South Pacific Studies Vol.37, No.1, 2016

Economic Efficiency of Smallholder Peanut Farming: An
Application of Data Envelopment Analysis to Smallholder
Producers in the Markham Valley of Papua New Guinea
Manus Peter A.

Department of Agriculture, PNG University of Technology, Private Mail Bag, LAE, MP 411,
Papua New Guinea.
E-mail: peter.manus@pnguot.ac.pg

Abstract

The study, conducted using primary data of the smallholder peanut farming in the Kaiapit Districtof Markham Valley, was to examine the economic efficiency of these farmers in their farm production.
The input-oriented Data Envelopment Analysis (DEA) model was used to estimate technical andallocative efficiencies, the components of economic efficiency. The results establish that while about 6
percent of the farmers were both technically and allocatively efficient and therefore were economicallyefficient in their farming operations, the majority of the farmers were found to be economicallyinefficient. Decomposing technical efficiency into pure technical and scale efficiencies suggests thepresence of scale inefficiency by 23 percent. This implies that the farmers were not using their inputs
at optimum scale. Of the 69 percent (11 farmers) of the farmers who were found to be scale inefficient,
all of them (11 farmers) were operating in the region of increasing returns to scale. Since the majorityof the farmers were found to be technically and allocatively inefficient, it is useful for these farmers to
shift their resources to more profitable cropping alternatives.
Key words:

smallholder peanut production, economic efficiency, scale efficiency, resources allocation

Introduction
Agriculture, in Papua New Guinea (PNG), is an important sub-sector of the economy
where 85 percent of the population, who dwell in the rural sector, derive their livelihood.
Subsistence livelihood production systems can no longer meet the increased demands placedon them and in that perspective, their participation in the monetized cash economy is a step
forward. Consistent with their desire to participate in the cash economy, the rural masses
engage in various economic activities in agriculture and agriculture related production and――――――――――――――――――――――――――――――――――――――――――――――
Received: 23 March, 2016
Accepted: 14 October, 2016

10South Pacific Studies Vol.37, No.1, 2016

sales activities, both crops and livestock. Among crops is peanut, grown entirely on small
scale in many parts of the country. Although a small scale activity, farm surveys (Wemin etal. 2005, Kolopen et al. 2005) conducted from selected peanut growing areas indicated thatit contributes as much as 70 percent of family income for families who grow it.
The domestic market value estimated for peanut produced in four provinces
representing four agro-ecological zones, viz., Eastern Highlands (highlands), Markham
Valley of Morobe Province (Dry Lowland), National Capital District (Dry lowland & periurban) and East New Britain (Wet lowlands) amounts to K29 million (Wemin and Geob
2004). Given that small scale peanut is produced and sold in many provinces distributed
throughout the county, the market value for peanut would be significant and therefore an
important smallholder cash crop.
However, there is an apparent decline in current production. For example, peanut
production in 1998 was estimated to be 21,600 tonnes per annum while in 2004; it wasestimated to be about 12,600 tonnes per annum (Wemin and Geob 2004). Production has,over the 7 year period, declined by 33 percent. This amounts to a decline of about 5 percent
per annum. Such a decline in production, as a smallholder industry, is not encouraging.
The decline in production is the product of two related happenings. First, the fall in
production is due to the productivity differences in the peanut growing areas. In the highlands,
for example, the variations in productivity ranged between 0.6 and 1.5 tonnes per hectare(Ramakrishna et al. 2005). Wemin and Geob (2004), in their cross sectional smallholder
peanut production farm survey of 3 coastal and 1 highlands areas noted peanut productivity
to be between 0.9 and 1.2 tonnes per hectare which are within the range estimated by
Ramakrishna et al. (2005). The variation in productivity could be due to variation isaccessing suitable and improved planting seeds. Such a problem exists everywhere.
The second factor that causes a decline in production is the poor management andcultivation practices of peanut farming. The farmers’ lack of these skills is the direct resultof collapsed commodity extension systems, the function which has been decentralized to theprovinces and provided by Provincial Departments of Agriculture and Livestock (PDAL). In
sum, production declines are essentially, among others, the problem of collapsed extension
systems and unavailability of suitable planting seeds in peanut growing areas.
Peanut was, up to the mid-1980s, grown on small scale commercial basis in theMarkham Valley of the Morobe Province by a large number of smallholder producers.
This area was where the largest concentration of smallholder production took place, in themain, to feed a peanut export processing plant established at Atzera of Markham Valley. In
addition to the problems of differing productivity levels, poor access to improved planting
materials, and poor management and cultivation practices, the decline in production in theMarkham Valley was partly due to the closer of the peanut factory in mid-1980s.
In the recent past, location specific peanut trials (Wemin and Geob 2004) have been
conducted 1 with the objective of releasing better performing varieties to the farmers. The
1

Trials were conducted in Eastern Highlands, Markham of Morobe Province, National Capital District and East
New Britain Provinces to assess agronomic and yield performances of 40 varieties of peanut introduced fromthe Centre for International Crop Research Institute of Semi Arid Territories (ICRISAT).

Manus: Economic Efficiency of Smallholder Peanut Farming

11

trials conducted were spread across major agro ecological zones of the country. Among theselocation trials conducted, a few were conducted in the Markham Valley of Morobe Province,
where smallholder peanut production is active. These attempts to improve productivity are
made in the right direction and must be done by improving extension systems to improve
production and management practices.
While improving access to seeds with better management and cultivation practices
are necessary steps needed to be taken to improve production, no study has, however, been
conducted on the economic efficiency of smallholder peanut farming in PNG except for two
studies (Kwamillon 1986, Millicent 2008) on allocative efficiencies of the producers.
At a more general level, farmers were found to be allocatively inefficient and suggested
adjustment to resources which were not used in optimal proportions. Against these findings,
economic efficiency is not only influenced by allocative efficiency but technical efficiency
as well. Essentially, one needs to establish a clear picture of the productive performance ofthe farmers and resources allocation in peanut production as a smallholder industry.
Technical and allocative efficiencies of the farms are estimated relative to, for theformer, the production frontier and for the later, the cost frontier. If a farm is not located onthese frontiers, it is technically and allocatively inefficient (Farrell 1957). Since cost or
economic efficiency is the product of technical and allocative efficiencies, it is economic orcost inefficient as well if it is either technically or allocatively inefficient or both.
A farm is technically efficient if it produces a given level of output at a minimum
feasible level of input set or if it produces the maximum possible output with a given set of
inputs. The former is estimated in the input- oriented framework while the latter is estimatedin the output-oriented framework.
Although measurement approaches for estimating productive efficiency vary, they arebroadly categorized into two streams; the parametric (econometric) and the non-parametric.
The parametric stream requires specifying the functional forms as either deterministic
or stochastic. The estimation of the stochastic frontier requires one to make appropriate
distributional assumptions about the stochastic error term. This method was developed,
modified and extended based on the initial works of Aigner et al. (1977) and Meeusen
and van den Broeck (1977). For the non-parametric approach to estimating efficiency,
Data Envelopment Analysis (DEA) is popular and receives wide application. Unlike theparametric approach, DEA does not require one to make distributional assumptions inrelation to the functional form (Coelli et al. 2005, Headey et al. 2010).
DEA work is based on the initial work of Farrell (1957). Charnes et al. (1978),following the work of Farrel coined the term DEA and constructed their models under
the condition of constant returns to scale (CRS). The CRS DEA models developed byCharnes et al. (1978) were later modified by incorporating the variable returns to scale(VRS) condition (Banker et al. 1984). DEA models are formulated and analysed either
in the output-oriented mode or input-oriented mode (Coelli et al. 2002). The former has amaximization objective while the later has a minimization objective.
Although the peanut factory collapsed, smallholder peanut production is still active
and such production is distributed over most parts of the Markham Valley, particularly12South Pacific Studies Vol.37, No.1, 2016

areas located closer to the National Okuk Highway. Peanut production, in these areas, has
become a huge cash generating activity essentially made it possible by this road not only
linking buyers of Lae, the capital city of Morobe Province, but buyers from the highlands
provinces as well. Essentially, there exists a vibrant informal market economy which needs
to be promoted. The nature of support to be given to the farmers depends on the productive
performance of these farmers and how well resources, they command, are allocated.
This study2 is the application of DEA to historical smallholder peanut farming data in
Kiapit District of the Markham Valley to assess technical, allocative, economic and scaleefficiencies of these farmers.

Methodology and Data
The DEA procedure (Charnes et al. 1978, Fare et al. 1985, 1994) for estimating
technical, allocative, cost and scale efficiency measures can be constructed in either theconstant returns to scale (CRS) framework or the variable returns to scale (VRS) framework(Banker et al. 1984). Except where mentioned, this study uses the input-oriented CRS DEA
approach to estimate these measures.
CRS DEA for Technical Efficiency
Under the CRS DEA framework, the technical efficiency (TECRS) measure, also known as
the “overall” TECRS measure (OTE), can be derived as:

Subject to(1)

Where each farm of the study sample of n farms produces a single output by using m inputs.
Thus, yi is the output produced by the ith farm, xi is the (m x 1) vector of inputs used by theith farm, Y is the (n x 1) vector of outputs produced by the n farms, X is the (m x n) matrix
of inputs used by the n farms, and 𝜆𝜆 is the (n x 1) vector of weights attached to each of
the efficient farms. The DEA in equation (1) is estimated n times, one for each farm andtherefore, Q is a scalar and provides a measure of TE for each farm under CRS (TECRS). If
Q=1, the farm is on the frontier and is technically efficient. If on the other hand, Q < 1 the2

The study derives the input-output data from Mr Kwamillon’s MPhil Thesis completed in the Department ofAgriculture, University of Technology in 1986.The historical data is used to benchmark productivity analysis.
Presently, input-output data are being collected to assess productivity changes since 1986.

Manus: Economic Efficiency of Smallholder Peanut Farming

13

farm does not lie on the frontier and is thus technically inefficient.
CRS DEA for Cost Efficiency
When input prices are available, one runs the CRS cost minimization DEA model (Fareet al., 1985, 1994) as:∗∗

Subject to

∗

(2)
λ≥0

Where, wi=vector of input prices for the i th farmer, x*i is the cost minimizing vector of input
quantities for the i th farm, given the input prices wi and output levels, y i.
The CRS cost efficiency (CECRS) or economic efficiency (EECRS) measureis thenestimated as the ratio of minimum cost to the observed cost. Thus:∗EECRS =

(3)

Allocative Efficiency
The allocative efficiency (AECRS) index can then be calculated residually from the TECRS
measure estimated in equation (1) and EECRS index estimated in equation (3) as:
=

=

′

′ ∗

(4)

′
where
=
and ′
). If AE CRS = 1, the farms are allocatively
efficient. If on the other hand, AECRS< 1, the farms are allocatively inefficient, suggesting
they are not on the cost frontier.
Input Use Ratios
For each farm, the cost minimization DEA model in (2) produces the cost efficient input
quantities (x*i) while the TECRS DEA model in (1) produces the technically efficient input (Qxi)levels (Coelli et al. 2002). Given these efficient input quantities, one can estimate the farms
extent of input use in peanut farming by constructing the input use ratios (IR) (Coelli et al.
2002) as:∗(5)

By using equation (5) one can investigate, for each farm, if it either over or underutilize the resources used in its farming operations. An IR < 1 suggests underutilization of

14

South Pacific Studies Vol.37, No.1, 2016

inputs while an IR > 1suggests over use of inputs.
Input Slacks
Inputs slacks appeared to be handled in two different ways. The first approach is totreat the slacks as the source of technical inefficiency (Ali and Seiford 1993). The second
way is to threat the slacks as the source of allocative inefficiency (Ferrier and Lovell
1990, Sharma et al. 1999). In the later approach, equation (2) accounts for the input slacks
that were not captured by equation (1). This procedure suggests that slacks occur due toinappropriate input mixes used in production and therefore it is considered as a source ofallocative inefficiency. This paper followed the later approach to account for input slacks
DEA for Scale Efficiency
In the situation where the TECRS index, Q is less than 1, farms are not operating at the
optimal scale. In this case one needs to estimate the extent of scale efficiency (SE), often
done, by estimating TE under the condition of variable returns to scale (TEVRS). Essentially,
to account for TEVRS, the CRS model in equation (1) was modified by adding convexity
constrain; NI’𝜆𝜆 = 1 (Banker et al. 1984). Thus:

Subject to(6)

The DEA specification in equation (6) eliminates the scale inefficiency component fromthe TECRS estimate derived from the CRS DEA model. Essentially, SE is derived residuallyas:
SE = TECRS/ TEVRS(7)

A farm is scale efficient if its SE index = 1. In this case, the farm is operating in theregion of CRS. On the other hand, if SE < 1, the farmer is scale inefficient.
In the case where the farmers are found to be scale inefficient, a further VRS DEA
analysis can be conducted with the non-increasing returns to scale (NIRS) restriction (NI’𝜆𝜆≤1) imposed on equation (6) by replacing the convexity constraint; NI’𝜆𝜆 = 1 with NI’𝜆𝜆≤ 1.
Now let the estimated TE measure under the non-increasing returns to scale (NIRS) as
TENIRS. It follows that if TENIRS = TECRS, increasing returns to scale exists. If on the other hand,
TECRS < TENIRS, the farms are operating under decreasing returns to scale. These situations

Manus: Economic Efficiency of Smallholder Peanut Farming

15

indicate that scale inefficiency arise only if the farmers were operating in the regions ofincreasing and decreasing returns to scale (Coelli et al. 2005, Kumar and Gulati 2008).
Sample and Data
The input-output data was collected from 17 farmers located in Atzera, Amari, andLower Yaros in the Kaiapit District. The inputs were farm size measured in hectares, familylabor in man-days of work, and hired labor in man-days of work and seeds in kilograms andcapital in Kina3. The output, measured in kilograms, was sold as dried pods packed in 50 kg
bags. The smallholder farms varied in sizes between 0.1 and1.2 hectares. Peanut seeds andoutputs were valued at market prices. Capital tools such as spades and bush knives werevalued at cost price.
The farmers have easy access to the Okuk Highlands Highway which connects Lae,
the Capital City of Morobe Province and the provinces of the highlands region. Essentially,
the farmers grow and sell peanut on a regular basis to road side buyers in the production
locations.
Peanut can be grown twice in a year. This study conducted the productive performance
analysis of the farmers using the aggregated data covering both production cycles. Dueto the geographical spread of the farmers, a sample of 30 farmers was randomly selected.
At the time of interview, 13 farmers were not available and due to time and resources
constraints, 17 farmers were actually interviewed. To remove the influences of outliers, one
farm was dropped in the actual DEA analysis as it has contained input-output which wereabnormally high.

Results and Discussion
The summary statistics of inputs and output of the average farm in the study sample
are presented in Table 1. This farm cultivates 0.265 of a hectare of land to produce 279 kgof peanut. This production required an average of 62 man-days of labor, 17kgs of seed, andcosts K13.81 for tractor hire services and K2.09 for the purchase of farm tools. Farm work
was done by family members and hired hand of which hired labor contributed about 22
percent of the man-days of work.
Table 1. Summary statistics of inputs and output.
Variables
Minimum
Maximum
1. Output
Peanut Output (Kgs)48660
2. Inputs
Land planted (ha)
0.068
0.44
Labour (man-days)
19.7
137.48
Seed (kgs)
7.3
27.89
Tractor
6.76
24.89
Tools
0.77
3.33
3

A PNG Kina is worth about AU$ 0. 43

MeanStandard Deviation

279.06160

0.265
61.77817.09213.8112.099

0.103
34.645
6.665
5.073
0.664

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South Pacific Studies Vol.37, No.1, 2016

Table 2. Distribution of estimated technical, allocative and cost efficiencies.
Distributions
TECRS
AE
CE
< 0.500 (%)
25.00 (4)*12.50 (2)62.50 (10)0.500 – 0.599 (%)
12.50 (2)37.50 (6)18.75 (3)0.600 – 0.699 (%)
18.75 (3)18.75 (3)6.25 (1)0.700 – 0.799 (%)
12.50 (2)18.75 (3)6.25 (1)0.800 – 0.899 (%)
0.900 – 0.999 (%)
6.25 (1)1.000 (%)
31.25 (5)6.25 (1)
6.25 (1)Minimum
0.348
0.347
0.139
Maximum
1.000
1.000
1.000
Mean
0.708
0.642
0.461
Standard Deviation
0.235
0.167
0.212
*Figures in the brackets are number of farms located in that category of distribution.
Table 3. Input use ratios.
Land
LabourSeed
Tractor
Tools
Minimum
0.937
0.786
0.690
1.000
0.659
Maximum
3.356
3.424
5.411
18.43311.959
Mean
1.674
1.615
2.278
4.426
2.702
Standard Deviation
0.737
0.743
1.447
4.362
3.024
Over using farms (%)
87.50 (14)* 87.50 (14)81.25 (13)93.75 (15)75.00 (12)Under using farms (%)
11.76 (2)11.76 (2)11.76 (2)23.53 (4)*Figures in the brackets indicate the number of farms who over or under use the indicated farm inputs.

The DEA models described in the previous section were estimated using the program
DEAP 2.1 (Coelli 1996). The results on technical, allocative, cost and scale efficiencies
estimated from these models and together with their frequency distributions are summarizedin Table 2. The efficiency measures are observed to vary substantially among the sampled
farmers. Technical efficiency (TECRS) varied between 0.348 and 1.000 with a mean of 0.708.
This indicates that the average farm has produced less than the potential total output by
about 29 percent. Of the 16 farms, 5 (31.25%) of them were technically efficient.
Allocative efficiency (AECRS) varied between 0.347 and 1.000 with a mean of 0.642.
This index suggests that these farms, on average, need to reduce costs by 36 percent andgiven their prices that can be done by realigning the input mixes in the production process.
Of the 16 farms, only one farm was price efficient.
Economic or cost efficiency is the product of combining the TECRS and AECRS (foundat the tangency point of iso-cost and iso-product curves). Cost efficiency (CECRS) varied
between 0.139 and 1.000 with an average of 0.461, suggesting that the average farm iscost inefficient by 54 percent. Of the 16 farms, one farm was found to be technically andallocatively efficient and thus cost efficient. In sum, the foregoing DEA analysis suggests theexistence of substantial inefficiencies in smallholder peanut.
Allocative inefficiency suggests that the average farm did not equate the value ofmarginal product of inputs to their respective acquisition prices. Essentially, the extent towhich the inputs were used was investigated by constructing the input use ratios (Coelli etManus: Economic Efficiency of Smallholder Peanut Farming

Table 4. Distributions of technical, pure and scale efficiencies.
TEVRS
Distribution
TECRS< 0.500 (%)
25.00 (4)0.500 – 0.599 (%)
12.50 (2)0.600 – 0.699 (%)
18.75 (3)0.700 – 0.799 (%)
12.50 (2)12.50 (2)0.800 – 0.899 (%)
31.25 (5)0.900 – 0.999 (%)
1.000 (%)
31.25 (5)56.25 (9)Minimum
0.348
0.756
Maximum
1.000
1.000
Mean
0.708
0.922
Standard Deviation
0.235
0.095
IRS (%)DRS (%)CRS (%)-

17SE
23.53 (4)6.25 (1)12.50 (2)18.75 (3)6.25 (1)31.25 (5)
0.343
1.000
0.768
0.234
68.75 (11)031.25 (5)al. 2002) and these results are presented in Table 3. The mean input use ratios of the inputs
are greater than one, suggesting considerable overuse of these inputs. The overuse of seeds
was not expected as they were required to be bought from established sellers. By using
their own seeds, farmers used more seeds than needed to ensure adequate germination. Of
the 16 farms, 13 used more seeds than they actually needed. Farm labor used was higher
than normally required and this could possibly be due to farms providing prepared meals
to hired hand in addition to making, on occasions, a one of cash payment for a particular
activity that ought to be completed. The easy to attract hired hand for meals may suggest
disguised unemployment, a situation prevailing in most rural village communities. Of the 16
farms, 14 used more labor than they actually need. The overuse of tools may be expected.
The probable cause of this was the difficulty involved in apportioning tools to different andvaried farming activities performed in a given production cycle. Farm size and tractor hire
were inputs requiring adjustments as well. For tractor hire, the rate of hire may need to bereviewed, if at all, possible.
Mean scale efficiency (SE) score of 0. 768 were estimated and this result is given in
Table 4. The score suggests that the average farm need to increase scale efficiency by 23
percent. The mean TECRS score was reported to be 0.708 and the mean TEVRS score wasreported to be 0.922. If by improving SE by 23 percent, TECRS can be increased to 0.922, a
substantial gain in technical efficiency of 21.4 percent. These results suggest that technicalefficiency can be improved by adjusting the scale of farm size to operate in addition to
paying attention to improving allocative efficiency of peanut farming. Furthermore, of the
16 farms, 5 farms had constant returns to scale while the remaining 11 farms had increasing
returns to scale. In sum, the farms are essentially small and need to be increased to reduce
costs and improve profitability of peanut farming.
Given the above results, it is useful to highlight that the relevance of these results arelimited by two factors. First, the results were obtained from a study sample of 16 farms
which is less than 30 farms, the minimum required for statistical analysis. Thus the results

18South Pacific Studies Vol.37, No.1, 2016

may not be representative of the peanut farming population. Secondly, the use of historical
data may not provide the correct picture of the current productive efficiency of peanut
farming in the study area. It is therefore suggested that further research should be conducted
to validate these results and to provide information on productivity changes that mightoccurred over the past 30 years.

Conclusions
The paper analyzed, by using DEA, the technical, allocative, cost and scale efficienciesof smallholder peanut farming in the Markham Valley of Papua New Guinea. The DEA
analysis using historical production data was done not only to get some understandingof the productive efficiency of farms that prevailed then but itcan also become useful forcomparing productivity changes that might have occurred since 1986.
The findings indicate that technical, allocative, cost and scale efficiencies estimatedvaried substantially among the sampled farms. The average TECRS for the sampled farms
was estimated to be 0.708 which suggest that farms need to reduce inputs by 29 percent.
Allocative efficiency estimated amounts to 0.642 which suggest that farms, on average,
need to reduce cost by 36 percent. Of particular interest is the over use of labor which may
indicate disguised unemployment problem in the farming areas. The overuse of seed can beaddressed by using reliable seeds for planting while tractor hire rate may be appropriately
reviewed. The overuse of tools was expected as it was not possible to apportion their use inother farming activities.
The farms, moreover, were scale inefficient by 23 percent. Given the average
TE CRS of 0.708 and TE VRS of 0.922 and by improving scale efficiency by 23 percent,TECRS can be increased to 0.922, a massive gain of 21.4 percent in technical efficiency.
Essentially, technical efficiency can be improved by adjusting the scale of farm size while
simultaneously making adjustments to inputs to improve allocative efficiency in peanut
farming. While adjustments in the use of inputs are necessary to improve technical andallocative efficiencies, 96 percent of the farmers were economically inefficient in theirfarm operations. Given that farmers have other production activities in addition to peanut;it might be useful for them to shift resources to these alternatives which may be more
profitable. Moreover, given the small sample size and historical data used, further research is
required to validate the findings of this study and examine the extent of productivity changethat might have occurred since 1996.

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