The GROWEST program calculates monthly and weekly growth indices in point or grid form for selected plant temperature regimes using monthly and weekly input climate data and specified soil water properties. The GROWEST model was originally developed to run on weekly climate data by Fitzpatrick and Nix (1970). The model was expanded to permit additional temperature regimes and more fully described by Nix (1981). Several studies have demonstrated the robustness and simplicity of the GROWEST model in the analysis and characterisation of plant growth and production (Murray and Nix 1987, Rimmington and Charles-Edwards 1987, Hutchinson et al. 1992, Blumenthal and Ison 1993, White et al. 2001). GROWEST has been combined with a JAVA graphical user interface to form the GROWEST PLUS program in joint work with the Bureau of Rural Sciences (Brinkley et al. 2004). Both GROWEST and GROWEST PLUS are maintained and distributed by CRES.
The GROWEST program was first coded in FORTRAN as a point model by J.P.McMahon who subsequently incorporated the program as GROCLIM into the ANUCLIM package (Houlder et al. 2000). GROCLIM produces weekly and monthly plant growth indices, in point and grid form, from monthly mean climate surfaces based on the methodology described by Hutchinson (1991) and implemented in ANUSPLIN package (Hutchinson 2004).
The version of GROWEST described here has been comprehensively upgraded from the original point model to process general point and gridded inputs and outputs. The revised GROWEST program now accepts average monthly, actual monthly and actual weekly input climate data, supplied in either point or grid form. Basic soil moisture parameters may also be supplied in point or grid form. The revised program has been implemented in FORTRAN 90 to take advantage of the extensive vector processing capabilities of this language. This has facilitated processing of gridded input data.
The basic time step of the GROWEST model is weekly so monthly climate inputs are automatically interpolated to weekly input values. Point and gridded output weekly indices can also be aggregated to monthly average values. The output time step may thus be set independently of the time steps of the input climate data.
All input weekly interpolations and output monthly aggregations are performed making due allowance for the temporal coverage of each month by whole and partial weeks. This means in particular that annual averages of GROWEST output weekly indices and output monthly indices are identical when GROWEST is run on the same input data. The aggregation of weekly output indices to monthly output indices is appropriate when broader time scale responses are required, as for example in the assessment of drought. This can also be appropriate when the input climate data are supplied as monthly values.
The central output of the GROWEST program consists of weekly or monthly growth indices that characterise relative plant growth. Further calibration is required to quantify output growth indices in terms of actual biomass production. However the program can be used without such further calibration to perform a wide range of comparative assessments of plant growth over space and time, using minimal climatic inputs and basic soil moisture properties.
The GROWEST model is constructed from generalised functions that transform the dynamic, non-linear responses of plant growth to weekly solar radiation, temperature and available soil moisture into three primary dimensionless indices. Each of these indices: light index (LI), temperature index (TI) and moisture index (MI) represents plant dry matter production relative to dry matter production at non-limiting values of that factor. Thus each weekly index takes values ranging between zero (completely limiting conditions) and one (non-limiting conditions). The three primary indices are described by Nix (1981).
GROWEST normally calculates the weekly growth index (GI) as the product of the three primary climate indices given by
GI = LI x TI x MI
The GI therefore reaches a maximum value of one, corresponding to optimum or maximum relative growth, when all three primary indices are one. The GI reaches a minimum value of zero when any of the three primary indices is zero. Nix (1981) states that this multiplicative function is marginally superior to the "law of the minimum" where the growth index would be simply the value of the most limiting primary index.
For applications in irrigated environments, where moisture can be completely non-limiting, GROWEST permits the calculation of a partial growth index (PGI) that depends only on solar radiation and temperature given by
PGI = LI x TI
GROWEST can be calibrated to simulate actual dry matter production by using the growth index in a simple growth rate equation as described by Hutchinson et al. (1992). However, over typical growing seasons actual plant growth can be reasonably approximated by a linear function of accumulated weekly growth index (Nix et al. 1977). Thus Hutchinson et al. (1992) accumulated GROWEST indices over standard growing seasons of 13 weeks to generate the parameters for their global agroclimatic classification.
The GROWEST model may either be run using the GROWEST PLUS graphical user interface or as a standalone executable. GROWEST PLUS (Brinkley et al. 2004) was designed to simplify the task of accessing and organising the numerous input and output files, as well as analysing and plotting GROWEST outputs.
GROWEST can be executed in standalone mode from a command-line shell. Under Unix operating systems use any terminal emulator window showing a shell prompt. Under Microsoft Windows start an MS-DOS shell. Then run GROWEST by typing, for example,
growest < job.cmd > job.log
job.cmd is the input command file and
job.log is the output log file.
The input command file can be compiled with a text editor according to the user requirements and the user directives specified below. It is strongly recommended to make use of the output log file to document the process. The output log file is especially useful when diagnosing problems.
- Blumenthal, M.J. and Ison, R.L. 1993. Use of water balance models to examine the role of climate in annual legume decline in southern Australia. Proceedings of the 17th International Grassland Congress, pp 61-62.
- Brinkley, T.R. Laughlin, G.P. and Hutchinson, M.F. 2004. GROWEST PLUS - A tool for rapid assessment of seasonal growth for environmental planning and assessment. In prep.
- Fitzpatrick, E.A. and Nix, H.A. 1970. The climatic factor in Australian grassland ecology. In: R. Milton Moore (ed), Australian Grasslands, Australian National University Press, Canberra.
- Houlder, D., Hutchinson, M.F., Nix, H.A. and McMahon, J.P. 2000. ANUCLIM Version 5.1. Centre for Resource and Environmental Studies, Australian National University, Canberra.
- Hutchinson, M.F. 1991. The application of thin plate smoothing splines to continent wide data assimilation. In: J.D.Jasper (ed) Data Assimilation Systems, BMRC Research Report No. 27, Bureau of Meteorology, Melbourne, 104-113.
- Hutchinson, M.F. 2004. ANUSPLIN Version 4.3. Centre for Resource and Environmental Studies, Australian National University, Canberra.
- Hutchinson M.F., Nix, H.A. and McMahon, J.P. 1992. Climate constraints on cropping systems. In: C.J.Pearson (ed), Ecosystems of the World: Field Crop Ecosytems, Elsevier, London, pp 37-58.
- Hutchinson, M.F., Nix, H.A and McTaggart, C. 2004. GROWEST Version 2.0. Centre for Resource and Environmental Studies, Australian National University, Canberra.
- Keig, G. and McAlpine, J. 1969. Instructions for the preparation of daily rainfall as data input to Land Research climate programs. Technical Memorandum 69/8. CSIRO Division of Land Use Research, Canberra.
- Murray, M.D. and Nix, H.A. 1987. Southern limits of distribution and abundance of the biting-midge Culicodes brevitarsis Kieffer (Diptera: Ceratopogonidae) in south-eastern Australia: an application of the GROWEST model. Australian Journal of Zoology 35: 575-585.
- Nix, H.A. 1981. Simplified simulation models based on specified minimum data sets: the CROPEVAL concept. In: A. Berg (ed), Application of Remote Sensing to Agricultural Production Forecasting, Commission of the European Communities, Rotterdam, pp 151-169.
- Rimmington, G.M. and Charles-Edwards, D.A. 1987. Mathematical descriptions of plant growth and development. In: Plant Growth Modelling for Resource Management, Vol. 1, Boca Raton, Fla. CRC Press, pp 3-15.
- White, D.H., Lubulwa, G.A., Menz, K., Zuo, H., Wint, W, and Slingenbergh, J. 2001. Agro-climatic classification systems for estimating the global distribution of livestock numbers and commodities. Environment International 27: 181-187.