Full Text - PDF - Science Web Publishing

Document technical information

Format pdf
Size 400.1 kB
First found Nov 13, 2015

Document content analysis

Language
English
Type
not defined
Concepts
no text concepts found

Organizations

Places

Transcript

©2014 Scienceweb Publishing
Journal of Agricultural and Crop Research
Vol. 2(12), pp. 228-235, December 2014
ISSN: 2384-731X
Research Paper
Analysis of socio-economic factors affecting the coffee
yields of smallholder farmers in Kirinyaga County,
Kenya
Minai, J. M.1* • Nyairo, N.2 • Mbataru, P.1
1
2
Coffee Research Institute, P.O. Box 4 - 00232, Ruiru, Kenya.
Kenyatta University, P.O. Box 43844 - 00100, Nairobi, Kenya.
*Corresponding author. E-mail: [email protected]
Accepted 19th November, 2014
Abstract. This study examined the socio-economic factors influencing coffee yields within the smallholder sector of
Kirinyaga County, Kenya. It also assessed the influence of coffee prices on re-investment and yields. A total of 251
farmers were selected from the study area using the stratified random technique. The data was analyzed by use of
descriptive statistics, regression and correlation analysis using Stata (version 11). The results indicated that the mean
age of the farmers were 52.95 years and the average yields were 2.31 kg of cherry per tree. The multiple regression
2
analysis showed an R of 0.5217 for all variables investigated which means that 52.17% of the variation in yields can be
explained by these variables. The explanatory variables which were statistically significant were access to adequate
credit, having some source of cash from other enterprises or employment and consulting extension agents. It was also
found out that there is a strong positive relationship between price and the level of reinvestment (Pearson’s r = 0.814).
This indicates that higher prices encourage reinvestment in coffee. However, the correlation analysis between price and
yields showed a Pearson’s correlation coefficient of 0.154 which was statistically insignificant. This implies that although
price influence yields positively, it may not necessarily lead to significantly higher yields. Higher prices need to be
supported by the three significant variables in order to increase yields significantly. It is concluded that agricultural policy
effort on small holder coffee farming should thus focus on ensuring farmers get access to adequate credit, diversification
of farm income base and training.
Keywords: Yields, socio-economic factors, price, coffee, Kenya.
INTRODUCTION
Coffee is one of the key agricultural export commodities
in the Kenyan economy. Prior to 1998, it was Kenya’s top
foreign exchange earner and currently ranks fourth after
tea, tourism and horticultural sub-sector (Government of
Kenya (G.o.K), 2010). Currently, coffee contributes about
10% to total agricultural export earnings, and up to 30%
of the labor force employed in the agriculture sector. The
coffee industry contributes significantly to the sustenance
of rural livelihoods. It supports about 700,000 smallholder
growers and up to 4,000 small and medium estate
growers (Coffee Board of Kenya (C.B.K), 2012). In 1963,
coffee production stood at 43,778 tons from a total
hectarage of 45,538 and this rose to approximately
130,000 tons from 170,000 ha by 1988. In the last two
and a half decades, production has declined to about
50,000 metric tons in 2011/12 (C.B.K, 2012).
To revive the industry, the government introduced a
phased liberalization since 1992. It introduced the
payment of coffee in US Dollars in 1992 and liberalized
coffee milling in 1994. In 2001, the Coffee Act no.9 was
enacted following the repeal of Coffee Act Cap 333. This
de-linked the marketing and regulatory functions
undertaken then by the former Coffee Board of Kenya
(now renamed Coffee Directorate under the Agriculture
J. Agric. Crop Res. / Minai et al.
Food and Fisheries Authority). The marketing function
was privatized in the year 2002 and the Board’s role was
redefined as regulatory, overall development and
promotion of the industry (C.B.K, 2012). Other measures
included inter-alia: Establishment of the Coffee
Development Fund in 2006, Debt Waiver to growers
amounting Ksh 3.2 billion in 2006 and a further waiver of
about Ksh 2 billion in 2012 (CBK, 2012).
However, despite the measures undertaken by the
Kenya government and the improvement in coffee prices
since 2002, yields have remained low. This is not in
tandem with the basic law of supply which states that as
the price of a commodity rises, producers expand their
supply into the market (Lipsey, 1986). In 2010/11 for
instance, the Nairobi Coffee Auction posted an average
of 329.12US dollars per 50 kilogram bag (C.B.K, 2012) –
a 293% increase in price from 83.73 US dollars posted in
2003/04 but there has not been a corresponding increase
in yields. Since coffee farming is an important activity in
Kirinyaga County with many smallholder farmers
depending on its proceeds for their livelihoods, low yields
have affected the coffee farmers’ economic wellbeing due
to the loss of income. There was therefore need to study
the socio-economic factors influencing the low yields and
assess the influence of the improved coffee prices on the
small holder production in the county.
Among the problems that are hampering coffee yields
are non-affordability of agricultural inputs such as
fertilizers and agrochemicals coupled with inaccessible
credit. Kamau (1980) reported that adoption of weed
control recommendations in coffee production was
influenced by cash flow constraints and availability and
cost of labour. While looking at the factors affecting the
technical efficiency of Arabica coffee producers in
Cameroon, Amadou (2007) found out that the
educational level of producers and access to credit are
the main socio-economic variables that significantly affect
the technical inefficiency of farmers. He also found out
that age has a negative effect on technical efficiency,
implying that older farmers are technically more inefficient
than younger ones. Other variables that are positively
associated with adoption of technologies and hence
higher yields are: increased farming experience, access
to extension services and access to credit services
(Aneani et al., 2012; Jatoe et al., 2005; Mazuze, 2007;
Namwata et al., 2010).
Battese and Coelli (1995) and Ajibefun et al. (1996)
found a positive relationship between the degree of
inefficiency and the producer’s age and a positive
relationship between the degree of efficiency and the
educational level of the producers. According to Oniah
and Kuye (2012), older farmers are less likely to have
contact with extension workers and are equally less
inclined to adopt new techniques and modern inputs than
younger farmers. Seyoum et al. (1998) also found that
the farmers’ educational level positvely influence yields.
Coelli and Battese (1996) analyzed the factors affecting
229
the technical inefficiency of Indian coffee farmers, and
found a negative correlation between inefficiency and
variables such as farm size, the level of education and
age of the farmer. Oluyole and Sanusi (2009) carried out
a study in Cross River State, Nigeria and reported that
increased farm size improves farm output. Amusa et al.
(2011) and Kebede et al. (1990) also found out that farm
size was positively related to the output of cocoyam.
The general objective of this study was to analyze the
causes of the low coffee yields in Kirinyaga County
despite the improved coffee prices. The specific
objectives were:
i) To determine the socio-economic factors causing low
coffee yields in the small holder sector of Kirinyaga
County.
ii) To assess the influence of prices on the level of
reinvestment in coffee farming.
iii) To assess the influence of coffee prices on yields in
Kirinyaga County.
MATERIALS AND METHODS
Study area, design and sampling technique
The study was carried out in Kirinyaga County located on
the slopes of Mount Kenya. The county was chosen
because it is has all the agro-ecological zones where
coffee can grow and is centrally placed within the major
coffee growing region and thus a good representative of
other counties. The study used a survey design
employing both quantitative and qualitative methods. The
sample was selected using the stratified random
technique from the target population of 47,610 coffee
farmers (G.o.K, 2012). The population was stratified
according to the various Agro-ecological zones (AEZs)
outlined as suitable for coffee farming by Jaetzold et al.
(2007) and further into coffee co-operative societies and
factories. At the factory level, random selection of
individual farm households was done to avoid bias. The
total population was first divided into several subpopulations. These were the coffee – tea zone (upper
midland one - UM1), the main coffee zone (upper midland
two -UM2) and the marginal coffee zone (upper midland
three - UM3). Sixty two farmers were sampled from UM1,
131 from UM2 and 58 from UM3 since according to G.o.K
(2006), approximately 25% of the coffee farmers are in
UM1, 50% in UM2 and about 25% in UM3 .
To achieve this, three co-operative societies cutting
across the three zones were randomly selected and
fourteen wet mills representing the various AEZs further
selected to represent each stratum. Finally, farm
households were randomly selected from each of the
selected factories using the Tippets random number
tables. Farmers’ membership numbers were used as the
farmers’ exclusive identity. A structured questionnaire
230
J. Agric. Crop Res. / Minai et al.
was used to collect data from the respondents. The data
was analyzed using descriptive and inferential statistics.
The descriptive statistics used to summarize the socioeconomic characteristics of the farmers were measures
of central tendency (means, frequency distribution and
percentages and measures of dispersion (variance,
standard deviation and range) while regression model of
the log-linear form was used to estimate factors and
determinants of coffee productivity in the study area.
Correlation analyses were done to determine whether a
linear relationship between price and investment and
between price and yields existed.
Econometric model
The regression model was as expressed implicitly as:
LnYi = β0 + β1CA+ β2IOS + β3FS+β4ESC + β5EHH +
β6GHH + β7AHH +ε.
Where:
Ln Yi = Log of the production per unit (Kilograms of cherry
per tree), CA = Access to adequate credit (dummy
variable), IOS = Income from other sources other than
credit - such as employment, tea, dairy, etc (Measured in
Kenya shillings), FS = Farm size (Acres), ESC=
Extension services consultation– either trainings,
demonstrations or other educational contacts (dummy
variable), EHH = Education level of House hold Head
(number of years in school), GHH = Gender of House
hold Head (dummy variable) and AHH = Age of
Household Head (years), β0 is the Y intercept, β1 to β7 the
slope coefficients and ε the error terms.
In this model, the slope coefficient measures the
percentage change in Y for a given absolute change in
the value of the regressor (Gujarati, 2007).
RESULTS AND DISCUSSION
Socio-economic characteristics of the farmers
Results in Tables 1 and 2 show that 87.65% of the
household heads were male while 12.35 % were female
indicating that most households are male headed. The
average age of the farmers was 52.95 years with the
youngest farmer being 20 years old and the oldest 91.
This suggests that the small holder coffee farming cluster
is skewed towards the ageing. This concurs with the
findings of the Coffee Research Foundation (2010)
baseline survey conducted under quality coffee and
commercialization project. Since most coffee production
operations in the farm are manual, this has the potential
to limit productivity. The results also agrees with the
findings of Adesoji and Farinde (2006) who found that
farmers older than 52 years had a tendency of getting
less yields.
The findings revealed that 6.05% of the household
heads had no formal education, 51.21% had primary
education, 34.68% had secondary education and 8.06%
had tertiary education.The mean number of years of
education was 8.16 years with a standard deviation of
3.7. The minimum number of years of schooling was 0
while the maximum was 16 years as shown by Table 2.
Similar observations were made by Mumba et al. (2011).
Generally, the more educated people are, the more
efficient producers they become (Battese and Coelli,
1995). Low literacy levels can therefore hamper coffee
production.
The study showed that 48.21% of the farmers in the
study area had farm sizes of 1 acre or less, 43.83% had
farm sizes of between 1.1 and 5 acres and only and only
7.97% had 5.1 acres or more. This showed that the
farmers in the area have small farm holdings. The
average acreage in the area is 2.23 acres with a standard
deviation of 2.37 while the minimum and maximum
holdings is 0.25 and a maximum of 25 acres respectively
as shown in Table 2. Further, majority of the farmers over 55%, have 0.5 acres of coffee or less. Only 3% have
more than 2 acres as indicated by Table 1. The average
area under coffee was 0.63 acres while the minimum and
maximum acreage was 0.04 and 8.93 acres, respectively.
The average number of coffee trees per farmer was 348
with the minimum number and maximum number being
35 and 4820 respectively as shown in Table 2. The
percentage of farmers who consulted extension staff or
attended training in the last three years was 72.11 % . Of
these, 94.74% attended field training while only 5.26%
went to an office to seek advice as shown by Table 1.
This means that field based trainings would reach out
more farmers than waiting for farmers to seek information
themselves.
Table 1 shows that 76.52% of the farmers need need
credit to farm their coffee. Of these, 81.04% had access
to credit while 18.95% indicated that they had no access.
Of those who were able to access credit, 54.98% did not
get adequate credit while 45.02% got adequate credit.
This inadequacy of credit is primarily because most
societies lend depending on the number of kilograms
delivered at the factory. Majority of the co-operative
societies limit the credit to Ksh 10 per kilograms of cherry
delivered. This creates a vicious circle of low yields since
only those who have cash from other sources can afford the
fertilizers and pesticides needed for coffee production. As
Junge et al. (2009); Okoedo-Okojie and Onemolease
(2009) observed, credit enables farmers to adopt new
technologies more readily since they are able to plan
ahead. Most of the credit, 86.6% was sourced from the
co-operative societies with only 4.24% being sourced
from the banks. None of the farmers indicated to have
borrowed from the Coffee development fund despite the
fund being in existence for over seven years.
J. Agric. Crop Res. / Minai et al.
Table 1. Socio-economic characteristics of the farmers interviewed during the survey.
Parameters
Gender
Male
Female
Relative frequency (%)
87.65
12.35
Age (years)
18 - 35
36 - 45
46 - 55
56 - 65
Over 65
15.14
16.73
29.48
17.93
20.70
Education level of household heads
No education
Primary
Secondary
Tertiary
6.05
51.21
34.68
8.06
Farm size (acres)
0.0- 0.5
0.51-1.0
1.1- 2.0
2.1 - 5.0
5.1-10.0
Over 10 acres
17.53
30.68
18.73
25.10
5.98
1.99
Farmers with various acreage under coffee (acres)
< 0.5
0.51 - 1.0
1.1 - 2.0
> 2.0
54.98
33.07
8.76
3.19
Farmers who consulted extension staff
Consulted
Did not consult
72.11
27.89
Extension forum
Field training / demonstration
A visit to an agricultural office
94.74
5.26
Farmers who need credit
Need credit
Don't need credit
76.52
23.48
Percentage of farmers who have access to some credit
Have access
Do not have access
81.04
18.96
Percentage of farmers who got adequate credit
Credit adequate
Credit not adequate
45.02
54.98
231
232
J. Agric. Crop Res. / Minai et al.
Table 1. Contd.
Sources of credit
Co-operative society
Commercial banks
SACCOS
Coffee deevelpoment fund
86.06
4.24
9.7
0
Yields (kg/tree)
0.00 – 1.00
1.01 – 2.00
2.01 – 3.00
3.01 – 5.00
5.01 – 10.00
Over 10
34.66
24.30
13.94
19.52
6.37
1.2
Source: Author (2013)
Table 2. Summary of the characteristics of various variables in the model.
Variable
Age of head household (years)
Years of education (years)
Farm size (acres)
Area under coffee (acres)
Total no. of trees
Average yields per tree (kg)
1
Mean
52.95
8.16
2.23
0.63
348.21
2.31
1
Std. Dev
14.73
3.70
2.57
0.80
432.82
2.47
2
Min
20
0
0.25
0.04
35
0.1
3
Max
91
16
25
8.93
4820
19.9
Standard deviation, 2 Minimum, 3 Maximum. Source: Author (2013)
Majority of the farmers, 72.91% were producing 3 kg of
cherry per tree or less. About 19.52% were producing
between 3 and 5 kg, 6.37% between 5.01 and 10 kg and
only 1.2% were producing over 10 kg as shown in Table
1. The mean yield was 2.31 kg per tree with a standard
deviation of 2.47. The lowest yield was 0.1 and the
highest was 19.9 kg. This confirmed the secondary data
collected before the study as in G.o.K (2006).
Regression analysis results
The analysis of variance (Table 3) for the regression
analysis yielded an F-value of 37.87, with a p-value of
0.000, indicating that the model was statistically
significant even at the 1% level. The coefficient of
2
determination (R ) was 0.5217, meaning that
approximately 52.17% of variability of the dependent
variable (yields) was accounted for by the explanatory
variables in the model. Thus the regression model was
adequate since in determining model adequacy, features
2
such as the R and the F-value are observed (Gujarati,
2007).The remaining 47.13% could be due to
measurement errors or factors not accounted for in the
model such as soil and climatic factors.
Access to adequate credit had a positive coefficient of
1.2493 with a p-value of 0.000 which is significant at 1%.
This means that all other predictors held constant, having
access to adequate credit increases yields by 125%.
Similar results were obtained by Binam et al. (2004) and
Amadou (2007) while undertaking studies on small scale
coffee farmers in Cameroon. They argued that access to
adequate credit permits a farmer to enhance efficiency by
overcoming liquidity constraints which may affect their
ability to apply inputs and implement farm management
decisions on time. Use of credit therefore loosens
financial constraints, ensures timely acquisition and use
of inputs and results in increased economic efficiency.
The results also agree with the findings of Adesoji and
Farinde (2006) as well those of Nyagaka et al. (2009)
who found that farmers with access to credit tend to
exhibit higher levels of yields.
The coefficient for income from other sources was
0.0149 with a p-value of 0.003 which was statistically
significant at 1%. This means that for every unit increase
in cash amount from other sources other than coffee (one
unit = Ksh10,000 as defined in chapter 3), yields increase
by 1.5%. This is because farmers usually swivel finances
from one enterprise to the other in their operations. The
results agree with those of Namwata et al. (2010) as well
those of Franzel (1999) who argued that higher income
farmers may be less risk averse, have more access to
J. Agric. Crop Res. / Minai et al.
233
Table 3. Multiple regression results showing the influence of various regressants on yields.
Variable
Access to adequate credit
Income from other sources
Farm size
Years of education
Extension services consultation
Gender of house hold head
Age of household head
Constant
Coefficient
1.24939
0.0148505
-0.036068
0.0141456
0.2120942
-0.0759403
- 0.0027915
-.2225871
Std error
.0907784
0.0049541
0.0185508
0.0139061
0.0991875
0.139685
0.0035457
0.2653446
t-statistic
13.76
3.00
-1.94
1.02
2.14
-0.54
-.79
-.084
p >|t|
0.000***
0.003***
0.053*
0.310
0.033**
0.587
0.432
0.402
Number of Observations = 251 F (7, 243) = 37.87 Prob > F = 0.0000***
R2 = 0.5217 Adjusted R2 = 0.5080
****, **, * Signify significant at 1%, 5% and 10% levels respectively
Source: Author (2013)
Table 4. Results showing the correlation between price and re-investment and between price and yields.
Price
(Ksh)
Re-investment (ksh)
Pearson correlation (r) Sig. (2-tailed)
0.814*
0.048*
Covariance
77559.809
Average yields (kg/tree)
Pearson correlation (r) Sig. (2-tailed)
0.154
0.693
Covariance
3.309
*Correlation is significant at the 0.05 level (2-tailed). Source: Author (2013)
information, have longer-term planning horizon, and have
greater capacity to mobilize resources and hence
increased likelihood of adopting new technologies.
Farm size had a coefficient of -0.0361 with a p-value of
0.053. Although this was not statistically significant, the
results indicate that farmers with smaller farms are more
efficient in resource use. The results agree with the
findings of Adesoji and Farinde (2006) who found out that
increase in farm size decreases the yields of arable
crops. Years of education had a coefficient of 0.0141 with
a p value of 0.310. Although not significant statistically,
the results shows a positive relationship between
education and yields. More educated farmers are able to
perceive, interpret and respond to new information and
adopt improved technologies such as fertilizers and
pesticides much faster than their counterparts. This
agrees with the findings of Nyagaka et al. (2010) who
used the Tobit model and found out that farmers with
more years of formal schooling were more efficient than
their counterparts. Aneani et al. (2012) also obtained
similar results.
Extension services consultation had a coefficient of
0.2121 with a p value of 0.033 which was statistically
significant at 5 %. This means that consulting extension
agents on what needs to be done increases yields by
21%. Nyagaka et al. (2010) argued that frequent visits to
the farmers by extension agents provides the farmer with
necessary information about the availability of needed
resources, market prices as well as the profitability
status. Nchare (2007), further argued that extension
workers play a central role in informing, motivating and
educating farmers about available technologies. The
results also concurs with Seyoum et al. (1998) who found
a 14% difference in technical efficiency between farmers
who had access to extension services and those who did
not. The gender of house hold head had a coefficient of 0.076 with a p-value of 0.587and thus not significant. This
means that being male or female does not significantly
affect yields. The results contradict the findings of
Aworemi et al., (2010) who found that the male gender
had higher yields. The age of household head had a
coefficient of -0.0028 with a p-value of 0.432 which was
not significant. However, it means that older people are
more likely to have less yield than the younger ones
perhaps due to the manual nature of coffee operations.
The results concur with those of Ayoola et al. (2011) who
found out that age negatively affects rice yields.
We can therefore reject the null hypothesis and
conclude that socio-economic factors influences the level
of yields in the smallholder coffee sub-sector. The study
therefore disagrees with the findings of Rondinelli (1983)
that socio-economic factors have no significant influence
on performance but supports the findings of Aworemi et
al., (2010).
Correlation analyses between price, level of reinvestment and yields
There was a strong relationship (Pearson’s r = 0.814)
between price and the level of reinvestment which was
statistically significant at 5% as shown by Table 4. This
234
J. Agric. Crop Res. / Minai et al.
means that changes in price were strongly correlated with
changes in reinvestment. The higher the price (payment
per kilogram of cherry) the more the farmers were
motivated to invest in coffee. This agrees with the basic
law of supply which states that as the price of a
commodity rises, so producers expand their supply onto
the market (Lipsey, 1986).
Table 4 also shows that there was a weak relationship
(Pearson’s r = 0.154) between price and yields. This
relationship was also statistically insignificant at 5%. This
means that although there is a positive relationship
between price and yields, as per the basic law of supply,
a good price alone may not necessarily guarantee
siginificantly higher yields. It therefore implies that there
are constraints that are making farmers not to invest
adequately in order to cause signifinant increase in yields
thus conflicting the basic law of supply. These constraints
are highlighted by the regression analysis results.
CONCLUSIONS
The explanatory variables found to have significantly
contributed to the dependent variable (yield) were access
to adequate credit, having some source of cash from
other enterprises or employment and consulting
extension agents. It was also found out that although
price has a positive influence on yields, the impact of
price on yields is dampened by the socio-economic
factors that farmers finds themselves in. This implies that
although good prices encourage farmers to invest in
coffee, there is need for an enabling environment in
terms of adequate credit, extension services provision
and diversification of farmers’ incomes in order to
increase coffee yields significantly. Further, it was found
that only 35.4% of farmers were aware of the Coffee
Development Fund despite the institution having been
formed in 2005 to offer credit to coffee farmers
suggesting that there is need to create awareness about
the institution and the services it offers.
RECOMMENDATIONS
To enlarge the income base and the sources of cash,
farmers should be encouraged to diversify by having
other enterprises such as dairy, bananas and macadamia
as income generating enterprises. The government
should also endeavor to have at least one coffee
extension officer per sub-county to enhance provision of
coffee extension services.
The government needs to streamline provision of credit
to make it accessible. It should be provided in amounts
that are adequate to meet the cost of inputs and labour.
There is therefore need to create awareness on the
existence of the Coffee Development Fund and carry out
further research on the challenges in loan application,
processing and repayments. Given the level of average
yields in the sub-sector, initial capital can be given out to
the farmers to jumpstart production followed by provision
of adequate credit. There is also need to undertake a
similar study to look at factors affecting some other
counties that were formerly large coffee producers.
ACKNOWLEDGEMENTS
The authors are grateful to the Coffee Research Institute
management and the African Network for Agriculture,
Agroforestry and Natural Resources Education (ANAFE)
for their financial and logistical support support in carrying
out this study.
This article is published with the permission from the
Institute Director, Coffee Research Institute.
REFERENCES
Adesoji SA, Farinde AJ (2006). Socio-economic factors influencing
yield of arable crops in Osun state, Nigeria. Asian J. plant sci.
5(4):630-636.
Ajibefun A, Battese GE, Daramola AG (2002). Determinants of
Technical Efficiency in Smallholder food crop farming: Application of
Stochastic frontier production function. Int. Agric. Quart. J. 41:225240.
Amadou N (2007). Analysis of factors affecting the technical efficiency
of Arabica coffee producers in Cameroon. Volume 163 of AERC
research paper, African Economic Research Consortium. ISBN
9966778063.
Amusa NA (2010). Man and microbes in a continuous battle. An
inaugural lecture delivered in the Faculty of Science, January 12,
2010, Olabisi Onabanjo University, Ago-Iwoye, Nigeria.
Aneani F, Anchirinah VM, Owusu, Asamoah M (2012). Adoption of
some cocoa production Technologies by cocoa farmers in Ghana.
Retrieved from http://dx.doi.org/10.5539/sar.v1n1.
Aworemi JR, Adegoke AI, Opoola N A (2010). Impact of socioeconomic factors on the performance of small -scale enterprises in
Osun state, Nigeria. Int. Bus. Res.3:2.www. cceenet.org/ibr.
Ayoola JB, Dangbegnon C, Daudu CK, Kudi TM, Amapu JO,
Adeosun JO, Ezui KS (2011). Socio-economic factors influencing
rice production among male and female farmers in Northern Guinea
savanna, Nigeria. Lessons for promoting gender equity in action
research. Agric. biol. J. North Am. 2(6):1010-1014.
Battese GE, Coelli TJ (1995). A Model of technical inefficiency effects
in a stochastic function for panel data. Empir. Econ. 20:325-332.
Binam JN, Tonye J, Wandji N, Nyambi G, Akoa M (2004). Factors
affecting the technical efficiency among smallholder farmers in the
slash and burn agriculture zone of Cameroon. Food Pol. 29:531-545
Coelli T, Battese G (1996). Identification of factors which influence the
technical inefficiency of Indian farmers. Austr. J. Agric. Econ.
40(2):103-128.
Coffee Board of Kenya (2010, 2012). Annual reports, Nairobi,
Government printers.
Franzel S (1999). Socio-economic factors affecting the adoption
potential of improved tree fallows in Africa. Agrofor. Syst. 47:305-321.
Gujarati DN (2007). Essentials of Econometrics, 4th edition. New Delhi,
Mc-Graw-Hill.
International Coffee Organization, (2010). Exporters guide. London:
International Coffee Organization.
International Coffee Organization (2000). Coffee Statistics. London :
International Coffee Organization.
International Coffee Organization (1997). Coffee price determination
and volatility. London: International Coffee Organisation.
Jatoe JB, Al-Hassan R, Abatania LN (2005). Factors affecting the
adoption of imroved sorghum among farm households in the
northwest Ghana: a probit analysis. Ghana J. Dev. Stud. 2(1):37-50
J. Agric. Crop Res. / Minai et al.
Jaetzold R, Schmidt H, Hornetz B, Shisanya C (2007). Farm
management handbook of Kenya Vol. II, 434. Nairobi: Government
printers.
Kamau PC (1980). Economics of herbicide use in coffee.Kenya-Coffee,
45(529), 111-119.
Lipsey RG (1986). An introduction to positive Economics, 6th edition.
London: Buttler and tanner Ltd. ISBN 0 297 182 66 5.
Mazuze FM (2007). Analysis of adoption of orange-fleshed sweet
potatoes: The case study of Gaza Province in Mozambique.
Research Report Series No. 4E. Institute of Agricultural Research of
Mozambique, Windhoek, Mozambique.
Namwata BM, Lwelamira J, Mzirai OB (2010). Adoption of improved
agricultural technologies for Irish potatoes (Solanumtuberosum)
among farmers in Mbeya Rural district, Tanzania: A case of
Ilunguward. J. Anim. Plant Sci. 8(1):927-935.
Nchare A (2007). Analysis of the factors affecting the technical
efficiency of Arabica coffee farming. Afr. Econ. Res. Consort. p.163.
Nyagaka DO, Obare GA, Omiti JM, Nguyo W (2010). Technical
efficiency in resource use: Evidence from smallholder irish potato
farmers in Nyandarua North District, Kenya. Afr. J. Agric. Res.
5(1):1179-1186.
235
Okoedo-Okojie DU, Onomolease EA (2009). Factors affecting the
adoption of yam storage technologies in the Northern ecological zone
of Edo State, Nigeria. J. Hum. Ecol. 27(2):155-160.
Oniah MO, Kuye OO (2012). Econometric analysis of factors affecting
yields of small scale yam farmers on inland valleys of obubra local
government area of cross river state, nigeria.International J. Res.
Manage. Econ. Comm. 2:5. www.indusedu.org
Oluyole KA, Sanusi RA (2009). Socioeconomic variables and cocoa
production in Cross River State, Nigeria. J. Hum. Ecol. 25(1):5-8.
Rondinelli DD (1983). Implementing decentralization programmes in
Asia: A Comparative analysis, Public Admin. Dev. 3(3):181-208.
Seyoum ET, Battese GE & Fleming EM (1998). Technical efficiency
and productivity of maize producers in Eastern Ethiopia: a study of
farmers within and outside the Sasakawa - Global 2000 project.
Agric. Econ. 19:341-348.
http://www.sciencewebpublishing.net/jacr

Similar documents

×

Report this document