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Sustainable Development in Africa What role for agricultural research?

Katrien DescheemaekerWageningen University

Strategic Forum “Sustainable development in Africa”, 16 January 2016

Challenges for African agriculture and agricultural research

What role for agricultural research for development? 2 examples: Mali, Zimbabwe

Honest broker in innovation processes Partnerships in co-construction, co-execution and co-

evaluation Capacity building through co-learning processes

Outline

Agriculture is key for African Development• 70% of Africans, 80% of poor Africans live in rural areas,

mainly depending on agriculture for their livelihood• Agriculture + agribusiness: 45% of Africa’s GDP• 60% of Africa’s labour force in agriculture

Challenges for African Agriculture

1. Need to improve agricultural productivityIncreasing demands for food, energy

FAO, 2012

Human population


FAO, 2012

Agricultural land Water use

Challenges for African Agriculture1. Need to improve agricultural productivityIncreasing demands for food, energy, water, land

FAO, 2012

Rockstrom et al., 2009

Planetary boundaries

Challenges for African Agriculture1. Need to improve agricultural productivityIncreasing demands for food, energy, water, land from natural resource base under pressure

2. Problems of low productivity• rainfed agriculture• low input use • low soil fertility • degraded natural resources, declining

ecosystem services

Map of land degradationNachtergaele et al., 2010Tittonell and Giller, 2013


2. Problems of low productivity• Poor policies and institutional failures• Dysfunctioning markets• Poor access to information, inputs and

credit• Poor infrastructure


3. Climate change

IPCC, AR 5


Muller, 2013

Challenges for African Agriculture3. Climate change – Large impact on agriculture

Investments in agricultural R&D generally yield high rates of return (Alston et al., 1996; Waibel, 2006; Seck et al., 2013)Many examples of positive impacts of agricultural research on crop and livestock production, NRM, nutrition and food security Rates of return of agricultural R&D projects

Agricultural Research and Development

Still…Stagnation in yield improvementDisappointing progress in, e.g. - Poverty: 290 to 386 million poor people in 1990-2008- Hunger and malnutrition: 175 to 239 million in 1990-2012(source: World development indicators, World Bank)

Cere

al y

ield

(t/h

a)

Seck et al., 2013


Need for a Uniquely African Green

Revolution

Reasons for poor progress 1. Insufficient fundingStagnation and low level of public investments in agricultural R&DNARIS depend for 75% of their budget on donors

Public agricultural R&D investment

Source: Beintema and Stadt (2010)

Intensity ratios of public agricultural R&D investment

Recommended by World Bank

Recommended by FAO


Reasons for poor progress 2. Poor capacitySome statistics (Seck et al., 2013): - < 100 FTE scientist in 50% of countries (sample of 48)- 40% of African scientist work in just 5 countries (Ethiopia, Kenya,

Sudan, Nigeria, South-Africa)- 70 researchers to 1 million inhabitants (Japan: 4380 to 1 million)- Africa contributes 0.3% of global scientific outputs- Low investment in university education for science and technology- Ageing population of African agricultural researchers


Reasons for poor progress 3. Solutions not relevant or adapted to complex farmer contextContext is multi-scale, multi-dimensional and changingHeterogeneity in farming systemsBarriers and constraints at farm level and beyondVarious stakeholders with objectives and aspirations


So, how can we find relevant solutions for agricultural development?Through Farming Systems Analysis that is • Participatory• Interdisciplinary• Systems thinking• Forward looking, using scenarios• Adaptive learning cycles


OBSERVEREFLECT

ANALYZE CONCEPTUALIZE

PLANEXPERIMENT

DOEXPERIENCE LEARNING

Learning cycle – Kolb, 1984

Adaptive learning cycle

OBSERVEREFLECT


PLANEXPERIMENT

DOEXPERIENCE LEARNING

EXPLAIN

EXPLOREDESIGN

DESCRIBE

Learning cycle – Kolb, 1984DEED research cycle – Giller et al., 2008


OBSERVEREFLECT


PLANEXPERIMENT

DOEXPERIENCE CO-LEARNING

EXPLAIN

EXPLOREDESIGN

DESCRIBE


additive pattern substitutive pattern

cowpea soybeansorghummaize

maize – cowpea intercropping

Sustainable intensification in MaliOn-farm trials: ±200 small trials with ± 150 farmersTesting a basket of options on entire cropping system

Example 1: Looking for tailored solutions

Falconnier et al., 2016


Farmer field daysParticipatory appraisal of sources of yield variability- Soil type- Previous crop

Multi-criteria evaluation of options

Ronner et al., 2015


Farmer feedbackStatistical analysis with mixed models

Niches for the re-design of farm systems

Options


SORGHUM 3 ha

Medium farm

COTTON 3 ha

SOYBEAN .5 ha

MILLET 3 ha

GROUND-NUT 1 ha

20 40 60 80 100

Replace sorghum by soybean

SANDY SOIL CLAY SOIL

MAIZE 1 ha

AfterSorghum/Millet

AfterCotton/Maize

Re-design exercise with different farmer types


Medium farms – replace sorghum by soybean

Large farms – replace sole maize by maize cowpea intercropping

Small farms – replace sorghum by cowpea grain variety

1 ox

1 cow

1 donkey/calf

- Trade-off analysis

Example 1: Looking for tailored solutionsRe-design exercise with different farmer types

% replacement % replacement

Village workshops


FEEDBACK

Lessons Smallholder farming is a risky businessOptions can be tailored to farm types and “niche”, based on

- on-farm empirical work- modelling- evaluations at field and farm level, with multiple criteria

Farmer involvementAdaptive learning cycles


Crop-livestock farming systems in semi-arid Zimbabwe- Low crop productivity; e.g. 0.7 t/ha for maize, 0.5 t/ha for sorghum- Low livestock productivity; e.g. mortality rates 15%; < 1.5 l/cow/day- High poverty levels; 76% poor and 22% extremely poor- Food self-sufficiency: 3-10 months

High vulnerability to climate change

Example 2: Co-designing farming futures

Climate change− Temperature: Strong signal: +2 to +3.3oC− Precipitation: No strong signal: -0.7 mm/day to +0.5 mm/day; drier start of season

Adverse effects on crops, grazing land, water, livestock

RCP 8.5 mid century temperature scenarios for all GCMs in Nkayi, Zimbabwe

RCP 8.5 mid century precipitation scenarios for all GCMs in Nkayi, Zimbabwe


How to adapt to climate change and reduce vulnerability of rural households?

Complex question! Many uncertainties:- Economic, policy and institutional situation of future world- Climate scenarios

Many components:- Mixed farming systems: crops, grazing lands, livestock- Strong heterogeneity in rural communities - Various stakeholders with different interests and capabilities


How to adapt to climate change and reduce vulnerability of rural households?

Complex question! Many uncertainties:- Economic, policy and institutional situation of future world- Climate scenarios

Many components:- Mixed farming systems: crops, grazing lands, livestock- Strong heterogeneity in rural communities - Various stakeholders

Scenarios

Multi-model framework

Disaggregation by farm types

Participatory learning


Climate dataHistorical (1980-2010):

Mid century (2040-2070):

20 GCMs Projected changes in

temperature, precipitation

Climate dataHistorical (1980-2010):

Mid century (2040-2070):

20 GCMs Projected changes in

temperature, precipitation

Crop Model APSIM, DSSAT

Crop management: fertilizer, rotation,

varieties,…

Effects on on-farm crop production; rangeland

grass production

Crop Model APSIM, DSSAT

Crop management: fertilizer, rotation,

varieties,…

Effects on on-farm crop production; rangeland

grass production

Livestock modelLivSim

On-farm feed production; rangeland biomass

Effects on livestock production (milk, off-take, mortality rates)

Livestock modelLivSim

On-farm feed production; rangeland biomass

Effects on livestock production (milk, off-take, mortality rates)

Economic modelTOA-MD

Household characteristicsAgricultural production

Prices, costs

Economic effects of climate change and

adaptationson entire farms

Economic modelTOA-MD

Household characteristicsAgricultural production

Prices, costs

Economic effects of climate change and

adaptationson entire farms

Economic impacts Heterogeneous populationsTypes of households

All households (n=160)

Modelling framework


Stakeholder engagementBackground information

Conceptualization of 2nd scenario and

adaptation package

Model runs; Result interpretation

External revision

Re-designed systems

Expert discussion SSP2, RAP1


Stakeholder feedback


External revision


Conceptualization of 1st scenario and

adaptation package


First cycle: Incremental changeIndicators

Cultivated land area

- Intensified production on less land

Herd size + Small increase due to improved feed and animal management

Systems change

+ Better crop-livestock integration

Input use + Fertilizer and improved seed for maize

Legume cultivation

0 No change

Off-farm income

- Limited alternative options, people rely more on agriculture

Adaptation package-1 Microdosing fertilizer on maize, maize-mucuna rotation, and drought-tolerant maize


56%of farm households could be negatively

affected by climate change.

Impact of CC in the future

94%of farm households could benefit from

adaptation toclimate change.

Benefits of adaptation to CC

91%of farm households

in Nkayi will be below poverty line

Poverty Projection for 2050

Example 2: Co-designing farming futuresFirst cycle: Incremental change

Stakeholder engagementBackground information

Conceptualization of 2nd scenario and

adaptation package


External revision

Re-designed systems





External revision


Conceptualization of 1st scenario and

adaptation package


Indicators

Cultivated land area

- Intensified production on less land

++ Expansion of cultivated land; labor saving techn., better market access

Herd size + Small increase due to improved feed and animal management

++ Large increase; more fodder production, market incentives

Systems change

+ Better crop-livestock integration ++ Further crop-livestock integration; crop diversification, intensification

Input use + Fertilizer and improved seed for maize

++ Fertilizer and improved seed for all crops

Legume cultivation

0 No change + + Groundnut and legume forages

Off-farm income

- Limited alternative options, people rely more on agriculture

+ Growth in other sectors attracts people, income diversification

Adaptation package-2Shift to sorghum, crop rotation, drought tolerant and high-yielding varieties, fodder production, manure application

Second cycle: Transformative changeExample 2: Co-designing farming futures

0 cattle, extr. poor 1-8 cattle, poor >8 cattle, non-poor

0

2000

4000

6000

8000

10000

12000

curre

nt

incr

emen

tal

trans

form

ativ

eFarm

Net

Ret

urns

(US$

/yea

r) extr. poor

poor

non-poor

0

20

40

60

80

100

curre

nt

incr

emen

tal

trans

form

ativ

e

Pove

rty le

vels

(%)

extr. poor

poor

non-poor

Example 2: Co-designing farming futuresSystem comparison

Example 2: Co-designing farming futuresSecond cycle: Transformative change

43%of farm households

in Nkayi will be below poverty line

Poverty Projection for 2050

58%of farm households could be negatively

affected by climate change.

Impact of CC in the future

73%of farm households could benefit from

adaptation toclimate change.

Benefits of adaptation to CC

Interdisciplinary approach- Integrated modelling framework: assess effects on crops and

livestock, whole-farm economics- Assess climate change in conjunction with trends in the socio-

economic and institutional context


Lessons for semi-arid Zimbabwe:

- A high proportion of farms was vulnerable to climate change

- Incremental adaptation benefited most farms, but the gains were insufficient to lift people out of poverty

- Drastic farming system re-design can lead to substantial improvement in productivity, food self-sufficiency and income, but needs to be enabled by policy and institutional interventions

- Persistent poverty for a large group indicates need for opportunities outside agriculture


Huge potential to contribute to sustainable developmentChallenges require investment in adapted approach:• Embracing complexity• Multi-scale: field to farm to community to regional to …

(and back)• Multi-dimensional and interdisciplinary research• Looking forward to explore opportunities

What role for agricultural research for development?

Research as honest broker in innovation process• Decision support• Discussion support

Partnerships in co-construction, co-execution and co-evaluation• Tailored solutions• Evaluation with multiple criteria and trade-offs

Capacity building through co-learning process• Empowered smallholder farmers and their

organizations• Local research partners through collaboration and

student training• Our own understanding of complex systems

What role for agricultural research for development?

Thank you!

Acknowledging: Farmers, funders (McKnight Foundation, DFID), students (Gatien Falconnier, Mary Ollenburger, Esther Ronner),

partners (ICRISAT, IER, AMEDD, OSU, CSAG, ICRAF, Matopos Research), AgMIP, colleagues (Sabine Homann-Kee Tui, Patricia Masikati, Ken Giller, Olivier Crespo, Roberto Valdivia, Ousmane

Sanogo, Bouba Traoré, Ousmane Dembele, Arouna Bayoko, Salif Doumbia, Myriam Adam, Mink Zijlstra)

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