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for Climate prediction

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Summary of

Intensive Training Workshop

C o l u m b i a U n i v e r s i t y

L ink ing Sc i ence t o Soc i e t yL ink ing Sc i ence t o Soc i e t y

8 - 26 July 2002

Palisades, New York, USA

Co-organized with

Global Change System for Analysis, Research and Training (START)

Supporting co-sponsor

David and Lucile Packard Foundation

Published by the International Research Institute for Climate Prediction (IRI)

The Earth Institute at Columbia University

Palisades, New York, 10964, USA

IRI Publication IRI-CW/03/1

ISBN 0-9705907-9-2

©2003 International Research Institute for Climate Prediction. All rights reserved.

Advanced Training Instituteon Climate Variability

and Food SecuritySummary of the Intensive Training Workshop

Palisades, New York8-26 July, 2002

TRAINING DIRECTOR AND EDITORJames Hansen

Advanced Training Institute on Climate Variability and Food Security

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Contents

EXECUTIVE SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

WORKSHOP OVERVIEW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

CURRICULUM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Introduction: Setting the Scene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Module 1: Understanding and Predicting Climate Fluctuations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Module 2: Understanding and Predicting Agricultural Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Module 3: Analyzing Management Responses to Forecasts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Module 4: Understanding Decision Makers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Module 5: Institutionalizing Support for Forecast Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Module 6: Communicating Forecast Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Panel Discussion: Implementation Opportunities and Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

WORKSHOP EVALUATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Individual Questionnaire: Quantitative Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Group Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

PROJECT SUMMARIES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

PARTICIPANTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Trainees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Faculty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

International Advisory Committee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Additional Project Mentors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Contact Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55


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EXECUTIVE SUMMARY

The Advanced Training Institute on Climatic Variabilityand Food Security was designed to equip youngagriculture and food security professionals in developingcountries to apply advances in climate prediction at aseasonal lead time to their home institutions’ ongoingefforts to address climate-sensitive aspects of agriculturalproduction, food insecurity and rural poverty. TheInstitute comprises three components: intensive training,follow-up research, and a final workshop. TheInternational Research Institute (IRI) for ClimatePrediction designed, implemented and hosted the initialphase of the Training Institute, in coordination with theGlobal Change System for Analysis, Research and Training(START), and with generous financial support from theDavid and Lucile Packard Foundation.

Participant Selection

Nineteen outstanding early- to mid-career agriculture andfood security professionals from fourteen countries inAsia, Africa and Latin America participated. The selectionprocess was highly competitive. Seventy-seven peopleapplied for only twenty positions. Participants wereevaluated by the Director, other IRI scientists, and theInternational Advisory Committee based on a well-definedset of criteria that included:

• Academic foundation,

• Grasp of the central issues, expressed in a Statement ofInterest,

• Vision and strategy for applying concepts within asignificant follow-up project,

• Home institution’s relevance, capacity and connectionto target stakeholders,

• The applicant’s influence within their institution, and

• Potential for long-term impact.

Intensive Training Phase

The intensive training component of the Training Institutewas held at the IRI facility in Palisades, New York, 8-26July 2002. The workshop curriculum provided a balanceof relevant concepts and methods from the physical,biological, social and integrative systems sciences.Twenty-two faculty members led the participants throughthe following topics:

• Understanding and predicting climate fluctuations.

• Understanding and predicting agricultural impacts.

• Understanding decision makers.

• Analyzing management responses to forecasts.

• Communicating forecast information.

• Institutionalizing support for forecast applications.

• How all the pieces fit together in the design ofpractical, problem-focused projects.

• Developing proposals to obtain project resources.

The curriculum was heavily oriented toward methodology.Hands-on exercises in the afternoons reenforced conceptspresented in morning lectures. The trainees showedconsistent enthusiasm and a high degree of competencethroughout the workshop.

Project Implementation Phase

A unique element of this Training Institute is seed grantsfor follow-up project work. The competitive seed grantprogram clearly motivated participants. It provides amechanism to ensure immediate follow up and to embedthe concepts and approaches within the long-termprograms of the participants’ home institutions. Trainingin proposal development, feedback from peers andorganizers before and during the workshop, individualproposal clinics, and methods learned during the workshopimproved project design and presentation. Everyparticipant left with a funded project, and has beenassigned a senior scientist to serve as a mentor. Projectmentors (a) serve as a resource persons, (b) provideaccountability to ensure that the project stays on track,including approving mid-term and final reports, (c) helpexpand the participant’s network of contacts and (d) areavailable to serve as advocates with participant’s homeinstitutions. Home institutions have committed to supportthe projects with in-kind contributions. The immediateresult is 19 ongoing, funded, participant-led projects in 14countries, that focus on various aspects of climate andfood security.

Synthesis Phase

After completing projects, the participants will reconveneat a summary workshop to present their results, share theirexperiences, synthesize lessons learned, and prepare aresulting publication. The final workshop will tentativelycoincide with the Fourth International Crop Science


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Congress in Brisbane, Australia, in September 2004.

Advisory Committee

An International Advisory Committee of ten distinguishedscientists provided oversight. Their contributions includedevaluating applicants and selecting trainees, providinginput to the curriculum, evaluating follow-up projects andguiding grant award decisions and, in several cases,serving as project mentors.

A Suite of Intensive Training Institutes

The Advanced Training Institute on Climatic Variabilityand Food Security is the first of three Intensive TrainingInstitutes in Global Change Science, sponsored by STARTand the David and Lucile Packard Foundation. The othersare:

• Urbanization, Emissions and the Global Carbon Cycle(4-22 August 2003, National Center for AtmosphericResearch, Boulder, Colorado, USA)

• Assessing Vulnerability to Global Change and Global

Environmental Risks

All aim at enhancing the pool of trained young scientistsfrom developing countries who are able to play aleadership role in cross-disciplinary approaches to keyissues of global environmental change and sustainabledevelopment. All three incorporate funds for follow-upresearch grants and a summary workshop. The sponsorsbelieve that the momentum and critical mass mobilized inthe training institute coupled with follow-up research anda synthesis workshop will yield greater synergies thantraining alone, and lead to sustained collaborative researchnetworks.

This report summarizes the initial training component ofthe Advanced Training Institute on Climate Variability andFood Security. It describes the content and evaluation ofthe training workshop with summaries of each trainingmodule, lecture and exercise. It then presents plans foreach participant’s follow-up projects. Finally, itintroduces the participants – trainees, faculty, advisorycommittee members and mentors – who made the initialphase of the Training Institute a success.


5

WORKSHOP OVERVIEW

Week One: July 8-12, 2002SUNDAY EVENING, JULY 7 at 7:00pm: INFORMAL ORIENTATION AT THE IBM CENTER.

Monday Tuesday Wednesday Thursday Friday

8 July 9 July 10 July 11 July 12 July

MODULE: Introduction to theTraining Institute

Understanding andPredicting Climate

Fluctuations


Fluctuations


Fluctuations


Fluctuations

9:00am-10:30am The IRI

R. Basher

The Scientific Basis forSeasonal Prediction

N. Ward

Methods of Prediction

N. Ward

Statistical Transformationof Dynamic Model Output

H. Feddersen

Downscaling in Space

H. Feddersen

A Framework forDeveloping AgriculturalApplications of SeasonalForecasts

J. Hansen

Climate Data, ClimateMonitoring and the GlobalENSO Signal

C. Ropelewski

Dynamic Downscaling

A. Robertson

10:30am-11:00am

Coffee Break

11:00am-12:30pm

Keynote: Beyond ClimateInformation: What Else isNeeded for Food Securityand Poverty Alleviation?

J. Kijne

Ocean-AtmosphereInteractions and El Niño

L. Goddard

Statistical ForecastModels

S. Mason

Operational ForecastSystems: the Example ofthe IRI’s System

T. Barnston

Downscaling in Time

A. Robertson

Evaluating Predictability:Dynamic Climate Models

N. Ward

Evaluating ForecastQuality

S. Mason

12:30am-1:30pm Lunch

1:30pm-3:00pm Funding Your OwnResearch: How to Write aWinning Proposal

M. Fuchs-Carsch

Introduction to computers,software, data,procedures

Statistical rainfallprediction exercise

Model outputtransformation exercise

Compare skill of GCM,multivariate statistical andENSO phase forecasts

3:00pm-3:30pm Coffee Break

3:30pm-5:30pm Funding Your OwnResearch: How to Write aWinning Proposal

M. Fuchs-Carsch

Analyze observed SST-wind-precipitationrelationships

Statistical rainfallprediction exercise

Model outputtransformation exercise

Compare skill of GCM,multivariate statistical andENSO phase forecasts

Dinner

Evening Proposal clinic

M. Fuchs-Carsch, A.Freise

Proposal clinic



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Week Two: July 15-19, 2002



MODULE: Understanding andPredicting Agricultural

Impacts

Understanding andPredicting Agricultural

Impacts

Analyzing ManagementResponses to

Forecasts


Forecasts


Forecasts

9:00am-10:30am Weather-SensitiveAgricultural Models:Principles

J. Jones

Predicting RegionalProduction: Principles

J. Hansen

Reports from Tuesdayexercise

Constrained OptimizationModels for DecisionAnalysis

D. Letson

Eliciting DecisionStructure and DecisionRules

D. Letson

Predicting RegionalProduction: Applications

J. Jones

Interpreting HistoricalAgricultural Data

J. Hansen

Farm-level DecisionAnalysis: Introduction

J. Hansen

10:30am-11:00am

Coffee Break

11:00am-12:30pm

Weather-SensitiveAgricultural Models: CropSimulation andApplications

J. Jones

Linking Climate Predictionto Agricultural Models

J. Hansen

A Risk ManagementFramework J. Hansen

Multiple Criteria Decisions

D. Letson

Farm-level DecisionAnalysis: Farm LandAllocation Case Study

D. Letson

Linking Models with kNearest NeighborWeighted HistoricAnalogs

U. Lall

Principles ofRetrospective DecisionAnalysis

J. Hansen

Enterprise-level DecisionAnalysis

J. Hansen

Incorporating Market-level Effects: TomatoProduction Case Study

C. Messina


1:30pm-3:00pm Crop simulation tutorial Predict yields fromclimate forecasts

Analyze associationbetween yield data andENSO

Analyze value of ENSO-based forecasts foroptimal cropmanagement

Analyze value of forecastsfor optimal farmenterprise mix


3:30pm-5:30pm Crop simulation tutorial Predict yields fromclimate forecasts

Analyze associationbetween yield data andENSO

Analyze value of ENSO-based forecasts foroptimal cropmanagement

Analyze value of forecastsfor optimal farmenterprise mix

Dinner

SUNDAY, JULY 21: PROPOSAL PEER REVIEW PANEL 2:00PM-5:00PM JULY 21 AT THE IBM CENTER, PROPOSAL CLINIC AFTER DINNER


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Week Three: July 22-26, 2002



MODULE: UnderstandingDecision Makers

UnderstandingDecision Makers

InstitutionalizingSupport for Forecast

Applications

CommunicatingForecast Information

Project Design andEvaluation

9:00am-10:30am Understanding DecisionMakers

P. Hildebrand

Introduction toEthnographic LinearProgramming of SmallFarm Livelihood Systems

P. Hildebrand

Scientific and LocalKnowledge Systems:Bridging the Gaps

J. Gilles

Communicating ForecastUncertainty and CognitiveAnomalies

P. Hayman

Panel discussion:

ImplementationOpportunities andStrategies

Relevant Questions

P. Hildebrand

Diffusion of Innovations:An Overview

J. Gilles

Demonstration of two’games’ to relay conceptof probabilities in the field

10:30am-11:00am

Coffee Break

11:00am-12:30am

Food Security inDeveloping Countries:Livelihood Activities andPortfolios

C. Valdivia

Livelihood systemmatrices

The Role of theInnovation and the SocialSystem in Adoptions

J. Gilles

Basic Principles ofDecision Making

G. Marcus

Training workshopevaluation

DesigningCommunicationProcesses that helpDecision Makers

J. Phillips

From Decision SupportSystems to DiscussionSupport Systems

H. Meinke


1:30pm-3:00pm Livelihood system profiles Livelihood systemmatrices

Networks Using Scenarios in thePlanning Process

Project awards, projectguidelines, mentors


3:30pm-5:00pm The Sondeo

P. Hildebrand

Livelihood systemmatrices

Diffusion strategy Using Scenarios in thePlanning Process

Project awards, projectguidelines, mentors

Survey Design andInterpretation

C. Valdivia

Dinner

Evening Proposal clinic


Proposal clinic


Closing Ceremony


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Objective: Introduce trainees to the Training Institute, and instill a perspective of how its components fittogether and into the broader context of food security and development.

Faculty: Jacob Kijne, Reid Basher, James Hansen, Marian Fuchs-Carsch

Background reading:

Hansen, J.W., 2002. Realizing the potential benefits of climate prediction to agriculture: Issues, approaches,challenges. Agricultural Systems 74:309-330.

CURRICULUM

Introduction: Setting the Scene

LECTURE SUMMARIES

A Framework for Developing AgriculturalApplications of Seasonal Forecasts

James Hansen

Advances in our ability to predict climate fluctuationsmonths in advance suggest opportunity to improvemanagement of climatic risk in agriculture, but only ifparticular conditions are in place. I will relate thecomponents of the Training Institute to a set ofprerequisites to beneficial use of seasonal climateforecasts. The first prerequisite is that forecastinformation must address a need that is both real andperceived. Second, benefit arises only through viabledecision options that are sensitive to forecast information.Third, benefit depends on prediction of the components ofclimate variability that are relevant to viable decisions.Fourth, appropriate forecast use requires effectivecommunication of relevant information. Finally,sustained use requires institutional commitment andfavorable policies. Considering three phases of effort isuseful: an exploratory phase to gain understanding andassess potential, a pilot phase characterized by co-learningbetween researchers and target decision makers, and anoperational phase focusing on engaging and equippingrelevant institutions.

Keynote Address:Beyond Climate Information:

What Else Is Needed for Food Security and Poverty Alleviation?

Jacob Kijne

This presentation discusses the conditions under whichfood security needs to be achieved, includingtechnological changes, degradation of natural resources,competition for scarce water, the changing structure offarming, and international agricultural trade and itssubsidies. It analyses the link between food security andrural poverty, and presents food production trends indeveloping countries. The role of irrigation in foodproduction is discussed. Important institutional aspects,such as the need for farmers’ involvement in theimprovement of food production, will be illustrated byseveral case studies. The presentation ends by specifyingthe most promising options and opportunities forimproving food production in developing countries andthe role of climate information in this process.


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Afternoon Workshop:Funding Your Own Research:

How to Write a Winning Proposal

Marian Fuchs-Carsch

The primary purpose of proposal writing is to persuade,not to inform. Principles of persuasive writing apply.First, think about your reader. Second, spoon feed yourreader. Make your writing easy to read by using simple,direct sentences; bullets; active voice; and positivelanguage. Third, plan before you write. Writing topersuade requires appealing to the self-interest of thereader (i.e., the donor), and writing with passion.Development donors generally fall within a fewcategories, but represent a range of goals and constraints.Although development goals are priority, development

donors may fund projects with a research component.Rules differ, depending on whether a proposal is sole-source or a response to a request for proposals.Partnerships are always an asset. A project is acombination of inputs managed in a certain way, toachieve desired outputs, and ultimately desired impacts.The essential elements of a project are goals andobjectives, inputs, a management plan, activities, outputsand a budget. Objectives should be specific, measurable,achievable, realistic and time-bound. A concept note isuseful for gaining internal approval, enlisting partners, andgaining preliminary donor approval. A full proposal ismore involved, should follow any donor guidelines, andshould be subjected to a policy of in-house review. Thepresentation presents generic templates and extensiveguidance for writing concept notes and proposals.


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Objective: Equip trainees to (a) understand the basis for seasonal prediction and the range of methods used tocapture that predictability; (b) analyze relationships of both observed predictors and dynamic climate model outputto regional-to-local seasonal climate fluctuations, and characterize forecast skill associated with those relationships;and (c) appreciate the requirements for operational seasonal climate forecasting.

Faculty: Neil Ward, Andrew Robertson, Lisa Goddard, Chet Ropelewski, Tony Barnston, Simon Mason, HenrikFeddersen, Matayo Indeje

Background reading:

Ropelewski, C.F. and Halpert, M.S. 1996. Quantifying Southern Oscillation-precipitation relationships. Journalof Climate 9:1043-1059.

Murphy, A. H., 1993: What is a good forecast? An essay on the nature of goodness in weather forecasting.Weather and Forecasting 8:281-293.

Wilks, D. S., and R. L. Wilby, 1999: The weather generation game: A review of stochastic weather models,Progress in Physical Geography 23:329-357.

Module 1: Understanding and Predicting Climate Fluctuations

LECTURE SUMMARIES

The Scientific Basis for Seasonal Prediction

Neil Ward

Although the chaotic nature of the atmosphere precludesprediction of weather events beyond about 10-14 days,climate fluctuations can be anticipated months ahead dueto the influence of solar irradiance, land vegetation, sea iceand sea surface temperatures (SSTs) on the atmosphere.SST patterns influence climate patterns around the globe.Their persistence brings a degree of predictability. Asimplified model highlights one effect of SSTs on thetropical atmosphere. The bottom 3 km “boundary layer”over the tropical oceans is well mixed. Its temperaturereflects that of the ocean surface. The difference in weightbetween warm and cold air creates pressure gradients thatinduce winds that converge and rise over warm water. Asthe warm air rises, it cools, causing its moisture toprecipitate as rain. A 10-km effective lid causes rising airto spread and descend elsewhere, creating three-dimensional circulation patterns. Descending air inhibitsclouds and rain. Friction and the Coriolis force modifythe resulting circulation. Latent heating associated withlarge-scale ascent triggers atmospheric waves that travelinto mid-latitudes. Away from the tropics, the Coriolisforce has a greater influence than SSTs.

Ocean-Atmosphere Interactions and El Niño

Lisa Goddard

Coupled ocean-atmosphere interaction occurs in all thetropical ocean basins. However, the Pacific Ocean exertsa unique influence on the global climate system. Its largesize allows for: (a) relatively long time scales, leading topotential predictability of El Niño and La Niña; (b) largeamplitude growth of coupled anomalies; (c) potential forsustained oscillations; and (d) large spatial shifts inconvection and thus atmospheric heating, impacting globalcirculation. Longitudinal shifts in SST patterns associatedwith El Niño or La Niña induce anomalous low-levelwinds, leading to anomalous convergence and rainfall,resulting in anomalous upper-level winds, which finallyinfluences anomalous subsidence. Convergence andsubsidence feed back to low-level winds which, in turn,influence SST patterns. Atmospheric circulation changesinduced by El Niño or La Niña often modify SST in othertropical ocean basins. SST anomalies in the Indian andtropical Atlantic Oceans can play a significant role inaffecting climate variability of neighboring regions thatmay be modified by the atmospheric response to SSTanomalies in the tropical Pacific.


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Evaluating Predictability:Dynamic Climate Models

Neil Ward

General Circulation Models (GCMs) represent theatmosphere throughout a 3-dimensional grid, and simulateweather every few minutes through a season. The modelsrequire initial atmospheric conditions, and continuous SSTboundary conditions. Equations represent all keydeterminants of the atmosphere’s evolution. The modelatmosphere responds to incoming solar radiation at itsupper boundary and SSTs at its lower boundary.Equations specify how heat and moisture enter the lowestlayer and permeate through the atmosphere. There isalways a battle between the influence of SSTs and internalatmospheric process that limit predictability from SSTforcing. To address this, we run the model multiple timeswith different initial atmospheric conditions. Thedistribution among model runs indicates the model’s rangeof possible climatic conditions. The average among runscan be considered the model’s best forecast. Becausesome GCMs perform better than others in particularregions, conclusions about lack of prediction skill shouldnot be based on results of a single model. Evidence ofpredictability should be based on both GCM results andevidence of a plausible physical mechanism.

Methods of Prediction

Neil Ward

We consider five methods. First, coupled GCMs, includeocean process and their dynamic interaction with theatmosphere – the theoretically most appealing approach.They can predict ENSO-related SST evolution with goodskill and lead time. Second, operational forecast centerstypically use atmospheric GCMs forced with forecastSSTs, derived from coupled GCMs (Pacific) or statisticalmodels (Atlantic and Indian). Third, regional climatemodels (RCMs) simulate the atmosphere at a high (up to20 km) resolution over a limited region to resolve small-scale climatic influences, using GCM-derived boundaryconditions. Fourth, dynamic forecasts may be improvedthrough statistical transformations of model output,including, (a) correction of systematic error, (b) correctionof ensemble spread, (c) optimal model combination, and(d) downscaling. Fifth, statistical prediction methodssometimes perform well, but risk over-fitting and artificialskill unless predictor selection is based on physicalmechanism. Statistical downscaling estimates local-scaleresponses to large-scale model predictions, and can beextended to prediction of, e.g., hydrological, agriculturalor epidemiological impacts of the local climate. Wediscuss statistical and dynamic downscaling in detail.

Climate Data, Climate Monitoringand the Global ENSO Signal

Chet Ropelewski

There has been a revolutionary change in climate scienceover the past 2-3 decades due, in part, to the ability tostore and analyze large amounts of data using moderncomputers. Previously the emphasis was on the massivejob of getting observation data into computer-compatibleform. Much of that work has been completed and anumber of data libraries exist. We are now accustomed toaccessing and analyzing any number of data sets that havebeen prepared with climate analysis as their expresspurpose. However, almost all of the historical data usedin climate research were not gathered with climate studiesin mind. It is only over the past few years that programssuch as the Global Climate Observing System have beendesigned with climate monitoring as a primary focus. Thelack of a climate focus in most of the historical data setslimits our ability to describe, monitor and analyze theclimate. This presentation will discuss seasonal climatevariability from the viewpoint of the availableobservational data and the products derived from thesedata. The presentation will illustrate the capabilities andshortcomings of current climate data to define andmonitor the global ENSO signal.

Statistical Forecast Models

Simon Mason

An introduction to some of the most commonly usedstatistical models in seasonal climate forecasting ispresented. Deterministic-discrete models should belimited to exploration, when sample sizes are small.Contingency tables can transform a deterministic-discreteto a probabilistic model. The probabilistic-discrete modelcan be extended to multiple predictors throughdiscriminant analysis. Deterministic, continuous forecastsuse, e.g., regression. Prediction intervals allowtransformation to a probabilistic forecast. Canonicalmethods are useful with multiple predictors and/orpredictands. The similarities, differences, strengths andweaknesses of each of the models are discussed. We alsoprovide guidelines on how to interpret and compareforecasts from the different models. One of the commonpitfalls in statistical forecasting is the multiplicity problemand resulting “artificial skill.” Cross-validation andretrospective forecasting can help correct artificial skill.


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Statistical Transformation of Dynamic Model Output

Henrik Feddersen

Because substantial forecast uncertainty is due to modelerror, statistical postprocessing can often increaseprediction skill. The challenge is to find large-scalefeatures that GCMs simulate well and that correlate withgrid-cell observations. An ideal “index” is one thatmaximizes the explained variance. Principal componentsanalysis (PCA) applies successive linear transformationsthat maximize the remaining explained variance, such thatthe resulting eigenvectors (EOFs) are orthogonal. Thefirst PC of the observed field is an optimal “index,” and isestimated by the first PC of the GCM field. If the PCs ofthe model and observations correlate well in time, we canuse the model PCs as predictors. Singular valuedecomposition (SVD), finds patterns that maximize thefraction of explained covariance in time between the twofields. Canonical correlation analysis (CCA) findspatterns that maximize the correlation between time series.Regardless of the transformation method, the transformedpredictors and predictand are related by a regression modelthat is then used for prediction. A further transformationcorrects negative bias in the spread of ensemble membersfor probabilistic forecasting.

Operational Forecast Systems:The Example of the IRI’s System

Tony Barnston

Each month, the IRI forecasts global temperature andprecipitation for four overlapping 3-month periods. Theprocess uses predicted global SST anomalies, based on acombination of dynamic and statistical models, as input tofour atmospheric general circulation models (AGCMs).For the shortest lead time, the AGCMs are also run withrecent observed SST anomalies. To represent forecastuncertainty, the IRI expresses them in terms of probabilityof falling within low, middle and upper terciles, based inpart on the distribution of multiple runs of each AGCM.Predictions from the four AGCMs are combined into asingle forecast using two multi-model ensemblingtechniques that calculate optimal weights for each AGCM.IRI forecasts incorporate empirical adjustments, includingan ENSO-based probabilistic composite for the locationand season. Ongoing improvements of the IRI forecastsystem include improved SST prediction in the Indian andAtlantic Oceans. Future advances depend on improvedocean observation and increased computational resources.

Evaluating Forecast Quality

Simon Mason

We discuss methods of evaluating the quality of forecasts.Appropriate methods of forecast verification depend onthe type of forecast, i.e., whether discrete or continuous,and whether deterministic or probabilistic. A probabilisticforecast is never “wrong.” In the deterministic-discretecase, we consider rates of hits and misses. Skill comparesthe accuracy of one set of forecasts to that of another (e.g.,climatology, persistence, perpetual forecasts, randomforecasts). In the deterministic-continuous case, we use ameasure of association, e.g., correlation (which does notaccount for bias) or mean squared error. Forprobabilistic-discrete forecasts, the Brier score isanalogous to mean-squared error. Although aprobabilistic forecast can never be wrong, a forecaster’slevel of confidence can be. Reliability deals withcorrectness of probabilities. Sharpness, a function ofaccuracy, deals with the degree to which a forecast reducesuncertainty. Even for deterministic forecasts, there is nosingle measure of the various aspects of forecast quality:accuracy, skill, uncertainty.

Downscaling in Space

Henrik Feddersen

The linear transformation of the GCM output discussedpreviously is based on downscaling large-scale patterns ofvariability to a single grid cell. The transformation isconsidered a correction if the predictand is at the sameresolution as the predictor(s), and downscaling if thepredictand is at a finer (e.g., individual station) spatialresolution. The predictor and predictand need notrepresent the same field. For example, GCM mean sealevel pressure or 850 hPa wind fields might gives accuratedownscaling of rainfall than GCM rainfall. The modeloutput statistics (MOS) approach requires a long trainingperiod in which both GCM output and observations areavailable. Otherwise, a statistical relationship between thepredictand (e.g., station rainfall) and observations of adifferent field (e.g., mean sea level pressure or 850 hPawind) can be used to transform GCM output to estimatethe predictand. This approach is known as perfectprognosis. Whenever it is possible, the MOS approach ispreferred, as it removes systematic model bias, while theperfect prognosis approach does not. Statisticaldownscaling can be applied to statistical properties ofwithin-season variability, e.g., the number of rainy days.


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Dynamic Downscaling

Andrew Robertson

We present preliminary results and lessons from a dynamicdownscaling experiment carried out experimentally forNortheast Brazil. The project is a collaboration betweenthe IRI and FUNCEME. The approach is to nest theRegional Spectral Model (RSM) at an 80 km grid overGCM (ECHAM 4.5) output, then nest the RSM at 20 kmwithin the 80 km RSM. We now have a 20 memberensemble run for 30 years. Hindcast skill of mean rainfallis high in the March–May season and in the single monthof April, with anomaly correlations exceeding 0.7 in someareas. We are just starting to evaluate daily statistics. Thesimulated daily rainfall distribution is better from theRSM at 80 km than from the GCM or the RSM at higherresolution. The approach is promising, but is tedious andrelatively expensive. Future research will comparedynamic downscaling skill against statistical downscaling.

Downscaling in Time

Andrew Robertson

Forecast seasonal averages are not always useful toapplications such as agriculture and water resourcemanagement. This lecture discusses the characteristics ofthe weather within a season that are important forapplications. We then consider how these attributes can bequantified in a probabilistic way, and prospects forpredicting them up to several seasons in advance. Astochastic approach is key, since weather cannot bepredicted deterministically more than about two weeks inadvance. We would like to be able to predict daily weatherscenarios that can be used as inputs to, e.g., cropsimulation models. The stochastic weather generator is awell-established technique for generating daily sequencesof precipitation, daily maximum and minimumtemperatures and solar radiation. We show how theparameters for these models can be estimated fromobserved data and predicted from climate-modelseasonal-mean predictions. The lecture compares thisapproach to using historical analogues, and to direct useof (statistically corrected) daily climate-model output.

EXERCISE SUMMARIES

Analyze Observed SST-Wind-Precipitation Relationships

Matayo Indeje

Objective: Enhance understanding of the influence ofglobal SST patterns on global circulation and precipitationresponse.

Overview: Dr. Indeje will demonstrate the use ofGRADS software for visualizing climatic fields. You willthen use GRADS to view SST-wind-precipitationrelationships for several types of past years. The exercisewill include creating El Niño and La Niña composites. GRADS will enable you to visualize large-scale SST-wind-global rainfall coupling patterns that are typical of ElNiño and La Niña years. Please discuss what you see withyour peers and with the faculty (Matayo Indeje and NeilWard).

Statistical Climate Prediction

Matayo Indeje, Ashish Sharma, James Hansen

Objective: Learn appropriate use of (a) simple linearmethods to explore statistical association between SSTindices and regional precipitation, and (b) independentvalidation to assess statistical predictability (emphasizingthe importance of also proving a physical basis) ofregional precipitation.

Overview: You will use time series (1950-1998) of SSTindices from the Pacific, Atlantic and Indian Ocean asstatistical predictors of an index of October to DecemberEast Africa rainfall. Concurrent (i.e., October-December)SSTs will give an indication of how the ocean basinsinteract with East Africa precipitation. Use of August toSeptember SSTs and two forms of independent validationwill give a more honest assessment of predictabilityassociated with these SST indices.


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Model Output Transformation

James Hansen, Matayo Indeje, Henrik Feddersen

Objective: Become familiar with several statisticaltransformations that can correct climate model outputfields and improve prediction skill at particular locations.

Overview: You will first apply principal componentsanalysis (PCA) to time series of ensemble mean Octoberto December precipitation from 42 GCM (ECHAM 4.5)grid cells in a region around East Africa. You will beassigned a single station for this analysis.

Compare Skill of GCM, Multivariate Statisticaland ENSO Phase Forecasts

James Hansen, Simon Mason

Objective: Learn how to calculate several measures ofprediction skill, and use them to compare differentforecast systems. Gain experience in calculating skill ofa categorical (i.e., ENSO phases) forecast system withoutand with cross-validation.

Overview: You will use the same set of observedOctober-December precipitation from 10 stations inKenya that you used in Exercise 1.3. In addition to theraw GCM output and the model output transformationthat you applied to your assigned station in Exercise 1.3,you will create cross-validated hindcasts from ENSOphases and from the SST indices used in Exercise 1.2.You will then compare these four “forecast systems” interms of several measures of prediction skill.


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Objective: (a) Equip trainees to analyze and predict impacts of climate variability on agricultural production viahistoric data analysis and process-level agricultural models, and (b) instill appreciation of the capabilities andlimitations of both approaches.

Faculty: Jim Jones, James Hansen

Background reading:

Jones, J.W. and Luyten, J.C. 1998. Simulation of Biological Processes. In: R.M. Peart, and R.B. Curry (Eds.),Agricultural Systems Modeling and Simulation. Marcel Decker, New York. pp. 19-62.

Thornton P K and Herrero M (2001). Integrated crop-livestock simulation models for scenario analysis andimpact assessment. Agricultural Systems 70:581-602.

Hansen, J.W. and J.W. Jones. 2000. Scaling-up crop models for climate variability applications. AgriculturalSystems 65:43-72.

Thornton P K, Bowen W T, Ravelo A C, Wilkens P W, Farmer G, Brock J and Brink J E (1997). Estimatingmillet production for famine early warning: an application of crop simulation modelling using satellite andground-based data in Burkina Faso. Agricultural and Forest Meteorology 83:95-112.

IRI. 2000. Summary Report of the Workshop: Linking Climate Prediction Model Output with Crop ModelRequirements. IRI-CW/00/2. International Research Institute for Climate Prediction, Palisades, New York.

Lall, U. and A. Sharma. 1996. A nearest neighbor bootstrap for time series resampling. Water ResourcesResearch 32(3): 679-693.

Module 2: Understanding and Predicting Agricultural Impacts

LECTURE SUMMARIES

Weather-Sensitive Agricultural Models:Principles

Jim Jones

The variability of the environment in space and time, andthe complexity of crop responses to climate, soil andmanagement, call for an integrative systems approach.Crop models addresses the limitations of traditional fieldexperiments, integrate knowledge across disciplines, andbridge the gap between information created by disciplinaryresearch and needed for decision making. Forrestordiagrams are useful for representing conceptual models.Crop simulation models are based on understanding ofinteractions between plants, soil, weather, andmanagement; predict growth, yield and timing; requireinformation; and can be used for “what-if” experiments.They model mass and energy balances, and integrate theeffects of weather (processes temperature, CO2, day length,solar radiation, sometimes humidity and wind) on growth,

development and the dynamics of water availability.Experiments in Florida and Georgia demonstrate that themodels capture soybean response to weather. Ongoingwork is gradually addressing limitations including: factorsmissing from models, the challenge of spatial variability,input requirements, and model complexity, stability anddocumentation.

Weather-Sensitive Agricultural Models:Crop Simulation and Applications

Jim Jones

We present examples of crop model applications for (a)advancing agronomic research; (b) determining bestmanagement for a given site and situation; (c)understanding causes of yield variability and determininghow to optimize management across a spatially-varyingfield; (d) predicting effects of, and exploring adaptations


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to, projected changes in temperatures, precipitation andCO2; and (e) addressing tradeoffs between economic andenvironmental goals for land use decisions. Withinseasonal forecast applications, crop models have been usedto identify optimal field scale management and farm-scalecrop mix, and estimate forecast value. Credibility ofapplications hinges on model evaluation, and sometimesrequires model adaptation. DSSAT is a research tool,developed by international network of researchers, forcrop production analyses. It incorporates: (a)crop-soil-weather models of 16 crops that account formost of the global food crop production, (b) analysis tools(uncertainty, economics), (c) support software (graphics,data management), and (d) GIS linkages for spatialvariability analysis. DSSAT is used in most countries ofthe world for research, teaching and technology transfer.

Predicting Regional Production:Principles

James Hansen

When applying crop simulation models to heterogeneousregions, aggregation error can seriously bias predictions.I will discuss the nature and sources of aggregation errorby comparing with “perfect aggregation.” Aggregationerror includes both mean bias due to nonlinear modelresponse to averaged inputs, and bias of year-to-yearvariability due to imperfect correlation between yieldsfrom different spatial units. The key approaches forimproving spatially aggregated crop yield simulation aresampling model inputs in either geographic or probabilityspace, and calibrating either model inputs or outputs.Calibration depends critically on availability of qualitytime series data at the target spatial scale. I discuss issuesand approaches for dealing with heterogeneity of soil,meteorological and management inputs when predictingspatially-aggregated production.

Predicting Regional Production:Applications

Jim Jones

I present three case studies that illustrate how models canbe used to predict regional-scale yield response to climatevariations. A study of soybean yield variations in a singlecounty in Georgia shows that variations in yield associatedwith climate variations over time can be estimated withaccuracies on the order of 5-10%. Use of simulateddeviations and historical yields to predict yield variationsis a good first approximation (errors on the order of10-20%). Availability of historical yields can improvepredictions, reducing errors by half or more. From a studyof soybean yields throughout Georgia, I introduce the yieldgap concept and reasons for yield bias. RMSE averaged14% when predicting yields for independent years after

correcting for bias. Bias varied considerably across thestate, implying variations in management and other factorsnot accounted for in the model. Other approaches mayfurther reduce bias. Finally, a millet yield forecastingsystem for early warning application in Burkina Fasoillustrates the role of GIS, the use of satellite-derivedrainfall coupled with generated temperature and solarradiation values, and sequential updating of forecasts andtheir uncertainty through the growing season. Yieldsforecast at mid season were within 15% of final yields.Although more work is needed, this approach showsconsiderable promise.

Linking Climate Predictionto Agricultural Models

James Hansen

The spatio-temporal scale mismatch between dynamicclimate models and agricultural (e.g., crop) simulationmodels is an ongoing challenge for model applications.The set of plausible information pathways suggests severalpotential methods for linking climate prediction toagricultural simulation models. First, classification (e.g.,ENSO phases) and selection of historic analogs has longbeen the standard method. The second approach, direct useof dynamic climate model output, is unlikely to give goodresults due to distortion of day-to-day variability, unlesssophisticated statistical transformations are applied to thedaily climate model output. The third approach, stochastictemporal disaggregation, involves use of a stochasticweather generator to disaggregate seasonal or sub-seasonalpredictions into daily time series with appropriatestatistical properties. The fourth approach, directstatistical prediction, relates the simulated outputs ratherthan the meteorological inputs of a crop model to climaticpredictors. The final approach I discuss is to derive yieldpredictions from probability-weighted historic analogs,e.g., tercile forecasts or a k-nearest-neighbor approach(topic of the next lecture).

Applications of the k-Nearest NeighborMethod for Regression and Resampling

Upmanu Lall

The objectives of this lecture are to (a) provide astructured approach to exploring a regression data set, and(b) introduce and demonstrate the k-nearest neighbor (knn)method. As a weighted average, the knn method is a formof regression. The steps of data exploration are (a)examine any systematic trends, (b) understand the structureof inter-variable relationships, (c) and examine probabilitystructure. Brush and spin plots aid visualization ofrelationships in multivariate data sets. If relationships arelinear and distributions normal (without or with a


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transformation), then a linear model should be used.Otherwise, use the knn method with distance weights.Applying the knn method requires decisions aboutweighting method (i.e., the kernel function) and number ofneighbors (k). We discuss weighting based on rankdistance to the predictand vector, and criteria for choosingk.

Interpreting Historical Agricultural Data

James Hansen

Historical production records are useful for characterizingand understanding the impacts of predictable componentsof climate variability. Valid purposes of statisticalanalysis include exploration, hypothesis testing, andstatistical prediction. Because non-climatic factors

influence production, statistical analysis of time seriesdata must first account for trends. Choice of statisticaltechniques depend in part on whether the variables aretreated as continuous or discrete, the number of each typeof variable, and the distributions and behavior of each timeseries. I also discuss presentation method. Themultiplicity problem arises when a pair-wise statisticalhypothesis test is applied repeatedly to a data set. Acommon cause is “fishing” for predictability by evaluatinga number of potential predictors or evaluatingrelationships at multiple lead times. The problem alsoarises when considering multiple commodities orlocations simultaneously. Potential solutions includelimiting the variables considered, use of statisticalmethods that control experiment-wise significance, pre-filtering with principle component analysis, andBonferroni correction.

EXERCISE SUMMARIES

Crop Simulation Tutorial: Sensitivity Analysisof Maize to Weather and Management

Jim Jones

Objective: Gain experience in running a crop simulationmodel, comparing simulated results with observations,select different weather and management conditions,install input files for a new location, and graphicallyanalyze simulation results.

Overview: The crop simulation models in DSSAT v3.5can be used to help one understand the influence ofweather, soil, and management on crop yields. It isimportant to understand their operation and performancebefore they can be applied to real world situations andproblems. You will simulate a maize field experimentconducted in Gainesville, Florida, and examine and graphsimulation results. You will then set up, simulate, andconduct a sensitivity analysis of maize at Katumani, in thesemi-arid Machakos district of Eastern Kenya.

Crop Simulation Tutorial: Simulating Multiple Years for Analyzing Uncertainty

Jim Jones

Objective: Gain experience in simulating cropproduction, exploring variability over a range of weatheryears for different management alternatives, and analyzingproduction response probabilistically.

Overview: Crop simulation models can help oneunderstand the influence of annual or seasonal variationsin weather on yield and its variability from year to year.For maize in Katumani, Kenya, you will set up anexperiment file with different treatments, then run themodel is to be run for a number of years. This will causethe model to reset soil conditions and produce simulatedresults for each year specified. You will construct anempirical cumulative distribution of results, and estimateprobabilities of achieving particular yield goals.


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Predict Crop Yields from Seasonal Climate Forecasts

James Hansen

Objective: Become familiar with several promisingapproaches for linking coarse spatial and temporalresolution climate forecasts with crop simulation for topredict yield response to predicted climate fluctuations.

Overview: You will collectively implement and evaluatefive potential methods for producing crop yield forecastsfrom seasonal climate forecasts. Due to the number ofmethods we will consider, this exercise will be conductedin teams. Each of four teams will apply one method togenerate a time series (1970-1996) of maize yieldhindcasts based on seasonal climate hindcasts. Every teamwill also simulate yields with observed weather, calculateseveral measures of goodness of fit between hindcastyields and yields simulated with observed weather, andplot results. Each team will give a short (5 minutes)presentation of results on Wednesday morning. Theexercise will use the same Katumani, Kenya, data used inExercise 2.1. Teams 2-4 will use October-Decemberprecipitation hindcasts derived from ECHAM 3.6, eitherdirectly or as boundary conditions for a high-resolutionnested regional climate model (RSM). Because ECHAMwas run with observed SSTs, the results are not truehindcasts, and probably overestimate true prediction skill.

Four teams will evaluate five methods:

• Team 1: Selection of historic analogs by ENSO phase.

• Team 2: Direct use of daily GCM output andstochastic disaggregation of monthly precipitationtotals.

• Team 3: Direct statistical prediction.

• Team 4: Nearest neighbor weighted historic analogs.

Analyze Association Between Yield Data and ENSO

James Hansen

Objective: Gain experience with a process of analysis ofhistoric crop data, including exploratory graphics, dealingwith trends, and statistical hypothesis tests and graphicaldisplays of association between historical crop yields andENSO.

Overview: Historical crop data at a reporting district scalecan provide a valuable perspective of the possibleinfluence of ENSO or other potential climatic predictorson crop production. You will select either Kenya maizeor Florida tomato time series data, and evaluateassociation with both categorical and continuous measuresof ENSO.


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Objective: Equip trainees to perform realistic ex-ante evaluation of management responses to climate forecastsat several system levels.

Faculty: David Letson, James Hansen

Background reading:

Hardaker, J.B., Huirne, R.B.M., Anderson, J.R. (Eds.), 1997. Coping with Risk in Agriculture. CABInternational, Wallingford, UK. (Chapters 2 and 5)

Jones, J.W., Hansen, J.W., Royce, F.S., Messina, C.D., 2000. Potential benefits of climate forecasting toagriculture, Agriculture, Ecosystems and Environment 82:169-184.

Hazel, P.B.R., Norton, R.D., 1986. Mathematical Programming for Economic Analysis in Agriculture.Macmillan, New York. (Chapters 3 and 4)

Royce, F.S., J.W. Jones, and Hansen, J.W. 2001. Model-based optimization of crop management for climateforecast applications. Transactions of the American Society of Agricultural Engineers 44:1319–1327.

Jochec, K.G., Mjelde, J.W., Lee, A.C., Conner, J.R., 2002. Use of seasonal climate forecasts in rangeland-basedlivestock operations in West Texas. Journal of Applied Meteorology 40:1629-1639.

Module 3: Analyzing Management Responses to Forecasts

LECTURE SUMMARIES

A Risk Management Framework

James Hansen

Risk management entails balance between the level and thesecurity of livelihood. Decision trees are a useful way tothink about risky decisions, particularly the sequence ofdecisions, chance and outcomes. A decision tree exampleillustrates risk aversion and the concept of a certaintyequivalent. Expected utility provides a mathematicalfoundation for risk management that can handle a widerrange of scenarios, including those where decision optionsand states of nature are continuous. The shape of a utilityfunction embodies one’s attitudes toward risk. I discussabsolute and relative risk aversion. Although risk aversionis difficult to measure, experience suggests some commonranges of relative risk aversion. The assumptions behindexpected utility theory often do not hold underexperimental conditions. Nevertheless, the frameworkmakes the tradeoff between profit maximization and riskavoidance tractable, and remains useful when itslimitations are understood. Importantly, it provides anobjective basis for recommending incremental responsesthat are consistent with forecast uncertainty and risktolerance.

Principles of Retrospective Decision Analysis

James Hansen

Ex-ante, retrospective decision analysis is useful foranswering several relevant questions. Retrospectiveanalysis accounts for the probabilistic nature of forecastsby considering what optimal decisions and outcomeswould have been over a past series of years. Economistsdefine information value as the difference in expectedoutcomes between optimal use of the new information andoptimal use of the prior information. It is useful todistinguish between “objective” value based on expectedoutcomes, and “subjective” value based on certaintyequivalents. In spite of limitations, the forecast valueframework provides a useful metric for, e.g., identifyingwhich decisions are most sensitive to forecasts, comparingdifferent forecast systems, or evaluating tradeoffs betweenforecast lead time and skill. Optimal strategies identifiedfrom such an analysis can be an entry point for discussionwith decision makers or a starting point for traditionalresearch. The general framework is meaningful only in thecontext of a well-defined system.


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Constrained Optimization Models for Decision Analysis

David Letson

Ex-ante evaluation of climate forecast use requires thatwe model a range of decisions. Constrained optimizationis a useful framework for structuring decisions thatinvolve risk aversion, intermediate products, distributionnetworks, inventory management, and multiple objectives.The general steps to finding an analytical solution to aconstrained optimization problem are: (a) form aLagrangean, (b) differentiate with respect to all decisionvariables, and (c) form and interpret complementaryslackness conditions. Examples illustrate howmanagement of intermediate product processing in anindustry value chain, sequential inventory storagedecisions, and decisions with multiple objectives can beformulated and solved as constrained optimizationproblems.

Enterprise-Level Decision Analysis

James Hansen

The enterprise is the lowest level in the agriculturalsystems hierarchy that is managed. Production enterprisesare typically analyzed on a unit land area basis. Scaleindependence simplifies analyses and generalization ofresults, but precludes accounting for resource constraintsand management of risk. Enterprise-level analysisgenerally focuses on biophysical production, costs andprices. Due to the inability to account fully for risk at theenterprise level, analyses either assume risk neutrality orexamine risk efficiency. Steps in enterprise-level decisionanalyses are: (a) define the system and decision problem,(b) identify the range of viable decision options, developa realistic enterprise budget, (c) develop a stochasticbudget that accounts for climate variability, and (d)identify and evaluate superior strategies for climatologyand each forecast. I present an example of an enterprise-level analysis of maize management tailored to ENSOphases.

Eliciting Decision Structure and Decision Rules

David Letson

Jochec et al. (2001) effectively combined elements ofprescriptive and descriptive decision analyses in what isnow identified as a “participatory systems approach.”They used focus groups to (a) brief ranchers on theapproach and on seasonal climate outlooks, (b) elicit andconfirm decision rules in response to forecasts, and (c)verify the structure and results of an economic model. Themodeling approach combined forage prediction simulated

with a process-level simulation model using historicweather data, elicited decision rules, and a farm-leveleconomic accounting model. Rancher knowledge provedcritical for designing the decision analytic models,interpreting model results, understanding constraints toforecast use, and understanding rancher information needs.Whether or not you simulate ideal responses, start bylearning how climate-sensitive decisions are currentlymade.

Farm-Level Decision Analysis:Introduction

James Hansen

Climate-sensitive farm-level decisions include: (a) farmresource acquisition and allocation, (b) enterpriseportfolio / risk management, (c) managing interactionsbetween enterprises, (d) choice between farm andnon-farm activities, (e) choice between work and leisure,and (f) household consumption. To motivate thinking, Ipropose a series of questions related to each. I suggest thefollowing strategy. First, understand farmer goals.Second, inventory important decisions. Which aresensitive to the climate? What factors constrain each?Third, identify optimal (or risk efficient) strategies forindividual enterprises. Fourth, structure farm-leveldecisions and their determinants in a linear programmingtableau. Start simply and refine incrementally. Fifth,generalize objectives to consider, e.g., risk aversion.Finally, let farmers evaluate and refine the model. A farmlevel analysis cannot address market responses toaggregate decisions. Steep (inelastic) demand and a smallmarket relative to the aggregate production effects favornegative price impacts of forecast use.

Farm-Level Decision Analysis:Farm Land Allocation Case Study

David Letson

We present a case study of optimal management inresponse to ENSO information. The general problem is tochoose farming practices to maximize utility of expectedwealth, or its certainty equivalent. The value ofinformation (VOI) is the difference in certainty equivalentwith and without use of climate forecasts. The decisionmodel allocates farm land among 21 crop and management(fertilizer amount and planting date) alternatives tomaximize expected utility of year-end wealth, subject toland and labor constraints, and in response to pricevariability, risk aversion and climate variability. Theanalysis used synthetic weather data, crop simulationmodels, and stochastic prices. Maize is the favored cropfor favorable conditions, e.g., warm events and riskneutrality. Soybeans are favored in neutral and coldevents. Sunflower is a favored hedge, since its returns


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have low variability and low correlation with those ofmaize. Diversification increases with risk aversion, butnot dramatically. Expected VOI is positive in the long-term, but in any given season may be < 0. The level andspread of VOI important to potential users. Omitting pricevariability may give misleading results.

Incorporating Market-Level Effects:Tomato Production Case Study

Carlos Messina

Although ENSO’s influence on Florida winter tomatoyields suggests that farmers might benefit from ENSOinformation, the small number of producers andgeographic concentration of production raises concern thatnegative price response might reduce or negate any

production benefits. A study to assess potential benefitsof ENSO information included participation of growersand extension agents; and retrospective, model-baseddecision analysis that linked historic weather data toprocess-level yield prediction and economic optimization.Optimal transplanting date and distribution of tomatoproduction between South Florida and Puerto Rico variedwith ENSO phase. Labor available for planting andharvest constrains responses to ENSO. The estimatedvalue of optimal use of ENSO forecasts for a single ha isabout $900 ha–1 y–1. If all farmers were to respond toENSO independently, reduced prices would cancelproduction benefits. However, if all South Florida tomatofarmers were to coordinate their actions optimally, theaverage benefit would be about $300 ha–1 y–1, or about$2,000,000 y–1 for the entire state.

EXERCISE SUMMARIES

Analyze Value of ENSO-Based Forecastsfor Optimal Crop Management

James Hansen

Objective: Gain experience with linking biophysicalmodeling with stochastic budgeting and optimization, andwith calculating forecast value.

Overview: You will examine the potential value oftailoring maize management strategies to ENSO phases fora location in the Pampas region of Argentina. You willuse a systematic grid search to simulate yields andcalculate gross margins at each of a fixed number ofequally-spaced points in decision state space. The outputlends itself to plotting yield and gross margin responsesurfaces to pairs of decision variables. The grid searchwill provide the decision vector that maximizes expectedgross margins. You will calculate “forecast” valueassociated with the optimal management strategies forclimatology and for each ENSO phase. For the sake ofsimplicity, we will not apply cross validation.

Analyze Value of ENSO-based ForecastsUsing a Farm-Level Land Allocation Model

David Letson

Objective: Gain experience solving a nonlinear farmoptimization model in Excel and with calculating forecastvalue for a risk-averse decision maker.

Overview: You will use a simple spreadsheet-basedeconomic optimization model to identify optimalallocation of land among alternative cropping enterprisesfor a representative farm in Santa Rosa, in the Pampasregion of Argentina. You will use the built-in solver tomaximize certainty-equivalent farm income (CE) in a one-year decision period. CE is a nonlinear function ofvariability and risk aversion. You will look at sensitivityof the optimal solution to the degree of risk aversion.Solving the model for all years of weather data, andseparately for each ENSO phase allows you to calculatethe value of optimal use of the ENSO information. Forthe sake of simplicity, we will again not apply crossvalidation.


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Objective: Equip trainees to (a) understand the needs, perspectives, constraints and socioeconomic context oftarget decision makers in order to effectively evaluate, foster and guide appropriate use of forecast information,and (b) engage decision makers in identifying and evaluating appropriate use of forecast information.

Faculty: Peter Hildebrand, Corinne Valdivia

Background reading:

Chambers, R. and G. R. Conway. 1992. Sustainable Rural Livelihoods: Practical Concepts for the 21stCentury. Discussion Paper 296. Institute of Development Studies, London.

Hildebrand, P. E. 1981. Combining disciplines in rapid appraisal: the Sondeo approach. AgriculturalAdministration 8:423-432.

Norman, D.W., Worman, F.D., Siebert, J.D., and Modiakgotla, E., 1995. The Farming Systems Approach toDevelopment and Appropriate Technology Generation. FAO, Rome. Chapter 8 (available online athttp://www.fao.org/inpho/vlibrary/x0044e/X0044E00.htm#Contents).

Module 4: Understanding Decision Makers

LECTURE SUMMARIES

Understanding Decision Makers

Peter Hildebrand

Farmers respond to climate forecasts using their ownindicators, and constantly make decisions about what toproduce, where, how and when. In addition to climate,they must also factor in market conditions, policies,household composition, and their own susceptibility toshocks. All these factors, and more, add up to verycomplex and diverse livelihood systems. We assume thatyou are interested in how to get farmers to respond toclimate predictions, how to get information to farmers,and how to understand farmers’ subsequent responses. Toreach these goals, it is necessary to understand whydecision makers make the decisions they do. IF yourforecasts are better than the farmers’ AND your suggestedresponses are better than the farmers’, then you need toknow how best to deliver the information, to whom, andwhen.

Relevant Questions

Peter Hildebrand

What information do you need before developingpredictions and making recommendations for highlydiverse smallholder farmers with scarce resources? Smallfarm livelihood systems: (a) are highly complex, (b) havea large number of enterprises, (c) have stronginterrelationships among enterprises, and (d) showincreasing dependence upon infrastructure with increasingpopulation pressure. Farming system diversity exists atscales ranging from continents to within-household. Thebiophysical environment, cultural norms, infrastructureand household composition influence diversity. It isimportant to understand who makes what decisions withinthe household and community. Key decisions include on-farm production (crops, livestock, non-agricultural) andreproduction (e.g., rearing children) activities, and off-farm activities (e.g., education, employment). Theportfolio, extent and seasonality of these activities areinfluenced by a range of biophysical and socioeconomicfactors.


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Food Security in Developing Countries:Livelihood Activities and Portfolios

Corinne Valdivia

This session introduces the participants to the livelihoodsframework, a conceptual approach to understanding thecapabilities of people and their use of tangible andintangible assets resources and claims in making a livingin rural areas. The context in which individuals andhouseholds make decisions is also shaped by structures,institutions and climate. The session explains theseinteractions and how it can impact on coping with stressand shocks, and ability to make a living and improve it.From the conceptual framework, household livelihoodstrategies are analyzed with the economic portfolio model,evaluating the ex-ante and ex-post strategies to cope withshocks and stresses and adapt to climate variability.Finally the approach is applied to a case study on theidentification of profiles in the Bolivian Andes and howthis relates to access and use of information that improvescapabilities.

The Sondeo

Peter Hildebrand

The Sondeo, a reconnaissance survey method approachdeveloped in Guatemala to elicit information from farmersin a systematic way, is presented. In a Sondeo, livelihoodsystems are described, the agro-socio-economic situationof the farmer is determined, and the restrictions they faceare defined so that any proposed modifications of theirpresent technology are appropriate to their conditions. Ina relatively short time, it generates insights andinformation rarely obtainable in a formal survey.Generalizations beyond the sample interviewed arelimited. The Sondeo is characterized by a combination ofmethods used, fast turnaround time, close communicationbetween clientele and professionals, and aninterdisciplinary team approach. The presentationdiscusses the process and the applicability of thetechnique, in the context of the profiles of livelihoodsystems identified in the previous session.

Survey Design and Interpretation

Corinne Valdivia

This presentation focuses on the purpose andappropriateness of formal surveys in eliciting informationfrom farmers and their households. Formal surveysprovide a systematic way of obtaining information toanswer specific questions, in a manner that can be analyzedstatistically. Planning includes (a) specifying the questionsto be answered, (b) designing appropriate question contentand format, (c) defining respondents, (d) sample selection,(e) choosing interview method, and (f) establishinganalysis requirements. The order and manner in whichquestions are asked is important. Pre-testing is a necessarystep in the development of a formal survey. The mannerand order in which questions are asked is important,because it may influence the respondent’s answers. Anappropriate household survey is likely to includedemographic information, agricultural and non-agricultural activities, and climate. The advantages,constraints and purpose for the surveys are presented, aswell as the process to develop the survey, pretesting, dataentry, variable construction and analysis.

Introduction to Ethnographic Linear Programming of Small

Farm Livelihood Systems

Peter Hildebrand

This session describes the methodology to elicitinformation and describe a livelihood system, and identifythe livelihood strategies. Linear programming helps usunderstand highly-diverse livelihood systems, and servesas a basis for simulating household livelihood strategies.Simulating household livelihood strategies requiresethnographic data obtained with the participation of thefarmers. Ethnographic linear programming (ELP) helps usto design livelihood alternatives, and predict responses tochanges in technology, infrastructure, policy orenvironment. A linear programming matrix relatesresources, activities, goals, constraints and year-end cash.Household composition affects the amount and kind oflabor available, the amount of food required, and cashrequired for expenses. An example illustrates the stepsfollowed in developing a matrix for the LP model, and infinding feasible and realistic solutions that represent thelivelihood system.


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EXERCISE SUMMARIES

Livelihood System Profiles

Corinne Valdivia, Peter Hildebrand

Objective: Learn to use information elicited from a keyinformant to develop a farm livelihood system profile,including capabilities, stores and resources, and claims andaccess, and the key external drivers.

Overview: The participants are divided in four groupswith a leader that is familiar with the agriculture of aregion. The regions identified correspond to India,Vietnam, Ghana and Ethiopia. The exercise consists ofdescribing a livelihood system, guiding the process by theconcepts of livelihoods, relevant questions, and portfoliocomponents described in the morning session. Each grouphas 40 minutes to develop a profile. Then these arereported to the whole group and discussed.

Livelihood System Matrices

Corinne Valdivia, Peter Hildebrand

Objective: Gain experience in quantifying a livelihoodsystem profile within a linear programming matrix.

Overview: The procedures to develop the matrix werepresented in a group session. The next step was anexercise to develop a matrix to represent a livelihoodsystem and identify strategies. The exercise consists ofdeveloping the matrix for an Ethnographic LinearPrograming exercise. After the group presentation thestudents broke into the working groups identified the daybefore to develop the livelihood systems, and used thissystem to develop a matrix with coefficients to understandthe objectives, activities and constraints. The second partof the exercise consisted in constructing the matrix in thecomputer and making it work. In the second part of theafternoon a member of each team was chosen to presenttheir matrix. “What-if” scenarios were explored, as two ofthe groups used the matrix to understand what wouldchange if some of the original conditions changed. Thelast part of the afternoon was intended for this exploration.


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Objective: Instil in trainees (a) appreciation of how and under what circumstances agricultural producers canuse knowledge about climate forecasts and their application, (b) understanding of existing institutionalnetworks, including where their home institutions fit into those networks; and (c) appreciation of the elementsof institutional and policy support required for sustained application of climate forecasts by a large segment ofthe population.

Faculty: Jere Gilles, Corinne Valdivia

Background reading:

Rogers, E.M. and Burdge, R.J., 1972. Diffusion of Technological Innovations. Ch. 13 in: Social Change inRural Societies, 2nd Ed. Prentice-Hall, Englewood Cliffs, New Jersey.

Kloppenburg, Jack Jr. 1991 "Social Theory and the De/Reconstruction of Agricultural Science: LocalKnowledge for an Alternative Agriculture" Rural Sociology 56(4):519-548.

Module 5: Institutionalizing Support for Forecast Applications

LECTURE SUMMARIES

Scientific and Local KnowledgeSystems: Bridging the Gaps

Jere Gilles

Regardless of their education and sophistication,practitioners in a field approach knowledge quitedifferently than do scientists. The knowledge system ofpractitioners is termed local knowledge and is oftendistinct from traditional or customary knowledge.Priorities and emphases are different. Scientificknowledge tried to generalize across all settings and localknowledge concentrates on a particular place and time.The difference between these two knowledge systemsposes problems of translation and extension workers whooperate in the scientific tradition must learn to bridgebetween scientific knowledge and local knowledge if theyare to be successful. Given the different languages of thetwo systems, it is even more difficult to get feedback fromproducers. This session looks at these differences and triesto generate possible solutions.

Diffusion of Innovations: An Overview

Jere Gilles

What are the processes by which new ideas come to beadopted by most farmers in a population? This sessiongoes over the literature on the diffusion of innovation.The first distinction will be between awareness, trail andadoption because many programs encounter problemsbecause they do not distinguish between these threeelements. The second is between the different types ofadopters. The first people to adopt an innovation aregenerally very different from the remainder of thepopulation. Successful adoption by these "innovators,"though a crucial first step, will not assure the spread of aninnovation throughout all potential users.


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The Role of the Innovation and the Social System in Adoption

Jere Gilles

Once we have the general principles of theadoption/diffusion model down, it is necessary to examinehow the nature of the innovation itself and the nature ofthe culture and social structure of a farming communitywill influence the adoption of an innovation and therelative benefits of that innovation. Basically the nature of

an innovation may influence the ability of people to try itout, to derive benefits from it and to sustain its use. Thesocial structure is also important because ultimately thewide diffusion of an innovation in a population dependson the networks of information sharing, power andinfluence that exist within a community. These factors donot influence the awareness of an innovation nor do theyhave an impact on the people who are the first to try a newtechnology. These networks are crucial for the widespreaduse/adoption of new technologies.

EXERCISE SUMMARIES

Networks

Jere Gilles

Objective: Identify possible bottlenecks in the flow ofinformation in participant countries to and fromagricultural field staff, using examples of weather andmarket information.

Overview: Each participant will draw a diagram of theflows of information to and from the agricultural fieldstaff person most likely to see farmers. Where does thisperson get information? What their resources if they wantmore information? How often do they use this source?How quickly does it flow? Once students have mappedout the networks, they should discuss possible bottlenecksand challenges. What barriers must be addressed to ensurethe flow of forecast information from its source tofarmers? This activity can be done by individuals orgroups of 2-3 persons who share a similar region, countryof origin, or farming system. These results will be sharedwith other small groups but probably not with allparticipants.

Diffusion Strategy

Jere Gilles

Objective: Gain experience in developing a strategy fordiffusion of climate forecast information and associatedtechnology.

Overview: The use of improved weather/climateforecasting techniques should help agriculture producersreduce the risks associated with weather. Your challengeis to identify a plan for the diffusion of the use of climateforecasting knowledge and an associated package ofdrought resistant crops to producers in your region. Firstdescribe the package of innovations that you would useand the communities of target producers. What challengesdoes the package of technologies and the social context ofagricultural communities pose for the innovation?Outline a plan of diffusion. What would be the targetcommunities? How would information be disseminatedand what steps would be taken to overcome the challengesposed by the technology and social settings themselves?Are there any preconditions, institutional changes thatwould be required before this program of diffusion couldbe initiated? This would be done by groups of 2-5participants who would present their results to the largergroup.


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Objective: Instil in trainees an appreciation for the challenges of communicating information about skillful butuncertain forecasts in a manner that can be incorporated into decision making.

Faculty: Peter Hayman, Jennifer Phillips, Holger Meinke, Galit Marcus

Background reading:

Hayman, P. 2001. No certainties with climate - just choices chances and chocolate wheels. Australian FarmJournal, April 2001, p 12-13.

Nicholls. N. 1999. Cognitive illusions, heuristics and climate prediction. Bulletin of the AmericanMeteorological Society 80:1385-97.

Nelson, R.A. D.P. Holzworth, G.L. Hammer, and P.T. Hayman. 2002. Infusing the use of seasonal climateforecasting into crop management practice in North East Australia using discussion support software.Agricultural Systems 74:393-414.

Module 6: Communicating Forecast Information

LECTURE SUMMARIES

Communicating Forecast Uncertaintyand Cognitive Anomalies

Peter Hayman

Like most of us, farmers tend to prefer categoricalforecasts that can be slotted into an IF THEN ELSE rule.For example, IF the season is going to be wetter thanaverage, THEN apply more inputs. Despite this preferencefor certainty, climate science can only offer probability. Inthis session we discuss several issues. First, whycommunicating climate science to end users requires beingclear about uncertainty. Second, why we need to workwith intermediaries to shift from the language ofchoice-consequence to choice-chance-consequences.Third, evidence from psychology that humans are poorintuitive statisticians. We will also cover the counterarguments that point out that as humans we aresurprisingly good at sorting though masses of data andmaking acceptable decisions. Finally we discuss ways tosimplify messages without “dumbing down” to categoricalforecasts.

Basic Principles of Decision Making

Galit Marcus

Rationality refers to consistent and systematicdecision-making. One might argue that people are neverirrational; some goal always motivates actions. Peoplemake decisions in a variety of modes, including: cost-benefit, recognition-based (including non-deliberative,stereotype-based, case-based and principle-based), role-based, reason-based, and affect-based. Cost-benefit andaffective decision making, for example, might lead toconsideration and selection of very different alternatives.Obstacles to good decision-making include: limitedattention, aversion to uncertainty, aversion to tradeoffs,conflict between motivation and cognition, and egocentricbiases. Limited attention contributes to excessivesensitivity to how a decision problem is framed, and toheuristics that simplify decision making but sometimeslead to poor decisions (e.g., availability,representativeness, anchoring). ’Good’ decision makingbenefits from using decision trees to structure a decisionin a manner that makes alternative actions, probabilitiesand tradeoffs explicit; and from creative problem solvinginvolving open-minded and sufficient (but not exhaustive)evaluation of goals, alternatives and probabilities.


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Designing Communication Processesthat Help Decision Makers

Jennifer Phillips

Climate forecast information has value only if itinfluences actions by decision makers. The way in whichclimate information is communicated to the intendedbeneficiaries strongly influences the uptake and use of thatinformation. The common pitfalls of traditionaltechnology transfer include assuming that information isuseful before investigating the needs of users, ignoringsolutions already in place, and restricting informationexchange to one direction. Some lessons for addressingthese shortcomings in the communication process can bedrawn from the field of participatory development. Keyconcepts include interacting, facilitating, choice andadaptation. In practical terms, the use of discussiongroups is key, wherein everyone’s knowledge is respectedand ideas can be inspected, evaluated and accepted orrejected. Ideas for organizing discussion groups, the useof scenario-building as an activity, and integratingdiscussion groups with radio are presented here.

From Decision Support Systems to Discussion Support Systems

Holger Meinke

This talk illustrates how quantitative systems analysis, thatcombines simulation modeling with seasonal climateforecasting, can be useful in agricultural decision making.Quantitative systems analysis can help farmers replace“gut feeling” about their complex system with (a) harddata about the state of their system, and (b) probabilisticinformation about how the unknown can affect theoutcomes of decisions. Our participatory systemsapproach involving simulation-aided discussion withadvisers and farmers has led to the development ofdiscussion support software as a vehicle for facilitatinginfusion of forecasting capability into practice. WhopperCropper is an example, designed for farmer advisers.Whopper Cropper consists of a database of simulationoutput and a graphical interface for analyzing risksassociated with crop management options. Examplesillustrate how tactical responses to seasonal forecasts canincrease mean income. Although the approach does noteliminate the complexities of decision making, nor risksassociated with responses to a forecast, it does generatenew understanding of risks and opportunities. It providesquantitative data for meaningful comparison of options asa basis for informed discussion (“discussion support”).

EXERCISE SUMMARIES

Responding to Climate Forecasts: Using Scenarios in the Planning Process

Jennifer Phillips, Tahl Kestin, Cynthia Lawson

Objective: Explore and evaluate how predefined decisionscenarios communicate decision principles and improvedecision making under climatic risk. Participate in thereview of a web-based decision training module.

Overview: Trainees beta tested a web site,www.ccnmtl.columbia.edu/projects/iri/responding/index.html,designed to help people use forecast information indecision making. The site contains information on generalprinciples of decisions making, and a few case studiesillustrating these concepts. The assignment is to: (1) readthe web materials on scenario building and case studies,(2) develop your own case study, and (3) to fill out a briefquestionnaire regarding the usefulness of the site. Parts(1) and (2) will help you organize information that willhopefully lead to better decisions using forecasts. Part (3)will help us improve the site. Tutorials guide users toaccess IRI web-accessible resources, such as climate dataand forecast information. The three people who worked

most closely developing the materials were present toobserve and answer questions.

Crop Choice Simulation Game

Peter Hayman

Objective: Use a spreadsheet-based simulation game toexplore issues of using a skillful but uncertain forecast fordecision making.

Overview: The purpose of the game is to decide theportion of the farm that should be planted to a new higherreturn higher risk crop (wonder bean) and the portion toleave in the traditional lower return lower risk crop (stablecereal). There are three rounds. In the first roundparticipants hear from an agronomist how valuable thenew crop is and need to decide the portion to plant, thegame is cumulative and people start to learn fromexperience. The second round introduces the probabilitydistribution of the profit of the two crops and the thirdround involves seasonal climate forecasts.


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Panel Discussion: Implementation Opportunities and Strategies

Panelists: Reid Basher, Hartmut Grassl, Sulochana Gadgil, Holger Meinke, Hassan Virji, Roland Fuchs, Peter Gregory

The panel discussion provided a forum for participants tointeract with experts from diverse backgrounds regardinglong-term implications of the training. Discussionsfocused on a few themes:

• Regional climate outlook forums,• Forecast timing and content,• Engaging stakeholders, and• Related programs of the sponsoring institutions.

Climate Outlook Forums

Periodic climate outlook forums in Africa and LatinAmerica have been quite successful in improving themethodological basis and authority of seasonal forecasts,raising awareness of seasonal prediction, and initiatingdialogue between climate and stakeholder communities.Virji polled the trainees to see how many haveparticipated in the forums, and challenged them to attendthose in their regions. Some of the discussion focused onthe relevance of forecast information that the outlookforums produce, and how effectively they reach andinvolve poor farming communities. Waiswa indicatedthat the process of trickling from policy makers down tofarmers tends to be slow. Basher cited a recentcomprehensive review of the climate outlook forumprocess, the challenges involved in sustaining them, andthe prospects for reducing costs throughvideoconferencing.

Forecast Timing and Content

Grassl asked what forecast time scales are relevant, andchallenged participants to use the full range ofinformation, from daily weather forecasts to globalchange scenarios, that are available from internationalclimate institutions. Romero stressed the need to focuson critical periods for crop response, such as flowering.Waiswa indicated that farmers in eastern Africa wantforecasts of rainfall onset date, and complained that therainy season sometimes starts before climate outlookforms in the region. Adiku later reiterated the importanceof predictions of onset date, and emphasized the value ofcontinuous forecast updates within the season. Accordingto Singh, Indian farmers are concerned about breaks in themonsoon, and demand more information about within-

season variability. The group discussed the challenge offorecasting onset dates, and the potential for optimizingthe timing of forecast delivery and climate outlookforums. Gadgil first asked whether participants wereprepared to use relevant predictors other than ENSO.Later discussion emphasized the potential for satelliteremote sensing data, including spatially-contiguousestimates of rainfall and stored soil moisture, to addvalue to seasonal forecasts. According to Basher, the IRIprefers to build regional capacity, rather than tailor itsown global forecasts to local user need.

Engaging Stakeholders

Meza proposed that communication between farmers andresearchers is inadequate, and discussed the challenge ofmaintaining interest and learning during neutral years.Boer, Biru and Adiku called for greater efforts to equipextension personnel to function as intermediaries incommunicating forecast information. Gadgil counteredthat researchers need to interact directly with farmers tolearn from them, and not just rely on extensionintermediaries. Adiku highlighted the cultural andinstitutional barriers that sometimes limit directinteraction between researchers and farmers. Birusuggested that climate outlook forums may be a vehiclefor engaging farmer groups and extension programs. Theperceived need to effectively engage farming communitiesled naturally to a discussion of training for extensionpersonnel and for farmers. Fuchs-Carsch suggested theInternational Service for National Agricultural Research(ISNAR) as a potential starting point for building capacitywithin national agricultural research systems. Basherurged participants to also actively engage policy makersand senior scientists.

Related Programs

Panelists summarized relevant capacity buildingopportunities at START (Fuchs), the GlobalEnvironmental Change and Food Systems Program(GECAFS) (Gregory), CLIMAG projects in West Africa(Virji) and Asia (Meinke), and climate applications at theIRI (Basher).


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WORKSHOP EVALUATION

Summary

On the final day of the workshop, individualquestionnaires and a self-directed group evaluationprocess gave participants the opportunity to evaluate theirexperience. The evaluation questionnaires combinednumeric and open-ended questions about the overalldesign and implementation, individual training modules,and the proposal development and evaluation process.Numeric results are presented as histograms on thefollowing pages.

Open-Ended Questions

The first open-ended question was, “What was the mostvaluable aspect of the workshop?” Several answersrelated to integration – the conceptual design of theworkshop; combination of climate science, agriculture andsocio-economics; combination of theory and exercises;how to integrate various tools; how to make forecastsuseful to farmers. Others focused on people – the qualityand experience of faculty; interactions with faculty, IRIclimate scientists and other participants. Severalparticipants found the training and support for projectproposal development the most valuable aspect. Othersidentified a variety of particular topics.

Four open-ended questions solicited perceived weaknessesand proposed improvements. Topics identified as needingmore attention include: dynamic climate models, climateforecasting, downscaling, use of climate model output,crop modeling, understanding and modeling decisionmakers, communication, and integrating forecasts withfood security. Topics identified as needing less attentioninclude: dynamic climate models, climate forecasting,relationships between empirical and dynamic forecastmodels, crop modeling, analyzing decision makers, andcommunication. The diversity of answers clearly reflectsthe diversity of needs, interests and backgrounds of theparticipants. Although the target audience was restrictedto agriculture and food security professionals, theparticipants still brought a rich diversity of cultural,educational and professional experience to the Institute.

Participants offered a range of suggestions for improvingthe training. Several related to the exercises. Participantsdisagreed about whether there were too many or too fewexercises. However, several cited problems completingexercises in a manner that fostered learning. One proposedremedy was after-hour access to computers equipped withthe software necessary to complete exercises. Suggestions

related to the intensity of the materials included: reducingmathematical rigor; screening participants to ensureappropriate background; provide more introductoryreading material well in advance; splitting the curriculuminto two workshops; and scheduling time for interactionwith faculty and IRI scientists. Other general suggestionsincluded: introducing social science concepts and methodsearlier; allowing participants to analyze their own datasets; keeping participants in one computer lab; and makingsoftware tools available to follow-up projects and homeinstitutions. The diversity of suggested changes to thebalance of topics included in the curriculum reflect thediversity of participant interests and backgrounds.

Quantitative Results

Participants rated many aspects of their experience on five-point numeric scales. Results are presented as histogramsin the next section. Most agreed strongly that theworkshop was personally valuable, and showed moderateagreement that it met its objective of equipping them tobring climate information to bear on their institutions’ongoing work. Nearly all felt that the level, scope andbalance of material and duration of the workshop wereappropriate. Lectures, exercises and faculty were generallyrated as effective. All trainees considered proposaldevelopment and evaluation activities to be valuable.They were, however, divided on whether time spent onproposals detracted from learning. The proposalworkshop was regarded as the most effective component,and the peer review activities the least effective componentof the proposal development process during the workshop.

There was no clear segregation among modules in termsof accomplishing objectives, relevance, comprehensibility,or the challenge to think in new ways. Module 1influenced more proposals than did the other modules.Responses suggest that Modules 1, 2 and 6 are most likelyto influence participants’ ongoing work.

Group Evaluation

Trainees conducted and summarized a group workshopevaluation. The group rated the training workshop as asuccess overall. Their report highlighted “the remarkableblend or disciplines and topics and the logical linkagesbetween successive components.”

They expressed several concerns that were generally


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“The training course is very useful, and has provided ustheoretical and a little practical knowledge in climateforecasts and application in agriculture. The staff areexcellent, very supportive and enthusiastic. Logistics wereperfect.”

“The workshop was very well planned. The combination ofclimate science with agricultural and socio-economicsciences is a very good blend.”

“Meeting other participants, faculty, IRI and STARTmembers was very valuable. We could exchange ideas andexperiences and make contacts. I hope these relationshipswill be strengthened over time. This training will be veryvaluable in boosting my research and career.”

“The workshop is very well organized, and I appreciateJames Hansen and other faculty for their help sincerely.”

consistent with those raised in the individualquestionnaires. First, the time constraint limited theirability to fully assimilate the materials. Theyrecommended sending background reading materialsearlier. Second, participants generally wanted moreemphasis on practical methodology, and offered severalsuggestions related to exercises. The third issue related to

understanding climate data formats and access proceduressufficiently to allow them to use the IRI Data Libraryeffectively in their projects and ongoing work. Fourth, thereport cites issues of policy, markets and food securityresponse that participants would like to see receive moreattention. The group identified several additional topicsthat would be of interest in future training.


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Individual Questionnaire: Quantitative Results

Overall Workshop

Proposal Development and Evaluation Activities


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1 2 3 4 5

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Individual Training Modules


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“Discussions highlighted theremarkable blend of

disciplines and topics andthe logical linkages betweenthe successive components.”

Group Evaluation

Moderator: Francisco MezaRapporteur: Sibiry Traore Pierre

Participants believe that IRI,START and their partners met thedifficult challenge of organizingthis workshop. Discussionshighlighted the remarkable blendof disciplines and topics and thelogical linkages between thesuccessive components. Overall,participants agreed that theorganization of the ATI was asuccess, with a particular mention for the facilities, judgedexcellent.

Although the workshop duration appears to be appropriate,the time constraint has been pointed out by severalparticipants, for which proper assimilation of informationand technical skills received during lectures and exerciseswas being hampered by proposal development activities.The group recommends that background material be sentto participants much earlier prior to workshop kick-off inorder to enhance preparedness and reduce time pressureduring the workshop itself.

Participants feel that more emphasis should be put on thepractical side of the training. These include (but are notlimited to): more time and faculty allocated to exercises(with more flexibility to access computer rooms afterregular working hours), allowing for more discussions andsupport during the exercises sessions; increased relianceon case studies (instead of “blind” directive guidelines andhandouts that one follows rather “mechanically” – the lastweb-based exercise on 07/25/02 was the only oneinvolving case studies and this approach would have alsobeen very useful earlier in the workshop); possible use ofparticipants’ own data (which would need to be brought bythe trainees at the workshop); more exposure and trainingon data formats (this topic was not specifically scheduledand had to be squeezed in on 07/25/02; it would have beenuseful to include it early in the training process).

Participants think that the issue ofdata access needs to be included inthe training curriculum, as everyfollow up project will rely onsources of climate forecasts to anextent or another. It was suggestedthat the ATI coordinators sendsome electronic materialdocumenting IRI climate dataaccess procedures.

The issue of food security has also raised muchdiscussions. Some participants suggest that food securityper se has not been given enough attention despite itsexplicit mention in the theme of the ATI. The trainingcurriculum could include topics related to markets andpolicies as they are major factors acting on food securityalongside climate variability. The group recommends thata few policy makers be also invited to serve as lecturers inthe future. A participant suggested that ‘climate variabilityand food production’ would better describe the actualcontent of the ATI.

Other suggestions include: the need for a role-playinggame in the module on communicating forecastinformation; the need to expose trainees to alternativeapproaches for crop yield forecasting (some feel there istoo much reliance on process-based crop models – simplerempirical models could be other options to consider);finally, several participants suggested that opportunitiesfor meetings and discussions with actualpractitioners/users of climate forecasts (e.g. local farmers)should be explored for inclusion in the activities of thetraining institute.

Beyond these recommendations for further improvement,the group acknowledges the efforts made by the organizersand faculty for successful conduct of the training institute.


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PROJECT SUMMARIES

Exploring Options for Improving CropProductivity Within Some Farming Zones of Ghana Using Coupled Climate-Crop Models

Samuel G.K. Adiku

Mentor: James W. Jones

This project seeks to develop a climate-based decisionsupport system to guide agricultural workers (farmers,extension advisors and policy makers) in planning theirwork, with the view of improving the productivity ofmaize and peanut in farming areas within southern Ghana.The proposal is set out in three phases: First, theknowledge (both indigenous and formal) of agriculturalworkers in climate variability and their planning methodsto offset bad seasons or exploit good seasons will beinvestigated. Second, models of maize and peanut inconjunction with long-term historical climate data will beused to investigate the problem of yield variability at ten(10) selected farming sites in southern Ghana. Third, twoclimate forecast techniques will be evaluated for theirvalidity to forecast the yield of peanut at one of thefarming sites. One technique will use historical climatedata of analogue years corresponding to three ENSOphases (El Niño, Neutral and La Niña) to simulate peanutyields in hindcast mode. The other technique will useclimate data output from a stochastic weather generator(e.g., WGEN) whose parameters are conditioned uponENSO phases, to run the peanut model. In both cases, thehindcast yields will be cross-validated and compared withhistorical yields collected from the Ministry ofAgriculture, Ghana. An assessment of the skill of theforecast method will be based on correlation coefficientand the root mean square error.

Early Climate Warning to the AndeanFarmers: Linking Seasonal Forecast

Information to Integrated GIS and Biophysical Models

Guillermo A. Baigorria

Mentor: Corinne Valdivia

Mountain areas present harsh environment, poorinfrastructures with difficult access and many hazardsrelated to climate and terrain. Climate forecasts at aglobal scale are not useful due to their low resolution.Therefore, there is the necessity to develop forecastingtools that represent the potential concerns of farmers in

order to integrate this information into their agriculturaldecisions. The project will combine the knowledge onseasonal-climate forecasting and geospatial modeling tocreate a support system for decision-makers. The projectwill develop a statistical downscaling method, linking theclimate forecast from Global Climate Models with the GIS& Biophysical models Laboratory software (GABP-LAB).This software makes use of the Water Erosion PredictionProject (WEPP) and the Decision Support System forAgrotechnology Transfer (DSSAT). Both are in theprocess of being validated for the Andean highlands. Thecase study will be held in the La Encañada Watershed inthe northern Andes of Peru, ranging between 2500 to4000 m.a.s.l. Simulated crop production maps athigh-resolution levels will be produced using seasonalforecasts, and traditional and alternative managementfarmers’ portfolios. Outputs will be presented to farmersbefore the cropping season. The acceptance by farmerswill be analyzed comparing both, the decisions proposedby the model versus farmers decisions.

Designing a Climate Sensitive DecisionSupport System for South Eastern (Somali

Region) Pastoral Areas of Ethiopia

Ngist Biru

Mentor: Maxx Dilley

Pastoralists in Ethiopia have faced repeated severedroughts leading to massive livestock losses and humansuffering. The limited ability of pastoralists to cope,coupled with poor early warning systems contributed tothe 1999-2000 humanitarian crisis. Save the ChildrenUnited Kingdom has been working with variousstakeholders to improve the early warning system inpastoral areas, focusing on the Somali Region. The systemincorporates the Household Food Economy Approach, butdoes not use available remote sensing or seasonal climateprediction information. Improved information could beused by the government, NGOs, donors, and also by thepastoralists. The project will incorporate both remotely-sensed rainfall and NDVI data, and climate predictioninformation into early warning in two zones of the Somaliregion, and assess the resulting benefits. The food securityofficers of the two zones will be trained to processrelevant satellite imagery. The principal investigator willfollow up on the incorporation of the remote sensed dataand forecast information in monthly bulletins in a way thatis understandable by users. A survey of government,


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non-government and international agencies that use theinformation will determine whether it has triggeredadditional responses compared to the bulletins available inother zones.

Reducing Climate Risk for Chili andPotato Production at Pengalengan,

Bandung District, West Java

Rizaldi Boer

Mentor: Holger Meinke

The project will assess the relationship between rainfallvariability and ENSO indices in the Bandung district. Astochastic climate data generator model to manageclimatic data limitation problems in Indonesia and its useto assess year-to-year and site-to-site variability of soilwater condition and potato production in the Pengalengansub-district (Bandung District, West Java) will beevaluated. Evaluation of the use of stochastic climaticdata generator model conditioned for ENSO phases toassess the variability of soil water condition and potatoproduction at Pengalengan will also be conducted.Options for supporting farmers in decision-making on thechoice of cropping strategies based on climate forecastoutcomes, and assisting agriculture extension workers andlocal agriculture staff to use climate forecasts to guideagricultural planning will be explored andoptions/approaches to connect dynamic climate modeloutput to agricultural simulation models forbenchmarking and impact assessment will be provided.

An Early Warning and Visualization System toIndicate the Three Monthly Status and

Impact of the El Niño Southern Oscillation onNamibia

Albert Calitz

Mentor: Willem Landman

This project will develop a relay system that will localizethe seasonal precipitation forecast of the InternationalResearch Institute for Climate Prediction (IRI), forNamibia. The system will show the area in Namibiaaffected by the El Niño Southern Oscillation (ENSO) in a25 X 25 kilometer grid and will visualize the seriousnessof the impact in terms of a rainfall index. This system willserve as an early warning system for ENSO relateddroughts. The output will be used as input to strategicplanning of drought aid and agricultural productiondecisions. It will provide an important and much-neededplatform for mitigating the effects of extreme ENSO

events. It will aid in directing policies and activities tomanage La Niña-related floods and El Niño-relateddroughts and water shortages, and provide input fordetecting the overuse of farmlands and the misuse ofgroundwater.

Delivering Climate Forecast Products toFarmers: Knowledge-based Corn Yield

Forecasting System in Isabela, Philippines

William de los Santos

Mentor: James Hansen

Climate variability is one of the major sources offluctuations in Philippine food security. A one yearproject will be implemented in northeast Philippines to (a)to use suitable climate forecast products and downscalingmethodologies to develop seasonal crop forecasts; (b) todevelop a local protocol for corn yield forecasting; (c) toevaluate several mediums for communicating cropforecasts; and (d) to quantify the economic contribution ofadvanced seasonal climate information to corn farmers inIsabela, Philippines. Isabela, located in one of the mostdepressed regions in northern Philippines, is consideredthe top corn-producing province in the countrycontributing 24.8% of the total yellow corn production in2000. The overall goal of this project is to contributetoward improvement of the economic condition of Isabelacorn farmers by enhancing the stability of corn productionsystems in the province through the use of climate forecastproducts.

Application of Seasonal ClimateForecasts to Predict Regional Scale

Crop Yields in South Africa

Trevor G. Lumsden

Mentor: Emma Archer

The high inter-annual variability in South African climate,and resulting high variability in crop production, hasimplications for food security in the country, in particular,amongst resource-poor farming households. Theusefulness of available seasonal forecasts couldpotentially be enhanced if they were translated intoforecasts of crop yields through application of a cropmodel. The application of a crop model allows fordifferent management scenarios to be explored, and for anoptimum to be determined. The objectives of this researchproject include the linking of a crop yield model todownscaled seasonal climate forecasts derived from adynamical climate model, thus enabling the generation ofcrop yield forecasts for various agricultural regions inSouth Africa. These crop yield forecasts could be used in


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coarse scale agricultural planning. The potential ability toimprove crop management decisions will then bedemonstrated through simulation of a variety of possiblecrop practices, thus allowing for optimum strategies to bedetermined in response to the crop yield forecast. Areview of the forecast information needs of small-scaleagriculture in South Africa will be conducted to facilitaterecommendations for further development of the productsof this research.

Climate Information Systemand Food Security in Ethiopia

Alemu Asfaw Manni

Mentor: Maxx Dilley

The tragic death of many people in the recurrent Ethiopiandrought demonstrates the region’s vulnerability to climatevariability. By studying climate variability and makingclimate information palatable to farmers and other users,we hope to empower them to mitigate the consequences ofnatural hazards, particularly drought, through better-informed decision making. The objectives of this projectare to (a) increase access to climate information productstailored to farmers (including pastorals andagro-pastorals), (b) integrate climate information with theHousehold Economy Approach, and (c) create awarenessof opportunities to use climate information in farmingdecisions. The project will generate knowledge, increasethe use of climate information, and identify ways in whichinformation products tailored to user needs can be appliedto reduce vulnerability and enhance preparedness. Projectactivities will include: timely dissemination of forecaststailored to user needs, dissemination of climateinformation linked with other food security-relatedvariables to relevant user groups, and collection andarchiving of relevant data. This project will demonstratethat forecasts can be effectively tailored to the needs ofusers, with a focus on pastoralists and agro-pastoralists.Lessons learned can be extrapolated to other parts of thecountry and other sectors of the economy.

Where and When Do We Need Water? Development of a Regional Crop Yield and

Water Demand Model Based on Sea SurfaceTemperature Forecasts

Francisco J. Meza

Mentor: Guillermo Podestá

The operation of water resources in regions of largeclimatic variability represents one of the major challenges

of modern society. Competition for water acrosseconomic sectors, and natural variability of the system canimpose severe restrictions on water supply. For thisreason, the need for integrated and efficient watermanagement policies has become increasingly important.The central valley of Chile shows a significant ElNiño-Southern Oscillation (ENSO) climate footprint onits climatic regime that can affect both water demand andsupply. This feature and the current skill of climateforecast models suggests that there is valuable informationthat can be incorporated into the decision making processin order to explore efficient water management strategies.The main objective of this research is to characterize themain water components of the agricultural hydrologicalcycle as well as the possible crop yield outcomes ofirrigated sectors under the ENSO scenarios. By combiningcrop models and climatic variability information performan exploratory analysis of the applications of climateforecasts for water management purposes for the MaipoRiver Basin. This project will result in the developmentof a regional model to assess water demands, crop yieldimpacts and risk profiles based on ENSO information andwater availability.

Application of Climate Predictionin Rice Production in the

Mekong River Delta, Vietnam

Thuan Nguyen

Mentor: Michael Manton

The Mekong River Delta is the largest rice producing area,and one of the most densely populated areas of Vietnam.The majority of the population depends entirely onfarming for their livelihood. Water deficiency and waterexcess associated with seasonal climate variability lead toa remarkable variability in rice production. Much of thisvariability appears to be related to ENSO. Seasonalclimate forecasts would help agricultural workers andfarmers plan for the coming season, and prepare forunfavorable climate conditions. The research project aimsto identify the impacts of ENSO phases on rainfall andtemperature characteristics over Long An province.Downscaled climate forecasts for the province will beproduced and disseminated to the agricultural sector.Seasonal climate forecasts will be incorporated into a cropyield simulation model to generate crop yield forecasts,which will then be evaluated.


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Localized Climate Forecasting System:Seasonal Climate and Weather Prediction

for Farm Level Decision-making

Rengalakshmi Raj

Mentor: Sulochana Gadgil

The variability, trend, occurrence of the precipitation andits direct impact on crop growth and potential yield will bestudied for better understanding and planning using timeseries climate and production data sets. Needs,Constraints, opportunities and farmer’s indigenous copingstrategies are chronicled using participatory appraisaltechniques. MSSRF has established institutional linkageswith Indian Institute for Tropical Meteorology (IITM),Pune and Tamil Nadu Agricultural University (TNAU),Coimbatore to get seasonal climate predictions at onmonthly lead-time. And medium range weather forecastfrom National Center for Medium Range WeatherForecast (NCMRWF), New Delhi. It converts generalinformation (forecast) into locale specific farmerquantifiable terms through focus group discussion withthe farmers.

Will Climate Forecasting and New KnowledgeTools Help Resource-poor Farmers from Debt

to Prosperity? Farmers’ ParticipatoryApproach to Manage Climate Variability

V. Nageswara Rao

Mentor: Sulochana Gadgil

We plan to collect the long-term historical weather datafor Anantapur and Mahboobnagar sites, and identifypredictors that have good physical correlations forpredictand (rainfall) of the regions. Appropriate statisticaldownscaling techniques will be employed to predictclimate with dependable skill at local scale, and generateviable cropping options through APSIM modeling for useby farmers. A survey will be conducted to assess farmers’perception on relevance of climate forecasts in farmingdecisions. In partnership with NARS collaborators, weshall present to farmers the possible prediction skill ofclimate for these two regions as against historicalobservations, and the ensuing season’s forecast of climate.Besides, we will discuss with farmers, model-basedcropping options using yesteryears’ climate, which mighthave benefitted farmers, and options available withpredicted climate for the coming season. Forty farmersfrom four villages in two districts will be identified byexpression of interest, to participate and evaluate croppingoptions decisively in the study. During the crop season,observations will be recorded on farmers’ decisionsregarding cropping options, their input strategies, andreturn on investment to farmers who made use of climatepredictions. Farmers’ participation and discussions would

help evaluating the value of climate predictions to drylandagriculture in the region. As long-term benefits, we wishto create a local hub for climate prediction expertise byproviding training on downscaling techniques tocollaborators, and awareness among farmers aboutpotential gains as well as risk minimization throughapplying climate forecasting and crop modeling inagriculture.

Towards the Development of a SpatialDecision Support Systems (SDSS) for the

Applicationof Climate Forecasts in Californian

and Uruguayan Rice Production Systems

Alvaro Roel

Mentor: Walter Baethgen

Although forecasts make predictions of climate variablebehaviors for large regions of the world, these regions arenot uniform. Nevertheless, seasonal forecastrecommendations are traditionally applied as uniform invast regions. These had determined that in many situationsin some areas of these regions forecast recommendationswere suitable while in others not. A pilot project isproposed to evolve a system for the effective applicationof a seasonal climate forecast, which can address thenatural spatial variability in growing conditions thatcontrol productivity in rice ecosystems. The ultimate goalis to be able to predict for a given forecast how is going tobe the yield spatial structure within a field undertraditional management and according to the SDSS whatshould be the strategy to follow to maximize yieldproductivity under this climatic scenario. Therefore theOverall objectives of this project is: To integrate the "stateof the art" of the scientific knowledge regarding seasonalclimate forecast, crop simulation models and site-specificfarming into a Spatial Decision Support System for therice production systems of California and Uruguayan riceproduction sectors to decrease their vulnerability to interannual climatic variability.

Impacts of Climate on Agricultural Systemsand Food Production: Combining Models,Remote Sensing and Climate Forecasts to

Estimate and Predict Soil Water Status

Ricardo Romero

Mentor: Peter Gregory

The objective is to assess spatial and temporal variabilityin soil water availability in the Argentinean and Uruguayanpampas by integrating modeling and remote sensing. Thiswill also be applied as initial conditions to predict soilwater content using climate seasonal forecast. A soil


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water balance model will be used to integrate soil waterholding capacity and remote sensing variables:precipitation and transpiration. InfraRed and NormalizedDifferential Vegetation Indices will be combined toestimate precipitation and transpiration for the area ofstudy. Soil water modeling will be conducted to estimatesoil water availability and run off. GIS will be used toclassify and quantify areas of homogeneous water status.A seasonal outlook will be prepared and compared tothose obtained using normal climatology to determine theimpact of the climate impact on soil water.

Improving Food Security and Resource Use ofIrrigated Crop Production Systems Through

Climate Forecasts in Southern India

R. Selvaraju

Mentor: Holger Meinke

The overall objective of the project is to assess andmanage the impact of El Nino/Southern Oscillation(ENSO) on water availability and crop yields in theirrigated crop production systems to improve smallholderfood security. The project proposes the use of seasonalclimate information to improve the productivity andresilience of the irrigated crop production systemsinvolving food (rice, maize) and commercial (sugarcaneand cotton) crops in Western agro-climatic zone of TamilNadu, India. The major elements of the project are (i)climate, water resource inventory and prediction (ii)impact assessment of ENSO on water availability and cropyield through system simulation approaches (iii) ENSObased water allocation and crop area decision frameworkand (iv) demonstrating the benefits of seasonal climateforecasts to multiple stakeholders.

Application of Seasonal Climate Forecastfor Sustainable Agricultural Production

in Telangana Sub-division of Andhra Predesh, India

K.K. Singh

Mentor: James Hansen

A major part of the population in the semi-arid AndhraPradesh lives in rural areas, where per capita income isvery low and agriculture is the main source of livelihood.In the absence of adequate irrigation facilities, rainfall isthe most critical element dictating productivity. An erraticrainfall distribution in space and time, associated droughtsand floods, and other socioeconomic factors force manyresource-poor farmers to migrate to urban areas to seekemployment. Early warnings based on seasonal rainfallforecasts can help farmers to adjust crop managementstrategies to minimize impacts of malevolent climate and

maximize benefits of benevolent climate. This project willevolve a system for the application of the seasonal climateforecasts to rainfed agriculture in the semi-arid areas ofAndhra Pradesh, India. We will select two districts astarget areas for climate forecast application. At theselected locations, we will explore crop managementscenarios (of farmers’ choice) based on climate forecasts.Finally, we will seek to integrate the use of seasonalclimate forecasts with the Agrometeorological AdvisoryService (AAS) of the National Centre for Medium RangeWeather Forecasting (NCMRWF). Outcome of theproposed research will equip and strengthen the AASsystem of NCMRWF with scientific tools andmethodologies that can be used for the potentialapplication of seasonal forecasts in agriculture in otherzones.

Coping with Climate Risk in China: Developinga Food Security Warning System Using

Seasonal Climate Forecasts

Fulu Tao

Mentor: Hartmut Grassl

The climate in China is highly variable due to influencesof Atmosphere/Ocean oscillations (such as ENSO,monsoons, etc.). The agriculture in China is highlyvulnerable to this climate variability because manycropping systems are rain-fed with low technicaladaptation. It is becoming clear that skill in seasonalclimate forecasts offers considerable opportunities tofarmers via its potential to improve yields. This projectaims to develop a food security warning system usingseasonal climate forecasts as input for food productionestimates and provision of adaptation options, ahead ofcrop seasons. Firstly we use a crop model to simulate thetime series of yield anomalies during the last decades.Then we analyze the correlation between the yield anomalyand seasonal climate anomaly using a multivariatestatistical model; finally we apply the statistical relationsto predict yield variability for the next season withseasonal climate forecasts as inputs. The predictions(probability estimates) will be used to discuss adaptationoptions with farmers.


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Bytes for Bites: Translating Climate Forecastsinto Enhanced Food Security for the Sahel

Sibiry Traore Pierre

Mentor: Neil Ward

Climate variability is an urgent problem in the Sahel. Pastdramatic droughts and evidence of decreasing rainfall callfor new adaptative strategies to meet food requirements ofthe growing population living in these fragile and changingproduction systems. Modern science systems analysis andsimulation models have the potential to address thischallenge. Crop yield forecasts can be developed byappropriate combinations of climate and crop modeling,and incorporated into farmers practices. To achieve this,spatial and temporal scale constraints can be tackled withremote sensing techniques to increase local predictionskill. Use of leading climate and crop models will allowquantitative analysis of sorghum, maize and cottonresponses, and subsequent identification of optimal cropmixes under variable climate patterns in southern Mali.Outputs will include a decision-support matrix forproducers to minimize climatic risk, an evaluation ofcurrent forecasting skill and a digital land surface schemeof the region, and a method to apply climate forecasts toidentify production options in sudano-sahelian agriculture.Exposure of farmers and NGOs to products generated willprefigure the implementation of a full-size projectinvolving all partners along the information chain, withsensible impact on agricultural production expected byyear 2010.

Climate Information for Food Security:Responding to Users Needs

of Climate Information

Milton Michael Waiswa

Mentor: Jennifer Phillips

Currently climate scientists are able to use sea surfacetemperatures to forecast shifts in probabilities of rainfallamounts over a three-month period. Although this type ofinformation is important, the first major climateinformation needs of the farmers is knowing, at adequatelead times, the timing of the expected onset of seasonalrains. As a coping mechanism, farmers attempt to usetheir traditional indicators, particularly local winds andtemperatures, to forecast this important climate element.However, identification, validation and improvement ofthese indicators have not been done. Therefore this studywill identify details of traditional usage of temperature andwind information for forecasting onset of seasonal rains;validate the traditional environmental indicators for onsetof seasonal rains; develop statistical models forforecasting of seasonal rains and disseminate researchfindings. Identification of usage will be achieved throughconducting individual and group surveys of farmers inEastern (Tororo), Lake Victoria basin (Jinja), Central(Namulonge) and Western (Masindi) Uganda. Validationof environmental indicators will be based on the climatedata from synoptic weather stations in the four regions.Model development will be achieved by statisticalregression of validated temperature and wind indicatorswith rainfall onset dates formatted in pentads. Operationaluse of findings will be initiated through a stakeholders’workshop.


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PARTICIPANTS

Trainees

Samuel Adiku

Department of Soil Science, University of Ghana, Ghana

Adiku holds a Ph.D. in Environmental Sciences from Griffith University, Australia. His Ph.D.thesis was on modeling a maize-cowpea intercrop. Adiku is a Senior Lecturer and Head of theDepartment of Soil Science, University of Ghana, where he teaches and conducts research in soilphysics, soil conservation and soil-plant-atmosphere relationships. He is interested in croppingsystems research, and participates in a project that seeks to improve low-input peanut farming inthe coastal savanna zones of Ghana with the aid of crop models.

Guillermo A. Baigorria

International Potato Center (CIP), Peru

Baigorria is a Ph.D. candidate in Soil Sciences at the Wageningen University. His doctoral workinvolves the integrated use of biophysical models and GIS to evaluate watershed managementoptions. Also, he is a Meteorologist, and he received an M.S. in Crop Production at theUniversidad Agraria La Molina, Peru. He works at the Production Systems & Natural ResourcesManagement Department of CIP, where he is working on developing, calibrating and validatingmodels in climate, soils, crops and soil erosion.

Nigist Biru

World Food Program (WFP), Ethiopia

Biru has a B.A. in Economics from Addis Ababa University and an M.S. in EnvironmentalEngineering and Sustainable Infrastructure from The Royal Institute of Technology, Sweden. Sheworks as a Food Security Specialist in the WFP Vulnerability Analysis and Mapping Unit. Herresponsibilities include collecting and presenting food security early warning information toWFP’s operation, and targeting of WFP resources to those who need it most. WFP operationsin Ethiopia include relief food provision, food for work, school feeding, and feeding refugees.

Rizaldi Boer

Faculty of Mathematics and Natural Sciences, Bogor Agricultural University, Indonesia

Boer received degrees in Agroclimatology from Bogor Agricultural University, and a Ph.D. inAgriculture from the University of Sydney, Australia. His Ph.D. thesis focused on the analysisof climatic risk in crop production. Most of his researches related to the area of climate changeand climate variability in relation to agriculture and forestry. His specific interests lie on the useof climatic models and forecasts to improve decision-making and agriculture management.


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Albert Jacobus Calitz

Ministry of Agriculture, Water and Rural Development, Namibia

Calitz is a Senior Agricultural Research Officer. He received a B.S. Agriculturae (Pasture andAnimal Sciences) at the University of the Orange Free State, South Africa. He is second incommand of the Agro-ecological Zoning Program of Namibia, and is involved in developing theprogram’s crop growth and yield modeling and agricultural meteorology components. He hasexperience in natural resource surveys, and managing data, hardware and software for theNamibian Agriculture Resource Information System.

Trevor Lumsden

University of Natal, South Africa

Lumsden has an M.S. in Hydrology from the University of Natal. His dissertation involved thedevelopment and evaluation of techniques for forecasting sugarcane yields at a local mill supplyarea scale. He is currently working as a hydrological researcher at the University of Natal. Apartfrom his interest in the application of seasonal climate forecasts, he has been involved in severalprojects to investigate the impacts of land use on water resources in South Africa.

Alemu Asfaw Manni

USAID Famine Early Warning System Network (FEWSNET), Ethiopia

Manni works as a Food Security Economist in the FEWSNET project. His professional activitiesinclude compiling and analyzing food security-related data, participating in crop production andfood security assessment missions, reviewing and participating in monthly food security bulletins,and participating in drought task force and food and humanitarian activities. He plays a leadingrole in the periodic Cereal Availability Study Report. Manni holds an M.S. in Economics.

Francisco J. Meza

Catholic University of Chile, Chile

Meza has been an academic member of the department of Natural Resources at the CatholicUniversity of Chile since 1995, working in the field of agricultural meteorology. He receiveda M.S. degree in Hydrology from the Catholic University of Chile in 1998. He recently receivedhis Ph.D. in Atmospheric Science from Cornell University. His dissertation involves the use andvalue of seasonal climate forecasts for Chilean agriculture based on El Niño phases.

Thuan Nguyen

Center for Hydrometeorology of South Vietnam (CEHYMET), Vietnam

Nguyen received a Master of Engineering Science from the University of Melbourne (Australia).She worked as a weather forecaster at the Southern Regional HydroMet Center. She is now aresearcher at the Center for HydroMeteorology of South Vietnam (CEHYMET), where she isworking on the weather variation and climate change, including ENSO phenomenon. Her specialinterests lie on the use of climate models and forecasts from international centers to assist localprofessionals.


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Sibiry Traore Pierre

International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Mali

Pierre serves a research scientist and GIS manager, with a joint appointment between ICRISATand the Institute for Rural Economy (IER), Mali. His work focuses on improving productionsystems of the West African SAT through the integration of crop modeling, remote sensing andspatial modeling, and expanding regional use of a joint GIS-modeling facility. The work isdesigned to support improved decision making relative to crop production and natural resourcemanagement. He holds an M.S. in Remote Sensing.

Rengalakshmi Raj

M.S. Swaminathan Research Foundation, India

Raj is an agronomist who has been working in the M.S. Swaminathan Research Foundation forthe past six years. Her areas of research interest are agro-biodiversity conservation, food security,natural resources management and participatory research. She specializes in conservation andmanagement of Neglected and Underutilized Species (NUS).

Nageswara Rao Vajaha

International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), India

Rao received a B.S. in Agriculture from Andhra Pradesh Agricultural University and an M.S. inSoftware Systems from Birla Institute of Technology and Sciences. His interests focus on cropand soil carbon modeling applications. He has worked on legume-based cropping systems andnutrient management strategies to improve productivity and food security in low-input drylandagriculture in India’s SAT. He works on a farmer-participatory project developing and evaluatingsimulated crop management options in the Anantapur region of Andhra Pradesh.

Alvaro Roel

National Agricultural Research Institute (INIA), Uruguay

Roel received an M.S. in Agronomy from Texas A&M University, USA, and is completing hisPh.D. in the Ecology Graduate Group at University of California – Davis. His doctoral workinvolves understanding agronomic and ecological factors that affect spatial and temporal cropyield variability in California and Uruguayan rice production systems. Alvaro is a seniorresearcher with INIA, where he contributs to the integration of seasonal climate forecast in thedecision making processes of the agricultural sectors.

Ricardo Romero

National Agricultural Research Institute (INIA), Uruguay

Romero received an M.S. in Crop Production and Physiology from Iowa State University, USA.He is completing his dissertation at Iowa State on water use efficiency in irrigated maize inUruguay. Ricardo is a senior researcher with INIA, where he is contributing to develop anInformation and Decision Support System to improve the sustainable use of natural resources,including the efficiency of water use, in the agricultural sector.


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William de los Santos

Department of Agronomy, University of the Philippines Los Banos, Philippines

De los Santos has a Ph.D. degree in Agronomy & Soils from Auburn University, Alabama. Heis the 2002 University of the Philippines Outstanding Teacher Award recipient in the field ofbiological sciences. He is an Assistant Professor and currently the Head of the Crop ProductionDivision, Department of Agronomy, University of the Philippines Los Baños. He is a studyleader in decision support systems research of the Philippine National Corn Network. He is alsoinvolved in the Global Environmental Change and Food Systems (GECaFS) research initiative.

Ramasamy Selvaraju

Agrometeorology Department, Tamil Nadu Agricultural University (TNAU), India

Selvaraju has a Ph.D. in Agronomy with specialization in Agricultural Meteorology andmodeling. His responsibilities as Assistant Professor of Agricultural Meteorology includetranslating ENSO information into appropriate action using cropping system models,institutionalizing ENSO information for agricultural decision making, and whole farm economicanalysis, through the Agricultural Climatic Information System Unit of TNAU. He has beeninvolved in many activities related to agroclimatology and natural resource management.

Kamalesh Kumar Singh

National Center for Medium Range Weather Forecasting (NCMRWF), India

Singh is a Scientist-D in the Applications Division of NCMRWF. His work focuses oninterpreting GCM-based forecasts, using crop simulation modeling to tailor managementpractices to forecasts, and developing forecast-based agricultural advisories. Forecasts andadvisories are disseminated to the farming community through a nation-wide network of 82agrometeorology advisory service units. Plans for longer-term forecasts are underway. Singhholds a Ph.D. in Agricultural Meteorology and an M.S. in Meteorology.

Fulu Tao

Agrometeorology Institute, Chinese Academy of Agricultural Sciences, People’s Republic ofChina

Tao received a Ph.D. in ecology from Chinese Academy of Sciences, Research Center for Eco-Environmental Sciences. He is now a research associate at Chinese Academy of AgriculturalSciences, Agrometeorology Institute. He is currently involved in the researches on the regionalassessment of agroecosystem vulnerability to climate change and climate variability. His interestsinclude the regional assessments on ecosystem degradation associated with global environmentalchange, its mechanism and management by GIS and remote sensing technology.

Milton Waiswa

Department of Meteorology, Uganda

As the Agriculture Meteorologist for the Department of Meteorology, Waiswa’s responsibilitiesinclude the production, communication, and dissemination of climatic information and advisoriesto agricultural users; disaster preparedness and development. He is National Coordinator of theRANET Uganda Program. He trains agriculture extension agents on the use of climate forecasts.His background includes a B.S. in Agriculture, Post Graduate Diploma in Meteorology, and aCertificate in New Information Technologies.


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Faculty

James Hansen, Training Director

International Research Institute for Climate Prediction, USA

Hansen’s work focuses on use of seasonal climate prediction to improve agricultural decisionmaking in smallholder farming systems of the tropics. He previously served on the faculty ofAgricultural and Biological Engineering, University of Florida, where he worked within aninterdisciplinary program to foster and guide use of climate information for agricultural decisionmaking. The effort led to a statewide climate applications extension program in Florida, that isbeing replicated in neighboring states. Hansen’s applied research has also targeted thePhilippines, Colombia, Argentina, India and Mali. His research contributions include applicationsof agricultural systems methods to optimal use of climate information, farm economic risk andsustainability analysis, and land use decisions; spatial scaling issues in agricultural systemsmodeling; stochastic weather generation; and tropical soil fertility and intercrop ecology. Hansenholds a Ph.D. in Agricultural and Biological Engineering from the University of Florida, and M.S.in Agronomy and Soil Science from the University of Hawaii. He is co-Editor-In-Chief ofAgricultural Systems.

Tony Barnston


Prior to arriving to the IRI in 2000, Barnston was an operational seasonal climate forecaster anddevelopmental researcher in empirical prediction methodology at the Climate Prediction Centerof NOAA for 17 years. He has authored atlases, reports and journal papers on weather andclimate, the several best known of which were on statistical diagnosis on large-scale circulationpatterns and on empirical climate prediction. He was Editor of the Experimental Long LeadForecast Bulletin from 1992 to1997. Barnston has received awards from the Department ofCommerce and the American Meteorological Society. With his forecast staff, Barnston ensuresthe production of a range of IRI forecast products. His current goal is the improvement of theIRI’s forecast operation, through maximizing forecast accuracy, automating the forecast process,and facilitating the creation of new versions of the forecast tailored to specific user groups.

Henrik Feddersen

Danish Meteorological Institute, Denmark

Since he started at The Danish Meteorological Institute in 1994, Fedderson has been working onnatural climate variability and predictability on monthly to interannual time scales. He isinterested in the predictability of climate patterns such as ENSO and the North Atlantic/ArcticOscillation, and their connection to the global climate. Recently, he has explored methods tostatistically correct seasonal forecasts from dynamical climate models in order to be able tocompute reliable probability forecasts for seasonal precipitation and temperature on regionalscales. These methods have been developed within the European PROVOST project, and will befurther developed and applied in the DEMETER project. Before coming to The DanishMeteorological Institute he worked on numerical methods for calculation of discrete breathers,i.e., solutions that are periodic in time and localized in space to nonlinear Hamiltonian latticeequations, e.g., the discrete nonlinear Schrödinger equation, the discrete self-trapping equationand the discrete sine-Gordon equation.


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Marian Fuchs-Carsch

Independent resource mobilization consultant, UK

In the 1970s and 80s Marian Fuchs-Carsch worked for USAID in the Philippines, Pakistan, Ghanaand Mali. In various positions, she was involved in the design of projects in health, agriculture,energy, education, family planning and environment. For seven years she was the donorrelations/project development person for two institutes (IWMI, Sri Lanka, and ICLARM,Malaysia) of the CGIAR, where she worked to maximize fundraising efforts, coordinated centeractivities relating to donors, and supported scientists in proposal preparation. Since 1996, Fuchs-Carsch has worked as a freelance consultant, with clients in the US, Europe, Latin America,Africa and the Middle East, and Asia. Her work includes training scientists and development staffin project design and proposal preparation, advising managers on strategic planning and otherways of improving donor relations and fundraising, advising on the design and write up ofspecific projects, and a range of writing and editing tasks. Her doctorate (1970) is inPsycholinguistics, her masters in English, and her bachelors’ degree in Social Sciences.

Jere Gilles

Department of Rural Sociology, University of Missouri, USA

Biographical brief not available.

Lisa Goddard

International Research Institute for Climate Prediction (IRI), USA

Goddard has been working for the IRI, first at Scripps, within the Climate Research Division since1995. She has a Ph.D. in Atmospheric and Oceanic Sciences from Princeton University, whereshe did her thesis research on the physics and energetics of El Niño at GFDL with GeorgePhilander. Goddard is part of a team that studies climate dynamics and potential predictability,assesses climate prediction tools, advances strategies for research, development andimplementation of climate forecasts, and produces quarterly Net Assessment forecasts for the IRI.She has developed the tools and analysis methods used for examining and comparing the datafrom the atmospheric general circulation models used for climate forecasting. Her researchmainly focuses on climate dynamics and climate predictability with a continuing interest in ElNiño.

Peter Hayman

New South Wales Agriculture, Australia

After doing an undergraduate course in Agricultural Science majoring in Agronomy and an M.S.in crop physiology, Hayman worked as an extension officer with farmers in dryland farming inthe state of New South Wales. From this work he learned lessons about how the variable climatehad an enormous impact on farmer decision making, farm viability and land degradation. In 1994he was awarded an industry scholarship for a Ph.D. in Agroclimatic risk management. His Ph.D.dissertation, “Dancing in the Rain, Farmers and Agricultural Scientists in a Variable Climate,”explored the assessment and management of climate risks from the perspective of farmersmanaging the farming system and scientists studying the farming system. He is the Coordinatorof Climate Applications with New South Wales Agriculture, and has special interests in theapplication and communication of climate science to agricultural systems.


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Peter Hildebrand

Food and Resource Economics Department, University of Florida (UF), USA

With degrees in Animal Science and Economics, Hildebrand serves as Director of IFASInternational Programs at UF, and coordinates the university-wide Farming Systems Program.He joined UF in 1979 after living abroad and working on agricultural development projects for15 years in several countries. His research is in the areas of farming systems research-extensionmethods, gender analysis, small farm livelihood systems, and tropical conservation anddevelopment. He developed many of the ideas and approaches that are the foundation of FarmingSystems Research and Extension. He developed innovative approaches to training research andextension programs to reach food producers in the United States and abroad. Hildebrand’sinternational background encompasses work in over 31 countries over 35 years. He has hadassignments with the Office of Technology Assessment, U.S Congress, National ResearchCouncil, National Academy of Sciences and the U.S. Department of Agriculture. He wasfounding president of the global Association for Farming Systems Research and Extension.

Matayo Indeje


Indeje received his Ph.D. in 2000 from North Carolina State, where his thesis research was on theprediction and numerical simulation of the regional climate of equatorial Eastern Africa. Hereceived his M.S. from the University of Nairobi, Kenya. Prior to receiving his Ph.D., he workedas a Forecaster for the National Weather Service in Kenya from 1985-1988 and as a ResearchScientist for the World Meteorological Organization Drought Monitoring Centre (Nairobi) inKenya from 1989-1996. His research focus is on downscaling of global climate forecasts toregional scales using both numerical models and Model Output Statistics, and applications ofthese products to agriculture and water resource management. Indeje was one of the pioneers atthe IRI pilot training project in seasonal to inter-annual climate predictions in 1994.

Jim Jones

Agricultural and Biological Engineering Department, University of Florida (UF), USA

Jones joined UF in 1977 as Associate Professor, and now holds the position of DistinguishedProfessor. His areas of specialization are agricultural systems analysis and climate effects oncrop production. He leads several national and international research programs in the applicationof crop models for climate change and climate prediction studies. Jones and his co-workers havedesigned several decision support tools for analyzing agricultural productivity and sustainabilityat field, farm and watershed scales, targeting both farmers and their advisors, and researchers invarious disciplines. Besides teaching a graduate course on simulation of agricultural andbiological systems, he has organized and taught short courses on crop simulation in the USA,Canada, the Netherlands, Taiwan, India, South Africa, Venezuela, Egypt, Togo, and Mexico. Hehas broad experience in teaching and advising graduate students and postgraduate fellows. He hasauthored more than 200 scientific publications. He is a Fellow of the American Society ofAgricultural Engineers and of the American Society of Agronomy, Co-Chair of the InternationalConsortium for Agricultural Systems Applications He serves on the Board of Trustees of theInternational Center for Tropical Agriculture (CIAT) in Colombia, and on the InternationalAdvisory Board of the Wageningen Agricultural University in the Netherlands.


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Jacob Kijne

Independent irrigation consultant, UK

Kijne is currently an independent irrigation consultant. He has forty years of experience ininternational agriculture and water management that includes Pakistan, Sri Lanka, Tunisia,Yemen, Kenya, Australia and Venezuela. His positions have included head of the PakistanProgram and Director of Research for the International Water Management Institute, Sri Lanka;Director of Deventer Agricultural College, The Netherlands; and head of the Department ofAgricultural Engineering of the University of Nairobi, Kenya. He served on the first SteeringCommittee of the Global Water Partnership (Sweden), and as a consultant to FAO, UNESCO,Netherlands Foreign Aid and Japan Foreign Aid. Honors include the Van Dam Penning awardfor eminent service to international agricultural education in the Netherlands, and an award fromthe Board of Governors of the International Water Management Institute, Sri Lanka, inrecognition of outstanding contributions to the institute. Kijne has authored more than 70scientific papers. He holds a Ph.D. in Soil Physics from Utah State University.

Upmanu Lall

Columbia University, USA

Lall is Professor at the Department of Earth and Environmental Engineering, ColumbiaUniversity, and Senior Research Scientist at the IRI. Prior to joining the IRI, he was a professorfor the University of Utah and Utah State University. His research interests include hydro-climatemodeling, spatial data analysis and visualization, time series analysis and forecasting, Bayesnetworks for process modeling and decision making, risk and reliability, and water resourcemanagement using climate information. He has over 20 years of experience as a hydrologist. Heholds a Ph.D. and M.S. in Civil Engineering from the University of Texas.

David Letson

Rosenstiel School of Marine and Atmospheric Science, University of Miami, USA

Letson is Associate Professor of Marine Affairs and Economics, and has also served as anenvironmental economist with the Natural Resources and the Environment Division of the USDAEconomic Research Service. He has a Ph.D. in Economics from the University of Texas atAustin, with concentrations in natural resource and environmental economics, public finance, andmathematical economics. Letson specializes in natural resource economics and the economicsof regulation. Letson is participating in a multidisciplinary assessment of agriculturalapplications of climate forecasting in Argentina and the southeastern U.S. He is a member of theFlorida Consortium, a group of researchers from three Florida universities (Miami, Florida andFlorida State), in the disciplines of meteorology, agricultural engineering, economics, hydrologyand anthropology. In the second project, Letson is part of a multi-disciplinary team that isassessing the consequences of climate change for the South Florida environment for the USEPA’sNational Center for Environmental Research and Quality Assurance.

Galit Marcus, Training Coordinator


Marcus works at the IRI as research assistant and served as Training Institute Coordinator. Shehas studied environmental decision making with some of the leading academics in the field. Shehas experience in running workshops in East Africa and at home. Marcus has a Bachelors ofArchitecture degree and is currently a student in the Graduate School of Architecture, Planningand Preservation at Columbia University. Her interests lie in water resources, urbaninfrastructure, economics and policy in the Middle East.


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Simon Mason


Prior to joining the IRI in 1997, Mason was Deputy Director of the Climatology Research Groupat the University of the Witwatersrand, in South Africa, where he developed empirical models forpredicting southern African rainfall. He worked closely with the South African Weather Bureauto assist in developing their operational seasonal climate forecasting capability. Mason hasauthored numerous papers on southern African inter-annual climate variability, climate change,and seasonal forecasting. Since joining the IRI, Mason’s research interests have expanded toinclude dynamical as well as empirical methods of seasonal prediction. He was lead author ofan article detailing the IRI’s operational forecasting methodology during the 1997/98 El Niñoevent, and has played a primary role in the regional forecast fora activities, especially in Africa.Mason engages in research related to the IRI’s forecasting effort, and assists in producing the NetAssessment forecasts. His current responsibilities also include developing methods forimproving forecast skill, and facilitating diagnosis of forecast skill.

Holger Meinke

Agricultural Production Systems Research Unit (APSRU), Queensland Department ofPrimary Industries, Australia

Meinke is a Principal Scientist, co-leader and team member of APSRU, and acting ProgramLeader for the Queensland Center for Climate Applications. He is responsible for a range ofresearch projects, including a major, collaborative CLIMAG project that involves partners fromthe US (IRI), India, Pakistan and Indonesia, and aims to increase the economic and environmentalresilience of cropping systems against a background of climate variability and change. Meinke’sresearch emphasizes the development and delivery of agricultural systems analysis, appliedseasonal climate forecasting and climate variability assessments, and involves the developmentand application of dynamic agricultural simulation models and climatic databases. He haspublished over 60 papers on these issues. Much of his work involves working directly withclients (producers, agribusiness, policy makers and other scientists). Meinke represents theinterests of DPI on the APSRU management committee and thus is responsible for the strategicdirections and performance evaluation of the group.

Carlos Messina

Agricultural and Biological Engineering Department, University of Florida, USA

Messina is currently a Ph.D. candidate and graduate research assistant in Agricultural andBiological Engineering, University of Florida. He holds an M.S. in Crop Production (Universityof Buenos Aires). His research interests include simulation of crop and agricultural systems,climate forecast applications, crop ecophysiology and plant physiology and molecular biology.His research contributions include characterization of ENSO influence on rainfall and cropproduction, model-based tomato yield prediction, and evaluation of use of ENSO informationfor farm land allocation decisions in the Argentine Pampas and for tomato planting decisions insouthern Florida. His dissertation research focuses on gene-based-modeling approaches of cropproduction.


50

Jennifer Phillips

Center for Environmental Policy, Bard College, USA

Phillips received her Ph.D. in Agronomy from Cornell in 1994, then served as a post-doctoralassistant working on the implications of climate change for agricultural production at the NASAGoddard Institute for Space Studies. Since the mid-1990’s she has turned her focus towardsapplications of seasonal climate predictions. Her focus has been on Africa, and she is particularlyinterested in the issue of communicating climate information, and how uncertainty inherent inforecasts influences decision making. Phillips has led research in Zimbabwe, Kenya and Uganda,working with small farmers on the use of climate information. The key questions guiding herwork are how to improve access to quality information, mainly climate information, for resource-poor small-holders, and how can one incorporate information with uncertainty into the decisionmaking process. After two and a half fruitful years at the International Research Institute forClimate Prediction, she is now on the faculty at the Bard College Center for EnvironmentalPolicy. She is a former Fulbright scholar and NATO research fellow.

Andrew Robertson


Robertson joined the IRI from UCLA, where he was principal investigator on research grantsconcerned with climate variability on interannual-to-interdecadal time scales. His work at UCLAfocused on the phenomenology of climate variations using data and GCM experiments, andapplications-relevant research. Robertson received his Ph.D. in atmospheric dynamics from theUniversity of Reading in 1984. He has held research positions at the Universities of Paris andMunich. Robertson is interested in ocean-atmosphere interactions that give rise to predictableaspects of interannual-to-interdecadal regional climate variability, and the use of GCMexperiments to isolate them; and in probabilistic modeling of relationships between local dailyweather statistics and large-scale climate processes. Robertson seeks to advance understandingof short-term regional climate predictability and to develop useful predictions of applications-relevant quantities with small spatial and temporal scales.

Chet Ropelewski


Before joining the IRI, Ropelewski served as a research meteorologist with the ClimatePrediction Center. As Chief of the Center’s Analysis Branch, he directed research and operationalclimate monitoring for over a dozen senior climate research scientists. His primary researchinterests include studies of the El Niño/Southern Oscillation (ENSO) and its influence on rainfalland temperature, the analysis and display of climate information, the influence of the land surfaceon atmospheric processes and the detection of global climate change. He is the author of over50 scientific papers in the refereed literature and scores of reports. He has been a contributor tonational and international reports including the Inter-governmental Panel on Climate Change(IPCC). He currently Chairs the American Meteorological Society’s Climate VariationsCommittee. He was co-recipient of the WMO’s 1990 Norbert Gerbier Mumm Award for workdescribing rainfall patterns associated with ENSO. Ropelewski leads the IRI effort to developmethods and data sets to improve monitoring of the climate system, to disseminate climateinformation, and to implement the IRI Climate Information System.


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Corinne Valdivia

Agricultural Economics Department, University of Missouri, USA

Valdivia is Associate Professor, director of the Graduate Interdisciplinary Minor in InternationalDevelopment, and Program Director-Training, International Agriculture, College of Agriculture,Food and Natural Resources. She specializes in the microeconomics and political economy ofinternational development, and conducts household and community level research in semiarid andsub-humid rural East Africa and Latin America, measuring impacts of new technology andinformation on communities, households and individuals. Her interests include householdportfolios, diversification and risk, coping strategies and social capital, and impacts of change onwelfare. She leads a multi-institutional project, “Climate Variability and Household Welfare inthe Andes: Farmer Adaptation and Use of Weather Forecasts in Decision Making.”

Neil Ward


Ward joined the IRI in April of 2000 from the University of Oklahoma, where he was principalinvestigator for a range of climate research topics. Ward also has extensive experience inoperational forecast products and systems from his previous tenure with centers in Europe. Wardfocuses on the important link between forecast products and user applications. In this role, heworks in the field with collaborators to better understand requirements, and also with the forecastresearch and production team at the IRI, to ensure the feedback users provide becomesincorporated, whenever possible, into improved forecast developments.

International Advisory Committee

Walter Baethgen

International Fertilizer Development Center (IFDC), Uruguay

In his role as Senior Scientist at IFDC, Uruguay, Baethgen collaborates with national andinternational institutes to establish and coordinate regional research programs on information anddecision support systems for sustainable agricultural production and improved decision-making;assessment of climate impacts on agricultural sustainability; and measurement, monitoring andprediction of effect of soil and crop management on carbon sequestration. Baethgen was a leadauthor for IPCC’s Second and Third Assessments Reports, and review editor for IPCC’s specialissue on Technology Transfer. He has been a member of the advisory committees of the IRI andof CLIMAG (WMO), and was a steering committee member during the establishment of the Inter-American Institute for Global Change Research. He holds M.S. and Ph.D. degrees in Crop andSoil Environmental Sciences from Virginia Polytechnic Institute and State University, USA.

Sulochana Gadgil

Center for Atmospheric and Oceanic Sciences (CAOS), Indian Institute of Science, India

Gadgil is Professor at CAOS. She holds a Ph.D. from Harvard and M.S. from Poona University.Her research interests include monsoon dynamics, ocean dynamics, ocean-atmosphere coupling,and rainfall variability and its impact on agriculture. Awards and honors include the UGC CareerAward, the B. N. Desai Award of India Meteorological Society, the Vasvik Award, the Shri HariOm Ashram Prerit Dr.Vikram Sarabhai Award, the Norman Borlaug Award, and the AstronauticalSociety Award. Gadgil is a Fellow of the Indian Academy of Sciences, Bangalore, the IndianNational Science Academy, and the Indian Meteorological Society.


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Hartmut Grassl

World Climate Research Program, World Meteorological Organization, Switzerland

Grassl is the Director of the Joint Planning Staff for the World Climate Research Programme(WCRP), World Meteorological Organization, Geneva, Switzerland. Prior to taking this positionin 1994, Grassl was director of the Max Planck Institute for Meteorology in Hamburg, Germany,and responsible for developing remote sensing algorithms and global aerosol models. Grassl isnoted for his work with the international climate research community on programs that studyglobal atmospheres, oceans, sea and land ice, and land surfaces.

Peter Gregory

Department of Soil Science, University of Reading, UK

Gregory is Professor of Soil Science and Pro-Vice-Chancellor responsible for research, and waspreviously Department Head, and Dean of the Faculty of Agriculture and Food. He has a Ph.D.in Soil Science from The University of Nottingham. He is a Fellow of the Institute of Biology.Gregory has been employed as soil physicist at Nottingham; lecturer at Reading, and visitingscientist and later principal research scientist at CSIRO Division of Plant Industry in Perth,Australia. His research interests include non-invasive techniques for imaging roots growing insoil, chemical and physical properties of root exudates, modeling water and nutrient uptake byplant root systems, and developing sustainable systems of crop production. Gregory contributesto undergraduate teaching in soil management, plant nutrition and rhizosphere processes.

Jim Jones

Agricultural and Biological Engineering Department, University of Florida, USA

See biographical brief under Faculty.

Michael Manton

Bureau of Meteorology Research Center, Australia

Manton is the chief of the Bureau of Meteorology Research Center (BMRC) in Australia. He haspublished in fluid mechanics (mainly on turbulence), oceanography (waves), and meteorology(mainly on cloud physics and boundary layers). Manton is Editor-in-Chief of the AustralianMeteorological Magazine. He has completed an 11-year term as a member of the Joint ScientificCommittee of the World Climate Research Program (WCRP) and is currently chair of theAtmospheric Observation Panel for Climate, which is sponsored jointly by the Global ClimateObserving System and WCRP. He also has been a member of the Steering Committee for theSystem for Analysis Research and Training (START).

Giampiero Maracchi

University of Florence and Applied Meteorology Foundation (FMA), Italy

Maracchi is Professor of Agrometeorology and Climatology at the University of Florence andPresident of FMA. He directs the Institute for Agro-meteorology and Environmental Analysisfor Agriculture of National Research Council, and is President of the Center for ComputerScience in Agriculture - Accademia dei Georgofili - Florence. He is national representative ofscientific and technical commissions of WMO, FAO and EU, and Vice President of the ScientificCouncil of the AGRHYMET Center of WMO in Niger. Maracchi has worked to promote anddevelop climatology and computer science applied to agriculture and environment. He is authorof more than 200 scientific, technical and didactic papers.


53

Guillermo Podestá

Rosenstiel School of Marine and Atmospheric Science, University of Miami, USA

Podestá holds a Ph.D. in Biological Oceanography, University of Miami, and a B.S. in Agronomy,University of Buenos Aires. After graduating from Miami, Podestá spent one year as a NationalResearch Council Fellow at NASA’s Goddard Space Flight Center. He returned to the RosenstielSchool as a postdoctoral associate, and joined the faculty in 1991. Podestá has recently becomeinvolved in studies of ENSO-related climate variability and applications of climate predictionsto climate-sensitive sectors of society such as agriculture. Podestá’s interests include satelliteremote sensing of ocean dynamics using sea surface temperature, ocean color, and sea surfaceheight fields; applications of satellite and in situ observations to understanding oceanic variabilityand biological responses; fishery oceanography; and fishery ecology.

Cynthia Rosenzweig

Goddard Institute for Space Studies (GISS), Columbia University, USA

Rosenzweig is a research scientist at NASA’s GISS, where she leads the Climate Impacts Groupin its mission to investigate the interactions of climate variability and change on systems andsectors important to human well-being.. She is an adjunct research scientist at the ColumbiaEarth Institute and an adjunct professor at Barnard College, with degrees from Rutgers Universityand the University of Massachusetts. Her research focuses on the impacts of environmentalchange, including increasing carbon dioxide, global warming, and El Niño, on regional, national,and global scales. Rosenzweig has organized and led large-scale interdisciplinary, national, andinternational studies of climate change impacts and adaptation. She is the Co-Leader of theMetropolitan East Coast Regional Assessment of the U.S. National Assessment of the PotentialConsequences of Climate Variability and Change.

Ramadjita Tabo

International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Mali

In his position as Agronomist with ICRISAT, Tabo seeks to improve productivity andsustainability of integrated crop-livestock systems in the dry savannas of West and Central Africain partnership with farmers, NARES, NGOs, and scientists from other IARCs and advancedinstitutions. His research focuses on agronomic and physiological responses of cropping systemsinvolving sorghum, millet, cowpea, soybean, pigeonpea and groundnut to management, drought,soil fertility, genotypes, and biotic stresses; and modeling these systems in relation to resourceuse. He has contributed to capacity building of NARES through training, meetings and jointresearch planning. He has served as Team Leader of the Integrated Systems Project 2, consultantto the WB Assisted Adaptive Research Project in Chad, and consultant for the WB mid-termreview of the Ghana National Agricultural Research System. He serves on the Pan-AfricanCommittee for the Global Change System for Analysis, Research and Training (START).


54

Additional Project Mentors

Emma Archer

Dept. Environmental & Geographical Science, University of Cape Town, South Africa

Archer is a visiting scientist at the University of Cape Town. She previously worked as a Post-doctoral Associate at the IRI, co-hosted by Pennsylvania State University. At the IRI, she workedon a pilot climate impacts and forecast use study in Mangondi village, Northern Province, SouthAfrica, in collaboration with the Agricultural Research Council’s Institute for Soil, Climate andWater. She was also involved in the development of a larger-scale, “Defining Pathways ofSuccessful Applications of Seasonal Forecasting in South Africa,” that seeks to identifyconditions that allow forecasts systems to evolve to benefit vulnerable farmers in South. Shereceived her Ph.D. in Geography from Clark University, where her dissertation work was fundedthrough Fulbright and NASA Earth Systems Science fellowships.

Maxx Dilley


Dilley is a Geographer with experience in applied disaster and risk management in Africa, LatinAmerica and Asia. Prior to joining the IRI, he worked at the World Bank Disaster ManagementFacility and at the USAID Office of Foreign Disaster Assistance. His specialization includesclimate and hydro-meteorological hazards, food security, and GIS applications in disastermanagement. Dilley earned a Ph.D. and M.S. in Geography at Pennsylvania State University. Hiscurrent research interests include assessment of disaster risk and vulnerability, communicatingclimate information, factors affecting the sustainability of disaster early warning and responsesystems, and improving the global database for analyzing disaster impacts. Dilley is responsiblefor developing the IRI's program in disaster and risk management, and plays a role in IRIpartnership development.

Willem Landman

South Africa Weather Service, South Africa

As a senior research scientist, Landman is responsible for conducting and supervising researchon interannual climate variability over Southern Africa, and for developing and producingseasonal climate forecasts for the region in collaboration with neighboring countries (NamibiaBotswana, Lesotho, Swaziland). Since he participated in the Pilot Project of the IRI in 1993,Landman has been involved in seasonal to interannual climate prediction and research at the IRIand in South Africa. He holds a B.S.(Honors) and M.S. in meteorology from the University ofPretoria, and Ph.D.(Science) from the University of the Witwatersrand. His contributions includedownscaling GCM simulations over southern Africa to rainfall and streamflow/runoff using aregional climate model and model output statistics, and developing statistical forecasts of near-global sea-surface temperatures.


55

Contact Information

Samuel AdikuDepartment of Soil ScienceUniversity of GhanaPO Box 245, LegonAccraGHANAPhone: +233-21-500-467Fax: +233-21-500-467E-mail: [email protected]

Emma ArcherClimate Systems Analysis GroupEnvironmental & Geographical ScienceUniversity of Cape Town, Private BagRondebosch 7701SOUTH AFRICAPhone: +27 21 650 2999Fax: +27 21 650 5773E-mail: [email protected]

Walter E. BaethgenResearch and Development DivisionIFDC Uruguay OfficeJuan M. Perez 2917 Apt 501Montevideo 11300URUGUAYPhone: +598-2-712-088Fax: +598-2-711-6958E-mail: [email protected]

Guillermo BaigorriaInternational Potato Center (CIP)Av. La Molina 1895Lima 12PERUPhone: +51-1-349-6017Fax: +51-1-317-5326E-mail: [email protected]

Tony BarnstonInternational Research Institute for Climate

PredictionP.O. Box 1000, 61 Route 9WPalisades, NY 10964-8000USAPhone: +1-845-680-4447Fax: +1-845-680-4865E-mail: [email protected]

Nigist BiruWorld Food ProgrammePO Box 25584, Code 1000Addis AbabaETHIOPIAPhone: +251-1-51-51-88Fax: +251-1-51-44-33E-mail: [email protected]

Rizaldi BoerFaculty of Mathematics and Natural

SciencesBogor Agricultural UniversityBogor, West Java 16144INDONESIAPhone: +62-251-376817Fax: +62-251-376817, 313384E-mail: [email protected]

Albert CalitzMinistry of Agriculture, Water & Rural

DevelopmentGovernment Office Park, Luther Street,

Office No. 067WindhoekNAMIBIAPhone: +264-61-208-7079Fax: +264-61-208-7039, 7768E-mail: [email protected]

Maxx DilleyInternational Research Institute for Climate


Henrick FeddersenDanish Meteorological InstituteLyngbyvej 100DK-2100 CopenhagenDENMARKFax: +45-3915-7460E-mail: [email protected]

Amy FreiseInternational START Secretariat2000 Florida Ave. NW Suite 2000Washington, DC 20009USAPhone: 1-202-462-2213Fax: +1-202-457-5859E-mail: [email protected]

Roland FuchsInternational START Secretariat2000 Florida Ave. NW Suite 2000Washington, DC 20009USAPhone: +1-202-462-2213Fax: +1-202-457-5859E-mail: [email protected]

Marian Fuchs-CarschRose CottageCherry Tree LaneHemel HempsteadHerts HP2 7HSUNITED KINGDOMPhone: +44-2-232 814E-mail: [email protected]

Sulochana GadgilCentre for Atmospheric and Oceanic

SciencesIndian Institute of ScienceBangalore 560012INDIAPhone: +91-80-309-2505, 360-0450Fax: +91-80-360-0865E-mail: [email protected]

Jere GillesDepartment of Rural Sociology101 Sociology BuildingUniversity of MissouriColumbia, MO 65211USAPhone: +1-573-882-3791Fax: +1-573-882-1473E-mail: [email protected]


56

Lisa GoddardInternational Research Institute for Climate


Hartmut GrasslMax Plank Institute for MeteorologyBundesstrasse 55D-20146 HamburgGERMANYFax: +49-40-41173-350E-mail: [email protected]

Peter GregoryDepartment of Soil ScienceUniversity of ReadingWhiteknightsPO Box 233Reading RG6 6DWUNITED KINGDOMPhone: +11-118-9318-911Fax: +44-118-9316-666E-mail: [email protected]

James HansenInternational Research Institute for Climate


Peter HaymanNSW Agriculture.RMB 944 Calala LaneTamworth 2340AUSTRALAPhone: +61-2-6763-1256Fax: +61-2-6763-1222E-mail: [email protected]

Peter HildebrandFood and Resource Economics

DepartmentUniversity of FloridaPO Box 110240Gainesville, FL 30611-0240USAPhone: +1-352-392-1845E-mail: [email protected]

Matayo IndejeInternational Research Institute for Climate


Jim JonesAgricultural and Biological Engineering

DepartmentUniversity of FloridaPO Box 110570Gainesville, FL 32611USAPhone: +1-352-392-1864 ext. 289Fax: +1-352-392-4092E-mail: [email protected]

Jacob KijneRose CottageCherry Tree LaneHemel HempsteadHerts HP2 7HSUNITED KINGDOMPhone: +44-2-232-814E-mail: [email protected]

Upmanu LallDepartment of Earth and Environmental

Engineering500 West 120th StreetColumbia University, MC 4711New York, NY 10027USAPhone: +1-212-854-8905Fax: +1-212-854-7081E-mail: [email protected]

Willem LandmanSouth African Weather BureauPrivate Bag X097Pretoria 0001SOUTH AFRICAE-mail: [email protected]

David LetsonUniversity of Miami – RSMAS4600 Rickenbacker CSWYMiami, FL 33149-1098USAPhone: +1-305-361-4083Fax: +1-305-361-4675E-mail: [email protected]

Trevor LumsdenSchool of Bioresources Engineering &

Environmental HydrologyUniversity of NatalPrivate Bag X01Scottsville 3209SOUTH AFRICA.Phone: +27-33-2605490 Fax: +27-33-2605818E-mail: [email protected]

Alemu Asfaw ManniUSAID – FEWSNETP.O. Box 23429 Code 1000Addis AbabaETHIOPIAPhone: +251-1-510488, 510088Fax: +251-1-510043E-mail: [email protected]

Michael MantonBureau of Meteorology Research CentrePO Box 1289K13th Floor, 150 Lonsdale St.Melbourne, Victoria 3001AUSTRALIAPhone: +61-3-9669-4444Fax: +61-3-9669-4660E-mail: [email protected]

Galit MarcusInternational Research Institute for Climate


Gampiero MarracchiFondazione per la Meteorologia ApplicataVia Giovanni Caproni, 850145 – FlorenceITALYPhone: +39-055-30-1421Fax: +39-055-30-8910E-mail: [email protected],

[email protected]

Simon MasonInternational Research Institute for Climate


Holger Meinke


57

Agricultural Production Systems ResearchUnit (APSRU)

Department of Primary Industries (DPI)PO Box 102Toowoomba, QLD 4350AUSTRALIAPhone: +61-7-4688-1378Fax: +61-7-4688-1193E-mail: [email protected]

Carlos MessinaAgricultural and Biological Engineering

DepartmentUniversity of FloridaPO Box 110570Gainesville, FL 32611USAPhone: +1-352-392-1864Fax: +1-352-392-4092E-mail: [email protected]

Francisco J. MezaFacultad de Agronomia y Ingenieria

ForestalPontificia Universidad Catolica de ChileCampus San JoaquinAv Vicunia Mackenna 4860 MaculSantiagoCHILEPhone: +56-2-686-4101E-mail: [email protected]

T. H. Thuan NguyenCentre for Hydrometeorology of South

Vietnam (CEHYMET)19 Nguyen Thi Minh Khai St.District OneHo Chi Minh CityVIETNAMPhone: +84-8-8243815Fax: +84-8-8243816E-mail: [email protected]

Jennifer PhillipsBard Center for Environmental PolicyBard CollegeAnnandale-on-Hudson, NY 12504-5000USAPhone: +1-845-758-7845Fax: +1-845-758-7636E-mail: [email protected]

Sibiry Traore PierreICRISATP. O. Box 320, SamankoBamakoMALIPhone: +223-22-3375Fax: +223-22-8683E-mail: [email protected]

Guillermo PodestáUniversity of Miami – RSMAS4600 Rickenbacker CswyMiami FL 33149USAPhone: +1-305-361-4142Fax: +1-305-361-4622E-mail: [email protected]

Rengalakshmi RajM.S. Swaminathan Research FoundationIII Cross Street, Taramani Inst'l. AreaChennai 600 113, Tamil NaduINDIAPhone: +91-44-254-1229, 1698, 2698Fax: +91-44-254-1319E-mail: [email protected]

Nageswara V. RaoICRISATPatancheru (P.O.) Medak Dt. 502 324Andhra PradeshINDIAPhone: +91-40-329-6161Fax: +91-40-329-6182, 324-1239E-mail: [email protected]

Andrew Robertson International Research Institute for Climate

PredictionColumbia UniversityP.O. Box 1000, 61 Route 9WPalisades, NY 10964-8000USAPhone: +1-845-680-4491Fax: +1-845-680-4865E-mail: [email protected]

Alvaro RoelUniversity of California, Davis402 Russell Park #8Davis, CA 95616USAPhone: +1-530-792-7087Fax: +1-530-752-4361E-mail: [email protected]

Ricardo RomeroNational Agricultural Research InstituteRuta 50 km 12INIA La EstanzuelaColonia, 70000URUGUAYPhone: +598-574-8000Fax: +598-574-8012E-mail: [email protected]

Chet RopelewskiInternational Research Institute for Climate


Cynthia RosenzweigGoddard Inst. for Space Studies2880 BroadwayNew York, NY 10025USAPhone: +1-212-678-5562Fax: +1-212-678-5648E-mail: [email protected]

William de los SantosDepartment of AgronomyUniversity of the Philippines Los BañosCollege, Laguna 4031PHILIPPINESPhone: +63-49-536-2466Fax: +63-49-536-2468E-mail: [email protected],

[email protected]

Ramasamy SelvarajuDepartment of Agricultural MeteorologyTamil Nadu UniversityCoimbatore 641003, Tamil NaduINDIAPhone: +91-422-430657Fax: +91-422-431672E-mail: [email protected]

Kamalesh Kumar SinghNCMRWFMausam Bhavan Complex, Lodi RoadNew Delhi, 110 003INDIAPhone: +91-11-4603195Fax: +91-11-4690108E-mail: [email protected]

Ramadjita TaboICRISAT - MaliNatural Resource Management ProgramBP 320BamakoMALIPhone: +223-22-33-75Fax: +223-22-86-83E-mail: [email protected]


58

Fulu TaoChinese Academy of Agricultural Sciences,

Agrometeorology InstituteZhangquancun South Street No. 12Beijing, 100081PEOPLES’ REPUBLIC OF CHINAPhone: +86-10-62119681Fax: +86-10-68975409E-mail: [email protected]

Corinne ValdiviaAgricultural EconomicsCollege of Agriculture Food and Natural

Resources200 Mumford HallColumbia, MO 65211-6200USAPhone: +1-573-882-4020Fax: +1-573-882-3958E-mail: [email protected]

Hassan VirjiInternational START Secretariat2000 Florida Ave. NW Suite 2000Washington, DC 20009USAPhone: +1-202-462-2213Fax: +1-202-457-5859E-mail: [email protected]

Milton WaiswaDepartment of Meteorology10th Floor Postel BuildingClement Hill RoadKampalaUGANDAPhone: +256 41 255609, 251798Fax: +256 41 251797E-mail: [email protected]

Neil WardInternational Research Institute for Climate


Advanced Training Institute Participants, July 2002

International Research Institute

for Climate Prediction

The Earth Institute at Columbia University

61 Rt. 9W

Palisades NY 10964-8000 USA

Phone: 845-680-4468

Fax: 845-680-4866

http://iri.columbia.edu

L ink ing Sc i ence t o Soc i e t y

Documents

The International researcH Institute for Climate ...cscop.iri.columbia.edu/uploads/1/8/4/1/18415949/atireport_web.pdfThe International researcH Institute for Climate prediction linking