Extension services as key determining factor for adoption of minimum tillage practice in Kenya: A plot level analysis

• Published date: 01 December 2022
• Author: Pradyot Ranjan Jena
• Date: 01 December 2022
• DOI: http://doi.org/10.1002/pa.2724

PRACTITIONER PAPER

Extension services as key determining factor for adoption

of minimum tillage practice in Kenya: A plot level analysis

Pradyot Ranjan Jena

National Institute of Technology Karnataka,

Mangalore, India

Correspondence

Pradyot Ranjan Jena, National Institute of

Technology Karnataka, Surathkal, Mangalore,

575025, Karnataka, India.

Email: jpradyot@gmail.com

There is a consensus that conservation agricultural practices increase crop productivity

and save resources. However, even after a decade of active promotion of conservation

agriculture in sub-Saharan Africa, the adoption of such practices as minimum tillage is

low. This article finds out the determining factors that influence the adoption decision of

smallholder farmers of Kenya about minimum tillage practice. The study is based on sur-

vey data from a randomly collected sample of 300 households in themaize-dominant

farming system of eastern Kenya. A plot-level analysis consisting of 437 plots is carried

out using the Probit regression to estimate the adoption decision model. Access to

extension information is found to be a key determinant of adoption decisions for mini-

mum tillage. Household labor availability, land allocated for maize crop, and plot-level

characteristics such as soil type, plot size, and intercropping practice followed on the plot

are also found to have influenced farmers' decision to adopt minimum tillage practice.

Extension services being a vital element in the efforts to promote minimum tillage prac-

tice,moreeffectiveimplementationofpromotion programs need to be undertaken by

the Government in Kenya in particular and sub-Saharan Africa in general.

KEYWORDS

adoption decision, conservation agriculture, extension service, Kenya, minimum tillage

1|INTRODUCTION

Conservation agriculture (CA) which is an integrated agricultural tech-

nological innovation has been widely promoted as a resource and

environmentally conscious agricultural practice (Erenstein et al.,

2012). Such agricultural practices hold significant relevance for the

resource-poor countries such as Kenya where food security is threat-

ened.CA employs three main principles in crop agriculture; continuous

minimal mechanical disturbance of the soils, use of permanent organic

soil cover, and diversified crop rotation or intercropping (FAO, 2008;

Hobbs, 2007). Continuous mechanical tillage (hoe or mouldboard) has

been observed to reduce soil organic matter causing weakening of soil

structure and making the soils prone to; soil erosion, loss of fertility,

and environmental degradation (FAO, 2008; Richardson &

King, 1995). On the other hand, minimum disturbance of the soil

(Minimum tillage) and mulching have been shown to improve the soil

structure while at the same time reducing surface runoff hence

conserving the soil (Arshad et al., 1999; Richardson & King, 1995).

The current study is based on the farmers' perception and adoption

decision of the minimum tillage aspect of CA in Kenya. Minimum till-

age practice which results in improved soil fertility leads to improve-

ment in crop yields; especially cereals. At the same time, it also cuts

down the costs associated with labor mainly for land preparation and

weeding thereby increasing farmers' overall net income (Ahmad

et al., 2014; Erenstein et al., 2008; Gathala et al., 2014; Jaleta

et al., 2016; Lahmar, 2010). An IFAD/FAO joint study has found out

that minimum tillage practice can save labor hours as much as 75%

(in the hand labor/hoe system). In the draught animal powers cate-

gory, the minimum tillage system (with knife roller and direct seed

drill) can reduce labor requirement by 80%. Ngoma (2018) shows that

adopting minimum tillage in Zambia is associated with an average

yield gain for several crops such as maize, groundnut, sunflower, soy-

bean, and cotton. Deines et al. (2019) applied a machine-learning

causal inference approach to satellite-derived datasets of tillage

Received: 12 March 2020 Revised: 18 November 2020 Accepted: 5 July 2021

DOI: 10.1002/pa.2724

J Public Affairs. 2022;22(Suppl. 1):e2724. wileyonlinelibrary.com/journal/pa © 2021 John Wiley & Sons Ltd. 1of6

https://doi.org/10.1002/pa.2724