| dc.description.abstract | Food insecurity in Kenya has been attributed to a number of factors, among them, fragile
ecosystems and a steady decline in the growth of the agricultural sector, population
pressure and slow growth in other sectors of the economy. This has led to the gradual but persistent degradation of the natural resource base. Through efforts have been made to develop and disseminate soil nutrient replenishment technology packages, adoption still
remains low even in the high potential Central highlands of Kenya. An example of such
interventions is the multi-institutional, multi-disciplinary project by the Kenya
Agricultural Research Institute, the Tropical Soil Biology and Fertility Institute, the
University of Nairobi and the Ministry of Agriculture that sought to address the perennial
problem of declining soil fertility in the Central highlands of Kenya. In a participatory
process, the researchers in this multi-institutional project facilitated the farmers through
an exercise where they classified themselves into 3 soil fertility management classes
based on their management ability. Soil fertility class 1 are those who were deemed to
have good SFM practices (high manure and fertilizer input, hybrid seed, terrace
maintenance) while class 3 are characterized by low input (including low soil fertility
replenishment) and output. To understand adoption based on this project activities and
enrich future efforts in dissemination of soil fertility based options, this risk assessment
study submitted that the risk attitude of the target farmers is a major factor influencing
adoption. Further, the study submitted that farmers adopt technologies/techniques based
on their available resources and past experience. Using the SFM classes, the main
objective of this risk assessment study was to quantitatively measure the trade-offs
between risks and returns for selected cropping systems amongst the 3 SFM farmers'
classes in Githunguri division, Kiambu district of Central Kenya. The Target-MOTAD
model was used to analyse for risk using farmers' input-output details. The model is a
two-attribute risk and return linear programming model that entails a constraint on
income deviations from a target level of income. The model results were used to develop
risk frontiers for various income and variance combinations for each class. The
coefficient of variation, derived from dividing the mean by the standard deviation, was
also used as a measure of risk. Input, output and risk efficiency results were distinctly
different for the three classes. The farm annual incomes at the time of data collection
averaged KES.114,877, KES.35,656 and KES.18,153 respectively for the 3 SFM classes
from respective average farm sizes of 1.04, 0.76 and 0.52ha. Out of the six priority
enterprises selected, maize, maize/beans, potatoes, bananas, tomato and dairy/Napier
production, only tomato and dairy enterprises entered the risk efficient plans. Based on
the specified resource availability and obligations for the farmers interviewed, the
maximum attainable income levels (at zero-risk) was KES.278,551, KES.79,036 and
KES.47,618 respectively. Coefficient of variation (CV) showed higher risk levels as one
moved from farmers practice, through various risk levels, to optimal results for classes 1
and 3. Deriving risk efficient frontiers using research trial results, the income as well as
risk levels were much higher than farmers' current income and risk levels. Lowest risk
levels were depicted by CV of 51%, 22.6% and 45% for the 3 classes. Only class 2
farmers activities had higher CV compared to 3 of the plans in the normative plan. Risk is
important to the smallholder resource-poor farmers of the study area and their active
participation in formulating research agendas is inevitable. The study concluded that in
Integrated SFM research in Kiambu district, what is more urgently needed may not be
blanket new technologies but a research focus to improve productivity and enterprise
selection in face of the varied levels of resource availability. Whereas class 1 farmers
may be able to take higher risks, most class 3 farmers might not be able to increase
productivity sufficiently based on their current resource levels. Class 2 farmers, who form
50% of the area population, would be an effective target group for researcher knowledge
in order to increase efficiency in resource use. In terms of enterprises, researchers should
move towards including high value crops in their analysis, away from the traditional use
of maize in experimental designs. This will help get better and affordable options for
increasing income using sustainable soil fertility replenishment options. | en |
