Soybean
1 Soybean
Source: Models.PMF.Plant
The model has been developed using the Plant Modelling Framework (PMF) of Brown et al., 2014. This framework provides a library of plant organ and process submodels that can be coupled, at runtime, to construct a model in much the same way that models can be coupled to construct a simulation.This means that dynamic composition of lower level process and organ classes(e.g.photosynthesis, leaf) into larger constructions(e.g.maize, wheat, sorghum) can be achieved by the model developer without additional coding.
Waterlogging Model (7th May 2026)
Sotirios Archontoulis, Isaiah Huber, Ke Liu, Matthew Harrison
Excess soil moisture could affect several processes within the soil-plant-atmosphere system. The new functions affect root depth, radiation use efficiency, phenology, leaf senescence, and grain components, as reported in the literature.
With the new additions, APSIM crop models can simulate both types of water stress: drought and excess water. We used SWIM as the primary soil water model to parameterize the new routines; however, users can use either SWIM or SoilWat; the new functions work with both soil water models.
Further documentation can be found here
The model is constructed from the following list of software components. Links provided will direct the user to the code for each model. Details of the implementation and model parameterisation are provided in the following sections.
| Component Name | Component Type |
|---|---|
| Arbitrator | Models.PMF.OrganArbitrator |
| Phenology | Models.PMF.Phen.Phenology |
| Leaf | Models.PMF.Organs.SimpleLeaf |
| Grain | Models.PMF.Organs.ReproductiveOrgan |
| Root | Models.PMF.Organs.Root |
| Nodule | Models.PMF.Organs.Nodule |
| Shell | Models.PMF.Organs.GenericOrgan |
| Stem | Models.PMF.Organs.GenericOrgan |
| MortalityRate | Models.Functions.Constant |
| SeedMortalityRate | Models.Functions.Constant |
| CumulativeExcessWaterStress | Models.Functions.AccumulateFunction |
| Waterlogging | Models.Soils.Waterlogging |
1.1 Composite Biomass Components
| Component Name | Component Organs | Live Material | Dead Material |
|---|---|---|---|
| AboveGround | Leaf Stem Grain Shell | True | True |
| BelowGround | Root Nodule | True | True |
| AboveGroundLive | Leaf Stem Grain Shell | True | False |
| Total | Leaf Stem Grain Shell Root Nodule | True | True |
| TotalLive | Leaf Stem Grain Shell Root Nodule | True | False |
| TotalDead | Leaf Stem Grain Shell Root Nodule | False | True |
| Pod | Shell Grain | True | True |
2 Phenology
| Number | Name | Type | Start Stage | End Stage |
|---|---|---|---|---|
| 1 | Germinating | Models.PMF.Phen.GerminatingPhase | Sowing | Germination |
| 2 | Emerging | Models.PMF.Phen.EmergingPhase | Germination | Emergence |
| 3 | Vegetative | Models.PMF.Phen.GenericPhase | Emergence | StartFlowering |
| 4 | EarlyFlowering | Models.PMF.Phen.GenericPhase | StartFlowering | StartPodDevelopment |
| 5 | EarlyPodDevelopment | Models.PMF.Phen.GenericPhase | StartPodDevelopment | StartGrainFilling |
| 6 | EarlyGrainFilling | Models.PMF.Phen.GenericPhase | StartGrainFilling | EndCanopyDevelopment |
| 7 | MidGrainFilling | Models.PMF.Phen.GenericPhase | EndCanopyDevelopment | EndPodDevelopment |
| 8 | LateGrainFilling | Models.PMF.Phen.GenericPhase | EndPodDevelopment | EndGrainFill |
| 9 | Maturing | Models.PMF.Phen.GenericPhase | EndGrainFill | Maturity |
| 10 | Ripening | Models.PMF.Phen.GenericPhase | Maturity | HarvestRipe |
| 11 | ReadyForHarvesting | Models.PMF.Phen.EndPhase | HarvestRipe | Unused |
Source: Models.PMF.Phen.Phenology
The phenological development is simulated as the progression through a series of developmental phases, each bound by distinct growth stage.
In the new model we simplified phenology by taking out stages that are not measurable (e.g. end of juvenile stage) and by adding new stages that are measurable (e.g. start pod). The new phenology follows the V/R staging system.
Figure [#FigureNumber]: Comparison of soybean phenological stages for APSIM Classic and APSIM Next Generation.
The key differences for cultivars are mostly phenological parameters (e.g., Vegetative.Target, ReproductivePhotoperiodModifier). In some cases, some additional parameters have been changed, see parameter values in the cultivar section.
3 Organ Arbitrator
Source: Models.PMF.OrganArbitrator
The Arbitrator class determines the allocation of dry matter (DM) and Nitrogen between each of the organs in the crop model. Each organ can have up to three different pools of biomass:
- Structural biomass which is essential for growth and remains within the organ once it is allocated there.
- Metabolic biomass which generally remains within an organ but is able to be re-allocated when the organ senesces and may be retranslocated when demand is high relative to supply.
- Storage biomass which is partitioned to organs when supply is high relative to demand and is available for retranslocation to other organs whenever supply from uptake, fixation, or re-allocation is lower than demand.
The process followed for biomass arbitration is shown in the figure below. Arbitration calculations are triggered by a series of events (shown below) that are raised every day. For these calculations, at each step the Arbitrator exchange information with each organ, so the basic computations of demand and supply are done at the organ level, using their specific parameters.
- doPotentialPlantGrowth. When this event occurs, each organ class executes code to determine their potential growth, biomass supplies and demands. In addition to demands for structural, non-structural and metabolic biomass (DM and N) each organ may have the following biomass supplies:
- Fixation supply. From photosynthesis (DM) or symbiotic fixation (N)
- Uptake supply. Typically uptake of N from the soil by the roots but could also be uptake by other organs (eg foliage application of N).
- Retranslocation supply. Storage biomass that may be moved from organs to meet demands of other organs.
- Reallocation supply. Biomass that can be moved from senescing organs to meet the demands of other organs.
- doPotentialPlantPartitioning. On this event the Arbitrator first executes the DoDMSetup() method to gather the DM supplies and demands from each organ, these values are computed at the organ level. It then executes the DoPotentialDMAllocation() method which works out how much biomass each organ would be allocated assuming N supply is not limiting and sends these allocations to the organs. Each organ then uses their potential DM allocation to determine their N demand (how much N is needed to produce that much DM) and the arbitrator calls DoNSetup() to gather the N supplies and demands from each organ and begin N arbitration. Firstly DoNReallocation() is called to redistribute N that the plant has available from senescing organs. After this step any unmet N demand is considered as plant demand for N uptake from the soil (N Uptake Demand).
- doNutrientArbitration. When this event occurs, the soil arbitrator gets the N uptake demands from each plant (where multiple plants are growing in competition) and their potential uptake from the soil and determines how much of their demand that the soil is able to provide. This value is then passed back to each plant instance as their Nuptake and doNUptakeAllocation() is called to distribute this N between organs.
- doActualPlantPartitioning. On this event the arbitrator call DoNRetranslocation() and DoNFixation() to satisfy any unmet N demands from these sources. Finally, DoActualDMAllocation is called where DM allocations to each organ are reduced if the N allocation is insufficient to achieve the organs minimum N concentration and final allocations are sent to organs.
4 Leaf
4.1 Leaf
Source: Models.PMF.Organs.SimpleLeaf
This organ is simulated using a SimpleLeaf organ type. It provides the core functions of intercepting radiation, producing biomass through photosynthesis, and determining the plant's transpiration demand. The model also calculates the growth, senescence, and detachment of leaves. SimpleLeaf does not distinguish leaf cohorts by age or position in the canopy.
Radiation interception and transpiration demand are computed by the MicroClimate model. This model takes into account competition between different plants when more than one plant is present in the simulation. The values of canopy cover, LAI, and plant height (as defined below) are passed daily by SimpleLeaf to the MicroClimate model. MicroClimate uses an implementation of the Beer-Lambert equation to compute light interception and the Penman-Monteith equation to calculate potential evapotranspiration. These values are then provided back to SimpleLeaf which uses them to calculate photosynthesis and soil water demand.
Light Interception
The interception of light by live and dead leaf material is calculated seperately within the SimpleLeaf model, with both calculations based upon the Beer-Lambert approach for light extinction (Monsi et al., 2005).
Calculations are as follows:
CoverGreen = 1.0 - exp (-ExtinctionCoefficient x LAI)
CoverDead = 1.0 - exp (-Kdead x LAIDead)
CoverTotal = 1.0 - (1 - CoverGreen) * (1 - CoverDead)
where
| Name | Description | Units |
|---|---|---|
| ExtinctionCoefficient | Live Canopy extinction coeffient for short wave radiation | unitless |
| LAI | Leaf Area Index for live leaf | (m2/m2) |
| Kdead | Dead canopy extinction coefficient for short wave radiaton | unitless |
| LAIDead | Leaf Area Index for dead leaf | (m2/m2) |
The formulations used to calculate the daily ExtinctionCoefficient are described later within this document.
CO2 Impact on Photosynthesis and Stomatal Conductance
A potential stomatal conductance is provided to the Microclimate model for use in calculating daily potential water use.
This conductance accounts for the effects of temperature, vapor deficit and plant nutrition.
Potential water use is then calculated by the microclimate model, and actual water use subsequently by the soil arbitrator model using data also provided by the root model regarding potential water uptake.
The impact of atmospheric CO2 concentration on stomatal conductance is dependant upon temperature and the related impact of CO2 concentration on photosynthesis.
Atmospheric CO2 concentration is specified by the user along with meteorological data when constructing each simulation.
StomatalConductance = Gsmax350 * FRGR * stomatalConductanceCO2Modifier;
stomatalConductanceCO2Modifier = PhotosynthesisCO2Modifier x (350 - CP)/(CO2 - CP)
CP = (163.0 - T) / (5.0 - 0.1 * T)
for C3 plants
PhotosynthesisCO2Modifier = (CO2 - CP) x (350 + 2 x CP)/(CO2 + 2 x CP) x (350 - CP)
for C4 plants
PhotosynthesisCO2Modifier = = 0.000143 * CO2 + 0.95
where
| Name | Description | Units |
|---|---|---|
| StomatalConductance | The influence of stomatal opening on rate of diffusion of water vapour exiting through the stomata of a leaf. | (mm/s) |
| Gsmax350 | Potential stomatal conductance at atmospherical CO2 concentration of 350ppm | (m/s) |
| FRGR | A factor that accounts for the relative growth rate of the plant | (0-1) |
| stomatalConductanceCO2Modifier | A factor that accounts for changes of Gsmax with CO2 concentration | (0-1) |
| PhotosynthesisCO2Modifier | A factor that accounts for changes in photosynthesis with CO2 concentration (Reyenga et al., 1999) | (0-1) |
| CP | The CO2 compensation point | (ppm) |
| T | The daily average temperature | oC |
Photosynthesis
Source: Models.Functions.SupplyFunctions.RUEModel
Potential daily photosynthesis is calculated as the product of intercepted short wave radiation and its conversion efficiency, the radiation use efficiency (RUE) (Monteith et al., 1977).
Note: RUE in this model is expressed as g/MJ for a whole plant basis, including both above and below ground growth.
The radiation use efficiency is adjusted from a base value appropriate for historical levels of atmospheric CO2 concentration (ie 350ppm - see previous section). Daily values of potential photosynthesis are then modified for whichever is the most severe effect of plant nitrogen status, temperature and atmospheric vapour pressure deficit. These same relative growth factors are provided to the MicroClimate model to moderate the stomatal conductance terms incorporated into the Penman-Monteith formulation. Finally, the daily growth rate is moderated in response to the relative water supply:demand ratio (FW) to capture the effect of daily plant water status.
This calculation for photosynthesis is then provided to the organ arbitrator as a potential daily DM fixation supply for arbitration with all other DM supplies and demands.
DMFixationSupply = RUE x PhotosynthesisCO2Modifier x Min(FT, FN, FVPD) x FW
where
RUE (Constant)
Radiation Use Efficiency for potential daily growth (g/MJ/m2)
RUE = 1.2
FT (WeightedTemperatureFunction)
Relative growth rate factor for Temperature (0-1)
MaximumTemperatureWeighting = 0.5
FN (LinearInterpolationFunction)
Relative growth rate factor for Nitrogen status (0-1)
| [Leaf].Fn | FN |
|---|---|
| 0.0 | 0.0 |
| 1.0 | 1.0 |
| 1.5 | 1.0 |
FVPD (LinearInterpolationFunction)
Relative growth rate factor for Vapour Pressure Deficit (0-1)
| [Leaf].Photosynthesis.VPD | FVPD |
|---|---|
| 0.0 | 1.0 |
| 10.0 | 1.0 |
| 50.0 | 1.0 |
FW (MinimumFunction)
Relative growth rate factor for plant water status (0-1)
Note: SimpleLeaf has two options to define the canopy: the user can either supply a function describing LAI or a function describing canopy cover directly. From either of these functions SimpleLeaf can obtain the other property using the Beer-Lambert equation with the specified value of extinction coefficient. The effect of growth rate on transpiration is captured by the Fractional Growth Rate (FRGR) function, which is passed to the MicroClimate model.
5 Grain
Source: Models.PMF.Organs.ReproductiveOrgan
This organ uses a generic model for plant reproductive components. Yield is calculated from its components in terms of organ number and size (for example, grain number and grain size).
6 Root
Source: Models.PMF.Organs.Root
The root model calculates root growth in terms of rooting depth, biomass accumulation and subsequent root length density in each soil layer as well as the access of soil resources (water, NO3 and NH4).
Note: Calculations are undertaken for each rooting zone for simulations where the plant has roots in multiple spatial zones.
Soil Water Uptake
The approach used for soil water uptake comes from [Meinke_Hammer_Want_1993]. A simple first order decay coefficient is used to describe the exponential decay in available soil water (ie water above the crop lower limit) over time.
For each layer to the rooting depth
AvailableSWmm = SWmmlayer - LLlayer x Thicknesslayer x LLmodifierlayer
Supplylayer = Max(0.0, KLlayer] x KLmodifierlayer x AvailableSWmm x RootProportionlayer
where
| Name | Description | Units |
|---|---|---|
| SWmm | The soil water content from the soil water model for a given soil layer | mm |
| Thickness | The width of the soil layer used within the soil water model | mm |
| LL | The crop lower limit obtained from SoilCrop node for the soil within the relevant Zone | mm3/mm3 |
| KL | The first order decay soil water uptake parameter obtained from SoilCrop node for the soil within the relevant Zone | /d |
| LLmodifier | A function used to modify LL to account for the effect of differing root geometry (usually set to 1.0) | 0-1 |
| KLmodifer | A function used to modifty KL to account for the effect of plant size (ie root length) on water uptake ability. | 0-1 |
| RootProportion | The fraction of the layer occupied for roots (e.g. 0.5 if roots occupy the top half of a layer only) | 0-1 |
Soil Nitrogen Uptake
Nitrogen uptake uses a second order decay approach for both NO3 and NH4, as implemented in several crop models within earlier versions of APSIM (eg APSIM 7.10 and earlier).
for each layer to the rooting depth
NO3Supplylayer = Math.Min(zone.NO3Nlayer * kno3 * NO3ppmlayer * SWAFlayer * RootProportionlayer, (maxNUptake - NO3Uptake));
NO3Uptake += NO3Supplylayer;
NH4Supplylayer = Math.Min(zone.NH4Nlayer * knh4 * NH4ppmlayer * SWAFlayer * RootProportionlayer, (maxNUptake - NH4Uptake));
NH4Uptake += NH4Supplylayer;
| Name | Description | Units |
|---|---|---|
| NO3 | The NO3 content from the nutrient model for a given layer | kg/ha |
| NO3ppm | The NO3 concentration from the nutrient model for a given layer | ppm |
| NH4 | The NH4 content from the nutrient model for a given layer | kg/ha |
| NH4ppm | The NH4 concentration from the nutrient model for a given layer | ppm |
| SWAF | The soil water availability factor to modify nitrogen uptake for a given layer. | 0-1 |
| RootProportion | The fraction of the layer occupied for roots (e.g. 0.5 if roots occupy the top half of a layer only) | 0-1 |
| maxNUptake | The maximum plant N uptake for a given day. | kg/ha/d |
| kno3 | The second order decay coefficient for NO3 uptake (ie uptake rate at 1 ppm). | /d/ppm |
| knh4 | The second order decay coefficient for NH4 uptake (ie uptake rate at 1 ppm). | /d/ppm |
Root Length
Root length is calculated from root biomass using a value for specific root length (mm/g). Proliferation of roots into different layers is calculated using a simple approach similar to the generalised equimarginal criterion approach used in the field of economics. It is assumed the maximal return on a plant's investment into roots is achieved when uptake per unit root mass is uniform across the soil profile. Daily allocation of root mass into layers is calculated as follows to provide proliferation of roots into areas of higher resource return, taking into account for previous allocation into those areas, such as near surface layers undergoing regular rewetting or below-ground capilliary fringes immediately above water tables.
// First calculate a root activity for water (RAW) for current root mass within the layer
for each layer in root profile
RAWlayer = WaterUptakelayer / Root.Live.Wtlayer x Thicknesslayer x RootProportion
// Then use these root activity values to partition daily allocation of growth into root layers as follows:
for each layer in root profile
DailyAllocationtoRootMasslayer = TotalDailyDMAllocationToRootMass x RAWlayer / sum(RAW)
| Name | Description | Units |
|---|---|---|
| RAW | The root activity for water uptake in relation to root mass | mm/g/m2 |
| WaterUptake | The daily water uptake by the plant model for a given layer | mm |
| Root.Live.Wt | The live root mass within a given layer | g/m2 |
| Thickness | The depth of the soil layer used within the soil water model | mm |
| RootProportion | The fraction of the layer occupied for roots (e.g. 0.5 if roots occupy the top half of a layer only) | 0-1 |
| TotalDailyDMAllocationToRootMass | The amount of daily growth provided to the root model by the organ arbitrator | g/m2 |
7 Nodule
Source: Models.PMF.Organs.Nodule
This organ simulates the root structure associate with symbiotic N-fixing bacteria. It provides the core functions of determining N fixation supply and related costs. It also calculates the growth, senescence and detachment of nodules.
8 Shell
Source: Models.PMF.Organs.GenericOrgan
This organ is simulated using a GenericOrgan type. It is parameterised to calculate the growth, senescence, and detachment of any organ that does not have specific functions.
9 Stem
Source: Models.PMF.Organs.GenericOrgan
This organ is simulated using a GenericOrgan type. It is parameterised to calculate the growth, senescence, and detachment of any organ that does not have specific functions.
10 CumulativeExcessWaterStress
Source: Models.Functions.AccumulateFunction
11 Waterlogging
Source: Models.Soils.Waterlogging
APSIM Waterlogging Functions Documentation
Sotirios Archontoulis, Isaiah Huber, Ke Liu, Matthew Harrison
Excess soil moisture could affect several processes within the soil-plant-atmosphere system. Here, we describe new waterlogging functions added to APSIM and tested in maize, soybean, canola, wheat, and barley. The new functions affect root depth, radiation use efficiency, phenology, leaf senescence, and grain components, as reported in the literature. With the new additions, APSIM crop models can simulate both types of water stress: drought and excess water. We used SWIM as the primary soil water model to parameterize the new routines; however, users can use either SWIM or SoilWat; the new functions work with both soil water models.
Summary table of processes affected by waterlogging
| Process | Driver | Model Approach | Crop-specific? |
|---|---|---|---|
| Root growth | AFPS | XY Modifier on root front velocity | Yes |
| RUE | Wet root fraction | Min(stress today, stress from legacy effect) | Crop and Stage Specific |
| Phenology | Cumulative Excess Water Stress | Phyllochron Modifier | Optional |
| Senesence | Cumulative Excess Water Stress | Leaf model modifier | Yes |
| Grain components | Cumulative Excess Water Stress | Penalty Functions | Yes |
Roots
We incorporated into the model the approach we already had in APSIM Classic (Ebrahimi-Mollabashi et al., 2019; Archontoulis et al., 2020, Liu et al. 2021, 2023). Excess moisture affects the root front. More specifically, when the air-filled pore space exceeds 97% of saturation, the root front velocity decreases for the period of excess stress. Model simulations showed good agreement with experimental findings (see graph). The users can alter the root parameters by altering the XY pairs: “[Root].RootFrontVelocity.AFPSFactor.XYPairs”
Radiation Use Efficiency
Excess moisture stress affects RUE like drought stress. Hence, we updated the model to calculate water stress on RUE by considering both “Deficient” and “Excess” moisture stress via the “Minimum Function” (i.e. RUE FW = min(F_deficient, F_excess), see model structure). The driver for excess moisture stress is the wet root fraction, which is calculated as (sw-dul)/(sat-dul)). A 0 to 1 daily value is computed as the average of it weighted by root length density. Then we use this information (x-axis) to develop a modifier (y-axis), which was added in the RUE module (name = Excess).
Different crop species could have different sensitivities to excess stress. Also, different crop growth stages have different sensitivities to excess stress; for example, maize is more sensitive early in the season, while soybeans are more sensitive later in the season. To address this, we made the XY function phase-specific (3 pairs of XY functions; early, middle, and late phase, user-defined, see below). This addition proved very important during model calibration.
The last aspect we implemented in the model was a “legacy” factor to reflect the time required for crops to recover after a period of excess moisture stress (as shown by the persistent reduction). The legacy effect is modeled as an exponential decay function.
While measured RUE data were not available, biomass data were used as a proxy to evaluate model performance, which was judged to be good. An example for maize is provided below (measured data by Lizaso and Ritchie).
Crop Phenology
Excess moisture could delay phenology (Liu et al., 2020). We model this phenomenon by adjusting the phyllochron parameter via an XY modifier. In the model, the driver for this delay (x-axis) is the cumulative excess water stress (“CumulativeExcessWaterStress”). This is off by default but remains open as a pathway the user could utilize via a custom cultivar.
Leaf Senescence
Excess moisture could accelerate canopy senescence. We capture this by adjusting leaf senescence via an XY modifier using “CumulativeExcessWaterStress” as the driver, similar to phyllochron; see diagrams below. Currently, there are two distinct leaf models in APSIM: “Leaf”, which is used by maize, and “SimpleLeaf”, which is used by soybean and canola. While implementation required different approaches for different crop models, the concept is similar. The conceptual diagram with the driver and new functions are presented below:
Grain components or harvest index
While it was expected that changes in senescence rate or RUE would capture reduced grain number or weight, or harvest index, this was not the case, indicating that modeling waterlogging is quite challenging. Therefore, we added functions to capture the reduction in grain components due to excess moisture. In maize, we model this as a cumulative penalty for water stress-driven excess (implemented as a 0-1 multiplier) on maximum grains per cob. In soybeans, there is a similar penalty on the potential harvest index. In canola, we increase the maximum potential grain size as cumulative excess water stress days increase beyond 5. Please see model structure for the XY modifiers.
Sensibility test
We run three soybean simulations reflecting 3 weather scenarios (normal, drought, and excess moisture; see below).
Both water stress simulations reduced biomass production and grain yield. The effects were evident in all plant organs, including N-fixation. We present some figures below:
Validation
We refer users to the APSIM sims to view the validation plots. Some examples are presented below.
References
Ebrahimi-Mollabashi E, Huth NI, Holzwoth DP, Ordonez RS, Hatfield JL, Huber I, Castellano MJ, Archontoulis SV, 2019. Enhancing APSIM to simulate excessive moisture effects on root growth. Field Crops Research 236: 58–67.
Pasley HR, Huber I, Castellano MJ, Archontoulis SV, 2020. Modeling flood-induced stress in soybeans. Frontiers Plant Science 11:62, doi:10.3389/fpls.2020.00062.
Archontoulis SV, Castellano MJ, Licht MA, Nichols V, Baum M, Huber I, Martinez-Feria R, Puntel L, Ordónez RA, Iqbal J, Wright EE, Dietzel RN, Helmers M, Vanloocke A, Liebman M, Hatfield JL, Herzmann D, Cordova SC, Edmonds P, Togliatti K, Kessler A, Danalatos G, Pasley H, Pederson C, Lamkey KR, 2020. Predicting Crop Yields and Soil-Plant Nitrogen Dynamics in the US Corn Belt. Crop Science, 60: 721–738.
Garcia-Vila M, M dos Santos Vianna, MT Harrison, K Liu, R de S. Nóia-Júnior, T Weber, J Zhao, M Acutis, SV Archontoulis, S Asseng, P Aubry, J Balkovic, B Basso, X Chen, Y Chen, Q de Jong van Lier, M Delandmeter, A de Wit, B Dumont, R Ferrise, C Folberth, M Gabbrielli, T Gaiser, A Gorooei, G Hoogenboom, KC Kersebaum, YU Kim, D Kraus, B Liu, L Martin, K Metselaar, C Nendel, G Padovan, A Perego, DM Seserman, C Scheer, V Shelia, V Stocca, F Tao, E Wang, H Webber, Z Zhao, Y Zhu, T Palosuo (2025) Gaps and strategies for accurate simulation of waterlogging impacts on crop productivity. Nat Food, 6: 553-562. doi:10.1038/s43016-025-01179-y
Liu K, Harrison MT, Yan H, Liu DL, Meinke H, Hoogenboom G, Wang B, Peng B, Guan K, Jaegermeyr J, Wang E, Zhang F, Yin X, Archontoulis S, Nie L, Badea A, Man J, Wallach D, Zhao J, Benjumea AB, Fahad S, Tian X, Wang W, Tao F, Zhang Z, Rötter R, Yuan Y, Zhu M, Dai P, Nie J, Yang Y, Zhang Y, Zhou M, 2023. Silver lining to a climate crisis in multiple prospects for alleviating crop waterlogging under future climates. Nature Communications 14, 765.
Liu, K., Harrison, M. T., Archontoulis, S. V., Huth, N., Yang, R., Liu, D. L., Yan, H.L., Meinke, H., Huber, I., Ibrahim, A., Zhang, Y.B. Tian, X.H & Zhou, M. (2021). Climate change shifts forward flowering and reduces crop waterlogging stress. Environmental Research Letters, 16(9), 094017
Liu, K., Harrison, M. T., Ibrahim, A., Manik, S. N., Johnson, P., Tian, X., Meinke, H., Zhou, M. (2020). Genetic factors increasing barley grain yields under soil waterlogging. Food and Energy Security, 9(4), e238
12 Appendix 1: Cultivar specifications
| Name (Aternatives) | Overrides |
|---|---|
A6785 |
[Phenology].EarlyFlowering.Target.FixedValue = 200 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.055 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 728 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 12.33, 15.46 [Phenology].Vegetative.Target.FixedValue = 416 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 12.33, 15.46 |
AsgrowAG4403_MG40 |
[Phenology].EarlyFlowering.Target.FixedValue = 123 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.30 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 535 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 13.09, 16.49 [Phenology].Vegetative.Target.FixedValue = 292 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.09, 16.49 |
AsgrowAG5701_MG50 |
[Phenology].EarlyFlowering.Target.FixedValue = 131 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.30 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 549 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 12.83, 16.13 [Phenology].Vegetative.Target.FixedValue = 404 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 12.83, 16.13 |
Becks321NRR_MG32 |
[Phenology].EarlyFlowering.Target.FixedValue = 117 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.20 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 522 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 13.45, 18.0 [Phenology].Vegetative.Target.FixedValue = 272 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.45, 18.0 |
Becks367NRR_MG37 |
[Phenology].EarlyFlowering.Target.FixedValue = 112 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.26 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 506 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 13.27, 17.2 [Phenology].Vegetative.Target.FixedValue = 267 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.27, 17.2 |
Bowyer |
[Phenology].EarlyFlowering.Target.FixedValue = 80 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.05 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 405 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 11.9, 15.5 [Phenology].Vegetative.Target.FixedValue = 377 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.75, 15.5 |
Buchanan |
[Grain].MaximumNConc.FixedValue = 0.067 [Leaf].AreaLargestLeaf.FixedValue=0.009 [Phenology].EarlyFlowering.Target.FixedValue = 100 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.015 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 467 [Phenology].MidGrainFilling.Target.FractionofMidToLateGrainfilling.FixedValue=0.33 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 12.39, 15.61 [Phenology].Vegetative.Target.FixedValue = 480 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 12.39, 15.61 [Stem].PotentialGrowth.PartitionFraction.StemGrowthPhase.StemFraction.FixedValue = 0.5 |
Bunya |
[Leaf].AreaLargestLeaf.FixedValue=0.009 [Phenology].EarlyFlowering.Target.FixedValue = 80 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.05 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 405 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 10.9, 15.5 [Phenology].Vegetative.Target.FixedValue = 550 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.75, 18 |
Cowrie |
[Leaf].AreaLargestLeaf.FixedValue=0.009 [Phenology].EarlyFlowering.Target.FixedValue = 80 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.05 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 425 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 12.75, 16 [Phenology].Vegetative.Target.FixedValue = 390 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 12.75, 16 |
Davis |
[Grain].MaximumNConc.FixedValue = 0.067 [Leaf].AreaLargestLeaf.FixedValue=0.012 [Phenology].EarlyFlowering.Target.FixedValue = 100 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.015 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 467 [Phenology].MidGrainFilling.Target.FractionofMidToLateGrainfilling.FixedValue=0.33 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 12.39, 15.61 [Phenology].Vegetative.Target.FixedValue = 480 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 12.39, 15.61 [Stem].PotentialGrowth.PartitionFraction.StemGrowthPhase.StemFraction.FixedValue = 0.5 |
Djakal |
[Leaf].AreaLargestLeaf.FixedValue=0.009 [Phenology].EarlyFlowering.Target.FixedValue = 100 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.05 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 405 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 11.9, 15.5 [Phenology].Vegetative.Target.FixedValue = 380 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.75, 17 |
Djakal1 |
[Leaf].AreaLargestLeaf.FixedValue=0.009 [Phenology].EarlyFlowering.Target.FixedValue = 80 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.05 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 440 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 13.26, 16.67 [Phenology].Vegetative.Target.FixedValue = 332 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.26, 16.67 |
F148_7 |
[Leaf].AreaLargestLeaf.FixedValue=0.009 [Phenology].EarlyFlowering.Target.FixedValue = 80 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.05 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 485 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 12, 18 [Phenology].Vegetative.Target.FixedValue = 314 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.75, 17 |
FiskebyV |
[Phenology].EarlyFlowering.Target.FixedValue = 80 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.05 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 405 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 13.75, 18 [Phenology].Vegetative.Target.FixedValue = 285 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.75, 18 |
Generic_MG0 |
[Phenology].EarlyFlowering.Target.FixedValue = 120 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.422 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 616 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 14.1, 19.95 [Phenology].Vegetative.Target.FixedValue = 336 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 14.1, 19.95 |
Generic_MG00 |
[Phenology].EarlyFlowering.Target.FixedValue = 100 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.467 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 600 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 14.35, 21.11 [Phenology].Vegetative.Target.FixedValue = 320 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 14.35, 21.11 |
Generic_MG000 |
[Phenology].EarlyFlowering.Target.FixedValue = 100 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.475 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 590 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 14.6, 22.35 [Phenology].Vegetative.Target.FixedValue = 310 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 14.6, 22.35 |
Generic_MG1 |
[Phenology].EarlyFlowering.Target.FixedValue = 120 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.411 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 632 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 13.84, 18.77 [Phenology].Vegetative.Target.FixedValue = 340 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.84, 18.77 |
Generic_MG10 |
[Phenology].EarlyFlowering.Target.FixedValue = 200 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.053 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 748 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 11.78, 14.65 [Phenology].Vegetative.Target.FixedValue = 470 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 11.78, 14.65 |
Generic_MG2 |
[Phenology].EarlyFlowering.Target.FixedValue = 120 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.386 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 648 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 13.59, 17.61 [Phenology].Vegetative.Target.FixedValue = 348 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.59, 17.61 |
Generic_MG3 |
[Phenology].EarlyFlowering.Target.FixedValue = 120 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.361 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 664 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 13.4, 16.91 [Phenology].Vegetative.Target.FixedValue = 380 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.4, 16.91 |
Generic_MG4 |
[Phenology].EarlyFlowering.Target.FixedValue = 140 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.324 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 664 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 13.1, 16.49 [Phenology].Vegetative.Target.FixedValue = 388 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.1, 16.49 |
Generic_MG5 |
[Phenology].EarlyFlowering.Target.FixedValue = 160 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.072 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 696 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 12.83, 16.13 [Phenology].Vegetative.Target.FixedValue = 396 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 12.83, 16.13 |
Generic_MG6 |
[Phenology].EarlyFlowering.Target.FixedValue = 180 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.056 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 712 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 12.58, 15.8 [Phenology].Vegetative.Target.FixedValue = 404 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 12.58, 15.8 |
Generic_MG7 |
[Phenology].EarlyFlowering.Target.FixedValue = 200 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.055 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 728 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 12.33, 15.46 [Phenology].Vegetative.Target.FixedValue = 416 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 12.33, 15.46 |
Generic_MG8 |
[Phenology].EarlyFlowering.Target.FixedValue = 200 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.054 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 744 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 12.07, 15.1 [Phenology].Vegetative.Target.FixedValue = 430 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 12.07, 15.1 |
Generic_MG9 |
[Phenology].EarlyFlowering.Target.FixedValue = 200 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.053 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 748 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 11.88, 14.82 [Phenology].Vegetative.Target.FixedValue = 460 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 11.88, 14.82 |
Hayman |
[Phenology].EarlyFlowering.Target.FixedValue = 200 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.054 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 744 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 12.07, 15.1 [Phenology].Vegetative.Target.FixedValue = 430 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 12.07, 15.1 |
Hedou19 |
[Leaf].AreaLargestLeaf.FixedValue=0.02 [Leaf].Phyllochron.Phyllochron.FixedValue=45 [Phenology].EarlyFlowering.Target.FixedValue = 120 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.361 [Phenology].EarlyPodDevelopment.Target.FixedValue = 180 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 600 [Phenology].MidGrainFilling.Target.FractionofMidToLateGrainfilling.FixedValue=0.45 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 13.57, 16.07 [Phenology].Vegetative.Target.FixedValue = 370 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.57, 16.07 |
Hooper_MG40 |
[Leaf].AreaLargestLeaf.FixedValue=0.009 [Phenology].EarlyFlowering.Target.FixedValue = 80 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.05 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 440 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 13.26, 16.67 [Phenology].Vegetative.Target.FixedValue = 380 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.26, 16.67 |
HornbeckHBK4891_MG40 |
[Phenology].EarlyFlowering.Target.FixedValue = 123 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.30 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 535 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 13.09, 16.49 [Phenology].Vegetative.Target.FixedValue = 292 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.09, 16.49 |
Hutcheson_MG50 |
[Phenology].EarlyFlowering.Target.FixedValue = 112 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.30 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 505 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 12.58, 15.88 [Phenology].Vegetative.Target.FixedValue = 438 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 12.58, 15.88 |
IA1006_MG10 |
[Phenology].EarlyFlowering.Target.FixedValue = 118 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.05 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 518 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 14.37, 19.30 [Phenology].Vegetative.Target.FixedValue = 336 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 14.37, 19.30 |
IA2008_MG20 |
[Phenology].EarlyFlowering.Target.FixedValue = 150 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.05 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 580 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 13.92, 17.93 [Phenology].Vegetative.Target.FixedValue = 329 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.92, 17.93 |
Jiuyuehuang |
[Grain].PotentialHarvestIndex.PotentialHarvestIndex.FixedValue=0.25 [Leaf].AreaLargestLeaf.FixedValue=0.013 [Phenology].EarlyFlowering.Target.FixedValue = 140 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.2 [Phenology].EarlyPodDevelopment.Target.FixedValue = 140 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 420 [Phenology].MidGrainFilling.Target.FractionofMidToLateGrainfilling.FixedValue=0.45 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 12.94, 15.44 [Phenology].Vegetative.Target.FixedValue = 540 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 12.94, 15.44 |
Kuranda |
[Phenology].EarlyFlowering.Target.FixedValue = 200 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.054 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 744 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 12.07, 15.1 [Phenology].Vegetative.Target.FixedValue = 430 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 12.07, 15.1 |
Lambert_MG0 |
[Phenology].EarlyFlowering.Target.FixedValue = 81 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.05 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 442 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 14.29, 20.14 [Phenology].Vegetative.Target.FixedValue = 294 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 14.29, 20.14 |
Leichhardt |
[Leaf].AreaLargestLeaf.FixedValue=0.009 [Phenology].EarlyFlowering.Target.FixedValue = 80 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.05 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 425 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 12.75, 14.7 [Phenology].Vegetative.Target.FixedValue = 806 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 12.75, 14.7 |
Macon_MG30 |
[Phenology].EarlyFlowering.Target.FixedValue = 163 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.175 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 607 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 14.05, 17.56 [Phenology].Vegetative.Target.FixedValue = 367 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 14.05, 17.56 |
Manark |
[Leaf].AreaLargestLeaf.FixedValue=0.009 [Phenology].EarlyFlowering.Target.FixedValue = 80 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.05 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 425 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 12.75, 15 [Phenology].Vegetative.Target.FixedValue = 440 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 12.75, 15 |
MG3_Wayne_WL |
[Leaf].Photosynthesis.FW.Excess.PersistentReduction.C = -0.02 [Leaf].Photosynthesis.FW.Excess.PersistentReduction.MovingSumFunction.NumberOfDays = 15 [Leaf].Photosynthesis.FW.Excess.Today.SowToStartFlower.EarlySeason.XYPairs.X = 0, 0.95, 1 [Leaf].Photosynthesis.FW.Excess.Today.SowToStartFlower.EarlySeason.XYPairs.Y = 1, 1, 0 [Leaf].Photosynthesis.FW.Excess.Today.StartFlowerToStartGF.MidSeason.XYPairs.X = 0, 0.8, 1 [Leaf].Photosynthesis.FW.Excess.Today.StartFlowerToStartGF.MidSeason.XYPairs.Y = 1, 1, 0 [Leaf].Photosynthesis.FW.Excess.Today.StartGFToEndCrop.LateSeason.XYPairs.X = 0, 0.8, 1 [Leaf].Photosynthesis.FW.Excess.Today.StartGFToEndCrop.LateSeason.XYPairs.Y = 1, 1, 0 [Leaf].Photosynthesis.RUE.FixedValue = 0.9 [Leaf].SenescenceRate.Reproductive.MaximumFunction.FOXSenescence.XYPairs.X = 0, 5 [Leaf].SenescenceRate.Reproductive.MaximumFunction.FOXSenescence.XYPairs.Y = 0, 0.02 [Leaf].SenescenceRate.Vegetative.Rate.FOXSenescence.XYPairs.X = 0, 5 [Leaf].SenescenceRate.Vegetative.Rate.FOXSenescence.XYPairs.Y = 0, 0.02 [Phenology].EarlyFlowering.Target.FixedValue = 110 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.30 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 557 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 13.54, 16.3 [Phenology].Vegetative.Target.FixedValue = 280 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.54, 16.3 [Root].RootFrontVelocity.AFPSFactor.XYPairs.Y = 0.0, 1.0, 1.0 [Root].RootFrontVelocity.PotentialRootFrontVelocity.early.Function.FixedValue = 28 [Root].RootFrontVelocity.PotentialRootFrontVelocity.late.Function.FixedValue = 0 [Root].RootFrontVelocity.PotentialRootFrontVelocity.PreEmergence.Function.FixedValue = 2.5 |
MG4_1989 |
[Grain].PotentialHarvestIndex.FOXhi.XYPairs.X = 0, 5, 10 [Grain].PotentialHarvestIndex.FOXhi.XYPairs.Y = 1, 0.85, 0.75 [Leaf].Photosynthesis.FW.Excess.PersistentReduction.C = -0.055 [Leaf].Photosynthesis.FW.Excess.PersistentReduction.MovingSumFunction.NumberOfDays = 15 [Leaf].Photosynthesis.FW.Excess.Today.SowToStartFlower.EarlySeason.XYPairs.X = 0, 0.995, 1 [Leaf].Photosynthesis.FW.Excess.Today.SowToStartFlower.EarlySeason.XYPairs.Y = 1, 1, 0 [Leaf].Photosynthesis.FW.Excess.Today.StartFlowerToStartGF.MidSeason.XYPairs.X = 0, 0.7, 1 [Leaf].Photosynthesis.FW.Excess.Today.StartFlowerToStartGF.MidSeason.XYPairs.Y = 1, 1, 0 [Leaf].Photosynthesis.FW.Excess.Today.StartGFToEndCrop.LateSeason.XYPairs.X = 0, 0.8, 1 [Leaf].Photosynthesis.FW.Excess.Today.StartGFToEndCrop.LateSeason.XYPairs.Y = 1, 1, 0 [Leaf].SenescenceRate.Reproductive.MaximumFunction.FOXSenescence.XYPairs.X = 0, 5 [Leaf].SenescenceRate.Reproductive.MaximumFunction.FOXSenescence.XYPairs.Y = 0, 0.25 [Leaf].SenescenceRate.Vegetative.Rate.FOXSenescence.XYPairs.X = 0, 5 [Leaf].SenescenceRate.Vegetative.Rate.FOXSenescence.XYPairs.Y = 0, 0.02 [Phenology].EarlyFlowering.Target.FixedValue = 140 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.324 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 664 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 13.1, 16.49 [Phenology].Vegetative.Target.FixedValue = 388 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.1, 16.49 [Root].RootFrontVelocity.AFPSFactor.XYPairs.Y = 0.0, 1.0, 1.0 |
MG4_Oxdef |
[Grain].PotentialHarvestIndex.FOXhi.XYPairs.X = 0, 5, 10 [Grain].PotentialHarvestIndex.FOXhi.XYPairs.Y = 1, 0.95, 0.85 [Leaf].Photosynthesis.FW.Excess.PersistentReduction.C = -0.1 [Leaf].Photosynthesis.FW.Excess.PersistentReduction.MovingSumFunction.NumberOfDays = 15 [Leaf].Photosynthesis.FW.Excess.Today.SowToStartFlower.EarlySeason.XYPairs.X = 0, 0.9, 1 [Leaf].Photosynthesis.FW.Excess.Today.SowToStartFlower.EarlySeason.XYPairs.Y = 1, 1, 0.3 [Leaf].Photosynthesis.FW.Excess.Today.StartFlowerToStartGF.MidSeason.XYPairs.X = 0, 0.6, 1 [Leaf].Photosynthesis.FW.Excess.Today.StartFlowerToStartGF.MidSeason.XYPairs.Y = 1, 1, 0 [Leaf].Photosynthesis.FW.Excess.Today.StartGFToEndCrop.LateSeason.XYPairs.X = 0, 0.6, 1 [Leaf].Photosynthesis.FW.Excess.Today.StartGFToEndCrop.LateSeason.XYPairs.Y = 1, 1, 0 [Leaf].Photosynthesis.RUE.FixedValue = 0.9 [Leaf].SenescenceRate.Reproductive.MaximumFunction.FOXSenescence.XYPairs.X = 0, 5 [Leaf].SenescenceRate.Reproductive.MaximumFunction.FOXSenescence.XYPairs.Y = 0, 0.06 [Leaf].SenescenceRate.Vegetative.Rate.FOXSenescence.XYPairs.X = 0, 5 [Leaf].SenescenceRate.Vegetative.Rate.FOXSenescence.XYPairs.Y = 0, 0.02 [Phenology].EarlyFlowering.Target.FixedValue = 140 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.324 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 664 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 13.1, 16.49 [Phenology].Vegetative.Target.FixedValue = 388 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.1, 16.49 [Root].RootFrontVelocity.AFPSFactor.XYPairs.Y = 0.0, 1.0, 1.0 |
MG4_Rhine |
[Leaf].Photosynthesis.FW.Excess.PersistentReduction.C = -0.09 [Leaf].Photosynthesis.FW.Excess.PersistentReduction.MovingSumFunction.NumberOfDays = 15 [Leaf].Photosynthesis.FW.Excess.Today.SowToStartFlower.EarlySeason.XYPairs.X = 0, 0.75, 1 [Leaf].Photosynthesis.FW.Excess.Today.SowToStartFlower.EarlySeason.XYPairs.Y = 1, 1, 0.75 [Leaf].Photosynthesis.FW.Excess.Today.StartFlowerToStartGF.MidSeason.XYPairs.X = 0, 0.5, 1 [Leaf].Photosynthesis.FW.Excess.Today.StartFlowerToStartGF.MidSeason.XYPairs.Y = 1, 1, 0.5 [Leaf].Photosynthesis.FW.Excess.Today.StartGFToEndCrop.LateSeason.XYPairs.X = 0, 0.5, 1 [Leaf].Photosynthesis.FW.Excess.Today.StartGFToEndCrop.LateSeason.XYPairs.Y = 1, 1, 0 [Leaf].Photosynthesis.RUE.FixedValue = 0.8 [Leaf].SenescenceRate.Reproductive.MaximumFunction.FOXSenescence.XYPairs.X = 0, 5 [Leaf].SenescenceRate.Reproductive.MaximumFunction.FOXSenescence.XYPairs.Y = 0, 0.075 [Leaf].SenescenceRate.Vegetative.Rate.FOXSenescence.XYPairs.X = 0, 5 [Leaf].SenescenceRate.Vegetative.Rate.FOXSenescence.XYPairs.Y = 0, 0.01 [Phenology].EarlyFlowering.Target.FixedValue = 140 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.324 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 664 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 13.1, 16.49 [Phenology].Vegetative.Target.FixedValue = 388 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.1, 16.49 [Root].RootFrontVelocity.AFPSFactor.XYPairs.Y = 0.0, 1.0, 1.0 |
Nandou12 |
[Grain].PotentialHarvestIndex.PotentialHarvestIndex.FixedValue=0.35 [Leaf].AreaLargestLeaf.FixedValue=0.013 [Phenology].EarlyFlowering.Target.FixedValue = 120 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.2 [Phenology].EarlyPodDevelopment.Target.FixedValue = 180 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 440 [Phenology].MidGrainFilling.Target.FractionofMidToLateGrainfilling.FixedValue=0.45 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 12.63, 15.13 [Phenology].Vegetative.Target.FixedValue = 500 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 12.63, 15.13 |
NK622_MG60 |
[Phenology].EarlyFlowering.Target.FixedValue = 93 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.30 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 467 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 12.39, 15.61 [Phenology].Vegetative.Target.FixedValue = 436 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 12.39, 15.61 |
Pioneer92MGI_MG26 |
[Phenology].EarlyFlowering.Target.FixedValue = 120 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.125 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 529 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 13.77, 17.57 [Phenology].Vegetative.Target.FixedValue = 271 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.77, 17.57 |
Pioneer94B01_MG40 |
[Phenology].EarlyFlowering.Target.FixedValue = 143 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.30 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 567 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 13.05, 16.46 [Phenology].Vegetative.Target.FixedValue = 322 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.05, 16.46 |
PioneerP22T61_MG22 |
[Phenology].EarlyFlowering.Target.FixedValue = 106 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.3 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 499 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 13.59, 17.6 [Phenology].Vegetative.Target.FixedValue = 328 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.59, 17.6 |
PioneerP22T69R_MG22 |
[Phenology].EarlyFlowering.Target.FixedValue = 106 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.35 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 580 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 13.59, 17.6 [Phenology].Vegetative.Target.FixedValue = 328 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.59, 17.6 |
PioneerP92Y75_MG27 |
[Phenology].EarlyFlowering.Target.FixedValue = 106 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.14 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 499 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 13.59, 17.6 [Phenology].Vegetative.Target.FixedValue = 328 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.59, 17.6 |
PioneerP932T16R_MG32 |
[Phenology].EarlyFlowering.Target.FixedValue = 106 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.40 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 499 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 13.59, 17.6 [Phenology].Vegetative.Target.FixedValue = 328 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.59, 17.6 |
PioneerP93M11_MG31 |
[Grain].MaximumNConc.FixedValue = 0.065 [Leaf].AreaLargestLeaf.FixedValue=0.013 [Phenology].EarlyFlowering.Target.FixedValue = 110 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.30 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 457 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 13.4, 16.0 [Phenology].Vegetative.Target.FixedValue = 250 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.4, 16.0 [Shell].PotentialHarvestIndex.FixedValue = 0.5 |
PioneerP9504_MG50 |
[Phenology].EarlyFlowering.Target.FixedValue = 112 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.30 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 505 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 12.83, 16.13 [Phenology].Vegetative.Target.FixedValue = 404 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 12.83, 16.13 |
Richmond |
[Phenology].EarlyFlowering.Target.FixedValue = 180 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.056 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 712 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 12.58, 15.8 [Phenology].Vegetative.Target.FixedValue = 404 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 12.58, 15.8 |
Soya791 |
[Leaf].AreaLargestLeaf.FixedValue=0.009 [Phenology].EarlyFlowering.Target.FixedValue = 80 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.05 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 425 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 12.75, 15.3 [Phenology].Vegetative.Target.FixedValue = 430 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 12.75, 15.3 |
Stephens_MG40 |
[Leaf].AreaLargestLeaf.FixedValue=0.009 [Phenology].EarlyFlowering.Target.FixedValue = 80 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.05 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 440 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 13.26, 16.67 [Phenology].Vegetative.Target.FixedValue = 360 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 13.26, 16.67 |
Texuan13 |
[Grain].PotentialHarvestIndex.PotentialHarvestIndex.FixedValue=0.35 [Leaf].AreaLargestLeaf.FixedValue=0.013 [Phenology].EarlyFlowering.Target.FixedValue = 200 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.2 [Phenology].EarlyPodDevelopment.Target.FixedValue = 170 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 460 [Phenology].MidGrainFilling.Target.FractionofMidToLateGrainfilling.FixedValue=0.45 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 12.63, 15.13 [Phenology].Vegetative.Target.FixedValue = 500 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 12.63, 15.13 |
Trial_MG00 |
[Phenology].EarlyFlowering.Target.FixedValue = 76 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.05 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 432 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 14.43, 21.19 [Phenology].Vegetative.Target.FixedValue = 268 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 14.43, 21.19 |
Warrigal |
[Leaf].AreaLargestLeaf.FixedValue=0.009 [Phenology].EarlyFlowering.Target.FixedValue = 80 [Phenology].EarlyGrainFilling.Target.FractionofGrainfilling.FixedValue = 0.05 [Phenology].LateGrainFilling.Target.EntireGrainfillPeriod.FixedValue = 425 [Phenology].ReproductivePhotoperiodModifier.XYPairs.X = 12.75, 15 [Phenology].Vegetative.Target.FixedValue = 490 [Phenology].VegetativePhotoperiodModifier.XYPairs.X = 12.75, 15 |
13 References
Monteith, J. L., Moss, C. J., 1977. Climate and the Efficiency of Crop Production in Britain [and Discussion]. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 281 (980), 277-294.
Reyenga, P.J., Howden, S. M., Meinke, H., McKeon, G.M., 1999. Modelling global change impacts on wheat cropping in south-east Queensland, Australia. Environmental Modelling & Software 14, 297-306.