Household Dynamics in Adoption of Climate Resilient Agricultural Technologies in Semi-arid Kenya

Daniel Kangogo and Pascal Sanginga

Presented by;

Daniel K. Kangogo

Our Common Future under Climate Change Conference, 7-10 July, 2015Paris, France

Outline Research background

Research problem Research questions

Methodology

Results

conclusions

Research Background Climate Change is Real!

Sub-Saharan Africa is largely vulnerable to adverse impacts of climate change given their inadequate capacity to adapt.

Hence, strengthening agricultural production systems is fundamental to improving household resilience.

At national levels, this requires substantial investment in drought and heat tolerant seed varieties among other interventions.

Most of the interventions have focused on adoption of single technologies to improve productivity, yet to build resilience, diversified adoption of resilient technologies is critical.

Emerging concerns are moving from increasing yields to building resilience

Research Background Resilience, ability to anticipate, adapt to, and recover from the effects of shocks in a

manner that protects livelihoods, accelerates and sustains recovery There is an urgent need to understand the critical resilience dimensions in the face

of changing climate

This study was carried out within the Canadian International Food Security Research Fund (CIFSRF) project of IDRC

Objective- to enhance food security in developing countries by funding applied agricultural research.

How- through a participatory approach to evaluate agricultural practices in semi-arid Kenya

Objective- to catalyze adoption of appropriate agricultural practices

Project Intervention Some of the practices implemented through the project

Indigenous chicken Improved maize varieties Improved green gram varieties

Improved pigeon pea varieties

Water management

Improved sorghum varieties

Research Problem Over time researchers have focused on the adoption of single technologies with

the aim of increasing productivity No study on adoption of multiple technologies to improve household resilience to

climate change.

In this study we; o Analyse simultaneous adoption of a portfolio of climate resilient technologies

to demonstrate how adoption decisions can be used to explain household resilience.

(Households that have diversified livelihood options are relatively resilient)

Past studies have compared Male Headed Households (MHHs) Vs. Female Headed Households (FHHs)

o This way of analysis masks the dynamics that come along with different household structures

Household Dynamics We distinguish 3 different types of households

o MHHs – households where male and female are present o de facto FHHs – female headed households with absentee

husband o de jure FHHs – female headed households with no male

(widowed, divorced, separated or never married) This allows for the analysis of different household structures.

Research questions Do the different household structures exhibit different adoption behaviours?

How does household structure influence the adoption of climate resilient farming technologies?

Do household structure affect household resilience?

Methodology Using multistage sampling procedure, 300 households were surveyed: 240

project participants and 60 non-project members from Machakos and Makueni Counties, Kenya

To analyse adoption decisions and household resilience, three technologies were considered;

o Maize, Green grams and Indigenous chicken and their combinations (level of diversification) in the form of:

o Maize + green grams o Maize + IC o Green grams + IC o Maize + green grams + IC

Crop ent.

Crop-poultry ent.

Increasing level of diversification

Adapted from DFID, 2012

Econometric models Multivariate Probit (MVP) model – since farmers adopt

technologies as compliments or substitutes

MVP takes into account the potential correlation between adoption decisions (-/+)

However, the MVP model does not draw distinction between households that adopted one technology and those that adopted multiple technologies

Ordered logit model to determine the influence household structures on the resulting household resilience category

The ordered logit model allows for the analysis of the factors that influence the adoption of single technologies and the various combination.

An ordinal dependent outcome is generated from the nature of household adoption behaviour

Ordinal Outcome Score

if a household adopted any single technology Maize/Green grams/IC

0

if adopted Maize + green grams 1

if adopted Maize + IC or Green grams + IC 2

if adopted maize + green grams + IC 3

Econometric models

Descriptive resultsQ1. Do the different household types exhibit different adoption behaviours? technologies?

Table 1. Descriptive statistics comparing different household types

Variables MHHs De facto FHHs De jure FHHsProportions Proportions Proportions

Number of observations 195 49 56Indigenous chicken (IC) 0.93 0.92 0.91 Improved maize varieties 0.74 0.84 0.57*** Improved green gram varieties 0.76 0.93 0.79* Technology combination Improved maize varieties and IC 0.69 0.78** 0.52*Improved green gram varieties and IC 0.53 0.51 0.54 Improved maize and green gram varieties 0.45 0.53 0.41 Improved maize, green gram and IC 0.42 0.49 0.39

*-MHHs vs. de facto *-De facto vs. de jure

Q2. How does household structures influence the adoption of climate resilient technologies?

Regression resultsTable 2. Multivariate Probit model results

Improved maize technology

Improved green gram technology Indigenous chicken

Explanatory variables Coeff. Std. Err. Coeff. Std. Err. Coeff. Std. Err.

Male-headed household -0.888** 0.445 -0.857** 0.410 0.275 0.437De jure FHHs -0.980** 0.502 -0.714 0.481 0.897*** 0.574Belong to mkt group 0.047 0.238 0.538** 0.236 -0.040 0.363Ln(Off-farm income) -0.071* 0.043 0.038 0.034 0.074 0.048Project member 0.761** 0.302 -0.323 0.298 0.007** 0.396

Note: De facto female-headed household is the reference category where other household types are compared.

Yi= 0 (collapse) Yi= 1 (recover, but worse than before)

Yi= 2 (bounce back) Yi= 3 (bounce back better)

Explanatory variables ME SE ME SE ME SE ME SE

MHHs 0.040 0.042 0.013 0.014 0.022** 0.027 -0.075 0.082De jure FHHs 0.050** 0.066 0.014 0.017 0.017 0.015 -0.081** 0.096Belong to mkt group -0.077** 0.034 -0.024** 0.011 -0.041* 0.022 0.141** 0.063Ln(Distance to mkt) 0.033** 0.017 0.010* 0.006 0.016* 0.009 -0.060** 0.030Ln(Farm income) -0.021** 0.009 -0.007** 0.003 -0.010** 0.005 0.038** 0.016Project member -0.077 0.051 -0.021 0.013 -0.021** 0.011 0.119* 0.068

County -0.084** 0.034 -0.025** 0.011 -0.040** 0.019 0.150*** 0.058

Regression resultsTable 2. Multivariate Probit model results Q3. How does household structures influence the adoption of climate resilient technologies?

Note: De facto female-headed household is the reference category where other household types are compared.

Conclusions

If we consider households structures to consist of only MHHs and FHHs we miss important development outcomes.

Acknowledgment