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Title: Vulnerability and Adaptation Assessments Hands-On Training Workshop Impact, Vulnerability and Adaptation Assessment for the Agriculture Sector


1
Vulnerability and Adaptation Assessments
Hands-On Training WorkshopImpact,
Vulnerability and Adaptation Assessmentfor the
Agriculture Sector
2
Outline
  • Climate change, agriculture and food security
  • Methods, tools, and datasets
  • Practical applications
  • Integrated assessments

3
Climate Change, Agriculture, and Food Security
  • Climate change is one stress among many affecting
    agriculture and the population that depends on it

4
Multiple Interactions, Vulnerability, and
Adaptation
Systems and social groups that needs to adapt
5
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.

6
Multiple Interactions, Stakeholders Define
Adaptation
Policy makers
Scientists
Civil stake-holders
7
Multiple Interactions
  • Climate change is one stress among many now
    affecting agriculture and the population that
    depends on it
  • Integration of results is essential to formulate
    assessments relevant to policy
  • Potential future consequences depend on
  • The region and the agricultural system Where?
  • The magnitude How much? Scenarios are
    important.
  • The socioeconomic response What happens in
    response to change? Adaptive capacity (internal
    adaptation) and planned adaptation.

8
Where? Systems and Social Groups
9
How Much? Climate and SRES Scenarios
Had CM2 model, 2050s
Temperature change
Precipitation change
10
What Happens in Response to Change?
  • Adaptive capacity (internal adaptation)
  • Planned adaptation

11
Climate Change Affects Crop Production
  • Changes in biophysical conditions
  • Changes in socioeconomic conditions in response
    to changes in crop productivity (farmers income
    markets and prices poverty malnutrition and
    risk of hunger migration)

12
How Might Global Climate Change Affect Food
Production?
Percentage change in average crop yields for the
Hadley Center global climate change scenario
(HadCM2). Direct physiological effects of CO2 and
crop adaptation are taken into account. Crops
modeled are wheat, maize, and rice. Source
NASA/GISS Rosenzweig and Iglesias, 1994.
13
Limits to Adaptation
  • Technological limits (e.g., crop tolerance to
    water-logging or high temperature water
    reutilization)
  • Social limits (e.g., acceptance of
    biotechnology)
  • Political limits (e.g., rural population
    stabilization may not be optimal land use
    planning)
  • Cultural limits (e.g., acceptance of water price
    and tariffs)

14
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
C02 (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
Developed-Developing) () 10 10 8 10 7 9 9
15
Additional People at Risk of Hunger
Parry et al., 2004
16
Additional People at Risk of Hunger (continued)
  • Overall, the potential for additional people with
    risk of hunger is greater with the unstabilized
    scenario, although there are decadal variations
  • In all decades, the unstabilized scenario is
    the warmest
  • In the 2020s, the warming is beneficial for
    aggregated crop production
  • In the 2080s, the warming exceeds the threshold
    of optimal crop tolerance in many low latitude
    regions with more people at risk

17
Interaction and Integration Water
18
Conclusions
  • Although 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)

19
Methods, Tools, and Datasets
  • The framework
  • The choice of the research methods and tools
  • Demand-driven methods responding to stakeholders
  • Key characteristics, strengths, weaknesses
  • Examples
  • Datasets sources, scales, reliability

20
Frameworks
  • Adaptation Policy Framework (APF), US Country
    Studies, IPCC, seven steps
  • All have the essential common elements
  • Problem definition
  • Selection and testing of methods
  • Application of scenarios (climate and
    socioeconomic)
  • Evaluation of vulnerability and adaptation
  • The studies may want to use a framework as
    guidance or draw from the best elements of all of
    them

21
Demand-Driven Methods
  • Need quantitative estimates
  • Models are assisting tools
  • Surveys are assisting tools for designing
    adaptation options
  • Key variables for agronomic and socioeconomic
    studies crop production, land suitability, water
    availability, farm income,

22
Quantitative Methods and Tools
  • Experimental
  • Analogues (spatial and temporal)
  • Production functions (statistically derived)
  • Agroclimatic indices
  • Crop simulation models (generic and
    crop-specific)
  • 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
    (demand-driven science), provide expert judgment
  • Integrators GIS

23
Experimental Effect of Increased C02
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.
http//www.ars.usda.gov
24
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). Range for ranking is 1 (least amount) to 5 (most demanding).
Example growth chambers, experimental fields.
25
Analogues Drought, Floods
Africa vegetation health (VT - index) Vegetation
health Red stressed, Green fair, Blue
favorable Source NOAA/NESDIS
26
Analogues Drought
27
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). Range for ranking is 1 (least amount) to 5 (most demanding).
Example existing climate in another area or in
previous time
28
Production Functions
Statistically derived functions (Almeria Wheat)
Yield
Irrigation demand
Iglesias et al., 1999
29
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). Range for ranking is 1 (least amount) to 5 (most demanding).
Example Derived with empirical data.
30
Agroclimatic Indices
Length of the growing periods (reference climate,
1961-1990). IIASA-FAO, AEZ
31
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). Range for ranking is 1 (least amount) to 5 (most demanding).
Example FAO, etc.
32
Crop Models
33
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

34
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

35
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). Range for ranking is 1 (least amount) to 5 (most demanding).
Example CROPWAT, CERES, SOYGRO, APSIM, WOFOST,
etc.
36
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?

37
Economic Models (continued)
  • 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

38
Farm Models Differences
Small holder farmer Commercial farmer
Strategy of production Stabilize food production Maximize income
Risk Malnutrition and migration Debt and cessation of activity
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
Price of food crops Local for primary crops and partially global for industrial crops, with some interference of governments Global with some interference of policies
39
Agricultural Trade Models
Parry et al., 1999.
40
Social Sciences Tools
  • Surveys and interviews
  • Allow the direct input of stakeholders
    (demand-driven science), provide expert judgment
    in a rigorous way

41
Surveys and Interviews
  • Development of adaptation options with
    stakeholders

42
Surveys to StakeholdersDesigning Adaptation
Options
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 Risk-sharing (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
43
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). Range for ranking is 1 (least amount) to 5 (most demanding).
Examples Farm, econometric, I/O, national
economies, BLS,
44
Integrators GIS
45
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). Range for ranking is 1 (least amount) to 5 (most demanding).
Example . All possible applications .
46
Conclusions
  • The merits of each approach vary according to the
    level of impact being studied, and they may
    frequently be mutually supportive
  • For example, simple agroclimatic indices often
    provide the necessary information on how crops
    respond to varying rainfall and temperature in
    wide geographical areas crop-specific models are
    use to test alternative management that can in
    turn be used as a component for an economic model
    that analyses regional vulnerability or national
    adaptation strategies
  • Therefore, a mix of approaches is often the most
    rewarding

47
Datasets
  • Data are required data to define climatic,
    nonclimatic environmental, and socioeconomic
    baselines and scenarios
  • Data are limited
  • Discussion on supporting databases and data
    sources

48
IPCC Working Group 1 A Collective Picture of a
Warming World
Source of data GISS/NASA
49
Climate
50
FAOCLIM
51
Precipitation Annual 1901-1995
Source of data NOAA, NCDC
52
Global Land Cover Classification
De Fries et al., 1998
7 Wooded grasslands/shrubs 8 Closed bushlands
or shrublands 9 Open shrublands 10 Grasses 11
Croplands 12 Bare 13 Mosses and lichens
1 Evergreen needle leaf forests 2 Evergreen
broad leaf forests 3 Deciduous needle leaf
forests 4 Deciduous broad leaf forests 5 Mixed
forests 6 Woodlands
53
Population
54
Lights are Related to Income and Population
Map of the night-time city lights of the
world DMSP NASA and NOAA
55
Soils FAO
56
FAO and the World Bank
57
FAO
Food aid received from external sources 2000
58
USGS, FEWS, USAID
  • FEWS NET in cooperation with USGS and US AID
  • Botswana village flood watch
  • Carbon sequestration
  • Environmental monitoring and information system
  • Land cover performance
  • Madagascar conservation
  • Rift Valley fever
  • Sahel land use
  • Sustainable tree crops

59
The projected change in annual temperature and
precipitation for the 2050s compared to the
present day, for two GCMs, when the climate
models are driven with an increase in greenhouse
gas concentrations defined by the IPCC
business-as-usual scenario.
60
Data Scales, Sources, Reliability
61
Practical Applications Policy Questions
  • What components of the farming system are
    particularly vulnerable and may thus require
    special attention?
  • Can the water/irrigation systems meet the stress
    of changes in water supply/demand?
  • Will climate significantly affect domestic
    agriculture?

62
Practical Applications DSSAT
  • Question What components of the farming system
    are particularly vulnerable, and may thus require
    special attention? crop models (e.g., DSSAT)

http//www.icasanet.org/
http//www.clac.edu.eg
63
Practical Applications DSSAT
  1. Overview and previous examples of use
  2. Guided use of model (three practical applications
    to be done by the participants)

64
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
65
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
    Leaf Area Index (LAI)

66
Crop Model Validation
Source Iglesias et al., 1999
67
Examples
  • Can optimal management be an adaptation option
    for maize production in Zimbabwe?
  • Can adaptation be achieved by optimizing crop
    varieties?
  • Does the start of the rainy season affect maize
    yield in Kasungu, Central Malawi?

68
Can Optimal Management be an Adaptation Option
for Maize Production in Zimbabwe?
Muchena, 1994
Agroclimatic zones
69
Impacts Zimbabwe
Impacts of climate change CERES-Maize model
Muchena, 1994
70
Adaptation Zimbabwe
Adaptation strategies in Gueru CERES-Maize model
  • Increased inputs and improve management
  • Fertilizer
  • Fertilizer and irrigation

Muchena, 1994
71
Can Adaptation be Achieved by Optimizing Crop
Varieties?
Crop Coefficients Corn
72
Does the Start of the Rainy Season Affect Maize
Yield in Kasungu, Central Malawi?
73
Practical Applications
  • Effect of management (nitrogen and irrigation) in
    wet and dry sites (Florida, USA, and Syria)
  • Effect of climate change on wet and dry sites
  • Sensitivity analysis to changes in temperature
    and precipitation (thresholds) and CO2 levels
  • Adaptation Changes in management to improve
    yield under climate change

74
Application 1. Management
  • Objective Getting started

75
Weather
Syria Florida, USA
SR (MJ m2 day1) 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
76
Input Files Needed
  • Weather
  • Soils
  • Cultivars
  • Management files (.MZX files) description of the
    experiment

77
Open DSSAT . . .
78
Examine the Data Files . . .
Weather file
Soil file
Genotype file (Definition of cultivars)
79
Location of the Cultivar File . . .
80
Select the Cultivar File . . .
81
Examine the Cultivar File . . .
82
Location of the Weather File . . .
83
Selection of the Weather File . . .
84
Examine the Weather File . . .
85
Calculate Monthly Means . . .
86
Calculate Monthly Means . . . (continued)
87
Program to Generate Weather Data . . .
88
Location of the Input Experiment File . . .
89
Select the Experiment File . . .
90
Examine the Experiment File (Syria)
91
Examine the Experiment File (Florida)
92
. . . The Experiment File Can Be Edited Also
With a Text Editor (Notepad)
93
Start Simulation
94
Running . . .
95
Select Experiment . . .
96
Select Treatment . . .
97
View the Results . . .
98
Select Option . . .
99
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

100
Analyse and Present Results
101
Exp 2. Sensitivity to Climate
  • Objective Effect of weather modification

102
Start Simulation . . .
103
Sensitivity Analysis . . .
104
Select Option
105
Analyze Results . . .
106
Exp 3. Adaptation
  • Objective For advanced participants

107
  • Can the water/irrigation systems meet the stress
    of changes in water supply/demand? irrigation
    models (e.g., CROPWAT)

http//www.clac.edu.eg
108
Experiments
  1. Calculate ET0
  2. Calculate crop water requirements
  3. Calculate irrigation requirements for several
    crops in a farm

109
Start CROPWAT
110
Retrieve Climate File . . .
111
Examine Temperature . . .
112
Examine ET0 . . .
113
Calculate ET0 . . .
114
Examine Rainfall . . .
115
Retrieve Crop Parameters . . .
116
View Progress of Inputs . . .
117
Define and View Crop Areas Selected . . .
118
Define Irrigation Method . . .
119
Input Data Completed . . .
120
Calculate Irrigation Demand . . .
121
Calculate Irrigation Schedule . . .
122
View Results . . .
123
  • Will climate significantly affect domestic
    agriculture? model integration GIS integration

124
Integration of Agriculture and Other Sectors
  • Discussion on how to integrate the VA methods
    and tools into comprehensive assessments relevant
    to policy
  • Examples
  • Agriculture land use, water use (Egypt)
  • Agriculture socioeconomic issues
    (Mediterranean)
  • Agriculture water (Global)

125
Integrated Assessment in Egypt
Source Strzepek et al., 1999
126
Integrated Assessment in Egypt
  • Methods
  • Scenario development
  • Vulnerability evaluation using agronomic,
    economic, and water allocation models
  • Results Future vulnerability
  • Significant decreases in crop yield and agronomic
    water use efficiency with climate change
  • Overall crop production further deteriorated as
    result of a reduction in agricultural land due to
    sea-level intrusion, and population increase

127
Adaptation Limits of Current Technology
Data FAOSTAT
128
Adaptation
  • On-farm adaptation Use of alternative existing
    varieties and optimization of the timing of
    planting may improve yield levels or water use
    (no cost). In Egypt this is a very limited
    option.
  • Essential changes in resource management (crops,
    water and land) would lead not only to adaptation
    to climate change but also to the overall
    improvement of the agricultural systems (no
    regret options).
  • Explicit guidance to farmers regarding optimal
    crop selection, irrigation, and fertilization.
    Should institute strong incentives to avoid
    excessive water use.

Pioneer, April 00 - 128
129
Socioeconomic Issues
  • Policy, stakeholders
  • Technology

130
Understanding the Stakeholder Linkages and
Decision Process
131
Policy Decisions
  • Adaptation is, in part, a political process, and
    information on options reflects different views
    about the long-term future of resources,
    economies, and societies.
  • (Downing, 2001)

132
Tunisia National Strategy on Water Management
Current and projected water demand ()
1996 2030 Drinking 11.5
17.7 Irrigation 83.7 73.5 Tourism 0.7
1.5 Industrial 4.1 7.3
  • Resources management
  • Mobilization, storage (over 1,000 hill
    reservoirs in 10 years), and transfer of the
    resources
  • Use of the nonconventional resources saline and
    wastewater for irrigation (95,400 and 7,600 ha)
  • Desalinization
  • Demand management
  • Water saving in irrigation (up to 60 government
    subsidies), industry, and other uses

133
Crop Liberalization
  • Example the recent Egyptian policy of crop
    liberalization is giving farmers the possibility
    of adapting to more suitable crops in each area
    as result of this policy, the area sown with
    cotton has sharply decreased in recent years
    while the cereal area has increased.

134
Drought Management in the Mediterranean
  • Disaster management could be an effective
    adaptation option
  • Decreasing drought vulnerability is a win-win
    adaptation option

135
Water for Agriculture
  • WATER is a fundamental requirement for
    agriculture. That requirement is certain to
    increase along with the growth of population and
    living standards, especially in view of the
    prospect of a warmer climate imposed by the
    enhanced greenhouse effect.

136
Methods
SCENARIOS Population, Development, Technology
SCENARIOS GCMs variability
WATBAL Streamflow PET
CLIMATE Precip., Temp. Solar Rad.
WEAP Evaluation Planning
CERES Crop water demand
CROPWAT Regional irrigation
137
Methods
  • Crop yields, water demands, and nitrogen leaching
    are estimated with process based crop models
    (calibrated and validated). The ratios (Kc)
    between simulated and actual crop ET are used to
    estimate regional water demand with CROPWAT, and
    are then adjusted by a regional irrigation
    efficiency.

138
Working with Different Models Consistency,
Scales, Calibration
139
Projections Using the Suite of Models
  • Changes in runoff, water demands, and water
    system reliability
  • Actual changes in crop yield based on consistent
    projections of changes in water supply and demand
  • Changes in environmental stress due to human use
    of water resources
  • Changes in water quality
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