“Incorporating International Best Practices in the Preparation of Agricultural Outlook and Situation Analysis Reports for India” Capacity training workshop:

“Incorporating International Best Practices in the Preparation of Agricultural Outlook and Situation Analysis Reports for India” Capacity training workshop: Improved capacity in undertaking medium term projections and access to global market information

Trade & Markets Division
Dr. Holger Matthey Economist Trade and Markets Division of the Food and Agriculture Organization Headquarter Rome My name is... I hope, my presentation will provide useful background information so you can better understand and evaluate the baseline results presented later. Mention Schedule, breaks,... I think in terms of procedure, please interrupt me at any time you have a question or need clarification. Its better than to wait to the end.

Agro-food Trade and Market Department OECD - Paris
Gregoire Tallard Economist Agro-food Trade and Market Department OECD - Paris My name is... I hope, my presentation will provide useful background information so you can better understand and evaluate the baseline results presented later. Mention Schedule, breaks,... I think in terms of procedure, please interrupt me at any time you have a question or need clarification. Its better than to wait to the end.

Objectives of collaborator workshop
Use of Troll software and the collaborators version In depth review of the current India module, and its properties, data sources, etc. Generation of the India standalone baseline projections and scenarios Handling of global Aglink-Cosimo model India model revision Preparation of an India chapter in the Outlook

Outline of Aglink-Cosimo training
Training builds on the introductory workshop, provides in-depth knowledge and practical experience (see handout) Topics: Introduction to TROLL Review of Aglink-Cosimo Data Country module India Parameters Simulation Introduction of the Aglink-Cosimo “All Linked Model” Future Work

Monday

OUTLINE 1. Introduction to Troll

Introduction of the Troll Simulation Software
Integrated software for econometric, modeling and statistical analysis State-of–the-art model simulation engine designed for large systems Complete integration of various tasks (calculation, model editing, estimation, simulation...) Hundreds of built-in functions and sophisticated modeling language Efficient interface to MS-EXCEL Available for any platform, MS-WINDOWS or UNIX See course material for more details

Installation of the Troll Simulation Software
Computer requirements: Version: File structure:

OUTLINE 2. Review of the AGLINK-COSIMO project

Vision and main objectives
Provide consensus analyses on the future evolution of international commodity markets. Develop increasingly integrated systems that link short, medium and long term projections. Publication of the annual “OECD-FAO Agricultural Outlook” in collaboration with the OECD. Construct scenarios analyzing emerging market and policy issues using the Aglink-Cosimo model. The vision for our team is to Provide credible consensus analyses on the evolution of international commodity markets and particularly of the policy factors and emerging issues which influence them. Develop increasingly integrated systems that link short, medium and long term projections to better describe the continuum of agricultural development Main objective is the publication of the annual “OECD-FAO Agricultural Outlook”, governed by a collaboration agreement with OECD Outlook serves as a baseline and reference scenario for other simulations analyzing emerging market and policy issues using the Aglink-Cosimo model. Let me introduce the team to you

The Outlook: Forecast or Baseline?
Expectation for the future Based on clearly defined assumptions A prediction, as of coming events or conditions = Forecast A measurement, calculation, or location used as a basis for comparison = Baseline This is always a controversial topic and Carlos and I spent quiet a bit of time discussing this issue at the beginning of this projects. This issue seems to be a bit of hairsplitting at first, but is important to correctly interpret the results of the exercises. A model like Cosimo is a bit of both it makes statements about the future, so it is a prediction, so a forecast in a sense but with certain qualifications, from a forecast, like the weather forecast you expect that it tells you what is most likely to happen, and there is the difference, Cosimo outlook does not necessarily predicts what is most likely to happen, because it is based on fairly rigid assumptions, such as: only legally enacted policies are taken into account, likely changes are ignored. A forecast would incorporate them. in this sense it is a baseline, because the baseline projections can be used to later check the effects of these new policies and other shocks to the markets as we did in the scenarios for this project.

OECD-FAO Agricultural Outlook
Aglink – Cosimo commodities Data requirements OECD-FAO Agricultural Outlook Annual time series for: Historic (endogenous) prices supply (area, yield, animal numbers...) demand (food, feed, crush...) trade (exports, imports) Projection (exogenous) macroeconomic data (GDP, ex. rate...) policy variables (tariffs, CAP...) Aglink-Cosimo model Wheat Beef Skim Milk Powder Coarse Grains Sheepmeat Whole Milk Powder Rice Pigmeat Cheese Oilseeds Poultry Butter Vegetable Oils Eggs Fresh Dairy Products Oilseed Meals Cotton Roots and Tubers Sugar Biofuels OECD-FAO Agricultural Outlook Commodities Global Coverage As I have mentioned, our world revolves around the Outlook. It is our main output. We use the model to generate it, it is global in coverage, covers 20 commodities Countries and regions Data requirements Aglink-Cosimo model Aglink Cosimo Countries Regions Australia Algeria Kazakhstan Tanzania LDC L. America Argentina Bangladesh Nigeria Thailand Other LDC Africa Brazil Chile Malaysia Turkey Other LDC Asia Canada Colombia Mozambique Ukraine Other LDC Oceania China Egypt Pakistan Uruguay Other W. Europe EU-27 Ethiopia Paraguay Vietnam Other C. Asia Japan Ghana Peru Zambia Other E. Europe S.-Korea India Philippines Other M. East Mexico Indonesia Saudi Arabia Other N. Africa N-Zealand Iran South Africa Other Africa USA Israel Sudan Other S. America Norway Switzerland Partial equilibrium model, driven by elasticities, technical parameters and policy variables, provides representations of national and global agricultural markets where all of the major agricultural sectors are assumed to be connected, outlook simulation tool that constructs projections over a ten year period so that all of the main characteristics of the crops and livestock sectors influence the final equilibrium. OECD-FAO Agricultural Outlook Produced annually from December to June. Ten-year projection of global supply, demand and trade Assessment of driving factors in commodity markets Theme Chapter – shared messages for FAO-OECD Growing scope – fish, cotton, land, fertilizers.... Collaborative effort between various organizations FAO - OECD teamwork is critical to success

Applications of Aglink-Cosimo
Study A Study B Study C …… Give a representative overview over projects that were realized using Aglink Cosimo over the years.

OUTLINE 3. DATA

Projection Output Model Data Parameters Exo Endo What is it?
CO.SI.MO. is an FAO project undertaken jointly with OECD, that builds on OECD’s existing Aglink model, and unites various databases, economic modeling activities and expert judgments to enhance our analytical capacity to look at markets, policies and emerging issues. CO.SI.MO. is a partial-equilibrium world agricultural model, currently encompassing about 48 countries and regions and 18 commodities. What can it do? Enhance understanding of the role of commodity markets and policies in agriculture and food security Support studies on the impact of policy reforms in agriculture and on food security Enrich discussions on domestic and trade policy reform Allow timely assessment of emerging issues to inform members Generate baseline projections for policy analysis How does it work? Structured by country and region modules Linked to in-house and external databases Driven by elasticities, technical and policy parameters Solved in TROLL and managed in Visual Basic Parameters

IND_MK_QC Country Coding (see handout) Mod View Parm Mod
see handout – Table 1 “Coding” Parm Mod

Coding IND_MK_QC Commodity

Coding IND_MK_QC Activity

Coding IND_MK_QC Country Commodity Activity

Annual time series for:
supply (area, yield, animal numbers, ...) demand (food, feed, crush, ...) trade (exports, imports) prices macroeconomic data (GDP, ex. rate, ...) policy variables (tariffs, CAP, ...) sources: OECD country questionnaires, FAO stat and own division databases (more frequently updated), literature historical time series endogenous data, up to 2006 for the 2007 baseline, a convention we made, knowing that this has its risks since the closing date for the historic data is December 2006 for marketing year 06/07 numbers, the soybeans in Brazil are barely in the ground and we treat the production figure as data. In the 2005 baseline, the 2004 Argentina soybean production was 5 mill tons off, but this is done to be consistent with our short term publications that come out at the same time. these are supply, demand, trade, prices (for stand alones exogenous) exogenous numbers are provided till 2014 macro economic numbers, policy variables (tariffs, subsidies, taxes) policy variables are held constant at their last data point, if nothing else is known for sure.

Data sources for endogenous data
national statistics international databases surveys, questionnaires literature calculations

Exogenous data Population GDP deflator Consumer price index
Exchange rate GDP index GDP current World prices

Data sources for exogenous data
Sources (historic series and projections): national statistics international databases surveys, questionnaires literature calculations Exogenous projections may have to be done by you! Methods: trend time series methods regression expert opinion

TROLL Database construction
Convert spreadsheets of various commodities into a TROLL readable database. (use Indiafor.csv with descriptions as datafile, maybe split up into endo and exo, or more and write simple databasebuilder to illustrate the process) ….. INDdatabasebuilder INDdataexo INDdataendo

Questions? Crush is a function of the crush margin
meal extraction rate (.78) time meal price plus oil extraction rate (.18) divided by oilseeds price last years crush, represents a proxy for installed capacity, crush is not only price dependent and a trend for economic growth

Tuesday

Creating the standalone projections for India
OUTLINE 4. COUNTRY MODULE Creating the standalone projections for India

Introduction of the Aglink–Cosimo Standalones
Standalone model construction and baseline generation how modules are put together, solution process Analysis and scenario exercises demonstration exercises running these scenarios, compare, discuss one example chosen by the group Indsys.inp step2.inp Country_Viewer

Introduction to the Sys file
Functions of the Sys file data-generation calibration simulation Overview over model INDsys.inp INDmod_2013.inp What is it? CO.SI.MO. is an FAO project undertaken jointly with OECD, that builds on OECD’s existing Aglink model, and unites various databases, economic modeling activities and expert judgments to enhance our analytical capacity to look at markets, policies and emerging issues. CO.SI.MO. is a partial-equilibrium world agricultural model, currently encompassing about 48 countries and regions and 18 commodities. What can it do? Enhance understanding of the role of commodity markets and policies in agriculture and food security Support studies on the impact of policy reforms in agriculture and on food security Enrich discussions on domestic and trade policy reform Allow timely assessment of emerging issues to inform members Generate baseline projections for policy analysis How does it work? Structured by country and region modules Linked to in-house and external databases Driven by elasticities, technical and policy parameters Solved in TROLL and managed in Visual Basic

Introduction to the Viewer
Overview over viewer Review the results Projections Indicators Ratios Introduce revisions Endogenous Exogenous What is it? CO.SI.MO. is an FAO project undertaken jointly with OECD, that builds on OECD’s existing Aglink model, and unites various databases, economic modeling activities and expert judgments to enhance our analytical capacity to look at markets, policies and emerging issues. CO.SI.MO. is a partial-equilibrium world agricultural model, currently encompassing about 48 countries and regions and 18 commodities. What can it do? Enhance understanding of the role of commodity markets and policies in agriculture and food security Support studies on the impact of policy reforms in agriculture and on food security Enrich discussions on domestic and trade policy reform Allow timely assessment of emerging issues to inform members Generate baseline projections for policy analysis How does it work? Structured by country and region modules Linked to in-house and external databases Driven by elasticities, technical and policy parameters Solved in TROLL and managed in Visual Basic INDview2013_indicate

OUTLINE SIMULATION The joint presentation between Martin and myself is divided into three main parts. I will do the general introduction, structure, and procedures Martin: architecture of the EU model, present The outlook for the EU

Practical exercises using the country module
Simulation exercises with stand alone model Baseline generation create a projection with model, put output in viewer Adjustments of endogenous and exogenous variables read in new r-factors, modify the macro variables Parameter modifications modify parameters run model, Structural changes exercises to change equation structure (diff average in RH) Model improvement discussion INDparm.inp INDmod2013.inp Country_Viewer

Wednesday

OUTLINE COUNTRY MODEL DETAILS
The joint presentation between Martin and myself is divided into three main parts. I will do the general introduction, structure, and procedures Martin: architecture of the EU model, present The outlook for the EU

Equilibrium world price FAOSTAT/OECD Database
Aglink-Cosimo model Net trade (exports – imports) World trade balance ∑EX = ∑IM Equilibrium world price Production Area/ livestock Yield Opening stocks Exports Income Population Consumption (food, feed, other use) Ending Imports FAOSTAT/OECD Database Time series for production, consumption, stocks, GDP, tariffs, exchange rates, prices, costs... Domestic price (internal market clearing)

Supply Crop production: Oilseed products
Area=f(Return/ha, Paymt/ha, Costindex,Area(-1),Rfactor) Yield=f(Return/t,Paymt/t,Costindex,trend,Rfactor) Production=Area*Yield Oilseed products OilseedMeal production=mealyield*oilseed crush Protein meal production=oilseed meal + others… Oilseedoil production=oilyield*oilseed crush Veg oil production=oilseed oil + palm oil + others… Divide the IndiaModule-HM file into the respective parts and link to the sides Mod_cr.doc

Supply Meat production: Milk production:
Livestock Inventory=f(Meatprice,Paymt/hd, Feedpriceindex,Costindex,Inventory(-1),trend,Rfactor) Indigenous meat production=f(Meatprice,Paymt/tn, feedprice index,costindex,Inventory(-1), production(-1),trend,Rfactor) Milk production: Cow Inventory=f(Milkprice,Paymt/hd, Feedpriceindex,Costindex,Inventory(-1),trend,Rfactor) Cow yield=f(Milkprice,Paymt/tn, feedprice index,costindex, trend,Rfactor) Milk production= Inventory*yield Milk products=f(fatprice,proteinprice) Mod_li.doc

Demand Food demand Food demand= f(Ownprice, other prices, pricedeflator, Income/person,trnd,Rfactor) Demand constraints apply in log linear relationship Crop Feed demand Feed demand=f(Ownprice, otherprices, nonruminant prod, ruminant prod,trnd, Rfactor) Fish meal, DDGs are deducted Bio-fuel crop feedstock demand Feedstock demand=f(Pricebiofuel, feedstock price, mandate,subsidy, Rfactor) Crop Other use Other use=f(Price, income,trnd,Rfactor) Mod_de.doc

Stocks Crops Stocks= f(Ownprice/(average(ownprice), production, Rfactor) Meat and dairy products Stocks=f(Ownprice, production, Rfactor) Mod_st.doc

Trade Exports Exports= f(producerprice/exportprice, Rfactor) Imports
Imports=f(producerprice/importprice, Rfactor) Mod_tr.doc

Prices Export price Exportprice= worldprice*(1+exportwedge)*exchangerate Import price Importprice=worldprice*(1+tariff+importwedge)*exchangerate Producer price: domestic market clearing Production+stocks(-1)+imports=consumption+exports+stocks Consumer price consumerprice=f(producerprice,deflator) Mod_pr.doc

India specific model characteristics
ADDEQ bottom, IND_CG_ST1: LOG(IND_CG_ST1'N) = C.IND_CG_ST1.CON'C-0.5*LOG((IND_CG_PP/IND_MA_MSP)** 3)+0.2*LOG((IND_CG_QC+IND_CG_QC(-1))/2)+LOG(R.IND_CG_ST1) , ; REPEQ IND_CG_ST IND_CG_ST: IND_CG_ST = (IF (IND_CG_PP<IND_MA_MSP) THEN ((IND_MA_MSP-IND_CG_PP)* 2000) ELSE 0)+IND_CG_ST1 , IND_RI_ST1: LOG(IND_RI_ST1'N) = C.IND_RI_ST1.CON'C-0.1*LOG((IND_RI_PP/IND_RI_MSP)** 3)+1*LOG((IND_RI_FO+IND_RI_FO(-1))/2)+LOG(R.IND_RI_ST1) , IND_RI_ST2: IND_RI_ST2'N = (IF (IND_RI_PP<IND_RI_MSP) THEN ((IND_RI_MSP-IND_RI_PP )*8000) ELSE 0)+IND_RI_ST1 , REPEQ IND_RI_ST IND_RI_ST: IND_RI_ST = IF (IND_RI_ST2<8000*IND_ME_POP/ ) THEN (8000* IND_ME_POP/ ) ELSE IND_RI_ST2 , IND_WT_ST1: LOG(IND_WT_ST1'N) = C.IND_WT_ST1.CON'C-1*LOG((IND_WT_PP/IND_WT_MSP))+0.5*LOG((IND_WT_FO+IND_WT_FO(-1))/2)+LOG(R.IND_WT_ST1) , IND_WT_ST2: IND_WT_ST2'N = (IF (IND_WT_PP<IND_WT_MSP) THEN ((IND_WT_MSP-IND_WT_PP )*14000) ELSE 0)+IND_WT_ST1 , REPEQ IND_WT_ST IND_WT_ST: IND_WT_ST = IF (IND_WT_ST2>4000*IND_ME_POP/ ) THEN IND_WT_ST2 ELSE (4000*IND_ME_POP/ ) ,

Thursday

OUTLINE PARAMETERS The joint presentation between Martin and myself is divided into three main parts. I will do the general introduction, structure, and procedures Martin: architecture of the EU model, present The outlook for the EU

Parameters are the link between variables
determine the properties of the model INDparm.inp What is it? CO.SI.MO. is an FAO project undertaken jointly with OECD, that builds on OECD’s existing Aglink model, and unites various databases, economic modeling activities and expert judgments to enhance our analytical capacity to look at markets, policies and emerging issues. CO.SI.MO. is a partial-equilibrium world agricultural model, currently encompassing about 48 countries and regions and 18 commodities. What can it do? Enhance understanding of the role of commodity markets and policies in agriculture and food security Support studies on the impact of policy reforms in agriculture and on food security Enrich discussions on domestic and trade policy reform Allow timely assessment of emerging issues to inform members Generate baseline projections for policy analysis How does it work? Structured by country and region modules Linked to in-house and external databases Driven by elasticities, technical and policy parameters Solved in TROLL and managed in Visual Basic

Strategy for parameter choices
Parameters Strategy for parameter choices Use available estimates. Use systems / appropriate constraints Estimate: research estimation agenda Model validation by country / by commodity Emphasis on consultation with experts Ask them what their sources are? Where they could get information from... one of the most contentious issues, especially in large models with poor data sources where estimation is only possible in a few exception and even then sue to simple specifications the results might not be practical. different ways to chose them look in scientific literature where they were estimated in studies, or look into other models ask experts in house or who you know and trust in demand systems we calculate them through theoretical constraints. if there are enough observations, run a regression to estimate them but especially if estimated tests and validations are needed. shock the model and check the response, discuss with analysts if the response is reasonable I’d say the overriding idea in this model is that it has to satisfy basic economic theory but then it has to work, and it has to work in two ways, which as you all know is sometimes almost asking too much, it has to produce a smooth, believable baseline and for scenarios deliver a reasonable response to shocks. Since we do this as a service and not an and in itself, at least on the FAO side, the analysts who have to work with the model have to support the parameter choices or at least the characteristics of the model they imply.

Parameters Introduction to parameters types function sources
What is it? CO.SI.MO. is an FAO project undertaken jointly with OECD, that builds on OECD’s existing Aglink model, and unites various databases, economic modeling activities and expert judgments to enhance our analytical capacity to look at markets, policies and emerging issues. CO.SI.MO. is a partial-equilibrium world agricultural model, currently encompassing about 48 countries and regions and 18 commodities. What can it do? Enhance understanding of the role of commodity markets and policies in agriculture and food security Support studies on the impact of policy reforms in agriculture and on food security Enrich discussions on domestic and trade policy reform Allow timely assessment of emerging issues to inform members Generate baseline projections for policy analysis How does it work? Structured by country and region modules Linked to in-house and external databases Driven by elasticities, technical and policy parameters Solved in TROLL and managed in Visual Basic

Parameters Steps to develop and modify the parameter set
example of non-estimation method based on available estimates INDparm2013.xlsm What is it? CO.SI.MO. is an FAO project undertaken jointly with OECD, that builds on OECD’s existing Aglink model, and unites various databases, economic modeling activities and expert judgments to enhance our analytical capacity to look at markets, policies and emerging issues. CO.SI.MO. is a partial-equilibrium world agricultural model, currently encompassing about 48 countries and regions and 18 commodities. What can it do? Enhance understanding of the role of commodity markets and policies in agriculture and food security Support studies on the impact of policy reforms in agriculture and on food security Enrich discussions on domestic and trade policy reform Allow timely assessment of emerging issues to inform members Generate baseline projections for policy analysis How does it work? Structured by country and region modules Linked to in-house and external databases Driven by elasticities, technical and policy parameters Solved in TROLL and managed in Visual Basic

OUTLINE GLOBAL AGLINK-COSIMO MODEL

Introduction to the global “all linked” model concept
All country modules are merged Models Data files Parameter files World price endogenized Equilibrium solution for all countries and all commodites What is it? CO.SI.MO. is an FAO project undertaken jointly with OECD, that builds on OECD’s existing Aglink model, and unites various databases, economic modeling activities and expert judgments to enhance our analytical capacity to look at markets, policies and emerging issues. CO.SI.MO. is a partial-equilibrium world agricultural model, currently encompassing about 48 countries and regions and 18 commodities. What can it do? Enhance understanding of the role of commodity markets and policies in agriculture and food security Support studies on the impact of policy reforms in agriculture and on food security Enrich discussions on domestic and trade policy reform Allow timely assessment of emerging issues to inform members Generate baseline projections for policy analysis How does it work? Structured by country and region modules Linked to in-house and external databases Driven by elasticities, technical and policy parameters Solved in TROLL and managed in Visual Basic

Introduction of the Aglink-Cosimo “All Linked Model”
Merged model construction and baseline generation how modules are merged, solution process, Analysis and scenario exercises with complete Aglink-Cosimo demonstration exercises running these scenarios, compare, discuss one example chosen by the group driver os_3_viewer aglink

Aglink – Cosimo Outlook Process
OECD Commodity Groups OECD Questionnaire Responses OECD Experts Iterations, exchanges of modifications Solve for domestic prices Calibrated Stand-alone Models Aglink Country Model Development OECD-FAO World Agricultural Outlook Adjusted Stand-alone Models Cosimo This shows a summary of how a OECD-FAO baseline is produced, the analyst teams update, develop, maybe with the help of local consultants, the individual country modules for the new outlook. OECD: questionnaire responses come in, providing the base for the stand-alones, country models are calibrated to them. Experts provide their input, not all national numbers are taken 1:1 and not all countries provide numbers. At the end of the first phase is a standalone model FAO: take the model, and we are still in the Solve for world prices FAO Databases FAO Experts

World Prices: market clearing
Wheat, Coarse grain, rice, oilseeds, raw sugar, poultry, sheepmeat, butter, cheese, skim powder, whole powder, ethanol, biodiesel Global exports = Global imports Bovine and pig meats: Pacific market: exports=imports Atlantic market: exports = imports Rest of world: exports=imports

Potential Applications
Baseline projections for agricultural markets Scenarios involving: yields areas costs macroeconomic variables domestic support policies trade policies disease outbreaks The previous slides showed that there are differences of opinions about these projections scenario analysis are a way to address these differences in a bit of a systematic way. we thought about where are the main sources of uncertainties in the soybean sector and isolated the following variables:

Scenario Types Two types of scenarios were simulated:
Standardized scenarios standardized shifts of variables symmetric response allow easy comparisons between countries Customized scenarios variable shifts are customized asymmetric response allow setting of ranges around the baseline These methods were used in the two types of scenarios we simulated in the first run we raised and lowered all the shock variables by 20% compared to their baseline values. the exogenous stayed there, the endogenous ones went back a bit toward their original values. all shifts are symmetric so its easy to say how Argentina responded compared to Brazil or Paraguay but these are not likely boundaries. the 20% are not meaningful as limits, just chosen to initiate movements and show complex shifts and relative responses Then became clear that likely areas around the baseline solution are needed for this project, band of likely outcomes are what planning can be based on ideally a stochastic solution would be desirable where we have probabilities assigned to certain outcomes. next best thing are expert-set limits for critical variables second set of scenarios used those and simulated solutions that allowed to establish highs and lows for production, trade....

Scenario Methods Different methods were used:
shift exogenous variables e.g. set new exchange rate level shift endogenous variables change adjustment factor exogenize and set new level the variables we picked are exogenous as well as endogenous like exchange rate or yield for exogenous, you just set a new level endogenous variables can be changed 2 ways giving them an initial shock to the adjustment factor each equation has an adjustment factor, the error term of a regression, used to calibrate the calculation to the actual value in the last year of history. Adding to this term in, lets say yield, raises yields by the predetermined factor. But now disequilibrium in the model, need to solve for new equilibrium prices if more yield, more supply, lower price, lower yield, lower supply, higher price and so forth to new equilibrium. So clear, that new yield in solution will be lower than the 20% we initially raised it. Model fights back... if we do not want this effect, we think we know what the yield should be, need to exogenize it, no more price dependency. Loss of richness, but only way to stick to predetermined values

Practical exercises using the global model
Simulation exercises with global model Baseline generation create a projection with model, put output in viewer Adjustments of other countries read in new r-factors, modify the macro variables Parameter modifications modify parameters run model, Structural changes exercises to change equation structure (diff average in RH) Model improvement discussion coefs.txt Scenario_Viewer

Friday

OUTLINE Future Work

Discussion of future co-operation
Data review Country module development Pulses model Scenario work What is it? CO.SI.MO. is an FAO project undertaken jointly with OECD, that builds on OECD’s existing Aglink model, and unites various databases, economic modeling activities and expert judgments to enhance our analytical capacity to look at markets, policies and emerging issues. CO.SI.MO. is a partial-equilibrium world agricultural model, currently encompassing about 48 countries and regions and 18 commodities. What can it do? Enhance understanding of the role of commodity markets and policies in agriculture and food security Support studies on the impact of policy reforms in agriculture and on food security Enrich discussions on domestic and trade policy reform Allow timely assessment of emerging issues to inform members Generate baseline projections for policy analysis How does it work? Structured by country and region modules Linked to in-house and external databases Driven by elasticities, technical and policy parameters Solved in TROLL and managed in Visual Basic

Co-operators Program Benefits
Participate in annual world commodity projection Access to data, models, FAO staff Participate in analysis projects Access to capacity building – training Tap global expertise Costs Depends on commitment– 2-6 staff months Provide data on markets and policies Advice as to policy issues etc. Input into projection– country questionnaire What is it? CO.SI.MO. is an FAO project undertaken jointly with OECD, that builds on OECD’s existing Aglink model, and unites various databases, economic modeling activities and expert judgments to enhance our analytical capacity to look at markets, policies and emerging issues. CO.SI.MO. is a partial-equilibrium world agricultural model, currently encompassing about 48 countries and regions and 18 commodities. What can it do? Enhance understanding of the role of commodity markets and policies in agriculture and food security Support studies on the impact of policy reforms in agriculture and on food security Enrich discussions on domestic and trade policy reform Allow timely assessment of emerging issues to inform members Generate baseline projections for policy analysis How does it work? Structured by country and region modules Linked to in-house and external databases Driven by elasticities, technical and policy parameters Solved in TROLL and managed in Visual Basic

Levels of Cooperation A Level (1 month staff time)
Fill out historical data questionnaire Provide judgments on projections B Level (2 months staff time) Participate in projection process Attend meetings in Rome C Level (6 months qualified staff time) All of above Create new country specific module Training on modules – Cosimo provided by FAO Use module/model for projection work The previous slides showed that there are differences of opinions about these projections scenario analysis are a way to address these differences in a bit of a systematic way. we thought about where are the main sources of uncertainties in the soybean sector and isolated the following variables:

Time Requirements Annual Cycle – from November to April/May
Questionnaire on historical data and projection judgements Participation in baseline process Meetings – OECD/FAO Model maintenance - research sources: FAO stat, own division databases, literature historical time series the longer the better, especially if estimations are planned. endogenous data, where projections are made by the model, are historical numbers only, up to 2004 for the 2005 baseline these are supply, demand, trade, prices (for stand alones exogenous) exogenous numbers are provided till 2014 macro economic numbers, policy variables (tariffs, subsidies, taxes)

Steps to Formalize Cooperation
Set up Task Force (identify members) Sign agreement on what is required/offered by both FAO and collaborator Identify initial funding sources to build capacity Identify process of collaboration Identify technical support requirements Prepare work plan for new country model development and maintenance Identify policy questions to be addressed Undertake country report: Medium term prospects for agriculture and food security

Special feature on India Outline and resource requirements

Outline of theme chapter on Indian Agriculture
Introduction – motivation - background The success of India’s agriculture very notable aspects – growth, food security, technology, trade policy approach The Outlook for India Setting - many factors conditioning the outlook – Macro factors Constraints Policies conditioning the outlook Commodity Outlook Risks and uncertainties – scenarios Conclusions What is it? CO.SI.MO. is an FAO project undertaken jointly with OECD, that builds on OECD’s existing Aglink model, and unites various databases, economic modeling activities and expert judgments to enhance our analytical capacity to look at markets, policies and emerging issues. CO.SI.MO. is a partial-equilibrium world agricultural model, currently encompassing about 48 countries and regions and 18 commodities. What can it do? Enhance understanding of the role of commodity markets and policies in agriculture and food security Support studies on the impact of policy reforms in agriculture and on food security Enrich discussions on domestic and trade policy reform Allow timely assessment of emerging issues to inform members Generate baseline projections for policy analysis How does it work? Structured by country and region modules Linked to in-house and external databases Driven by elasticities, technical and policy parameters Solved in TROLL and managed in Visual Basic

Resource requirements
National consultants for quantitative research on: Price transmission Market structures and drivers Dairy sector Land tenure, GMO, …. Travel Workshops, meetings TSS for FAO staff Project funding requested, LoA prepared demand and supply. Demand drivers including price and including elasticities. Supply elasticities. What about an other study on policies and modeling them. This could include price transmission.

“Incorporating International Best Practices in the Preparation of Agricultural Outlook and Situation Analysis Reports for India” Capacity training workshop:

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