1. Assignment : CERES-Maize Model By:
SWE 604
Assignment :
CERES-Maize Model
By:

2. INTRODUCTION
Many soil, climatic, plant, and management factors affect the way a crop will respond to irrigation, fertilizer, and other management practices.
Computer simulation models of the soil/crop/atmosphere system can make a valuable contribution to both furthering our understanding of the processes determining crop responses and predicting crop performance in different areas.
User-oriented simulation models facilitate the task of optimizing crop and nutrient management and can be used to investigate environmental and sustainability issues of agro-ecosystems.
Advances in climate science, climate monitoring, computer technology and information technology are combining to create new opportinities for provision of information.
The crop simulation model involves the dynamic simulation of multitude of shoots of various ages and how these grow and develop.

3. Agro-Climatology of the Maize Crop
1- Maize is a warm-season annual crop. Suitable areas : where the average air temperature is from 20-30C for 4 consecutive months.
2- Planting of corn : Tave of air is approximately 10C and soil temp is near 13C. But optimal air and soil temperatures for germination are in the range of 20-25C.
3- Moisture stress during flowering causes greater yield reduction than temperature stress.
4- Greatest yield reduction due to water stress : during the silking stage. Because moisture stress postpones silking, pollen may be shed before silk appears. High temp. and high atm. moisture demand combined with low level of available soil moisture during tasseling and silking can cause reduction in yields an average of 5 percent per day.
5- Corn becomes extremely sensitive to any kind of stress during the tasseling and silking.
6- The rate of water use varies with the phenological state. It is from planting to 30cm height, 2.2 to 2.5 mm/day; at tasseling mm/day; reaching at kernel formation period, 5-6mm/day; and dropping at early maturity to 3.0-2mm/day respectively.

4. Phenological Stages of Maize
Tassel
Emergence
Tassel
Silk
Establishment
15-25 days
Vegetative
25-40 days
Flowering
15-20 days
Yield
Formation
35-45days
Milk
Ripeness
10-15days P1 P5
9 Stages taken into account by the CERES-Maize Model:
1)Sowing )End of Leaf Growth and 75% Silking
2)Germination )Begining of Effective Grain Filling Period
3)Seedling Emergence )End of Effective Filling Period
4)End of Juvenile )Physiological Maturity
5)Tassel Initiation

5. Soil-Plant-Water Relation
Water content of plant tissue is mostly dependent on the magnitude of suction or tension in the various plant parts and the soil matrix. Whenever plant water content falls below its level of full turgidity, plant water stress occurs. Water stress can also be explained by introducing the concept of a free energy gradient. Movement of water from soil through the plant and into the atmosphere results from a free energy gradient. Water moves into the plant by the existence of a free energy gradient between the root and the soil matrix. If the turgidity of a plant lowers, the solute concentration becomes higher and its free energy decreases.
Turgidity of a plant is determined by the transpiration rate. When the turgidity is decreased, the plant closes its stomata introducing a barrier to the loss of water. When the plant's leaf loses water, free energy for water in the leaf decreases relative to other parts of the plant, plant water content in the lower portion of the stem and root system moves to the point where the lower energy state exists. Under a non-turgid condition, water is transpired faster than it is taken up by the roots.

6. Description of Simulation Model
1- A model is a simplified representation of a system, a limited part of reality with well defined boundaries.
2- Simulation can also be described as a representation of behaviour of plant growth by a computer program.
3- Additionally, system simulation is to use dynamic modelling as a technique of dealing with plant processes.
4- A dynamic model is a model with time-indexable trajectory or structural behaviour. A set of mathematical equations describes a system in simulation studies. The status of the system is defined at every instant by a complement of state variables and its environment by a set of driving variables. simultaneous solution of this set of equations is carried out by the computer at every time step of the simulation process.
5- Agricultural researchers have realized that there is no way to fully describe the plant and its interactions with the environment in exact mathematical terms.

7. Basic Physiological Principles of Simulation Models
1- Simulation has been used to study individual plant processes such as photosynthesis, stomatal action, and water uptake by roots.
2- Simulation of photosynthesis involves simulation of the energy received, absorbed and reflected, the temperature of system is based on an energy balance, the storage and transport of the photosynthetic product.
3- Stomatal action can also be simulated as a series of the interrelated processes of water transport to and from epidermis, water transport to and from guard cells, ion transport to and from the guard cells, and biochemical control processes occuring inside the guard cells.
4- Simulation of water uptake by roots involves the simulation of movement of water in the soil, the uptake by the root, and the transport through the plant system to the leaves.
Since the supply of water to the leaf has an effect on stomatal action, one can see that all three of these examples exhibit interactions and feedback.

8. CERES MAIZE MODEL
It is a USER OREINTED, daily incrementing Simulation model of maize Growth ,Development and Yield ( Jones and Kiniry1986)
Two Versions
1-Standard Version
Simulates the effect of Genotype ,Weather and Soil parameters on maize growth ,yield
2- Nitrogen Version
Simulates soil and plant nitrogen dynamics and their effect on the crop

9. Flow Chart of CERES-MAIZE Model showing the Interactions between the Various Model Components

10. The model takes into account the following processes:
Phenological development as affected by Genetics and weather
Extension growth of leaves ,stems ,roots
Biomass accumulation and partitioning.
Soil water balance and water used by the crop.
Soil nitrogen uptake by the crops and partitioning among plant parts.

11. The CERES-Maize model is designed to simulate the effects of :
Cultivars
Planting density
Weather
Soil Water
Nitrogen
On crop growth and development

12. Limitations of the model
The following events are not considered by the model:
The effect of weeds
Insect and diseases
Nutrient deficiencies and toxicities
Extreme weather conditions like floods and wind damage

13. Water Balance Method PI + SW ± RO - D - ET = 0 where,
The CERES-Maize model uses the hydrological approach for estimating the soil-water balance. It calculates redistribution of water due to irrigation and rainfall, drainage, potential evapotranspiration, soil evaporation, and plant transpiration. Run-off is calculated by the U.S. Soil Conservation Service (SCS) curve number method. The water balance subroutine also accounts for the movement of upward flow and downward flow of soil water in the top 5 soil layers. Volumetric soil-water changes according to the movement of water in soil layers. The hydrologic or water balance method was stated by Rosenberg, et al., (1983):
PI + SW ± RO - D - ET = 0
where,
PI = the rainfall and/or irrigation.
SW = the change in content of water stored in the soil.
RO = the run-off.
D = the percolation or deep drainage.
ET = the evapotranspiration.

14. Review of Some INPUT Definitions-I
SOIL-WATER STATUS, SW(L):
It can be defined as the amount of plant available water within the soil-water system. Hillel (1972) noted that the soil-water system is an active system with a capacity to hold water and channels for water transport. Actual soil-water content for soil layer L is measured as volume fraction (cm³water/cm³ soil).
DRAINED UPPER LIMIT SOIL-WATER (or FIELD CAPACITY), DUL(L):
The soil-water content after the drainage of the gravitational water is called the "field capacity". It is also known as the upper limit of the water held in the soil. One third bar tension has been commonly used to define the moisture percentage to estimate the field capacity. Drained upper limit soil-water content for soil layer L is measured as volume fraction (cm³water/cm³ soil).
LOWER LIMIT SOIL WATER(or WILTING POINT), LL(L):
The wilting point is the soil-water content at which plants permanently wilt, (i.e can not recover even after water is added by natural causes or by irrigation). At the wilting point (or the permanent wilting percentage), the water is held in the soil against an extraction pressure of approximately 15 bars.The water held by the soil between the field capacity and the wilting point is considered as available or extractable water. Lower limit soil-water content for soil layer L is measured as volume fraction (cm³ water/cm³ soil).

15. Review of Some INPUT Definitions-II
GROWING DEGREE DAYS, P1, P5:
The concept of degree days is accepted as a means to relate plant growth, development and maturity to temperature. This concept assumes that each plant has its own particular base or treshold temperature below which growth does not occur. The amount of heat accumulated during the day, as obtained by substracting the plant’s base temperature from the mean temperature for the day, is termed the degree day accumulation. Degree-days may be accumulated for a week, for a month, or until plant maturity is reached. Average Base Temperature for corn is between 8 and 10C.
Growing Degree Days=((Max.Temp+Min.Temp)/2)-BaseTemp
DRAINAGE RATE COEFFICIENT, SWCON:
SWCON=(Porosity-DUL) / Porosity
where, porosity is defined as
Porosity= 1-(Bulk Density/2.65) .
Bulk density is measured as g/cm³.

16. Review of Some INPUT Definitions-III
SOIL ALBEDO, SALB:
If a radiation is allowed to fall simultaneously upon a black and a white surface, tha black surface absorbs most of the radiation, while the white surface reflects most of the radiation. Albedo is the fraction of the total incident solar radiation that is reflected back into space without absorption. The emissivity of any real substance is between 0 and 1, and may vary with wavelength. Albedo of blackbody is 0, but a new-fallen snow is 0.9. Blackbody radiation is isotropic; i.e, the radiance is independent of direction. Albedo can be described in terms of organic matter(%) and soil texture of uppermost layer.
Organic Matter Soil Texture Albedo
> all
all
all
< silt loam, silt
< fine sandy loam
< fine sand, very fine sand

17. Review of Some INPUT Definitions-IV
ROOT DISTRIBUTION WEIGHTING FACTOR, WR(L):
WR(L)=exp(-4 * ZL /200) where ZL is depth(cm) to centre of the soil layer.
PHOTOPERIOD SENSITIVITY COEFFICIENT, P2:
The relative sensitivity of Maize to daylength(hour) at which developmental progress occurs, is observed and recorded. P2 is the slope of the curve with x axis showing day length(hour) from 11 to 19 and y axis showing relative sensitivity from 0 to 1.
RUNOFF CURVE NUMBER, CN2:
Runoff is the water which is not absorbed by the soil and flows to lower ground, eventually draining into a stream, river. Example of runoff curve numbers from National Engineering Handbook, USDA, Hydrology Section 4,1972:
LAND USE TREATMENT HYDROLOGIC HYDROLOGIC SOIL GROUP
(PRACTICE) CONDITION A B C D
Row Crops Straight row poor
good
Terraced poor
good
Where A:high permeability, B:Moderate permeability, C:Slow permeability and D:Poorly drained;clay soils with high swelling potential; permanent high water table.

18. Review of Some Definitions of Simulated Variables
POTENTIAL (maximal) EVAPOTRANSPIRATION, EO:
EO is a measure of the ability of the atmosphere to remove water from the surface through the processes of evaporation and transpiration assuming no limitation on water supply. Actual evapotranspiration under non-water-stress condition defined as the maximal evapotranspiration (mm/day).
ACTUAL (reference) EVAPOTRANSPIRATION, ET:
ET is the amount of water that is actually removed from a surface due to the processes of evaporation and transpiration. Under water stress condition, water stress coefficient is introduced to account for the influence of water status in root zone upon evapotranspiration (mm/day). ET= Ks * EO where Ks is the water stress coefficient.
LEAF AREA INDEX, LAI:
The planimeter is the most accurate (0.76% relative mean square error per leaf) although not the quickest in its time req. (90seconds/corn leaf).The photometric is the quickest(35 seconds /corn leaf), but not so accurate (2.44% RMSE per leaf).
LAI=(m² LeafArea/m² FieldArea)

19. The CERES-Maize model uses 2 data input files
1st file : Parameter input file
2nd file : daily weather file
PARAMETER INPUT FILE
It is used to assign values to the variable that control the input execution and output.
Line 1: Title or name of the treatment.
Line 2: Details of input and output (DOS, plant population)
Line 3: Genetic information for the cultivar.
Line 4: Silking date, grain yield etc.
Line 5: General soil information for whole profile.

20. Soil Layer Information
Line 6 :Information regarding individual soil layers one line per layer
DLAYR: Thickness of the layer (cm)
LL : Lower limit of plant extractable water (cm/cm)
DUL : Drained upper limit (cm/cm)
SAT : Water content at saturation (cm/cm)
WR : Weighting factor for root distribution
SW : Initial water content

21. THE PARAMETER INPUT FILE (param1.txt)
The first five lines of this parameter file contain five types of values including switches, control numbers, management inputs, initial conditions, and field measured values.
The measured values are used to make comparison with values predicted by the model.
The switches control whether particular sections and subroutines of the model are accessed during the program run.
The frequency of the output is determined by the output control numbers.
Initialization values such as sowing date, initial plant population, and irrigation dates (and amounts) are done by management inputs.

22. Description of Values Used in the Parameter Input File of the CERES-Maize Model(param1.txt)
"MISSOURI, SW=DUL"
"B73 X MO17"
00.0

23. Description of Values Used in the Parameter Input File (param1
Description of Values Used in the Parameter Input File (param1.txt) (Continued-1)
Variable EXPLANATIONS VALUES USED
LINE
TITLE Title of treatment to be simulated MISSOURI, SW=DUL
LINE
ISOW Sowing date(day of year)
PLANTS Plant population(plants/m)
SDEPTH Sowing depth(cm)
LAT Latitude of location(degree)
KOUTWA Frequency in days of water balance output
KOUTGR Frequency in days of growth output
IIRR Switch or device number describing irrrigation
00:No irrigation is applied, 0 to 1:Irrigation is applied as specified by user,
>1:Irrigation is automatically applied.
INSOIL Indicator for initial soil water
0.00:set to lower limit(LL), :Set to drained upper limit(DUL),
0.00<INSOIL<1.0:set to LL+INSOIL*(DUL-LL),
1.00<INSOIL:Initial soil water input by user
ISWSWB Switch for water balance
00:Water balance not used, 01:water balance used

24. Description of Values Used in the Parameter Input File (param1
Description of Values Used in the Parameter Input File (param1.txt) (Continued-2)
Variable EXPLANATIONS VALUES USED
LINE
NAME Cultivar name B73 X MO17
LINE
P Growing Degree Days from seedling emergence to
the end of the juvenile phase (C day, base: 8C)
P Photoperiod sensitivity coefficient
P Growing Degree Days from silking to physiological
maturity (C day, base: 8C)
G Potential kernel number (kernels/plant)
G Potential kernel growth rate(mg kernel/day)
SALB Soil albedo (unitless)
U Upper Limit of stage 1 soil evaporation (mm)
SWCON Whole profile drainage rate coefficient
CN Runoff curve number used to calculate daily runoff

25. Description of Values Used in the Parameter Input File (param1
Description of Values Used in the Parameter Input File (param1.txt) (Continued-3)
Variable EXPLANATIONS VALUES USED
LINE
ISLKJD Measured 50% silking date(day of year)
MATJD Measured physiological maturity date (day of year)
XYIELD Measured grain yield (kg/ha at 15.5% moisture)
XGRWT Measured kernel dry weight at maturity (gr/kernel)
XGPSM Measured grain number at maturity (grains/m²)
XGPE Measured grain number at maturity (grains/ear)
XLAI Measured maximum leaf area index (m²/m²)
XBIOM Maximum above-ground biomass at maturity (kg/ha)
LINE 6 THROUGH
DLAYR:Layer thickness (cm)
LL(L) :Lower limit of plant-extractable soil water content in layer L as volume fraction
DUL(L):Drained upper limit soil water content in layer L as volume fraction
SAT(L):Soil water content at saturation in layer L as volume fraction
SW(L) :Actual soil water content in layer L -volume fraction(i.e. cm³water/cm³ soil).
WR(L) :Weighting factor for soil depth L to determine new root growth distribution

26. Weather Input File Daily weather data read by the main program.
One line of the data is used for each day .The first 7 columns of each line are used as an identification field or comment field.
IYR : Year
JDATE : Day of the year
TEMPMAX : Maximum air temperature
TEMPMIN : Minimum air temperature
SOLRAD : Solar Radiation
RAIN : Precipitation

27. THE WEATHER INPUT FILE YR DAY SOLRAD TMAXC TMINC RAIN(mm) .

28. FERTILIZAER INPUT JFDAY : Each line contains the day of the year.
AFERT : Amount of the elemental N applied.
DFERT : Depth of incorporation.
IFTYE : Type of fertilizer N applied.
One line is used for each application of N fertilizer.
After the last line of Fertilizer information added , a line with JFDAY set equal to zero
Zero is necessary to signal the end of fertilizer data
If no fertilizer is used ,zeros are entered for the day of the year.

29. BIOMASS OUTPUT FILE Written in subroutine ( OUTGRO)
Output control number (KOUTGR) in the parameter file defines the interval at which output is written (OBIO.DAT)
The following is written in the file
: Day of the year
: The number of the leaves plant has produced
: LAI
: Root weight ( g/plant)
: Stem weight ( g/plant)
: Grain weight ( g/plant)
: Leaf weight ( g/plant)
: Root Depth ( cm)
: Root length density (cm root/cm3 soil) in the five soil layers

30. The following information are written
SOIL WATER OUTPUT FILE
The following information are written
Day of the year
Plant transpiration (EP.mm)
Evapotarnspiration (ET.mm)
Potential ET (E0 .mm)
Solar radiation (SR,cal/cm3)
Maximum and Minimum temp. (Tmax & Tmin)
Total precipitation
The Volumetric soil water content in each soil layer( L1-L5)
The total plant-extractable soil water in the soil profile (PESW.cm) are printed for the day of the output.

31. SOIL NITROGEN OUTPUT FILE
The following data are printed
Day of the year.
Total immobilization of mineral nitrogen.
Mineralization of nitrogen from the fresh organic pool.
Mineralization of nitrogen from the humus nitrogen pool (average daily amount for the period).
The concentration of elemental Nitrogen ( g/mg) as nitrate and ammonium are written for the top five soil layers.

32. Simple methods for estimating soil and genetic variables
MODEL INPUTS
Simple methods for estimating soil and genetic variables
SOIL WATER INPUT
SOIL ALBEDO (SALB)
SALAB = 0.1 for dry, dark soils with OM
SALAB = 0.3 for light desert sands
Stage I .Soil Evaporation (U) Coefficient
6 mm :In sands and heavy shrinking soils
9 mm :In loams
12mm : In clay loams
Drainage coefficient (SWCON)
(SWCON) is calculated from each soil layer (L) from porosity (Po(L)) and drained upper limit ( DUL(L)) of each layer.
Po(L) = 1- BD(L)/2.65
SWCON(l) =( PO(L)) - DUL(L)/PO(L)
Where,
BD(L) : moist bulk density of the layer.
: is the approximate particle density.

33. SOIL WATER CONTENT(SAT)
Water content at saturation (SAT)
SAT can be calculated at
Drained upper limit ( DUL)
Drained lower limit ( LL) of plant extractable water
SAT(LL) is calculated for each layer upto 2m,by allowing a well developed maize crop to extract water from a plastic cover plot until it is at the point of death due to stress.
ROOT DISTRIBUTION WEIGHTING FACTOR( WR)
Used to estimate the relative root growth in all soil layers
WR( I ) = Exp ( -4*Z( I )/200)
where,
WR( I ) = 1.0 in the top layer of the soil
Z( I ) = is the depth ( cm ) to the center of the layer I.

34. Mean Temp. during grain filling = 20-30 ºC
GENETIC INPUTS
P1 : GDD ( base 8ºC) Emergence- Juvenile [ ]
P2 : Photoperiod sensitivity coefficient ( I/hr)
P5 : GDD from silking to Physiol. maturity
G2 : Potential kernel number ( kernel /plant)
G5 : Potential kernel growth rate ( mg/kernel ) 6-11mg
Mean Temp. during grain filling = ºC

35. SUBROUTINE STRUCTURE The CERES-Maize model is divided into :
1- Main program
2- Subroutine
Main Program :
Opens input & output files
Calls Sub-routines-PROGR1 & SOILR1
PROGR1 : reads the programme title and first five lines of the parameter file
: It also initilizes parameters ( variables)
Main Program : enters a daily loop:
1 - It reads the year ( IYR)
2 - It reads the day of year( JDATE)
3 - It reads the solar radiation (SOLARD)
4 - It reads the Max air temp. (TEMPMX)
5 - It reads the Min air temp (TEMPMN)
6 - It reads the pricipitation (RAIN)

36. SUBROUTINE
CALDAT : Calculates the month and day of the year from JDATE
WATBAL : Simulates soil water balance.
PHENOL : Calculates the rate of plant development.
GROSUB : is called if the crop is growing ( crop growth rate is less than 6).
WRITE : called to write daily climate, soil water, and crop growth data.The model returns to beginning of daily loop.
SUBROUTINE PROG I
Main program calls it on first day of simulation:
: It reads first 5 lines of parameter file and READ irrig. data
: Produces headings for the OUTPUT file and initiate
the variables.

37. SUBROUTINE SOILR1
:Reads from the parameter file for any No. of soil layers ( NLAYR) up to 10.
: Lower limit of plant-extractable soil ware( LL)
: Drained upper limit ( DUL)
: Soil water content at saturation ( SAT)
: Root Growth weighting factor (WR)
: Initial soil water content (SW)
INSOIL : Initial Soil Water ,
if INSOIL is > 0
:SW( NLAYER) is read from parameter file.
if INSOIL is ≤ 0 Calculates initial soil water
SW(NLAYER) = LL( NLAYER)+(DUL(NLAYER)-LL(NLAYER)*INSOIL
Initial SW may be set higher than that given by previous eq’n because crops rarely extract all plant water available below 110cm.
SWR: Real amount of plant extractable soil water is calculated from any layer (I)
SWR = ( SW (I)-LL ( I ))/ DUL ( I ) – LL ( L)

38. SUMES I = U = cumu. stage I Soil Evaporation.
SWR is used to initialize SUMES2 SUMES 2 : cumulative stage 2 soil Evaporation. If SWR < 0.9 SUMES2 = * SWR
SUMES I = U = cumu. stage I Soil Evaporation.
SUMES I = Cumulative Soil Evaporation. in stage 1 ( mm)
The upper limit of stage 1 evaporation. and the time after the begining of step 2
Evaporation ( T ) = ( SUMES 2 /3.5 ) ** 2
If SWR ≥ 0.9 ,SUMES 2 & T are set to 0
SUMS 1 = 100 – 100 * SWR
Subroutine calculates initial values the upper ( DLI ) and lower
( DL2) depth
Calculates plant-extractable soil ware ( ESW(L)) for each layer.
ESW ( L ) = DUL ( L ) – LL ( L )
The subroutine wrires DL 1, DL 2, LL ( L )
SAT ( L ) ,ESW ( L) , SW ( L ) and WR ( L ) on the output file OYLD.DAT

39. CUMDEP : Cumulative depth of profile
CUMDEP : Cumulative depth of profile. TSW : Total Soil water in the profile. TPESW : Total plant-extractable soil water in the profile.
TLL : Total soil water in the profile at the lower limit of plant extractable water ( TLL )
TDUL : Total soil water in the profile at the drained upper limit
TSAT : Total soil water in the profile at saturation is calculated.
WF( L ) : Weighting factor WF ( L ) is used to determine runoff.
WX = * ( 1- Exp ( * CUMDEP/DEPMAX))
W F( L ) = WX – XX
where, XX = 0 in the surface layer
and in other layers, XX = WX in the layers above.

40. RTDEP , TLL, TDUL , TSAT, TPESW, TSW.
CUMDEP : cumulative depth of the soil profile ( cm )
RTDEP : The depth of rooting
DEPMAX : Maximum depth of root zone ( mm )
The following total values for the profile are written to the output file ( OYLD.DAT):
RTDEP , TLL, TDUL , TSAT, TPESW, TSW.

41. SUBROUTINE WATBAL
It is called daily by the main program if the soil water switch( ISWSWB )≠ 0
Its principal functions are to calculate the redistribution of water due to irrigation, precip, Drainage , PET, Soil evaporation and plant evaporation ( transpiration).
It also contains calculation of potential soil evapotranspiration.

42. SUBROUTINE WETBAL Calculates pot.evaporation,PET soil
TD = 0.6 * TEMPMAX *TEMPMN
Where, TD : Day time mean temp when both soil and plant
Evaporation are greatest.
ALBEDO : The integrated crop and soil albedo is calculated from base soil albedo ( SALB) and LAI.
If the crop is not growing :
ALBEDO = SALB
If the crop is growing :
ALBEDO = 0.23 –( 0.23 – SALAB ) * Exp ( * LAI)
The equilibrium evaporation rate ( EEQ ) is calculated from SOLRAD , ALEBDO & TD
EEQ = SOLARAD * ( 2.O4 E E-4*ALBEDO)*( TD+29)
PET( EO) is estimated as a function of TEMPMX
EO = EEQ * If ºC <TEMPMAX < 35 ºC

43. POTENTIAL RATE OF SOIL Evap.(EOS)
IF TEMPMAX > 35 ºC : E0 = EEQ * (( TEMPMAX – 35 )* ) IF TEMPMAX < 5.0 ºC : E0 = EEQ * 0.01 * Exp ( 0.18 * ( TEMPMAX + 20 ))
POTENTIAL RATE OF SOIL Evap.(EOS)
When LAI < 1.0 ,
EOS = EO * ( * LAI )
When LAI > 1.0 ,
EOS = EO /1.1 * Exp ( -0.4 * LAI )

44. SUBROUTINE PHENOL SUBROUTINE PHASE 1
Called by the main programme on the day of the
sowing until the crop is mature
It uses temperature , photoperiod and gemetic characteristic to determine the date the crop begins a new growth stage.
It calls subroutine PHASE 1
SUBROUTINE PHASE 1
Called by subroutine PHENOL
When the crop completes a growth stage ( ISTAGE)
Initializes the variables
ISTAGE is uploaded each time subroutine PHASE1 is called.
Several important variables are calculated within subroutine

45. SUBROUTINE OUTWA SUBROUTINE WRITE
SUBROUTINE GROSUB - Called from the main programme. - Calculates leaf area development , light interception and portioning of biomass into various plant parts.
SUBROUTINE OUTWA
Called by subroutine WRITE specified by the variable in the parameter file.
Calls CALDAT and writes soil-water related output to the file OWAT.DAT
SUBROUTINE WRITE
Called daily from the MAIN programme.
Calculates cumulative variables related to hydrology and calls the output subroutine OUTWA and OUTGR respectively.

46. SUBROUTINE OUTGR Called by subroutine WRITE at the interval specified by the variable KOUTGR in the parameter file Calls subroutine CALDAT and writes growth related output to the output file .
SUBROUTINE SOILNI
In the N version, it is called from main program on
the first day of simulation.
Fertilizer data are read from parameter file.
N content of STRAW and ROOT residue are calculated
from the weight of residues and their C/N ratio from the parameters file on the assumption that 40% of dry weight
is carbon.

47. GENERAL OUTPUT FILE General Output File
Title of the experiment
Years in which the program starts
Cultivars name
Plant population
Genetics coefficients

48. The General Output File (OYLD.DAT) Generated by the CERES-Maize Model
The general output file prints:
1- Information about management and soil characteristics
contained in the parameter input file.
2- All water contents are in volumetric fractions.
3- The date for each phenological event is listed including the simulated dates of silking and physiological maturity.
4- These yield and growth information are presented:
Grain yield (kg/ha at 15.5% water content), dry single kernel weight (g/kernel), grain number (grains/m² and grains/ear), maximum leaf area index, and the final above-ground biomass (kg/ha).

49. The Biomass Output File (OBIO.DAT) (continued-1)
CERES MAIZE BIOMASS OUTPUT SUMMARY
"MISSOURI, SW=DUL"
DAY LEAF LAI ORGAN WEIGHT ROOT -ROOT LENGTH DENSITY--
NO ROOT STEM EAR LEAF DPTH L1 L2 L3 L4 L5
m2/m g/plant cm ---cmRoot/cm3Soil-----

50. The Soil-Water Output File (OWAT
The Soil-Water Output File (OWAT.DAT) Generated by the CERES-Maize Model
The model includes the calculations for the water balance to be written into the soil-water output file, OWAT.DAT, if the switch, ISWSWB, is set to a value larger than zero in the parameter file. This file contains day of the year, plant transpiration (EP, mm), evapotranspiration (ET, mm/day), potential evapotranspiration (EO, mm /day), solar radiation (SR, langley), maximum and minimum temperatures (TMAX and TMIX in C), total precipitation (PRECIP, mm/day), the volumetric soil-water content of the top five soil layers (L1-L5), and the total plant extractable soil-water in the whole soil profile (PESW, cm).
The output control number, KOUTWA, in the parameter file determines the output’s interval written to OWAT.DAT. Here, an example of OWAT.DAT:
CERES MAIZE SOIL-WATER OUTPUT SUMMARY
"MISSOURI, SW=DUL"
DAY -- AVERAGE VOLUMET SOIL WATER TOTAL
EP ES ET EO SR TMAX TMIN PRCPT DRN RNF L1 L2 L3 L4 L5 PESW
---mm/day LY ---- C ---- MM MM MM --cm³Water/cm3Soil CM

51. The General Output File(OYLD
The General Output File(OYLD.DAT) Generated by the CERES-Maize Model(Continued-1)
CERES MAIZE GENERAL OUTPUT SUMMARY
Program Begins Day : Project Name:MISSOURI,SW=DUL
Population(plants/m2):4.0 Cultivar :"B73 X MO17"
P1: P5:880.0 P2: G2:730.0 G3:10.0
SALB:0.14 U: SWCON:0.13 CN2:82.0
Soil Layer LL DUL SAT ESW SW WR
Depth(cm) (cmWater/cm3soil)
TotalProfile:
IRRIGATION (mm):
NO IRRIGATION WAS APPLIED

52. MODEL EVALUATION Common Input Errors
Development ,Verification and Validation of CERES-Maize model is an iterative process.
Measured & Simulated results are composed.
Comparison of measured results with simulated results, give logical errors and inadequate calibration.
Input errors are more serious than logical.
Common Input Errors
In accurate estimates of initial soil water content
The lower limit of plant-extractable water (LL)
The drained upper limit ( DUL)
Inaccurate estimates of rooting depth due to restrictions

53. YIELD SIMULATION
Simulated yields are sensitive to LL , DUL and initial soil water when yields are limited due to water stress.
Model is less sensitive to these input parameters when rainfall or irrigation is abundant.

54. SUMMARY & CONCLUSIONS
1- The CERES-Maize model provided very useful information about the plant-soil-water status.
2- The CERES-Maize model simulates the soil-water status through estimation of evaporation from the soil, transpiration from the plants, run-off from the soil surface, and drainage from the bottom of the soil profile.
3- It is recommended that further research focus on the verification of the estimates of biological performance by the model.
4- This calibration should compare simulated yield with actual measurements of growth stages, leaf area index and root growth.
5- Plant growth simulation needs to be improved in the CERES-Maize model for obtaining a better information about partitioning of the photosynthate during the different stages of development and under varying temperature and water stress.