ISHS Acta Horticulturae 610: V International Symposium on Kiwifruit
MODELLING OF SCLEROTINIA DISEASE IN NEW ZEALAND KIWIFRUIT (ACTINIDIA DELICIOSA) |
| Authors: | S.M. Hoyte, P. Long, R.M. Beresford |
| Keywords: | Systems analysis, simulation, apothecial density, phenology, potential disease, crop loss |
| Abstract:
Sclerotinia sclerotiorum (Lib.) de Bary causes up to 17% crop loss in New Zealand kiwifruit [Actinidia deliciosa (A. Chev.)]. Although growers use fungicides for disease control, cost-effective disease management decisions require a reliable measure of disease potential within an orchard. A systems dynamics approach was used to model sclerotinia disease development using ithink™ software. The model’s construction was based on interactions between the pathogens life cycle, crop phenology and the environment. A positive relationship between apothecial density and disease incidence was used, with a series of biological clocks and environmental thresholds, to predict disease potential at the end of flowering. Crop loss was predicted from flowering to mid-season in response to daily rainfall exceeding thresholds. Simulations were performed using five years climate data from Te Puke Research Orchard, standard mean daily temperature and rainfall thresholds of 15°C and 10 mm day-1 and an apothecial density of 1.0 m-2. The responsiveness of simulated disease potential and crop loss, to changes in apothecial density and environmental thresholds, ranged from -54% to +100%, relative to the standard inputs. Changing the date of bud-burst by 7 days affected these outputs by -56% to +61%, depending on the season and region. The model produces stable and realistic outputs under conditions typical of New Zealand kiwifruit vineyards, although validation with independent experimental data is required. The systems dynamics approach has provided a platform that readily incorporates phenological and environmental interactions into a framework of the pathogens life cycle over a multi-year timeframe. Alterations to the model can be made to incorporate new relevant experimental data and new components, including other kiwifruit models. This model could also be revised to describe other pathosystems, through simple modifications and replacement of key epidemiological equations within the model. Specific uses of this model include: interactive research planning tools and decision support for packhouses and growers. | |
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