Agrometeorological Monitoring and Forecasts for Pest and Disease Control Simone Orlandini Department...
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Agrometeorological Monitoring Agrometeorological Monitoring and Forecasts for Pest and and Forecasts for Pest and
Disease ControlDisease Control
Simone Orlandini
Department of Plant, Soil and Environmental Science
University of Florence
International Workshop on Addressing the Livelihood Crisis of FarmersBelo Horizonte, Brazil, 12-14 July 2010
OutlineOutline
o Background
o Input data
o Models for crop protection
o Use and application
o Dissemination of information
OutlineOutline
o Background
o Input data
o Models for crop protection
o Use and application
o Dissemination of information
BackgroundBackground
Widening of biological
knowledge
BackgroundBackground
Development of computer science and
telecommuncations
Most affected regions: tropics and developing Countries
Causes:
o Lack of technologies
o Crop successions
o High temperatures
o Possibility to have more than one cycle per year
Area Crop losses (%)
Europe 25
Oceania 28
North and central America
29
URSS e China 30
South America 33
Africa 42
Asia 43
BackgroundBackground
Constant level of crop losses
0
5
10
15
20
25
30
35
40
anni 50 anni 60-70 oggi
insetti malattie infestantipest disease
weed
1960-19701950 current
High level of pesticide utilisation
BackgroundBackground
NeedsNeeds of information of information
o There is the need of information disseminated to the growers to rationalise crop protection.
o Usually farmers carry out their decision in condition of high risk and uncertainty.
o The lack of knowledge increases the level of risk in farm management, and farmers have to increase the quantity of chemical and energy inputs, without solving the problems.
o A way to help growers during their activity is represented by the acquisition of high quality elaborated information, so reducing decision making uncertainty minimising the use of chemical and energy inputs.
Agrometeorological modelling can be the suitable tool Agrometeorological modelling can be the suitable tool
to provide this informationto provide this information
OutlineOutline
o Background
o Input data
o Models for crop protection
o Use and application
o Dissemination of information
Field stationsField stations
Leaf Leaf wetness wetness sensorssensors
Remote sensing Remote sensing – input data– input data
maps of downy maps of downy mildew infectionmildew infection
1.00E+00
1.00E+01
1.00E+02
1.00E+03
1.00E+04
1.00E+05
1.00E+06
1.00E+07
1.00E+08
1.00E+09
1.00E+10
06.0
5.
11.0
5.
16.0
5.
21.0
5.
26.0
5.
31.0
5.
05.0
6.
10.0
6.
15.0
6.
20.0
6.
25.0
6.
30.0
6.
05.0
7.
10.0
7.
15.0
7.
measured LW
dropben LW
sweb LW
control LW
Radar (RAINFALL)
Epidemiological model
Ground stations
LWD model
Remote sensing Remote sensing – identification of symptoms – identification of symptoms on crop canopies using multispectral imageson crop canopies using multispectral images
Numerical weather Numerical weather modelsmodels
Seasonal forecastSeasonal forecast
GISGIS
Map of number of days for the outbreak of the current infection
Number of generation of Bactrocera oleae
Simulated impacts Simulated impacts of leaf-damaging of leaf-damaging
pest infestation on pest infestation on maize yield at maize yield at
regional scale (30-regional scale (30-arcminute grids in arcminute grids in
Tanzania). Leaf Tanzania). Leaf damages was damages was implemented implemented
through a leaf area through a leaf area coupling point in coupling point in
the the DSSAT model DSSAT model
www.regional.org.au
Crop modelsCrop models
OutlineOutline
o Background
o Input data
o Models for crop protection
o Use and application
o Dissemination of information
Mechanistic Mechanistic EmpiricalEmpirical
http://www.ipm.ucdavis.edu
Cornell University in Geneva, New York
Other approaches: Other approaches: fuzzy, neural networkfuzzy, neural network
fuzzy fuzzy inferenceinference
de-de-fuzzificatifuzzificati
ononyes yes or or nono
TTRHRH
fuzzificatiofuzzificationn
fuzzificatiofuzzificationn
base of base of rulesrules
Quantitative Quantitative informationinformationQualitative Qualitative informationinformation
&&
R. D. Magarey, T. B. Sutton, and C. L. ThayerDepartment of Plant Pathology, North Carolina State University, Raleigh 27696..
Main modelsMain models
Coltura Malat. Mod.ABETE 3 3AGRUMI 1 1AVENA 2 2AVOCADO 1 1BANANA 2 4BARBABIET. 2 2BEGONIA 1 1CACAO 1 1CAFFÈ 1 1CANNA ZUC. 1 1CAROTA 2 2CASTAGNO 1 1CAUCCIÙ’ 2 3CAVOLO 2 3CEREALI 4 6CILIEGIO 2 2CIPOLLA 2 2COCOMERO 1 1COTICO ERB. 1 1COTONE 3 4CRESCIONE 1 1DUGLASIA 1 1FAGIOLO 4 4FRAGOLA 4 5GINEPRO 1 1GIRASOLE 2 2WHEAT 10 58LUPPOLO 1 3
Coltura Malat. Mod.MAIS 4 4MANDORLO 1 1MANGO 1 1MEDICA 2 3APPLE 4 18MELONE 1 1PEANUT 5 13NOCCIOLO 1 1OLMO 1 1BARLEY 5 13POTATO 4 21PESCO 1 1PINO 4 4PIOPPO 3 3PISELLO 1 1POMODORO 4 6QUERCIA 1 1RAPA 2 4RICE 4 17SEDANO 1 1SEGALE 1 1SOIA 5 9SORGO 7 7SPINACI 1 1SUSINO 1 1TABACCO 2 2TRIFOGLIO 1 1GRAPEVINE 4 17
Source: Erick D. DeWolf and Scott A. Isard, 2007. Disease Cycle Approach to Plant Disease PredictionAnnu. Rev. Phytopathol. 2007. 45:203–20
Year of formulationYear of formulation
ContinentContinent
0
10
20
30
40
50E
ur.
Asi
a
Afr
ica
S. A
m.
N. A
m.
Oce
ania
Input example: Plasmopara viticola Input example: Plasmopara viticola simulation modelssimulation models
Type Model Temp. Precip. RH LWDGoidanich G GRule of 3 10 G GDM CAST O O O OEPI Winter M 10 G
Empirical EPI Summer G 3 O dPOM GPCOP G GDyonis G 3 O dMILVIT 3 O 3 OVINEMILD O O O
O d O d O dMechanistic 15 MI n 15 MI n 15 MI n
Freiburg O O O OPLASMO O O O O
Rules
PRO O
Output example: Output example: Plasmopara Plasmopara
viticolaviticolasimulation simulation
modelsmodels
Model OutputGoidanich SINGLE INFORMATIONReg. 3 10dm CAST INFECTION POTENTIALEPI Inv.EPI Est.POMPCOPDyonisMILVIT SPECIFIC BIOLOGICAL ANDVinemild EPIDEMIOLOGICAL DATAPROFreiburgPLASMODM sim.
Example of variables included into different kinds of modelsExample of variables included into different kinds of models
L. M. Contreras-Medina, I. Torres-Pacheco, R. G. Guevara-González, R. J. Romero-Troncoso, I. R. Terol-Villalobos, R. A. Osornio-Rios, 2009. Mathematical modeling tendencies in plant pathology. African Journal of Biotechnology Vol. 8 (25), pp. 7399-7408
OutlineOutline
o Background
o Input data
o Models for crop protection
o Use and application
o Dissemination of information
Condition of applicationCondition of application
o Climatic classification
o Future climatic scenario for climate change and variability analysis
o Field monitoring and forecast for crop protection
Climatic Climatic classificationclassification
Potato late blight riskPotato late blight risk
Climatic risk for potato late blight in the Andes region of Venezuela (Beatriz Ibet Lozada Garcia; Paulo Cesar Sentelhas; Luciano Roberto Tapia; Gerd Sparovek, 2008)
Predicted severity of phoma stem Predicted severity of phoma stem canker (L. maculans) at harvest canker (L. maculans) at harvest (Sc) on winter oilseed rape crops.(Sc) on winter oilseed rape crops.
a)1960-1990
d) 2050 LO
c)2020 HI
b)2020 LO
e) 2050 HI
Zhou et al. 1999)
Climate change Climate change impactimpact
Probable number of Probable number of generations of leaf generations of leaf miner (Leucoptera miner (Leucoptera coffeella) on coffee coffeella) on coffee
plant in Brazilplant in Brazil
Source: Ghini R. et al., 2008. Risk analysis of climate change on coffee nematodes and leaf miner in Brazil. Pesq. agropec. bras. vol.43 n.2.
To treatTo treat
Not to treatNot to treat
Field monitoring and forecast for crop Field monitoring and forecast for crop protectionprotection
Information utilisation Information utilisation
For using information obtained by models or by decision making systems in order to define the field treatment epochs, different aspects have to be highlighted
Necessary to treat when
the pathogen is present
the crop is susceptible
the treatment is efficacious
To avoid treatments
in advance, for losses of efficacy due to the product degradation and to the growth of plants
late, for losses of efficacy due to a too developed infective process
Factors to consider
character of the farmer
need to have all the information concerning the disease and the crop
position of the threshold of action and damage
application with strategic or tactical aims
State Disease Crop Pathogen Benefit
UK Stem canker, light leaf spot
oil rape Leptosphaeria maculansPyrenopeziza brassicae
increase average yields by up to 0.5 t/ha (equivalent to £75/ha or £15 million/annum if benefits occur on 200,000ha)
1
Virginia (USA)
leaf spot peanut growers
Cercospora arachidicola 1987-1990: input costs reduced by 33% or $57 per ha1990-1995: input costs reduced by 43% or $66 per ha
2
Italy Grapevine downy mildew
grapevine Plasmopara viticola The threshold for an economical convenience in the adoption of the agrometeorological system is about 6 ha.
3
Florida Brown spot citrus Alternaria alternata 4
1. Dr Peter Gladders, ADAS Boxworth, Cambridge. LK0944: Validation of disease models in PASSWORD integrated decision support for pests and diseases in oilseed rape. HGCA conference 2004: Managing soil and roots for profitable production
2. Phipps PM, Deck SH,Walker DR. 1997.Weather-based crop and disease advisories for peanuts in Virginia. Plant Dis. 81:236–443. L. Massetti, A. Dalla Marta and S. Orlandini, Preliminary economic evaluation of an agrometeorological system for Plasmopara viticola infections management.4. Alka Bhatia, P. D. Roberts, L. W. Timmer, 2003. Evaluation of the Alter-Rater Model for Timing of Fungicide Applications for Control of Alternaria Brown Spot of
Citrus. Plant Disease / September 2003.
Model application economic benefitsModel application economic benefits
Costs and benefits of Costs and benefits of Alter-Rater Model Alter-Rater Model
Benefits from the Benefits from the IPM impact IPM impact
studiesstudies
Economic Impacts of Integrated Pest Management in Developing Countries:Evidence from the IPM CRSPTatjana HristovskaThesis submitted to the faculty of the Virginia Polytechnic Institute and State University, 2009
Other benefitsOther benefits
reduction of chemical inputs in the ecosystemsoil fertility conservationsmaller amount of chemical residuals in food work quality improvementreduction in the development of resistant formssafeguarding of natural predatoryreduction of new diseases
ImplementationImplementation of the model of the modelTables for manual calculations
Simplicity of application, difficulty to obtain information for an efficacious use
Electronic plant stations
Collocation in field, complete automation, imprecise results, frequent damages
Computer
Rapidity of intervention (tactic), possibility to analyse past conditions, possible simulation with future scenarios (strategic), automatic collection of data, use for different aims, precision of results
Manual calculation: Mills table (apple scab)Manual calculation: Mills table (apple scab)
LEAF WETNESS HOURSTemperature Light Medium Severe
8 18 23 349 15.5 20.5 30
10 12.5 19 2811 11.5 17 2612 10.5 16 2413 10 14 22.514 9.5 13 2115 9 12.5 2016 9 12.5 1917 9 12.5 1818 9 12.5 1819 9 12.5 1820 9 12.5 1821 9 12.5 1822 9 12.5 1823 9 12.5 1824 9.5 12.5 1925 10.5 14 21
Electronic Electronic plant plant
stationstation
Personal computer and Personal computer and network of network of
meteorological sensors meteorological sensors and stationsand stations
OutlineOutline
o Background
o Input data
o Models for crop protection
o Use and application
o Dissemination of information
Conditions of applicationConditions of application
Farm: in this case the model is applied directly by farmers, with evident benefits in the evaluation of real epidemiological condition and microclimate evaluation. On the other hand, the management of the simulations and the updating of the systems represent big obstacles.
Territory: it is probably preferable because it allows a better management and updating of the system. This solution requires the application of suitable methods for the information dissemination among the users.
Information dissemination: the bulletinsInformation dissemination: the bulletins
o Advises and information to the users can be disseminated by using: personal contact, newspaper and magazines, radio and television, videotel, televideo, telefax, mail, phone, INTERNET, SMS.
Mobile phone Mobile phone
From Omondi Lwande and Muchemi Lawrence (2008)
Powdery mildew Powdery mildew riskrisk
http://www.apsnet.org/online/feature/pmildew/
Veneto (Italy) – infection rainfall mapVeneto (Italy) – infection rainfall map
www.mausam.gov
In Florida AgroClimate.org provides a set of tools to help producers reduce risks associated with climate variability.
In particular, the Strawberry Diseases Tool can help you with recommendations for timing fungicide applications for Anthracnose and Botrytis fruit rot.
Maps of light leaf spot forecast Maps of light leaf spot forecast http://www.rothamsted.bbsrc.ac.ukhttp://www.rothamsted.bbsrc.ac.uk
Lupin bean yellow mosaic virus Lupin bean yellow mosaic virus (BYMV) Forecast(BYMV) Forecast
www.syngenta-crop.co.uk/brassica-alert.aspx
Syngenta Crop Syngenta Crop Protection UK LtdProtection UK Ltd
Growers and agronomists already registered on the Syngenta website will automatically have free access to Brassica Alert
Agrometeorological Monitoring Agrometeorological Monitoring and Forecasts for Pest and and Forecasts for Pest and
Disease ControlDisease Control
Simone Orlandini
Department of Plant, Soil and Environmental Science
University of FlorenceInternational Workshop on Addressing the Livelihood Crisis of Farmers
Belo Horizonte, Brazil, 12-14 July 2010
Thank you for your attention!!!
Thank you for your attention!!!