10th CBMS Conference: Session on Climate Change Adaptation and Food SecurityCrowne Plaza Manila Galleria, 24-26 March 2014

Assessments of Climate Change Impacts and

Mapping of Vulnerability to Food Insecurity under

Climate Change to Strengthen Household

Food Security with Livelihood Adaptation

Approaches

Introduction AMICAF is a comprehensive framework by the Food and

Agriculture Organization (FAO) of the United Nations that aims to address climate change impacts and adaptation planning targeted at improving the food security of vulnerable household groups.

In the Philippines, the AMICAF framework is currently being implemented in cooperation with the Department of Agriculture with funding from the Japanese government.

The Philippine activities started in January 2012 and it will end on October 2014 for a 3-yr implementation.

AMICAF Framework: Addressing the Linkage Between Climate Change and Food Security

To assist developing countries to address climate change assessment and adaptation, to improve food security through a comprehensive framework.

This framework would bridge climate change impact assessment, food Insecurity vulnerability analysis and livelihood adaptation approaches.

Step 1Impacts of Climate

Change on Agriculture

Step 2 Food Insecurity

Vulnerability Analysis at

household level

Step 3 Livelihood

Adaptation to Climate Change

Step 4 Institutional Analysis and

Awareness Raising

Global delivery Global Guideline for

Implementation in other countries

Step 1: Impacts of climate change on agriculture MOSAICC – Modeling System

for Agricultural Impacts of Climate Change

Multiple impact models (Climate downscaling, Crops, Hydrology, Economy) in one package

Crop model uses downscaled climate data, not hydrology outputs. Hydrology and crop models run in parallel. The results from hydro and crop are together used in PAM

Developed Partial Equilibrium model, named PAM (Provincial Agricultural Market Model) as link to VA (Step 2)

Climate projectionsdownscaling

Historical weatherrecords

Downscaled Climate projections

Hydrological Model

Crop growth Simulation

IPCC GCMLow resolution

projections

Historical dischargerecords

Water availabilityfor irrigation

Historical water usestatistics

Historical Yield records

Yield projections

Crop parameters

Soil data

Technology trend scenarios

Soil and Land usedata

Dam data

Provincial AgriculturalMarket Impact

Step2 (Food Insecurity Vulnerability Analysis)

Provincial AgriculturalModel (PAM)

Partners: PAGASA, UP-NIGS, PhilRice, NEDA

Climate Downscaling for Precipitation2 climate scenarios and 3 models

PAGASA has completed the final runs of the statistical downscaling for two scenarios (A1B, A2) and 3 models (BCM2, CNCM3, ECHMA5) with topography as a major consideration

Results have high statistically probability functions

Analysis confirmed the maximum interpolation parameter set at 2000 mm;

GCMs with highest percent changes in precipitation (i.e. 1162%: MPEH5 A1B) are still found to be reasonable; and

December-January-February and March-April-May have positive % changes, which mean dry months are becoming wetter.

% change in rainfall

Hydrology modelling using STREAM by UP-NIGS

UP-NIGS has finalized results using the hydrology model (STREAM ) on 14 major river basins and provinces

Projections for river discharges were up to 2050 for 3 models and 2 climate change scenarios using climate projections from PAGASA

Crop Modeling (WABAL) by PhilRice WABAL Crop model uses historical BAS data on crop yield linked with calculated

PET from downloaded rainfall, temperature & crop coefficient Runs using final climate data uploaded in MOSAICC server has been done for

four provinces representing 4 climate types (Nueva Ecija, Isabela, Agusan del Sur and Camarines Sur)

Review by FAO technical officers of the results are on-going before proceeding to other provinces

Provincial Agricultural Market Modeling by NEDA

PAM through partial equilibrium model (excel based) has been developed for irrigated & rainfedrice, and corn production linked to climate variables

Yield output from crop model is used and area harvested change in PAM according to supply/demand

Covers 79 provinces and 2 cities with 2010 as base year and projection is until 2030

PAM is calibrated with prices statistics from BAS

Retail price, Provincial level

Step 2: Food Insecurity Vulnerability Analysis

Develops an analytical econometrics model with the best available national household datasets; Climate information will be used to construct relevant climate shocks (level, volatility and seasonality) to assess their impact at household welfare to characterize vulnerability

Identify variables associated with highest levels of vulnerability; Explore and identify the channels through which climate changes pass through at the household (farm) level and welfare

Identify and map vulnerable groups (profiling); Considering adaptive capacity options of farmers (economic level)

Exploration of the efficiency of different policy tools (simulations

Partners: CBMS-DLSU, FNRI

Households who experienced hunger, by region, APIS, 2011FIVIMS

Household vulnerability effects of rainfall(preliminary results using 2009 FIES data)

Coef. Sig. Std. Err. Coef. Sig. Std. Err. Dy/dx Coef. Sig. Std. Err. Coef. Sig. Std. Err. dP(foodp=1)/dx

ln of income from farming 0.088 *** 0.03 0.088 -0.8 *** 0.048 -0.167

Age of HH -0.004 *** 0.001 0.006 *** 0 0.006 -0 *** 0.001 -0 *** 0.001 -0.004

Female Head 0.385 *** 0.032 -0.101 *** 0.016 -0.101 0.38 *** 0.032 0.42 *** 0.044 0.091Highest Educational Attainment of HH head

No Grade base base

Elem undergraduate_2 -0.241 *** 0.047 0.101 *** 0.017 0.101 -0.2 *** 0.047 -0.2 *** 0.053 -0.055

Elem graduate_3 -0.171 *** 0.049 0.165 *** 0.017 0.165 -0.2 *** 0.049 -0.3 *** 0.055 -0.062

HS undergraduate _4 -0.191 *** 0.054 0.172 *** 0.019 0.172 -0.2 *** 0.054 -0.4 *** 0.064 -0.09

HS graduate_5 -0.181 *** 0.053 0.247 *** 0.019 0.247 -0.2 *** 0.053 -0.4 *** 0.065 -0.091

College undergraduate_6 -0.18 *** 0.063 0.294 *** 0.022 0.294 -0.2 *** 0.062 -0.5 *** 0.089 -0.106

College graduate_7 0.009 0.072 0.44 *** 0.024 0.44 0 0.072 -0.9 *** 0.248 -0.18Amt received from abroad

-0.057 ** 0.027 0.136 *** 0.009 0.136 -0.1 ** 0.027 -0.3 *** 0.049 -0.066

HH with strong construction materials of walls and roof

0.193 *** 0.024 0.067 *** 0.01 0.067 0.19 *** 0.024 0.01 0.038 0.001

HH has safe water -0.105 *** 0.024 0.053 *** 0.009 0.053 -0.1 *** 0.024 -0.2 *** 0.027 -0.037

Number of radios 0.142 *** 0.021 0.032 *** 0.009 0.032 0.14 *** 0.021 0.02 0.032 0.003

Number of TV sets 0.131 *** 0.026 0.103 *** 0.009 0.103 0.13 *** 0.026 -0.2 *** 0.045 -0.041

Number of telephones 0.143 *** 0.024 0.034 *** 0.009 0.034 0.14 *** 0.024 -0.1 *** 0.04 -0.021

Number of cars 0.583 *** 0.053 0.205 *** 0.025 0.205 0.58 *** 0.053 -0.4 . 0.294 -0.097

Number of motorcycles 0.214 *** 0.03 0.074 *** 0.012 0.074 0.21 *** 0.03 -0.3 *** 0.076 -0.055

Cash loan payments 0.138 *** 0.024 -0.007 0.009 -0.007 0.14 *** 0.024 -0 0.039 -0.01HH expenditure on insurance and premiums

-0.201 *** 0.031 0.145 *** 0.012 0.145 -0.2 *** 0.031 -0.7 *** 0.077 -0.144

Climate type

1 base base

2 0.046 * 0.027 0.05 ** 0.023

3 -0.208 *** 0.03 -0.2 *** 0.027

4 -0.089 *** 0.033 -0.1 * 0.03

Annual Rainfall8.48x1

0-5*** 0 8.12x10 *** 0

Urban 0.088 *** 0.029 0.04 . 0.027

Constant 9.459 *** 0.093 5.515 *** 0.297 9.52 *** 0.091 8 *** 0.439

ln of income from farming

ln of food expenditure per capita

ln of income from farming

Food poor A unit increase in rainfall will increase per capita

food expenditure by 0.0000075 A seasonal rainfall categorized as climate type 4

(SI > 0.56) may translate to 0.008 decrease in per capita food expenditure

A unit increase in rainfall will lower the probability of being food poor by 0.00001

A seasonal rainfall categorized as climate type 4 (SI > 0.56) will increase the prob. of being food poor by 0.010

Annual rainfall was recorded at 3263mm in 2009 In 2030, annual rainfall will decrease to 3170mm

while by 2050 annual rainfall will decrease to 2939 as predicted using the CNCM3 A2 scenario

Average predicted annual rainfall for 2031-2050 is 3081 mm

The decrease in the annual rainfall by 2030 will lead to 0.0013 increase in the probability of being food poor

Similarly, the decrease in annual rainfall by 2050 will lead to increase in the probablity of being food poor by 0.44 percent

Subsistence poverty by province

Step 3 Livelihood Adaptation to Climate Change

Identification, validation, field-testing, and evaluation of good adaptation practices at local context through participatory processes and capacity development under the framework of Climate-smart Farmer Field Schools

Field-testing sites are Camarines Sur and Surigaodel Norte with drought, flooding and saline intrusion issues

Partners: DA RFU 5 (Bicol) , DA RFU 13 (Caraga) , PhilRice Agusan, ATI

Bacuag

Testing of CCA options in stressed areas under CS-FFSAMICAF Partners: DA RFU 5, DA Caraga and PhilRice Agusan, selected LGUs

The CCA options (multi-stress tolerant rice lines and rice-duck system) are tested side by side with farmer’s practices in drought, saline-intrusion and submerged areas - managed by CSFFS with AEWs

Products developed for local CCA… A Facilitator’s manual for LGU

AEWs in conducting CSFFS (in collaboration with RWAN) has been distributed and are now used as reference for similar FFS initiatives by other projects

A reference manual for LGUs in setting up and operation of climate information center with PAGASA and RWAN – for printing

Other GPOs introduced through the FFS

• Use of a simple rain gauge to monitor the precipitation at community and farmer’s level;

• importance of using farm weather bulletin (developed by DA 5) in farm decision making and schedule of field operations;

• Typhoon tracking exercises by farmers to monitor the path of typhoons;• PalayCheck system for irrigated and rainfed rice like use of one seedling per

hill, use of 40 kg/ha seeding rate, use of MOET and LCC, as well as IPM and AESA to monitor crop performance over time;

• Intercropping upland rice with corn plants (as border plants) for additional food and source of income;

• Homestead gardening to FFS farmers by provision of vegetable seeds; • Use of IRRI superbag for storing farmer seeds up to 1-year; and• Use of burnay earthen jars for storing some amount of emergency food during

calamities.

Step 4 Institutional analysis and awareness raisingPartners: Climate Change Commission, DA-CCO and NEDA

NEDA and FAO has concluded the discussion for the conduct of policy analysis and simulation studies; training of NEDA staff on policy simulation done, July 1, 4

A workshop for institutional analysis on Sept. 17, 2013 with CCC as lead convenor: Overview of sectoral climate change

frameworks and programs: CCC, NDRRMC, DA, DENR, DAR, DOST

— Initiatives and focus towards convergence and mainstreaming

— Participants from both government, NGOs and academic institutions

Policy Simulation using PAM model

The ratio of government agriculture expenditure vs. total is statistically significant and it leads to an increase in irrigated yield.

Both max and min temperature have a negative impact on both irrigated and rainfed palay yield. However, only max temperature is statistically significant.

Precipitation has a positive impact on rainfed yield and is statistically significant.

Policy/ Climate Variables Irrigated Yield Rainfed Yield

Ratio of Agriculture Expenditure

Max Temperature

Min Temperature

Precipitation

Impact of Policy and Climate Variables on Palay Yield, 1981-2010 (Preliminary)

Projection of Yield under Climate Change ScenarioProjected Average Palay Yield under Climate Change Scenario, 2011-2030

Note: Figures are 2011-2030 averages; Simulation refers to 5% annual increase in government agriculture expenditure ratio; C.V. is the coefficient of variation.

• Palay yield is projected to be lower under A2 Scenario (4.219 mt/ha) compared to A1B Scenario (4.248 mt/ha) given higher temperature and drier climate under A2 scenario;

• Rainfed palay yield is projected to be more volatile than irrigated palay yield;• A 5% increase in the ratio of government agriculture expenditure will increase palay yield to

4.279 mt/ha (A2) and 4.309 mt/ha (A1B).

Mean Mean C.V.w/ 5%

ExpenditureMean C.V.

w/ 5% Expenditure

precip 2699.277 2620.112 0.125 2416.238 0.101tmax 30.356 30.482 0.008 30.600 0.006tmin 21.868 21.959 0.006 22.009 0.007yield 2.998 4.248 0.073 4.309 4.219 0.073 4.279

irrigated 3.450 4.606 0.063 4.584 0.064rainfed 2.243 3.447 0.104 3.401 0.103

A1B (BCM2) A2 (BCM2)Variable

Hindcast/Actual (1981-2010)

Projection (2011-2030)

For further inquiries:

Dr. Eulito U. BautistaNational Project Managereulito.bautista@fao.org