low can lead to over production of woody material in the simulation Initial values of 0 01 to 0 1 are suggested for most woody species code variable name sturnOver units percent range 0 to 5 precision 0 005 Cold Unit Accumulation to Freeze Leaf Damage This parameter is defined as the accumulated negative differences between current days average temperature minus the base temperature The susceptibility of tissue death is a result of a certain amount of cold accumulated below the base temperature Generally observation of killing frost and computation of temperatures that occurred during the event is the best way to compute this variable Data can also be obtained from published literature code variable name cuaFreeze units Celcius range 0 to 2000 precision 10 Canopy Base Diameter This is the maximum canopy diameter at the base of the plant canopy at peak expression under optimal conditions at the site being simulated It can be determined from direct measurement or published literature code variable name cbasedia units cm 23 range 0 to 50000 precision 1 0 Canopy Crown Diameter This is the maxim
12. brush the major element Cover Description Pasture grassland or range continuous forage for H OO E CO oods grass combination orchard or tree farm oode P Farmsteads buildings lanes driveways and urrounding lots Average runoff condition and la 0 2S Poor lt 50 ground cover or heavily grazed with no mulch Fair 50 to 75 ground cover and not heavily grazed Good 75 ground cover and lightly or only occasionally grazed gt Poor lt 50 ground cover Fair 50 to 75 ground cover Good gt 75 ground cover 4 Actual curve number is less than 30 use CN 30 for runoff computations CN s shown were computed for areas with 50 woods and 50 grass pasture cover Other combinations of conditions may be computed from the CN s for woods and pasture boor Forest litter small trees and brush are destroyed by heavy grazing or regular burning Fair Woods are grazed but not burned and some forest litter covers the soil Good Woods are protected from grazing and litter and brush adequately cover the soil e ENS EENS e PE SJal Nj oN amp RO TER alol Ea Iall o o KR LO Oo or oo OINI co N Cu CH ou e I RI RA D EA EA EI RI A NI NM ketcoizdikM ioib lOc iz4l o A ST NTO TTT NNT SS CICA NI BO oO afli oo E all 40 Table 2 Runoff curve numbers for arid and semiarid rangelands Table 2 2d From USDA NRCS 210 VI TR 55 2nd Edition June 1986 Curve Numbe
13. crop is increasing relative to green standing crop lowered the number For shrubs and trees this value can range from 0 5 to 3 but a good default value is 1 75 If shrubs or trees grow out of control increase this value slightly If they decline decrease this number slightly Leaf Green to Dead Rate During This is a sensitive parameter during dormancy because of water stress or Dormancy temperature stress For grasses a value of 2 5 is suggested Increase or decrease it based on the look of the green standing crop vs dead standing crop output Also look at the green and dead standing crop and follow the same rules as above to examine slight changes you need to make For trees and shrubs use a default of 1 5 Canopy Base Diameter Specific to species Canopy Crown Diameter Specific to species Height at Canopy Start Specific to species Height at Beginning of Canopy Specific to species Curvature Max Leaf Litter Decomposition Specific to species Rate Max Stem Litter Decomposition Specific to species Rate Leaf Litter Water Store Capacity use a default of 1 Stem Litter Water Store Capacity use a default of 1 Contribution to Range Site If it is a dominant species on the site and would affect runoff this needs to Hydrologic Condition be close to 1 If it is less dominant and or does not affect runoff use values of less than 1 based on the degree to which this species would affect runoff or dominance Minimum Required Day Length
14. it applies to the litter from standing dead wood Normally this applies to species such as termites only 4 2 Grazer Simulation The model steps through each parameterized grazer every day of the simulation and updates the forage amounts in each of the preference classes then causes the grazer to consume from that available forage according to its preferences CYGRgi k Current Year s Growth for day i 1 yesterday for species k CYGRgi1k Current Year s Growth Turnover for day i 1 for species k If CYGRgi 1k CYGRgk Current Year s Growth new growth for day i and species k if plant is in rapid growth CYGDgk Current Year s Growth new growth for day i and species k if plant is in declining growth CYGDrix Current Year s Growth new growth for day i and species k if plant is in Dormancy CYGDdi Current Year s Growth new dead material for day i and species k 0 Then CYGDrix CYGRGi 1k 15 Else if CYGRGi 1k gt CYGTHi tk Then CYGRGix CYGRGi 1k Else if CYGRGi 1k lt CYGRGi 1x Then CYGDgix CYGRGi 1k WRGi 1k WRgi 1k WRGik WDgix WDrik WDdik Wood Growth for day i 1 yesterday for species k Woody material turnover for day i 1 for species k New wood growth for day i and species k if plant is in rapid growth New wood growth for day i and species k if plant is in declining growth New wood growth for day i and species k if plant is in Dormancy New dead woody material for day i and species
15. k If WRGi 1k 0 Then WDrix WRGi 1k Else if WRgi 1k gt WTri 1k Then WRgik WRgi 1k Else if WRgi 1k lt WRgi 1k Then WDgik WRgi 1k SDji APDU MnPDUj MxPDU j MnSRj MxSR ji Sp Stocking Density of grazer j on day i Actual P D and U forage available for grazer j on day i Minimum PDU as specified in user input described in section 4 1 Maximum PDU as specified in user input section 4 1 Minimum Stocking Rate as specified in user input section 4 1 Maximum Stocking Rate as specified in user input section 4 1 Stocking Rate Increment as specified in user input Section 4 1 If APDU lt MnPDUj Then SD MnSR Else if APDU gt MxPDUj Then SD MxSR Else SDji APDU MnPDU MxPDU MnPDU MxSRj MnSR SRI SRI MnSRji Quirk and Stuth 1995 DD PD UD ED TD NDj TF TDD The amount of forage grazer j wants to eat on day i from among the desirable species available The amount of forage grazer j wants to eat on day i from among the preferred species available The amount of forage grazer j wants to eat on day i from among the undesirable species available The amount of forage grazer j wants to eat on day i from among the emergency species available The amount of forage grazer j wants to eat on day i from among the toxic species available The amount of forage grazer j wants to eat on day i from among the non consume
16. observed outgoing long wave radiation J Climate 11 137 164 79
17. of the following lt gt 0r code variable name grazer 25 units n a range n a precision n a Planning Horizon This is the period of time that the stocking rules will impact It represents the period of time in which a management cycle for stocking and destocking would be implemented Normally it is 365 days 1 year but there may be other courses of time that can be specified If you specify 365 then the stocking rules will be repeated every 365 days if it was 730 then they would be repeated every 730 days code variable name grazer units days range n a precision n a Decision Day A decision day is the Julian day of the year when a change in stocking rate takes place in terms of the number of animals per unit of land for each grazer type code variable name aday units hd ha range 1 to end of Planning Horizonprecision n a Minimum Stocking Rate Minimum Stocking Rate is the desired number of animals that will be retained on the landscape for a specified minimum standing crop value for each animal kind regardless of environmental conditions The standing crop value is represented by the sum of the values for the Palatable Desirable and Undesirable species described below code variable name minsr units hd ha range n a precision 0 05 Maximum Stocking Rate Maximum Stocking Rate is the desired number of animals that will be retained on a given landscape for a spec
18. percentage Taken as a percent of standing crop the turnover rate varies from the active growing season to the dormant periods Summer dormancy or winter dormancy Heat Units Accumulation at Seed Heat units to seed set is determined by adding up the positive increments of differences between base temperature and average daily temperature until the seeds are fully formed on the plant Heat Unit Accumulation at Death Heat units are determined by adding up all positive increments of the difference between the average daily temperature and the base temperature of species In this case you add up the heat units from initiation of growth to full development of the plants typically when stem elongation has fully developed or flowering is initiated Maximum Rooting Depth cm For grasses determine the maximum rooting depth possible for this species Do not confuse this with the depth of the soil PHYGROW will make the adjustments if the maximum root depth is greater than the rooting depth of the soil Special care needs to be exercised for woody plants as they can penetrate deep into the soil and can access deep drainage water when the herbs and grasses are not able This will reduce the level of tree competition between grasses and trees and maintain the greenness when the grasses and herbs are dry and not growing Please be aware that the lower soil layer is for deep rooted species and goes below the normal classified lower soil layer See s
19. this value should be set to 0 code variable name huiDead units Celcius range 0 to 2000 precision 10 Rooting Depth Rooting depth defines the maximum rooting depth of the species under non water stress conditions on the site that is being parameterized This parameter can be determined from published values in the literature or field experiments Online databases such as ECOCROP http ecocrop fao org also provide information on this for some of the species catalogued code variable name rootDepth units cm range 0 to 10000 precision 1 Canopy Height Canopy height defines the maximum canopy height of the species under non water stress conditions on the site being parameterized This parameter can be determined from published values in the literature or from direct measurements in the field Online databases such as ECOCROP http ecocrop fao orqg also provide information on this for some of the species catalogued code variable name canopyHeight units cm range 0 to 100000 precision 1 Maximum Above Ground Biomass This attribute establishes the maximum amount of biomass both wood and leaf for a species on the site being simulated under optimal growth conditions This parameter can be determined from published values in the literature or from direct measurements in the field Online databases such as ECOCROP http ecocrop fao org also provide information on this for some of the species catalogu
20. 10 months of full stocking and 2 months of 80 stocking this assumes that the planning horizon is 365 and these intakes repeat themselves every year across 12 months Theoretically you can put in daily intake by representing 365 intake values or every week by putting in 52 values Another example could be that all cows are moved but none of the sheep or goats thus the stock densities for cows would be zero 0 during the days they are moved and the sheep and goats would stay at 1 The logic also allows representation of rotational grazing systems without adjusting the stocking rate code variable name sdens units ratio range 0 1 precision 0 0001 Forage demand Intake of dry matter kg head day Unlike stocking rate and herd proportion which depend on planning horizon Intake is always based on a set 365 day year Forage demand is the average dry matter intake for an average specified head of a grazer type The logic works the same way as herd proportion That is if intake is 9 kg day on average yearlong one could put in one number of 9 0 and it would apply to all days in the year If a seasonal difference in intake is desired 12 values of dry matter intake that is a weighted average of the monitoring herd would need to be input This intake value would be the average for the classes of animals reflected in the herd and their relative proportions e g if 10 cows and 5 heifers and 5 steers had intakes of 10 6 and 5 kg day hea
21. 19076 2 3025 0 8783 0 8828 10 16 9033 02934 0 9255 19864 2 4991 09012 0 9039 11 17 2956 0 3046 0 9363 20736 2 6588 09832 0 9631 12 17 4863 0 3203 0 979 2 068 2 9048 O9743 0 9513 13 19 4102 0 347 1 058 2 2476 3 1889 1 0727 1 0349 14 20 1501 0 9625 1 0879 2 4062 3 4222 1 0991 1 0598 15 19 1259 0 39395 1 0476 2 2835 3 3662 1 0724 1 016 16 17 8857 0 3211 O9557 20944 2 7824 1 0242 0 9888 17 17 4001 0 528 0 9633 20959 26669 09985 0 9823 18 17 1051 0 3318 O9745 2 0754 2 585 0 962 0 961 19 15 0534 0 2953 08243 1 8344 2 2502 0 8792 0 8788 20 16 6532 0 302 09103 19077 2 3647 0 9227 0 9314 21 15 4887 0 3058 0 898 1 8734 23108 0 8585 0 88 22 16 917 0 3043 0 9242 1 9443 2 319 0 9314 0 9267 e an Av rs DOC DOC annon 52009 DDO o oce Di Leafcrowth J IKI PS Ma Ready a A Each of the species listed and configured for the plant community are in a column and contain an average value for each day Excel could be used to create other graphs from the data perform statistics and help draw conclusions about the total leaf growth of the population This procedure can be done for any graph and data set within the Phygrow model 39 Appendix A Table 1 Runoff curve numbers for other agricultural lands Table 2 2c From USDA NRCS 210 VI TR 55 2nd Edition June 1986 Curve Numbers for Hydrologic Soil Group Hydrologic Poor pe f9 air d Good Ee E E and generally mowed for hay Brush brush weed grass mixture with
22. A amp M jwstuth cnrit tamu edu has a dataset for Central and South Texas species Stem Water Store Capacity This affects stemflow and throughfall Values for many African species can be obtained from the Livestock Early Warning System project http cnrit tamu edu lews Dr Jerry Stuth at Texas A amp M jwstuth cnrit tamu edu has a dataset for Central and South Texas species Fraction of Water Transferd from This affects stemflow and throughfall use 1 or 2 Leaf to Stem Stem Turnover This number is 1 for grasses This is a very sensitive parameter for trees and shrubs A value of 0 1 for the initially run on woody plants is suggested After the initial run examine standing crop for the shrub or 47 tree If it is growing out of control extreme upward trend over time then this number needs to be increased slightly e g 0 15 If is rapidly declining over time decrease this number e g 0 01 or 0 005 Try not to go below 0 005 with this parameter Incremental changes in this value and the leaf green to dead rate should allow one to stabilize the run going below 0 005 with this parameter Freeze Leaf Damage Leaf Green to Dead Rate This is a very sensitive parameter For grasses a value of 3 is suggested After the initial run examine the green standing crop vs dead standing crop If accumulating too much green standing crop is accumulating vs dead standing crop increase this number to 3 5 or 4 If too much dead standing
23. For herbaceous species this is determined using a representative stand that is sampled for grass basal cover forb frequency ina5x5cm frame and effective woody plant canopy cover The preferable method for gathering data for this parameter is field collection on a transect using a modified point frame However historical cover data or expert knowledge can be used to define the relative proportions of the species being simulated The model is sensitive to this parameter as it defines the proportionality of an individual species compared to its site potential along with its proportionality to the species that are competing with it on the site code variable name percentMax units decimal percent range 0 to 100 precision 0 001 Site Nutrient Factor This parameter is used as a scalar to increase or decrease the growth rate for a species due to increased or decreased nutrients or climate change In most cases this value should be set to 1 meaning no change in growth Values greater than 1 will have proportional increases in the growth rate to the value entered Values less than 1 will decrease growth rate proportionally code variable name nutrientFactor units n a range 0 01 to 2 precision 0 01 B Species Specific Parameters 17 LAI Leaf Area Index Leaf area index LAI is ratio of the total area of all leaves on a plant to the area of ground covered by the plant It is an important structural property of a pla
24. Green to Dead Rate During Dormancy i e Daily Green to Litter Transfer Rate at Dormancy of Current Years Growth Green Standing Crop This parameter defines the percentage of total green biomass that is transferred to standing dead leaf biomass on a daily basis when leaves are dormant due to temperature or water stress In field situations this can be determined from leaf mapping or leaf marking techniques followed over a period of time It can also be estimated from scientific studies where litterfall and leaf turnover have been measured Canopy Base Diameter cm This is the maximum canopy diameter at the base of the plant canopy at peak expression with water unlimited i e maximum genetic expression Canopy Crown Diameter cm This is the maximum canopy diameter expected by the plant at peak expression with water unlimited i e maximum genetic expression Height at Canopy Start cm This is the height at which the canopy diameter begins Height at Beginning of Canopy Curvature cm This is the height to the widest part of the canopy diameter of the plant at peak expression with water unlimited i e maximum genetic expression 64 Maximum Leaf Litter Decomposition Rate litter standing crop This parameter defines the optimal leaf litter decomposition rate as a daily percentage of weight loss to the total litter biomass This data can be gathered during field experiments using litter bag techniques and values for m
25. Italian annual ryegrass 3 Russian wildrye 3 Altai wildrye 3 Sideoats grama 1 1 Big bluestem 1 4 Little bluestem 3 4 Alamo switchgrass 4 7 Indiangrass 3 4 Alfalfa 2 Sweetclover 2 5 Sesbania 5 Flax 2 5 Sunflower 46 Pine 1 5 Oak 1 5 Poplar 3 Honey mesquite 1 6 45 Appendix Table 7 General base optimum and maximum temperatures along with suggested left and right shape numbers for the poisson density function used for simulating plant growth response to temperature derived from the Century Parameterization Workbook http www nrel colostate edu projects century CENTURY 20Parameterization 20Workbook pdf Base Optimum Maximum Left Right Species Temp Temp Temp Shape Shape Winter wheat barley 0 18 35 0 7 5 Corn 8 30 45 1 2 5 Soybean 10 27 40 1 2 5 C4 grass 12 30 45 1 2 5 C3 grass 0 15 32 1 3 5 Alfalfa 4 22 35 0 8 3 5 46 Appendix B General Guidelines on Selecting Initial Values for Plant Parameters for PHYGROW Model Runs Comments Name Comments e O LAI Leaf Area Index This should range from 1 5 to 4 Grasses are generally 3 Sedges would be between 2 and 3 depending on growth form Shrubs are 3 and lower Dense canopy trees and shrubs can be close to 4 Dry Matter to Radiation ratio Grasses should generally be higher that shrubs Grasses range from 9 to 2 3 Panicum maximum has a verified number as 2 3 A good mid range number for grasses is 1 5 Deciduous
26. K x The model includes a coefficient Cs which is set at 0 1 in CREAMS and this value is used in PHYGROW MF Cs SWik SWik SWO SWiktt SWOkz1 gt THK such that SW ix SW0 k SW j ket SWO k1 gt O CREAMS equation l 54 2 5 Runoff The calculation of runoff Williams et al 1990 Smith and Williams 1980 follows that of ERHYM II which is that presented in CREAMS except for the equations associated with calculations of CN which are obtained from EPIC as specified Runoff EP Effective precipitation on day i Pi Precipitation on day i SM Snow melt on day i EP P i SMi SLT k Soil Layer thickness cm of soil layer k user input B Proportion of soil layer thickness to maximum depth B gt k SLTk gt k 4 SLT partial ERHYM II equation 6 Wk Depth weighting factor for water retention Wk k Wutel EP 58 SPUR equation 11 CWS Current water storage SW ix Volumetric soil water cm cm on day i in soil layer k SWik SX SWix CWS UL e788 Wy adapted from SPUR equations 10 and 11 RNGF Factor between 0 and 1 weighted for each plant species that sets current days CN values user input in plant section SCS USDA NRCS curve number for infiltration potential of a site CN 2 Moisture condition II CN USDA SCS Hydrology Handbook 1972 CN 2 SCSmax SCSmax SCSmin RNGF Hansen et al 1972 for CN 2 values CN 3 Curve number for moist or wet soil conditions CN S
27. L else SWik SWix PRix Eaix Taix Pa en ee Precipitaticn Lape Evoporation of Water a Intercepied by Host Evaparaten al Evaporaice of bee fom SC Free Surlace Water i Si Irfiliration A KR Deep Ore nage Beyond the Reach of lor s er Figure 2 5 1 Diagram of major processes and pathways of water movement through a watershed Water inflow water outflow storage Thurow 1991 Before the complete soil water balance is calculated in the model the plant communities must be added to the soil because the soil water balance equation requires the effective infiltration effective percolation actual evaporation and transpiration and micropore flow into or out of the soil layer 60 Chapter 3 Plant Submodel 3 1 Plant Parameterization Phygrow requires initial parameterization of site specific and species specific data Information about location of the management unit for which the simulation is being run must be provided by the user If the user wishes to estimate a parameter value using empirically derived data perhaps the best way to visualize this exercise is to think about placing a square meter frame on the ground where it is totally occupied by the first plant species under consideration no interspecies competition Imagine further that the site has been irrigated such that water is not a limiting factor and estimate the following for each species in a plant community e Leaf area index cm of total lea
28. Modeling System Welcome to the Phygrow Software Home Page Bo E E internet Click on the Download link from the menu on the left of the screen 2 The Phygrow Forage Modeling System Microsoft Internet Explorer File Edit View Favorites Tools Help Back gt Q A EpersonalBar Search jFavorites 23 B Sp Pi Address ja http Uert tamu edu phygrow Download D Go Links gt 4 The Phygrow Forage Modeling System Here s How to Get and Use Phygrow Phygrow is available free of charge To get the software download the installer program The installer will walk y through the installation process and then you are ready to mai Phygrow Download ST http ent tamu edu rsg phyafowsdownloads Phygrow 2003 09 26 exe ITT le Internet 2 Click the HERE link in the main frame to begin downloading the PHYGROW model to the local machine The following screen will appear Select the option to Save this program to disk option File Download Save this program to disk M Always ask before opening this type of file Cancel More Info 3 Then click the OK button 4 A Save As window will appear prompting for the location to save the file Select a location on the hard drive The window captured below is saving the file to the My Documents folder It is important to know where the file was saved so that it can be executed or copied onto a CD for other installs that need to be perf
29. NREL Publication Fort Collins Colorado USA Pitts W E Yoo K H Miller Goodman M S and de los Santos W L Application of WEPP and GLEAMS to predict runoff and sediment losses from grazed pastureland Paper American Society of Agricultural Engineers 1997 No 972223 Quirk M F and J W Stuth 1995 Preference based algorithms for predicting herbivore diet composition Ann Zootech 44 Suppl p 110 Renard K G Shirley E D Williams J R and Nicks A D Hydrology component upland phases ARS U S Dep Agric Agric Res Serv 1987 Dec 63 17 30 Smith R E and J R Williams 1980 Simuation of the surface water hydrology In Knisel W G ed CREAMS A field scale model for chemicals runoff and erosion from agricultural management systems USDA Conservation Research Report 26 13 35 Thurow T L 1991 Hydrology and erosion pages 141 159 In R K Heitschmidt and J W Stuth editors Grazing Management An Ecological Perspective Timber Press Portland Oregon Wight J R 1987 ERHYM II Model description and user guide for the BASIC version U S Department of Agriculture Agricultural Research Service ARS 59 Williams J R C A Jones and P T Dyke 1984a The EPIC model and its application pp 111 121 n Proc ICRISAT IBSNAT SYSS Symp on Minimum Data Sets for Agrotechnology Transfer March 1983 Hyderabad India Xie P and Arkin P A 1998 Global monthly precipitation estimates from satellite
30. PHYGROW Phytomass Growth Simulator User s Guide Techical Documentation Ranching Systems Group Department of Rangeland Ecology and Management Texas A amp M University Jerry Stuth Dan Schmitt R C Rowan Jay Angerer and Kristen Zander SEPTEMBER 2003 PHYGROW USER S GUIDE TABLE OF CONTENTS IS lINS TAMIA TION EE 3 IL FRUININING THE MODEL EE 7 Ill PHYGROW INPUT FAbR MEHERR SS ege geed ue 13 IV VIEWING RESULTS AND MANIPULATING GRAPHS 0 30 VI EXPORTING DATA FOR FURTHER ANALYSIS eee terete 36 APPENDIX ee 40 PON IB tas a a San a aan ate anda atlanta Sati as a a tana ns a Ld 47 APPENDIX eech Eed Eege edel ed See REESEN 49 Bu KEE 50 BN wl e ET RTE 51 PHYGROW Installation and User s Guide I Installation To install PHYGROW on a machine from a CD go to Windows Explorer or the My Computer icon on the desktop Double click on the CD drive letter Double click on the folder named install then double click on the file named Phygrow 2003 09 26 exe Skip to step number 7 of this section if installing from the CD If you do not have a CD available with the Phygrow install open up Internet Explorer or Netscape after connecting to the Internet 1 Go to http cnrit tamu edu phygrow gro orage Modeling z oso erne plore 8 x Fie Edt View Favorites Tools Help Back gt Q A Epersonalpar Search GaFavortes lt 4 B Sp i Address http fenvit tamueduphygrowe Go men The Phygrow Forage
31. Python Perl and Java web applications The model has a unique capability to be started and stopped at any point in the computational process to allow full integration with data acquisition systems automation systems and and or other models The PHYGROW model engine is written in the C programming language and uses an object oriented design thus allowing high efficiency in the incorporation of new scientific relationships when necessary Because of the start restart features of the model simulations can be integrated at various spatial scales in terms of explicit areas across a landscape or in a virtual landscape representing multiple plant communities and soil combinations via a spatially explicit multiple run mode The PHYGROW model can be run in an automated environment across multiple platforms including all forms of Unix including Linux in server mode or server grid computing mode This allows streaming of data from many weather sources thus allowing near real time computation of plant growth A stand alone version of the model is also available for use on Windows 98 2000 NT and XP platforms Data input in the stand alone version is done using comma separated value or tab delimited text files containing the parameters These text files can be generated from a wide array of database formats The user interface in the stand alone Windows PC or Unix Linux version is provided via a publicly available software called Common Modeling Environmen
32. Wood Rapid Growth Preference A preference code P D U E T N is assigned for each grazer as it applies to the growth periods of a plant species where live wood growth rate is higher than the senescence rate of wood Generally this only applies to grazers that impact standing wood such as termites elephants etc Wood Declining Growth Preference A preference code P D U E T N is assigned for each grazer as it applies to the growth periods of a plant species where live wood growth rate is lower than the senescence rate of wood Generally this only applies to grazers that impact standing wood such as termites elephants etc Wood Dormancy Preference A preference code P D U E T N is assigned for each grazer as it applies to the growth periods of a plant species where live wood growth is zero Generally this only applies to grazers that impact standing wood such as termites elephants etc Wood Dead Preference A preference code P D U E T N is assigned for each grazer as it applies to the growth periods of a plant species where wood growth is the standing dead pool Current Year s Growth Litter Preference A preference code P D U E T N is assigned for each grazer as it applies to the litter from current year s growth Normally this applies to species such as termites or to certain species where consumption in extended dry periods is expected Wood Litter Preference A preference code P D U E T N is assigned for each grazer as
33. a species that the animal would eat but not actively seek out Undesirable U This class is assigned to a plant species when the percentage of that species in the diet of the animal is less than its percentage in the field on a relative weight basis regardless of standing crop Emergency E This class is assigned when the percentage of a Preferred Desirable and Undesirable plant species in the diet of the animal being simulated is almost zero It would represent a plant component for a species that would be eaten only inextreme conditions Toxic T This class is assigned when the percentage of Preferred Desirable and Undesirable is almost zero in the diet of the animal being simulated It represents a plant component that is eaten and it results in death or health problems Non consumed N This class is assigned when a component of a plant species is not recognized as a food item i e non cognitive IV Weather Attributes A Landscape Components Weather Weather is header to indicate the beginning of the weather component of the parameter file It must not contain any of the following lt gt or The longitude reading of the point that is being simulated is placed in the same cell as the header weather This is followed by the Latitude reading of the point and a name for the site that the weather applies to Both Longitude and Latitude have to be in decimal degrees Year This parameter indicates the listin
34. am Moderately slow Moderately fine sandy clay Slow 0 15 0 50 silty clay SE and very clay 0 00 0 15 Cd horizon Natric horizon fragipan ortstein Very slow or impermeable 42 Table 4 General guidelines for estimating bulk density when no data are available from http www mo10 nrcs usda gov mo1Oquides moistbulkdensity html Texture Terms Moist Bulk Density g cm Coarse sand 1 70 1 80 Sand 1 60 1 70 Fine sand 1 60 1 70 Very fine sand 1 55 1 65 Loamy coarse sand 1 60 1 70 Loamy sand 1 55 1 65 Loamy fine sand 1 55 1 65 Loamy very fine sand 1 55 1 60 Coarse sandy loam 1 55 1 60 Sandy loam 1 50 1 60 Fine sandy loam 1 50 1 60 Very fine sandy loam 1 45 1 55 Loam 1 45 1 55 Silt loam 1 45 1 55 Silt 1 40 1 50 Sandy clay loam 1 45 1 55 Clay loam 1 40 1 50 43 Silty clay loam 1 45 1 55 Sandy clay 1 35 1 45 Clay 35 50 1 35 1 45 Clay 50 65 1 25 1 35 Silty clay 1 40 1 50 Table 5 Soil water content cm3 cm3 at 0 1 3 and 15 bars based upon soil textural classes from Rawls et al 1982 VWC 0 bars VWC 1 3 bars VWC 15 bars Texture Low Ave High Low Ave High Low Ave High Sand 374 437 500 018 091 164 007 033
35. any species can be derived from litter decomposition studies published in the literature Maximum Stem Litter Decomposition Rate litter standing crop This parameter defines the optimal woody stem litter decomposition rate as a daily percentage of weight loss to the total woody stem litter biomass This data can be gathered during field experiments using litter bag techniques and values for many species can be derived from litter decomposition studies published in the literature Leaf Litter Water Storage Capacity g H20 g dry matter This parameter defines the amount of water grams that adheres to the surface of the leaf litter and then evaporates into the atmosphere Data for this parameter can be gathered from field surveys in much the same was as leaf water storage capacity described above Published values can also be obtained from water balance studies where leaf interception was measured Stem Litter Water Store Capacity g H20 g dry matter This parameter defines the amount of water grams that adheres to the surface of the woody stem litter and then evaporates into the atmosphere Data for this parameter can be gathered from field surveys in much the same was as woody stem water storage capacity described above Published values can also be obtained from water balance studies where leaf interception was measured Contribution to Range Site Hydrologic Condition Each species can be assigned a value of 1 to 1 0 which reflects its co
36. arameter Leaf Water Storage Capacity g H20 g DM This parameter defines the amount of water grams that adheres to the surface of the leaves and then evaporates into the atmosphere Data for this parameter can be gathered in field surveys where current year s growth leaves and new stem growth are collected and weighed Leaves and new stem growth are then dipped in water or sprayed until saturated They are then shaken 3 times and weighed again The difference between the two weights represents the weight of water sticking to the leaves that will evaporate back into the atmosphere The plant tissue is then placed in an oven to derive dry matter weight and allow the adhesion to be expressed as g water per g dry matter Published values for this are rare in the literature Stem Water Storage Capacity g H20 g DM The amount of water that adheres to woody material woody stems is determined the same way that leaf water storage capacity is determined Woody material adhesion is about 10 of current year s adhesion levels Fraction of Water Transferred from Leaf to Stem The water captured by the leaf canopy in excess of leaf storage capacity will either drip off of the end of the leaf or flow down the stem This is the fraction of the total amount of water that travels from leaf to stem 63 Stem Turnover Rate eg woody stem This variable is the percent wood biomass that turns over to the litter pool each day expressed as a perce
37. cy in a 5 x 5 cm frame and effective woody plant canopy cover The preferable method for gathering data for this parameter is field collection on a transect using a modified point frame However historical cover data or expert knowledge can be used to define the relative proportions of the species being simulated The model is sensitive to this parameter as it defines the proportionality of an individual species compared to its site potential along with its proportionality to the species that are competing with it on the site Site Nutrient Factor This parameter is used as a scalar to increase or decrease the growth rate for a species due to increased or decreased nutrients or climate change In most cases this value should be set to 1 meaning no change in growth Values greater than 1 will have proportional increases in the growth rate to the value entered Values less than 1 will decrease growth rate proportionally 3 1 Water Stress a loop The water stress constraint on daily biomass production in PHYGROW is function that artificially divides the maximum rooting depth for species j into four sections I 1 4 such that the soil moisture in each respective soil layer occupying a proportion of a given artificial layer is available for root extraction based on an effective rooting function decreasing with soil depth e m 40 30 20 10 MRD RTP RTP RTE m CLD SWik W Maximum rooting depth of plant species
38. d species available The total amount of actual forage available for grazer j on day i Total Daily Demand for grazer j on day i 76 DF The amount of actual forage available from among grazer j s desirable species on day i PFi The amount of actual forage available from among grazer j s preferred species on day i UF The amount of actual forage available from among grazer j s undesirable species on day i NF The amount of actual forage available from among grazer j s non consumed species on day i DFact The percentage of total daily forage demand that grazer j will take from it s desirable species of plants PFactj The percentage of total daily forage demand that grazer j will take from it s preferred species of plants UFact The percentage of total daily forage demand that grazer j will take from it s undesirable species of plants Feed The amount of feed that would be required to supplement grazer j s intake on day i due to total available forage being insufficient to meet demand FVljix The Forage Value Index of grazer j s intake of species x on day i GLCjix The amount of consumption for grazer j on day that was taken from the live Current Year s Growth of plant species x DCS The amount of consumption for grazer j on day i that was taken from the dead portion of the Current Year s Growth standing crop of plant species x WC The amount of woody material grazer j took
39. d thhe weighted average intake would be 10 10 5 6 5 5 10 5 5 7 75 kg day per head The software NUTBAL PRO available from Texas A amp M can be used to run a hypothetical quality profile for a 12 month period 12 cases with the typical herd structures profiles that are going to be used in the PHYGROW simulation to get derive 12 good estimates of seasonal shifts in intake In the PHYGROW simulation the total forage demand for each herbivore is computed by multiplying the stocking rates by the herd proportion and the daily intake per head code variable name demand units kg head day range 1 200 precision 0 0001 Preference The PHYGROW model allows a user to categorize components of plant species current year s growth and wood preference by animal species e each grazer according to phenological stage i e Current Year s Growth during Rapid Growing Current Year s Growth during Declining growth Current Year Growth during Dormancy no growth Current Year Growth during death by assigning one of the following preference classes Preferred P This class is assigned to a plant species that a particular animal is consuming when the percentage of that species in the diet of the animal is greater than its percent in the field on a relative weight basis Desirable D This class varies according to the percentages of the Preferred and 27 Undesirable classes in the diet of an animal It would represent
40. d from water balance studies where leaf interception was measured Forage production in PHGYROW is relatively insensitive to this parameter as it mostly affects interception loss in the water balance code variable name stemLitterStore units grams of water grams of dry matter range 0 to 5 precision 0 001 Contribution to Range Site Hydrologic Condition This parameter defines the impact of the species being simulated on the SCS Condition Curve Number Each species can be assigned a value of 1 to 1 0 which reflects its contribution to runoff of the site The higher the value the more it will drive the curve number to the lower value higher infilration rate less runoff while the opposite happens when it decreases code variable name nutrientFactor units ratio range 0 to 1 precision 0 01 Minimum Required Day Length to Grow This parameter defines the photoperiod or the number of hours of daylight required for growth of deciduous species It can be derived from field observation or published literature Itis a sensitive variable in PHYGROW as it will cause all live biomass to go into standing dead biomass when day length falls below this value code variable name requiredDayLength units hours range 0 to 24 precision 0 001 3 Grazer Data Grazer User defined name for the type of the animal species to be modeled Generic animal types such as cow sheep goat are generally used The name must not contain any
41. del to execute and produce results Click the OK button when you are ready to run the model lil PHYGROW INPUT PARAMETERS 1 Soil Attributes A Landscape Components Surface Water Storage This parameter defines the average depth to which water will pond in surface depressions during a rainfall event when the soil is at saturation on the surface It is a function of the percent of the soil surface that ponds and the average depth of each surface depression For instance if 20 of the soil surface ponds and average depth of the surface depressions are 1 0 cm then the average depth is 0 2 cm The water balance is sensitive to this variable as it influences the amount of water that can infiltrate into the soil and also the amount of evaporation Increases in this number increase infiltration during large rainfall events This parameter can be derived from field observations or estimated by experts in the region Code variable name furrow units cm range 0 to 50 precision 0 01 Surface Slope Slope is the number of meters of rise or fall in each 30 meters of land and is expressed in percent This value influences infiltration and runoff For PHYGROW the slope given for the soil series should be used For field situations where the soil information cannot be found use the average slope of the site being parameterized code variable name slope units percent range Oto 100 precision 0 1 Max SCS Condition Curve Number
42. ed This is an important variable in PHYGROW because it established the maximum productivity of a species on a site Incremental changes in this number will subsequently lead to incremental changes in the standing crop of the species being simulated code variable name maxWeight 21 units kg ha range 0 to 500000 precision 1 Leaf Above Ground Biomass Ratio This parameter defines the ratio of leaf biomass to total above ground biomass kg leaf kg total biomass It is important for partitioning out leaf biomass and wood biomass For grasses sedges and non woody dicots this value should be set to 1 For shrubs trees and woody forbs this value can range from 0 03 to 0 95 Information to derive this parameter can be gathered from published literature where biomass components have been separated and weighed In field surveys whole plants can be collected and leaf and wood material can be separated oven dried and weighed to derive this variable code variable name leafRatio units ratio range 0 to 1 precision 0 005 SAI Stem Area Index Stem area index SAI is ratio of the total area of all woody stems on a plant to the area of ground covered by the plant Data for this parameter can be collected in much the same way as leaf area index described above It is generally not collected during field surveys and published values for this in the literature are rare A default value of 10 of leaf area index LAI defin
43. ed above can be used in situations where you cannot measure this parameter In PHYGROW this parameter influences the amount of throughfall stemflow and interception loss Forage production is relatively insensitive to changes in this value code variable name SAI units ratio range 0 to 1 precision 0 01 Leaf Water Store Capacity This parameter defines the amount of water grams that adheres to the surface of the leaves and then evaporates into the atmosphere Data for this parameter can be gathered in field surveys where current year s growth leaves and new stem growth are collected and weighed Leaves and new stem growth are then dipped in water or sprayed until saturated They are then shaken 3 times and weighed again The difference between the two weights represents the weight of water sticking to the leaves that will evaporate back into the atmosphere The plant tissue is then placed in an oven to derive dry matter weight and allow the adhesion to be expressed as g water per g dry matter Published values for this are rare in the literature Forage production in PHGYROW is relatively insensitive to this parameter as it mostly affects interception loss in the water balance code variable name leafStore units grams water gram dry matter range 0to5 precision 0 001 Stem Water Store Capacity This parameter defines the amount of water grams that adheres to the surface of the wood and then evaporates into the atmos
44. emely sensitive variable especially for woody plants Values that are too high can lead to too much standing dead biomass and a lack of photosynthetic material Default values of 3 are suggested for grasses and forbs 1 75 for deciduous woody species and a value of 1 for evergreen species code variable name greentoDead units percent range 0 to 10 precision 0 01 Leaf Green to Dead Rate During Dormancy This parameter defines the percentage of total green biomass that is transferred to standing dead leaf biomass on a daily basis when leaves are dormant due to temperature or water stress In field situations this can be determined from leaf mapping or leaf marking techniques followed over a period of time It can also be estimated from scientific studies where litterfall and leaf turnover have been measured In PHYGROW this is sensitive variable but not as sensitive to growth as the leaf green to dead rate described previously Default values of 2 5 are suggested for grasses and forbs 1 5 for deciduous woody species and a value of 0 5 for evergreen species code variable name dormlturn units percent range 0 to 10 precision 0 01 Leaf Turnover Leaf turnover expresses the daily loss of leaf biomass that occurs from the total standing dead pool to the leaf litter biomass pool In field situations this can be determined from leaf mapping or leaf marking techniques followed over a period of time It can also be estimated from
45. ences and weather The scientific algorithms used in PHYGROW are presented in logical computation flow in the remainder of this document Chapter 2 Soils Water Dynamics 2 1 Soil Parameterization The process of simulating the distribution of natural rainfall or irrigation water throughout the soil horizon is perhaps the most crucial operation in plant 52 growth modeling Because water is a limiting factor to plant growth on most rangelands at some point during the growing season the entire water balance accounting including interception runoff evaporation infiltration percolation transpiration and deep drainage become critical simulation processes that makes water available or unavailable to plant roots The PHYGROW model accommodates an unlimited number of soil layers k lt n to delineate differences in soil texture saturated hydraulic conductivity water holding capacity and rock factor that could occur at the site being simulated The following parameters are required for each soil in PHYGROW e Surface water storage This reflects the amount water that can be ponded on the soil surface after the soil is saturated and rained upon Itis a function of the percent of soil surface that ponds and the average depth of each micropond For instance if 20 of the surface ponds and average depth is 1 0 cm then the average depth is 0 2 cm This means water can be held on the surface and allowed to infiltrate or evaporate if the
46. es phygrow folder or the location where the LeafGrowth csv file was saved Open 21x Look in phygrow D ge DI X Ci E Tools bin data History lib Campo_validation_Brazil a LeafGrowth L uninstall My Documents Desktop Za L Favorites My Network a il Places iles i Cancel Make sure All Files is selected as the file type or the saved data will not show up in the selection box The data will import straight into the spreadsheet program and look something like the following screen 38 Ei Microsoft Excel LeafGrowth E E el x SI Fie Edit view Insert Format Tools Data Window Help Acrobat la x AE TE 2 arial 10 RUSS Al e Julian Functions on Phygrow CS Brazil Leaf Growth e S Julian Fulnctions on Phygrow CSV Brazil Leaf Growth Julian MeaJulian MeaJulian MeaJulian MeaJulian MeaJulian MeaJulian Mean VValteria albicans O 16 721 0 2592 0 8929 2 027 2 4064 1 0181 0 9699 1 15 9022 0 2475 0 8805 1 9439 2 284 0 9643 0 9317 2 16 7653 0 2658 0 93919 20138 2 4915 09573 0 9454 3 16 597 0 2502 09008 1 9773 2 388 0 958 0 9349 4 175538 0 2782 0 9366 20125 2 4859 1 0112 0 9643 5 16 669 0 2685 0 9061 2 006 2 6513 09632 0 9245 6 16 1737 0 2556 0 673 1 8967 25061 09434 0 9072 7 15 9754 0 2686 0 8711 1 877 2 2586 0 9391 0 9132 8 15 19 0 2467 0 8459 1 8602 20173 O9277 0 9275 9 16 3997 0 2649 09218
47. f area cm of unit land area This attribute essentially sets the maximum leaf area index of an ungrazed plant at full expression when water is non limiting To determine this parameter requires either access to prior data expert opinion or field measurements e Dry Matter to Radiation Conversion Ratio grams dry matter mega joule radiation This attribute reflects the amount of biomass g that is produced per unit of radiation received on the site mega joules when water is non limiting This is usually estimated by experts but can be set up in an experimental situation where the plants can be irrigated or have high soil water during normal peak growth periods when temperature is optimum and soil water is not limiting growth e Suppression Temperature iC This is the average daily temperature at which leaf extension will cease when water is not limiting growth This is best measured when soil moisture is good and in a period when maximum daily temperatures are at their highest Leaf extension rates are useful measures of this phenomenon by making a mark on the leaf non toxic india ink then measure extension increments from the mark through the period at 3 day intervals until no extension is noted soil moisture is good during these periods Note the average daily temperature when it ceases elongation and that is the suppression temperature e Base Temperature C This is the average daily temperature at which leaf extension wil
48. f soil to water movement As the resistance increases the hydraulic 14 conductivity decreases Resistance to water movement in saturated soil is primarily a function of the arrangement and size distribution of pores Large continuous pores have a lower resistance to flow and thus a higher conductivity than small or discontinuous pores Soils with high clay content generally have lower hydraulic conductivities than sandy soils because the pore size distribution in sandy soil favors large pores even though sandy soils usually have higher bulk densities and lower total porosities total pore space than clayey soils Data from this parameter can be found in laboratory data for the soil series provided by the NRCS Appendix Table 3 can also be used as a guideline when laboratory data are not available The Soil Water Characteristics Hydrologic Properties Calculator developed by Washington State University http www bsyse wsu edu saxton soilwater is also helpful in determining this parameter when laboratory data are not available In the PHYGROW model this is a primary variable influencing water movement into the soil via infiltration and subsequent movement of water through layers under saturated conditions code variable name hydrConduct units cm hr range 0 to 100 precision 0 005 Bulk Density The bulk density parameter is defined as the mass of oven dry soil per unit grams divided by the total volume of soil cm at a water
49. from day i s standing crop of species x LLCjx The amount of day i s consumption that grazer j took from the leaf litter on the ground of species x WLCjix The amount of day i s consumption that grazer j took from the wood litter on the ground of species x PFact 1 e 3 65 E f UF act 0 031971 e 289 UF TF DFact 1 0 UFact PFact FVljx PFact 100 Dach 50 UFact 25 If Us gt 0 and TFji gt TDDj Then PDji TDDj KW PFact j If Pdiji lt TDD Then DDji TD s DFact If Pdi Du lt TDDji Then UD TD S Ufactj If PDji DDji UDji lt TDDji Then EDji EE TDDji PDji DDji UDji If EDji PDji DDji UDji lt TDDji Then 77 TDji TFii TDD PDj DDji UD EDji Else if TFii 0 Dn PDji UDji EDji TDji and ND all equal 0 Else if TF lt TDD Then PDji PDji DE TFii Dy Dy 2 DE TFii UD UD S UE TFii EDji EE TDDji PDji DDji UDji TDji TFii S TDDj PDj DDji UD EDji Chapter 4 Weather Weather data is input to the model as part of the comma separated value csv parameter file There is no subsequent manipulation of the weather data once read in each day Daily values of year day minimum temperature maximum temperature precipitation and radiation are used as input variables References Butt T B McCarl J Angerer P Dyke and J Stuth 2003 The economic and food security implications
50. g of the simulation years from beginning to the end code variable name Year units year range variable set by the user precision n a Day This parameter indicates the listing of the simulation julian days from the beginning to the end code variable name day units julian day range 1 366 precision n a Minimum Temperature This parameter indicates the minimum daily temperature for each day of the simulation period code variable name minT units degrees celsius range n a precision n a Maximum Temperature This parameter indicates the maximum daily temperature for each day of the simulation period code variable name maxT units degrees celsius range n a 28 precision n a Rainfall The parameter indicates the daily precipitation for each simulation day code variable name rain units cm range n a precision n a Radiation This parameter indicates the daily total solar radiation load for each day in the simulation profile This parameter is generated from minimum and maximum temperatures If minimum and maximum temperature are known Solar Radiation can be calculated as follows RS KT RA TD40 5 59 6 0 55 TD RS incident solar radiation KT coefficient 0 15 0 18 RA extraterrestrial radiation depends on latitude and month of the year TD max temperature minus minimum temperature More advanced weather generators can also be used to estimate this variable or ac
51. ge graph for the Brazilian validation study is shown below 31 SS Standalone Phygrow DI x Die v1 8 1 Fri Sep 26 2003 Help Model Status Accumulate DV Brazil Total Forage Available Accumulate from Start a bk Aver Done Phygrow CSV Brazil age Horse ER Average from Start S Ki Julian Functions veado Camper Constrain Data Text Files 400 300 Decade Decade 2 Decade 3 It is often useful to view a subset of the grazers so that a user can identify more closely the exact amount available for an animal kind To perform this function in PHYGROW select View from the menu in the graph then click on View Subset as illustrated above Select a grazer from the Select Graph Items box Select Graph Items ow Horse eado Campeiro Click the OK button to continue 32 This will produce a graph that shows just the Total Available Forage for the cow grazer configured in the model amp Standalone Phygrow Gei E File v1 8 1 Fri Sep 26 2003 Help Model Status Phygrow CSV Brazil Total Forage Available i You can also perform Julian Functions within PHYGROW For instance on the data displayed above it would be useful to know what the average daily value was for grazed standing crop on each day of the year over the 23 year period that the run represents To produce this graph in Phygrow go to View in the graph window and select Julian Functions Done Phyg
52. ified maximum standing crop value for each animal kind regardless of environmental conditions The standing crop value is represented by the sum of the values for the Palatable Desirable and Undesirable species code variable name maxsr units hd ha range n a precision 0 05 Stocking Rate Increment The stocking rate increment is the increment of change that must occur before an adjustment should be made code variable name srinc units hd ha range n a precision 0 00001 Proportion of Herd Stock density 26 This parameter indicates the proportion of the herd utilizing an area the site being simulated on a given day The proportion herd changes will depend on how you set your planning horizon i e number of days specified 365 730 etc You may put up to 365 proportions for 365 days planning horizon representing the proportion of a herd that can be present on each day of the year for that particular herbivore If you put in a value of 1 that means that all the specified animals are there each day and that the decision rules and stocking rates are driving the process This allows one to initially set simple decision rules and then put in secondary decisions For example one could parameterize it such that on the 6th month 20 of the cows are moved to a new location and then returned in the 8th month Therefore the parameterization if PHYGROW would take the form of 1 1 1 1 1 8 8 1 1 1 1 which represents
53. ile for a validation study done in Embrapa Pantana South Maso Grosso State Brazil in which forage standing crop estimates from PHYGROW were compared with data collected in grazing 11 experiments on 151 hectares Select the campox3 file to load this validation study into the user interface C plant IN Sample_csv IN soil TN modstor dta TN weather TN param TN param2 TN phygrow mecf After the filename appears in the File Name text box click the Open button to load the parameter file into the window as seen below Standalone Phygrow floods Image 0 44 0 06 0 06 2 75 0 06 2 75 0 04 1 Plant pant Plant Name Axonopus op 12 The various parts of the parameter file are explained briefly in the demonstration video The parameter file can be modified in an external program such as Excel and saved as a comma separated value or CSV file as indicated on the button within the user interface Use the param txt file in the data directory of the installation as a template for creating a new parameter file After the file is created load the parameter file into PHYGROW using the steps outlined above and demonstrated in the video The following section contains detailed explanations of each parameter required to complete a PHYGROW run Each of the parameters should be entered into the appropriate place within the csv file and must be filled out in order for the mo
54. imum Stem Litter Decomposition Rate This parameter defines the optimal woody stem litter decomposition rate as a daily percentage of weight loss to the total woody stem litter biomass This data can be gathered during field experiments using litter bag techniques and values for many species can be derived from litter decomposition studies published in the literature code variable name stemdcomrate units percent range 0 to 100 precision 0 01 Leaf Litter Water Store Capacity 24 This parameter defines the amount of water grams that adheres to the surface of the leaf litter and then evaporates into the atmosphere Data for this parameter can be gathered from field surveys in much the same was as leaf water storage capacity described above Published values can also be obtained from water balance studies where leaf interception was measured Forage production in PHGYROW is relatively insensitive to this parameter as it mostly affects interception loss in the water balance code variable name leafLitterStore units grams of water grams of dry matter range 0 to 5 precision 0 001 Stem Litter Water Store Capacity This parameter defines the amount of water grams that adheres to the surface of the woody stem litter and then evaporates into the atmosphere Data for this parameter can be gathered from field surveys in much the same was as woody stem water storage capacity described above Published values can also be obtaine
55. in If you do see the Download Complete window like the one below click the Open button to begin installation Download complete om Mm Download Complete Saved Phygrow 2003 09 26 exe from cnrit tamu edu CELLAT Downloaded 9 97 MB in 48 sec Download to C Docume Phygrow 2003 09 26 exe Transfer rate 212 KB Sec 7 Close this dialog box when downl Open Open Folder 7 The Phygrow setup will prompt you for an installation folder The default folder is C Program Files phygrow which is shown below d completes ie phygrow Setup Installation Folder Install phygrow Select the folder to install phygrow in C Program Files phygrow Browse Space required 13 9MB Space available 983 5MB Cancel Nullsoft Install System v2 0b3 To accept the default folder click the Install button To change the location of the Phygrow model highlight the text in to the left of the Browse button and indicate where the model should be installed on the local machine 8 The Phygrow Setup window will appear showing the progress of the files being copied from the setup program to the hard disk When the process is complete the following window will appear fe phygrow Setup Completed i t Ol x Completed Show details Gancel Nullsoft Install System yzZ 0b3 Click the Close button on this screen to complete installation ll Running the model To start the Standalone Phygrow mode
56. ired for each soil layer in Phygrow Soil Layer Depth cm The thickness of each soil horizon needs to be specified in cm There needs to be a 0 01 2 cm thick evaporative layer with similar characteristics of the top horizon except for the impact of increased organic matter if applicable The remaining thickness of the upper layer needs to be reflected with its attributes A deep rooting layer needs to be added to the description to accommodate deep tree root layers We normally recommend 200 cm as the typical thickness Ifa bottom type of 0 or 2 is used then the deep rooting layer is not needed Rock Factor cm3 cm3 This is the percent of the layer volume in cm3 cm3 proportion that is occupied by rock Saturated Hydraulic Conductivity cm hour This parameter is the rate that water flows through the horizon when it is saturated Bulk Density g cm3 This parameter measures the weight of soil per unit volume of soil moist Volumetric Water Content 0 bars cm3 cm3 This is the volume of water per layer volume of soil when it is saturated Volumetric Water Content 1 3 Bars cm3 cm3 This is the volume of water per layer volume of soil when at field capacity Volumetric Water Content 15 Bars cm3 cm3 This is the volume of water per layer volume of soil when it is below extraction capacity of the plants set arbitrary at 15 bars called permanent wilting point Dry Bulk Density g cm3 The parameter measures the weight of s
57. is given a soil series name In PHYGROW this parameter influences soil cracking thus influencing infiltration and macropore movement of water code variable name DrybulkD units g cm range 0 to 5 precision 0 001 2 Plant Attributes A Site Specific Components Plant Name User defined name for plant species that is being parameterized Name can be alphanumeric but must not contain any of the following lt gt or Generally a species code that is linked to a database common names or latin names are used code variable name plantfl units n a range n a precision n a Initial Standing Crop This parameter defines the initial standing crop kg ha for the species being parameterized for the starting day of the model run This variable can be estimated from or measured during a field survey of species being modeled For long term gt 5 years simulations the model is not sensitive to this parameter However an accurate representation of this number would be required for short term simulations Note this parameter should not exceed the value obtained when multiplying the decimal percent of maximum expression times the maximum aboveground biomass code variable name initialSC units kg ha range 0 to 1000000 precision 0 01 Percentage of Maximum Expression This parameter reflects the percent of potential niche occupancy that a species is currently expressing in a plant community
58. j artificial partitioning of total rooting depth into layers I 1 4 MRD j 25 I 1 4 water stress constraint for growth on day i for species j Effective rooting function in 4 artificial layers 40 30 20 and 10 Current soil layer depth cm in layer k Volumetric soil water in layer k on day i cm cm Dist 4 RTE m CLD RTPI SWix 66 For example Fig 3 2 1 shows a plant growing on a soil with four layers of varying depths For purposes of explanation the depths of the soil layers are unimportant but for calculations of soil moisture in each soil layer layer depth and layer texture must be known The maximum depth at which plant roots will penetrate is divided into four artificial layers which have different capabilities for plant roots to extract soil water based upon effective root mass Figure 3 2 1 Graphical representation of effective root distribution in four artificial layers affecting water stress on plant growth 3 2 Temperature Stress Plant photosynthetic activity may also be constrained by air temperatures outside a plant specific growth range Potential production is a function of a genetic maximum defined for each plant species and scalars with values ranging from 0 1 reflecting the effects of soil temperature moisture status shading by canopy and dead vegetation and seedling growth The maximum potential production of a species unlimited by temperature moisture or nutrient stresses is prima
59. ke advantage of the zooming capability within the PHYGROW user interface Close the Julian Function graph by clicking on the X in the upper 34 right hand corner of the graph window The application should return to the Total Available Forage graph for the cow grazer Using the zoom functionality it is possible to investigate the incidence of low forage availability and visually compare the results to the Julian Average calculated above Note that the third quarter average had a minimum standing crop of around 500 kg ha Place the mouse somewhere slightly to the left of Decade 3 on the x axis and high enough in the graph to enclose all of the standing crop points Hold down the left mouse button and drag a box around the lines within the graph to enclose all of the data from your mouse click to the bottom right hand corner of the graph as illustrated here File v1 8 1 Fri Sep 26 2003 Help Model Status el Eer SE 7 File Edit View Phygrow CSV Brazil Total Forage Available Done Phygrow CSV Brazil j Graphs Text Files X 7226 491 Y 1299 9668 X 8384 6475 Y 297 20264 300 Decade 1 Decade 2 Decade 3 rize Status Results When the mouse button is released the graph will zoom to the selected resolution 35 E stanaaione phyarow EE File v1 8 1 Fri Sep 26 2003 Help Model Status Phygrow CSV Brazil Total Forage Available Done Phygrow CSV Brazil j
60. l go to your desktop and double click on the icon that was installed during installation du Documents ACT 3 e 7 gudget Micre My Computer My Network Places FOLS Recycle Bin A black screen will appear which indicates that the client application is starting on the machine During startup the required files must be loaded for the Java application to run This command prompt screen gives you information regarding the success of this process Using modelClient version v1 8 1 Fri Sep 26 2663 Serving Models from Host localhost Reading Specifications for Phygrow CSU Reading Specifications for Phygrow CSU Parts java net SocketException factory already defined at java rmi server RMISocketFactory setSocketFactory CRM SocketFactory ja TEES ED at rsg ui ModelClient lt init gt lt Unknown Source at rsg phygrow ui Phygrow lt init gt lt Unknown Source at rsg phygrow ui Phygrow mainCUnknown Source modelClient version v1 8 1 Fri Sep 26 2003 After the model is loaded a Java user interface will appear with a Window title of Standalone Phygrow and Common Modeling Environment on the CME tab like the one below You can click the maximize window icon in the upper right hand corner the middle button to make the window larger and more easy to view PO standalone Phygrow EK lol x File v1 8 1 Fri Sep 26 2003 Help The Common Modeling Environment A user interface and common data repository to facilitate rem
61. l commence if water is not limiting growth This parameter can typically can be assessed by asking individuals to record the date when plants are observed to initiate growth when soil moisture is good published literature or plant growth databases e Optimum Temperature ef This parameter defines the optimum temperature at which a species will grow Above or below this 61 temperature plant growth will incrementally decline as the base temperature or the suppression temperature are approached Information on this parameter can be gathered from published literature for the species or from online databases such as ECOCROP http ecocrop fao org Left Side of Temperature Curve This parameter defines the left curve shape for parameterization of a Poisson Density Function curve to simulate temperature effect on growth Values for the left side of the curve can range from 1 to 5 A value of 1 makes the curve side more spread out A value of 5 makes the curve side thin and skewed toward the midpoint Right Side of Temperature Curve This parameter defines the right curve shape for parameterization of a Poisson Density Function curve to simulate temperature effect on growth Values for the left side of the curve can range from 1 to 5 A value of 1 makes the curve side more spread out A value of 5 makes the curve side thin and skewed toward the midpoint Appendix A table 7 provides several examples of this number for several species Leaf Turnover
62. lculator developed by Washington State University http www bsyse wsu edu saxton soilwater is also helpful in determining this parameter when laboratory data are not available code variable name volWater0O units mem range 0 to 1 precision 0 001 Volumetric Water Content at 1 3 Bar This parameter represents the water content of the soil when the water is held in the soil at 1 3 bar of tension also known as field capacity Field capacity is defined as the volume of water remaining in a soil 2 or 3 days after having been wetted with water and after free drainage is negligible As with Volumetric Water Content at 0 bars the rock fragments rock factor above are included into the calculation of this parameter in PHYGROW so that their effect on the water availability is accounted for Volumetric water content is generally measured in the laboratory from samples collected during a field soil survey In the absence of laboratory data Appendix A Table 5 can be used for a guideline if texture is known The Soil Water Characteristics Hydrologic Properties Calculator developed by Washington State University http www bsyse wsu edu saxton soilwater is also helpful in determining this parameter when laboratory data are not available code variable name volWater 333 units mem range 0 to 1 precision 0 001 Volumetric Water Content at 15 Bar This parameter represents the water content of the soil when the water is he
63. ld in the soil at 15 bars of tension also known as permanent wilting point Permanent wilting point is defined water content of a soil at which indicator plants growing in that soil wilt and fail to recover when placed in a humid chamber As with Volumetric Water Content at 0 bars the rock fragments rock factor above are included into the calculation of this parameter in PHYGROW so that their effect on the water availability is accounted for Volumetric water content is generally measured in the laboratory from samples collected during a field soil survey In the absence of laboratory data Appendix Table 5 can be used for a guideline if texture is known The Soil Water Characteristics Hydrologic Properties Calculator developed by Washington State University http www bsyse wsu edu saxton soilwater is also helpful in determining this parameter when laboratory data are not available code variable name volWater 15 units cr cm range 0 to 1 precision 0 001 Dry Bulk Density The dry bulk density parameter is defined as the mass of oven dry soil per unit grams divided by the total volume of soil cm at a water tension 15 bars It is not commonly measured during field soil surveys but is sometimes determined in the laboratory In the absence of data for this parameter use the bulk density parameter above or use the Map Unit User File MUUF program http www wcc nrcs usda gov wetdrain wetdrain tools html for estimating th
64. mum Stocking Rate The minimum amount of head acre that will be placed on a particular site e Maximum Stocking Rate The maximum amount of head acre that will be placed on a particular site e Stocking Rate Increment The units of change by which the head acre ratio will be incremented decremented e Minimum PDU The minimum amount represented in kg ha of Preferred Desirable and Undesirable forage that must be present in a field before grazers will be introduced there and the amount below which the animals will be removed from the field e Maximum PDU The maximum amount represented in kg ha of Preferred Desirable and Undesirable forage that will affect stocking decisions Demand There must be at least 1 forage demand number specified for each grazer as follows e Demand The total amount of forage dry matter represented as kg head day that the specified grazer will extract from the site under perfect conditions There can be an unlimited number of different demand numbers entered and they will be dispersed evenly across the planning horizon For example 12 demand numbers can be entered on a model run that has a 365 day planning horizon to model different intake amounts for each of the 12 months of the year Preferences In order to allow selective grazing in PHYGROW each animal s preferences for a specific species of plant are broken down into the parts of the plant and it s different stages of growth described bel
65. ndition Bottom type 0 Bottom is permeable unconsolidated material water drains freely Bottom type 1 Bottom is impermeable rock or indurated layer allows water to perch Bottom type 2 Bottom is a water table water is present at all times code variable name bottom units none range 0 to 2 precision 1 B Soil Layer Components Soil Name User defined name for each layer of the soil that is being parameterized Name can be alphanumeric but must not contain any of the following lt gt or code variable name name units none range n a precision n a Rock Factor The volume of rock fragments unattached pieces of rock 2 mm in diameter or larger that are present in the soil layer It is a ratio of the volume of rock fragments to the total volume of the layer It is commonly determined as part of field and laboratory analyses for soil surveys and can be found in the attribute data for published soil surveys This parameter directly influences the soil water holding capacity and therefore plant available water Increases in this parameter decrease plant available water code variable name rockfactor units em lem range 0 to 1 precision 0 001 Saturated Hydraulic Conductivity This parameter relates the soil water flow rate flux density to the hydraulic gradient and is a measure of the ease of water movement in soil Saturated hydraulic conductivity is the reciprocal of the resistance o
66. ns or modeling many years of weather data with multiple grazers and plants species Click on the graphs button on the left You will see a list of available graphs generated for the model 30 Select Graphs E xj weather nc Day weather nc Maximum Temperature weather nc Minimum Temperature weather nc Rainfall weather nc Solar Radiation xplantc nc Canopy Height xplantc nc Cold Units xplantc nc Decomposition Factor Temp Moist Scroll down to the xgrazeo nc category and find the graph for Total Available Forage Select Graphs E xj xgrazeo nc Prefered Forage Available xgrazeo nc Preferred Forage Consumed xgrazeo nc Stocking Rate xgrazeo nc Total Forage Available xgrazeo nc Total Forage Consumed xgrazeo nc Toxic Forage Consumed xgrazeo nc Undesirable Forage Available xgrazeo nc Undesirable Forage Consumed Click the OK button to bring up the Total Available Forage graph Total available forage which is the standing crop for the population of each target animal modeled will be seen in the window Each grazer is represented by a different color on the graph The x axis is automatically scaled to show all of the years included in the run based on the number of years of weather data entered into the parameter file The y axis is scaled to include all values between the maximum and minimum total available forage in kilograms per hectare of the specified period for each grazer The Total Available Fora
67. nsumed A species classified as non consumed is not recognized as a food item by an animal or is a non cognitive species Essentially forage that is out of reach by the animal or species that will not be eaten under any circumstance which is not toxic falls into this category These preferences are used to describe a specific grazer s preference for the following growth stages parts of the plants Current Year s Growth Rapid Growth Preference A preference code P D U E T N is assigned for each grazer as it applies to the growth periods of a plant species where green current year s growth is higher than the senescence rate of current year s growth Current Year s Growth Declining Growth Preference A preference code P D U E T N is assigned for each grazer as it applies to the growth periods of a plant species where green current year s growth is less than the senescence rate of current year s growth Current Year s Growth Dormancy Preference A preference code P D U E T N is assigned for each grazer as it applies to the growth periods of a plant species where green current year s standing crop is greater than zero but growth rate is equal to zero e g standing green material but no growth Current Year s Growth Dead Preference A preference code P D U E T N is assigned for each grazer as it applies to the growth periods of a plant species where current year s growth is in the standing dead pool 74
68. nt canopy because it defines the number of equivalent layers of leaves the vegetation displays relative to a unit ground area thus defining the photosynthetic area as well as water interception area of the species being simulated The data for this parameter can be gathered from field surveys or published reports and literature Field data collection requires collection of leaves from a species within a known ground area The collected leaves are then fed into a scanning device that measures the area of the leaves Leaf area can then be determined by dividing the scanned leaf area by the ground area Leaf area is commonly measured in water balance and photosynthesis studies The Oak Ridge National Laboratory has a compilation of the leaf area index for over 1000 species and can be accessed at this web address http www daac ornl gov VEGETATION lai_des html PHYGROW is sensitive to this parameter because it defines the leaf area available for photosynthesis thus influencing plant growth for the species being simulated Interception loss is also influenced by this parameter as it defines the area of the plant that has the potential to intercept rainfall code variable name LAI units ratio range 0 to 60 precision 0 01 Dry Matter to Radiation Ratio This parameter defines the efficiency to which the plant converts radiation into biomass Mathematically it is the slope of the linear relationship between biomass production and photosynthe
69. nt of current standing wood biomass In field situations this can be determined from stem mapping or stem marking techniques followed over a period of time It can also be estimated from scientific studies where litterfall and stem turnover have been measured Cold Unit Accumulation to Freeze Leaf Damage Cold temperatures will kill tissue if too low The accumulated negative differences between current days average temperature minus the base temperature is the cold degree day The susceptibility of tissue death is a result of a certain amount of cold accumulated below the base temperature Generally observation of killing frost and computation of temperatures that occurred during the event is the best way to compute this variable Leaf Green to Dead Rate i e Daily Green Dead Transfer Rate of Current Years Growth Green Standing Crop This is the rate or percent of green current year s growth that transfers to dead standing material eg senescence rate It is expressed as percent of green biomass It is assumed to be a constant rate but changes due to rate of growth or green tissue formation The faster a plant grows the faster it will die and go to the standing dead component the use of marked tillers or stems is useful in determining this parameter Measuring the amount of green length that transfers to dead length before shatter is critical and must measured frequently This concept is particularly critical to perennial grasses Leaf
70. ntribution to the hydrology of the site The higher the value the more it will drive the curve number to the lower value higher infiltration rate while the opposite happens when it decreases This is similar to the method used for crop cover adjustment due to LAI changes for crop species in other models Minimum Required Day Length to Grow for each plant hours A value of 0 means it always grows 24 means never grows The initial parameterization of site specific plant information is required as follows Initial Standing Biomass kg ha for each Species This parameter defines the initial standing crop kg ha for the species being parameterized for the starting day of the model run This variable can be estimated from or measured during a field survey of species being modeled For long term gt 5 years simulations the model is not sensitive to this parameter However an accurate representation of this number would be required for short term simulations Note this parameter should not exceed the value 65 obtained when multiplying the decimal percent of maximum expression times the maximum aboveground biomass Percentage of Maximum Expression for Each Species or Functional Group This parameter reflects the percent of potential niche occupancy that a species is currently expressing in a plant community For herbaceous species this is determined using a representative stand that is sampled for grass basal cover forb frequen
71. of climate change in Mali Climatic Change accepted with revisions Center for Natural Resource Information Technology CNRIT 2003 Impact of New Farming Systems Technology in the Central Rift Valley For Enhancing Future Food Security in Kenya Texas A amp M University Online link http cnrit tamu edu cnrit pdf riftvalley pdf Hanson J D B B Baker and R M Bourbon 1992b SPUR II Model Description and User Guide GPSR Technical Report No 1 USDA ARS Great Plains Systems Research Unit Ft Collins Colorado Higgins R W W Shi and E Yarosh 2000 Improved United States precipitation quality control system and analysis NCEP Climate Prediction Center Atlas Number 7 40 pp Online http Awww cpc ncep noaa gov research_papers ncep cpc_atlas 7 index html Laflen J M Flanagan D C Ascough J C Il Weltz M A and Stone J J The WEPP model and its applicability for predicting erosion on rangelands SSSA Spec Publ 1994 38 11 22 Leonard R A W G Knisel and D A Still 1987 GLEAMS Groundwater Loading Effects of Agricultural Management Systems Trans ASAE 30 14031418 78 Lemberg B J W Mjelde J R Conner R C Griffin W D Rosenthal and J W Stuth 2002 An interdisciplinary approach to valuing water from brush control Journal of the American Water Resources Association 38 409 422 Parton W J B McKeown V Kirchner and D S Ojima 1992 CENTURY Users Manual Colorado State University
72. oil parameterization section 62 Maximum Canopy Height cm This is the maximum canopy height expected by the plant at peak expression with water unlimited i e maximum genetic expression Maximum Above Ground Biomass at Maximum Expression kg ha This attribute establishes the maximum amount of biomass both wood and leaf for a species on the site being simulated under optimal growth conditions This parameter can be determined from published values in the literature or from direct measurements in the field Online databases such as ECOCROP http ecocrop fao org also provide information on this for some of the species catalogued Leaf to Aboveground biomass ratio The user is asked to provide this value for forbs shrubs and trees This value can be measured or estimated by dividing the mass of leaves on the plant kg by the mass of total aboveground biomass kg Grasses and grass like plants are considered to be all leaf for this entry so the value would be 1 Stem Area Index Stem area index LAISAI is ratio of the total area of all woody stems on a plant to the area of ground covered by the plant Data for this parameter can be collected in much the same way as leaf area index described above It is generally not collected during field surveys and published values for this in the literature are rare A default value of 10 of leaf area index LAI defined above can be used in situations where you cannot measure this p
73. oil per unit volume of soil dry 2 2 Soil Water Calculations wilting Volumetric soil water content at saturation field capacity and agronomic point SWox SW3 and SW15 and the saturated hydraulic conductivity SHC of each soil layer k are obtained from user entered values as indicated above Since 50 bar soil moisture estimates are generally unavailable that value is estimated by extrapolating from soil moisture relationships between field capacity and agronomic wilting point to an assumed rangeland wilting point of 50 bar SWok SW3k SW15k beta AWTk AWT k water content Campbell 1974 as follows Saturated volumetric cm cm soil water content 0 bar Field capacity volumetric soil water content 1 3 bar Agronomic permanent wilting point volumetric water content 15 bar 1 653212514 log SW3 v SWox log SW15 SWOx SPUR equation 3 air entry water tension for soil layer k 340 SW3 SWo 54 SPUR source code subroutine SOILC Renard et al 1987 SW50x Permanent wilt point soil water content for rangelands 50 bar is assumed since range plants can grow at soil moisture tensions below the 15 bar agronomic limit Suen SWo0x 51 000 AWT p assuming 1020 cm bar and SW0 SW3 and SW50 are in volumetric units cm cm SPUR source code subroutine USER Renard et al 1987 The upper limit of soil water storage capacity UL and water storage cm of wate
74. on Esp Potential soil evaporation cm LAI Leaf area cm of leaf area per unit area of land surface cm Esp ETp e 0 4 LAI WEPP equation 7 2 5 56 2 4 3 Soil evaporation TLR Days since last rain from day i SP Snow pack cm on day i Esa Actual soil evaporation cm on day i Esa Esp sqrt TLRi such that Esa gt 0 ERHYM II equation 15 2 4 4 Potential Transpiration Leaf area index LAI has a profound effect on the amount of water taken up by plant roots and transpired into the atmosphere As a plant community or monoculture establishes multiple layers of leaves above the ground surface the ability to shade the ground and reduce evaporation from the soil surface increases Thus a threshold is reached at approximately LAI 3 such that potential plant transpiration in plant communities of LAI 3 is equal to potential evapotranspiration In PHYGROW communities with LAI s less than 3 are potentially capable of transpiring at lower rates than potential evapotranspiration in proportion to their LAI as follows ETp Potential evapotranspiration on day i cm Tppi Potential plant transpiration cm When LAI lt 3 Tpp ETp LAI 3 0 EPIC equation 2 56 or WEPP equation 7 2 10 or SPUR equation 27 else Tppi ETpi EPIC equation 2 57 or WEPP equation 7 2 10 or SPUR equation 27 2 4 5 Percolation This element was not included in ERHYM II and is taken from SPUR Wight and Skiles 1987
75. onsumed by grazers from plant x on day i DLT Dormant Leaf Turnover Rate for species x GTD Green to Dead rate for species x CL Accumulated green biomass for species x up to day i If T is below freezing SGT CL Freeze kills all leafs Else if plant is dormant SGT CL DLT Else SGT j CL GTDx DCH Dry Matter to Radiation Conversion Ratio SNF Site Nutrient Factor for plant x input PEG The Physical Effect of Grazing on plant x on day i CTG Consumption to Growth ratio on day i of plant x If plant is in Rapid Growth and CTG gt 10 Then PEG 0 92 Else PEG 1 0 008 CTG i If plant is in Declining Growth and CTG gt 25 Then PEG 0 8 Else PEG 1 0 008 CTGx If plant is in Quiescence and CTG gt 15 Then PEG 0 88 Else PEG 1 0 008 CTG If there are no grazers on day i then NGxi DCR 20 PAR WSj TSi SNF Else NGxi DCR PEG 20 PAR WSi TS SNF LTx Leaf Turnover for species x on day i LTR Leaf Turnover Rate for species x as input SD Total Stocking Density on day i CDA Accumulated Dead Leaf of species x up to day i 71 If grazers are present a 0 29950315 b 7 2210757 c 1 681793 l y a 1 b e Thesis of Brown Leite Fernandes at TAMU LTR LTR y If plant is in Rapid Growth LT CDL 1 5 LTR Else LT CDL 0 7 LTRx 3 5 Wood Growth and Turnover WG Wood Growth for
76. ormed on machines that do not have access to the Internet J autosysTem C filelib ACN BELL 2002 Gas E Nutbal addresses carpay E grant proposal EI NUTBA Adobe cheerleading C History C person africa Church letters picture AGED610 CI cme C Lorena C Portfol AGED611 EI CORYELL 2002 E Lorena Scouting E propos AGED640 CountySoils Cl map stuff I REICHI AGED685 Downloads E My eBooks CIRLEM6 AGED689 Elaine My Music E Secret AGED690 FantasyFootball el My Pictures E Securit File name Phygrow 2003 09 26 DI Application DI Cancel My Network ES Save as type 5 The setup file is called Phygrow 2003 09 26 exe When the file begins downloading you will see a status box appear like the one below which indicates the progress of the download Depending on your connection speed this can take from 1 5 minutes Make sure the Close this dialog box when down process oad completes option is not checked during the download Afe E 18 of Phygrow 2003 09 26 exe Completed Saving Phygrow 200 C Docume Phygrow 2003 09 26 exe 270 KB Sec Tl Close this dialog box when download completes Open Open Folder cance Tranfter rate 6 If you do not see a Download Complete window appear when the progress bar reaches 100 you must go to Windows Explorer or the My Computer icon locate the folder where you saved the installation and double click the Phygrow 2003 09 26 exe file to beg
77. ote access to agricultural research models Funded B Texas CEET Experiment Station Developed By The Center for Natural Resource Information Technology Ranching Systems esi Integrated Information Management Labratory Impact Assessment Group 1 CHE Mode Parameterize Status Results To begin a model run click on the Models tab at the bottom of the interface GER File v1 8 1 Fri Sep 26 2003 Help Host add localhost Phygrow CSV localhost Phygrow CSV Parts This is the screen where you name the model run and select the host where the model is to be executed PHYGROW allows connections to remote servers configured for model runs This function speeds up processing times and does not tie up resources on the client machine In this example we will assume the client does not have access to a server running the Phygrow backend process and run the model on the localhost First give the model run a name by placing the cursor to the right of the Phygrow CSV button and typing in a descriptor for the site amp Standalone Phygrow File v1 8 1 Fri Sep 26 2003 Host Ada localhost Phygrow CSV Brazil localhost Phygrow CSV Parts Added localhost E Models Then click the Phygrow CSV button This means that you are going to run PHYGROW with a parameter file of comma separated values and that all of the informa
78. ow Preferences are input as single letter designations as follows e P Preferred A preferred species is one that is selected in greater proportion in the diet than found in the plant community composition by weight regardless of current standing crop kg ha Animals seek 73 these species D Desirable A desirable species is selected based on the associated composition of preferred and undesirable species Animals accept these species by increasing consumption as composition of undesirables increases and decreasing consumption as composition of preferred species increases U Undesirable An undesirable species is selected in less proportion in the diet than found in the plant community composition by weight regardless of the current standing crop kg ha Animals avoid these species where possible E Emergency A species classified as emergency is considered a food item only when preferred desirable and undesirable species have been depleted Normally these species are species that have high anti quality chemicals or physical restrictions to consumption T Toxic A species classified as toxic will result in death or cause severe aversive conditioning response of the animal if eaten resulting in removal of the behavior in the gene pool if death occurs or psychological aversion to future consumption They will eat the species only if starving and no other forage is available N Non Co
79. phere Data for this parameter can be gathered in much the same way as that described for leaf material above Forage production in PHGYROW is relatively insensitive to this parameter as it mostly affects stemflow in the water balance code variable name stemStore 22 units grams water gram dry matter range 0 to 5 precision 0 001 Fraction of Water Transferred from Leaf to Stem This parameter defines the amount of water captured by the leaf canopy in excess of leaf storage capacity that will either drip off the end of the leaf or flow down the woody stem Itis the fraction of the total amount of water that travels from leaf to woody stem Published values are rare for this in the literature and it is hard to determine during field surveys A proxy value for this can be determined from water balance studies where stemflow has been collected One can take total stemflow and divide it by total incident precipitation to derive a value for this parameter Forage production in PHGYROW is relatively insensitive to this parameter as it mostly affects stemflow in the water balance code variable name stemDrip units proportion range 0 to 1 precision 0 01 Stem Turnover This variable is the percent wood biomass that turns over to the litter pool each day expressed as a percent of current standing wood biomass In field situations this can be determined from stem mapping or stem marking techniques followed over a period of time It can
80. poration on day i in soil layer k 1 calculated from equation in Evapotranspiration 55 SMi SMi Snow melt on day i 0 07087 AT if AT lt 0 C then SP SPi4 Pi Esai if AT gt 0 C then SP SPi4 SMi Esaik 2 4 Infiltration Potential evapotranspiration for rangelands on day i ETrp i is determined from potential evaporation Esp i and potential transpiration Tpp i on day i Actual evaporation and transpiration on day i Ea and Taj respectively are determined from Esp and Tppi Evaporation is a modification of that presented in ERYHM II Wight 1987a ERHYM Il Actual evaporation Esa j on the ith day is calculated from potential evaporation on day i Esp and the number of days since the last precipitation was received TLR 2 4 1 Potential evapotranspiration A Albedo 0 0 1 As Gol albedo 0 0 1 Cls Soil cover index 0 1 0 C Sum of aboveground biomass plus plant residue kg ha CI d e 0 000029 C WEPP equation 7 2 3 A 0 23 1 0 Cls As 24 WEPP equation 7 2 2 with soil albedo set to constant of 24 VP Slope of saturated vapor pressure curve at mean air temperature AT Average daily air temperature K VP l 5304 AT 7 A e 21 25 5304 ATi WEPP equation 7 2 4 SR Solar radiation Langleys ETp Potential evapotranspiration on day i cm Etpi 0 128 SR 1 0 A 58 3 VP VP i 0 68 WEPP equation 7 2 1 2 4 2 Potential evaporati
81. r per layer thickness at field capacity FC are estimated for each soil layer using volumetric cm cm soil water content at saturation SWO field capacity SW3 and rangeland permanent wilting point SW50 in addition to the rock factor and soil layer thickness according to the following equations 2 2 1 Upper limit of soil water capacity RF Percent rock factor in soil layer k SLT Thickness cm of soil layer k UL Available upper water storage content cm per layer of soil layer k UL SWok SW50 1 RF SLT SPUR equation 4 2 2 2 Field capacity FC Available field capacity storage cm per layer of soil layer k FC SW3x SW50 1 RF SLT SPUR equation 5 2 3 Precipitation and Snow pack Actual precipitation the build up and decline of the snow pack and the effective precipitation on day i Pi SP and EP d leading to ponding infiltration and runoff are related as follows The initial snow pack value is determined by the first day on which ATi lt 0 C and a rainfall event occurs The value is accumulated throughout the simulation using the following equations which accumulate additional snow as related to precipitation and temperature and subtract snow melt and soil evaporation ATi Average air temperature on day i direct input from weather parameterization SP Snow pack on day i P Esai Rainfall on day i direct input from weather parameterization Actual soil eva
82. rent year s growth on species x on day i CDL Accumulated amount of dead current year s growth on species x on day i LF yi Ratio of Live current year s growth to the total amount of current year s growth LE CLy CLy CDA CHU Accumulated heat units for species x on day i HUS Heat Units required for species x to seed HUD Heat Unit level at which plant x dies CR Consumption Ratio of species x on day i CRx 100 GLC PG 1 FACT Factor by which growth is affected by the defoliation by grazers If plant x is in rapid growth and CR gt 10 then FACT 0 92 Else FACT 1 0 008 CR If plant x is in declining growth and CR gt 25 then FACT 0 8 Else FACT 1 0 008 CR 69 If plant x is in Quiescence and CR gt 15 then FACT 88 Else FACT 1 0 008 CR If GLC 0 Then If HUS lt CHU lt HUD Then EG Aa LAI EA Else EGLAI is equal to the lesser of LAI LFxi and MinL Else EGLAI LAI LF i FACT 3 4 Leaf Growth Death and Turnover 70 SGL Standing Green biomass for plant x on day i SGT Standing Green biomass for plant x that turned into standing dead material on day i SR Solar Radiation on day i weather input PAR Photosynthetically Available Radiation on day i PAR 0 02092 SR 1 e 285 Plan source WEPP SPUR NG New Green growth for plant x on day i Ti Temperature on day i GLC Green biomass c
83. rily determined by the level of photosynthetically active radiation the maximum net assimilation rate of photosynthesis the efficiency of conversion of carbohydrate into plant constituents and the maintenance respiration rate van Heemst 1986 Thus the parameter for maximum potential production has both genetic and environmental components Century 2003 A Poisson function is used in Phygrow to determine temperature stress on plant growth using the following set of equations Ts Suppression temperature C above which no plant growth occurs 67 Tp To Ta Ta Ti T Ge RTC LTC TSi If Ta lt Tp Else RELATIVE PRODUCTION KO Ka Ne Base temperature C above which plant growth occurs Optimum temperature C for species specific maximum growth Average daily temperature C Tmax Tmin 2 Ratio of where the present days avg temperature is in relation to when the poisson function is at no growth or U and at 1 or optimum growth Ts Ta Ts To Century 2003 Right curve shape for parameterization of a Poisson Density Function curve to simulate temperature effect on growth Valid range 0 0 10 0 Right curve shape for parameterization of a Poisson Density Function curve to simulate temperature effect on growth Valid range 10 0 to 5 0 multiplier for exponent in final equation 1 TI Century 2003 temperature stress constraint on growth on day i for species j
84. row CSV Brazil 5 Text Files Delete 300 Decade 1 Status Results B Standalone Phygrow I x File v1 8 1 Fri Sep 26 2003 Help Model Status File Edit View Accumulate SV Brazil Total Forage Available Accumulate from Start Done Phygrow CSV Brazil g Average a Average from Start E 2 Constrain Data View Subset Data Statistics Text Files 300 Decade 1 Decade 2 Decade 3 33 Select the default Julian Mean in the Average Over a Period window Average Over A Period e Julian Mean _ Julian Mode Julian Min _ Julian Max Julian Bottom Quartile _ Julian Top Quartile Click the OK button The following graph will appear Standalone Phygrow iol x File v1 8 1 Fri Sep 26 2003 Help Model Status File Edit View Julian Functions on Phygrow CSV Brazil Total Forage Available Julian Mean Cow Done Phygrow CSV Brazil j Text Files om _StatusResults Note that in the third quarter total available forage was very low over the 23 year average This would be a time when feeding supplements and alternate management plans should have been explored in the region based on low forage availability within the modeled plant community To further explore this point a user may want to ta
85. rs for Cover Description Hydrologic Soil Group Cover type Hydrologic Condition Cems El a Por Herbaceous mixture of grass weeds and low Kc growing brush with brush the minor element a ee Good JL P PE Aspen mountain mahogany bitter brush Poor E bapo maple and other brush Fair d wepe EEGEN Poor f pepe Pinyon juniper pinyon juniper or both grass j i inde ELSE R Poor IC E po ES Sagebrush with grass understory Far Su B bn Desert shrub major plants include saltbush Por BI D B greasewood creosotebush blackbrush Fair P b E Be bursage palo verde mesquite and cactus Good CUBS e fo b4 Average runoff condition and la 0 2S For range in humid regions use Table 1 Poor lt 30 ground cover litter grass and brush overstory Fair 30 to 70 ground cover Good gt 70 ground cover Curve numbers for group A have been developed only for desert shrub 41 Table 3 Soil textural classes amp related saturated hydraulic conductivity classes from http www mo10 nrcs usda gov mo1 Oguides properties html Texture Textural Class Ksat Class Ksat Rate cm hr gt 50 8 Coarse sand Very rapid Coarse Sands Rapid Loamy sands 15 24 50 8 Sandy loam Moderately Moderately 5 07 15 24 Fine Sandy Loam coarse Rapid Very fine sandy loam Moderate 1 52 5 07 Loam Silt loam Silt Medium 0 50 1 52 Clay loam Sandy clay loam Silty clay lo
86. s also linked to a fuel moisture fire behavior model that uses with NOAA s 9 day weather forecasting system 2 5 x 2 5 km gridded hourly d weather data There have been numerous studies conducted using the PHYGROW model including bioeconomic studies for climate change Butt et al 2003 brush control investment and hydrology policy analysis Lemberg B et al 2002 mixed species grazing weather forecasting impacts on rancher decisions and impact studies on intensification of agriculture CNRIT 2003 Intensive validation studies have been conducted in an array of ecosystems including the dry and wet tropics temperate grassland shrublands and desert regions of the US and semi tropic regions of the southern US PHYGROW has been explicitly used for the assessment of forage losses in terms of computation of percent deviation in normal forage standing crop and percentile ranking of current standing crop relative to historical standing crops for the purposes determining the feasibility of forage insurance programs for the United States The model has been successfully used in automated forage monitoring programs which cover large regions with streaming weather data for over 5 years Given the modular design of PHYGROW and platform neutrality the system can be implemented a large number of computational configurations PHYGROW is comprised of four major modules including soils plant communities species attributes grazer stocking rules plant prefer
87. scientific studies where litterfall and leaf turnover have been measured In PHYGROW this is a sensitive variable Values that are too high can lead to too much litter biomass and values that are too low can lead to a build up of standing dead biomass Default values of 3 are suggested for grasses and forbs 1 75 for deciduous woody species and a value of 1 for evergreen species code variable name IturnOver units Percent range 0 to 100 precision 0 01 Heat Unit Accumulation at Seed The growth and development of annual plants in PHYGROW is regulated by accumulated heat units Heat units are the positive increments of differences between the base temperature and 20 average daily temperature of the simulation The Heat Unit Accumulation at Seed defines the number of heat units that need be accumulated before the species can set seed This can be determined from published literature for the species in question or determined from field measurements For species that are not annuals this value should be set to 0 code variable name huiSeed units Celsius range 0 to 2000 precision 10 Heat Unit Accumulation at Death The Heat Unit Accumulation at Death defines the number of heat units that need be accumulated before the species will die under water non limiting conditions This can be determined from published literature for the species in question or determined from field measurements For species that are not annuals
88. shrubs would range from 0 8 to 1 3 Evergreens 0 9 or less Right Side of Temperature use a default of 1 5 Curve Leaf Turnover Start with a value of 3 and look at your green standing biomass to dead standing biomass If dead standing biomass exceeds green biomass then the number needs to be higher If dead biomass is very low compared to green biomass you may want to decrease this number Heat Unit Accum at Seed Leave as zero initially Heat Unit Accum at Death Leave as zero initially Rooting Depth Specific to species Canopy Height Specific to species Max Above Ground Biomass Specific to species Leaf Above Ground Biomass This should be 1 for grasses 1 for non woody forbs 0 35 to 0 85 for woody Ratio forbs 0 05 to 35 for shrubs and trees A value of 0 15 for trees and shrubs is a good one to start with and then the standing biomass can be examined If the species is growing out of control increase this number in increments of 0 1 until the growth stabilizes If it is declining quickly decrease this number in increments of 0 1 Only a few tree species have values of 0 5 so try to avoid going this low SAI Stem Area Index This does not directly affect plant growth so it is not a critical number A good default is 0 1 Leaf Water Store Capacity This affects stemflow and throughfall Values for many African species can be obtained from the Livestock Early Warning System project http cnrit tamu edu lews Dr Jerry Stuth at Texas
89. soil remains saturated The water balance is sensitive to choice of this variable impacting the amount of evaporation and opportunity for deep drainage if adequate mechanism to percolate water is established e g crack flow e Slope The percent of slope on average for the site should be reflected in this variable e Maximum and Minimum SCS Curve Number The USDA NRCS has a standard curve number or abstraction coefficient to reflect infiltration potential of a site depending on land use and cover type PHYGROW allows entry of an upper and lower range for a given soil Runoff is very sensitive to the choice of this range along with the choice of the hydrologic coefficient of the species occupying the site e Bottom Type 0 Permeable 1 Impermeable rock 2 Water Table o 0 makes a deep duplicate layer where roots can go It creates a 10000cm deep layer for water stress purposes only it does not contribute to the water balance and is assumed to track with the soil moisture on the last layer o 1 won t let any water out of the bottom of the parameterized layers or let any roots grow beyond the bottom of the parameterized layers Water bounces and roots can t go o 2 isawater table where roots can go and there is lots of water by creating a 10000cm deep layer for water stress purposes only it does not contribute to the water balance and it is assumed to be fully saturated all the time 53 The following parameters are requ
90. species x on day i WT Wood Turnover for species x on day i WTR Wood Turnover rate for species x as input LS seat Support ability of current wood on plant x on day i LR Leaf to Wood Ratio for plant species x as input CS Accumulated wood for species x up to day i LSxi CS LR 1 LRx If NGyi lt La Then WG 0 Else WG NG LSx 1 LR WTyi CSx WTRx Chapter 4 Grazer Simulation 4 1 Grazer Parameterization The grazing model within PHYGROW requires specific knowledge of general ranching practices in the area being modeled The first parameter that must be known is the planning horizon e Planning Horizon This is a number which specifies the number of days which constitute a full planning cycle for de stock restock rules This is typically 365 or one year meaning the de stocking and restocking decisions are made based on a one year cycle Each grazer in PHYGROW has a set of specific parameters that determine how the grazer will interact with the plant communities in the model There can be an unlimited number of unique grazers specified and each one must contain values for all the following parameters 72 Stocking Rules Ranching practices must be specified so that PHYGROW knows how many animals will be present in a specific field at any given time These rules are as follows e Decision Day The day of the planning cycle upon which stocking rules change begin e Mini
91. t developed by Texas A amp M University When run in automated batch mode an interface is not required PHYGROW is one of the foundation technologies of a 4 country livestock early warning system in East Africa which has been on going since 1998 with 3 more countries to come on line in 2004 European Union s METEOSAT weather satellite temperature data coupled with NOAA s rainfall estimator RFE 2 0 Xie 51 and Arkin 1998 provide primary driving variables for PHYGROW in an automated system PHYGROW was also used as the foundation analytical tool for three pilot regions in the Texas Livestock Early Warning System that was initiated in 2002 For the Texas system NOAA s national degree weather system 0 25 degree nation grid Higgins et al 2000 is used to drive PHYGROW in a fully automated system Just recently PHYGROW was selected as the foundation technology for the USAID sponsored Mongolian GOBI 2 initiative s national forage monitoring system for livestock producers in that country For the Mongolian system The NOAA CMORPH data http www cpc ncep noaa gov products janowiak cmorph_description html which combines microwave infrared sensor satellite technology is used to produce a 0 1 degree gridded weather data set that is used to drive the PHYGROW model f A new fire risk assessment system is currently under development at Texas A amp M University using PHYGROW This system is driven by the NOAA daily weather described above PHYGROW i
92. tension of 1 10 bar or 1 3 bar Itis commonly measured during field soil surveys or determined from field collected soil peds sent to a laboratory It is commonly reported in attribute data published by NRCS for each layer in a soil series In the absence of field collected or laboratory data Appendix A Table 4 can be used as a guideline The Soil Water Characteristics Hydrologic Properties Calculator developed by Washington State University http Awww bsyse wsu edu saxton soilwater is also helpful in determining this parameter when laboratory data are not available In the model this parameter influences crack flow and water holding capacity code variable name bulkD units g cm range 0to5 precision 0 001 Volumetric Water Content at 0 Bar This parameter represents the total possible water content of the soil when the soil is completely saturated In PHYGROW it represents the volume of water in the soil at saturation i e all pore spaces are full without rocks included During the calculation of the available water in PHYGROW the rock fragments rock factor above are included into the calculation so that their effect on the water availability is accounted for Volumetric water content is generally measured in the laboratory from samples collected during a field soil survey In the absence of laboratory data Appendix Table 5 can be used for a guideline if texture is known The Soil Water Characteristics Hydrologic Properties Ca
93. tically active radiation PAR intercepted by the canopy It is commonly determined in plant growth experiments and is a parameter in many plant growth models Data for this variable can be gathered from field experiments however specialized equipment is needed to measure PAR and data must be collected over a period of time to develop the relationship Published values can be found in the literature for many forage and rangeland species Appendix Table 6 provides Dry Matter to Radiation ratios that can be used for these same or similar species in PHYGROW PHYGROW is sensitive to this parameter as is controls the amount of biomass that a species can grow each day As biomass accumulates so does leaf area index and wood in woody plants only thus further increasing the potential for growth code variable name BioCvt units grams Megajoule range 0 to 6 precision 0 01 Suppression Temperature This parameter defines the maximum temperature at which a species can grow Beyond this temperature plant growth will cease in the model Information on this parameter can be gathered from published literature for the species or from online databases such as ECOCROP http ecocrop fao org If published values are not available Appendix A table 7 can be used as a general reference The plant growth curve in PHYGROW is sensitive to this number as it defines the end point of the growth curve code variable name supTemp units Celsius range
94. tics Text Files M A ml d Wi AM M i pet aN Nin Le Ubeade 1 EE SE 3 Status Results N T ba a iebsig Follow the procedure in section V for obtaining the Julian Mean function on the graph shown above After the Julian Mean graph appears go to File then save data on the graph window Standalone Phygrow y Yg Te ET Die v1 8 1 Fri Sep 26 2003 Help Edit View Print Graph Julian Functions on Phygrow CSV Brazil Leaf Growth Julian Mean Axonopus purpusii Done Tiea CSV Brazil Julian Mean Panicum laxum Julian Mean Andropogon spi j Julian Mean Melochia simple Julian Mean Richardia gra In a Julian Mean Reima och pa Hrasiliensis Julian Meah Walterig albicans Graphs Text Files Mala ir Geen SE ge te Lass S en Y 1Q 2 YA a3 Y1Q 4 Y 2Q 1 Status Results The default location for saving data within Phygrow is in the installation directory or C Program Files phygrow Give the data set a name ending with the csv extension as shown below 37 save xi cook mem OE TN Campo_validation_Brazil TN LeafGrowth TN totalLasr2Years TN uninstall File Name LeafGrowth csv Files of Type All Files v sca Then click the Save button Open Excel or any other spreadsheet or statistical package capable of reading comma separated value files and navigate to the C Program Fil
95. tion is saved in a single file The Phygrow CSV Parts button tells the model that you are going to perform a run with separate comma separated value files for the soil plant grazer and weather sections of the site The default installation example video and this user s guide reviews only the steps required to run the model with a single file Using the Phygrow CSV Parts function is an advanced feature of the system and is beyond the scope of this manual Standalone Phygrow _ 5 xj File v1 8 1 Fri Sep 26 2003 Help 10 Then go to Fjle within the Phygrow CSV window and select the word Load B Standalone hygrow v1 8 1 Fri Sep 26 2003 D E Surface SI Max SCS Min SCS C Bottom Type SoilName Soil Depth Ges j RockFactor Saturated Bulk Density Volumetric Volumetric Volumetric Dry Bulk D T Plant Plant Name Initial Stan Percentag Gite Nutrie 7 f l t This will bring up the Open dialog box like the one shown below SS Open x ant ien I TN Cambo_validation_Brazil TN totalL ysr2Years G uninstall File Name Files of Type All Files a Open Cancel Double click on the data folder to open it The default installation contains a parameter f
96. tual values from weather station radiometers can be directly used in the weather file code variable name rad units Langely range n a precision n a 29 IV Viewing results and manipulating graphs The Standalone Phygrow window will automatically move to the Status Results tab where you will see a man running on the buttons on the right When the model run is complete you will see a Done message appear on the button on the left hand side of the screen like the one below Standalone Phygrow 10 x File v1 8 1 Fri Sep 26 2003 Help On a Windows operating system the run can take anywhere from 10 20 minutes This is a function of processor speed disk input output capability number of plants number of grazer number of soil layers and length of the weather file The PHYGROW engine internally sets its run priority within the machine to allocate 100 of CPU resources to the application Consequently when a simulation is being run the operating system slows down It is recommended when running PHYGROW on a local host that other applications be closed when the actual simulation is being performed If the run was being sent to a model server the model server would accept the parameter file and perform all of the calculations using the server processor and would not tie up the CPU on the client machine This cuts the model time in half or more in most cases and is advised when doing a large number or ru
97. um canopy diameter expected by the plant at peak expression under optimal conditions at the site being simulated It can be determined from direct measurement or published literature code variable name ccrowndia units cm range 0 to 50000 precision 1 0 Height at Canopy Start This is the height at the start of the canopy ground to start of canopy extension expected by the species at peak expression under optimal conditions at the site being simulated It can be determined from direct measurement or published literature code variable name htcanopyst units cm range 0 to 50000 precision 1 0 Height at Beginning of Canopy Curvature This is the height at where the canopy begins curvature ground to start of canopy curvature expected by the species at peak expression under optimal conditions at the site being simulated It can be determined from direct measurement or published literature code variable name htcurcanopy units cm range 0 to 50000 precision 1 0 Maximum Leaf Litter Decomposition Rate This parameter defines the optimal leaf litter decomposition rate as a daily percentage of weight loss to the total litter biomass This data can be gathered during field experiments using litter bag techniques and values for many species can be derived from litter decomposition studies published in the literature code variable name leafdcomrate units percent range 0 to 100 precision 0 01 Max
98. ypically reflected as a head ha value and is normally found by listening sessions with producers extension bulletins or research articles published for the area in question This value is typically reflected as a proportion of the total herd being simulated that is grazing during that simulation day It is normally found by listening sessions with producers extension bulletins or research articles published for the area in question There are numerous texts available that contain this information along with a large national database in the Grazingland Animal Nutrition Lab GANLAB at Texas A amp M University that was compiled by MLRA for USDA NRCS and maintained by GANLAB National Research Council publications provide typical values for cattle sheep goats and horses The Journal of Animal Science Journal of Range Management Livestock Production Science and many other animal production journals provide a multitude of values that can be used The NUTBAL software distributed by GANLAB can be used to derive this value as well The definition and assignment of preference is in the USDA NRCS National Rangeland Handbook The Ranching System Group at Texas A amp M University designed the system for USDA NRCS 49 Literature Lane L J and J J Stone 1983 Water balance calculations water efficiency and aboveground net production Hydrol and Water Resour in Ariz and the Southwest 13 27 34 Rawls W J D L Brakensiek and K E Saxton

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