2. Introduction
Soybean (Glycine max) is a legume crop largely grown at
the central of the country (Malawi).
It dominates the international oilseed market as it comprises
about 54% of the world’s total oilseed production (NAMC,
2009).
The crop has the potential to transform the economy due to;
the increase in both domestic and international demand
(ICRISAT,2013)
3. Statement of the Problem & justification
The crop is well adapted in all agro-ecological zones of Mw.
Yields are still low as farmers obtain 40 percent less (800
kg/ha) on average than the potential yield of 2000-2500 kg/ha
(ICRISAT, 2013).
Increasing production through area expansion may not be
possible in most parts of the country due to:
population pressure on the land (IITA, 2012).
4. Justification continued
On average Soybean yield increased from 961kg/ha in 2010
to 982kg/ha in 2011 and then decreased to 970kg/ha in
2012.This implies that there are production problems locking
the sector (ICRISAT, 2013).
However, there is little documentation on the likely causes of
the drop in soybean yield.
Study was conducted to establish the socio-economic and
institutional factors causing efficiency differentials in
smallholder farmers’ soybean production.
5. Objectives
Underlying objective
The study aimed primarily to assess the technical efficiency of
soybean production among smallholder farmers.
Specific Objectives
To estimate the efficiency level of soybean production in the
study area.
To determine the effect of socio-economic and institutional
factors on technical efficiency of soybean smallholder farmers
in the study area.
To identify the sort of returns to scale characterized by soy
bean farmers in the study area.
6. Hypothesis
Smallholder soybean farmers are technically
efficient and their socio-economic as well as
institutional factors do not influence technical
efficiency of soybean production.
The Cobb-Douglas specification is an adequate
representation of the stochastic frontier model.
Soybean farmers have constant returns to scale.
7. Methodology
Study Area
The study focused on four EPAs;
Chitekwere and Nyanja of Lilongwe district
Madisi and Nachisaka of Dowa district
Areas chosen purposively for their popularity in
soybean production.
8. Sampling & Data collection methods
Sample size:
300 soybean farmers were considered enough for the
study.
Data collection:
2013/14 Secondary cross-sectional data was obtained
from IITA , (TLII-MW-2013/14 Survey data).
Peer journal articles & national economic
reports were reviewed accordingly.
9. Data analysis
Data analysis was done using both descriptive and
inferential statistical techniques in STATA & SPSS
Descriptive statistics
Means, standard deviations , charts etc.
Inferential statistics
Econometric model (TSPF model)
10. Empirical Model Specification
The Translog stochastic production frontier model was
used to determine the technical efficiency scores
through Maximum Likelihood Estimation.
The model is specified as;
lnYi = lnϢ0 +
j=1
n=3
Ϣ𝑖lnXj + 1
2
j=1
n=3
Ϣ𝑖lnXij
2
+
j=1
n=3
Ϣ𝑖lnXij ∗ lnXij + (u − v)
where; X1 = land hactares , X2 = soy bean seed (kgs)
X3 = labour (man days) , Ϣ1 … Ϣ9 =
model parameters , u − v = composite error term.
11. The multiple regression model jointly estimated in
frontier for factors contributing to inefficiency of
soybean farmers was specified as given;
U𝑖 = Ω0 +
k=1
10
Ωk Xik + ei
where Ui= inefficiency level of the ith farm,
X1 … X10 =age, educ., experience, extension contacts,
farm size, hh size, gender, modern seed, credit
access, farm club membership. Ω0 … Ω10 = model
parameters, ei= disturbance error term
Model specification cont’d
12. Results and discussion
Table 1:Summary statistics of soy bean farmers
variable units Mean Std. dev. Min. Max.
Age Years 45.81 14.88 18 76
Household
size
Persons 5.15 2.16 1 12
Education Years 5.19 3.54 0 16
Experience Years 10.81 3.67 4 25
Extension Contacts
no.
5.22 2.06 0 13
Farm size Hectares 0.52 0.44 0.1 2.97
Labour Man days 23.58 17.17 9 35
Seed Kgs 10.18 16.84 4.87 22
output Kgs 459.17 522.27 101.32 1350
13. Table 2: Frequency of farmer characteristics
Characteristic Frequency (n=300) Percentage (%)
Gender
a. Male 154 51.33
b. Female 146 48.67
Modern seed adoption
a. Adopter 177 59
b. Non-adopter 123 41
Club membership
a. Member 137 45.67
b. Non-member 163 53.33
Credit access
a. Access 138 46
b. No access 162 54
15. *,**,*** imply significance at 10%, 5% and 1% respectively
Variance parameters parameter Coeff. Std. err. T-ratio
sigma-squared (𝝈 𝒗
𝟐 + 𝝈 𝒖
𝟐) 𝜎𝑠
2 0.153** 0.031 4.94*
Gamma (𝝈 𝒖
𝟐/(𝝈 𝒗
𝟐 + 𝝈 𝒖
𝟐)) Γ 0.724** 0.120 6.03**
lambda ( 𝝈 𝒖
𝝈 𝒗) Λ 1.620* 0.0473 34.25*
Log-likelihood -47.651
Squared terms show relationship btwn input factors with
output on their continuous usage.
The interaction terms entail the substitutability or
complementarity of the input factors .
Negative term entails substitutes and positive term entails
complements (Adzawla, 2013).
16. Table 4: Determinants of technical inefficiency of soy
bean farmers
Variable units parameter Coeff. Std. err. T-statistic
Constant - 𝜴 𝟎 0.314** 0.1074 2.924
Farm size Acres 𝜴 𝟏 0.032** 0.0083 3.855
Age Years 𝜴 𝟐 -0.124 0.0324 -3.827
Education Years 𝜴 𝟑 -0.004** 0.0016 -2.50
Experience Years 𝜴 𝟒 0.002** 0.0007 2.83
Gender Dummy 𝜴 𝟓 0.018** 0.0081 2.22
Household size Persons 𝜴 𝟔 0.012* 0.0021 5.714
Extension Contacts 𝜴 𝟕 -0.013** 0.0019 -0.842
Modern seed Dummy 𝜴 𝟖 -0.018 ** 0.0092 -1.957
Farmer club Dummy 𝜴 𝟗 -0.006** 0.0088 -0.681
Credit access Dummy 𝜴 𝟏𝟎 -0.003*** 0.0068 -0.441
*,**,*** imply significance at 10%, 5% and 1% respectively
17. Table 5: Hypothesis testing
Null Hypothesis Chi-bar (X2) F-statistic Decision
H0 : γ = Ω1 +…+ Ω10 = 0 17.05** rejected
H0: Ϣ4 +…+ Ϣ9 = 0 242.40** rejected
Returns to scale Estimation Coeff. T-value decision
H0: Ϣ1 +… + Ϣ3 =1 3.370 8.73** rejected
** implies significance at 5%
H1: socio-econ and institutional factors have influence on TE of soybean
production in the study area.
H1: The TSPFM was a perfect and flexible model specification for the
estimation of TE of soy bean production in the study area.
H1: Farmers have increasing returns to scale with the use of available
inputs and technology levels.
18. Table 6:Technical efficiency levels of soy bean
farmers
Variable Mean (%) Std. dev. Min. Max.
TE 78.91 9.81 21.28 96.40
Range Frequency Percentage (%)
TE 20 - 40 4 1.3
41 - 60 10 3.3
61 - 80 118 39.3
81 above 168 56.0
Total 300 100
Aver. Farmer is 78.91% TE: a shortfall of 21.09% : still room for further increase
in soy bean output under the same Tech. level & without increasing amount of
inputs used.
Majority (56%) were at least 81% TE in the study area.
19. Table 7: TE with respect to EPAs
EPA Mean Technical Efficiency level
(%)
Chitekwere 77.98
Madisi 77.36
Nachisaka 79.52
Nyanja 80.83
Soybean farmers in Nyanja EPA were more TE compared
to the other EPAs in the study area
Farmer clubs had intensified the agron. husbandry
practices on soybean with credits obtained from
ICRISAT in form of seed and inoculants.
20. TE and socio-economic &inst. factors
A B
Aver. TE was higher for Modern seed adopters than Non-adopters
Female farmers were more TE than Male farmers in the study area
Female farmers actively participated in farmers clubs than their
male counterparts; males were mostly involved in Non farm
agriculture. activities for income.
21. C D
Aver. TE increased with increase in number of years a farmer spent in
formal education.
ability to utilize extension messages and optimally utilization of production
inputs hence, enabling maximum output.
Increase in the amount of land allocated to soybean negatively influenced
TE of production;
Large size of land demanded more of other inputs which were scarce
and of low quality to the Farmers.
22. E F
Aver. TE increased with increase in number of visits conceived by a
farmer.
Mainly farmers accessed extension services in groups & non
members were visited occasionally sometimes no visits at all.
TE decreased with increase in hhsize ; need for extra economic activities
to raise income for food at the hh & little focus given to soy bean
production, despite available for econo. Active age groups.
23. Conclusion
Basically, land, seed and labour were the main factors of soy
bean production. The Translog production function best fitted
the estimation of TE of soybean farms in the study area.
Moderately TE (78.91%) with TE affected by random shocks,
farm or farmer level socio-economic and institutional factors.
Soybean farmers in the study area could increase output by
21.09% without increasing levels of factor inputs and under
the same technology levels.
Soy bean farmers in the study area experience IRTS.
24. Recommendations
1. Farmers’ access to institutional factors be enhanced through:
provision of modern soy bean seed on credit
strengthening the capacity of extension services through;
deployment of extension field staff with relevant information
pertaining to soy bean production.
improvement in logistical support for agricultural extension agents
2. Extension staff should emphasize the need for soy bean farmers to work in
groups, in order to ensure effective spread of extension messages to most
farmers and enhance uptake of new technologies such as modern seed
adoption.
25. 3) National budget allocation of funds to legume
production –a welcome devpt. to the sector.
Under the presidential initiative for promotion of
legume production in the country.
4) Need for another study investigating allocative
efficiency of soy bean farmers in the study area.
