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1 The APSIM AgroforestrySystem ModelThe APSIM Agroforestry model has been developed to facilitate the modelling of tree-crop and silvopastoralsystems. It has been tested on a range of datasets from constrasting agorforestry systems, soils and climates. Itmakes use of APSIM's ability to simulate multiple zones within a production system Holzworth et al., 2014 forsimulating soil and plant processes at different locations within a windbreak, isolated tree or alley system.

The role of this model is not to simulate tree production, but to simulate crop or pasture production inagroforestry systems by calculating tree-crop interactions via microclimate and soil effects.

The proxy tree component of this model is intended also to be a guide for developing active trees for use in thismodel, as such trees would need to interact similarly with the crop or pasture component of the system.

2 APSIM DescriptionThe Agricultural Production Systems sIMulator (APSIM) is a farming systems modelling framework that is beingactively developed by the APSIM Initiative.

It is comprised of

1. a set of biophysical models that capture the science and management of the system being modelled,2. a software framework that allows these models to be coupled together to facilitate data exchange

between the models,3. a set of input models that capture soil characteristics, climate variables, genotype information, field

management etc,4. a community of developers and users who work together, to share ideas, data and source code,5. a data platform to enable this sharing and6. a user interface to make it accessible to a broad range of users.

The literature contains numerous papers outlining the many uses of APSIM applied to diverse problem domains.In particular, Holzworth et al., 2014; Keating et al., 2003; McCown et al., 1996; McCown et al., 1995 havedescribed earlier versions of APSIM in detail, outlining the key APSIM crop and soil process models andpresented some examples of the capabilities of APSIM.

Figure 2: This conceptual representation of an APSIM simulation shows a “top level” farm (with climate, farmmanagement and livestock) and two fields. The farm and each field are built from a combination of modelsfound in the toolbox. The APSIM infrastructure connects all selected model pieces together to form a coherentsimulation.*

The APSIM Initiative has begun developing a next generation of APSIM (APSIM Next Generation) that is writtenfrom scratch and designed to run natively on Windows, LINUX and MAC OSX. The new framework incorporatesthe best of the APSIM 7.x framework with an improved supporting framework. The Plant Modelling Framework(a generic collection of plant building blocks) was ported from the existing APSIM to bring a rapid developmentpathway for plant models. The user interface paradigm has been kept the same as the existing APSIM version,but completely rewritten to support new application domains and the newer Plant Modelling Framework. Theability to describe experiments has been added which can also be used for rapidly building factorials ofsimulations. The ability to write C# scripts to control farm and paddock management has been retained. Finally,all simulation outputs are written to an SQLite database to make it easier and quicker to query, filter and graphoutputs.

The model described in this documentation is for APSIM Next Generation.

APSIM is freely available for non-commercial purposes. Non-commercial use of APSIM means public-goodresearch & development and educational activities. It includes the support of policy development and/orimplementation by, or on behalf of, government bodies and industry-good work where the research outcomesare to be made publicly available. For more information visit the licensing page on the APSIM web site

3 Model descriptionThe APSIM AgroforestrySystem model calculates interactions between trees and neighbouring crop or pasturezones. The model is therefore derived from the Zone class within APSIM and includes child zones to simulatesoil and plant processes within the system. It obtains information from a tree model within its scope (ie a child)and uses information about the tree structure (such as height and canopy dimensions) to calculate microclimateimpacts on its child zones. Below-ground interactions between trees and crops or pastures are calculated by theAPSIM SoilArbitrator model.

Windbreaks are simulated using an approach Huth et al., 2002 that calculates windspeeds in the lee ofwindbreaks as a function distance (described in terms of multiples of tree heights) and windbreak opticalporosity.

4 Validation4.1 WarraThis test evaluates the performance of the APSIM AgroforestrySystem model in simulating the crop and soilwater data for the data of Huth, 2010. Monitoring of tree-crop interactions was undertaken on a farm near thetownship of Warra, Qld (26.93oS, 150.93oE), where a belt of four rows of Eucalyptus argophloia atapproximately 5 x 5 m spacing had been planted along the edge of fields used to grow wheat (Triticum aestivumL.), cotton (Gossypium hirsutum L.) and chickpeas (Cicer arietinum L.). The trees were about 7 years old andnearly 10 m high at the start of the study. Tree-crop interactions were monitored over 3 cropping seasons (two

winters and one summer) in two bays on opposite sides of the eucalypt belt. Agronomic management in the twocropping bays varied as they were at different stages of a cropping rotation. The bay used in this model test wassown to wheat in the winter of 2004 and remained fallow during the ensuing summer and winter growingseasons. Prior to harvest of the crop, plant samples were taken from quadrats consisting of 1 m of each plantrow in 5 linear transects out to 50 m from the shelterbelt Huth et al., 2010. As germination failure was significantnext to the trees, sampling positions were at 25, 30, 35, 40 and 50 m. The monitoring of soil moisture and itsextraction by both trees and crops was undertaken using electromagnetic induction measurements Huth et al.,2007 taken at 5 m intervals in four 50 m transects from the belt of trees.

The simulations were developed using data from various sources. Rainfall data were obtained from anAustralian Bureau of Meteorology rainfall station (No. 41291) situated on a farm 4.1 km from the study site. Soilproperties were based on the data of Huth et al., 2010.

NOTE: Yield predictions at this site are adequate, but an issue with simulating failed germination has beenidentified. Data from the trial show complete germination failure out to a distance of 22 m from the trees. TheAPSIM-Wheat model was not able to capture this germination failure, though much of the yield impact wascaptured through competition for water resources.

4.2 MooraData from Western Australia Lefroy et al., 2001 present results for the fodder tree tagasaste (Chamaecytisusproliferus Link.) at alley and plantation densities on a deep sand at Moora. Alleys were planted as single rows oftrees 30 m apart orientated north–south with 0.7 m between trees within a row. The soil was a coarse, veryinfertile acid sand with pH (CaCl2) 5.2 at the surface and 4.8 between 1 and 3 m. Water holding capacity wasvery low, with a drained upper limit of 0.10 and a lower limit of 0.03 m3/m3. A permanent fresh (<1 dS/m)perched watertable has developed over a clay layer approximately 10 m below the surface. Soil water contentwas measured with neutron probes at 20-cm intervals to a depth of 3.7 m from July 1996 to December 1998 at2-week intervals in winter and monthly during summer. Transpiration was measured using sapflow sensors. Thedata shown here are for the alley-fallow plots, which had Tagastaste rows spaced at 30m with no crop plantedin between.

4.3 MachakosThis test evaluates performance of the APSIM AgroforestrySystem model in simulating crop and soil water dataof Odhiambo et al., 1999; Odhiambo et al., 2001; Wilson et al., 1998. The experiment was conducted atICRAF’s field station at Machakos, Kenya (1o 33’S and 37o 8’E) at a mean elevation of 1660 m. Bimodal annualrainfall averages 740 mm.

Treatments were Gliricidia-maize, Grevillea-maize and sole maize. Grevillea and Gliricidia were planted inOctober 1993 as a single row in the middle of each 20 m x 18 m plot (1 m within-row spacing). Tree rows wereoriented east-west to minimize shading effects on crops. Grevillea was pruned to 2.5. m height, but Gliricidia,which was grown as a hedgerow, was heavily cut back before each rainy season. Maize (‘Katumani composite’) was sown at the start of each ‘long’ season (March-July), and beans at the start of each ‘short’ season(October-February). Results were reported for two ‘long’ seasons 1996-1997, but beans failed during theintervening ‘short’ season. Tree heights during 1996-1997 were approximately 5-7 m for Grevillea and 4-5 m forGliricidia.

This experiment should not be confused with the CIRUS experiment reported by Howard, 1997; Lott et al., 2009and intervening papers and reports, but it was conducted at the same research station and over-lappedtemporally. This experiment was funded by DFID (Project T01065s5) led by Prof. Chin Ong (currently,University of Nottingham, UK). Some site data, including meteorological data, were used from these sources, orfrom data supplied by researchers from the two studies. For example, the met file was collated mainly from datacollected using a Campbell weather station managed by Nick Jackson, and supplemented with data from aDelta-T weather station managed by James Lott. These weather station data were provided by Julia Wilson(Centre for Ecology and Hydrology, Natural Environment Research Council, UK). Watch Forcing Data Weedonet al., 2011 interpolated data helped fill some data gaps.

Soils are well-drained dark brown sandy clays. These soils are weakly to moderately leached with a pH of 6 to6.5 and possess medium base saturation (50 to 80%) in the topsoil. Nitrogen and phosphorus contents wereconsidered less than optimal, but like other nutrients, adequate for growth [mathuva1998improving].

At the beginning of each wet season, 1 to 2 m deep trenches (depending on depth to bedrock) were dug at themargins of each plot to stop roots from one plot penetrating into and exploiting adjacent plots. All cultivationswere by hand tools, which mimiced farmer practise and caused minimal disturbance to tree roots. Similarly,each season, weeds were controlled by cutting/uprooting them with hand tools on two occasions after cropgermination. Key measurements reported include transects from the tree row out to 8 maize rows for: soil water,tree and crop root length density, tree growth, and maize germination, biomass and grain yield. Maizegermination and growth was severely limited by low rainfall and tree competition for water.

NOTE: Germination failure was an important factor in determining yield impacts of trees within this trial. TheMaize model was not able to simulate changes in plant population density away from the tree row, but instead itwas specifically coded into the model for this case. Also, we do not simulate the intervening bean crop betweenthe two maize seasons. We add fertiliser to the second crop to provide N that would have been provided by thebean crop.

NOTE: Soil water data were obtained from digitising graphs of the measured data from the technical report forthe trial Wilson et al., 1998. As such, exact dates for observations are not known and error in estimates ofmeasurement dates will cause problems in the following graphs. For example, where observations occur closeto rainfall events.

4.3.1 Grevillea

4.3.2 Gliricidia

5 SensibilityYield data from cereal and pulse crops Bennell et al., 2008 have been collected from windbreak sites throughthe eastern agricultural districts of South Australia using a harvester equipped with a yield monitor. The yieldresponse in the sheltered benefit zone was +3.7% for cereal yield averaged across zones (2.2H–9.7H) outsidethe competition zone. They found, for wheat, that the average width of expression of the competition zone to be2 tree heights (2H) with a 4% increase in yields within the sheltered zone (2H-11H).

This sensibility test assumes a tree windbreak with a of height of 10 m. The competition zone should thereforebe evident out to 20m from the trees with only a marginal increase in yield at 20-110 m from the trees.

6 ReferencesBennell, M. R., Verbyla, A. P., 2008. Quantifying the response of crops to shelter in the agricultural regions of

South Australia. Australian Journal of Agricultural Research 59 (10), 950-957.

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 andSoftware 62, 327-350.

Howard, Stephen, 1997. Resource capture and productivity of agroforestry systems in Kenya..Doctor ofPhilosophy.University of Nottingham.

Huth, Neil Ian, Carberry, P.S., Poulton, P.L., Brennan, L.E., Keating, Brian A., 2002. A framework for simulatingagroforestry options for the low rainfall areas of Australia using APSIM. European Journal of Agronomy18, 171-185.

Huth, N. I., Poulton, P. L., 2007. An electromagnetic induction method for monitoring variation in soil moisture inagroforestry systems. Australian Journal of Soil Research 45 (1), 63-72.

Huth, N. I., 2010. Measuring, modelling and managing tradeoffs in low rainfall agroforestry for Australia’ssubtropics..Doctor of Philosophy.University of Queensland.

Huth, N. I., Robertson, M. J., Poulton, P. L., 2010. Regional differences in tree-crop competition due to soil,climate and management. Crop & Pasture Science 61 (9), 763-770.

Keating, B. A., Carberry, P. S., Hammer, G. L., Probert, M. E., Robertson, M. J., Holzworth, D., Huth, N. I.,Hargreaves, J. N. G., Meinke, H., Hochman, Z., McLean, G., Verburg, K., Snow, V., Dimes, J. P., Silburn,M., Wang, E., Brown, S., Bristow, K. L., Asseng, S., Chapman, S., McCown, R. L., Freebairn, D. M.,Smith, C. J., 2003. An overview of APSIM, a model designed for farming systems simulation. EuropeanJournal of Agronomy 18 (3-4), 267-288.

Lefroy, E. C., Stirzaker, R. J., Pate, J. S., 2001. The influence of tagasaste (Chamaecytisus proliferus Link.)trees on the water balance of an alley cropping system on deep sand in south-western Australia.Australian Journal of Agricultural Research 52 (2), 235-246.

Lott, JE, Ong, CK, Black, CR, 2009. Understorey microclimate and crop performance in a Grevillea robusta-based agroforestry system in semi-arid Kenya. Agricultural and forest meteorology 149 (6), 1140-1151.

McCown, R. L., Hammer, G. L., Hargreaves, J. N. G., Holzworth, D., Huth, N. I., 1995. APSIM: an agriculturalproduction system simulation model for operational research. Mathematics and Computers in Simulation39 (3-4), 225-231.

McCown, R. L., Hammer, G. L., Hargreaves, J. N. G., Holzworth, D. P., Freebairn, D. M., 1996. APSIM: a NovelSoftware System for Model Development, Model Testing and Simulation in Agricultural SystemsResearch. Agricultural Systems 50 (3), 255-271.

Odhiambo, HO, Ong, CK, Wilson, Julia, Deans, JD, Broadhead, J, Black, C, 1999. Tree-crop interactions forbelow ground resources in drylands: root structure and functions. Annals of Arid Zone 38 (3), 221-237.

Odhiambo, HO, Ong, CK, Deans, JD, Wilson, Julia, Khan, AAH, Sprent, JI, 2001. Roots, soil water and cropyield: tree crop interactions in a semi-arid agroforestry system in Kenya. Plant and Soil 235 (2), 221-233.

Weedon, G. P., Gomes, S., Viterbo, P., Shuttleworth, W. J., Blyth, E., Österle, H., Adam, J. C., Bellouin, N.,Boucher, O., Best, M., 2011. Creation of the WATCH Forcing Data and Its Use to Assess Global andRegional Reference Crop Evaporation over Land during the Twentieth Century. Journal ofHydrometeorology 12 (5), 823-848.

Wilson, Julia, JD (Doug) Deans, Ong, CK, Khan, AH, Odhiambo, HO, 1998. Comparison of tree: intercropinteractions of Gliricidia and Grevillea in semi-arid Kenya. International Centre for Research inAgroForestry (ICRAF).