Improving nutrition through increased utilisation of local agricultural biodiversity in Kenya. Presentation given by Gudrun Keding, Postdoctoral Research Fellow, Bioversity International. Find out more about this research: http://www.bioversityinternational.org/news/detail/improving-nutrition-through-local-agricultural-biodiversity-in-kenya/

1. INULA: Improving nutrition through increased
utilization of local agricultural biodiversity in Kenya
Gudrun Keding, PhD
28 August 2014

2. Using and
conserving
agricultural and
forest biodiversity
for…

3. 3
Productive & Resilient Ecosystems
Livelihoods
Sustainability
Nutrition

4. 4
The need for agricultural biodiversity
The heavy reliance on a narrow diversity of crops puts future
food and nutrition security at risk.
Source: ‘Dimensions of Need: An atlas of food and agriculture’. FAO, 1995.

5. Bioversity Research Programmes
• Nutrition and Marketing Diversity Programme
• Agrobiodiversity & Ecosystem Services Programme
• Forest Genetic Resources Programme
• Conservation & Availability Programme
• Commodity Systems & Genetic Resources Programme

6. INULA –
Improving nutrition
through local
agrobiodiversity

7. Research objective
To demonstrate the evidence that
agrobiodiversity has an impact on
dietary diversity and quality, and on
nutritional health of women and
children under two.
Bioversity InternationalG. Keding

8. Research questions
1. Does the local agrobiodiversity available in
farmers’ fields and on markets translate into
dietary diversity of women and children under
two?
2. What are reasons/ constraints for not
diversifying i) farmer’ fields and ii) children’s
and mother’s diets?
3. Does nutrition education for mothers on the
increased integration of local ABD into diets
have an impact on dietary diversity of children
under two?
Bioversity InternationalG. Keding

9. Study sites

10. Materials & Methods
Nutrition survey:
4 districts
15 villages per district
10 households per village
 600 households/ mother-child pairs
(baseline)
Bioversity InternationalG. Keding
Agricultural survey: sub-sample of 10%:
4 districts
3 villages per district
5 households per village
Bioversity InternationalG. Keding
 60 households/farms

11. Timeframe
2012
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Start of
INULA
Baseline survey
nutrition
• First farm inventory
• FGD agriculture
• Market survey
FGD
nutrition
Middle
survey
nutrition
Second farm
inventory
2013
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Nutrition Education
Sessions
“Complementary
Feeding”
1 + 2
Follow-up
visits
Nutrition Education
Sessions
“Complementary
Feeding”
3 + 4
Endline survey
nutrition
2014
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
• Extra survey nutrition
(2 districts only)
End of
INULA
• Third farm inventory
• Second market survey
(2 districts only)

12. The team
Lydiah Waswa, PhD student at Giessen University, Institute of
Nutritional Sciences, under the supervision of
Prof. Dr. Michael Krawinkel: April 2012 – March 2015;
Jacqueline Kipkorir, PhD student at Kenyatta University,
Department of Food, Nutrition and Dietetics, under the supervision of
Prof. Dr. Judith Kimyiwe: March 2012 – March 2015;
Mary Kanui, Mphil/ PhD student at Oxford University, School of
Geography and the Environment, under the supervision of
Dr. Shonil Bhagwat: August 2012 – March 2015;
Oliver Mundy, MSc student at Giessen University, Institute for
Agricultural Policy and Market Research, under the supervision of
Prof. Dr. Ernst-August Nuppenau: July 2012 – August 2014;
Laura Bender, BSc student at Bayreuth University, Geography and
African Studies, Intern at Bioversity (August – October 2012), returned to
Bioversity Kenya for data collection for her BSc thesis in March/April 2013
Johanna Lubasch, BSc student at Giessen University, Institute of
Nutritional Sciences, under the supervision of
Prof. Dr. Michael Krawinkel: April – September 2014.
Bioversity InternationalL. Waswa
Bioversity InternationalG. Keding

13. Statistical
Analysis
Plan

14. Statistical Analysis Plan I
INULA research questions Data Statistical analysis Data source
MSc + PhD
theses
Papers
1. Does the local
agrobiodiversity available in
farmers’ fields and on
markets translate into
dietary diversity of women
and children under two?
1. 1 x 24h recall
2. 1 x farm survey
3. 1 x market survey
1.-3. Multilevel
analysis for WDDS
and CDDS
separately
Farm survey I;
Market survey
I; Nutrition
survey I;
2. What are reasons/
constraints for not
diversifying i) farmer’ fields
and ii) children’s and
mother’s diets?
1. FGD agriculture
2. FGD nutrition
3. Individual farmers
perception
4. IYCF practices
5. Mothers nutrition
knowledge
1.-3. Qualitative
analysis of FGD data
(see chapter 3.4)
4. Analysis of
repeated cross-sectional
nutrition
survey data before
the intervention
FGD
agriculture;
FGD nutrition;
Farm survey I
+ II;
Nutrition
survey I + II;
i) MSc
thesis OM;
PhD thesis
MK
ii) PhD
thesis LW;
PhD thesis
JK;
Paper by
MK to …
2. Does nutrition education for
mothers on the increased
integration of local ABD into
diets have an impact on
dietary diversity of children
under two?
1. 2 x 24h recall
2. 2 x mothers
nutrition
knowledge
3. 2 x IYCF
practices
1-3. Analysis of
cross-sectional
survey data before
and after intervention
Nutrition
baseline
survey;
Nutrition
endline survey
PhD thesis
LW; PhD
thesis JK;
Paper by
LW to PHN

15. Statistical Analysis Plan II
RESEARCH QUESTION 1: Does the local agrobiodiversity available in
farmers’ fields and on markets translate into dietary diversity of women and
children under two?
Dependent (outcome) variables (DV):
• Child DDS; Mother DDS
Independent variables (predictors) (IV):
• agrobiodiversity: availability (farm and markets)
• nutritional functional diversity
 use method similar to the one used in “Scoring system for child feeding index” by
Ruel and Menon (2002)
Covariates:
• socio economic status (Wealth Index) (individual and community level)
• education of mother
• age of the child
• AEZ (?)  or part of IV?

16. Statistical Analysis Plan III
RESEARCH QUESTION 3: Does nutrition education for mothers on the increased
integration of local ABD into diets have an impact on dietary diversity of children
under two?
Dependent (outcome) variable (DV):
• CDDS
Independent variable (predictors) (IV):
• “nutrition education” (knowledge score, attendance rate esp. of ABD education
session, …)
 use a method similar to the one used in “Scoring system for child feeding index” by
Ruel and Menon (2002)
Covariates:
Wealth (individual and village)
AEZ
Education mother
…

17. Statistical Analysis Plan IV
Main questions
1. Independent (predictor) variable “agrobiodiversity availability”
 develop a score: which variables should be part of this
score?
 “Agrobiodiversity” as the dependent variable?
2. Independent (predictor) variable “nutrition education”
 develop a score: which variables should be part of this
score?
 replace dependent variable CDDS by a child feeding index
(CFI)

18. Thank you
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