Scaling up Ethiopia’s ‘Seeds for Needs’ approach of using agro-biodiversity to

adapt to climate change

Carlo Fadda, Senior Scientist, Bioversity InternationalWorld Bank, Addis Ababa, 15/01/2015

In partnership with:

Presentation’s Outline

Climate change

Green economy strategy: agricultural targets

Seeds for Needs: what is all about

Upscaling seeds for needs

Climate Change: Is It Real?

Climate Change: Some Evidence

•Climate change, floods, droughts, unpredictable temperatures and rainfall

•Changing pest and pathogen populations and levels of pollination efficiency

•Increased soil and land degradation

Major Environmental Threats to Sustainable Production

Adaptation to climate change: recommended actions by the IPCC

Improving crop tolerance to high temperature is a frequently

identified adaptation for almost all crops and environments

worldwide as high temperatures are known to reduce both

yield and quality

Adaptation to climate change: recommended actions by the IPCC

Improving gene conservation and access to extensive gene

banks could facilitate the development of variety with

appropriate thermal time and thermal tolerance characteristics

Adaptation to climate change: recommended actions by the IPCC

Indigenous Knowledge (IK) has developed to cope with

climate hazards contributing to food security in many parts of

the world

Ethiopian Green Economy Strategy

Target set for agriculture:

• Boost agricultural productivity (+40% increased production for major crops such as teff, maize, wheat);

• Intensify agriculture through usage of improved inputs and better residue management resulting in a decreased requirement for additional agricultural land that would primarily be taken from forests;

• Create new agricultural land in degraded areas through small-, medium-, and large-scale irrigation to reduce the pressure on forests if expansion of the cultivated area becomes necessary

• Introduce into cultivated areas lower-emission agricultural techniques, ranging from the use of carbon- and nitrogen-efficient crop cultivars to the promotion of organic fertilizers

Under conditions of change

(reducing the probability of loss of agricultural

productivity in the future, while enhance productivity

today)

The fundamental question:

Productivity and reduced vulnerability

How can we ensure that agricultural productivity increases are

accomplished in ways that create and enhance ecosystem

resilience and services for the poor?

Minimum Goals for 2050

Environmental Goals Development Goals

Total Agricultural Production

Nutritionally Complete Production

Biodiversity Conserved

Carbon Sequestered

Food Security Goals

Increased Farmer Livelihoods

And Resilience

Increase Farm Self Reliance

Adapted from Foley et al 2011

The Case of Durum Wheat

Improve Human Health

Food Distribution and Access

Conserve agrobiodiversity

Water Conserved

Improved Water Quality

Soil Formed

Unexploited Potentials in Landraces

• disease resistance (Leppik, 1970; Negassa, 1986; Klindworthet al., 2007; Jemanesh et al., 2013);

– e.g. Ethiopian landraces are the source for Sr13 gene, which is responsible for stem rust resistance

• Drought tolerance/resistance (Tesfaye, 2001; Mondini et al., 2010, our study)

• Very diverse for qualitative and quantitative traits

What We Did

Phenotyping trials

The genotypes, 373 landraces and 27 improved wheat varieties, were phenotyped attwo locations (Hagreselam and Geregera) in 2012 and 2013 main cropping seasons for 10important traits:

A. Phenological traits

• days to 50% booting (DB);

• days to 50% flowering (DF) and;

• days to maturity (DM)

B. Morpho – agronomic traits

• plant height (PH);

• number of effective tillers per plant (NET);

• spike length (SPL);

• number of seeds per spike (SPS);

• above ground dry biomass (BY);

• grain yield (GY).

Phenotyping (external characteristics)

0

10

20

30

40

50

60

70

80

<µ - 2SD µ - 2SD to µ -SD

µ - SD to µ +SD

µ + SD to µ+2SD

>µ +2SD

DISTRIBUTION OFGENOTYPES INTO

VARIOUS CLASSES

HS GER Com

Performance of Genotypes Across Locations

Landraces Performance Compared With the Best Improved Variety

The table tells that:

•21%, averaged over traits, of

the landraces are superior to

the best performer IM variety

•Many landraces mature

earlier than the IM varieties

•A yield advantage of 61%

obtained from the best

landrace over the best IM

variety (Robe)

Trait

Superior

(IM)

Superior

(LRs) no‡ %age

No

Geregera

%

Geregera

DB* 59.69 55.54 1 0.3 1 0.3

DF* 70.8 69.88 1 0.3 5 1.6

DM* 116.59 109.34 57 18.4 71 23.0

PH 110.34 115.07 8 2.6 5 1.6

NET 7.14 7.48 90 29.1 48 15.5

SPL 7.94 9.5 125 40.5 19 6.1

SPS 41.67 41.83 1 0.3 2 0.6

BY 7.17 9.99 97 31.4 47 15.2

GY 2.17 3.49 68 23.9 22 7.1

Ethiopian Unique Genetic Diversity

Landraces vs. Improved Genetic Divergence

Crop Improvement

Grain Yield as

quantitative trait in

Hagereselam 2012

Plot overall

performance in

Hagereselam 2012

Modified from Yu et al. 2008

Principal Facts• 52 RIL families

• 180 – 200 lines

• > 9,000 F6 lines in

Dec. 2014

• wide phenotypic

variation

Development of a Structured Multiparental PopulationNested Association Mapping - NAM-Population

Misiko (2013)

Limitations

Seeds for Needs

(1)

Genetic

diversity

(2)

Selection &

cultivation

(3)

Harvest

(4)

Value addition

(5)

Marketing

(6)

Final

use

Outcomes

Empowerment of communities: more

resilient to eco-socio-economic changes,

more resilient food systems

Outcome

Preservation of options

for resilient systems

Outcome

Self-reliance of value chain

actors on broader set of

options, making them more

resilient to market changes

From Farm to Fork: Biodiversity Contribution along the Value Chain

IMPACTImproved

nutrition,

incomes and

other

livelihood

benefits

3. Farmers test

and report back by

mobile phone

3. Environmental data

(GPS, sensors) to assess

adaptation4. Data are used

to detect

demand for new

varieties and

traits

4. Farmers receive tailored variety

recommendations and can order seeds

The process

2. Each farmer gets a different

combination of varieties

1. A broad set of varieties

is evaluated

Participatory Evaluation

• 30 farmers per location (15

male + 15 female)

• Individual score on 5 traits for

800 plots

• > 200,000 data points

29

Participatory Variety Selection (PVS)Mother and Baby Trial Approach

3. Farmers test

and report back by

mobile phone

3. Environmental data

(GPS, sensors) to assess

adaptation

1. A broad set of varieties

is evaluated

4. Data are used

to detect

demand for new

varieties and

traits

4. Farmers receive tailored variety

recommendations and can order seeds

The process

2. Each farmer gets a different

combination of varieties

Crowdsourcing

31

- 2 woredas

- 12 villages

- 85 km by 32 km cover

- 2400 to 3100 masl

• 32 genotypes (pre-selected by farmers)

• 24 farmers

• 4 genotype per farmer

• 3 times replication of each genotype

Mother and Baby Trial Approach

32Monthly report by a group of farmers Method of communication with farmers

I-button in each farmer plot

Crowdsourcing

• 4 genotypes per farmer

• All package was sent to each

farmer

• 200 farmers, 12 different villages

• 21 genotypes, 1 common for all 200

farmers

• 30 times replication of each genotype

Crowdsourcing

• Enumerator selection and training

• Farmers trained

• Bylaws developed

35

iButtons and Rain Gauge

iButton holder preparation

Different Stage Performances

Undergone meeting every month at each village and made report

Researchers data collection

2. Each farmer gets a different

combination of varieties

1. A broad set of varieties

is evaluated

4. Data are used

to detect

demand for new

varieties and

traits

4. Farmers receive tailored variety

recommendations and can order seeds

The process

3. Farmers test

and report back by

mobile phone

3. Environmental data

(GPS, sensors) to assess

adaptation

group number FourFarmers Name: 1. Guzguz Gelaw2.Yeshi Nega 3. Aregitu Moges 4. Abebabye Mebrate 5.Melkam Tseganew

Farmer

No

Farmer Name የአ/አደርስም

Plot No.

የመደብ

ቁጥር

Treat

No.

የተጠኝ

ቁጥር

Acc

Name

የዝርያ ስም

Earliness/ፈጥኖ ደራሽነት Tillerig C/የጋቻ አመታት Spike Q/የዛላ ሁኔታ Disease/የበሽታ ሁኔታ Overall/አጠቃላይ

Remark/ማስታ

ወሻየአ/አ ተ.ቁ 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

አራጋውመብራት 1 8 222854 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 3 2 2 2 2 2 3 2 2

Aragaw Mebrat 2 9 238576 3 3 2 2 2 2 2 2 2 2 2 2 2 3 2 3 3 2 2 2 2 2 3 2 33 22 236300 4 3 3 2 2 1 1 1 1 1 1 2 1 2 1 2 2 1 2 2 2 3 2 1 34 25 222736 1 2 2 2 1 2 2 1 1 1 2 2 1 2 1 2 2 1 2 2 1 2 2 1 2

ምስጋናውሙሉጌታ 1 2 204488 4 4 4 4 3 3 3 3 3 2 4 4 3 4 3 3 3 3 3 3 3 4 3 3 3

Misganaw Mulugeta 2 11222816B 3 4 4 3 3 3 3 3 3 3 3 4 3 4 4 3 3 3 4 3 4 4 3 4 33 24 206551 5 5 5 3 4 3 3 3 3 3 3 3 3 4 4 4 3 3 3 3 3 4 4 5 34 27 222408 2 3 3 3 2 3 3 3 4 3 3 4 3 4 4 3 4 3 4 3 4 5 4 5 4

የሽ ነጋ 5 5 5 4 4 2 3 3 4 3 3 2 3 3 3 3 2 2 3 3 3 3 3 3 2

Researcher Data

Farmers score Mother trial

Farmers score Baby trial

Both farmer data and researcher data has been recorded and is being processed

39

Harvesting Biomass

Grain yieldNumber of seeds per spike

Balance was distributed to all villages

Harvesting and data collection

3. Farmers test

and report back by

mobile phone

2. Each farmer gets a different

combination of varieties

3. Environmental data

(GPS, sensors) to assess

adaptation

1. A broad set of varieties

is evaluated

4. Data are used

to detect

demand for new

varieties and

traits

4. Farmers receive tailored variety

recommendations and can order seeds

The process

Strengthening Community Seed Systems

Upscaling and Outscaling Seeds for Needs

Reaching more farmers and for more crops

• Capacity development

• Approach institutionally embedded in extension services and agro-

dealer networks

• Methodology improved and expanded using ICT-based solutions

Crowdsourcing plan

• Initial investment for a new crop such as teff, sorghum, pulses,

targeting 500 farmers/site for 2 years

• Crop technical characterization

• Participatory evaluation

• Capacity development

• Crowdsourcing

• Subsequent distribution through crowdsourcing: targeting

10,000,000 HH over 3-4 years including seed multiplication

Strengthening Seed Systems

Institutional

genebanks(National, private, experimental

stations, universities…)

Community Seedbanks

CGIAR genebanks

International Genebanks

Regional

genebanks

To strengthen the seed network one needs more than one

seed bank/landscape (roughly 10 to reach 10,000

households).

Goal: to reach 200,000 households in 20 landscapes

across the country