Verfyning en verbetering van 'n donsige skimmel waarskuwingsmodel vir die Wes-Kaap

[摘要] English : Downy Mildew (Plasmopara viticola) is known as one of the most important vineyard diseases in the Western Cape, because it has the capability to develop and spread very fast, and so cause large crop losses in certain years. In 1992 an Austrian researcher developed the Metos automatic weather station and associated software, to predict the occurrence of primary and secondary infection of downy mildew. This Metos weather station's software was adapted for South African climatic conditions during 1995 and is known as the Metos-2 model. The Metos-2 model however had some shortcomings that needed to be improved. The most important of this was that the model was not sensitive enough to accurately calculate infections, and furthermore it gives only a 'Yes/No warning of possible primary and/or secondary infections. The Metos-2 model makes use of measured leaf wetness values from a leaf wetness sensor that is probably considered as one of the most inaccurate meteorological sensors. During 1995 - 2005 the Metos-2 model has been thoroughly tested and used by the disease management division of ARC Infruitec-Nietvoorbij, to warn the industry of possible downy mildew outbreaks. Results over these years have shown that more sprays were needed within the preventative spraying programs, as opposed to recommendations of the Metos-2 model, for the same or even improved control of downy mildew. On the other hand the results of the Metos-2 model compared to the Metos model, gave similar warnings for both primary and secondary infections. It is however very difficult to get clear similarities/differences between what the Metos-2 model has calculated and what had really occurred in the vineyards. This can be attributed mainly to the accumulation effect of downy mildew infections. With the development of the Downy Mildew Early Warning Model (DSVWmodel), two important changes were made, namely the leaf wetness was replaced with a mathematical, non-linear regression and the Metos-2 model's 'Yes/No warnings for downy mildew infections were replaced with four classes of possible risks. The calculated leaf wetness of the DSVW-model, that uses measured relative humidity and air temperature as input values, had a significant coefficient of determination of 0.70, compared with measured leaf wetness. The DSVW-model's four risk classes of possible infections (primary and secondary) are as follows: zero infection (0 %), low infection (1 - 34 %), medium (35 - 74 %) and a high risk class (75 - 100 %). To test the DSVWmodel's accuracy and reliability, historical weather data (1998 - 2003) and measured disease outbreak data in the Stellenbosch, Robertson and Paarl areas were used to run both the Metos-2 and the DSVW-models. Primary as well as secondary infections were predicted by the models. When the DSVW-model and the Metos-2 model's infection warnings were correlated with disease outbreaks, of the two, the DSVW-model showed consistently similar or better correlations with the measured disease outbreak data. The DSVW-model also calculated on a regular basis more primary and secondary infections, than the Metos-2 model, which at times did not warn of any downy mildew infections, although outbreaks of downy mildew did occur soon after. Producers can use the new DSVW-model with confidence, together with one or other prevention spray program, for the control of downy mildew.

[发布机构] University of the Free State