1 The APSIM Eucalyptus 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.

Eucalyptus Model Notes

Plant Modelling Framework

The APSIM Eucalyptus model has been developed using the Plant Modelling Framework (PMF) of Brown et al., 2014 within APSIM Next Generation Holzworth 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 processes and organ classes (e.g. photosynthesis, leaf) into larger constructions (e.g. maize, wheat, eucalyptus) can be achieved by the model developer without additional coding.

###Pecularities of Eucalyptus and This Model

The Eucalyptus model consists of:

• a phenology model to simulate development through sequential growth phases
• 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 forest models such as described by Huth et al., 2002,Huth et al., 2001 and Huth et al., 2008.

Eucalyptus is a reasonably straight forward perennial crop to model. This model has been set up for simulation of even-aged plantations (transplanted seedlings) or native forests (also assumed to start the simulation as a transplanted seedling, but in practice it would be sown from naturally distributed seed). Plants grow in accordance with available resources and conditions, which in this version of the model are temperature, radiation, available soil water, and available soil nitrogen. Leaves, branches and roots senesce, remain attached for some time, then detach to produce litter. Above-ground biomass is the main target of production, which is made up of organs that develop from default partitioniing targets that are modified daily in response to organ demand. Forest managers also deal with tree size, which are set in the model as empricial functions of aboveground biomass. During model development, we found that Eucalyptus model performance (plant or stand development) was particularly sensitive to leaf lifespan/longevity, specific leaf area, dead leaf detachment, partitioning to roots and shoots, mortality and thinning, and weeds (if present).

After stem dry weight is determined, it is then empirically split into bark and wood (based on individual stem weight), and the volumes of each set using bark thickness. This enables under and over bark properties to be calculated for BA, Vol and MAI, and finally a the calculation of wood and bark densities. Volume is calculated as a stand rather than summing individual trees. Many of these attributes of stem metrics are highly site, genotype and management specific, and some forestry plantation companies keep there own parameterisations confidential.

There are many Eucalyptus genotypes (species, closely related genera, provenances, families, clones, and hybrids) that can behave differently in response to their growing environment. The Eucalyptus model was calbirated on datasets of species (E. globulus, E. grandis, E. nitens, E. saligna), hybrids (E. grandis x E. urophylla, E. globulus x E. urophylla), and two clones of the E. grandis x E. urophylla hybrid.

###Including a Eucalyptus crop in an APSIM simulation

A example Eucalyptus simulation is available by clicking the "Open an Example" tab available when APSIM Next Gen is opened. This provides a demonstration of how to simulate a Eucalyptus crop, and it provides some useful graphs as suggestins for viewing model behaviour and performance.

To incluce a Eucalyptus crop in a simulation the "Eucalyptus" model needs to be added to the paddock, field or zone in which it is to be grown. This can be done by (a) right clicking on the "Paddock", selecting "Add model..." then "PMF", then selecting "Eucalyptus" from the list that comes up, or (b) copying and pasting the model from the example simulation. A TreeSowingRule needs to be set up to start the crop. Harvesting and replanting are included in the 'EucalyptusRotation' example.

This document provides a more detailed description of the model, describes the validation and test datasets, and model performance.

Major Eucalyptus model developments:

2017-2019

• Developed and released the first version of the Eucalptus model in APSIM Next Generation. That verison was based on Australian and Brazilian datasets covering tropical and sub-tropical genotypes - mainly E. grandis. Publications include Smethurst et al., 2020 and Elli et al., 2020.

2020-2022

• Included temperate species, i.e. E. globulus and E. nitens.
• Included an expansion of stem metrics beyond just diameter at breast height (DBH, cm), height (m), and overbark stem volume (Vol, m³/ha). New stem metrics include underbark parameters of stem volume (Volub) and wood density, and basal area (BA, m²/ha) the mean annual increment of overbark and underbark volumes (m³/ha/year).This required partitioning of stem biomass into bark and wood, calculation of bark thickness, and an estimation of volumes overbark and underbark. All stem metrics are empricially calculated rather than process-based. These metrics are known to be highly effected by stem taper, bark thickness and wood density, which in-turn are highly influenced by site, genetics and management. Few data are available on wood density (underbark, whole tree), so it is included here only as a check that underlying calculations are sensible. Validations of these metrics are included. In comparison, it remains that only DBH, height and overbark stem volume are validated for the tropical and sub-tropical genotypes.

Suggested future developments:

1. Create a set of functional weeds specifically for use with these forestry models, e.g. N-fixing/non-N-fixing X herbaceous/shrub/tree X tropical/sub-tropical/temperate.
2. Coppicing
3. Self-thinning rule or process-based mortality
4. Improve effects of stocking, if necessary. Leaf allocation as a function of aboveground.wt (g/m²) rather than individual tree weight (g/tree) has been included, but further checking of this is required to see that if that is all that is needed for a wide range of stockings.
5. Waterlogging – I (Philip) would have thought it was important, particularly for some euc and pine genotypes, but so far I haven’t run into a really need for it amongst our current observed datasets.
6. Add observed data for the tropical and sub-tropical genotypes for the more advanced stem metrics, and recalibrate the model for those genotypes if necessary. This would be a good postgrad project for a Brazilian student with industry collaborators.
7. Soil P (and K) and fertiliser responses
8. Pruning and effects on knot-free wood (wood quality)
9. Geo-locate and interact adjacent plots for predicting area-based metrics like stream flow and wood production

10. Tree and log size class distributions
    

11. Development of outputs for greenhouse accounting (water use, C sequestration, greenhouse gases, biodiversity idiexes)

Root:shoot ratio, and allometric relationships for height (Ht, m), stem diameter (DBH, cm, over bark at 1.3 m height), and their derivatives (stem volume Vol, and mean annual increment MAI) were developed as a function of above-ground biomass from Almeida, 2003, Almeida et al., 2004, Borges, 2009, Cromer et al., 1993, and Nogueira, 2005. Similarly, above-ground biomass as a function of stem weight or wood weight was developed from the same datasets plus Turner, 1986, Byrne, 1989, Bradstock, 1981, Polglase et al., 1995, Snow et al., 1999, Snow et al., 1999, Myers et al., 1996, Myers et al., 1998, and Melo et al., 2015.

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 Age

Accumulates a child function between a start and end stage.

1.4.2 MortalityRate

A constant function (name=value)

1.4.3 SeedMortalityRate

A constant function (name=value)

1.4.4 Phenology

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

1.4.5 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.

1.4.6 IndividualTreeLiveWt

A class that divides all child functions.

Returns zero if nominator is zero, returns double.maxValue if denominator is zero.

Note that this property does not include branches. It is used in calculating DM Demands of several plant components.

1.4.7 IndividualTreeStemWt

A class that divides all child functions.

Returns zero if nominator is zero, returns double.maxValue if denominator is zero.

1.4.8 Leaf

This organ is parameterised using a simple leaf organ type which provides the core functions of intercepting radiation, providing a photosynthesis supply and a transpiration demand. It also calculates the growth, senescence and detachment of leaves.

1.4.9 Branch

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.

For many of the parameters in this organ, see comments for similar parameters in the leaf organ.

1.4.10 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.

For many of the parameters in this organ, see comments for similar parameters in the leaf organ.

1.4.11 CoarseRoot

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.

For many of the parameters in this organ, see comments for similar parameters in the leaf organ.

1.4.12 FineRoot

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

For many of the parameters in this organ, see comments for similar parameters in the leaf organ.

1.4.13 RootShootRatio

A class that divides all child functions.

Returns zero if nominator is zero, returns double.maxValue if denominator is zero.

2 Validation

Validation datasets have been included to assist with validation during model development. Validation datasets cover a range of environmental (soil and climate) conditions, management options (populations, nitrogen rates, irrigation) and genetic backgrounds (different regions, provenence, clones). These datasets have been grouped and ordered alphebetically by site within a climatic zone. Graphs of model performance are provided for stocking, canopy development, biomass production, stem metrics, and soil water. Where a tropical or sub-tropical dataset did not include aboveground biomass, but instead a related parameter like stem volume or biomass, the latter was converted to aboveground biomass by a regression based on the rest of the validation dataset for that climatic region. However, this estimation was not conducted for temperate datasets.

Observed data are shown compared to predictions, with statistics for model skill.

2.1 Combined Validation

These graphs are for the combined datasets of Tropical and SubTropical and Temperate climatic zones.

Graphs from individual sites, particularly temporal trends of observed and predicted values, are available but currently disabled. If you wish to view these graphs, please download the validation from GitHub, run it, and enable those graphs and-or add others.

2.2 Tropical and SubTropical

2.2.1 Tropical and SubTropical Validation

These graphs are for the Tropical and SubTropical datasets.

2.2.2 Australia Egrandis

2.2.2.1 CoffsHarbour

Data are from Turner, 1986. The research is also reported in Byrne, 1989 and Bradstock, 1981. These data are from a chronosequence of approximately even-aged stands of native forests of E grandis and include the oldest stands simulated so far by the APSIM Eucalyptus model. They are the only native forest data in this set of simulations; all other simulations are of plantations.

2.2.2.2 Gympie

This experiment is described in Cromer et al., 1993 and Cromer et al., 1993. Some soil input data are from Ross, 1991. Experimental treatments were factorial combinations of two levels each of irrigation and fertilisation applied to E. grandis. Most growth response was to fertiliser, which included NPK, but only N is simulated, which assumes that other nutrients were present at adequate levels.

2.2.2.3 Wagga

This experiment is described in Polglase et al., 1995, Snow et al., 1999, Snow et al., 1999, Myers et al., 1996, and Myers et al., 1998. The experiment included effluent-irrigated E. grandis and some weeds; only the irrigated treatement was included here.

2.2.3 Brazil Egrandis and others

2.2.3.1 Aracruz

These data are described in Almeida, 2003 and Almeida et al., 2004. Two clones of E. grandis are included that had measured differences in root:shoot ratio. These differences were simulated by specifiying partitioning targets as a genetic property.

2.2.3.2 Curvelo

These data are described in Borges, 2009. Two clones of the hybrid E. grandis x E. urophylla were included (C3334, C3336), but their measured differences were not significantly different. Hence, clonal differences were not simulated by specifiying partitioning targets as a genetic property.

2.2.3.3 Itacambira

These data are described in Borges, 2009. Two clones of the hybrid E. grandis x E. urophylla were included (C3334, C3336), but their measured differences were not significantly different. Hence, clonal differences were not simulated by specifiying partitioning targets as a genetic property.

2.2.3.4 Luisantonio

These data are described in Melo et al., 2015. One clone of the hybrid E. grandis x E. urophylla was grown with 4 rates of N fertilisation.

2.2.3.5 Mogiguacu

These data are described in Melo et al., 2015. One clone of the hybrid E. grandis x E. urophylla was grown with 4 rates of N fertilisation.

2.2.3.6 MonteDourado

These data are described in Silva, 2006. One clone of the hybrid E. grandis x E. urophylla was included, and it was grown at two sites on contrasting soils.

2.2.3.7 Paulistania

These data are described in Melo et al., 2015. One clone of the hybrid E. grandis x E. urophylla was grown with 4 rates of N fertilisation.

2.2.3.8 Ribasdoriopardo

These data are described in Melo et al., 2015. One clone of the hybrid E. grandis x E. urophylla was grown with 4 rates of N fertilisation.

2.2.3.9 SantanadoParaiso

These data are described in Nogueira, 2005. One clone of the hybrid E. grandis was included, and it was grown in a factorial experiment of two levels each of irrigation and fertilisation.

2.3 Temperate

2.3.1 DroughtRiskSites

These experiments are described in Mendham et al., 2011, White et al., 1996, White et al., 1998, White et al., 2009, White et al., 2010 and White et al., 2014. Experimental treatments were combinations of stocking and N fertilisation starting 2 years after planting. Additional data were provided by D. Mendham. A second rotation is described in these papers and data included in the observed file, but treatments were reallocated at the begining of the second rotation, and coppice rather than seedlings were used in most treatments for the second rotation. It would be useful and possible to include coppicing in the Eucalytpus model, but this has not yet been attempted. Other models in APSIM provide a basis for including coppicing, e.g. gliricidia, and lucerne.

2.3.2 Forico

These data describe adjacent plantations of E. globulus and E. nitens, with part of the E. nitens plantation being thinned. The site was known as St Georges Road. We thank G. Holz, K. Joyce, and L. Cannon of Forico for data and other information about the site (formally the site was owned or managed by Gunns, North Forest Products and APPM).

2.3.3 FSAGrowthPlots

These data are for Forestry SA growth plots, and were provided Jim O'Hehir, University of South Australia. As they are in a region with a water table containing nitrate that can be reached by roots, these components were added to the simulation. However, nitrate was not described as a concentration in groundwater, but instead nitrate was applied as nitrate fertiliser at 2.5 m depth or greater four times per year. Thinned and unthinned stands are included.

2.3.4 Furadouro

This experiment is described in Madeira et al., 1990, Madeira et al., 1995, Pereira et al., 2012, Madeira et al., 2002, Fabi~ao et al., 1995, K\atterer et al., 1995, Quilh'o et al., 2001, Pereira et al., 1994, Pereira et al., 1989, and Fontes et al., 2006. Experimental treatments were combinations of irrigation and fertilisation applied to E. globulus. Most growth response was to fertiliser, which included NPK, but only the IL treatment was simulated here, which assumes that nutrients othere than N were also present at adequate levels.

2.3.5 Lewisham

This experiment is described in White et al., 1998, White et al., 1996, and Worledge et al., 1998. The experiment included a comparison of E. globulus and E. nitens under supplemented-rainfed and well-irrigated conditions. Rainfed plots were up-slope of the irrigated plots. Soil was mostly derived from basalt, which was present at a shallow depth.

2.3.6 Shepparton

This experiment is described in Bren et al., 1993, Baker, 1998, Baker et al., 2005, Duncan et al., 1998, Wong et al., 2000, Hopmans et al., 1990, and Stewart et al., 1990. Additional data and information were provided by T.G. Baker and H. Stewart. The experiment included coppiced and seedling E. grandis and E. globulus, which were irrigated with sewerage effluent. The soil was a duplex, and there for poorly drained. Eearly growth was quite impressive, but by 10 years trees had noticably mortality due to pests and diseases, and also due to other conditions that did not suit these species (poor drainage, frost).

2.3.7 Westfield

This experiment is described in Smethurst et al., 1997, Smethurst et al., 2003, Smethurst et al., 2004, Smethurst et al., 2004, Misra et al., 1998, Misra et al., 1998, and Resh et al., 2003. The experiment included nil to high cumulative rates of N and P fertilisers in an E. nitens plantation. Other research suggested that three was little or no response to the P component of the fertiliser. Very high rates of fertiliser might have started to induced a base cation deficiency (Ca, Mg or K) by the latter stage of the rotation, as some acidification had occurred, but this was not investigated further.

3 Sensibility

A series of sensibility tests have been employed to test the behaviour of the model in regions not explicitly included in the previous test set. Furthermore, these tests explore the emergent behaviour of the model under a range of changing climate, fertility and management scenarios to ensure that simulated patterns agree with expected behaviours.

3.1 MAI in SE Australia

Representative growth rates for Eucalyptus grandis have been published for south-eastern Australia (Victoria and South Australia) by [Wong2000forecasting]. Some of the sites within this publication had previously been used for improved pasture and had minimual fertility constraints. Simulations for 4 sites have been presented here to capture a range of environmental conditions. Stocking rates used at each site match those obtained at each site within the published study. MAI at age 10 years should be approximately 10 cubic metres per annum for Mount Worth and Stockdale and approximately 20 cubic metres per annum for Tostaree and Mount Lofty. Climate data has been taken from nearby towns and common soil properties have been used for all sites, with soil properties reflecting a relatively high state of fertility.

3.2 Response to Rainfall

Eucalyptus should respond to changes in rainfall such that peak MAI should increase with rainfall. Long term "climax LAI" should also increase with mean annual rainfall. This simulation experiment explores the changes in MAI and canopy cover along a rainfall gradient within SE Queensland Australia. Mean annual rainfall decreases from approximately 1200 mm to 660 mm. Fertiliser is applied within the simulations to remove any confounding of results due to site fertility. Data from Specht, 1972 show that canopy cover should be almost complete for the wetter sites in this study, and decrease to approximately 50% at the drier sites.

3.3 Response to Soil Fertility

Site fertility is an important driver of the pattern of tree growth rates. As site fertility declines, the long term growth rate (e.g. MAI) should also decrease, but the time to obtaining peak MAI should increase. This sensibility test uses a single location in SE Queensland at which Eucalyptus grandis occurs naturally. A range of soil fertility states are applied in this experiment. Peak MAI should decrease with decreasing fertility, but the time required to achieve this should increase.

3.4 Reponse to N Fertilizer

Eucalyptus responses to rate of fertiliser are often asymptotic, the plateau of which is determined by other limiting factors (Rubilar et al., 2018). In the sensibility tests presented here, soil from Wodonga, Australia, was used as the basis for the simulations, except soil organic C and C:N were set to represent site 1 in Columbia in Albaugh et al., 2015 and Rubilar et al., 2018. Two climates are used in these simualtions ('WodongaI0ClimateWodonga' and 'WodongaI0ClimateCoffs'. Management was set similar to site 1 in Rubilar et al., 2018, i.e. E grandis was fertilised at 2 years of age and the 3-year stem volume response assessed at 5 years of age. For a highly responsive site in Columbia, a plateau in growth response occurred at about 800 kg N/ha, when it was speculated that other factors became limiting.

In 'WodongaI0ClimateWodonga', a low rainfall site (739 mm/year average longterm), the response to N was simulated to plateau at a rate of about 800 kg N/ha. The N rate inflexion point here is similar to that in Rubilar et al., 2018, but the limiting factor above this N rate in this simulation was mainly water, whereas in Albaugh et al., 2015 we can specualte that it was base cation deficiency.

In 'WodongaI0ClimateCoffs', a high rainfall site (1635 mm/year average longterm), the water limitation was removed and the response to N has not plateaued even at a rate of 3000 kg N/ha.

Values of MAI and other outputs are in the range of expectation.

3.5 GXE Brazil

The graphs shown here demosntrate a genotypeXsite interaction. Inhambupe is a dry site in NE Brazil. Botucatu is a highly productive site in SE Brazil. Sao Gabriel is a wet site in S Brazil. Bocaiuve is dry site in SE Brazil.

3.6 ScottRiverNResponse

This experiment is described in Cromer et al., 1993 and Cromer et al., 1993. Some soil input data are from Ross, 1991. Experimental treatments were factorial combinations of two levels each of irrigation and fertilisation applied to E. grandis. Most growth response was to fertiliser, which included NPK, but only N is simulated, which assumes that other nutrients were present at adequate levels.

4 Interface

4.1 Eucalyptus

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

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Almeida, AC, 2003. Application of a process-based model for predicting and explaining growth in Eucalyptus plantation..

Almeida, Auro C, Landsberg, Joe J, Sands, Peter J, 2004. Parameterisation of 3-PG model for fast-growing Eucalyptus grandis plantations. Forest Ecology and Management 193 (1-2), 179-195.

Baker, T, Duncan, M, Stackpole, D, others, 2005. Growth and silvicultural management of irrigated plantations. New forests: wood production and environmental services, 113-134.

Baker, TG, 1998. Tree growth in irrigated plantations at Wodonga and Kyabran, Victoria (1976-1994)..

Borges, JS, 2009. Parameterization calibration and validation of the 3PG model for the cerrado of Minas Gerais..

Bradstock, R, 1981. Biomass in an age series of Eucalyptus grandis plantations. Australian Forest Research 11 (2), 111-127.

Bren, L, Hopmans, P, Gill, B, Baker, T, Stackpole, D, 1993. Commercial tree-growing for land and water care. 1. Soil and Groundwater Characteristics of the Pilot Sites. Trees for Profit Research Centre, University of Melbourne, 46.

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.

Byrne, GF, 1989. Above-ground dry matter accumulation by Eucalyptus grandis and its relation to standard meteorological data. Agricultural and forest meteorology 46 (1-2), 65-73.

Cromer, RN, Cameron, DM, Rance, SJ, Ryan, PA, Brown, M, 1993. Response to nutrients in Eucalyptus grandis. 1. Biomass accumulation. Forest Ecology and Management 62 (1-4), 211-230.

Cromer, RN, Cameron, DM, Rance, SJ, Ryan, PA, Brown, M, 1993. Response to nutrients in Eucalyptus grandis. 2. Nitrogen accumulation. Forest Ecology and Management 62 (1-4), 231-243.

Duncan, M, Baker, TG, Wall, G, 1998. Wastewater irrigated tree plantations: productivity and sustainability. Proceedings of the 61st Annual Water Industry Engineers and Operators’ Conference, 18-26.

Elli, Elvis, Huth, Neil, Sentelhas, Paulo, Carneiro, Rafaela, Alcarde Alvares, Clayton, 2020. Global sensitivity-based modelling approach to identify suitable Eucalyptus traits for adaptation to climate variability and change. in silico plants 2.

Fabi~ao, Ant'onio, Madeira, Manuel, Steen, E, K\atterer, T, Ribeiro, C, Ara'ujo, C, 1995. Development of root biomass in an Eucalyptus globulus plantation under different water and nutrient regimes. Plant and Soil 168 (1), 215-223.

Fontes, Luis, Landsberg, Joe, Tom'e, Jos'e, Tom'e, Margarida, Pacheco, Carlos A, Soares, Paula, Araujo, Clara, 2006. Calibration and testing of a generalized process-based model for use in Portuguese eucalyptus plantations. Canadian Journal of Forest Research 36 (12), 3209-3221.

Holzworth, Dean P., Huth, Neil I., deVoil, Peter G., Zurcher, Eric J., Herrmann, Neville I., McLean, Greg, Chenu, Karine, van Oosterom, Erik J., Snow, Val, Murphy, Chris, Moore, Andrew D., Brown, Hamish, Whish, Jeremy P. M., Verrall, Shaun, Fainges, Justin, Bell, Lindsay W., Peake, Allan S., Poulton, Perry L., Hochman, Zvi, Thorburn, Peter J., Gaydon, Donald S., Dalgliesh, Neal P., Rodriguez, Daniel, Cox, Howard, Chapman, Scott, Doherty, Alastair, Teixeira, Edmar, Sharp, Joanna, Cichota, Rogerio, Vogeler, Iris, Li, Frank Y., Wang, Enli, Hammer, Graeme L., Robertson, Michael J., Dimes, John P., Whitbread, Anthony M., Hunt, James, van Rees, Harm, McClelland, Tim, Carberry, Peter S., Hargreaves, John N. G., MacLeod, Neil, McDonald, Cam, Harsdorf, Justin, Wedgwood, Sara, Keating, Brian A., 2014. APSIM – Evolution towards a new generation of agricultural systems simulation. Environmental Modelling and Software 62, 327-350.

Hopmans, P, Stewart, HTL, Flinn, DW, Hillman, TJ, 1990. Growth, biomass production and nutrient accumulation by seven tree species irrigated with municipal effluent at Wodonga, Australia. Forest Ecology and Management 30 (1-4), 203-211.

Huth, N. I., Carberry, P. S., Cocks, B., Graham, S., McGinness, H. M., O'Connell, D. A., 2008. Managing drought risk in eucalypt seedling establishment: An analysis using experiment and model. Forest Ecology and Management 255 (8-9), 3307-3317.

Huth, N. I., Snow, V. O., Keating, B. A., 2001. Integrating a forest modelling capability into an agricultural production systems modelling environment-current applications and future possibilities.., Eds: Zerger, A. and Argent, R.M., 1895-1900.

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