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A Quantitative Approach to Innovation in Agricultural Value Chains: Evidence from Kenyan Horticulture

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Abstract

In less developed countries such as Kenya, trade is increasingly occurring through, and employment is found within, global and local value chains. Yet, although innovation is widely recognised as crucial for development, the endogenous relationship between small-scale innovations and participation in global value chains (GVCs) has yet to be explored sufficiently. This endogeneity is highlighted using the 3L’s of labels, linkages and learnings as key overlapping factors that affect both the processes of innovation as well as GVC participation. Drawing on a survey of 320 fresh fruit farmers and 55 interviews in Kenya, we develop a novel method to quantify small-scale agricultural innovations, which are categorised into two overarching types. The first, formal, emanate from meeting standard requirements; the second, informal, evolve from local contexts and are less codified. We find that GVC farmers perform more formal innovations, while local farmers perform similar levels of informal innovation to GVC farmers.

Dans les pays moins développés tels que le Kenya, le commerce se passe de plus en plus de la même façon que l’on trouve un emploi, c’est-à-dire par le biais des chaînes de valeur mondiales et locales. Dans le même temps, l’importance de l’innovation à petite échelle est de plus en plus reconnue. Cependant, la relation endogène entre les innovations à petite échelle et les chaînes de valeur mondiales (CVM) n’a pas encore été suffisamment explorée. Empiriquement, en se basant sur un sondage auprès de 320 cultivateurs de fruits frais et sur 55 entretiens au Kenya, nous quantifions systématiquement l’innovation agricole à petite échelle: il y a celles qui émanent de pressions extérieures formelles liées à la chaîne de valuer mondiale; et celles qui évoluent à partir de contextes locaux informels qui sont plus difficiles à codifier. Nous constatons que les agriculteurs de la chaîne de valeur mondiale réalisent des innovations plus formelles, alors que les agriculteurs locaux ainsi que ceux de de la chaîne de valeur mondiale ont des niveaux d’innovation informelle presque similaires. Nous soulignons également que les 3 “L”, c’est-à-dire les labels, liens et leçons apprises, sont des facteurs clés qui se cumulent et impactent à la fois les processus d’innovation ainsi que la participation à la chaîne de valeur mondiale.

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Acknowledgements

The authors would like to acknowledge the support from Peter Knorringa, Kunal Sen, Rory Horner, Kate Meagher, Stephanie Barrientos and Khalid Nadvi for their comments on earlier drafts of this paper. We also thank the reviews for their valuable insights.

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Authors and Affiliations

  1. Global Development Institute, University of Manchester, Oxford Rd, Manchester, M13 9PL, UK

    Aarti Krishnan

  2. Overseas Development Institute, Blackfriars Road, London, SE1 8NJ, UK

    Aarti Krishnan

  3. Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK

    Chistopher Foster

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  1. Aarti Krishnan
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Correspondence to Aarti Krishnan.

Appendices

Appendix A: Linkages – Breakdown of Relationships

Actors

Relationship of farmer with actors

GVC

LVC

Seed suppliers

0 = weak (% of each farmer category)

2.68

0.41

 

1 = intermediate (% of each farmer category)

63.6

50

 

2 = strong (% of each farmer category)

33.72

49.59

Agro-vets

0 = weak (% of each farmer category)

4.6

7.41

 

1 = intermediate (% of each farmer category)

51.72

62.3

 

2 = strong (% of each farmer category)

43.68

29.59

Local credit givers

0 = weak (% of each farmer category)

3.45

0.00

 

1 = intermediate (% of each farmer category)

90.42

88.21

 

2 = strong (% of each farmer category)

6.13

11.79

Extension officers

0 = weak (% of each farmer category)

12.2

44.83

 

1 = intermediate (% of each farmer category)

47.56

36.78

 

2 = strong (% of each farmer category)

40.24

18.39

Main buyers (exporters for GVC farmers and local buyers for LVC)

0 = weak (% of each farmer category)

19.51

54.39

1 = intermediate (% of each farmer category)

44.31

24.72

2 = strong (% of each farmer category)

33.74

20.88

Brokers

0 = weak (% of each farmer category)

31.42

19.51

 

1 = intermediate (% of each farmer category)

50.57

36.99

 

2 = strong (% of each farmer category)

14.56

8.54

Appendix B: Summary of variables used in regression

Variables

Variable explanation

Stage 1 regression

Stage 2 regression

Labels

Having a local code of conduct or international certification dummy; values in  %

Yes

Yes

Tacit learning × linkages

Interaction of total share of tacit learning with index of backward/forward linkages

Yes

Yes

Explicit learning × linkages

Interaction of total share of explicit learning with index of backward/forward linkages

Yes

Yes

Asset Index

Index of assets possessed before participation in current chain

Yes

Yes

Alternate livelihoods

Other livelihoods possessed dummy; values in  %

Yes

Yes

Sex

male dummy; values in  %

Yes

Yes

Part of farmer group

Membership if farmer group dummy; values in  %

Yes

Yes

Rejection levels

Dummy for if they have rejections; values in  %

Yes

Yes

Duration

Number of years in specific chain

Yes

No

Contract

Dummy if they have a contract with main buyer

Yes

No

Dependent variables

 

Binary variable GVC farmer or not

Index of formal innovations; Index of informal innovations

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Krishnan, A., Foster, C. A Quantitative Approach to Innovation in Agricultural Value Chains: Evidence from Kenyan Horticulture. Eur J Dev Res 30, 108–135 (2018). https://doi.org/10.1057/s41287-017-0117-0

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