improving nutrition through local agricultural biodiversity in kenya

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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/

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INULA: Improving nutrition through increased utilization of local agricultural biodiversity in KenyaGudrun Keding, PhD28 August 2014

Using and conserving agricultural and forest biodiversity for…

3

Productive & Resilient Ecosystems

Livelihoods

Sustainability

Nutrition

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The need for agricultural biodiversityThe 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.

Bioversity Research Programmes

• Nutrition and Marketing Diversity Programme

• Agrobiodiversity & Ecosystem Services Programme

• Forest Genetic Resources Programme

• Conservation & Availability Programme

• Commodity Systems & Genetic Resources Programme

INULA – Improving nutrition through local agrobiodiversity

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 International\G. Keding

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 International\G. Keding

Study sites

Materials & MethodsNutrition survey:

4 districts

15 villages per district

10 households per village

600 households/ mother-child pairs (baseline)

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Agricultural survey: sub-sample of 10%:

4 districts

3 villages per district

5 households per village

60 households/farms

Timeframe

2012

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecStart 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 DecNutrition 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)

The teamLydiah 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 International\L. Waswa

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Statistical Analysis Plan

Statistical Analysis Plan IINULA research questions Data Statistical analysis Data source MSc + PhD

thesesPapers

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 recall2. 1 x farm survey3. 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 agriculture2. FGD nutrition3. Individual farmers

perception4. IYCF practices5. 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 MKii) 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 recall2. 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

Statistical Analysis Plan IIRESEARCH 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?

Statistical Analysis Plan IIIRESEARCH 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)AEZEducation mother…

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)

www.bioversityinternational.org

Thank you

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