Plant
Plant module Scope
The Plant module simulates the growth of a number of different species on a daily time-step (on an area basis not single plant). Plant growth in this model responds to climate (temperature, rainfall and radiation from the Met module), soil water supply (from the Soilwat module) and soil nitrogen (from the SoilN module). The Plant module returns information on its soil water and nitrogen uptake to the Soilwat and SoilNmodules on a daily basis for reset of these systems. Information on crop cover is also provided to the Soilwat module for calculation of evaporation rates and runoff. Plant tops and root residues are ‘passed’ from Plant to the Residue and SoilN module respectively at harvest of the plant crop.
Currently, the crops that are included in the Plant module are chickpea, mungbean, cowpea, soybean, pigeonpea, stylosanthes, peanut, faba bean, lucerne, canola, weed, mucuna, lupin, wheat and navybean (Table 1).
A list of the module outputs is provided in the ‘Plant module outputs’ section listed below. The module will predict on a daily basis: phenological variables (leaf and node appearance, occurrence of stages of development, thermal time progression), leaf area development, nitrogen content and biomass of plant parts (including grain), depth and distribution of roots in the soil profile, root water and nitrogen uptake, water, oxygen and nitrogen deficit stress factors, and nitrogen fixation from the atmosphere.
Plant module History
The Plant module replaces previous modules covering the relevant crops. The module was developed so that disparate pieces of source code residing with different plant modules could be consolidated into the one module, thus cutting down on on-going maintenance costs, source code management, and version control problems. The underlying premise was that the basic physiological principles needed to be simulated were essentially the same across species and that species differences could be captured successfully through different parameter inputs. The functions on which the crop growth module is based originate from a mixture of sources including; values/functions from published literature/models (e.g. Sinclair, 1986), functions derived directly from experimental data and model calibration to experimental data sets.
While the original intent of the Plant module was to simulate legume species, it now simulates non-legume species such as canola, wheat and weeds .
Ownership of the crop species science remains with the original module owners. Documentation of the history of the evolution of the Plant module is available upon request from the module convener, Michael Robertson.
Table 1: Plant species simulated by APSIM-Plant
| Plant species | Species science “owner” | Former APSIM module |
| Current | ||
| Chickpea | Michael Robertson CSIRO / APSRU | APSIM-Chickpea (Carberry, 1996; Turpin et al., 1998) |
| Mungbean | Michael Robertson CSIRO / APSRU | APSIM-Mungbean (Carberry, 1996) |
| Cowpea | Michael Robertson CSIRO / APSRU | APSIM-Cowpea (Adiku et al. 1993) |
| Soybean | Michael Robertson CSIRO / APSRU | APSIM-Soybean (Carberry, 1996) |
| Pigeonpea | ICRISAT(Gary O’Leary) & CSIRO / APSRU (Peter Carberry) | None |
| Stylo | Peter Carberry CSIRO / APSRU | APSIM-Stylo (Carberry et al., 1996) |
| Navybean | Michael Robertson CSIRO / APSRU
Graeme Wright QDPI |
None |
| Lucerne | Michael Robertson CSIRO / APSRU | Probert et al. (1998) |
| Peanut | Mike Bell QDPI
Graeme Wright QDPI Michael Robertson CSIRO / APSRU |
QNUT |
| Fababean | Michael Robertson CSIRO / APSRU | None |
| Lupin | Michael Robertson CSIRO / APSRU | None |
| Mucuna | Michael Robertson CSIRO / APSRU | None |
| Canola | Michael Robertson CSIRO / APSRU | None |
| Weed | Michael Robertson CSIRO / APSRU | None |
| Wheat | Neil Huth CSIRO / APSRU | APSIM Wheat, APSIM NWheat, APSIM IWheat |
Plant module Structure
Phenology
There are 11 crop stages and 10 phases (time between stages) in the Plant module (Table 2), and commencement of each stage (except forsowing to germination which is driven by soil moisture) is determined by accumulation of thermal time.
Table 2: Stages of crop development simulated in the module
| Stage code | Stage name |
| 1 | Sowing |
| 2 | germination |
| 3 | emergence |
| 4 | end_of_juvenile |
| 5 | floral_initiation |
| 6 | flowering |
| 7 | start_grain_fill |
| 8 | end_grain_fill |
| 9 | maturity |
| 10 | harvest_ripe |
| 11 | end_crop |
Each day the phenology routines calculate today’s thermal time (in degree days) from 3-hourly air temperatures interpolated from the daily maximum and minimum temperatures. Thermal time is calculated using the relationship in Figure 1 (base temperature, optimum and maximum) with the eight 3-hour estimates averaged to obtain the daily value of thermal time (in growing degree days) for the day. These daily thermal time values are accumulated into a thermal time sum which is used to determine the duration of each phase. Between the stages of emergence and flowering the calculated daily_thermal_time is reduced by water or nitrogen stresses, resulting in delayed phenology when the plant is under stress.
The duration and timing of 6 of the 10 crop phases are determined by fixed thermal time values input in the parameters section of the ini file.
Sowing to germination is dependent on soil water levels. If soil water in the soil layer in which the seed is sown is sufficient (specified bypesw_germ ) then germination takes place one day after sowing.
The phase germination to emergence includes an effect of sowing depth on the thermal time target. The thermal time target equals a lag period before linear shoot growth starts ( shoot_lag ) plus a shoot elongation rate ( shoot_rate ) which determines the thermal time taken to reach the soil surface and emerge.
The thermal duration of the phase emergence to end_of_ juvenile in some species is affected by the number of cumulative vernalising days experienced during the period. The relationship between a fraction of a vernalising day and mean daily temperature is specified by the table x_vernal_temp vs y_vernal_days .
The phase between end_of_ juvenile and floral initiation is determined by a cultivar’s photoperiod (daylength) sensitivity – note that the Plant module can cope with short-day species (e.g. cowpea), long-day species (e.g. chickpea) and species that exhibit the qualitative response (e.g. pigeonpea). The photoperiod sensitivity is specified in the parameters section of the ini file with a table for the relationship between photoperiod and thermal time between end-of-juvenile and floral intiation
Biomass accumulation (Photosynthesis)
Radiation_interception is a function of the fraction of radiation intercepted and daily radiation. The fraction of radiation intercepted is determined by the leaf area index and the extinction coefficient, which varies as a function of row spacing, inter-row skip-row configuration and intra-row ‘skip-plant’ configuration.
In crop species that produce a significant layer of green photosynthesising pods (eg canola) it is possible to specify the rue, extinction coefficient and specific pod area (that converts pod weight to area) of the pods ( rue_pod, extinct_coef_pod, spec_pod_area ).
Each day two estimates of the daily biomass production are calculated, one limited by available water for transpiration (eqn 1), and the other limited by radiant energy (eqn 2). The minimum of these two estimates is the actual biomass production for the day.
delta_drymatter_transpiration = soil_ water_ supply * transpiration_efficiency eqn 1.
Note: transpiration_efficiency is derived from the transpiration_efficiency_coefficient and the vapour pressure deficit (vpd) estimated from daily temperatures.
dlt_drymatter_potential = rue *radiation_interception eqn 2.
Note: rue (radiation use efficiency) incorporates temperature, oxygen deficit (waterlogging) and nitrogen stresses. The value of rue is not limited by temperature over a range between the first and second optima. Temperatures outside this range reduce rue to zero at a base and maximum temperature. Rue is linearly interpolated between the phenological stages specified in a table.
Biomass partitioning
Daily biomass production is partitioned to six different plant parts in different ratios depending on crop stage (Table 3).
Table 3: Plant parts and their description in the Plant module.
| Element in the plant part array | Plant part | Description |
| 1 | root | Below-ground fibrous roots |
| 2 | leaf | Leaf lamina |
| 3 | stem | Stem |
| 4 | pod | Hull (or pod wall) |
| 5 | meal | Grain (or seed) meal, excluding the oil |
| 6 | oil | Oil contained in the grain |
Roots are grown daily in a fixed proportion to the tops production. This proportion ( ratio_root_shoot ) is specified for each growth stage.
On the day of emergence, biomass (and nitrogen) in plant parts are initialised. Between emergence and flowering a proportion of biomass produced ( frac_leaf ) is partitioned to leaf and the remainder to stem. However, if the amount of carbon partitioned to leaves is more than is required for the calculated increase in leaf area (the leaves have a maximum thickness, sla_min ) then the residual is partitioned to stems. Likewise if the carbon partitioned to leaves is too little to grow the potential increase in leaf area, leaf area increase is reduced (see leaf area development section).
Between flowering and start-of-grainfill the same procedure is used for determining leaf biomass ( frac_leaf ). Of the remaining carbon a proportion goes to stem and pod in the ratio specified by the parameter frac_pod .
Between the start-of-grainfill and maturity biomass is partitioned between grain, pod and stem. Partitioning to grain depends on calculated grain-demand (see below). The pod wall accounts for a fraction of the grain demand ( frac_pod ). If (because grain demand is lower than supply) there is any biomass remaining it goes to leaf as specified by frac_leaf , with the remainder going to stem. In this way if there is low demand for assimilate by grain during grainfill, leaf area may be produced, as occurs in indeterminate species and cultivars.
Grain demand for carbohydrate (biomass) is driven using a cultivar-specific daily rate of harvest index (HI) increase ( hi_incr ). The demand for biomass to be partitioned to grain on any day is calculated using HI i.e. the ratio of grain-biomass to tops-biomass. Each day HI is increased by hi_incr until it reaches a maximum hi_max_pot . In species in which there is an energy cost to grain dry weight synthesis (e.g. oilseeds such as soybean), above that which is standard for grain carbohydrate, account must be taken of the extra assimilate required. This is specified by the parameters grain_oil_conc and carbo_oil_conv_ratio (fractional oil content of grain and carbohydrate:oil conversion ratio respectrively), and these are used to calculate the energy used to produce the oil content and accumulate the oil plant part. Energy is not included in the summing of plant parts to give the weight of biomass, but must be accounted for when calculating grain demand for carbohydrate. Grain weight at commercial moisture content (variable = yield-wet) is calculated using the parameter grn_water_cont .
Crop height (mm) is a function of stem weight per plant, as specified for each cultivar.
Biomass retranslocation
If the grain demand for carbohydrate cannot be met through partitioning of daily biomass production it is retranslocated from other plant parts to meet (if possible) this grain demand. The Plant module allows a total retranslocation of no more than leaf_trans_frac of leaf weight,stem_trans_frac of stem weight, and pod_trans_frac of podwall weight that is present at the start of grain filling.
Leaf development
On the day of emergence, leaf area per plant ( initial_tpla ) and leaf number per plant ( leaf_no_at_emerg ) are initialised. Node appearance rate per plant is driven by thermal time, specified by the lookup table between x_node_no_app vs y_node_app_rate . Leaf appearance is driven by a number of leaves appearing per node as specified by the x_node_no_leaf vs y_ leaves _per_node relationship.
Potential LAI is a product of potential leaf number, leaf-size (which is a function of nodal position) ( x_node_no vs. y_leaf_size (mm 2 )), number of plants per m 2 and the water stress factor for expansion (see water deficits section below)
Actual LAI is less than the potential LAI if there is not sufficient biomass partitioned to leaf on that day. Maximum specific leaf area ( sla_max ) defines the maximum leaf area (m 2 ) that can be expanded per gram of biomass. sla_max declines with increasing LAI i.e. smaller, younger crops are able to produce thinner leaves.
Leaf senescence
There are four causes of leaf senescence; age, light competition, water stress and frost. The plant senescence routines calculate a senesced LAI for each stress each day and take the maximum of the four values as the day’s total senescence.
A fraction of the oldest green leaf dies each day after flowering. This senescence due to age occurs a rate of leaves per day. This is calculated from the day’s thermal time, the rate of node senescence per o Cd ( node_sen_rate ) and a fraction of the total green leaves on the plant that senesce for each node that is senescing ( fr_lf_sen_rate ). This number of dead leaves is then converted to a senesced LAI.
A rate of senescence of other plant parts can also be specified (such as stems) in terms of a fraction of dry weight senesced for each fraction of canopy senesced.
Above an LAI of 4.0 light competition causes leaf area to be lost. The LAI senesced because of light competition is related to the amount LAI exceeding lai_sen_light .
Water stress during crop growth will cause leaf senescense
sensLAI_water_fac = 0.05 * (1 – plant_swdef(photo))
delta_sensLAI_water = LAI * sensLAI_water_fac
Note: the calculation of the water stress factor plant_swdef(photo) is descibed in the ‘water deficits’ section below.
Frost senescence. Low minimum temperatures will cause a linearly increasing loss of leaf area from 0 to 100% respectively, as defined by the relationship between temp_senescence and senescence_fac .
From the values of senesced LAI the Plant module calculates the biomass and nitrogen in that leaf area that is senesced, however a proportion of the carbon and nitrogen of these leaves is retranslocated to stem before senescence.
Regrowth
Depending upon the relative height of harvesting, differing fractions of leaf and stem can be left remaining ( fr_height_cut vs fr_stem_remain ).
Regrowth routines allow growth after harvest in the Plant module. Regrowth in ensured if the parameter min_tpla is set to a value greater than zero. At present this only occurs in the lucerne module. The phenological stage that the crop is set back to upon harvest is specified by the tablestage_code vs stage_stem_reduction_harvest . Re-setting of phenology can also occur when the growing point is killed by a “kill_stem” action. This could be due to frost damage, grazing, herbicide, insect damage. The stage at which phenology is reset to is specified by stage_code vsstage_stem_reduction_kill_stem .
In some species harvesting or similar actions cause the module to use a different set of “ crop_class ” parameters listed in a separate section of the ini file. The section(s) of the ini file that are read upon the receipt of a particular action are listed at the top of the ini file. For example in lucerne.ini the table:
class_action = harvest kill_stem
class_change = regrowth regrowth
means that a harvest action will cause a change of crop class to regrowth, while a kill_stem action will cause a similar change of crop class.
Water uptake
To determine the amount of water supply to the crop on any day, first the total available water above the lower limit for all soil layers with roots is summed. If roots are only partially through a layer, available soil water is scaled to that portion that contains roots. The kl constant (value differs for each soil layer) is then used to limit the amount of water available on any day. The kl factor is empirically derived, incorporating both plant and soil factors which limit rate of water uptake – it represents the fraction of available soil water that can potentially be taken up on that day from that layer, and values typically vary between 0.01 for deep layers with low root length densities to 0.10 for surface layers with high root length densities
do layer = 1, deepest_layer (do loop to calculate available water for all layers)
sw_avail = sw(layer) – ll (layer)
sw_supply(layer) = sw_avail * kl (layer)
Soil water demand is calculated as in the ‘biomass accumulation’ section above where potential biomass production is a function of radiation interception and rue . This potential biomass production is converted to water demand using transpiration efficiency. Transpiration efficiency is calculated from the transpiration effieicny coefficient ( transp_eff_cf ), which can vary with growth stage, and vapour pressure deficit. Soil water demand is capped by the atmospheric evaporative demand (eo) adjusted by the proportion of green canopy cover (cover_green) and a crop factor (eo_crop_factor) i.e. eo_crop_factor * eo * cover_green .
Water uptake is the minimum of the supply and demand.
Water deficits affecting plant growth
Soil water deficit factors are calculated to simulate the effects of water stress on different plant growth processes. Four water deficit factors are calculated which correspond to four plant processes each having different sensitivity to water stress i.e. photosynthesis (photo), phenology (pheno), leaf-expansion (expansion) and nitrogen fixation (fixation).
A water availability ratio is calculated by dividing actual soil water supply (sw – ll) by the potential soil water supply (dul – ll). This ratio is used in the relationships illustrated in Figure 3a to derive stress factors for nitrogen fixation and phenological development. A factor of 0 is complete stress and 1 no stress. Likewise, Figure 3b shows the relationship between the stress factors for photosynthesis and leaf expansion and the ratio of supply to demand for soil water.
Nitrogen uptake and retranslocation
In order to calculate nitrogen demand today, first, potential biomass production is re-calculated unlimited by water, nitrogen or temperature i.e. as a function of rue and radiation-interception. This dry matter (biomass) is then partitioned into plant parts according to their current relative weights. The Plant module has a defined minimum, critical and maximum N concentration for each plant part. Demand for nitrogen in each part attempts to maintain nitrogen at the critical (non stressed) level. Nitrogen demand on any day is the sum of the demands from the pre-existing biomass of each part required to reach critical N content, plus the N required to maintain critical N concentrations in today’s potentially assimilated biomass..
A nitrogen uptake maximum is defined as the nitrogen uptake required to bring all plant part N contents to the maximum allowable concentration.
Nitrogen supply is the sum of nitrogen available via mass flow and by diffusion (otherwise known as active uptake).
no3_massflow (layer) = no3_conc * delta_sw (layer)
no3_diffusion (layer) = sw_avail_frac *no3_conc
note: these layer values are summed to root depth and sw_avail_frac is ratio of extractable soil-water over total soil-water.
If nitrogen demand cannot be satisfied by mass flow then it is supplied by either diffusion or fixation. The preference by a species for diffusion or fixation is specified by the parameter n_supply_preference (options are “active” or “fixation”) . Demand can only be exceeded by supply from mass flow (up to the nitrogen uptake maximum). If both mass flow and diffusion supplies can’t satisfy demand then nitrogen is sought from N fixation (see next section).
Nitrogen available for uptake is distributed to plant parts in proportion to their individual demands.
Nitrogen for grain is retranslocated from other plant parts, N is not directly taken up from the soil or atmosphere to meet grain demand. Nitrogen is available for retranlocation from all parts except for grain and roots; other plant parts will translocate nitrogen until they reach their defined minimum N concentration. Grain nitrogen demand is again driven by critical N content but this demand is lowered if the plant is under N stress. Grain N demand is also affected by temperature and water stress using eqns below.
N_grain_temp_fac = 0.69 + 0.125 * aver_temp
N_grain_sw_fac = 1.125 – 0.125 * swdef (expansion)
The greatest of these two factors is multiplied by the previously calculated N demand i.e. if temperature is high or swdef(expansion) is low (water stressed) the N demand will be increased above the level required to reach the critical N concentration.
During leaf senescence, leaf nitrogen is reduced in the newly senesced leaves and the excess is retranslocated to green stem.
N fixation
The daily rate of nitrogen fixation at potential, is a function of the crop N fixing capacity ( N_fix_rate ), which varies with growth stage, crop biomass (i.e. the size of the crop) and soil water stress.
N_fixation = N_fix_rate * biomass * swdef (fixation)
Nitrogen deficits affecting plant growth
There are three N availability factors (0-1), one each for the photosynthesis, phenology and grain filling processes. A N concentration ratio is calculated for the stover (stem + leaf), which is used as a measure of N stress, then different constants are used to convert that ratio to a deficit factor for each of the processes. A factor of 1 is used for affecting grain N concentration, 1.25 for photosynthesis (reduces rue) and 5.75 to slow phenological development. As a value of 1 is no stress and 0 complete stress, phenology is least sensitive to nitrogen deficiency and grain N the most.
N_conc_ratio = (N_conc_stover – N_conc_stover_min) / (N_conc_stover_crit – N_conc_stover_min)
Root growth and distribution
Root depth at emergence is initialised at initial_root_depth . Between emergence and grain filling, the increase in root depth is a daily rate (root_depth_rate ) multiplied by an exploration factor ( xf ), a soil water availability factor for the layer than the deepest roots are currently passing across, and a temperature factor ( x_temp_root_advance vs. y_rel_root_advance ). In severe water deficit the roots depth increase can be slowed and even stopped by the function between sw_supply_demand_ratio and root depth increase, expressed by x_ws_root vs.y_ws_root_fac . The parameter root_depth_rate varies with growth stage and is typically zero after the start of grain filling. Root depth is constrained by the soil profile depth.
The amount of biomass partitioned daily to the root system is described in the ‘biomass partitioning’ section above. Root biomass is partitioned among the soil layers currently occupied by roots according to three factors: the exploration factor ( xf ), the soil water availability factor, and the root branching factor ( rel_root_rate ). Root biomass is converted to root length using the parameter specific_root_length (currently assumed as 60000 mm/g for all species).
Roots are senesced during the life of the crop (0.005 of the length in each layer per day), and are immediately detached and sent to the SoilNmodule. At harvest all roots senesce and distributed as fresh organic matter in the profile according to their distribution on the day of harvest.
Oxygen deficits (waterlogging) affecting plant growth
Oxygen deficit (waterlogging) affects photosynthesis. The oxygen deficit stress factor is calculated as the fraction of the whole plant root length that is exposed to water contents above the drained upper limit (i.e. near-saturated soil conditions).
Temperature stress
There are no generic temperature factors, as for water and nitrogen stress, but as discussed in sections above temperature does influence grain N content, rate of senescence and radiation use efficiency (rue).
Plant death
All or some of the plants can be killed due to a variety of stresses;
If the crop hasn’t germinated within 40 days of sowing, due to lack of germinating moisture, all plants are killed.
If the crop does not emerge with 150 o Cdays of sowing, because it was sown too deep, then all plants are killed.
If crop is past floral initiation and LAI = 0, then all plants are killed due to total senescence.
If the cumulative phenological water stress factors exceed 25, all plants are killed due to water stress prolonging phenology.
A fraction of plants will be killed by high temperatures immediately following emergence.
A fraction of plants can be killed by a kill_stem action from the manager to simulate the effect of severe frost.
A specified fraction of plants can be killed by a kill_crop action from the manager.
Detachment
In the module the user can specify the fraction detached from each part of a dead plant or senesced pool per day. Currently, only senesced roots are assumed to be detached.
Plant Module Parameterisation
Crop lower limit, kl values, and exploration factor ( xf ) values are need for each soil layer. An optional parameter uptake_source can also be specified for simulations where the uptake of water and solutes (in this case NO 3 ) is calculated by another module in APSIM. The possible setting for this parameter are ‘calc’ (= calculate own uptakes) or ‘apsim’ (= get uptake data using the APSIM messaging system). The default value of ‘calc’ is used in the absence of the parameter specifier.
test. cowpea.parameters
uptake_source = calc ! calculate uptake of water and nitrate
ll = 0.200 0.200 0.200 0.220 0.250 () ! crop lower limit
kl = 0.08 0.06 0.04 0.02 0.01 () ! kl need calibrating for each crop and soil type
xf = 1.00 1.00 1.00 1.00 1.00 (0-1) ! exploration factor for root growth
Cultivar parameters are needed, and are specified in the ini file. The example below is for cowpea cv. Banjo.
standard.cowpea.banjo
| x_pp_hi_incr | = | 1 24 | (hours) | ! photoperiod |
| y_hi_incr | = | 0.014 0.014 | (1/days) | ! rate of HI increase |
| x_hi_max_pot_stress | = | 0.0 1.0 | () | ! average stress at flowering |
| y_hi_max_pot | = | 0.6 0.6 | () | ! maximum harvest index potential |
| cum_vernal_days | = | 0 100 | ||
| tt_emerg_to_endjuv | = | 552.0 552.0 | (oCd) | ! TT from emergence to end of juvenile phase |
| est_days_emerg_to_init | = | 20 | (days) | ! estimated days from emergence to floral init. |
| x_pp_endjuv_to_init | = | 13.3 18.0 | (hours) | ! photoperiod |
| y_tt_endjuv_to_init | = | 0 229 | (oCd) | ! TT from end juvenile to floral initiation |
| x_pp_init_to_flower | = | 1 24 | (hours) | ! photoperiod |
| Y_tt_init_to_flower | = | 20.0 20.0 | (oCd) | ! TT from initiation to flowering |
| x_pp_flower_to_start_grain | 1 24 | (hours) | ! photoperiod | |
| y_tt_flower_to_start_grain | = | 100.0 100.0 | (oCd) | ! TT from flowering to start grain fill |
| x_pp_start_to_end_grain | = | 1 24 | (hours) | ! photoperiod |
| y_tt_start_to_end_grain | = | 280.0 280.0 | (oCd) | ! TT from start grain fill to end grain fill |
| tt_end_grain_to_maturity | = | 20 | (oCd) | ! TT from end grain fill to maturity |
| tt_maturity_to_ripe | = | 5.0 | (oCd) | ! TT from maturity to harvest ripe |
| x_stem_wt | = | 0 15 | (g/plant) | ! stem weight |
| y_height | = | 0 1000 | (mm) | ! plant height |
PLANT MODULE OUTPUTS
The following Plant variable can be output through the report module
| Variable Name | Units | Description |
| plant_status | character | Status of crop (e.g. alive, dead, out) |
| stage | Current phenological stage (real value) | |
| dlt_stage | Daily increase in phenological stage | |
| stage_code | Current phenological stage (integer value) | |
| stage_name | Current phenological stage (description) | |
| crop_type | character | crop type (e.g. mungbean, chickpea, cowpea, etc) |
| crop_class | crop class (e.g. plant, regrowth, etc) | |
| dlt_tt | o Cd | Daily increase in thermal time |
| phase_tt (max_stage = 12) | o Cd | Thermal time target for each phenological phase |
| tt_tot (max_stage = 12) | o Cd | Thermal time elapsed for each phenological phase |
| days_tot (max_stage = 12) | d | days elapsed for each phenological phase |
| das | days after sowing | Days since sowing |
| flowering_date | day of year | Flowering date |
| flowering_das | days after sowing | Flowering date |
| maturity_date | day of year | Maturity date |
| maturity_das | days after sowing | Maturity date |
| leaf_no (max_node = 1000) | number of fully expanded leaves during each node development | |
| node_no (max_stage = 12) | number of nodes on the mainstem, developed in each phase | |
| dlt_leaf_no | daily increase in number of leaves | |
| dlt_node_no | daily increase in number of nodes | |
| leaf_no_dead (max_node = 1000) | no of dead leaves in each phenological phase | |
| leaf_area (max_leaf = 1000) | mm 2 | leaf area of each leaf |
| height | mm | canopy height |
| root_depth | mm | depth of roots |
| plants | plants/m 2 | plant density |
| cover_green | 0-1 | fraction of radiation reaching the canopy that is intercepted by the green leaves of the canopy |
| cover_tot | 0-1 | total crop cover fraction |
| lai_sum | m 2 /m 2 | leaf area index of all leaf material live + dead |
| tlai | m 2 /m 2 | total lai (senseced plus green) |
| slai | m 2 /m 2 | area of leaf that senesces from plant |
| lai | m 2 /m 2 | live plant green lai |
| tlai_dead | m 2 /m 2 | total lai of dead plants |
| root_wt | g/m 2 | root biomass |
| leaf_wt | g/m 2 | leaf biomass |
| stem_wt | g/m 2 | stem biomass |
| pod_wt | g/m 2 | pod biomass |
| grain_wt | g/m 2 | grain biomass |
| dm_green (max_part = 6) | g/m 2 | live plant dry weight (biomass) of each plant part |
| dm_senesced (max_part = 6) | g/m 2 | senesced plant dry wt of each plant part |
| dm_dead (max_part = 6) | g/m 2 | dead plant dry weight of each plant part |
| yield | kg/ha | grain yield dry wt |
| biomass | kg/ha | total above-ground biomass |
| green_biomass | kg/ha | total above-ground biomass of green material |
| biomass_wt | g/m 2 | total above-ground biomass |
| dlt_dm | g/m 2 | the daily biomass production |
| dlt_dm_green (max_part = 6) | g/m 2 | daily plant biomass growth of each plant part |
| dlt_dm_green_retrans (max_part = 6) | g/m 2 | daily plant biomass retranslocation of each plant part |
| dlt_dm_detached (max_part = 6) | g/m 2 | daily biomass detached from live plants of each plant part |
| dlt_dm_dead_detached (max_part = 6) | g/m 2 | daily biomass detached from dead plants of each plant part |
| n_green (max_part = 6) | g/m 2 | plant nitrogen content of each plant part |
| n_senesced (max_part = 6) | g/m 2 | plant n content of senesced plant of each plant part |
| n_dead (max_part = 6) | g/m 2 | plant n content of dead plants of each plant part |
| dlt_n_green (max_part = 6) | g/m 2 | actual n uptake into plant of each plant part |
| dlt_n_retrans (max_part = 6) | g/m 2 | nitrogen retranslocated out from parts to grain of each plant part |
| dlt_n_detached (max_part = 6) | g/m 2 | actual n loss with detached plant of each plant part |
| dlt_n_dead_detached (max_part = 6) | g/m 2 | actual n loss with detached dead plant of each plant part |
| swdef_pheno | water deficit factor for phenology | |
| swdef_photo | water deficit factor fo photosynthesis | |
| swdef_expan | water deficit factor for leaf expansion | |
| swdef_fixation | water deficit factor for nitrogen fixation | |
| oxdef_photo | oxygen deficit (waterlogging) factor for photosynthesis | |
| ep | mm | Transpiration (Total water uptake from profile) |
| cep | mm | cumulative water uptake |
| sw_uptake(max_layer = 100) | mm | Water uptake from each profile layer |
| sw_demand | mm | total crop demand for water |
| sw_supply | mm | Total water supply over profile |
| sw_supply_layr(max_layer = 100) | mm | water supply in each profile layer |
| esw_layr (max_layer = 100) | mm | plant extractable soil water in each profile layer |
| n_conc_stover | % | sum of tops (leaf, stem and pod) actual n concentration |
| n_conc_crit | % | sum of tops (leaf and stem) critical n concentration |
| n_conc_leaf | % | actual n concentration in leaf |
| n_conc_stem | % | actual n concentration in stem |
| n_conc_grain | % | actual n concentration in grain |
| n_conc_min | % | minimum n concentration in tops (leaf and stem) |
| n_uptake or biomass_n | g/m 2 | cumulative total n uptake (minus roots): live, dead & senesced |
| green_biomass_n | g/m 2 | cumulative total n uptake by live (green) parts (minus roots) |
| n_uptake_stover | g/m 2 | n uptake by stover (green leaf, stem and pod) |
| no3_tot | g/m 2 | total no3 in the root profile |
| n_demand | g/m 2 | sum n demand for plant parts |
| n_supply_soil | g/m 2 | n supply from soil |
| dlt_n_fixed_pot | g/m 2 | daily potential N fixation |
| dlt_n_fixed | g/m 2 | actual daily N fixation |
| n_fixed_tops | g/m 2 | cumulative N fixed in above-ground biomass |
| nfact_photo | N deficit factor for photosynthesis | |
| nfact_grain | N deficit factor for grain N content | |
| rlv (num_layers) | mm/mm 3 | root length density in soil layer |
| no3_demand | kg/ha | plant demand for nitrate (when using APSSWIM) |
| root_length (max_layer = 100) | mm/mm 2 | total root length per unit ground surface area in each profile layer |
Validation
The Plant module has been described by Robertson et al. (2002). Previous models covering the species now in the Plant module have been validated by Adiku et al. (1993) (cowpea), Carberry (1996) (chickpea, mungbean, cowpea, soybean), Carberry et al. (1996a,b) (stylosanthes), lucerne (Probert et al. 1998), fababean (Robertson et al., in press), canola (Robertson, et al.,1999) and pigeonpea (Robertson et al., 2002).
References
Adiku S.K., Carberry P.S. Rose, C. W., McCown, R.L. & Braddock, R. (1993). Assessing the performance of maize (Zea mays – cowpea (Vigna unguiculata) intercrop under variable soil and climate conditions in the tropics. Proceedings of the 7th Australian Society of Agronomy Conference, September 1993, Adelaide , South Australia , p. 382.
Carberry, P.S. 1996. Assessing the opportunity for increased production of grain legumes in the farming system. Final Report to the Grains Research and Development Corporation, Project CSC9, 33pp.
Carberry, P.S.; Adiku, S.G.K.; McCown, R.L. and Keating, B.A. 1996b. Application of the APSIM cropping systems model to intercropping systems. In: O Ito, C Johansen, JJ Adu-Gyamfi, K Katayama, JVDK Kumar Rao, and TJ Rego (Eds.) Dynamics of Roots and Nitrogen in Cropping Systems of the Semi-Arid Tropics, pp. 637-648. Japan International Research Centre for Agricultural Sciences.
Carberry, P.S.; McCown, R.L.; Muchow, R.C.; Dimes, J.P.; Probert, M.E.; Poulton, P.L. and Dalgliesh, N.P. 1996b. Simulation of a legume ley farming system in northern Australia using the Agricultural Production Systems Simulator. Aust. J. Exptl. Agric. 36: 1037-48.
Probert, ME, Robertson, MJ, Poulton, PL, Carberry PS, Weston, EJ and Lehane, KJ (1998) Modelling lucerne growth using APSIM. Proceedings of the 1998 Australian Agronomy Conference, Wagga Wagga.
Robertson MJ, Carberry PS 1998. Simulating growth and development of soybean in APSIM. Proceedings 10th Australian Soybean Conference, Brisbane 15-17 September, 1998: pp. 130-136.
Robertson MJ, Holland JF, Kirkegaard JA, Smith CJ. 1999 Simulating growth and development of canola in Australia . In “Proceedings 10th International Rapeseed Congress. 1999” (CD-Rom) (Eds. N. Wratten and P.A. Salisbury).
Robertson MJ, Carberry PS, Huth NI, Turpin JE, Probert ME, Poulton PL , Bell M, Wright, GC, Yeates SJ and Brinsmead RB 2002. Simulation of growth and development of diverse plant species in APSIM. Australian Journal of Agricultural Research 53 , 643-651.
Sinclair, T.R. 1986. Water and nitrogen limitations in soybean grain production. Field Crops Res. 15: 125-141.
J.E. Turpin, M.J. Robertson, C. Haire, W.D. Bellotti, A.D. Moore and I. Rose (in press). Simulating fababean development, growth and yield in Australia . Australian Journal of Agricultural Research.