Vulnerability and Adaptation Assessment Agriculture Sector Jakarta, Indonesia

Vulnerability and Adaptation Assessment Agriculture Sector Jakarta, Indonesia

Vulnerability and Adaptation Assessment Agriculture Sector Jakarta, Indonesia 23 March 2006 Ana Iglesias Universidad Politcnica de Madrid 1 Objective To provide participants with information on V&A assessment for the agriculture sector A general discussion on the impacts of

climate variability and change on agriculture and food security Methods, tools and issues to assess V&A PC based training on methods, tools, issues 2 Outline Climate variability and change, agriculture and food security ( h) Key differential vulnerabilities ( h) Key issues ( h) 1. 2. 3. 1. 2. 3. 4.

Integration and cooperation (social, water) Calibration Extreme events Uncertainties PC based training: Models, assisting tools for stakeholders, risk management (3 h) 4. 1. 2. 3. Designing the framework ( h) Participatory evaluation and prioritization of adaptation ( h) PC based training (2 h) Total: (4 h) 3 Agenda

9:15 10:45 1. 2. 3. Climate variability and change, agriculture, and food security Key differential vulnerabilities Key issues 10:45 11:00 Coffee 11:00 12:30 4. Models, assisting tools for stakeholders, risk management

1. 2. 12:30 13:30 Lunch 13:30 15:00 4. Designing the framework Participatory evaluation and prioritization of adaptation Models, assisting tools for stakeholders, risk management 3. PC based training 4

Climate, agriculture, and food security Climate change is one stress among many affecting agriculture and the population that depends on it 5 Observations: Increased drought Persistent drying trend in parts of Africa has affected food production, including freshwater fisheries, industrial and domestic water supplies, hydropower generation (Magazda, 1986; Benson and Clay, 1998; Chifamba, 2000; Iglesias and Moneo, 2005) Maize production, Zimbabwe

6 Drought in the Mediterranean 624mm Correlation betwen total rainfall and agricultural production r=0.82 Annual Rainfall (mm) 650 Kairouan (Tunisia) 550 111mm

450 Rainfall 350 250 150 50 1951 1955 1959 1963 1967 1971 1975 1979 1983 1987 1991 1995 1999 20 Q/ha Cereal Yields 15 10 5 0

1980 1983 1986 1989 Rendement/SE (Qx/ha) 1992 1995 1998 2001 Rendement/SR (Qx/ha) Source: R. Mougou, INRGREF 7

Drought in the Mediterranean Wheat yield in Spain Probability of yield (%) 100% 80% high yield 60% medium yield low yield 40% 20% 0% all years

dry years normal years wet years Source: Iglesias and Moneo, 2004 8 Longer growing seasons In Australia, climate change appears to have increased wheat yield by about 10 to 20% since 1952 (Nicholls, 1997) 9 Multiple interactions, vulnerability and adaptation Climate change

Systems and social groups that need to adapt Economic, social, demographic, land use changes Social vulnerability 10 Social vulnerability Starvation is the characteristic of some people not having enough food to eat. It is not the characteristic of there being not enough food to eat. While the later can cause the former, it is but one of many possible causes.

A. Sen, Poverty and Famines, An Essay on Entitlement and Deprivation, 1981, pg 1 11 Multiple interactions: Stakeholders define adaptation Scientists Policy makers Civil stakeholders 12 Concepts are important: The big picture Conclusions for policy Models Assumptions

Theory Data 13 Agriculture: empirical evidence 14 Source: Wei Xiong, Erda Lin, Xiu Yang, et al., 2006 15 POSSIBLE BENEFITS POSSIBLE BENEFITS POSSIBLE BENEFITS Possible benefits CO CO

CO 222 POSSIBLE BENEFITS POSSIBLE BENEFITS CO2 2 CO CARBONDIOXIDE DIOXIDE CARBON DIOXIDE CARBON FERTILIZATION FERTILIZATION FERTILIZATION CARBONDIOXIDE DIOXIDE CARBON

FERTILIZATION FERTILIZATION LONGER LONGER LONGER GROWING GROWING GROWING SEASONS SEASONS SEASONS LONGER LONGER GROWING GROWING SEASONS SEASONS INCREASED INCREASED INCREASED

PRECIPITATION PRECIPITATION PRECIPITATION INCREASED INCREASED PRECIPITATION PRECIPITATION POSSIBLEDRAWBACKS DRAWBACKS POSSIBLE DRAWBACKS POSSIBLE Possible drawbacks POSSIBLE DRAWBACKS PESTS POSSIBLE DRAWBACKS PESTS

PESTS MORE MORE MORE FREQUENT FREQUENT FREQUENT DROUGHTS DROUGHTS DROUGHTS MORE MORE FREQUENT FREQUENT DROUGHTS DROUGHTS HEAT HEAT HEAT PESTS PESTS

STRESS STRESS STRESS HEAT HEAT STRESS STRESS FASTER FASTER FASTER GROWING GROWING GROWING PERIODS PERIODS PERIODS FASTER FASTER GROWING GROWING PERIODS

PERIODS INCREASED INCREASED INCREASED FLOODINGAND AND FLOODING AND FLOODING SALINIZATION SALINIZATION SALINIZATION INCREASED INCREASED FLOODINGAND AND FLOODING SALINIZATION SALINIZATION 16

Weeds, pests and diseases Weeds, pests, and diseased damage about one half of the potential production every year 17 Climate change affects crop production POSSIBLE BENEFITS CO2 CARBON DIOXIDE FERTILIZATION LONGER GROWING SEASONS INCREASED PRECIPITATION

POSSIBLE DRAWBACKS MORE FREQUENT DROUGHTS PESTS HEAT STRESS FASTER GROWING PERIODS INCREASED FLOODING AND SALINIZATION Changes in biophysical conditions

Changes in socio-economic conditions in response to changes in crop productivity (farmers income; markets and prices; poverty; malnutrition and risk of hunger; migration) 18 How might global climate change affect food production? 2020s Percentage change in average crop yields for the Hadley Center global climate change scenario (HadCM3). Direct physiological effects of CO2 and crop adaptation are taken into account. Crops modeled are: wheat, maize, and rice. 2050s

2080s Source: NASA/GISS; Rosenzweig and Iglesias, 2002; Parry et al, 2004 Yield Change (%) -30 -20 -10 -5 -2.5 0 2.5 5

10 20 30 40 19 Limits to adaptation Technological limits (i.e., crop tolerance to water-logging or high temperature;

water reutilization) Social limits (i.e., acceptance of biotechnology) Political limits (i.e., rural population stabilization may not be optimal land use planning) Cultural limits (i.e., acceptance of water price and tariffs) 20 Developed-Developing country differences Potential change (%) in national cereal yields for the 2080s (compared with 1990) using the HadCM3 GCM and SRES scenarios (Parry et al., 2004) Scenario A1FI A2a A2b A2c

A2c B1a B2b CO2 (ppm) 810 709 709 709 527 561 561

World (%) -5 0 0 -1 -3 -2 -2 Developed (%) 3 8

6 7 3 6 5 Developing (%) -7 -2 -2 -3 -4

-3 -5 DevelopedDeveloping) (%) 10 10 8 10 7 9 9 21

Additional people at risk of hunger Additional Millions of People 200 160 120 80 40 0 2020 2050 A2 - Regional Enterprise 2080 B2 - Local Stewardship Parry et al., 2004 22

Interaction and integration: Water Additional population under extreme stress of water shortage Population (millions) 120 80 40 0 2020 2050 2080 University of Southampton 23 Conclusions

While global production appears stable, . . . . . . regional differences in crop production are likely to grow stronger through time, leading to a significant polarization of effects, . . . . . . with substantial increases in prices and risk of hunger amongst the poorer nations Most serious effects are at the margins (vulnerable regions and groups) 24 Agenda 9:15 10:45

1. 2. 3. Climate variability and change, agriculture, and food security Key differential vulnerabilities Key issues 10:45 11:00 Coffee 11:00 12:30 4. Models, assisting tools for stakeholders, risk management 1. 2.

12:30 13:30 Lunch 13:30 15:00 4. Designing the framework Participatory evaluation and prioritization of adaptation Models, assisting tools for stakeholders, risk management 3. PC based training 25 Key differential vulnerabilities

Climate change is one stress among many now affecting agriculture and the population that depends on it Integration of results and stakeholder definition of adaptation strategies are essential to formulate assessments relevant to policy Potential future consequences depend on: The region and the agricultural system [Where?, The baseline is important] The magnitude [How much? Scenarios are important] The socio-economic response [What happens in response

to change? Adaptive capacity (internal adaptation) and planned stakeholder adaptation and policy] 26 Where? Systems and social groups Map of the night-time city lights of the world (DMSP: NASA and NOAA) 27 How much? Climate and SRES scenarios Had CM2 model, 2050s Temperature change Precipitation change 28 What happens in response to change?

Adaptive capacity (internal adaptation) Planned adaptation 29 Definition of key vulnerabilities Expert judgement Stakeholder consultation Empirical evidence Scientific knowledge of processes Models are assisting tools 30 Check list and ranking of potential

vulnerabilities - Examples Components of the farming system particularly vulnerable Stress on water/irrigation systems Domestic agricultural production Food shortages that lead to an increase in hunger Agricultural exports Prices to consumers Government policies such as agricultural pricing, support,

research and development Greater stress on natural resources or contribute to environmental degradation (e.g., through land-use change, soil degradation, changes in water supply and water quality, pesticide use, etc.) Research/extension system capability for providing adaptation advice to farmers Technological options in place 31 Key vulnerabilities Who can adapt? Who is vulnerable? Individuals particularly vulnerable to environmental change are those with .

Relatively high exposures to changes High sensitivities to changes Low coping and adaptive capacities Low resilience and recovery potential 32 Agenda 9:15 10:45 1. 2. 3. Climate variability and change, agriculture, and food security Key differential vulnerabilities Key issues 10:45 11:00

Coffee 11:00 12:30 4. Models, assisting tools for stakeholders, risk management 1. 2. 12:30 13:30 Lunch 13:30 15:00 4. Designing the framework Participatory evaluation and prioritization of adaptation

Models, assisting tools for stakeholders, risk management 3. PC based training 33 Key issues Integration and cooperation (social, water) Calibration Extreme events Uncertainties 34

Key issues: Pressures and solutions Water Population Economic and social development Technology (water desalination, reuse, efficiency) Agricultural technology Cooperation Improved management 35

Albania 0 Yemen Viet Nam Vanuatu Uzbekistan Tuvalu Turkmenistan Tonga Thailand Tajikistan Solomon

Singapore Samoa Philippines Palau Pakistan Niue Nepal Nauru Mongolia Micronesia,Fed Maldives

Malaysia Lebanon Laos Kyrgyzstan Kuwait Korea, Korea, Dem Kiribati Kazakhstan Jordan Iran, Islamic

Indonesia India Cook Islands China Cambodia Bhutan Bangladesh Bahrain Bahamas Water Agricultural water use % of total (2004)

100 80 60 40 20 36 -2 -4 Bahrain Bahamas 0

Yemen Viet Nam Vanuatu Uzbekistan Tuvalu Turkmenistan Tonga Thailand Tajikistan Solomon Singapore

Samoa Philippines Palau Pakistan Niue Nepal Nauru Mongolia Micronesia,Fed Maldives Malaysia

Lebanon Laos Kyrgyzstan Kuwait Korea, Korea, Dem Kiribati Kazakhstan Jordan Iran, Islamic Indonesia

India Cook Islands China Cambodia Bhutan Bangladesh -6 Albania Population Rural population change % (1993-2003) 6 4

2 -8 -10 -12 -14 37 -7,000 Bahrain Maldives Malaysia Lebanon

Laos Kyrgyzstan Kuwait Korea, Korea, Dem Kiribati Kazakhstan Jordan Iran, Islamic Indonesia India

Cook Islands China Cambodia Bhutan Bangladesh Albania 8,000 3,000 Vanuatu Viet Nam Yemen Vanuatu Viet Nam Yemen

Tuvalu 13,000 Uzbekistan Agricultural trade balance (exportts-imports) value (million $) (2004) Tuvalu Turkmenistan Tonga Thailand Tajikistan Solomon Singapore

Samoa Philippines Palau Pakistan Niue Nepal Nauru Mongolia Micronesia,Fed Maldives Malaysia

Lebanon Laos Kyrgyzstan Kuwait Korea, Korea, Dem Kiribati Kazakhstan Jordan Iran, Islamic Indonesia

India Cook Islands China Cambodia Bhutan Bangladesh Bahrain Bahamas 2,000,000 1,800,000 1,600,000 1,400,000 1,200,000 1,000,000

800,000 600,000 400,000 200,000 0 Uzbekistan Turkmenistan Tonga Thailand Tajikistan Solomon Singapore Samoa

Philippines Palau Pakistan Niue Nepal Nauru Mongolia Micronesia,Fed -12,000 Albania -2,000 Bahamas

Economic and social development GDP 2004 (millions of US dollars) -17,000 -22,000 38 Integration and cooperation Additional population under extreme stress of water shortage Population (millions) 120 80 40 0 2020

2050 2080 Source: University of Southampton 39 Water The agriculture sector needs water supply scenarios Policy defines how much water can be used by agriculture Water policy and rights are extremely hard to change 40

Water conflicts Evolucin del balance Demandas - Disponibilidades 1000 900 800 700 hm 3 600 Valmayor El Atazar 500 Nuevos pozos Tr. S. Juan Valmayor Sequa 1982

Sequa 1992 Imp. Picadas 400 300 200 100 0 1970 1975 1980 1985 1990 1995 Capacidad de suministro

2000 2005 2010 2015 2020 Demanda 41 Transboundary surface and groundwater Water can lead to political hostilities and many regions with political conflicts also share water resources www.bgr.de/app/whymap/

42 Political and cultural process The political process reflects the view about future of the resources and economies, therefore defines the range of adaptation options Cultural impediments to change traditional water management add complexity to the design of adaptation strategies Irrigation Area: 2000 and 2010 4000 Irrig Area (ha x 1000) 2000 2010

3000 2000 1000 0 France Spain Italy Greece Portugal Source: EEA, CEDEX 43 Tunisia: National strategy

on water management (Source: R. Mougou) Current and projected water demand (%) Drinking Irrigation Tourism Industrial 2030 17.7 73.5 1.5 7.3 Resources management

1996 11.5 83.7 0.7 4.1 Mobilization, storage (over 1,000 hill reservoirs in 10 years), and transfer of the resources Use of the non conventional resources: saline and waste water for irrigation (95,400 and 7,600 ha) Desalinization Demand management Water saving in irrigation (up to 60% Government subsidies) Example: Integrated assessment in Egypt

Aim Analysis of no regret options for the future Current vulnerability Dependence on the Nile as the primary water source Large traditional agricultural base Long coastline already undergoing both intensifying development and erosion Problems derived from population increase Agriculture entirely based on irrigation (water from the Nile, and to lesser degree from groundwater) Soil conditions and water quality deteriorating Source: El-Shaer et al., 1997; Strzpek et al., 1999 45 Cooperation and integration Your expert opinion, consultation 46

Calibration of models This afternoon Documentation 47 Extreme events Your expert opinion, consultation Large knowledge based on risk management of natural disasters Empirical evidence is essential (external shock, impacts, vulnerability) 48

Uncertainties Your expert opinion, consultation Climate change scenarios Climate variability Stakeholder adaptation Agricultural models Effects of CO2 on crops Issues of scale Socio economic projections 49 Thanks for your attention!

Visit MEDROPLAN on the web www.iamz.ciheam.org/medroplan [email protected] 50 Agenda 9:15 10:45 1. 2. 3. Climate variability and change, agriculture, and food security Key differential vulnerabilities Key issues 10:45 11:00

Coffee 11:00 12:30 4. Models, assisting tools for stakeholders, risk management 1. 2. 12:30 13:30 Lunch 13:30 15:00 4. Designing the framework Participatory evaluation and prioritization of adaptation

Models, assisting tools for stakeholders, risk management 3. PC based training 51 The process: Example Set up a Multidisciplinary Stakeholder Team (Organizational component) Evaluate the legal, social, and political process (Organizational component) Public review and Revision

Public dissemination (Operational component) Select and identify priority actions, based on agreed criteria (Operational component) Identify risk and potential vulnerabilities (Methodological component) www.iamz.ciheam.org/medroplan 52 Agenda 9:15 10:45 1.

2. 3. Climate variability and change, agriculture, and food security Key differential vulnerabilities Key issues 10:45 11:00 Coffee 11:00 12:30 4. Models, assisting tools for stakeholders, risk management 1. 2. 12:30 13:30

Lunch 13:30 15:00 4. Designing the framework Participatory evaluation and prioritization of adaptation Models, assisting tools for stakeholders, risk management 3. PC based training 53 Bottom-up stakeholder adaptation

Objective of the strategy: To minimize impacts of a warmer and drier climate while maintaining rural agricultural production and minimizing the environmental damage Consideration of effectiveness to minimize the impacts of a warmer and drier climate, cost, and feasibility Adequacy for situation without climate change (win-win strategy) 54 Bottom-up stakeholder adaptation Possible tool: MCA WEAP 55

Bottom-up stakeholder adaptation Surveys: Adaptation to climate change in Tunisia, Source: R. Mougou 56 Bottom-up stakeholder adaptation Stakeholder group Adaptation Level 1 Adaptation Level 2 Adaptation Level 3 Small-holder farmers or farmers' groups

Tactical advice on changes in crop calendar and water needs Management of risk in water availability (quantity and frequency) Education on water-saving practices and changes in crop choices Commercial farmers Tactical on

improving cash return for water and land units Investment in irrigation technology; Risksharing (e.g., insurance) Private sector participation in development of agro-businesses Resource Managers Education on alternatives for land and water management

Integrated resource management for water and land Alternatives for the use of natural resources and infrastructure 57 Water harvesting Source: T. Oweis, 2004 58 Bottom-up stakeholder adaptation Examples 1. 2. 3.

4. 5. 6. 7. 8. 9. 10. 11. 12. Tactical advice crop calendar Tactical advice water needs Improve cash return for water and land units Management of risk in water Investment Integrated resource management for water and land

Education Private sector participation Alternatives for the use of natural resources and infrastructure Crop residue incorporation Access to fertilizer Extension services 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25.

26. Indigenous knowledge Short-duration varieties Crop diversification New crop varieties New crops Agroforestry Food storage Agrometeorological advice Construction of a dam Irrigation (new scheme) Irrigation (improved system) Water harvesting Water desalination / reutilization Cease activity 59 Example: Use MCA WEAP

60 Agenda 9:15 10:45 1. 2. 3. Climate variability and change, agriculture, and food security Key differential vulnerabilities Key issues 10:45 11:00 Coffee 11:00 12:30 4.

Models, assisting tools for stakeholders, risk management 1. 2. 12:30 13:30 Lunch 13:30 15:00 4. Designing the framework Participatory evaluation and prioritization of adaptation Models, assisting tools for stakeholders, risk management 3.

PC based training 61 Assisting tools to stakeholders Need quantitative estimates Models are assisting tools Surveys to stakeholders are assisting tools for designing bottom-up adaptation options Key variables for agronomic and socioeconomic studies: crop production, land suitability, water availability, farm income, 62 Before getting started .

Models are assisting tools, stakeholder participation is essential The use of models requires high degree of technical expertise The merits of each model and approach vary according to the objective of the study, and they may frequently be mutually supportive Therefore, a mix of tools and approaches is often the most rewarding 63 Quantitative methods and tools

Experimental Analogues (spatial and temporal) Production functions (statistically derived) Agro-climatic indices Crop simulation models (generic and cropspecific) Economic models (farm, national, and regional) Provide results that are relevant to policy Social analysis tools (surveys and interviews) Allow the direct input of stakeholders (demanddriven science), provide expert judgment Integrators: GIS 64

Experimental Value Spatial scale of results Season to decades Time to conduct analysis Site Data needs 4 to 5 Skill or training required 1 Technological resources 4 to 5 Financial resources 4 to 5 Range for ranking is 1 (least amount) to 5 (most demanding). Example: growth chambers, experimental fields. 65 Experimental: Effect of Increased CO2 http://www.whitehouse.gov/media/gif/Figure4.gif

Near Phoenix, Arizona, scientists measure the growth of wheat surrounded by elevated levels of atmospheric CO2. The study, called Free Air Carbon Dioxide Enrichment (FACE), is to measure CO2 effects on plants. It is the largest experiment of this type ever undertaken. 66 http://www.ars.usda.gov Analogues (space and time) Value Spatial scale of results Decades Time to conduct analysis Site to region Data needs 1 to 2

Skill or training required 1 to 3 Technological resources 1 to 3 Financial resources 1 to 2 Range for ranking is 1 (least amount) to 5 (most demanding). Example: existing climate in another area or in previous time 67 Analogues: drought, floods Africa vegetation health (VT - index) Vegetation health: Red stressed, Green fair, Blue favorable Source: NOAA/NESDIS 68 Production functions Value Spatial scale of results

Season to decades Time to conduct analysis Site to globe Data needs 2 to 4 Skill or training required 3 to 5 Technological resources 3 to 5 Financial resources 2 to 4 Range for ranking is 1 (least amount) to 5 (most demanding). Example: Derived with empirical data. 69 Production functions Statistically derived functions (Almeria Wheat) Yield Irrigation demand 400

Irrigation (mm) Dryland Yield (kg ha-1) 8000 6000 4000 2000 300 200 100 Irrigation Dryland Yield 0

-150 Predicted Values -100 -50 0 50 100 Yr PP Change (%) 150 0 -150 Predicted Values -100

-50 0 50 100 150 Yr PP Change (%) Iglesias, 1999; Iglesias et al., 2000 70 Agroclimatic indices Value Spatial scale of results Season to decades Time to conduct analysis Site to globe

Data needs 1 to 3 Skill or training required 2 to 3 Technological resources 2 to 3 Financial resources 1 to 3 Range for ranking is 1 (least amount) to 5 (most demanding). Example: FAO, etc. 71 Agroclimatic Indices Length of the growing periods (reference climate, 1961-1990). IIASA-FAO, AEZ 72 Crop models Value Spatial scale of results

Daily to centuries Time to conduct analysis Site to region Data needs 4 to 5 Skill or training required 5 Technological resources 4 to 5 Financial resources 4 to 5 Range for ranking is 1 (least amount) to 5 (most demanding). Example: CROPWAT, CERES, SOYGRO, APSIM, WOFOST, etc. 73 Crop models Based on Understanding of plants, soil, weather, management

Calculate Water Growth, yield, fertilizer & water requirements, etc Carbon Require Information (inputs): weather, management, etc Nitrogen 74 Models - Advantages

Models are assisting tools, stakeholder interaction is essential Models allow to ask what if questions, the relative benefit of alternative management can be highlighted: Improve planning and decision making Assist in applying lessons learned to policy issues Models permit integration across scales, sectors, and users 75 Models - Limitations

Models need to be calibrated and validated to represent reality Models need data and technical expertise Models alone do not provide an answer, stakeholder interaction is essential 76 Economic and social tools Value Spatial scale of results Yearly to centuries Time to conduct analysis Site to region Data needs 4 to 5 Skill or training required

5 Technological resources 4 to 5 Financial resources 4 to 5 Range for ranking is 1 (least amount) to 5 (most demanding). Example: Farm, econometric, I/O, national economies, MCA WEAP 77 Economic models Consider both producers and consumers of agricultural goods (supply and demand) Economic measures of interest include:

How do prices respond to production amounts? How is income maximized with different production and consumption opportunities? Microeconomic: Farm Macroeconomic: Regional economies All: Crop yield is a primary input (demand is the other primary input) Economic models should be built bottom-up 78 Differences in farming systems Small holder farmer Commercial farmer

Strategy of production Stabilize food production Maximize income Risk Malnutrition and migration Debt and cessation of activity Primary source of risk Weather Weather, markets

and policies Non-structural risk avoidance mechanisms Virtually nonexistent Insurance, credit, legislation Inputs and farm assets Very low Very significant 79 Social sciences tools

Surveys and interviews Allow the direct input of stakeholders (bottom-up approach is emphasized) Provide expert judgment in a rigorous way 80 Integrators: GIS Value Spatial scale of results monthly to centuries Time to conduct analysis region Data needs 5 Skill or training required 5

Technological resources 5 Financial resources 5 Range for ranking is 1 (least amount) to 5 (most demanding). Example: . All possible applications . 81 Conclusions The merits of each approach vary according to the level of impact being studied, and they may frequently be mutually supportive

Therefore, a mix of approaches is often the most rewarding Data are required data to define climatic, nonclimatic environmental, and socio-economic baselines and scenarios Data is limited Discussion on supporting databases and data sources 82 Data: Scales, Sources, Reliability Iirrg Area (ha x 1000) 450 Irrigation Area Tunisia (1970 - 1998) 350 250 150 50 1970

1975 1980 1985 1990 Year FAO Data USDA ERS Data 1995 83 PC Based examples DSSAT CROPWAT 84

Can crop models explain observations? 2002 Egypt Morocco Spain Tunisia Area (1000ha) Population (1000) Population 2030 (1000) Population in agriculture (% of total) Population in rural areas (% of total) Population in rural areas 2030 (% of total) 100,145 70,507

109,111 35 57 44,655 30,072 42,505 35 43 50,599 40,977 39,951 7 22 16,361 9,728 12,351 24 33

46 29 15 22 Agricultural Area (% of total) Irrigation area (% of agricultural) Wheat Yield (kg/ha) (World = 2,678) 3 100 6,150 69 4 1,716 58 12

2,836 55 4 3,853 Agricultural Imports (million $) Agricultural Exports (million$) Fertiliser Consumption (kg/ha) 3,688 774 392 1,740 811 12 12,953 16,452 74

1,022 391 12 No Low 17 No Low 14 Yes High 4 No Low 12 4,000

3,900 21,200 6,800 Crop Drought Insurance Agricultural Subsidies Agriculture, value added (% of GDP) GDP Per capita (US$) UN derived from purchasing power parity (PPP) Data: FAOSTAT 85 Some crops are more complicated than others . 86

Practical Applications: DSSAT International Consortium for Agricultural Systems Applications http://www.icasanet.org/ http://www.clac.edu.eg 87 Applications of DSSAT to answer adaptation questions What components of the farming system are particularly vulnerable, and may thus require special attention? Can optimal management decrease vulnerability to climate? What are the characteristics of optimized crop varieties? 88

DSSAT Decision Support System for Agrotechnology Transfer Components Description DATABASES Weather, soil, genetics, pests, experiments, economics MODELS Crop models (Maize, wheat, rice, barley, sorghum, millet, soybean, peanut, dry bean, potato, cassava, etc) SUPPORTING SOFTWARE Graphics, weather, pests, soil,

genetics, experiments, economics APPLICATIONS Validation, sensitivity analysis, seasonal strategy, crop rotations 89 Input Requirements WEATHER: Daily precipitation, maximum and minimum temperatures, solar radiation SOIL: Soil texture and soil water measurements MANAGEMENT: planting date, variety, row

spacing, irrigation and N fertilizer amounts and dates, if any CROP DATA: dates of anthesis and maturity, biomass and yield, measurements on growth and LAI 90 ESSENTIAL STEP 1. Crop Model Validation Source: Iglesias, 1999 91 Key issues Limitations of datasets Limitations of models Lack of technical expertise and resources

Limitations of the studies due to lack of integration with: Water availability and demand Social and economic response 92 Datasets Data are required data to define climatic, non-climatic environmental, and socioeconomic baselines and scenarios Data is limited Discussion on supporting databases and data sources

93 Guided examples Effect of management (nitrogen and irrigation) in wet and dry sites (Florida, USA, and Syria) Effect of climate change on wet and dry sites 1. 2. Sensitivity analysis to changes in temperature and precipitation (thresholds), and CO2 levels 94 Application 1. Management

Objective: Getting started 95 Weather Syria Florida, USA SR (MJ m2 day-1) 19.3 16.5 T Max (C) 23.0

27.4 T Min (C) 8.5 14.5 Precipitation (mm) 276.4 1364.3 Rain Days (num) 55.7 114.8 96

Input files needed Weather Soils Cultivars Management files (*.MZX files) description of the experiment 97 Open DSSAT 98 Examine the data files Weather file Soil

file Genotype file (Definition of cultivars) 99 Location of the cultivar file 100 Select the cultivar file 101 Examine the cultivar file 102 Examine the cultivar file 103

Location of the weather file 104 Selection of the weather file 105 Examine the weather file 106 Calculate monthly means 107 Calculate monthly means 108 Program to generate weather data

109 Location of the input experiment file 110 Select the experiment file 111 Examine the experiment file (Syria) 112 Examine the experiment file (Florida) 113 The experiment file can be edited also with a text editor (Notepad) .

114 Start simulation 115 Running 116 Select experiment 117 Select treatment 118 View the results 119

Select option 120 Retrieve output files for analysis C:/DSSAT35/MAIZE/SUMMARY.OUT C:/DSSAT35/MAIZE/WATER.OUT C:/DSSAT35/MAIZE/OVERVIEW.OUT C:/DSSAT35/MAIZE/GROWTH.OUT C:/DSSAT35/MAIZE/NITROGEN.OUT There are DOS text files Can be imported into Excel 121

Analyse and present results 12000 Management: Maize Yield Florida and Syria Grain Yield (kg/ha) 10000 8000 6000 Florida Syria 4000 2000 0 Rainfed Low N Rainfed High N Irrig Low N

Irrig High N 122 Application 2. Sensitivity to climate Objective: Effect of weather modification 123 Start simulation 124 Sensitivity analysis 125 Select option

126 Analyse results . Climate Change: Maize Yield Florida 2500 Grain Yield (kg/ha) 2000 1500 1000 500 0 Florida Base Florida -50% pp

127 Proposed application: Adaptation For advanced participants 128 Adaptation Management strategy: Explicit guidance to farmers regarding optimal crop selection, irrigation, and fertilization, and should institute strong incentives to avoid excessive water use Use the DSSAT models to evaluate the use of alternative existing varieties and

changes in the timing of planting to optimize yield levels or water use 129 Pioneer, April 00 - 129 Applications of CROPWAT to answer adaptation questions Can the water/irrigation systems meet the stress of changes in water supply/demand? Will climate change significantly affect agricultural water demand production? 130 CROPWAT is a decision support system for irrigation planning and management. http://www.clac.edu.eg http://www.fao.org/ag/agl/aglw/cropwat.htm

131 Experiments 1. 2. 3. Calculate ET0 Calculate crop water requirements Calculate irrigation requirements for several crops in a farm 132 Start CROPWAT 133 Retrieve climate file 134

Examine temperature 135 Examine ET0 136 Calculate ET0 137 Examine rainfall 138 Retrieve crop parameters 139 View progress of inputs

140 Define and view crop areas selected 141 Define irrigation method 142 Input data completed 143 Calculate irrigation demand 144 Calculate irrigation schedule 145

View results 146 Review Climate variability and change, agriculture and food security Key differential vulnerabilities Key issues Models, assisting tools for stakeholders, risk management

Designing the framework Participatory evaluation and prioritization of adaptation PC based training [email protected] 147 Review Climate variability and change, agriculture and food security Key differential vulnerabilities Key issues 1. 2. 3. 1. 2. 3. 4.

Integration and cooperation (social, water) Calibration Extreme events Uncertainties PC based training: Models, assisting tools for stakeholders, risk management 4. 1. 2. 3. Designing the framework Participatory evaluation and prioritization of adaptation PC based training 148

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