1 The APSIM STRUM Model

The STRUM model

A simple representation of the shape and mass of rows of fruit trees which can be used in a multizone simulation with a row of mown grass to model the water N and C balance of an orchard system.

The model has been developed using the Plant Modelling Framework (PMF) of Brown et al., 2014. This new 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.

STRUM: the Simple Tree Resource Uptake Model

Hamish Brown, Edith Khaembah, Rogerio Cichota, Xiumei Yang and Jo Sharp, New Zealand Institute for Bioeconomy Science

Neil Huth and Dean Holzworth, Commonwealth Scientific and Industrial Research Organisation

Steven Reeves, Queensland Department of the Environment, Tourism, Science and Innovation

The STRUM model is designed and built along the same lines and the SCRUM and SPRUM models. Its primary purpose is the represent the contributions of orchard trees to the nitrogen and water balance of an orchard. It can also be used to assess the impacts of water and nitrogen supply on tree stress and potential fruit production. It can be used where a simple plant model is required to reduce structural uncertainty in simulations or where simulations of tree crop systems are required but no detailed tree crop model exists.

The trees phenology is represented in a very simple manner with a series of phases that represent dormancy; a period of canopy expansion; a period or full canopy and a period of leaf fall returning to the Dormant phase: The dates that the model transitions between these phases are user inputs provide in the model configuration. Date of flowering and subsequent fruit growth are also provided as user inputs. As such it is easy to parameterize the timing canopy and fruit development to occur at times that are sensible for the given context. However, the crops phenology does not respond to temperature or photoperiod so the model is not appropriate where users are wanting to simulate the effects of environment and management on tree development.

In most cases STRUM is used in a multizone simulation with the STRUM model growing in a “Row” zone along-side an “Alley” zone that may have a crop or pasture model growing in it. A schematic representation of a typical Row/Alley structure is shown below.

Attention has been paid to modelling the interception of solar radiation by the tree row zone and the interaction of roots with other plants growing in the alley. A modified implementation of the Gou et al., 2017 radiation interception model has been implemented and tested in the APSIM Microclimate module to simulate the radiation of a row of tree canopies and a shorter alley understory. Briefly, the tree canopy is treated as a cube and the proportion of diffuse radiation that would be intercepted by this cube is calculated firstly assuming it is a black body. Then transmission through the cube is calculated based on the fractional radiation interception of the canopy (based on LAI and extinction coefficient). Radiation that is not captured by the tree canopy is then partitioned between the Row and Alley understories. The STRUM canopy intercepts radiation with both green leaves (which drives photosynthesis and transpiration) and trunk area (which shades the understory). An example of a typical seasonal pattern of radiation interception is shown in the figure below. While the tree has no green leaves it is intercepting a small proportion of the total radiation over the simulation area (Tree_FRad, as shown by the black line). At Bud break the leaf area starts expanding and the fractional radiation interception of the green canopy increases (Canopy_FRad as shown by the green line). Canopy_FRad remains constant from the start of full canopy until the start of leaf fall but Tree_FRad continues to increase during this time as the trees hight and width increases and it captures more of the total radiation.

The STRUM model consists of 4 organs:

1. Leaf which represents leaves on a tree.
2. Fruit which represents the biomass and nitrogen that may be removed from the orchard each year as harvested product
3. Trunk which represents the perennial biomass of the trees and will grow a small amount each year but is mostly pruned out at the end of the year and may be returned to the soil surface.
4. Root which extracts water and nitrogen from the soil for plant growth, grows a small amount of biomass each year and senesesses a proportion of this to the soil.

Using STRUM in simulations

• STRUM is programmed to work with rectangular zones so users must ensure this zone type is used. It also requires the TreeRow strip crop radiation model to be selected for each zone.
• While the STRUM model will run as a stand alone model, in most cases fruit trees and vines are grown in rows with a grass or herb ley growing in the alleys. As such it is important to set up a simulation with two rectangular zones, one containing the STRUM fruit tree model and another with the understory crop or pasture. In most cases the SPRUM model is adequate to represent the energy, water and nitrogen balance of the alleys.
• STRUM has been designed to repeat through annual growth cycles over any number of years. If STRUM is included in a simulation it will establish itself on the first day of the simulation. The annual cycle is based around the winter solstice so it is best to start simulations including STRUM on the 21st of June (Southern Hemisphere) or 21st of December (Northern Hemisphere). Some working examples are provided in the STRUM.apsimx file in the released example folder.

The zone representing the tree row needs to have the STRUM model included and a STRUMTreeInstance where the parameters for the orchard layout and tree behaviour are set. BiomassRemovalEvents models must also be included to specify the dates and amounts of biomass removed by picking and pruning. The STRUMTreeInstance has the following parameters:

• Tree Type: The user specifies if the tree is deciduous or evergreen which influences leaf senescence and winter cover patterns
• Distance between rows, Distance between trees within rows and Relative alley width: These parameters are used to set the size of the Row and Alley zones. Note that STRUM overwrites the length and width properties on the Rectangular zone model when it is established so those properties have not effect in simulations containing STRUM.
• Tree age at start of simulation: STRUM simulates an increase in its dimensions annually until it reaches its maximum dimension. This parameter is used to determine the size of the tree relative to its maximum on the day of initialisation.
• Age of tree at maximum dimension: The trees dimensions will no longer increase annually once this age is reached
• Trunk mass when maximum dimension is reached. Trunk is defined as all above ground material that is not leaf or fruit. Set this as the weight that a tree would typically reach when it is at the maximum dimension that it would be pruned back to annually.
• Date for Budbreak, Start of full canopy, Start of leaf fall and End of leaf fall represent typical dates for these canopy development stages for the location where the tree is being simulated. Some experience with the tree crop being modelled is required to ensure sensible values are set here.
• Grow roots into Alley zone: The trees roots will be present in the Row zone, this option specifies if they also grow into the alley zone.
• Root depth when mature: Sets the root depth that the tree will achieve at the Age of maximum dimension (see above). If the Age of the tree at the start of the simulation is less that the age at maximum dimension the root system will start shallower that this and grow down to this depth over time. If XF parameters are set to zero at any depth the roots will only achieve that depth and not reach the depth specified here.
• Root, Leaf and Trunk biomass proportions set what proportion of the total photosynthesis is demanded by these organs each day. Note that fruit biomass demand is determined by fruit size, number and growth pattern parameters (see below) and its demands are given a higher priority so Root, Leaf and Trunk might not get the full proportion of biomass demanded when fruit are growing.
• Height of the bottom of the canopy is the height at which tree leaves are held above the ground. This should be higher than the top of the alley crop to avoid errors in the radiation interception predictions.
• Height and Width of the mature tree before pruning is the maximum hight and width that the trees reach before they are pruned each year.
• Height and Width of the mature tree after pruning is the height and width the tree is pruned down to each year. Note, the model adjusts its height and width independent of the Pruning Biomass removal event and it is assumed that pruning occurs annually on the winter solstice.
• Nitrogen content of Leaf, Trunk, Root and Fruit are used to determine the N demands for the growth of each of these organs
• Extinction coefficient is used to derive maximum LAI from cover.
• Winter cover of the tree canopy is only relevant for evergreen trees and sets the cover that is maintained through the winter
• Maximum cover sets the cover that the tree crop achieves between Start of full canopy and Start of leaf fall. Note for this (and the winter cover) it is the fractional radiation interception of the area of the canopy. The radiation interception model also uses the width of the tree (relative to row spacing) and the height of the canopy to determine total radiation interception by the tree canopy.
• Radiation use efficiency is the amount of biomass (g) that is produced by photosynthesis per unit of radiation (MJ) intercepted by the canopy. This is then partitioned to leaf, root, trunk and fruit. The base, optima and maximum temperatures specify the seasonal response of photosynthesis to temperature. Note that temperatures represent daily mean temperatures not maximums.
• Maximum canopy conductance and Net radiation at 50% canopy conductance determine the trees water demand. Typical values for trees are 0.005 and 50 respectively.
• KL in the top soil layer specified the kl in the top 30 CM of soil. Note that STRUM overwrites the KL values specified on the STRUMSoil node on the Soil.Physical Node so there is no need to pay attention to setting these values. Instead STRUM calculates KL for each soil layer assuming an exponential decline from the top layer values to zero at the maximum root depth.
• It is also possible to turn off the crops response to water stress which will stop the canopy and biomass production being affected by any water shortages.
• Fruit number retained is the number of fruit that will be left on the tree after thinning. It is specified per m^2 of canopy so the total number of fruit per tree will increase as the tree grows
• Potential Fruit weight is the average fresh weight of individual fruits.
• Fruit DM concentration is the dry matter concentration of fresh fruit which is used to calculate fruit dry weight from fresh weight
• Fruit Density is used to calculate fruit size assuming fruit are spherical.
• Date of maximum bloom is the date that flowering occurs and is when slow fruit growth starts. An approximate sigmoidal increase in fruit mass is assumed with 1% of the final mass reached at Start Linear Growth and 90% reached on end linear growth. Users need to have some experience with the crop to ensure appropriate dates are entered for flowering and the points of fruit growth to ensure sensible fruit growth patterns.

STRUM sets the area of Row and Alley zones so that a simulatoin is representing a single tree and the area or row and alley zone that goes with it. The user needs to be careful when considering STRUM model outputs becaue the outputs are generally on a per tree basis.

The model is constructed from the following list of software components. Details of the implementation and model parameterisation are provided in the following sections.

1.1 Plant Model Components

1.2 Child Components

1.2.1 Arbitrator

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 Figure [FigureNumber]. 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.

1. 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.

2. 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).
3. 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.
4. 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.

Figure [FigureNumber]: Schematic showing the procedure for arbitration of biomass partitioning. Pink boxes represent events that occur every day and their numbering shows the order of calculations. Blue boxes represent the methods that are called when these events occur. Orange boxes contain properties that make up the organ/arbitrator interface. Green boxes are organ specific properties.

The partitioning of biomass to fodder beet organs follows a "Relative Allocation" routine (see Arbitrator module documentation) guided by coefficients derived from the October sowing date data of the test dataset

1.2.2 RootUptakesArbitrator

Interface between soil arbitrator and Plant model instance All supplies, demands and uptakes passed to and from the Soil Arbitrator are in liters for water and kg for N. This is because the Soil Arbitrator work independently of area. This interface needs to adjust uptakes back to the appropriate units to send them back to plan.

1.2.3 Phenology

The phenological development is simulated as the progression through a series of developmental phases, each bound by distinct growth stage.

1.2.4 Root

This is the basic organ class that contains biomass structures and transfers

1.2.5 Leaf

This is the basic organ class that contains biomass structures and transfers

1.2.6 Trunk

This is the basic organ class that contains biomass structures and transfers

1.2.7 Fruit

This is the basic organ class that contains biomass structures and transfers

1.2.8 AccumulatedWaterStress

Accumulates a child function between a start and end stage or start and end events.

Water Stress is accumulated while the tree has a canopy and reset each year at the start of canopy expansion. This value is referenced by NextYearsFruitingBudSurvival when the tree goes dormand to determine to set the proportion of buds that will have survived this year. That value is held over into the following season so stress effects on fruiting bud number from this season are carried over to affect fruit load for next season.

1.2.9 AboveGround

This is a composite biomass class, representing the sum of 1 or more biomass objects.

1.2.10 AboveGroundDead

This is a composite biomass class, representing the sum of 1 or more biomass objects.

1.2.11 BelowGround

This is a composite biomass class, representing the sum of 1 or more biomass objects.

1.2.12 Total

This is a composite biomass class, representing the sum of 1 or more biomass objects.

1.2.13 TotalLive

This is a composite biomass class, representing the sum of 1 or more biomass objects.

1.2.14 TotalDead

This is a composite biomass class, representing the sum of 1 or more biomass objects.

1.2.15 SeedMortalityRate

A constant function (name=value)

1.2.16 MaxPrunedHeight

A constant function (name=value)

1.2.17 MortalityRate

A constant function (name=value)

1.2.18 Height

A class that returns the sum of its child functions.

1.2.19 Width

A class that returns the sum of its child functions.

1.2.20 RelativeAnnualDimension

A linear interpolation model, where an

1.2.21 RelativeSeasonalDimension

A linear interpolation model, where an

1.2.22 RadiationIntercepted

A class that returns the sum of its child functions.

1.2.23 RowWidth

A constant function (name=value)

1.2.24 TranspirationPerTree

A class that returns the product of its child functions. Performance note: This function returns zero as soon as any of its child functions return zero. Therefore, speed gains can be achieved by placing children that are likely to return zero values at the top of the list of children

1.2.25 InterRowSpacing

A constant function (name=value)

1.2.26 CanopyArea

Returns special values if the numerator is 0 or if the denominator is 0.

Currently used in sorghum/maize code to mimic divide functions in old apsim which return 10 if the denominator is 0 or 0 if the numerator is 0.

1.2.27 TranspirationPerLeafArea

Returns special values if the numerator is 0 or if the denominator is 0.

Currently used in sorghum/maize code to mimic divide functions in old apsim which return 10 if the denominator is 0 or 0 if the numerator is 0.

2 PatternTests

I this test canopy size is held constant and the size of the soil zone is changed. As Zone gets larger supply of nitrogen and water (kg) to the soil arbitrator should increase in proportion to zone size. Demand for water and nitrogen should remain constant as they are driven by canopy size rather than zone size. Uptakes should be the minumu of supply and demand

I this test canopy size is held constant and the size of the soil zone is changed. As Zone gets larger supply of nitrogen and water (kg) to the soil arbitrator should increase in proportion to zone size. Demand for water and nitrogen should remain constant as they are driven by canopy size rather than zone size. Uptakes should be the minumu of supply and demand

I this test canopy size is held constant and the size of the soil zone is changed. As Zone gets larger supply of nitrogen and water (kg) to the soil arbitrator should increase in proportion to zone size. Demand for water and nitrogen should remain constant as they are driven by canopy size rather than zone size. Uptakes should be the minumu of supply and demand

3 NZ AppleExperiments

• Based on Green et al 2013 - PFR report for the Hawkes Bay Regional Council

• Location: Hastings, NZ

• Cultivar: Pink Lady

• Transpiration measured using sap flow sensors.

• Based on Green et al 2013 - PFR report for the Hawkes Bay Regional Council

• Location: Hastings, NZ

• Cultivar: Pink Lady

• Transpiration measured using sap flow sensors.

4 USA AppleExperiments

• Based on Ebel e al (2001) - HORTSCIENCE, VOL. 36(7)

• Trial site: Roza irrigation district in the lower Yakima valley near Prosser, Wash. (lat. 45°N; long. 119°W and 380 m above sea level)

• Soil: Warden fine sandy loam (coarse, silty, mixed, mesic, Xerollic Camborthids) that was uniform down the profile and overlaid fractured basalt bedrock

• The permanent wilting and field capacity were 8% (80 mm·m–1) and 28% (280 mm·m–1) by volume, respectively - total available soil water was 200 mm·m–1 for the two orchards.

• The shallow soil (mean depth = 0.8 m) and deeper soil (average depth = 1.2 m) had 160 and 240 mm of total water available at field capacity (FC), respectively.

• Irrigation treatments: withholding irrigation all season or from 3, 5, 7, 9, 11, 13, 15 or 17 weeks before harvest.

• Total pan evaporation was 1005 mm and precipitation was negligible from May through September.

• Apples grown in Washington, e.g. Red/Golden Delicious, Gala, and Granny Smith, are in season from August to early November.

• Apple trees in Washington should be sown in early spring.

5 NZ Avocado

6 NZ Kiwifruit Expts

• Correct soils entered

• Correct climate data entered

• Approx start growth and season-end added

• This is a Gold3 orchard

• This is a Gold3 orchard

• This is a Gold3 orchard

7 Interface

7.1 STRUM

Properties (Outputs)

Links (Dependencies)

Events published

Methods (callable from manager)

7.2 SowingParameters

Parameters which control how a plant is sown.

Properties (Outputs)

7.3 Phenology

The phenological development is simulated as the progression through a series of developmental phases, each bound by distinct growth stage.

Properties (Outputs)

Links (Dependencies)

Events published

Methods (callable from manager)

8 References

Brown, Hamish E., Huth, Neil I., Holzworth, Dean P., Teixeira, Edmar I., Zyskowski, Rob F., Hargreaves, John N. G., Moot, Derrick J., 2014. Plant Modelling Framework: Software for building and running crop models on the APSIM platform. Environmental Modelling and Software 62, 385-398.

Gou, Fang, van Ittersum, Martin K., Simon, Elisabeth, Leffelaar, Peter A., van der Putten, Peter E.L., Zhang, Lizhen, van der Werf, Wopke, 2017. Intercropping wheat and maize increases total radiation interception and wheat RUE but lowers maize RUE. European Journal of Agronomy 84, 125-139.