1 The APSIM Barley Model

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.

Brown, H.E., Huth, N.I., Khaembah E.N., Zyskowski, R., and Holzworth, D.P.

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 Composite Biomass

1.3 Cultivars

1.4 Child Components

1.4.1 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 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.

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.

1.4.2 Phenology

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

Barley exhibits a range of developmental responses to environment and these are strongly influences by genotype characteristics. Temperature effects development increasing development rates and decreasing phase durations as temperatures increase. Theset affects are captured by thermal time, . However, Barley also exhibits vernalisation and photoperiod sensitivities in its Vegetative phase and further photoperiod sensitivity in the EarlyReproducivePhase. Photoperiod responses are seen as a reduction in the length of a phase for a photoperiod sensitive genotype in response to a longer photoperiod. For vernalisation sensitive varieties (Winter types) exposure to cool temperatures or short photoperiods during the Vegetative phase will reduce the thermal time duration of the vegetative phase.

This model draws on the Kirby Framework to capture these vernalisation and photoperiod responses. This framework assumes that the timing of anthesis is a result of the timing of flag leaf and an additional thermal time passage from there to anthesis. It also assumes the timing of flag leaf is a result of the Final Leaf Number which sets a target, and leaf appearance rate, which sets the rate of progress toward this target. Leaf appearance rate is a function of Thermal time and a genotype specific Phyllochron which changes with Haun stage (Jamieson et al., 1995).

Final Leaf Number (FLN) is modeled as the sum of three numbers:

FLN = MinLeafNumber + VernalLeaves + PhotoLeaves

Where MinLeafNumber is the number of leaves that a barley crop will produce when vernalisation is satisified early in the crops duration (before 2nd true leaf) and it is grown in a long photoperiod. VernalLeaves are the number of leaves that are added due to vernalisation effects. For insensitive varieties this will always be zero but this is potentially a larger number for sensitivie varieties and the number progresively decreases as the crop encounters more vernalisation. PhotoLeaves are the number of leaves that are added to the minimum leaf number as a result of short day exposure. For insensitive varieties this will be zero but is potential larger for more sensitivie varieties and decreases as day length increases. More detailed explinations of the components of phenology are provided below.

1.4.3 Structure

The structure model simulates morphological development of the plant to inform the Leaf class when and how many leaves and branches appear and provides an estimate of height.

Barley exhibits a range of developmental responses to environment and these are influences by genotype characteristics. Temperature effects development increasing development rates and decreasing phase durations as temperatures increase. These affects are captured by thermal time. However, barley also exhibits vernalisation and photoperiod sensitivities in its Vegetative phase and further photoperiod sensitivity in the EarlyReproducivePhase. Photoperiod responses are seen as a reduction in the length of a phase for a photoperiod sensitive genotype in response to a longer photoperiod. Vernalisation responses are more complicated as they are driven by temperature but interact with photoperiod. For vernalisation sensitive varieties (Winter types) exposure to cool temperatures or short photoperiods during the Vegetative phase will reduce the thermal time duration of the vegetative phase.

We draw on the Kirby Framework to capture these vernalisation and photoperiod responses. This framework assumes that the timing of anthesis is a result of the timing of flag leaf and an additional thermal time passage from there to anthesis. It also assumes the timing of flag leaf is a result of the Final Leaf Number which sets a target, and leaf appearance rate, which sets the rate of progress toward this target. Leaf appearance rate is a function of Thermal time and a genotype specific Phyllochron which changes with Haun stage as described by Jamieson et al., 1995.

Final Leaf Number (FLN) is modeled as the sum of three numbers:

FLN = MinLeafNumber + VernalLeaves + PhotoLeaves

Where MinLeafNumber is the number of leaves that a wheat crop will produce when vernalisation is satisified early in the crops duration (before 2nd true leaf) and it is grown in a long photoperiod.. VernalLeaves are the number of leaves that are added due to vernalisation effects. For insensitive varieties this will always be zero but this is potentially a larger number for sensitivie varieties and the number progresively decreases as the crop encounters more vernalisation. PhotoLeaves are the number of leaves that are added to the minimum leaf number as a result of short day exposure. For insensitive varieties this will be zero but is potential larger for more sensitivie varieties and decreases as day length increases. More detailed explinations of the components of phenology are provided below.

Wheat exhibits a range of developmental responses to environment and these are strongly influences by genotype characteristics. Temperature effects development increasing development rates and decreasing phase durations as temperatures increase. Theset affects are captured by thermal time, . However, wheat also exhibits vernalisation and photoperiod sensitivities in its Vegetative phase and further photoperiod sensitivity in the EarlyReproducivePhase. Photoperiod responses are seen as a reduction in the length of a phase for a photoperiod sensitive genotype in response to a longer photoperiod. Vernalisation responses are more complicated as they are driven by temperature but interact with photoperiod. For vernalisation sensitive varieties (Winter types) exposure to cool temperatures or short photoperiods during the Vegetative phase will reduce the thermal time duration of the vegetative phase.

We draw on the Kirby Framework to capture these vernalisation and photoperiod responses. This framework assumes that the timing of anthesis is a result of the timing of flag leaf and an additional thermal time passage from there to anthesis. It also assumes the timing of flag leaf is a result of the Final Leaf Number which sets a target, and leaf appearance rate, which sets the rate of progress toward this target. Leaf appearance rate is a function of Thermal time and a genotype specific Phyllochron which changes with Haun stage as described by Jamieson et al., 1995.

Final Leaf Number (FLN) is modeled as the sum of three numbers:

FLN = MinLeafNumber + VernalLeaves + PhotoLeaves

Where MinLeafNumber is the number of leaves that a wheat crop will produce when vernalisation is satisified early in the crops duration (before 2nd true leaf) and it is grown in a long photoperiod.. VernalLeaves are the number of leaves that are added due to vernalisation effects. For insensitive varieties this will always be zero but this is potentially a larger number for sensitivie varieties and the number progresively decreases as the crop encounters more vernalisation. PhotoLeaves are the number of leaves that are added to the minimum leaf number as a result of short day exposure. For insensitive varieties this will be zero but is potential larger for more sensitivie varieties and decreases as day length increases. More detailed explinations of the components of phenology are provided below.

1.4.4 Grain

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).

1.4.5 Root

The root model calculates root growth in terms of rooting depth, biomass accumulation and subsequent root length density in each soil layer.

1.4.6 Leaf

The leaves are modelled as a set of leaf cohorts and the properties of each of these cohorts are summed to give overall values for the leaf organ. A cohort represents all the leaves of a given main stem node position including all of the branch leaves appearing at the same time as the given main stem leaf (Lawless et al., 2005). The number of leaves in each cohort is the product of the number of plants per m² and the number of branches per plant. The Structure class models the appearance of main stem leaves and branches. Once cohorts are initiated the Leaf class models the area and biomass dynamics of each. It is assumed all the leaves in each cohort have the same size and biomass properties. The modelling of the status and function of individual cohorts is delegated to LeafCohort classes.

1.4.7 Spike

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.

1.4.8 Stem

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.

1.4.9 MortalityRate

A constant function (name=value)

1.4.10 SeedMortalityRate

A constant function (name=value)

2 Validation

A test dataset has been developed to test the APSIM barley model for a range of environmental (soil and climate) conditions, management options (sowing dates, populations, nitrogen rates, irrigation) and genetic backgrounds (different regions, cultivar types). These tests have been groups into various geographical regions to allow the user to evaluate the suitability of the model for their particular region of interest. Graphs of model performance are provided for yield, biomass production, canopy development, phenological development, water and nitrogen uptake, and grain yield components.

2.1 Map

2.2 NewZealand

This experiment was conducted in A Block near Lincoln, New Zealand to determine if there were any differences in the water use efficiency of different commercial varieties of barley. The results are un-published to date.

Four cultivars of Barley ('Omaka', 'Dash', 'Sherwood' and 'Booma') were sown on the 1st of October 2009 in a randomised complete block design with 4 replicates and two other factors, Irrigation and Nitrogen. The irrigation treatments consisted of full irrigation (to replace measured evapotranspiration) or nil irrigation (recieving only rainfall). The nitrogen treatments were 150 kg N broadcast in the early vegetative stage or nil fertiliser. Biomass accumulation, organ N content, Leaf area index, radiation interception and soil water content were measured at regular frequencies throughout the experiment.

Following on from MCPD09_10, this experiment was conducted in A Block near Lincoln, New Zealand to test a wider range of barley genotypes for differences in water use efficiency. The results are un-published to date.

Treatments were simply 8 commercially available varieties of barley ('Booma','County','Dash','Hooded','Omaka','Optic','Quench' and 'Retriever')

Biomass accumulation, Leaf area index, radiation interception and soil water content were measured at regular frequencies throughout the experiment.

Following on from MCPD10_11, this experiment was conducted in A Block near Lincoln, New Zealand to test the effects of sowing time on apparent water use efficiency of three varieties of Barley. The results are un-published to date.

Treatments were simply 3 commercially available varieties of barley ('Booma','Dash' and 'Omaka') sown on three different sowing dates (13 Aug, 9 Sep and 15 Nov).

Biomass accumulation, Leaf appearance, leaf size, radiation interception and soil water content were measured at regular frequencies throughout the experiment.

This was a field experiment established at the New Zealand Institute for Plant & Food Research Limited rain-out shelter in October 2014. Barley cultivars cv Dash and cv Omaka, differing canopy characteristics and water extraction capabilities were established with a range of irrigation treatments aimed at producing a range of soil moisture deficits.

• High =Full replacement irrigation: replace weekly, measured crop water use;
• Mid =Intermediate replacement with reduced frequency and higher volume e.g. monthly application of ½ measured crop water use;
• Low =Nil (or near nil) irrigation, e.g. irrigated once at anthesis.

The treatments were monitored for:

• Soil water - this was measured continuously using auto logging TDR and weekly using a neutron probe;
• Canopy light interception;
• Biomass - quantified four times during crop growth and at final harvest
• Leaf area index

This experiment was conducted in the rainshelter at Plant and Food Research in Lincoln, New Zealand. The objective was to evaluate the effect of drought on the yield of barley crops. The experiment is fully described by Jamieson et al. (1995). Briefly, 'Triumph' barley was sown at 188kg/ha on 07 September 1988. This sowing rate resulted in a population of 291 ± 6 plants/m2. Plants were evaluated for their response to drought of varying duration. The treatments were:

• Full irrigation – measured ET replaced weekly (06 October to 29 December)
• Early Drought 1 – irrigated from 03 November to 05 January
• Early Drought 2 – irrigated from 17 November to 19 January
• Early Drought 3 – irrigated from 24 November to 19 January
• Early Drought 3 – irrigated from 01 December to 09 February
• Middle Drought 1 – irrigated (06/10/1988 to 03/11/1988) and (24/11/1988 to 29/12/1989)
• Middle Drought 2 – irrigated (06/10/1988 to 03/11/1988) and (01/12/1988 to 29/12/1989)
• Middle Drought 3 – irrigated (06/10/1988 to 03/11/1988) and (15/12/1988 to 29/12/1989)
• Middle Drought 3 – irrigated (06/10/1988 to 03/11/1988)
• Late Drought 1 – irrigated (06/10/1988 to 17/11/1988)
• Late Drought 2 – irrigated (06/10/1988 to 24/11/1988)
• Late Drought 3 – irrigated (06/10/1988 to 01/12/1989)

This is a water response trial conducted inside and outside the rain shelter at the New Zealand Institute for Plant & Food Research in Lincoln and described in De Ruiter et al (1999). Briefly, a vernalisation insensitive barley, variety "Valetta", was sown at 280 plant/m² on 20 October 1995 and subjected to five water treatments as follows:

• Full drought (in rain shelter) – was not irrigated except for 17 mm rainfall as a result of shelter failure 42 days after sowing;
• Early drought (in rain shelter) – also receive 17 mm rainfall and the drought was fully relieved at the onset stem elongation;
• Late drought (in rain shelter) – fully irrigated (i.e. net crop water use + soil water evaporation) up to stem elongation and nil irrigation thereafter;
• Rain-fed (outside the rain shelter) – received all the rain but no irrigation;
• Full irrigation (located outside the rain shelter) – received all the rain and irrigation to replace ET.

This is the mineral nitrogen (N) validation trial conducted by the New Zealannd Institure for Plant & Food Research from September 2015 to February 2016. The experiment was established at the Lincoln University Dairy Farm on a deep well-drained Templeton silt loam soil. Five N fertiliser treatments were evaluated:

• Nil (0%) N - the crop did not get any fertiliser N
• 50% N - the crop received 50% of the required fertiliser N
• 75% N - the crop received 75% of the required fertiliser N
• 100% N - the crop received the required fertiliser N as estimated by the fertiliser calculator
• 125% N - the crop received 25% fertiliser N above the requirement.

This was a sowing date by irrigation trial conducted at Lincoln. The following were evaluated:

• Two water treatments; Rainfed (nil irrigation) and irrigated (310 mm)
• Two fertiliser N treatments; 0N (no fertiliser) and 150N (150 kg N/ha)

2.2.1 CPT

This was a cultivar evaluation trial conducted in Lincoln New Zealand between 2003 and 2006. Crops were sown in either May or September. Observations made were of (i) DAS to flag leaf appearance and (ii) DAS to flowering.

2.3 Australia

2.3.1 MCVP

This experiment was funded as part of the Australian Managing Climate Variability Program. Barley cultivars were sown at two planting dates at three sites for the purpose of improving APSIM predictions of the phenology of commercial cultivars. The locations included Gatton in Queensland (subtropical location), Birchip in Victoria (temperate inland location) and Tarlee in South Australia (temperate location).

2.3.2 HermitageRS

This rain-out shelter experiment was undertaken at the Hermitage Research Station near Warwick, Queensland. Treatments included 3 irrigation experiments to provide combinations of early and later water stress. The experiment was described by Goyne et al., 1996.

2.3.3 Wellcamp

This experiment provides information on biomass accumulation, canopy development and final yield for Barley sown on the eastern Darling Downs. The experiment is explained in Goyne et al., 1996.

2.3.4 Hermitage1990

This experiment was conducted at Hermitage Research Station near Warwick, Queensland. Three sowing dates were used to study changes in phenology, biomass accumulation and canopy development. The experiment is explained in Goyne et al., 1993.

2.3.5 Roma1988

This sowing date x plant population experiment was undertaken near Roma, Queensland. Measurements include biomass accumulation, leaf area development and final grain yield. The experiment is explained in Goyne et al., 1996.

NOTE: Sowing was 12-Mar but simulations are sown on 30 March to capture delayed germination until following rainfall event

2.3.6 Gatton1984

This nitrogen rate (0 to 200 kg N/ha) experiment was conducted in 1984 at Gatton, Queensland. The experiment is explained in Birch et al., 1990.

NOTE: High N treatment logdged. Final grain sizes were less than expected.

2.3.7 NPIField2019

2.3.8 NPIField2020

2.3.9 ControlledEnvironment

3 Sensibility tests

This is a hypothetical experiment with Barley sown on the 15th of October every year for 20 years at Lincoln, New Zealand. The treatments applied are as follows:

• Two water treatments; Dry (nil irrigation) and Wet, with irrigation applied when soil water deficit reaches 60% to return water content to 100% of capacity.
• five fertiliser N treatments; 0, 50, 100, 200 and 400 kg N/ha with half of the N applied at sowing and the other half applied at growth stage 32.

The results for irrigted, High N treatments the range of yields are inline with expectations for the location of the simulations. There is no sensitivity to irrigation with zero nitrogen as N supply is the factor limiting production. As N inputs increase the crop becomes increasingly sensitive to water application and the crop is more responsive to nitrogen with irrigation. These results show the model is giving sensible predictions

4 Interface

4.1 Barley

Properties (Outputs)

Links (Dependencies)

Events published

Methods (callable from manager)

4.2 SowingParameters

Parameters which control how a plant is sown.

Properties (Outputs)

4.3 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)

5 References

Birch, C.J., Long, K.E., 1990. Effect of nitrogen on the growth, yield and grain protein content of barley (Hordeum vulgare). Australian Journal of Experimental Agriculture 30, 237-242.

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.

Goyne, PJ, Meinke, Holger, Milroy, Stephen, Hammer, G, Hare, JM, 1996. Development and use of a barley crop simulation model to evaluate production management strategies in north-eastern Australia. Australian Journal of Agricultural Research - AUST J AGR RES 47.

Goyne, PJ, Milroy, Stephen, Lilley, Julianne, Hare, JM, 1993. Radiation interception, radiation use efficiency and growth of barley cultivars. Crop and Pasture Science 44, 1351-1366.

Jamieson, P. D., Brooking, I. R., Porter, J. R., Wilson, D. R., 1995. Prediction of leaf appearance in wheat: a question of temperature. Field Crops Research 41 (1), 35-44.

Lawless, Conor, Semenov, MA, Jamieson, PD, 2005. A wheat canopy model linking leaf area and phenology. European Journal of Agronomy 22 (1), 19-32.