Climate Smart Agriculture

Measuring Policy impact

Jan Verhagen (Wageningen UR)

June 19, 2015

Challenges for agriculture

feeding the world

low vulnerability

low emissions

= Sustainable development

Two times

More Better Less

Food & Nutrition Security

Availability

Access

Utilization

production, stocks, trade, …

income, markets, prices, entitlement, gender, …

processing, preparation, hygiene, tradition, …

Sta

bility

Climate Smart Agriculture

1. sustainably increase agricultural productivity and incomes;

2. build resilience and the capacity of agricultural and food systems to adapt to climate change, and;

3. seek opportunities to reduce and remove greenhouse gases (GHGs) in order to meet their national food security and development goals

Adaptation under the Convention

From fragmentation to coordination and integration of adaptation

COP 7: Packages of decisions for

LDCs:

• Addressing urgent and immediate

adaptation needs through NAPAs

• Establishment of the LDC work

programme, including the NAPAs

• Establishment of the LEG

• Establishment of the LDCF

2007

COP 13 Bali Action

Plan:

• Long-term and

cooperative action by

Parties initiated

2001

Over time:

• Parties recognized the fragmented nature of adaptation

under the Convention and decided to address the issue of

cohesion of adaptation

2010

COP 16 Cancun Adaptation

Framework

• New institutional structures and

processes established to

address adaptation in a

coordinated and coherent

manner

Adaptation Committee

National adaptation plans

Work programme on loss and

damage

At an early stage

• Efforts made in improving the science of CC including

projections/scenarios

• Parties enabled the exploration of different frameworks to define

adaptation, small funds to test different ideas

COP 11: Establishment of NWP

2005

NAP Process

National Adaptation Plan process

To reduce vulnerability to the impacts of climate change, by building adaptive capacity and resilience;

To facilitate the integration of climate change adaptation, in a coherent manner, into relevant new and existing policies, programmes and activities

Decision UNFCCC 5/CP.17, paragraph 1.

Why metrics?

To inform decision-makers about the impacts (including trade off and synergies) of activities and allow learning and evaluating. Key criteria:

● Relevance for the decision maker ● Concise ● Quantifiable ● Link to or use existing monitoring systems ● Allowing monitoring of progress ● Easy to communicate

Metrics framework:

insight in the effects of activities applied by the decision maker to reach their goals;

insight in the connections and interdependencies between different decision makers;

insight in the trade-offs between food productivity, climate resilience and GHG emissions;

a basis for discussions with decision makers and stakeholders around trade-offs and thresholds.

Steps

1. Identify the decision maker and understand the objectives and activities the decision maker;

2. Understand the context (social, financial, technical, natural, human) of the decision maker;

3. Map activities undertaken by the decision maker to reach the objectives

4. Understand the impacts of the activities (trade-offs and synergies)

Scales

Global Global

Spatial scale Jurisdiction scale Main stakeholders

Farm/Household Household level/farm Farmer

Community Landscape Commodity processor

Macro(basin) District Weatherboard/ Retailer

National Consumer

Governments

Example: EU27 - level

Estimating impacts of climate and market changes for selected crops (wheat and potatoes) and grassland/milk on

● achievable production

● demand (food and feed) and compare with production

● regional changes in production area

Hermans, T., & Verhagen, J. (2008). Spatial impacts of climate and market changes on agriculture in Europe, 1–80. Hermans, C., Geijzendorffer, I. R., Ewert, F., Metzger, M. J., Vereijken, P. H., Woltjer, G. B., & Verhagen, J. (2009). Exploring the future of European crop production in a liberalised market, with specific consideration of climate change and the regional competitiveness. Ecological Modelling, 221(18), 2177–2187. http://doi.org/10.1016/j.ecolmodel.2010.03.021

Production regions in 2050

A1 B2 Wheat

Potato

High Medium Low <0.1% of production

Productivity x area

Concluding remarks EU 27

The effects of technology and market change are more pronounced than the effect of climate change;

A wide variability in regional production in Europe is predicted, due to

● differences in productivity of regions

● differences in competitiveness of regions;

Surplus of land is predicted in the global economic scenario A1 and a shortage of land in the regional environmental scenario B2.

Relevance for policymakers

‘Relative’ maps; regions are compared to each other;

Possible policy responses should differ depending on the region;

● Need of support for agricultural innovation

● Need of support for conservation of nature, landscape, water

● Need of support for provision of alternative employment

Example: Farm household study

Context & Assets

Ghana

Northern region

Kenya

Wamaluma

Zimbabwe

Makoni

Sample size (#FHH) 104 97 98

Number of growing seasons 1 2 1

Average annual rainfall 1981-

2010 (mm)

984 1754 863

Agro-ecological zone Tropical

warm/subhumid

Tropical cool, humid Tropical warm/semi-

arid

Population density (per km2) 61-70 1200 30

Average land holding size (ha) 3.1 0.6 1.6

Average FHH size (capita) 5.1 4.2 3.8

Land availability (ha/cap) 0.61 0.14 0.42

Tropical Livestock Units per FHH

(#)

4.4 1.4 1.9

% female household heads 6 47 40

% FHH with income > 75% from

farming

85 58 47

Number of cultivated crop types 4.4 3.5 2.9

Hengsdijk, H., Franke, A. C., van Wijk, M. T., & Giller, K. E. (2014). How small is beautiful?: food self-sufficiency and land gap analysis of smallholders in humid and semi-arid sub Saharan Africa.

Minimum required maize yield

0

1000

2000

3000

4000

5000

6000

0 25 50 75 100

maize yield

(kg/ha)

% Farm households

Minimum maize yield for household food

self-sufficiency

yield at 18 kg N/ha

yield at 75 kg N/ha

0

1000

2000

3000

4000

5000

6000

7000

8000

0 20 40 60 80 100

maize yield

(kg/ha)

% Farm households

Minimum maize yield required for

household food sufficiencyyield at 18 kg N/ha

yield at 75 kg N/ha

0

1000

2000

3000

4000

5000

0 20 40 60 80 100

maize yield

( kg/ha)

% farm households

minimum maize yield required for

household food selfsufficiencyyield at 8.5 kg N/ha

yield at 75 kg N/ha

Ghana Kenya

Zimbabwe

Income effects

0

0.5

1

1.5

2

2.5

3

0 20 40 60 80 100

Crop

income /

capita

(USD/cap)

% Farm households

actual maize (31 kg N/ha) and actual soya

yieldimproved maize yield (150 kg N/ha) and

improved soya yieldpoverty line

0

1

2

3

4

5

6

0 20 40 60 80 100

crop

income/

capita

(USD/cap)

% farm households

actual maize (8.5 kg

N/ha) and actual soya

yield

improved maize yield

(150 kg N/ha), improved

soya yield

Kenya

Zimbabwe

Impact of CC

Negative in all regions: outcomes are worse than for baseline period (mainly related to increased

temperature): will have a negative affect the other indicators

2.0

2.5

3.0

3.5

4.0

4.5

1990 1995 2000 2005 2010

t/ha

current climate-current variety

future climate-current variety

future climate-modified variety

Kenya

Simulations of maize (with 75 kg N/ha)

GHGi

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

Ghana Kenya Zimbabwe

GHG emission intensity (kg CO2

eq/kg maize)

actual N

medium N

high N

Farm household study

Indicators based on relevance for stakeholder.

Models to quantify effects are available

Resilience is difficult to tackle: done via variability.

No clear smart picture emerges -> development priorities may link to adaptation (N application & farm size), GHG emissions/GHGi increase.

Key messages

Start from political and economic context (awareness and commitment to act)

Set indicators to address development and CC with focus on user group/scale

Make climate information available for everyday practices of stakeholders

● Indicators for and quantitative insight in effects of activities

● Quantitative insight in the trade-offs between food productivity, climate resilience and GHG emissions

Show realism in dealing with synergies and trade-offs

Effectiveness and efficiency not always easy: time scales

Adaptation should lead mitigation: priority remains with feeding the increasing population

Thanks