1 The APSIM Wheat 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. and Holzworth, D.P.

The APSIM wheat 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.

The wheat model consists of:

• a phenology model to simulate development through sequential developmental phases
• a structure model to simulate plant morphology
• a collection of organs to simulate the various plant parts
• an arbitrator to allocate resources (N, biomass) to the various plant organs

This work builds upon earlier APSIM Wheat models such as NWheat (S Asseng et al., 2002, BA Keating, 2001), NWheatS (S Asseng et al., 1998), Cropmod-Wheat (Wang et al., 2002), and the earlier versions developed in Plant (APSIM Wheat 7.5") and then within the Plant Modelling Framework (Brown et al., 2014).

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.

The wheat models outputs of phase names (Phenology.CurrentPhase.Name), stage names (Phenology.CurrentStageName) and decimal stage (Phenology.Stage) are consistent with the population culm development score (PCSD) as described by Corinne Celestina et al., 2023. Observations recorded following the protocole outlined by Corinne Celestina et al., 2023 are suitable for testing model accuracy and calibrating cultivar coefficients.

Wheat exhibits a range of developmental responses to environment and these are strongly influenced by genotype characteristics. Temperature is the primary driver of development, increasing development rates and decreasing phase durations as it increases. These affects are captured by thermal time. However, wheat also exhibits cold and photoperiod sensitivities in its Vernalisaing phase and further photoperiod sensitivity in the SpikeletDifferenation and HeadEmergence phases. 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 cool temperature but interact with photoperiod. For vernalisation sensitive varieties (Winter types), exposure to cool temperatures and/or short photoperiods during the Vernalising phase may reduce its thermal time duration.

APSIM wheat implements the Cereal Anthesis Molecular Phenology (CAMP) model to simulate development. It is based on the Kirby Framework which assumes the timing of anthesis is a result of the timing of flag leaf and an additional thermal time passage from there to heading then 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 the 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 set on the day that terminal spikelet occurs as:

FLN = 2.85 + 1.1 * TSHS

Where TSHS is the Haun stage on the day terminal spikelet stage occurs. Terminal spikelet is at the end of the SpikeletDifferentaiation phase which is preceeded by the Emerging and Vernalising phases. The mechanisums for progress through each of these phase are described 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.

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 Wheat model for a range of environmental (soil and climate) conditions, management options (sowing dates, populations, nitrogen rates, row spacing, irrigation), genetic backgrounds (different regions, cultivar types) and for special considerations such as crop damage. These tests have been grouped 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 Combined Results

Simulation results for the combined datasets from the various countries are shown in the following graphs. The model is able to adequately capture the influence of growing conditions (soil, climate) and management (population, Nitrogen, irrigation, sowing date).

2.2.1 Harvest Biomass

2.2.2 Leaf Area Index

2.2.3 Grain Number

2.2.4 Harvest Yield

2.2.5 Harvest Biomass N

2.2.6 Harvest Grain N

2.2.7 Grain Nitrogen Concentration

2.2.8 Haun Stage

2.2.9 Leaf Number

2.2.10 Flag Leaf Date

2.2.11 Flowering Date

2.2.12 Maximum Leaf Size

2.2.13 DailyBiomass

2.2.14 FLN

2.2.15 Heading Date

2.2.16 SpikeWt

2.2.17 StemWt

2.2.18 LeafWt

2.2.19 ET

2.2.20 NDVI

2.3 SE Queensland

South-eastern Queensland has a warm subtropical environment. Daytime temperatures are moderate due to the relatively low latitudes for wheat growing in Australia, but inland continental conditions can provide cool nights with occaisional frosts. Many of the datasets used here have been published as part of previous APSIM Wheat model tests. Further datasets have been added to provide information on phenological development of modern cultivars.

2.3.1 Harvest Yield

2.3.2 Harvest Biomass

2.3.3 Harvest Biomass N

2.3.4 Harvest Grain N

2.3.5 GrainNumber

2.3.6 Leaf Area Index

2.3.7 FloweringDate

2.3.8 FLN

This trial was conducted at Gatton in 1995 using the local variety, Hartog. It consisted of 4 Nitrogen rates (0,40,80,160) with two irrigation rates (minimal amount for establishment, fully irrigated). Yields ranged from 1.4 t/ha to 5.4 t/ha.

2.3.9 FW photo

2.3.10 StemNumber

2.3.11 Leaf Fn

2.3.12 FloweringDate

2.3.13 WaterPotential

2.3.14 C Supply and Demand

2.3.15 ET

2.3.16 RootDepth

2.3.17 Height

2.3.18 RootLengthDensity

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

2.3.19 NStress

2.3.20 Yield

2.3.21 Biomass

2.3.22 BiomassN

This simple experiment was conducted to investigate the impact of time of sowing on canopy development and growth of wheat. Wheat (cv. Hartog) was sown at Gatton on 30th of May and 30th of July in 1991. Data were collected on canopy development, biomass accumulation and yield.

2.3.23 LeafAppearance

2.3.24 LAI

2.3.25 TillerNumber

2.3.26 LeafSize

This simple experiment was conducted to investigate the light interception and subsequent growth of wheat under different populations invoked using row spacing. Wheat was sown at Gatton on 15th of June 2011 at 25cm row spacing. Soon after emergence, alternate rows were removed from selected plots to produce half populations at 50cm row spacing. A zero N treatment was used to identfy the inherent fertility of the site to assist in model parameterisation. Data were collected on light interception, canopy development, biomass accumulation and yield.

2.3.27 LeafAppearance

2.3.28 TillerNumber

2.3.29 LAI

2.3.30 AboveGroundWt

2.3.31 SpecificLeafArea

This simple experiment was conducted to investigate the impact of time of sowing on wheat. Wheat (cv. Hartog and Batavia) was sown at Gatton on six dates during 1994. Various data were collected. However final growth data was compromised by mouse damage. The dataset is used here to study the impact of sowing time on phenological development.

2.3.32 FloweringDate

2.3.33 FloweringDate vs TOS

2.3.34 LeafNumberTimeSeries

2.3.35 ZadokTimeSeries

2.3.36 LeafNumberTimeSeries

2.3.37 ZadokTimeSeries

2.3.38 Axe

2.3.39 Bolac

2.3.40 Derrimut

2.3.41 EagleHawk

2.3.42 Gregory

2.3.43 Mace

2.3.44 Lincoln

2.3.45 Scout

2.3.46 FLN

2.3.47 ZadokTimeSeries1

2.3.48 HaunStage

2.3.49 FLN

2.3.50 HaunStage

2.3.51 FLN

2.3.52 HaunStage

2.3.53 HaunStage

2.3.54 AddingValueToNVT

The "Adding Value to the National Variety Trials" project aimed to use measurement and modelling to explain GeneXEnvironmentXManagement interactions for Australian Wheat cultivars. A description of this national trial can be found in R.A. Lawes et al., 2016. Here we include some of the data from south-eastern Queensland.

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2.3.55 Phenology1996

This dataset includes observed heading date for six cultivars (Batavia, Cunningham, Hartog, Janz, Sunbri, Suneca) for a range of locations and planting dates in the northern grain-growing region of Australia.

2.4 Western Australia

The wheat belt of Western Australia has a Mediteranean climate (winter dominant rainfall patterns) with mostly sandy soils. Data from S Asseng et al., 1998. and some more recent studies have been included to extend the range of conditions studied and to include more modern cultivars.

2.4.1 Harvest Yield

2.4.2 Harvest Biomass

2.4.3 GrainNumber

2.4.4 Harvest Grain N

2.4.5 Harvest Biomass

2.4.6 Biomass

2.4.7 LAI

2.4.8 Harvest Yield

2.4.9 Harvest Biomass

2.4.10 GrainNumber

2.4.11 Harvest Yield

2.4.12 Harvest Biomass

2.4.13 GrainNumber

2.4.14 GrainN

2.4.15 Biomass

2.4.16 RootDepth

2.4.17 GrainSize

2.4.18 LAI

2.4.19 WaterStress

2.4.20 NStress

2.5 Turkey

The dataset of Ali Fuat Tari, 2016 includes 4 irrigation deficit treatments applied at each of 3 plant growth stages. The experiment was conducted at Konya in the Central Anatolia region of Turkey. Yields ranged from 2.88 t/ha to 6.82 t/ha. These treatments were reproduced over two growing seasons, resulting in 44 individual wheat crops including differing levels of water stress at different stages of development. Soil data have been estimated from that provided within the original publication.

2.5.1 Harvest Yield

2.5.2 Harvest Biomass

2.5.3 GrainNumber

2.5.4 GrainN

2.5.5 TotalSW

2.5.6 LAI

2.5.7 Stress

2.5.8 ZadokStage

2.5.9 Harvest Biomass

2.5.10 RootDepth

2.5.11 StemWt

2.5.12 Harvest Grain

2.5.13 GrainNumber

2.5.14 TotalSW

2.5.15 LAI

2.5.16 ZadokStage

2.6 New Zealand

2.6.1 Leaf Appearance

2.6.2 Harvest Yield

2.6.3 Harvest Biomass

2.6.4 GrainNumber

2.6.5 Harvest N

2.6.6 Grain Size

The design, management and yield results of this trial have been described in full by Craigie R.A., 2015. In brief, this trial was conducted to assess the impact of earlier sowing on potential yields of 'Wakanui' wheat grown at Wakanui (the cultivar was named after the area) in Mid Canterbury, New Zealand. it was a 4 x 4 factorial with 4 replicates of the following treatments:

1. Sowing date (20 February, 10 March, 28 March, 23 April)
2. Sowing density (50,100,150 and 200 plants/m²

In addition to yield, measurements of leaf appearance and senescence and canopy cover (measured with NDVI) were measured at 10 - 14 day intervals and biomass measurements were taken at growth stages 32 and 65.

Considerable canopy decay was observed during the winter for the early sown treatments and we have not introduced mechanisms into the model to capture this yet so there is a genral over prediction of canopy size and biomass in these first sowing dates.

2.6.7 FLN

This is a water response trial run in the rain out shelter at Plant and Food Research in Lincoln, New Zealand. It is described in full by Jamieson et al., 1995 but briefly. A winter crop of 'Batten' wheat was sown at 300 plants/m² on the 8th of June 1991 and range of irrigation amount and timing treatments were applied. Six of these treatments have been included in this validation:

1. Full irrigation where measured ET was replaced weekly
2. Short Early Drought where irrigation was withheld from sowing until late October then full irrigation was applied
3. Long Early Drought where irrigation was withheld from sowing until mid December then full irrigation was applied
4. Long Late Drought where full irrigation was applied from sowing until mid September then withheld for the rest of the season
5. Moderate Late Drought where full irrigation was applied from sowing until mid October then withheld for the rest of the season
6. Short Late Drought where full irrigation was applied from sowing until Early November then withheld for the rest of the season
7. Nil where no water was applied and the crop grew on soil stored water only.

Irrigation was applied at weekly intervals through and assemply of low rate drip emitters on each plot. Soil water content was measured weekly with a neutron probe and biomass was measured at 10 - 14 day intervals. Samples throughtout the crop were from two 0.1m² quatrants and the final harvest sample was from a 1 m² quadrant. Each treatment was replicated twice and there was considerable soil variation from plot to plot so each treatment was initianilised with unique soil parameters which best described the soil they were growing on.

2.6.8 SoilWaterProfile

2.6.9 AboveGroundWt

2.6.10 LAI

2.6.11 Stems

2.6.12 GrainNumbers

2.6.13 Population

2.6.14 Tillers

This trial has never been formally written up. It was conducted at Plant and Foods A Block, Lincoln, New Zealand with 'Batten' Wheat grown on a Templeton silt loam (160mm AWC/m). It was a 2 x 2 x 4 factorial with the following treatments:

1. Sowing Date (5 May 1992 and 5 Aug 1992)
2. Irrigation (Nil and 120 mm)
3. Nitrogen (0, Low, Medium and High)

The N applied to the Low, Medium and High N treatments was 100, 150 and 250 kg/ha, respectively, for the May sowing and 50, 100 and 150, respectively, for the August sowing.

Could not find informaiton confirming sowing rate so assumed typical values of 300 plants per m/2 for the spring sowing 100 plants per m/2 for May sowing. Emergence was recorded as the 28th of May for the first planting. The model was predicting this early so delayed sowing date to get emergence date correct..

The design, management and yield results of this trial have been described in full by Craigie R.A., 2015. In brief, this trial was conducted to assess the impact of earlier sowing on potential yields of 'Wakanui' wheat grown at Wakanui (the cultivar was named after the area) in Mid Canterbury, New Zealand. it was a 4 x 4 factorial with 4 replicates of the following treatments:

1. Sowing date (20 February, 10 March, 28 March, 23 April)
2. Sowing density (50,100,150 and 200 plants/m²

In addition to yield, measurements of leaf appearance and senescence and canopy cover (measured with NDVI) were measured at 10 - 14 day intervals and biomass measurements were taken at growth stages 32 and 65.

Considerable canopy decay was observed during the winter for the early sown treatments and we have not introduced mechanisms into the model to capture this yet so there is a genral over prediction of canopy size and biomass in these first sowing dates.

2.6.15 FLN

2.6.16 LeafTips

• Could not find informaiton confirming sowing rate. Protocole said aim for 300 plants per m/2 which is usual for a spring sowing but very high for an Autumn sowing. Assumed 100 plants per m/2 for May sowing and 300 for August.
• Emergence was recorded as the 28th of May for the first planting. The model was predicting this early so delayed sowing date to get emergence date correct..

This is a water response trial run in the rain out shelter at Plant and Food Research in Lincoln, New Zealand. It is described in full by E. Chakwizira et al., 2014 but briefly. An Autumn crop of 'Wakanui' wheat was sown at 165 plants/m² on the 28th of March 2013 and 6 irrigation timing treatments were applied:

1. Full irrigation where measured ET was replaced weekly .
2. Nill irrigation.
3. Very Early Drought where irrigation was withheld from sowing until the beginning of stem extension.
4. Early Drought where irrigation was withheld between Flag leaf and the beginning of grain fill.
5. Middle Drought where irrigation was withheld between Flag leaf and 1 week into grain fill.
6. Late Drought where irrigation was withheld from heading until harvest.

Irrigation was applied at weekly intervals through and assemply of low rate drip emitters on each plot. Soil water content was measured weekly with a neutron probe and biomass was measured on 5 occasions throughout the crop. Samples throughtout the crop were from a 0.43m² quatrant and the final harvest sample was from a 1 m² quadrant. Each treatment was replicated four times and there was considerable soil variation from plot to plot so each treatment was initianilised with unique soil parameters which best described the soil they were growing on.

The design, management and yield results of this trial have been described in full by Craigie R.A., 2015. In brief, this trial was conducted to assess the impact of earlier sowing on potential yields of 'Wakanui' wheat grown at Wakanui (the cultivar was named after the area) in Mid Canterbury, New Zealand. it was a 4 x 4 factorial with 4 replicates of the following treatments:

1. Sowing date (20 February, 10 March, 28 March, 23 April)
2. Sowing density (50,100,150 and 200 plants/m²

In addition to yield, measurements of leaf appearance and senescence and canopy cover (measured with NDVI) were measured at 10 - 14 day intervals and biomass measurements were taken at growth stages 32 and 65.

Considerable canopy decay was observed during the winter for the early sown treatments and we have not introduced mechanisms into the model to capture this yet so there is a genral over prediction of canopy size and biomass in these first sowing dates.

2.6.17 Harvest Yield

The design, management and yield results of this trial have been described in full by Craigie R.A., 2015. In brief, this trial was conducted to assess the impact of earlier sowing on potential yields of 'Wakanui' wheat grown at Wakanui (the cultivar was named after the area) in Mid Canterbury, New Zealand. it was a 4 x 4 factorial with 4 replicates of the following treatments:

1. Sowing date (20 February, 10 March, 28 March, 23 April)
2. Sowing density (50,100,150 and 200 plants/m²

In addition to yield, measurements of leaf appearance and senescence and canopy cover (measured with NDVI) were measured at 10 - 14 day intervals and biomass measurements were taken at growth stages 32 and 65.

Considerable canopy decay was observed during the winter for the early sown treatments and we have not introduced mechanisms into the model to capture this yet so there is a genral over prediction of canopy size and biomass in these first sowing dates.

This is a water response trial run in the rain out shelter at Plant and Food Research in Lincoln, New Zealand. It is described in full by E. Chakwizira et al., 2014 but briefly. An Autumn crop of 'Wakanui' wheat was sown at 165 plants/m² on the 28th of March 2013 and 6 irrigation timing treatments were applied:

1. Full irrigation where measured ET was replaced weekly .
2. Nill irrigation.
3. Very Early Drought where irrigation was withheld from sowing until the beginning of stem extension.
4. Early Drought where irrigation was withheld between Flag leaf and the beginning of grain fill.
5. Middle Drought where irrigation was withheld between Flag leaf and 1 week into grain fill.
6. Late Drought where irrigation was withheld from heading until harvest.

Irrigation was applied at weekly intervals through and assemply of low rate drip emitters on each plot. Soil water content was measured weekly with a neutron probe and biomass was measured on 5 occasions throughout the crop. Samples throughtout the crop were from a 0.43m² quatrant and the final harvest sample was from a 1 m² quadrant. Each treatment was replicated four times and there was considerable soil variation from plot to plot so each treatment was initianilised with unique soil parameters which best described the soil they were growing on.

Lincoln2012 (Rain-Shelter Trail)

Testing of APSIM Maize under New Zealand conditions was undertaken using the data of Teixeira et al., 2014. This dataset includes the impact of three N (0 to 250 kg/ha N) and two water regimes (dryland and fully irrigated) using a rain-shelter structure. Observations include biomass growth and nitrogen content of individual organs, soil water contents, leaf area index, phenology and yield components. Total biomass ranged from 8000 kg/ha for dryland nil N crops to up to 28000kg/ha for fully irrigated and N fertilised crops. Dryland crops recovered 25 percent less N from applied fertilizer than irrigated crops.

The design, management and yield results of this trial have been described in full by Craigie R.A., 2015. In brief, this trial was conducted to assess the impact of earlier sowing on potential yields of 'Wakanui' wheat grown at Wakanui (the cultivar was named after the area) in Mid Canterbury, New Zealand. it was a 4 x 4 factorial with 4 replicates of the following treatments:

1. Sowing date (20 February, 10 March, 28 March, 23 April)
2. Sowing density (50,100,150 and 200 plants/m²

In addition to yield, measurements of leaf appearance and senescence and canopy cover (measured with NDVI) were measured at 10 - 14 day intervals and biomass measurements were taken at growth stages 32 and 65.

Considerable canopy decay was observed during the winter for the early sown treatments and we have not introduced mechanisms into the model to capture this yet so there is a genral over prediction of canopy size and biomass in these first sowing dates.

2.6.18 Harvest Yield

The design, management and yield results of this trial have been described in full by Craigie R.A., 2015. In brief, this trial was conducted to assess the impact of earlier sowing on potential yields of 'Wakanui' wheat grown at Wakanui (the cultivar was named after the area) in Mid Canterbury, New Zealand. it was a 4 x 4 factorial with 4 replicates of the following treatments:

1. Sowing date (20 February, 10 March, 28 March, 23 April)
2. Sowing density (50,100,150 and 200 plants/m²

In addition to yield, measurements of leaf appearance and senescence and canopy cover (measured with NDVI) were measured at 10 - 14 day intervals and biomass measurements were taken at growth stages 32 and 65.

Considerable canopy decay was observed during the winter for the early sown treatments and we have not introduced mechanisms into the model to capture this yet so there is a genral over prediction of canopy size and biomass in these first sowing dates.

2.6.19 Harvest Yield

The design, management and yield results of this trial have been described in full by Craigie R.A., 2015. In brief, this trial was conducted to assess the impact of earlier sowing on potential yields of 'Wakanui' wheat grown at Wakanui (the cultivar was named after the area) in Mid Canterbury, New Zealand. it was a 4 x 4 factorial with 4 replicates of the following treatments:

1. Sowing date (20 February, 10 March, 28 March, 23 April)
2. Sowing density (50,100,150 and 200 plants/m²

In addition to yield, measurements of leaf appearance and senescence and canopy cover (measured with NDVI) were measured at 10 - 14 day intervals and biomass measurements were taken at growth stages 32 and 65.

Considerable canopy decay was observed during the winter for the early sown treatments and we have not introduced mechanisms into the model to capture this yet so there is a genral over prediction of canopy size and biomass in these first sowing dates.

2.6.20 Harvest Yield

2.6.21 FLN

2.6.22 CPTPhenology

A range of soon to be released cultivars have their phenology assesed each year at Plant and Food Research in Lincoln, New Zealand. Each cultivar is planted on 4 sowing dates representing Autumn, Winter, Early Spring and Late Spring sowings. (approx April, June, Aug, Nov). Each cultivar is observed for 3 years but a number of standards are included each year or for more than 3 years bacause of the value of the data they provided.

2.7 Southern Australia

2.7.1 Harvest Yield

2.7.2 FloweringDate

2.7.3 FLN

2.7.4 Biomass

2.7.5 Leaf

2.7.6 Stem

2.7.7 Spike

2.7.8 StemPopn

2.7.9 Grain

2.7.10 DeadLeaf

2.7.11 SurfaceOM

2.7.12 Harvest Yield

2.7.13 Phenology

INSERT TEXT HERE

2.7.14 FloweringDate

INSERT TEXT HERE

2.7.15 FloweringDate

INSERT TEXT HERE

2.7.16 FloweringDate

INSERT TEXT HERE

2.7.17 FloweringDate

2.7.18 LeafNumberTimeSeries

2.7.19 Axe

2.7.20 Bolac

2.7.21 Derrimut

2.7.22 EagleHawk

2.7.23 Gregory

2.7.24 Mace

2.7.25 Lincoln

2.7.26 Scout

2.7.27 LeafNumberTimeSeries

2.7.28 Axe

2.7.29 Bolac

2.7.30 Derrimut

2.7.31 EagleHawk

2.7.32 Gregory

2.7.33 Mace

2.7.34 Lincoln

2.7.35 Scout

This dataset includes observed flowering date for five cultivars (Batavia, Hartog, Sunbri, Sunco, Suneca) for a range planting dates at Tamworth.

2.7.36 Flowering DAS

2.7.37 Flowering DAS vs TOS

2.7.38 van Herwaarden et al 1998

2.7.39 Wagga1314

The dataset of K.T. Zeleke et al., 2016 includes plantings of two wheat varieties (Gregory, Livingston) under two water regimes (dry and irrigated) for 2013 and 2014 at the Wagga Wagga Agricultural Research Institute. Yields ranged from 1.63 t/ha to 6.01 t/ha.

2.7.40 YarrabahCreek

This trial was conducted on a Vertosol soil on the Liverpool Plains, central-eastern Australia in 2001. Hybrid Mercury was sown on 19th of June and biomass, leaf area, phenology, soil water (Neutron Moisture Meter) and water use (Bowen Ratio method) were monitored. More information can be found in Young et al., 2008.

2.7.41 Griffith

This dataset from Stapper et al., 0 includes observed phenological data for range of cultivars, three of which have been used here (Yecora, Egret, Hartog). Planting dates from 1983 and 1984 provide a range of climatic conditions.

2.7.41.1 Pre vs Obs Flowering DAS

2.7.41.2 Flowering DAS vs TOS

2.7.41.3 Pre vs Obs Haun Stage

2.7.41.4 Pre vs Obs Flowering DAS

2.7.41.5 Flowering DAS vs TOS

2.7.41.6 Yecora

2.7.41.7 Egret

2.7.41.8 Leaf Appearance

2.7.41.9 FLN

2.7.41.10 Pre vs Obs Flowering DAS

2.7.41.11 Flowering DAS vs TOS

2.8 Europe

2.8.1 Belgium

This trial was conducted at Lonzee near Gembuloux in Belgium and is described in detail by Dufranne et al., 2011 and Moureaux et al., 2008. The trial was run to measure carbon flux from wheat crops using eddy covariance but sufficient crop information was collected to make it suitable as a model validataion dataset also. Crops of wheat (cultivars 'Dekan', 'Rosario', 'Ararat' for the three respective sowing dates) were sown on 14/10/2004, 13/10/2004 and 13/11/2004. Standard management practices for winter wheat in this area were followed. Dates for the timing of key phenological events were used to determine the developmental coefficients for each of the cultivars.

2.9 NorthAmerica

2.9.1 Arizona

These FACE trials were conducted to investigate the effects of atmospheric CO2 concentrations and water stress on wheat growth and development [Hanksar_1996_FACE]. It was conducted at Maricopa, Arazona using Free Air Carbon Enrichment to create elevated CO2 treatments:

1. Normal CO2 (370 ppm) (that is no longer normal)
2. High CO2 (550 ppm)

Irrigation treatmetnts were also applied in the 1992 and 1993 experiments with:

1. High Irrigation (~600 mm)
2. Low irrigation (~265 mm)

Nitrogen treatments were applied in 1994 and 1995 with:

1. High Nitrogen ()
2. Low Nitrogen ()

Crop development, Biomass production and soil moisture were monitored throughout the crops duration.

2.10 Africa

This trial was run in the Jamma district of the Amhara region of the Ethiopia country of the Africa contenentand is described in full by Getu, 2012. 'HAR 1685' Wheat was sown in 20 cm row spacing. Different N fertiliser treatments were applied:

1. 0NUSG = 0kg N/ha
2. 23NUSG = 23 kg N/ha as Urea Super Granules (a slow release urea product)
3. 46NUSG = 46 kg N/ha as Urea Super Granules
4. 69NUSG = 69 kg N/ha as Urea Super Granules
5. 46NUC = 46 kg N/ha as Uncoated Urea

;From

Soil Characterization and Evaluation of Slow Release Urea Fertilizer Rates on Yield Traits and Grain Yields of Wheat and Teff on Vertisols of Jamma District of South Wollo Zone, Amhara Region b0

By

Abebe Getu (BSc), Hawassa University, Ethiopia

2.11 ControlledEnvironment

Experiment Name	Design (Number of Treatments)
LaTrobeCE	Treat x CV x Durat (276)
PalmerstonNorthCE	Treat x Cv x Durat (208)
LincolnCE	Treat x Cv x Durat (24)

2.11.1 FLN

2.12 NPIField2019

Experiment Name	Design (Number of Treatments)
WaggaWagga	TOS x Cv (512)
Callington	TOS x Cv (512)
Dale	TOS x Cv (512)
YanYean	TOS x Cv (512)

2.12.1 EmergenceDAS

2.12.2 FLN

2.12.3 HeadingDAS

2.12.4 FloweringDAS

2.12.5 HaunStage

2.12.6 FLN

2.12.7 EmergenceDAS

2.13 NPIValidation

Experiment Name	Design (Number of Treatments)
SGEHEAT_13GEHEAT-1	Cultivar (6)
SGEHEAT_13GEHEAT-2	Cultivar (6)
14SSOW-1	Cultivar (34)
14SSOW-2	Cultivar (35)
14SSOW-4	Cultivar (34)
14SSOW-5	Cultivar (31)
12JuneeJames-1-100	Cultivar (2)
12JuneeJames-1-50	Cultivar (1)
12JuneeJames-2-100	Cultivar (2)
12JuneeJames-2-50	Cultivar (1)
12JuneeJames-3-100	Cultivar (3)
12JuneeJames-4-100	Cultivar (2)
BCVT_Ardingly_1992-06-19	Cultivar (1)
BCVT_Badgingarra_2005-05-26	Cultivar (1)
BCVT_Badgingarra_2005-05-28	Cultivar (1)
BCVT_Badjaling_2003-05-30	Cultivar (1)
BCVT_Chapman_1992-06-23	Cultivar (1)
BCVT_Chapman_1992-06-24	Cultivar (1)
BCVT_Chapman_1994-06-10	Cultivar (1)
BCVT_Gairdner_River_2004-06-09	Cultivar (1)
BCVT_Georgina_1992-06-26	Cultivar (1)
BCVT_Konnongorring_1997-06-13	Cultivar (1)
BCVT_Konnongorring_1997-06-27	Cultivar (1)
BCVT_Kumarl_1999-05-22	Cultivar (1)
BCVT_Kunjin_2005-05-26	Cultivar (1)
BCVT_Meckering_2003-06-04	Cultivar (1)
BCVT_Merredin_1995-05-30	Cultivar (1)
BCVT_Merredin_1996-06-25	Cultivar (1)
BCVT_Merredin_1996-07-02	Cultivar (1)
BCVT_Merredin_1998-06-19	Cultivar (1)
BCVT_Merredin_2000-06-19	Cultivar (1)
BCVT_Merredin_2002-06-10	Cultivar (1)
BCVT_Merredin_2003-06-06	Cultivar (2)
BCVT_Mt_Madden_1994-06-13	Cultivar (1)
BCVT_Mukinbudin_2000-06-13	Cultivar (1)
BCVT_Mullewa_1993-06-04	Cultivar (1)
BCVT_Mullewa_2004-05-28	Cultivar (1)
BCVT_Munglinup_1994-06-08	Cultivar (1)
BCVT_Scaddan_1999-05-25	Cultivar (1)
BCVT_Speddingup_2001-05-30	Cultivar (1)
BCVT_Tammin_1999-06-09	Cultivar (1)
BCVT_Wannamal_1992-06-25	Cultivar (1)
BCVT_Wongan_Hills_1998-06-09	Cultivar (1)
BCVT_Wongan_Hills_2001-06-11	Cultivar (1)
BCVT_Wongan_Hills_2002-06-09	Cultivar (1)
BTOS_2008GE1	Cultivar (6)
BTOS_2008GE2	Cultivar (7)
BTOS_2008GE3	Cultivar (8)
BTOS_2008GE4	Cultivar (11)
BTOS_2008KA1	Cultivar (2)
BTOS_2008KA2	Cultivar (1)
BTOS_2008KA3	Cultivar (5)
BTOS_2008KA4	Cultivar (12)
BTOS_2008NM2	Cultivar (1)
BTOS_2008NM3	Cultivar (12)
BTOS_2008NM4	Cultivar (12)
BTOS_2009GE1	Cultivar (5)
BTOS_2009GE2	Cultivar (2)
BTOS_2009GE3	Cultivar (10)
BTOS_2009GE4	Cultivar (12)
BTOS_2009KA3	Cultivar (3)
BYIE_08HIR	Cultivar (4)
BYIE_08KA	Cultivar (7)
BYIE_08NB	Cultivar (13)
BYIE_08RS	Cultivar (13)
BYIE_08WH	Cultivar (13)
BYIE_09ER	Cultivar (12)
BYIE_09GN	Cultivar (13)
BYIE_09HIR	Cultivar (1)
BYIE_09KA	Cultivar (7)
BYIE_09MDL	Cultivar (14)
BYIE_09RS	Cultivar (5)
BYIE_09WH	Cultivar (14)
PTOS_10Kairi-1	Cultivar (8)
PTOS_10Kairi-2	Cultivar (8)
PTOS_10Kairi-3	Cultivar (7)
PTOS_10Kairi-4	Cultivar (7)
PTOS_10Kairi-5	Cultivar (9)
PTOS_10Mackay-1	Cultivar (8)
PTOS_10Mackay-2	Cultivar (8)
PTOS_10Mackay-3	Cultivar (7)
PTOS_10Mackay-4	Cultivar (7)
PTOS_10Mackay-5	Cultivar (9)
HAGT_09Roseworthy-1	Cultivar (2)
HAGT_09Roseworthy-2	Cultivar (2)
HAGT_09Roseworthy-3	Cultivar (2)
HAGT_09Roseworthy-4	Cultivar (3)
HAGT_09Roseworthy-5	Cultivar (3)
17CuryoMESW-TOS1	Cultivar (5)
17CuryoMESW-TOS2	Cultivar (5)
17CuryoMESW-TOS3	Cultivar (5)
17CuryoMESW-TOS4	Cultivar (5)
17HartMESW-TOS1	Cultivar (5)
17HartMESW-TOS2	Cultivar (5)
17HartMESW-TOS3	Cultivar (5)
17HartMESW-TOS4	Cultivar (5)
17LoxtonMESW-TOS1	Cultivar (5)
17LoxtonMESW-TOS2	Cultivar (5)
17LoxtonMESW-TOS3	Cultivar (5)
17LoxtonMESW-TOS4	Cultivar (5)
17MilduraMESW-TOS1	Cultivar (5)
17MilduraMESW-TOS2	Cultivar (5)
17MilduraMESW-TOS3	Cultivar (5)
17MilduraMESW-TOS4	Cultivar (5)
17MinnipaMESW-TOS1	Cultivar (4)
17MinnipaMESW-TOS2	Cultivar (4)
17MinnipaMESW-TOS3	Cultivar (4)
17MinnipaMESW-TOS4	Cultivar (4)
18HartMESW-TOS1	Cultivar (5)
18HartMESW-TOS2	Cultivar (5)
18HartMESW-TOS3	Cultivar (5)
18HartMESW-TOS4	Cultivar (5)
18LoxtonMESW-TOS1	Cultivar (5)
18LoxtonMESW-TOS2	Cultivar (5)
18LoxtonMESW-TOS3	Cultivar (5)
18LoxtonMESW-TOS4	Cultivar (5)
18MilduraMESW-TOS1	Cultivar (5)
18MilduraMESW-TOS2	Cultivar (5)
18MilduraMESW-TOS3	Cultivar (5)
18MilduraMESW-TOS4	Cultivar (5)
Inverleigh2013-TOS1	Cultivar (1)
Inverleigh2013-TOS2	Cultivar (1)
Temora2015-TOS1	Cultivar (7)
Temora2015-TOS2	Cultivar (7)
Temora2015-TOS3	Cultivar (7)
Temora2015-TOS4	Cultivar (7)
Brookstead2015-TOS1	Cultivar (3)
Brookstead2015-TOS2	Cultivar (3)
Brookstead2015-TOS3	Cultivar (3)
Emerald2015-TOS1	Cultivar (3)
Emerald2015-TOS2	Cultivar (3)
Emerald2015-TOS3	Cultivar (3)
Minnipa2015-TOS1	Cultivar (4)
Minnipa2015-TOS2	Cultivar (4)
Minnipa2015-TOS3	Cultivar (4)
Temora2016-TOS4	Cultivar (2)
Hart2015-TOS1	Cultivar (2)
Hart2015-TOS2	Cultivar (2)
Hart2015-TOS3	Cultivar (2)
Inverleigh2013-TOS3	Cultivar (1)
Inverleigh2013-TOS4	Cultivar (1)

2.14 NPIField2020

Experiment Name	Design (Number of Treatments)
WaggaWagga2020	TOS x Cv (504)
Urrbrae2020	TOS x Cv (504)
Dale2020	TOS x Cv (504)
YanYean2020	TOS x Cv (441)

2.14.1 EmergenceDAS

2.14.2 FLN

2.14.3 FlagleafDAS

2.14.4 HeadingDAS

2.14.5 FloweringDAS

2.14.6 HaunStage

2.14.7 FLN

2.14.8 EmergenceDAS

2.14.9 HaunStage1

2.14.10 FlagleafDAS

3 Sensibility

3.1 CO2AndTranspirationEfficiency

Experiment Name	Design (Number of Treatments)
CO2TE	CO2 (2)

This test examines the impact of a doubling of CO2 from historical (350ppm) on Transpiration Efficiency. Reyenga et al., 1999 suggest an increase of approximately 37% in Transpiration Efficiency over this range in CO2 concentration. In this test, a series of wheat crops are simulated for Dalby, Queensland, Australia. Nitrogen limitation is removed. The slope of plots of biomass production vs crop water use is used to quantify a gross seasonal TE. The change in slope should approximate the response suggested by Reyenga et al., 1999.

3.2 CO2AndTemperatureInteractions

Experiment Name	Design (Number of Treatments)
CO2XTemperature	CO2 x MaxT (16)

This test examines the impact and interactions between increasing temperature and increasing CO2. Constant weather conditions are applied with daily maximum temperature increasing between treatments (20C to 35C). CO2 is constant and but doubles between treatments (350ppm to 700ppm).

3.3 ProteinAccumulation

Experiment Name	Design (Number of Treatments)
ProteinAccumulation	NRate (2)
NResponse	NRate (8)
WaterResponse	Irrigation (5)
PotentialGrainSize	Value (3)

This sensibility test investigates the time course of protein and mass accumulation in grains in response to water stress levels.

This sensibility test investigates the time course of protein and mass accumulation in grains in response to water stress levels.

This sensibility test investigates the time course of protein and mass accumulation in grains in response to water stress levels.

This sensibility test investigates the time course of protein and mass accumulation in grains in response to water stress levels.

3.4 TerminalWaterStress

Experiment Name	Design (Number of Treatments)
TerminalWaterStress	Irrigation (2)

This sensibility test investigates the time course of protein and mass accumulation in grains in response to water stress levels.

3.5 DetailedDynamics

Experiment Name	Design (Number of Treatments)
DetailedDynamics	Irrigation (1)

This sensibility test investigates the time course of dry matter and nitrogen content in plant organs within a growing season.

4 Interface

4.1 Wheat

Properties (Outputs)

Name	Description	Units	Type	Settable?
Structure			IStructure	True
AboveGround			IBiomass	True
AboveGroundHarvestable			IBiomass	False
SowingData			SowingParameters	True
CultivarNames			String	False
SowingDate			datetime	True
Population		/m2	double	True
IsAlive			boolean	True
IsEmerged			boolean	False
IsReadyForHarvesting			boolean	False
DaysAfterSowing		d	int32	False
CoverGreen		-	double	False
CoverTotal		-	double	False
LAI		m2/m2	double	False
WaterUptake			double	False
NitrogenUptake			double	False

Links (Dependencies)

Name	Type	IsOptional?
summary	ISummary	False
clock	IClock	False
mortalityRate	IFunction	False
seedMortalityRate	IFunction	False
Phenology	Phenology	False
Arbitrator	IArbitrator	True
structure	Structure	True
Leaf	ICanopy	True
Root	IRoot	True

Events published

Name	Type
Sowing	Void Sowing (Object sender, EventArgs e)
PlantSowing	Void PlantSowing (Object sender, SowingParameters e)
Harvesting	Void Harvesting (Object sender, EventArgs e)
PostHarvesting	Void PostHarvesting (Object sender, HarvestingParameters e)
PlantEnding	Void PlantEnding (Object sender, EventArgs e)
Flowering	Void Flowering (Object sender, EventArgs e)
StartPodDevelopment	Void StartPodDevelopment (Object sender, EventArgs e)

Methods (callable from manager)

Name	Description
Sow	void Sow(String cultivar, double population, double depth, double rowSpacing, double maxCover, double budNumber, double rowConfig, double seeds, int32 tillering, double ftn)Sow the crop with the specified parameters.
Harvest	void Harvest(boolean removeBiomassFromOrgans)Harvest the crop.
EndCrop	void EndCrop()
ReducePopulation	void ReducePopulation(double newPlantPopulation)Reduce the plant population.
AddCultivar	void AddCultivar(Cultivar cultivar)Add a cultivar.

4.2 SowingParameters

Parameters which control how a plant is sown.

Properties (Outputs)

Name	Description	Units	Type	Settable?
Cultivar			String	True
Population		/m2	double	True
Seeds			double	True
Depth		mm	double	True
RowSpacing		mm	double	True
MaxCover			double	True
BudNumber			double	True
SkipType			double	True
SkipRow			double	True
SkipPlant			double	True
SkipDensityScale			double	True
TilleringMethod			int32	True
FTN			double	True

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)

Name	Description	Units	Type	Settable?
Structure			IStructure	True
StageNames			String	False
StageCodes			int32	False
AccumulatedTT			double	True
AccumulatedEmergedTT			double	True
Emerged			boolean	False
Stage			double	True
CurrentPhaseName			String	False
CurrentStageName			String	False
FractionInCurrentPhase			double	False
CurrentPhase			IPhase	False
Zadok			double	False

Links (Dependencies)

Name	Type	IsOptional?
plant	Plant	False
thermalTime	IFunction	False
zadok	ZadokPMFWheat	True
age	Age	True

Events published

Name	Type
PhaseChanged	Void PhaseChanged (Object sender, PhaseChangedType e)
StageWasReset	Void StageWasReset (Object sender, StageSetType e)
PlantEmerged	Void PlantEmerged (Object sender, EventArgs e)
PostPhenology	Void PostPhenology (Object sender, EventArgs e)

Methods (callable from manager)

Name	Description
IndexFromPhaseName	int32 IndexFromPhaseName(String name)Look for a particular phase and return it's index or -1 if not found.
StartStagePhaseIndex	int32 StartStagePhaseIndex(String stageName)Look for a particular stage and return it's index or -1 if not found.
EndStagePhaseIndex	int32 EndStagePhaseIndex(String stageName)Look for a particular stage and return it's index or -1 if not found.
SetToEndStage	void SetToEndStage()
SetToStage	void SetToStage(String newStage)A function that resets phenology to a specified stage
SetToStage	void SetToStage(double newStage)A function that resets phenology to a specified stage
SetAge	void SetAge(double newAge)Allows setting of age if phenology has an age child
OnStartDayOf	boolean OnStartDayOf(String stageName)A utility function to return true if the simulation is on the first day of the specified stage.
InPhase	boolean InPhase(String phaseName)A utility function to return true if the simulation is currently in the specified phase.
Between	boolean Between(int32 startPhaseIndex, int32 endPhaseIndex)A utility function to return true if the simulation is currently between the specified start and end stages.
Between	boolean Between(String start, String end)A utility function to return true if the simulation is currently between the specified start and end stages.
Beyond	boolean Beyond(String start)A utility function to return true if the simulation is at or past the specified startstage.
BeyondPhase	boolean BeyondPhase(int32 phaseIndex)A utility function to return true if the simulation is at or past the specified startstage.
BeforePhase	boolean BeforePhase(int32 phaseIndex)A utility function to return true if the simulation is before the specified phaseIndex.
PhaseStartingWith	IPhase PhaseStartingWith(String start)A utility function to return the phenological phase that starts with the specified start stage name.
PhaseBetweenStages	boolean PhaseBetweenStages(String startStage, String endStage, IPhase checkPhase)Helper function to check if a particular phase is present between specifice start and end stages.
ResetCampVernParams	void ResetCampVernParams(FinalLeafNumberSet overRideFLNParams)Resets the Vrn expression parameters for the CAMP model
OnCreated	void OnCreated()
SetEmergenceDate	void SetEmergenceDate(String emergenceDate)Force emergence on the date called if emergence has not occurred already
SetGerminationDate	void SetGerminationDate(String germinationDate)Force germination on the date called if germination has not occurred already
GetPhaseTable	DataTable GetPhaseTable()

5 Science Documentation

View science documentation here

6 References

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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.

Corinne Celestina, James Hunt, Hamish Brown, Neil Huth, Mariana Andreucci, Zvi Hochman, Maxwell Bloomfield, Kenton Porker, Melissa McCallum, Felicity Harris, Mary Matthews, Ben Biddulph, Ghazwan Al Yaseri, Dion Nicol, Jessica Hyles, Enli Wang, Bangyou Zheng, Zhigan Zhao, Michele Kohout, 2023. Scales of development for wheat and barley specific to either single culms or a population of culms. European Journal of Agronomy 147.

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Jamieson, P. D., Martin, R. J., Francis, G. S., 1995. Drought influences on grain yield of barley, wheat and maize. New Zealand Journal of Crop and Horticultural Science 23 (1), 55-66.

K.T. Zeleke, C. Nendel, 2016. Analysis of options for increasing wheat (Triticum aestivum L.) yield in south-eastern Australia: The role of irrigation, cultivar choice and time of sowing . Agricultural Water Management 166, 139 - 148.

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R.A. Lawes, N.I. Huth, Z. Hochman, 2016. Commercially available wheat cultivars are broadly adapted to location and time of sowing in Australia’s grain zone . European Journal of Agronomy 77, 38 - 46.

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Young, R.R., Derham, P-J, Dunin, F.X., Bernardi, A.L., Harden, S., 2008. High crop productivity with high water use in winter and summer on the Liverpool Plains, eastern Australia.. Australian Journal of Agricultural Research 59, 303–320.