IJERD – International Journal of Environmental and Rural Development (2023) 14-2

Research article
erdTechnical Efficiency of Potato Producers in Benguet
Province, Philippines
ASRES ELIAS*
Faculty of Agriculture, Tottori University, Tottori, Japan
Email: asres@tottori-u.ac.jp

CHERYLL CASIWAN LAUNIO
Research and Extension Publication Office, Benguet State University, Philippines

KUMI YASUNOBUFaculty of Agriculture, Tottori University, Tottori, Japan

YUKI TOYAMA
United Graduate School of Agricultural Science, Tottori University, Tottori, Japan

BIRARA ENDALEW
Graduate School of Sustainability Science, Tottori University, Tottori, JapanMARYAN J. ALTAKI
Philippine Rice Research Institute, Philippines
Received 25 September 2023

Accepted 28 November 2023(*Corresponding Author)

Abstract The Benguet Province of the Philippines is endowed with distinct agroecologicalzones favorable for the production of high-value vegetables. Potato is one of the top ten
vegetable crops in Benguet Province in terms of production area, volume, and value.
However, potato production in the Philippines in general is characterized by low yield, witha yield per hectare of 15.5 tons compared to the world average yield of 20.9 tons in 2018.
This study examined the technical efficiency (TE) and inefficiency determinants of potato
producers in Benguet Province, Philippines. Data from 101 farmers from the major potatoproducing municipalities in Benguet Province was collected in 2019. We employed the
Cobb-Douglas stochastic production frontier after the null hypothesis for the translog
function was rejected. The production function result revealed that land, hired labor, organic
fertilizer (chicken dung) and fungicide had significant effects on potato yield. We also foundthat the mean technical efficiency of potato farmers is about 0.89, implying that farmers canincrease their production by about 11%, with an average yield increase from 15.8 to 17.6
t/ha, using existing resources. The yield gap result further revealed that farmers are producing
below the production possibility frontier. However, the observed average yield is about 50%
lower than the potential yield (31 t/ha) found by Benguet State University. The inefficiency
model result depicted that a larger household size, higher level of education, using the
Granola potato variety, receiving training related to vegetable production, and partial
irrigation significantly improve technical efficiency, while hand tractor ownership andrainfed cultivation were the major sources of technical inefficiency in potato production. In
conclusion, while comprehensive plot-level studies including various parameters remain
important to understanding why potato producers’ yields are significantly lower than thepotential yields, we recommend promoting the Granola variety along with the use of organic
fertilizers and the provision of training to enhance the efficiency of producers.
Keywords potato production, technical efficiency, stochastic production function,
Philippines

INTRODUCTIONⒸ ISERD

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Vegetable production in the Philippines shows an increasing trend in terms of total value of
production compared to rice production which was the leading crop until 2020 (Department ofAgriculture, 2021). The vegetable production is divided into two main classifications based on
location and climatic conditions i.e., highland and lowland. Benguet province in the Cordillera
Administrative Region (CAR) is the leading highland vegetable producer area endowed with distinct
agroecological zones favorable to producing high-value vegetables. Potato is one of the top ten
vegetables in terms of production area, volume, and high value in Benguet. Among the six regionsthat grow potatoes in the Philippines, CAR contributes 73% and 83% of production area and volume
respectively (PSA, 2020). However, potato production is characterized by low yield in thePhilippines in general is among the lowest yield per hectare (15.5 tons) compared to the world
average yield (20.9 tons) in 2018. According to the JICA 2019 report, there is a reduction in the
production of cabbage, cauliflower, carrots, and potatoes in Benguet province. The decrease in thearea planted, extreme weather events, pest and disease problems, and low adoption of high-yielding
varieties were among the main causes. In this context, only a few studies exist including the statusof potato varieties (Kiswa et al., 2020); identification of potato varieties, agronomic characteristics,
and potential yields (Gonzales et al., 2016). As far as our knowledge, no studies have been conducted
in Benguet to investigate whether the declining production trend is attributed to the technicalefficiency of producers. Therefore, this study aims to understand the extent to which potato producers
can attain the utmost output from the available combination of inputs.
OBJECTIVES
The objectives of this research are as follows;
1) to estimate the technical efficiency of potato producers, and2) to identify major sources of technical inefficiency in potato production.
METHODOLOGY
The data used in this study was collected by Benguet State University (BSU) with funding from the
Department of Agriculture, Bureau of Agricultural Research (DA-BAR) in 2019, in reference to 2018
production season. Farm-level information including farm profile, facilities, input-output
information, perception of recommended practices, and other variables were collected from 700
farmers (7 crops) in eight selected municipalities of Benguet Province, Cordillera Administrative
Region, Luzon Island. This study used the potato data collected from 101 farmers in the Atok, Bakun,
Buguias, Kibungan, and Mankayan municipalities. Multistage sampling was employed first by
selecting the top five municipalities of Benguet with the largest area planted to potatoes. All thevillages in the sample municipalities with areas that planted potatoes were then shortlisted from
where three villages were randomly drawn.
Empirical Model
Following the neo-classical definition of technical efficiency, a production process is considered
technically efficient when it attains the highest achievable output from a given combination of inputs.
This study utilized the Stochastic Frontier (SF) approach, which is a parametric method forevaluating farm-level efficiency. The SF model dissects the error term into a two-sided random errorthat accounts for uncontrollable random effects beyond the firm's control (the decision-making unit),
and a one-sided efficiency component. In this research, we adopted the model outlined by Battese
and Coelli (1995), and the model specification is provided as follows:(1)

𝑌 = 𝑓(𝑥 ; 𝛽) exp(𝑣 − 𝑢 )where Y is the quantity of output (potato yield) on the 𝑖 th firm, x is a vector of inputs (production
inputs shown in Table 1) used, 𝛽 is a vector of parameters, 𝑓(𝑥 ; 𝛽) is a suitable production function,v is a random error term assumed to be independently and identically distributed as 𝑁(0, 𝜎 ) ,Ⓒ ISERD

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independent of u, which represents technical inefficiency and is identically and independently
distributed as truncated normal, with truncation at zero of the normal distribution (Battese and Coelli,1995). The maximum likelihood estimation of Eq. 1 yields an estimator for 𝛽 and 𝛾, where γ = 𝜎 /𝜎and 𝜎 = 𝜎 + 𝜎 . The total variation of output from the frontier, which is attributed to technicalinefficiency, is given by  and has a value between zero and one. Battese and Coelli (1995) proposeda model in which the technical inefficiency effects in a stochastic production frontier are a functionof other explanatory variables. In their model, the technical inefficiency effects, u are obtained bytruncation (at zero) of the normal distribution with mean, 𝑧 𝛿 and variance 𝜎 , such that:
(2)

𝑢 =𝑧𝛿

where 𝑧 is a vector of farm-specific explanatory variables (Table 1) and 𝛿 is a vector of unknown
coefficients of the farm-specific inefficiency variables.
The technical inefficiencies in Eq. 2 can only be estimated if the technical inefficiency effects,
ui are stochastic and have distributional properties (Coelli and Battese, 1995). These conditions leadto conducting different hypothesis tests using the generalized likelihood-ratio statistic, λ, given by
Eq. 3.
(3)𝜆 = −2[ln{𝐿(𝐻 )} − ln{𝐿(𝐻 )}]where L ( 𝐻 ) and L ( 𝐻 ) denote the values of the likelihood function under the null ( 𝐻 ) andalternative (𝐻 ) hypotheses, respectively. In the end, predictions of technical efficiency scores weredone as follows in Eq. 4.
(4)

𝑇𝐸 = exp −𝑢Summary statistics of output, input, and farm-specific explanatory variables are given in Table
1.
RESULTS AND DISCUSSION
Technical Efficiency of Potato Production
To select the accurate functional form for potato producers’ technical efficiency estimation, aloglikelihood ratio test was done. The test result revealed that the Cobb-Douglas production
functional form is appropriate to estimate potato technical efficiency. Moreover, gamma wascomputed following the Cobb-Douglas production function to confirm the presence of technicalinefficiency in the stochastic frontier model. The result shows that about 25.5% of the total variationin potato production is explained by technical inefficiency. Following the lower value of the gamma,
both the unrestricted (stochastic frontier model) and restricted (ordinary least square) estimations
were undertaken to confirm whether the presence of technical inefficiency is significant or not. For
this purpose, the loglikelihood ratio (LR) test result was generated by applying LR = -2[L(H0)-L(H1)4.
The LR test result (42.68) was statistically significant at p < 0.01, affirming the significance oftechnical inefficiency in potato production. This substantiates the appropriateness of employing thestochastic frontier model to estimate potato producers’ technical efficiency. Additionally, the loglikelihood of the stochastic frontier model was also statistically significant at p < 0.01, indicating theoverall fitness of the model is robust. This suggests that the model's parameters for the variables
differ significantly from zero.
The model result revealed potato productivity and the area allocated for potato production had
a negative and statistically significant relationship. This implies that an increase in area allocated topotato production results in a decrease in potato productivity. This implies that a large farm size isless productive than a small farm size. On the other hand, hired person day per hectare and potato
productivity had a positive and statistically significant relationship. For instance, a 1% increase in
hired person day per hectare will increase potato productivity by 0.041%. Similarly, chicken dung
4 L(H ) and L(H ) are the loglikelihoods of the unrestricted and restricted models, respectively.
0
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per hectare had positively and significantly affected potato productivity. This means that potato
productivity will be increased by 0.334% for a 1% increase in chicken dung per hectare. Moreover,the number of fungicides had a positive and statistically significant effect on potato productivity at
p<0.05. This implies that potato producers were effectively using fungicides to protect against
harmful fungal diseases that severely affect potato growth and productivity.
Table 1 Descriptive statistics and variables included in the stochastic frontier analysis
Variables

Description

MeanStandard
deviation
12.52

Potato yield
Potato yield ton/ha
15.78
Production inputs
Land
Potato planted area in hectare (ha)
0.48
0.37
Family labor
Total family and exchange labor in person day/ha
4108.00
8108.00
Hired labor
Total hired labor person day/ha
323.88617.49Chemical fertilizer
Total chemical fertilizer in kg/ha
543.18483.88Organic fertilizer
Total chicken dung fertilizer in kg/ha
9522.817904.91
Insecticides
Total insecticides kg a.i.* /ha
2.33
3.44
Herbicides
Total herbicides kg a.i.* /ha
2.97
3.20
Fungicides
Total fungicides a.i.* /ha
41.84
38.13
Technical inefficiency variables
Household size
Number of family members
4.68
2.19
Education
Number of years of schooling
9.52
3.36
Training
Dummy (1= if farmers have veg. production training, 0= No)
0.24
0.43
Potato seed variety
Dummy (1= Granola, 0= Igorota (PO3))
0.60
0.49
Land topography
Dummy (1= hilly/undulating terraced, 0= river/flood plain)
0.82
0.38
Hand tractor ownership
Dummy (1= yes, 0= No)
0.28
0.45
Partially irrigated
Dummy (1= if farmers use partial irrigation, 0= otherwise)
0.38
0.49
RainfedDummy (1= if farmers use fully rainfed, 0= otherwise)
0.63
0.48
Atok Municipality
Dummy (1= if Atok Municipality, 0= otherwise)
0.03
0.17
Bakun Municipality
Dummy (1= if Bakun Municipality, 0= otherwise)
0.10
0.30
Buguias Municipality
Dummy (1= if Bugaias Municipality, 0= otherwise)
0.39
0.49
Kibungan Municipality
Dummy (1= if Kibungan Municipality, 0= otherwise)
0.29
0.45
Mankayan Municipality Dummy (1= if Mankayan Municipality, 0= otherwise)
0.20
0.40
Number of observations
101.00Source: Benguate State University (BSU) in collaboration with the Department of Agriculture (DA), 2019
Note: Actual use in liters and kg/ha were converted to kg active ingredient (ai)using ai equivalent from FPA, 2021

On the other hand, herbicide and potato productivity had a negative and statistically significant
relationship. This shows that more herbicide users are less likely to be efficient in potato production
than their counterparts. The model result further reveals that potato productivity will be decreased
by 0.076% for a 1% increase in herbicide. The technical efficiency scores result shows that the score
ranges from 0.19 to 0.99 and the average score was 0.89. This shows that potato producers were
producing about 11% below the average production capacity. Moreover, the distribution of technicalefficiency was categorized into six groups considering statistical procedures. The result shows thatabout 78.2% of the technical efficiency level was 0.9 and above. Whereas, about 7.9% of thetechnical efficiency level was 0.8.
Factors Affecting Technical Inefficiency in Potato Production
The results in Table 2 demonstrated that various factors influenced the technical efficiency of potato
production. Household size had a negative effect on the technical inefficiency of potato production,
indicating that an increase in household size is associated with reduced technical inefficiency.
Agricultural production, especially potato farming, is known for its labor-intensive nature. Therefore,a larger household size facilitates the demanding tasks of farm plot preparation, sowing, follow-up,
and harvest. Furthermore, a larger household size plays a crucial role in lowering the costs of hiring
labor and reducing the time required to find labor during peak production seasons.
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Table 2 Parameter estimates of the stochastic frontier and inefficiency model
Variables
Coefficient
Std. Error
T-valueP-valueLog (Planted area)-0.275***0.091-3.0400.002
Log (Family labor)-0.0450.035-1.2700.205
Log (Hired labor)
0.042*
0.022
1.920
0.055
Log (Chemical fertilizer)-0.0180.030-0.6000.549
Log (Organic fertilizer)
0.334***
0.066
5.070
0.000
Log (Insecticide)
0.023
0.033
0.690
0.493
Log (Fungicide)0.135**
0.062
2.180
0.029
Log (Herbicide)-0.076***0.034-2.2700.023
Constant
6.092
0.562
10.8400.000
Technical inefficiency
Household size
-0.739**0.333-2.2200.026
Educational level-0.429**0.175-2.4500.014
Training-1.648**0.828-1.9900.047
Potato seed variety-1.078*0.604-1.7800.074
Land topography-1.230**0.607-2.0300.043
Hand tractor ownership3.117**
1.259
2.480
0.013
Partially irrigated-1.274**0.553-2.3000.021
Rainfed2.845**
1.355
2.100
0.036
Bakun
0.755
4.156
0.180
0.856
Buguias
0.559
4.099
0.140
0.892
Kibungan-2.1984.308-0.5100.610
Mankayan
3.226
4.006
0.810
0.421
Constant
3.942
4.581
0.860
0.389
σu
0.268**
0.120
2.230
0.026
σv
0.458***
0.035
13.1800.000
λ0.584***
0.126
4.640
0.000
𝛾
0.255
𝜎 =𝜎 +𝜎
0.281
Mean technical efficiency
0.898
Loglikelihood-64.59***-2[L(H0)-L(H1)42.68***
Number of observations
101
Source: Benguate State University (BSU) in collaboration with the Department of Agriculture (DA), 2019. *, **,*** indicate significant at 10%, 5% and 1% level respectively.

Similarly, there is a negative and statistically significant relationship between educational level
and technical inefficiency. As the educational level of the household head increases, the technicalinefficiency of potato production decreases. This implies that less educated farmers exhibit greater
inefficiency compared to those with higher educational attainment. This might be due to the
proficiency of literate household heads in crop production and management, understanding the
dynamics of production trends, and implementing yield-enhancing strategies that surpass that of lesseducated farmers. Likewise, a negative and statistically significant relationship occurred between
training access and technical inefficiency.
Granola potato seed had a negative and statistically significant effect on the technicalinefficiency of potato production. This shows that Igortoa seed users are more inefficient than
Granola seed users. Moreover, the mean comparison result confirms that the mean productivity ofGranola seed (16.8 tons/ha) is higher than Igorota seed (14.2 tons/ha). The cross-tabulation result
depicted that about 53.16% and 46.84% of Granola and Igorota seed users’ technical efficiency
distribution is 0.9 and above, respectively. This shows that Granola seed users are more efficient than
Igorota seed users. Therefore, the model result is congruent with the cross-tabulation and mean
comparison results. Additionally, land topography had negative and statistically significant effects
on the technical inefficiency of potato production. This implies that river/floodplain land topography
increases potato production inefficiency more than hilly/undulating terraced land topography. Thismight be due to river/floodplain land topography being more susceptible to soil erosion that resultsin poor soil fertility. Then, poor soil fertility directly affects potato growth and productivity.
Conversely, hand tractor ownership had a positive and statistically significant effect on thetechnical inefficiency of potato production. This shows that hand tractor users are more inefficientⒸ ISERD

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than non-users. The mean potato productivity comparison result also reveals that the mean potato
productivity of hand tractor owners and non-owners was 14.3 tons/ha and 16.4 tons/ha, respectively.
This result is unexpected and might be hand tractor owners renting out their tractors for better returns
instead of using them for potato cultivation, or the tractor type may not be suitable for potato
production. However, this requires further validation.
Whereas potato production through partial irrigation had a negative and statistically significant
relationship with technical inefficiency. In contrast, rainfed production and technical inefficiency had
a positive and statistically significant association. This illustrates that rainfed producers are more
inefficient than fully irrigated production users. This further shows that rainfed potato production
might be prone to water shortage due to drought and erratic rainfall as potato production needs morewater compared to other cereal crops.
CONCLUSION
This study found that the observed average potato yield (15.8 t/ha) in Benguet province is about 50%
below the potential yield found by the BSU-Northern Philippine Root Crops Research and Training
Center. About 60% of producers used the Granola variety obtaining 16.8 t/ha on average and theremaining used the Igorota variety (14.2 t/ha). Besides, the estimated mean technical efficiency ofpotato producers was 0.89. The model result depicted that yield and technical inefficiency of potato
production are determined by land size, hired labor, amount of organic fertilizer (chicken dung),
agro-chemicals, household size, education level, training access, Granola variety, topography,
irrigation use, and hand tractor ownership respectively. Therefore, we conclude that concerned
stakeholders such as the Department of Agriculture, universities, and research institutes, among others
should conduct a measured plot level study including various parameters to understand why potato
producers' yield is much lower than the potential yields of the two dominantly grown varieties (Igorota
variety (25-35 t/ha) and Granola (21-30 t/ha)) despite farmers’ relatively efficient potato production.
Furthermore, the promotion of the Granola variety, coupled with the application of organic fertilizers
and the provision of training related to potato production, is essential for enhancing the efficiency ofpotato producers and, consequently, boosting productivity in Benguet province.
ACKNOWLEDGEMENTS
We acknowledge Benguet State University and the Department of Agriculture, Bureau of
Agricultural Research for allowing us to use part of the data on “Highland Vegetable Value Chain
Analysis for Use in Policy Formulation and Impact Evaluation of Agricultural Training Posts”.
REFERENCES
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