Development of a hemp (Cannabis sativa L.) simulation model 4. Model description and validation
Lisson, S and Mendham, NJ and Carberry, PS, Development of a hemp (Cannabis sativa L.) simulation model 4. Model description and validation, Australian Journal of Experimental Agriculture, 40, (3) pp. 425-432. ISSN 0816-1089 (2000) [Refereed Article]
In studies assessing the prospects for a hemp industry, as well as in longer term research activities, the use of a hemp simulation model to complement the more traditional agronomic field trials would offer a number of potential advantages. In addition to being cost and labour intensive, field trials with hemp have political, social and security implications. With these implications in mind, a simulation model that captures the growth and development processes of hemp in response to management, genotypic, soil and climate factors, has the potential to increase research efficiency. The model could be used to assess the need, extent and nature of field trials, to help interpret field trial results, and to investigate temporal and spatial variability in selected crop responses. This paper describes a hemp crop growth and development model (APSIM-Hemp) and its validation against an independent dataset. The model was developed as a crop module within the framework of the larger systems model, Agricultural Production Systems simulator (APSIM), to extend the capability to encompass the agricultural system in which hemp is grown. APSIM-Hemp incorporates relationships developed in the previous papers in this series relating to pre- and post-emergent phenology and leaf area production. Other parameters relating to biomass partitioning, biomass production, water uptake and nitrogen uptake were derived from separate field studies and selected references. APSIM-Hemp adequately predicted phenology, leaf area and biomass production for the cultivar Kompolti at Forthside in north-western Tasmania, for a dataset comprised of results from trials conducted over 3 seasons and including treatments of sowing date, irrigation regime and plant density. Although performing well against this independent dataset, the performance of the model needs to be further validated over a range of other soil, climate and management conditions in order to assess its broader predictive capability. Notwithstanding these limitations, the sound basis of a model for simulating the growth and development of hemp has been developed.