Modeling Workshop Florida-Next Generation Modeling Implemented in ILCYM-JK-04.03.2015-Final For

Next generation crop insect pest models for integrated assessments

The Insect Life Cycle Modeling (ILCYM) software and options for integrated assessments

J. Kroschel M. Sporleder, P. Carhuapoma, H. Juarez, N. Mujica

DCE Crop Systems Intensification and Climate

AGMIP, February, 2015 February, AGMIP, Change (CSI-CC); Agroecology/IPM sub-program Outline

‹ Introduction • Development of ILCYM: For which purpose • Main features and advantages

‹ ILCYM applications • Pest Risk Analysis (PRA) on different scales • Climate change and adaptation planning • Regional assessments • Decision support tool in pest management

‹ Outlook: considerations in integrated assessments

‹ Conclusions ILCYM: For which purpose

Analytical tools for predicting, evaluating and understanding the dynamics of insect populations in agroececosystems.

1 Species distribution mapping (SDM) in support of pest risk assessment (PRA, IPPC) 2 Climate change / adaptation planning 3 Integrated Pest Management (Examples) - Simulation of pest life-table parameters under prevailing weather conditions as decision support for pest management - Identification of potential release sites for parasitoids (classical biocontrol) - Simulation of field performance of biopesticides and application rates Potato Tuber Moth, Phthorimaea operculella • Invasive species; reported from more than 90 countries • Adapted to wide range of different climates and agroecologies • Yield losses due to leaf and tuber infestation at harvest • Storage pest in tropical and subtropical countries Potato production areas and PTM distribution

Phthorimaea operculella Tecia solanivora Symmetrischema tangolias Framework for Pest Risk Analysis

Likelihood of entry and Consequences of establishment * establishment Economic impact Entry potential 1 potential Colonization potential Environmental impact 2 Establishment potential potential 3 Social impacts Spread potential *under current and future climates Insect Life Cycle Modeling (ILCYM)

Software package for developing temperature-based insect phenology models with applications for regional and global pest risk assessments and mapping . • Required input: life-table data collected under constant and fluctuating temperatures • Model builder: - Fitting functions for describing temperature- driven development processes under constant temperatures, i.e. development rate , mortality and reproduction • Validation and simulation module - Uses life-table data established under fluctuating temperature; can be used for deterministic and stochastic simulations (www.cipotato.org/ilcym) • Potential population and risk mapping module - Establishment index - Generation index - Activity index Pest risk mapping using ILCYM

Potential distribution at global, regional and local level With and without crop shapes Current climate: 2000 Future climate: 2050 (1950-2000: www.worldclim.org/) Down-scaled data SRES-A1B, IPCC (2007): http://gisweb.ciat.cgiar.org/GCMPage ∑ / ERI GI AI ERI = AI = log Risk mapping Risk indices GI = ∑ 365 / 365 Model validation

150 0.08 1.25 150 100 100 100 Simulation of life-table 1.15 50 0.04 50 1.05 50 parameters 0.00 0 10 20 30 0.95 0 0 1020 30 40 0 10 20 30 10 20 30 40 0 10 20 30 40 Net reproduction rate, generation time intrinsic and finite rate of increase, double time Temperature-based phenology

Model builder models Development rate and time Survival rate, oviposition, mortality Life-table studies A constant and fluctuating temperatures: oviposition, survival time, development time, (www.cipotato.org/ilcym ) mortality Model validation

100 40 do 90 Arequipa 2 cycle, 13.01.99 35 80 Eggs

Larva 30 C) 70 ° 25 60 50 20 Individuals 40 15 Temperature ( Temperature 30 Pupa 10 20 Adult 10 5 0 0 0 10 20 30 40 50 60 Time (days)

(Keller 2003) Model validation

100 40 90 Huancayo 2do. cycle, 24.11.98 35 80 Eggs C)

30 ° 70 Larvae Adults 60 Pupae 25 50 20

Individuals 40 15

30 ( Temperature 10 20 10 5 0 0 0 10 20 30 40 50 60 70 80 90 100 110 Time (days)

(Keller 2003) Three risk indices Establishment risk index (Survival risk index ): Indicates the capacity of invasive pest species to establish permanent populations

Index = ∑ /365 ()

The index is defined as the number of time intervals with a net reproduction rate, R 0, above 1 ( Ii=1 ) divided by the total number of time intervals within a year ( II).

Generation Index: Indicates the potential infestation of a crop Index = ∑ 365 / 12 The index is computed by averaging the sum of estimated generation lengths, Ti , calculated for each Julian day, x, where 365 is the number of days per year and Ti is the predicted average generation lengths in days during the month, i (i = 1, 2, 3, …, 12). Activity Index: Indicates pest potential population growth, spread and severity

Index = log where λλλ is the finite rate of increase at Julian day, x (x = 1, 2, 3, …365). Main advantages of ILCYM modeling (1)

• Complex simulation of an entire insect life system. • It uses process-based functions that provide a mechanistically detailed representation of the insects life history, and hence population growth. • Model functions are build based on statistical analysis of experimental data that allows determining errors in particular processes. • Full live history (development, survival, reproduction) is modeled and the software compiles the overall model automatically (reducing programming skills and time of users) • Models have parameters with straightforward interpretation of the biological system. • Individual functions of a model can be validated and improved • The information can be used in different modeling types; i.e. spatially (SDM through the use of specific risk indices), or in time (in a given area) for different inquiry (from basic ecological understanding to prediction, management and policy-making). Main advantages of ILCYM modeling (2)

• Since the model works with probability distributions for specific processes it is possible to conduct both deterministic and stochastic simulations. • The simulation uses an cohort-updating algorithm and quantifies the population age-stage specific. • Input data can be real or simulated temperature at variable intervals. • The simulation is implemented in R-language, which is open source software. ILCYM: Reference publications

Sporleder, M., J. Kroschel, M. R. Gutierrez Quispe & A. Lagnaoui (2004): A temperature- based simulation model for the potato tuber worm, Phthorimaea operculella Zeller (Lepidoptera; Gelechiidae). Environmental Entomology 33 (3): 477-486.

Kroschel, J., M. Sporleder, H.E.Z. Tonnang, H. Juarez, P. Carhuapoma, J.C. Gonzales & R. Simon (2013): Predicting climate change caused changes in global temperature on potato tuber moth Phthorimaea operculella (Zeller) distribution and abundance using phenology modeling and GIS mapping. Journal of Agricultural and Forest Meteorology 170 : 228-241.

Tonnang, E.Z.H., P. Carhuapoma, H. Juarez, J.C. Gonzales, D. Mendoza, M. Sporleder, R. Simon, J. Kroschel (2013): ILCYM - Insect Life Cycle Modeling (version 3.). User Guide . A software package for developing temperature-based insect phenology models with applications for regional and global analysis of insect population and mapping. International Potato Center, Lima, Peru. pp 193.

Sporleder, M., H.E.Z. Tonnang, P. Carhuapoma, J. C. Gonzales, H. Juarez & J. Kroschel (2013): Insect Life Cycle Modeling (ILCYM) software – a new tool for Regional and Global Insect Pest Risk Assessments under Current and Future Climate Change Scenarios. CABI Book publication , 412-427. ILCYM users

Number of ILCYM potential users that have visited the software webpage from March 1, 2010 until 20 March 2014 Applications of ILCYM: Pest Risk Analysis (PRA)

“The process of evaluating biological or other scientific and economic evidence to determine whether an organism is a pest, whether it should be regulated, and the strength of any phytosanitary measures to be taken against it”

Stage I: Stage II: Stage III: Stage IV: Initiation PR-Assessment PR-Management Documentation

Pest categorization Pest identification React Pathway Probability Test & evaluate Rational for Policy review X efficacy of phytosanitary PRA area Magnitude of measures requirements Pest taxonomy etc. econ./environ. (by NPPO) Impact

Pest Risk Mapping and Surveillance National and regional new framework to assess Diagnostics adaptation planning! climate change impacts! *(adopted from FAO-IPPC, 2007) Pest Risk Mapping Validation of risk maps by using geo-referenced distribution maps Example: potato tuber moth

Green points: countries with reported pest establishment Red points : geo-referenced distribution data Yellow points : permanent establishment not confirmed ILCYM: Risk mapping under current and future

Phthorimaea operculella:climates ERI 2000 and 2050 A range expansion into temperate regions of the northern hemisphere and in tropical mountainous regions

Asia: Europe: 8.3% (699,680 ha) North America: 2.73% (234,404 ha) 4.21% (32,873 ha)

Africa: 1.01% (11,605 ha)

South America: 11.9% (109,666 ha)

Oceania: 13.67% (7,314 ha)

(Kroschel et al., J. Agric. Forest Meterol., 2013) ILCYM: Risk mapping under current and future Phthorimaea operculella:climates GI 2000 and 2050 Abundance and damage potential will progressively increase in all regions where the pest already prevails today with an excessive increase in warmer cropping regions of the tropics and subtropics

(Kroschel et al., J. Agric. Forest Meterol., 2013) ILCYM: Risk mapping under current and future Phthorimaeaclimates operculella: GI Change >4 generations are developed on 30.2% (5.918.557 ha) of the total potato production area worldwide; this can pot. increase to 42% (8.328.531 ha)

Europe: Asia: 10.14% (1,115,432 ha) 15.29% (5,246,319 ha)

Oceania: 26.05% (42,465 ha) North America: 15.87% (280,180 ha)

Africa: South America: 2.87% (1,113,966 ha) 13.11% (530,116 ha)

(Kroschel et al., J. Agric. Forest Meterol., 2013) ILCYM: Risk mapping under current and future

P. operculella validation: observedclimates and predicted numbers of generations at different locations using different climate input data Life table ILCYM modeling ILCYM modeling studies under results using local results using Country/location local natural weather station WorldClim data conditions data Monthly records Peru : San Ramon, 12 12 9.5 800 m a.s.l. Peru : Arequipa, 71 6.7 7.2 1140 m a.s.l. Peru : Huancayo, 3-4 3.1 2.3 3250 m a.s.l. Yemen : Sana’a, 82 7.1 5.4 2150 m a.s.l. Egypt : Giza, 10 3 10 8.6 10 m a.s.l. (Kroschel et al., J. Agric. Forest Meterol., 2013) ILCYM: Risk mapping under current and future climates Effect of climate change on the number of generations at different locations

18 +4.6 +3.9 16 +2.5 +1.5 14 +3.7 12.5 +3.1 12 +2.0 12 +1.2 2.7 +2.8 + 10 10 10 +2.2 +2.4 +1.3 +1.4 8.0 +0.9 8 +0.8 7.0 6.7 7.0 6 +1.0 +1.2 +0.7 4 +0.4 3.0 2.9

Number of generations per year per generations of Number 2

PeruPRESENT - San Ramon PREDICTION (800 m) PeruPRESENT - Huancayo PREDICTION (3250 m) PRESENTPeru - Arequipa PREDICTION (1440) PRESENTYemen - Sana’a PREDICTION (2250) PRESENTEgypt - Giza PREDICTION (10 m) Leafminer fly, Liriomyza huidobrensis

• Center of origin in the neotropics; reported from more than 66 countries • Very polyphagous; in Peru pest in >27 horticultural crops • Yield loss without management up to 70% in potato

Green points: countries with reported pest establishment Yellow points : reported in protected crops Red points : geo-referenced distribution data ILCYM: Risk mapping under current and future Liriomyza huidobrensis:climates Regional maps Index change: ERI 2000 2050 2000-2050

GI ILCYM: Risk mapping under current and future Liriomyza huidobrensis:climates country maps for Uganda L. huidobrensis will potentially increase its risks in the northern, eastern and southern provinces due the high values of GI>12 and AI>17

2000 2050 Change Generation index Generation Activity index Activity Publication Project – Pest Risk Atlas Pest Distribution and Risk Atlas for Africa Potential global and regional distribution and abundance of agricultural and horticultural pests and associated biocontrol agents under current and future climates Potato pests Potato tuber moth, Phthorimaea operculella (Kroschel et al.) Guatemalan potato tuber moth, Tecia solanivora (Schaub et al.) Andean potato tuber moth, Symmetrischema tangolias (Sporleder et al.)

Sweetpotato pests Sweetpotato weevil, Cylas puncticollis (Okonya et al.) Sweetpotato weevil, Cylas brunneus (Paul Musana et al.) Sweetpotato butterfly, Acraea acerata (Okonya et al.) Sweetpotato white fly, Bemisia tabaci Type B (Gamarra et al.) White fly, Bemisia afer (Gamarra et al.)

Vegetable pests Serpentine leafminer fly, Liriomyza huidobrensis (Mujica et al.) Vegetable leafminer, Liriomyza sativae (Mujica et al.) American serpentine leafminer , Liriomyza trifolii (Le Ru et al.) Greenhouse whitefly, Trialeurodes vaporarium (Gamarra et al.) Publication Project – Pest Risk Atlas

3.4 Maize pests Spotted stemborer , Chilo partellus (Khadioli et al.) Maize stalk borer, Busseola fusca (Khadioli et al.) African pink stem borer , Sesamia calamistis (Khadioli et al.)

3.5 Cassava pests Cassava mealybug, Phenacoccus manihoti (Hanna et al.) Cassava green mite, Mononychellus tanajoa (Hanna et al.)

3.6 Fruit pests Oriental fruit fly , Bactrocera invadens (Hanna et al.) Banana aphid , Pentalonia nigronevosa (Hanna et al.) Climate change / adaptation planning Reducing crop vulnerability by adaptation to new and emerging pests

Mitigation Reduction of CO 2 emissions

Crop yields Crop pests Exposure

Temperature increase: 2 °C

Sensitivity + Adaptation (of IPM)

Pot. impacts on crop yields based on pest Resilient systems, new technologies, expansion/increased abundance: Higher tolerant and resistant varieties reduce pest infestations reduce yields by 20% caused losses by 15%

Vulnerability

Actual impact on crop yields by pests increase by 5% (Adapted from IPCC, 2001, 2007) Climate change / adaptation planning Stakeholder workshop

Objective To inform stakeholders in plant health, plant quarantine and pest management about future pest risks on global, regional and local scales and to use this information for crop-specific adaptation planning and to define priorities in pest management. ILCYM: regional assessments Climate data and map resolution 35 Max 30 25 20 • For estimating pest risk for specific locations, max. and min. Min 15 Min 10 temperature data in a 1 hr time step length are used to Temperature 5 0 consider the within-day temperature variability that may 0 4 8 12 16 20 24 affect insects. Time steps • For global and regional pest risk mapping, currently available temperature surfaces use monthly average aggregated climate data from 1960 to 2000 (www.worldclim.org ). • Aggregation and interpolation of temperature data has its limitations especially in mountainous regions due to local temperature variability (topographic effects) over short distances. • Higher accuracy of risk maps and predictions of potential pest distribution could be achieved through locally collected daily weather data processed and interpolated in ILCYM. ‰ Development of a tool which allows regional assessments of pest risks in mountainous regions at high resolution. ILCYM: regional assessments Map resolutions: from global to regional and local scales

18 x 18km (1x) ILCYM: regional assessments Map resolutions: from global to regional and local scales

189x9km x 18km (4x (1x) respect to 10’) ILCYM: regional assessments Map resolutions: from global to regional and local scales

189x9km4.5x4.5km x 18km (4x (1x) (16xrespect respect to 10’) to 10’) ILCYM: regional assessments Map resolutions: from global to regional and local scales

180.9x0.9km9x9km4.5x4.5km x 18km (4x (1x) (400x (16xrespect respect respect to 10’) to to 10’) 10’) ILCYM: regional assessments Map resolutions: from global to regional and local scales

180.9x0.9km9x9km4.5x4.5km x 18km (4x (1x) (400x (16xrespect respect respect to 10’) to to 10’) 10’)

90m x90m (40,000x respect to 10’) ILCYM: regional assessments Data collection

21 weather stations were installed in the Mantaro Valley, Peru ILCYM: regional assessments

Climate [Calculating the interpolator Indices (GI, ERI, (thin plate smothing spline) AI) per weather station] ANUSPLIN 4.1 (Australia) Data processing: 30 days versus 5 min . Index Interpolator tool (thin plate smothing spline) [Daily] Tmax n=365, Tmin n=365

GI, ERI, AI

[Mapping Index: GI, ERI, AI] ILCYM: regional assessments Use of ILCYM’s Index Interpolator Local risk maps for leaf miner fly, Mantaro Valley, Peru

ERI GI ILCYM: regional assessments Use of ILCYM’s Index Interpolator Local risk maps for leaf miner fly, Mantaro Valley, Peru

ERI+1 °C GI+1 °C ILCYM: regional assessments Use of ILCYM’s Index Interpolator Local risk maps for leaf miner fly, Mantaro Valley, Peru

ERI+2 °C GI+2 °C ILCYM: regional assessments Use of ILCYM’s Index Interpolator Local risk maps for leaf miner fly, Mantaro Valley, Peru

ERI+3 °C GI+3 °C RTB-Pest Risk Assessment project ILCYM: regional assessments

Impact of climate change on the livelihood of farmers

Household models

New entry RTB yield Current pests (PRA) models pests

Pest and crop models (yield gaps caused by insect pest )

Modeling climate change

(Framework of RTB Pest Risk Analysis (PRA) project) ILCYM: regional assessments 3000 Action sites of RTB PRA project: 2500 Burundi , Rusizi valley: 900 to 2600 m asl Rwanda, Ruhengeri: 1400 to 2600 m asl 2000 1500 - All RTB crops - Harmonization and standardization 1000 of household and field survey

Altitude (m asl) (m Altitude 500 methodologies and collection of climatic information along an altitudinal gradient 0 Weather stations Burundi Rwanda Pest management (1) Example: Evolution in decision support tools Determination of pesticide application date

1. Use of calendar date : Pest status assessed at a specific date or crop growth stage and pesticides are applied if the pests economic threshold is reached. Example : potato tuber moth Control threshold : 0.5 mines/plant Critical infestation period : GS 60 Mines / plant

Example : 2. Use of degree-days (DD) : The temperature Daily pest degree above the pest’s specific temperature day calculator of minimum is summed up (degree-day the Illinois State Water Survey, summation) and the pest status assessed University of when the pest specific temperature sum is Illinois reached ( improvement to 1 ). Pest management (2) Example: Evolution in decision support tools Determination of pesticide application date

3. Use of non-linear models for insect development : (daily development rates are summed up until reaching a value of 1 (further improvement to 2 , specially when temperature is in the non-linear range for development).

4. Process-based climatic response model (ILCYM phenology model): Temperature variability in autumn influences emerging time of insects in spring because the overwintering stage already past some development in autumn (Example: mountain pine beetle, Dendroctonus ponderosae (Hicke et al. 2006). Pest management (3) Example: Evolution in decision support tools Determination of pesticide application date

Example using ILCYM : Potato tuber moth: Rate of population increase within one year

Huancayo, Peru (3250 masl) Hermiston, Oregon, USA (144 masl) Pest management (4) Simulation of pest life-table parameters under prevailing weather conditions Leafminer fly, Liriomyza huidobrensis, under Peruvian lowland and highland conditions 0.18 1.2 35 1.18 0.16 30 0.14 1.16 1.14 25 0.12 1.12 0.1 20 1.1 0.08 1.08 15 0.06 1.06 10 0.04 net reproduction rate reproductionnet finite rate of increase of rate finite 1.04 5 intrinsic rate of increase of rate intrinsic 0.02 1.02 0 1 0 0 0 0 30 60 90 30 60 90 30 60 90 120 150 180 210 240 270 300 330 360 120 150 180 210 240 270 300 330 360 120 150 180 210 240 270 300 330 360

Julian days Lowland Julian days highland Julian days

90 0.7 90 80 80 0.6 70 70 0.5 60 60 50 0.4 50 40 40 0.3 30 30 0.2 20 Doubling time (days) time Doubling immature survival immature 20 10 0.1 (days) time generation 10 0

0 0 0 30 60 90 120 150 180 210 240 270 300 330 360 0 0 30 60 90 30 60 90 120 150 180 210 240 270 300 330 360 120 150 180 210 240 270 300 330 360 35 Julian days Julian days Julian days 25 30 20 C) 25 max. 20 15 15 10 10 Temperature (° Temperature min. 5 5 Temperature (°C) Temperature 0 0 0 100 200 300 0 100 200 300 (Mujica et al., -5 Julian days Julian days in preparation) Lowland agroecology (350 masl) Highland agroecology (3250 masl) Pest management (4)

Potential establishment (ER) and efficacy (number of generations, GI) of Orgilus lepidus (Hymenoptera: Braconidae), parasitoid for classical biocontrol of the potato tuber moth, Phthorimaea operculella ERI GI Pest management (5) Simulating Phop GV field performance A 14 0.14 24ºC r m = 0.1297 12 0.12 Egg 10 nat. increase 0.10 8 0.08 ln N 6

0.06 4 Larva distribution rate distribution 0.04 2 Pupa 0.02 0 Adult 0 30 60 90 120 0.00 Time (days) 0 10 20 30 40 50 age (days) B 14 18ºC 12

8 r m = 0.0622 ln N 6

0 0 30 60 90 120 (Sporleder et al. Agriculture and Forest Entomology 2007) Time (days) Pest management (5) Simulating Phop GV field performance A 14 24ºC r m = 0.1297 0.14 12 applications 0.12 10 Egg nat. increase 0.10 8

ln N 6 0.08 1. Appl.

0.06 4 Larva distribution rate distribution 2 0.04

Pupa 0 0.02 Adult 0 30 60 90 120 0.00 Time (days) 0 10 20 30 40 50 B 14 age (days) 18ºC 12 applications Application of 10 8 13 r m = 0.0622 1.2 ×10 granules/hectare ln N 6

0.08 ln N 6 2. Appl.

0.06 4 Larva distribution rate distribution 0.04 2

Pupa 0.02 0 Adult 0 30 60 90 120 0.00 Time (days) 0 10 20 30 40 50 age (days) B 14 18ºC 12 applications 1 - 2 Application of 10 8 13 r m = 0.0622 1.2 ×10 granules/hectare ln N 6

0.08 ln N 6 3. Appl. 0.06 4 Larva distribution rate distribution 0.04 2

Pupa 0.02 0 Adult 0 30 60 90 120 0.00 Time (days) 0 10 20 30 40 50 age (days) B 14 18ºC 12 applications 1 - 3 Application of 10 8 13 r m = 0.0622 1.2 ×10 granules/hectare ln N 6

4 ≈≈≈ 4,000 LE/hectare 2 ≈≈≈ 80% mortality (neonate larvae) 0 0 30 60 90 120 Time (days) (Sporleder et al. Agriculture and Forest Entomology 2007) Pest management (5) Simulating Phop GV field performance 14 nat. increase 0.14 A 24ºC r m = 0.1297 1. Appl. 12 2. Appl. 0.12 applications 1 - 8 3. Appl. Egg 10 4. Appl. 0.10 8 5. Appl. 0.08 6. Appl. ln N 6 7. Appl. 0.06 8. Appl. Larva 4 distribution rate distribution 0.04 2 Pupa 0.02 0 Adult 0 30 60 90 120 0.00 Time (days) 0 10 20 30 40 50 age (days) B 14 18ºC 12 applications 1 - 8 Application of 10 8 13 r m = 0.0622 1.2 ×10 granules/hectare ln N 6

4 ≈≈≈ 4,000 LE/hectare 2 ≈≈≈ 80% mortality (neonate larvae) 0 0 30 60 90 120 Time (days) (Sporleder et al. Agriculture and Forest Entomology 2007) Outlook: Integrated assessments Global economic assessments/first approximation of crop losses: Relate pest abundance (pot. no. of generations/season) to % crop loss; consider precipitation as co-factor Linking to crop models: Use of same input data (temperature); daily or hourly simulation interval (e.g., using InfoCrop, DSSAT, EPIC). ILCYM quantify pest populations by separating stage- and age-classes. Individuals in the damaging age-stage can be quantified over time. These numbers need to be expressed for a determined leaf area and further calculated into “crop damage-loss relationships”. Leaf miner fly : Apply foliar injury (%) crop loss functions in crop models; yields of early varieties (Desiree) are more affected than of late varieties (Yungay). 2008-Yungay 50 70 2008-Desiree y = 0.6155x - 0.0138 y = 1.9501x + 0.1364 R² = 0.7555 60 40 R² = 0.8941 P<.0001 50 30 40 P<.0001

20 30

20 10 Yield loss (%) loss Yield Yield loss (%) loss Yield 10

0 0 0 10 20 30 40 50 60 0 10 20 30 40 -10 -10 Foliar injury (%) Foliar injury (%) (Muijca, N. & J. Kroschel Crop Protection 2013). Next steps

ILCYM version 4 with a new interface fully in R-language (shiny- applications) to reduce required skills to maintain ILCYM software and to increase user-friendliness and flexibility to use the software.

Develop new sub-modules for assessing other influencing co- factors (e.g., precipitation) to improve pest predictions under climate change on related crop losses.

Combining pest models with crop models in order to better predict/quantify losses. Leafminer fly as model pest to integrate into crop models. Parametrize crop models with crop specific data at the Peruvian coast. Acknowledgements

Donors: • German Federal Ministry for Economic Cooperation and Development (BMZ/GIZ) • World Bank • El Fondo Regional de Tecnologia Agropecuaria (FONTAGRO) • CRP-RTB • CCAFS

Thank-you!