1 of 19

Farm resilience assessment using FAO’s adapted SHARP+ tool in Busia County, Kenya��Elizabeth Imbo NICE project country manager Kenya, �representing research of:�Charles Chigemezu Nwokoro, Sophie van den Berg, Aleksandra Wybieralska, Elizabeth Imbo, Marnie Pannatier, Jimena Monroy, Cornelia Speich, Kesso Gabrielle van Zutphen, Tanja Barth-Jaeggi,Dominique Barjolle�

2 of 19

Study took place in the context of the NICE project

The NICE project facilitates locally-led actions to improve nutrition of secondary cities’ population through agricultural, food and health sector collaborations, and public-private engagements, with strong emphasis on the role of women and youth entrepreneurs.

2

3 of 19

The NICE project consortium

3

More information and city factsheets on the NICE webpage: nice-nutrition.ch

4 of 19

Project locations

4

Rangpur

Dinajpur

Bangladesh

Kenya

Busia

Bungoma

Rusizi

Rwanda

Rubavu

5 of 19

Purpose of the research for NICE

M/E: baseline for NICE project objectives
Priorities: identify priorities to reinforce and build resilience

5

6 of 19

What is the FAO SHARP+ tool?

Resilience assessment tool combining: self-assessment of farmers + external assessment of agricultural systems
A methodology developed by FAO in 2014
In the context of CC adaptation projects, it has been implemented in 45+ countries

6

7 of 19

SHARP+ main outputs

Detailed information of farming systems and livelihoods

7

Resilience scores of farming systems and livelihoods

Quantitative & qualitative georeferenced data

8 of 19

Adaptation FAO SHARP+ tool

8

5. Integration of feedback from pretest

4. Computerization and pretest

3. Adapting of units in questions

2. Rephrasing of questions

1. Addition of questions

The SHARP+ tool is a flexible tool and allows for and should also be adapted to a certain extend to the local context.

In step 1, questions were added to generate baseline data on specific objectives in the NICE project, such as the level of adoption of agroecological farming practices (AFP) by the farmers, a result we will reflect on later in the presentation.
In step 2, questions and response options’ lists were rephrased to adapt to the local context, such as adding the names of local crop varieties and animal breeds.
In step 3, we changed units such as the land size definitions from hectares (as in SHARP+) to acres, the unit Farmers from Busia feel confident to report on.
In step 4, the computerization and pretest in the local setting with farmers allowed to check for understanding questions and consequent adaptations.
In step 5, the feedback after the pretest resulted in changes relating to steps 2 and 3, for example, the addition of a local water conservation practice as a listing option.

9 of 19

Research location

9

10 of 19

Multi-stage purposive sampling

10

4. Women and youth representation

3. Nutrition-sensitive value chain producers

2. Identification of farmers’ groups

1. Food producing counties

11 of 19

Enumerator training

11

12 of 19

12

Interviews with farmers

13 of 19

The 13 SHARP+ resilience indicators and …

13

1. Socially self-organized

2. Ecologically self-regulated

3. Appropriately connected

4. Functional and response diversity

5. Optimally redundant

6. Spatial and temporal heterogeneity

7. Exposed to disturbance

8. Coupled with local natural capital

9. Reflective and shared learning

10. Locally interdependent

11. Honours legacy

12. Builds human capital

13. Reasonably profitable

Resilience

14 of 19

… the overlap with agroecology

Adapted from Leippert et al. 2020

14

1. Socially self-organized

2. Ecologically self-regulated

3. Appropriately connected

4. Functional and response diversity

5. Optimally redundant

6. Spatial and temporal heterogeneity

7. Exposed to disturbance

8. Coupled with local natural capital

9. Reflective and shared learning

10. Locally interdependent

11. Honours legacy

12. Builds human capital

13. Reasonably profitable

Social

aspects

Ecological aspects

Economic aspects

Recycling

Human and

social values

Recycling

Human and

social values

Resilience

Responsible governance

Synergies

Circular economy

Synergies

Co-creation of knowledge

Synergies

Diversity

Efficiency

Circular economy

Culture and food traditions

as an outcome of agroecology

15 of 19

15

Indicator	Module	SHARP question	Response	Scale
4. Functional and response diversity Heterogeneity of features within the landscape and on the farm; diversity of inputs, outputs, income sources, markets, pest controls, etc.	6. Crop production	6. By surface covered, which crops do you cultivate? (seasonal and perennials)	# of seasonal and perennial crops	1=0; 2=1; 3=3; 4=5; 5=6; 6=8; 7+=10
	22. Trees	22. Approximately how many different types/species of trees grow on your land? (trees)	# count from list	1= 3, 2= 5, 3= 7, 4= 9, 5+= 10
	3. Production activities	3. Do you usually carry out any of these activities on your farm?	Animal production, crops, trees, bee keeping, agroforestry, etc.	1= 0, 2= 5, 3= 7, 4+= 10
	24. Income sources	24. Do you have any non-farm income generating activity?	Yes, all year Yes, seasonally Yes, occasionally No	Yes, all year=10 Yes, seasonally=7 Yes, occasionally=5 No=0

Example build-up score questions

To reflect on how these resilience scores are build up from the questions, we give you an example of an indicator, the questions, and the scores.

For example for the indicator functional and response diversity, which assesses the heterogeneity of features within the landscape and on the farm, the diversity of inputs and outputs, income sources, markets, pest controls, and other aspects, you can see here a few of the modules relating to this indicator.
If we take the module on trees, for example (*indicate with cursor), we ask the respondent how many different trees types/species grow on the farmers’ land. The farmer has then a list option to select the number of types of trees. A high number of types of trees impacts the resilience score on this indicator positively. With a low number of different types of trees, for example 1 type, the score on this question is low and will thus negatively impact the final score on this indicator. Each indicator has a number of questions relating to it. (These four questions are just a fraction of it).

16 of 19

Results on the indicators

16

Economic aspect

Social

aspects

Ecological aspects

These are the scores we found for the sampled farmers in Busia.

The scores go from 0-10.

If the score is below 3.5 we speak of very low resilience.

A score between 3.5 and 6 is considered as medium resilience.

A score from 6 to 10 is considered as high resilience.

The findings of Busia revealed, that three out of the five resilience indicators related to the social aspects of resilience, scored high.

Those are: socially self-organized, honours legacy, and locally interdependent.

Socially self-organized, defined by the extend to which farmers are able to self-ogranize in grassroot networks, such as advisory networks or farmers markets’ scored very high for Busia. The relation to resilience here is that a broader social safety networks gives a buffer against disturbances, thus increases resilience.

Also honours legacy, an indicator focusing on traditional cultivation techniques and use and replication of heirloom seeds, also scores high, indicating traditional knowledge and wider management skills are passed down through generations which contributes to resilience.

All indicators focusing on the ecological aspects of resilience, have a medium score. This shows that the farming households are equipped with some capacity to adapt and transform when a shock occurs. However, there is still a lack in some areas to be resilient due to a lack of knowledge, skills, or resources. Especially the economic aspect, the reasonably profitable indicator, which demonstrates if farmers earn a liveable wage, have some assets, and have savings if shocks occur or to reinvest in the farm, scores particularly low.

What stands out, is that the social components have an higher score than the ecological ones.

That means, that some socio-cultural elements are in place to foster resilience, which can also catalyze greater resilience on the ecological aspects.

17 of 19

Results on the agroecological practices

17

No.	Agroecological farming practices
1	Agroforestry
2	Crop diversification
3	Crop rotation
4	Excl. organic pesticide appl.
5	Part. organic fertiliser appl.
6	Part. organic pesticide appl.
7	Production and use of locally adapted seeds and breeds
8	Efficient & water-saving irrigation/water manag.
9	Excl. organic fertiliser appl.
10	Intercropping
11	Mulching
12	Barriers and terraces
13	Windbreaks and living fences
14	Other soil/water conserv. practices
15	Integr. of livestock/fishery with crop produc.
16	Rehabilitation of degraded grazing land
17	Controlled grazing

Another results from the rich results of SHARP+ is the results on agroecological practices.

The graph shows the %nof farmers of our sample engaged in agroecological farming practices.

We see that agroecological practices are used widely and the agroecological practices used are diverse.

The practices are ranging from biodiversity enhancement, input reduction, soil health, and synergies with livestock.

Reflecting on these results, we see that in this context of Busia, agroecological practices are most probably not seen as alternatives for conventional or mainstream practices, but are embedded in a wide range of Kenyan farming practices often based on systems of agroforestry, livestock integration etc.

This finding aligns with a recent finding of Agroecology TPP (Transformative Partnership Platform on Agroecology). Agroecology by default, could apply here.

18 of 19

Conclusion and outlook

Strengthening resilience of farming systems is imperative in the face of climate change
Priorities for lower resilience scores
SHARP+ rich in results and is scalable
Georeferenced data for comparison

18

19 of 19

Thanks!

ANY QUESTIONS?

You can find me at:

Elizabeth Imbo

Elizabeth.imbo@syngenta.com

Sophie van den Berg

consulting@sophievdberg.com

LinkedIn ‘Sophie Jasmijn van den Berg’

19