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ASSESSMENT OF TECHNICAL EFFICIENCY OF SOYBEAN PRODUCTION IN SELECTED DISTRICTS OF CENTRAL

MALAWI

By Dyson Ligomba

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)

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).

Justification continuedOn 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.

ObjectivesUnderlying objectiveThe study aimed primarily to assess the technical efficiency of

soybean production among smallholder farmers.

Specific ObjectivesTo 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.

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.

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.

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.

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)

Empirical Model SpecificationThe Translog stochastic production frontier model was

used to determine the technical efficiency scores through Maximum Likelihood Estimation.

The model is specified as;

where; (kgs) , .

The multiple regression model jointly estimated in frontier for factors contributing to inefficiency of soybean farmers was specified as given;

where = inefficiency level of the ith farm, age, educ., experience, extension contacts, farm size, hh size, gender, modern seed, credit access, farm club membership. model parameters, = disturbance error term

Model specification cont’d

Results and discussionTable 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

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

Table 3: Maximum Likelihood estimates of the Translog Stochastic Production Frontier ModelVariable Parameter Coeff. Std. err. T-ratio

Constant -5.645 0.586 -9.64***

Lnland -4.003 0.270 -14.81***

Lnseed 5.970 0.345 17.29***

Lnlabour 2.403 0.216 11.12***

(lnland)2 -0.485 0.039 -12.41***

(lnseed)2 -0.433 0.087 -4.99***

(lnlabour)2 0.166 0.044 3.77***

Lnland*lnseed 2.343 0.185 12.65***

Lnland*lnlabour 0.9182 0.124 7.40***

Lnlabour*lnseed -2.186 0.208 -10.48***

*,**,*** imply significance at 10%, 5% and 1% respectively

Variance parameters parameter Coeff. Std. err. T-ratio

sigma-squared () 0.153** 0.031 4.94*Gamma () Γ 0.724** 0.120 6.03**

lambda 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).

Table 4: Determinants of technical inefficiency of soy bean farmersVariable units parameter Coeff. Std. err. T-statisticConstant - 0.314** 0.1074 2.924

Farm size Acres 0.032** 0.0083 3.855

Age Years -0.124 0.0324 -3.827

Education Years -0.004** 0.0016 -2.50

Experience Years 0.002** 0.0007 2.83

Gender Dummy 0.018** 0.0081 2.22

Household size Persons 0.012* 0.0021 5.714

Extension Contacts -0.013** 0.0019 -0.842

Modern seed Dummy -0.018 ** 0.0092 -1.957

Farmer club Dummy -0.006** 0.0088 -0.681

Credit access Dummy -0.003*** 0.0068 -0.441

*,**,*** imply significance at 10%, 5% and 1% respectively

Table 5: Hypothesis testingNull Hypothesis Chi-bar (X2) F-statistic Decision

H0 : γ = Ω1 +…+ Ω10 = 0 17.05** rejected

H0: 4 +…+ 9 = 0 242.40** rejected

Returns to scale Estimation Coeff. T-value decisionH0:1 +… + 3 =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.

Table 6:Technical efficiency levels of soy bean farmersVariable 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.

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 areaFarmer clubs had intensified the agron. husbandry

practices on soybean with credits obtained from

ICRISAT in form of seed and inoculants.

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.

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.

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.

ConclusionBasically, 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.

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.

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.

Thank you all.Merci beucoup tous les mondes