3 ¿D ÓNDE ENCUENTRO INFORMACIÓN ?
3.2. U SANDO GOOGLE
Vietnam
R.C. Magarey{ XE "Magarey, R.C." }A, L.W. BurgessB, P.J. NielsenC and Ngo Van TuC
A
BSES Limited, PO Box 566, Tully, 4854, Queensland
B
Faculty of Agriculture, Food and Natural Resources, University of Sydney, 2006, New South Wales
C
NAT&L Sugar Factory, Quy Hop, Nghe An Province, Vietnam
INTRODUCTION
Sugarcane is a major crop in many South East Asian countries
and provides an important cash crop as a rotation in a farming
system often consisting of crops such as rice, corn, peanuts and
watermelon. In contrast to first world countries, cropping areas
in Vietnam are small with up to 24,000 farmers supplying
sugarcane from 1ha plots to the local factory. This compares to
around 250 farmers supplying each Australian sugar factory. In
the mid‐1990s, a new sugarcane disease called green grassy
shoot disease (GGSD) was identified in Thailand. Characterised
by the production of many small grassy tillers, and caused by a
phytoplasma, the disease had severe consequences on crop
yields. Several other diseases in neighbouring countries are also
caused by phytoplasmas; these include white leaf disease (WLD)
and grassy shoot disease (GSD). In 2006, symptoms of GGSD
were identified in the NAT&L factory area, Quy Hop, Nghe An
Province, Vietnam. This paper briefly describes GGSD symptoms
and the current epidemic occurring in Vietnam.
GGSD
Symptoms. The disease is characterised by the production of
many small green grassy tillers. These first appear at the base of
mature sugarcane stools late in the cropping period; in this crop,
yields are not unduly affected. Being a semi‐perennial crop,
second and third annual harvests (first and second ratoon crops)
are made from the same planting. The following ratoon crops
arising from an infested crop suffer very serious yield effects.
Healthy ratoon shoots are replaced by profuse green, grassy
shoots that lead to complete crop failure. Harvest yields often
progress from 80 tonnes biomass per ha in a largely disease‐free
plant crop to 15 tonnes / ha in the first ratoon crop; second
ratoon crops in susceptible cultivars often fail altogether. In
contrast to GSD and WLD, there is no chlorosis in leaves of GGSD
affected sugarcane.
Figure 1. Symptoms of GGSD in sugarcane crop (cultivar MY55‐14) in
Nghe An Province, Vietnam. Note the small green grassy tillers in the midst of normal ratoon shoots.
Causal agent. Research undertaken in Thailand suggests that a
phytoplasma is the causal agent of GGSD.
Transmission. As a vegetatively propagated crop, infected
planting material leads to diseased crops; the supply of disease‐
free seed‐cane is essential for limiting disease spread. There are
no recorded vectors for GGSD but circumstantial evidence, such
as speed of spread, suggests a vector is likely to be associated
with disease transmission.
Control. The most important control measures for GGSD are the
termination of heavily diseased crops, the planting of new crops
with disease‐free planting material and the choice of the most
resistant cultivars—though there are few resistant cultivars
currently available in Vietnam. Further importation of
germplasm into Vietnam is needed to select suitably‐resistant
cultivars. Research has shown that immersion of infested
planting material in water maintained at 50C for 3 hours (HWT)
leads to the elimination of the disease in >85% of the axillary
buds. The selection of the cleanest planting material for HWT
provides the best opportunity for producing disease‐free nursery
cane.
NAT&L sugar factory, Nghe An Province. The disease has been
widely detected in the two most widely planted cultivars MY55‐
14 and ROC 10; ROC 10 is more susceptible than MY55‐14. The
disease quickly expanded beyond the initial finding with severe
GGSD observed in >6,000ha of crops in early 2009; lighter
infection has been widely observed across the sugar factory
area. The sugar factory has pro‐actively addressed the problem
with incentives paid to farmers to eliminate badly diseased
crops. Concurrently an intense extension program has been run
by the factory in the local communes; over 175 commune
meetings were staged from January to May 2009. In late April‐
early May 2009, there has been an expanded program, with
further funding, focused on the elimination of infested crops in
an attempt to further reduce disease spread.
DISCUSSION
The extent of the disease in the Quy Hop sugar factory area, the
speed of spread and the effect on yield all suggest that GGSD is a
very significant threat to sugarcane crop production in Vietnam.
Not enough is known about the disease, including the nature of
possible vectors, the resistance of cultivars to the disease, and
potential replacement canes, and the distribution of the disease
in Vietnam. There is a suspicion that GGSD also occurs in other
Provinces of Vietnam, but at lower severity levels. Further
research is needed, not only with GGSD but also to develop
reliable diagnostic tools for GGSD, GSD and WLD. Findings of
white leaves associated with diseased cane crops suggest that
GSD and / or WLD may also be present in Vietnam. It is
important that the status of the various pathogens is known to
ensure appropriate control measures are applied.
ACKNOWLEDGEMENTS
We acknowledge the assistance provided by NAT&L factory staff
in gathering information on this disease.
Session
6B—Quarantine
and
exotic
pathogens
Molecular detection of Mycosphaerella fijiensis in the leaf trash of ‘Cavendish’ banana
S.G. Casonato{ XE "Casonato, S.G." }A, J. HendersonB and R.A. FullertonAA
The New Zealand Institute for Plant and Food Research Limited, Private Bag 92169, Auckland Mail Centre 1142, New Zealand
B
Tree Pathology Centre, 80 Meiers Road, Indooroopilly, Queensland, 4068, Australia
INTRODUCTION
Black Sigatoka (black leaf streak) caused by, Mycosphaerella
fijiensis Morelet (anamorph Paracercospora fijiensis (Morelet)
Deighton), the most destructive foliar pathogen of bananas
globally. The disease is present in commercial plantations in
Africa, Asia and Central and South America, where extensive
fungicide applications are required for its control. The potential
for M. fijiensis to be carried into countries free of the disease in
leaf trash carried in commercial consignments is unknown. This
study was undertaken to determine whether M. fijiensis could
be detected in leaf trash in cartons of bananas imported from
the Philippines to New Zealand.
MATERIALS AND METHODS
Samples of leaf tissue and banana skin were collected from
cartons of a commercial consignment of bananas imported to
New Zealand from the Philippines in December 2005. The
samples were stored at ‐20ºC until assayed in July 2006.
DNA extraction. DNA was extracted from 11 of the samples
supplied (Table 1) using a QIAGEN DNeasy® Plant Mini Kit.
Insufficient sample of S40 (particulate leaf material) was present
for extraction. Samples of M. fijiensis (748 ex banana leaf,
Tongatapu, Tonga) and M. musicola (yellow Sigatoka) (Mf589
[Cultures are held in the culture collection maintained by Dr R.A. Fullerton at Plant and Food Research, Mt Albert Research Centre,
Auckland.] ex banana leaf South Johnston, Queensland) were
used as control samples and had DNA extracted from mycelium
growing on a potato dextrose agar plate. DNA was quantified
using a NanoDrop spectrophotometer. DNA extracts were kept
at ‐20°C.
Table 1. List of banana leaf, floral and trash samples from which DNA
was extracted. DNA was also extracted from Mycosphaerella fijiensis and M. musicola cultures. Sample Type S2 Floral S7 Edge of leaf? S31 Leaf material S32 Leaf material S36 Particulate trash? S39 Leaf material S56 Stem/petiole on fruit
S113 Fruit spots under trash
S155 Particulate trash
S348 Unknown
S351 Unknown
Mf 748 Mycosphaerella fijiensis culture Fullerton
Mf 589 Mycosphaerella musicola culture Fullerton
Banana leaf Healthy glasshouse grown plant in NZ
PCR protocol. Samples were initially amplified using primers
MF137 and R635 (1). These primers were found to be not
specific for M. fijiensis and alternative primers were sought,
MFFor and R635‐mod (Henderson et al. unpublished). These
primers are designed to amplify part of the internal transcribed
spacer (ITS) regions between the 18S and the 28S rDNA subunits
of M. fijiensis. Prior to amplifying the extracted DNA, the
minimum detection limit of M. fijiensis for the PCR protocol
being used was determined using DNA extracted from a culture
of known identity (Mf 748). This was shown to be 0.243 fg/µL (1
fg = 10‐15 g) of pure M. fijiensis DNA. When assaying the
extractions from leaf trash, a PCR product of approximately 1050
bp indicated a positive amplification of M. fijiensis (Figure 1).
Where a positive result was achieved, the PCR reaction was
repeated a minimum of three times. A control of healthy,
uninfected banana leaf was also included in reactions.
Sequencing. Direct sequencing was carried out to confirm the
identity of the amplified products. PCR products were purified
using a QIAGEN MinElute PCR Purification Kit. The PCR product
was fluorescently labelled using a BigDye® Terminator v3.1 Cycle
Sequencing Kit (Applied Biosystems, Foster City, USA). Each 10
µL reaction contained approximately 25 ng/µL of PCR product, 2
µM primer (ITS‐1 or ITS‐4), 2 µL terminator‐ready reaction mix
and the volume made up with sterile Milli‐Q water. Sequences
obtained were compared with those in the database GenBank® using the Basic Local Alignment Search Tool (BLAST), BLASTN.
RESULTS AND DISCUSSION
Four of the 11 tissue samples consistently yielded a PCR product
using M. fijiensis specific primers MFFor and R635‐mod (Figure
1). They were: S2 (floral), S31 (leaf material), S36 (particulate
trash) and S56 (stem or petiole). Sequenced products were
homologous (at least 99%) with M. fijiensis sequences lodged in
GenBank. This study has shown that M. fijiensis was present in
fragments of leaf trash found in cartons of banana fruit imported
into New Zealand from the Philippines. The viability of the
organism within the sample cannot be ascertained from these
tests nor can the quantity of M. fijiensis be verified using these
techniques.
—A—B—C—D—E—F—G—H—I—J—K—L—M—N—O—P—Q
Figure 1. Amplified products of Mycosphaerella fijiensis using primers MFFor and R635‐mod. Reaction used 5 µL DNA per reaction. Lane A:100 bp ladder; B:Banana leaf; C:S2: D:S7; E:S31; F:S32; G:S39; H:S36; I:S56; J:S113; K:S155; L:S348; M:S351; N:Mf589 yellow sigatoka; O:Mf748 black sigatoka; P:blank (negative control); Q:100 bp ladder. Double‐ended arrow indicates product of approximately 1050 bp. Deteriorating DNA lessened the band brightness for some sampels. Note: gel has been cut.
REFERENCES
1. Johanson, A. and Jeger, MJ. 1993. Use of PCR for detection of Mycosphaerella fijiensis and M. musicola, the causal agents of Sigatoka leaf spots in banana and plantain. Mycological Research
97, 670–674.
Session
6B—Quarantine
and
exotic
pathogens
Optimising responses to incursions of exotic plant pathogens
M. Hodda{ XE "Hodda, M." } and D.C. CookCSIRO Entomology, GPO Box 1700, Canberra, 2601, ACT
INTRODUCTION
Biological, spatial and economic data, linked through modelling,
can assist in optimising responses to incursions of exotic plant
pathogens. The approach allows predictions of the behaviour of
linked biological and agronomic systems within defined bounds
despite many uncertainties involved in individual parameters.
Uncertainties are to be expected because each incursion of an
exotic pest into a new environment is a novel situation for which
there may be no precedents. The biological and agronomic
parameters having the greatest impact can be identified, and the
response designed to optimise the benefit:cost ratio.
The value of this approach is shown in examples of two relatively
recent incursions into Australia by exotic pathogenic nematodes;
(a) Bursaphelenchus hunanensis, a relative of the Pine Wilt
Nematode (1), (b) Potato Cyst Nematode (PCN) (2).
MATERIALS AND METHODS
The model initially simulated possible scenarios for the arrival,
establishment, and expansion of the geographic range of a pest
in the absence of biosecurity measures. The effects of various
measures were then added and the results compared with the
first run.
The model was a stochastic simulation model using random
number generators to simulate chance or random events.
Probability distributions were used as parameters within an
abstract model rather than point estimates, and a Monte Carlo
algorithm used to sample from each of these distributions (3).
Many parameters were used to estimate the ecological
processes of establishment, spread, population growth and crop
damage, together with their economic consequences in terms of
crop yields, testing for disease, and control measures. Each
parameter was given one of a number of statistical distributions
with a defined mean or modal value, depending on the
distribution chosen. In each of the 5,000 iterations of the model,
one value was randomly sampled across the range of each
distribution. The model used Markov chains to estimate
transitional probabilities between time periods of 1 year. The
model was run over 20 years and used a standard discount rate
of 8% (a margin of 3% on top of a real risk free rate of 5%).
RESULTS
Impacts of both pests studied were large over the time period
considered. Under most possible scenarios, annual impact rises
steeply initially, followed by slower growth, before eventually
declining (Fig. 1). Raw crop losses in the field were only a small
proportion of the aggregate impact. Parameters had different
effects and time courses on the aggregate impact of the pests.
Potential rate of geographic expansion of the pest was
important, but the cost of testing for the pest during its
expansion was also important. This cost occurs soon after
invasion; it can be largely independent of the actual expansion
rate or range, but is affected by the accuracy and efficiency of
the test. Efficacy of testing affects the impact of a pest on other
crops occurring in the region. Cost of mitigation of the pest may
be large, and the failure rate of control is an important cost.
Impacts related to trade in the crop, both in terms of quantity
and value are highly uncertain, but under most scenarios are
highly significant. With increasing distances from production to
market, the chances of barriers to trade arising or loss of
markets following arrival of a pest are increased. Disinfestation
and certification costs were substantial in the long term.
DISCUSSION
Rapid initial rise in impact of pests makes early detection and
action desirable, even when there is great uncertainty over the
future behaviour of the pest. The substantial impact beyond lost
crop production means that eradication or other control
measures are often the best option. The problem is that cost of
this strategy precedes any benefits. Benefits of control programs
may be wider than the direct crop losses, so wider contributions
to costs may be justified.
The predicted decline in annual impacts may be largely related
to discount rates and this requires further investigation since the
real costs of many forms of pest control, eg chemicals, are
increasing, along with environmental, social and regulatory
costs. 0 5 10 15 20 25 30 35 40 0 2 4 6 8 10 12 14 16 18 20 Time (years) E x p e c te d im p a c t ( m illio n $ ) mean standard error 95% confidence limit
Figure 1. Simulated impact of PCN in Australia.
REFERENCES
1. Hodda M, Smith DI, Smith IW, Nambiar L, Pascoe I (2008) Incursion management in the face of multiple uncertainties: a case study of an unidentified nematode associated with dying pines near Melbourne, Australia. In ‘Pine Wilt Disease—a threat to forest ecosystems’. (Eds P Viera, Mota M) pp. ()
2. Hodda M, Cook DC (in press) Economic impact from unrestricted spread of Potato Cyst Nematodes in Australia. Phytopathology 33, 3. Cook DC, Thomas MB, Cunningham SA, Anderson, DL and DeBarro
PJ 2007. Predicting the economic impact of an invasive species on an ecosystem service. Ecol Appl 17: 1832–1840