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(Acacia Mearnsii) Background (Risk Factors) Species Distribution

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(Acacia Mearnsii) Background (Risk Factors) Species Distribution 01/08/2017 Background (risk factors) Environmental triggers and impact . Few pests and disease have been reported on black wattle in assessment of a new rust disease caused by South Africa such as: Uromycladium acaciae on black wattle in South Africa Photo by Benice Sivparsad Thobile Nxumalo, Ilaria Germishuizen and Andrew Morris . Currently, is the new Uromycladium acaciae which is causing Forest Science Symposium-18-20 July 2017 disease on Acacia mearnsii © ICFR 2017 Species distribution Background (Acacia mearnsii) . Acacia mearnsii De Wild (black wattle) is one of the important plantation species in South Africa (SA) . It is native in Australia and grown in SA for timber and tannin production (Searl,1991) . Black wattle occupies about 110 000ha of plantation area out of 1.3M ha of planted forests . 85% of the revenue is from timber and the other 15% from bark (Chan et al. 2015) • Nine species of Uromycladium infect Australian acacias . Therefore, it is economically important plantation species and any • All Uromycladium species are autoecious (Fraser et al. 2017) risk factors needs to be managed • Three have been described in the country Searl S. 1991.The rise and demise of the black wattlebark industry in Australia. Technical paper no. 1. Canberra: CSIRO Division of Forestry • U tepperianum (Morris, 1987) Smith CW. 2002. Growth and yield prediction. In: Dunlop RW, MacLennan LA (eds), Black wattle: the South African research experience. • U. alpinum (Morris and Wingfield, 1988) Pietermaritzburg: Institute for commercial Forestry Research. P93-99 • U. acaciae (McTaggart et al. 2015) © ICFR 2017 1 01/08/2017 Background and Objectives . Due to the disease outbreak, a multi-faceted project was . initiatedRelate the to levels manage of infestations the pathogen- to environmental integrated approach factors . IPM,and identify defines triggers economic of outbreaks; injury and levels, it develops preventative management and monitoring systems that . willProvideBiology allow (taxonomy, ground-truthing curative epidemiology control and points measures forWattle developing rust are exclusion economical a plot remote trials and environmentallypopulation dynamics) sustainable Quantify growth impacts sensing ofbased the pathogen system for detecting and(ICFR,NMMU monitoring & FABI) wattle rust (ICFR & FABI) Monitoring sites Monitoring sites Remote sensing work Monitoring the disease outbreaks (ICFR, NCT, (ICFR, NCT & FABI) FABI & UKZN) © ICFR 2017 Uromycladium acaciae life cycle Spermogonia Basidiospores Telia Teliospores Dr Stuart Fraser, 2017 2 01/08/2017 4 Mean DS in the warm-wet (Cramond) Mean DS in the warm-wet (Hilton Mean DS in the warm-wet (World‘s view) 4 College) 4 Monthly rainfall(mm) and mean DS (warm-dry Monthly temp (°C) and mean DS (warm-dry Eston) 3 Eston) 3 3 140 4 25 4 Rainfall(mm) Mean DS Temp Mean DS 120 2 2 2 3 20 100 3 Mean DS (0-4) 1 1 1 80 15 2 2 0 0 0 60 Mean DS (0-4)Mean 10 Monthly temp (°C) temp Monthly 40 DS (0-4)Mean 1 1 5 Date of assessment (mm) rainfall Monthly 20 Mean DS in the warm-moist site Seven Mean DS in the warm-moist Harden 4 4 Oaks Heights 0 0 0 0 3 3 2 Months Months 2 Mean DS (0-4) 1 1 • Disease severity increased even with minimal amount of rainfall 0 0 • Disease severity increased around March- April in all the site type classes with the Mean DS in the warm-dry (Bloemendal) Mean DS in the warm-dry site Eston exception of two sites in the warm-wet site where the symptoms were observed earlier 4 4 3 3 • No major differences in RH were observed during the study. 2 2 Mean DS (0-4) 1 1 • Temperatures between 18 -25 °C had an effect on disease severity 0 0 Date of assessment Date of assessment Monthly rainfall(mm) and mean DS (warm-wet Monthly temp (°C) and DS (warm-wet Cramond) Cramond) KZN-weather data- (slide provided by Dr Stuart Fraser) 250 4 Temp Mean DS Rainfall Mean DS 25 4 200 3 20 3 150 15 Based on the preliminary work 2 2 100 10 KZN done at FABI, March and October Mean DS (0-4)Mean 1 1 DS (0-4)Mean months have conducive weather for 50 (°C) tempMonthly Rainfall Tmin Tmax Monthly rainfall (mm) rainfall Monthly 5 Uromycladium acaciae 120 30 0 0 0 0 • Night time temperature averages 100 25 12-17°C Months Months • Evening rainfall and fog – wet 80 20 Monthly temp (°C) and mean DS (warm-moist conditions overnight Monthly rainfall(mm) and mean DS (warm- Seven Oaks) moist Seven Oaks) 60 15 25 Temp Mean DS 4 160 Rainfall Mean DS 4 40 10 20 3 120 3 Mean rainfall (mm) Mean rainfall 20 5 15 Mean temperature (°C)temperature Mean 2 80 2 10 0 0 Mean DS (0-4)Mean Jul Jan Jun Oct Apr Feb Sep Dec Aug Nov Mar Mean DS (0-4)Mean May Monthly temp (°C) temp Monthly 1 40 1 5 Monthly rainfall(mm) Monthly 0 0 0 0 Months Months 3 01/08/2017 Results Acknowledgements Dr Kabir Peerbay (ICFR), Craig Norris (NCT), Johan Nel (TWK), Prof Jolanda Roux (SAPPI), Dr Alistar McTaggart (FABI),Dr Stuart Fraser (FABI) Forest Protection Technical team (ICFR) Marilyn Bezuidenhout and Enos Ngubo Comparing mean DS in MPU areas Comparing mean DS in KZN areas Hardus Hatting (contractor in Piet Retief) 4 4 Mool(a) Zoonstr norm Baynes HC Bloem 3 3 2 2 Mean DS (0-4)Mean Mean DS (0-4)Mean 1 1 0 0 Feb Mar Apr May Jun 01-Feb 01-Mar 01-Apr 01-May Months Months • The decrease in disease severity in Baynesfield maybe the high infestation of mirid damage on the site © ICFR 2017 Conclusion . The study showed that disease spread or severity is driven by moisture and temperature . Rainfall and temperatures between 18 and 25 °C can trigger the disease infection . The data will be used to develop a bioclimatic risk model to evaluate the geographic extent of the disease and potential hotspots © ICFR 2017 4.
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