Technical Efficiency of Wet Season Melon Farming

Ananti Yekti  1 , Dwidjono Hadi Darwanto 2 , Jamhari 3 , Slamet Hartono 4

1 Jurusan Penyuluhan Pertanian, Sekolah Tinggi Penyuluhan Pertanian Magelang 2,3,4 Fakultas Pertanian, Universitas Gadjah Mada

Permalink/DOI: http://dx.doi.org/10.15294/jejak.v10i1.9124

Received: June 2016; Accepted: September 2016; Published: March 2017

Abstract

Melon is one of high-value horticulture commodity which is cultivated widely in Kulon Progo regency. The nature of agricultural products is heavily dependent on the season, so it causes the prices of agricultural products always fluctuated every time. In wet season the price of agricultural products tends to be more expensive. Melon cultivation in wet season provide an opportunity to earn higher profits than in the dry season. The price of agricultural products tends to be more expensive in wet season, thus melon cultivation in wet season prospectively generate high profits. In order to achieve high profitability, melon farming has to be done efficiently. Objective of this study was to 1) determined the factors that influence melon production in wet season 2) measured technical efficiency of melon farming and 3) identified the factors that influanced technical efficiency. Data collected during April – June 2014. Location determined by multistage cluster sampling. 45 samples of farmers who cultivated melon during wet season obtained based on quota sampling technique. Technical efficiency was measured using Cobb-Douglas Stochastic Frontier. The result reveals that 1) land use, quantity of seed, K fertilizer contributed significantly increasing melon production, while N fertilizer decreased melon production significantly 2) technical efficiency indeces ranged from 0.40 to 0.99, with a mean of 0.77; 3) farmer’s experience gave significant influence to technical

efficiency of melon farming in wet season.

Key words : Melon, Stochastic Frontier, Technical Efficiency, Wet Season, Kulon Progo.

How to Cite: Yekti, A., Darwanto, D., Jamhari, J., & Hartono, S. (2017). Technical Efficiency of Wet Season Melon Farming. JEJAK: Jurnal Ekonomi Dan Kebijakan, 10(1), 12-29. doi:http://dx.doi.org/10.15294/jejak.v10i1.9124

 Corresponding author :

ISSN 1979-715X

Address: Jalan Kusumanegara No.2. Yogyakarta E-mail: anantiyekti@yahoo.com

JEJAK Journal of Economics and Policy Vol 10 (1) (2017): 12-29 13

INTRODUCTION

agricultural

multifunctionality involved qualitative dimensions that some of them

Agricultural sector has contributed to intangible (Sumaryanto, 2014). In line with the

reduce poverty, both directly or indirectly. agenda of Sustainable Development Goals of

Direct contribution is through increasing poverty reduction, can be achieved by improving

farmers' income, while indirect contribution the farm production efficiency. Producing

is through the mechanism of the link forward efficiently will reduce the gap between actual

or backward in the formation of output, value production with potential production which is

added, employment opportunities, forming expected to improve income and welfare of

foreign exchange, and providing need food farmers. Many research has been done to

consumption as well as a supplier of raw improve agricultural production through

materials for the development of economic enhancement farming efficiency, based on the

sectors, particularly the processing industry. resources and technology available. But most of

Agricultural sector provides employment to studies focused on food and crops commodities.

absorb excees labor better than other sectors. Studies on horticulture farming efficiency are

The main actor in agriculture is farmers and still limited. Bravo-Ureta et al., (2007) using

farm labors who most of them living in rural published data between 1979 to 2005 found 167

areas and lower welfare than others studies related to efficiency. The most

communities. Therefore, even relative commodities widely analyzed were rice, a dairy

contribution of agriculture in formation of farm, then whole farm. Studies in horticulture

Gross Domestic Product (GDP) is still low but were still limited, only about 2 percent of the

the role of this sector is strategic, both in the

overall studies.

achievement of the Millennium Development One of horticultural commodities that

Goals (MDGs) as well as the sequel agenda, have the potential to be developed are melon,

Sustainable Development Goals (SDGs) because it is widely consumed and has a high

(Sumaryanto, 2014). economic value (Asmara & Sulistyaningrum,

2008). Thus melon farming can be an alternative contribution among agricultural subsectors.

to increase farmers' income. It is one of Horticulture include fruits, vegetables,

alternative that can be done with the limited ornamental plants, and medicinal plants.

resources available. Therefore, efficient of Fruits has had the most substantial

contribution then others. GDP value of resources’ use will be an important

benchmarking in measuring the performance of horticulture in 2011 reached Rp88851.00

farming.

billion and the contribution of fruit products Yogyakarta is one of the melon-producing

were Rp 46735.62 billion or about 52.60 provinces in Indonesia, with the main

percent of the total GDP horticulture production area in Kulon Pogo regency. Based on

(Directorate General of Horticulture, 2014). It topography, Kulon Progo’s region categorized describes the contribution of fruits to GDP is into three parts, namely: 1) the northern is part

the highest, so it is important to be developed of plateau / Menoreh hills with elevations

further. between 500-1,000 meters above sea level,

Some empirical studies have showed consist of districts Girimulyo, Samigaluh,

that the role of agriculture in poverty Kalibawang, and Kokap 2) The middle part is

reduction was huge and included not only the backs hilly area with altitude between 100-500

quantitative aspects because the nature of

14 Ananti Yekti, et al., Technical Efficiency of Wet Season Melon Farming

meters above sea level, covering districts watermelons. Nevertheless, development of Nanggulan, Sentolo, Compassionate, and

production and productivity were fluctuated parts of Lendah 3) the south is lowland with

relatively. In 2012, melon productivity was still an altitude of up to 100 meters above sea level

below the peak achieved within five years, which including districts Temon, Wates, Panjatan,

was in 2008.

strain, and parts of Lendah. Development Productivity of melon in Kulon Progo was strategies of Kulon Progo Regency mapped

high aggregately, but not directly proportional to out central zone and southern zone intent for

the productivity of each farmer. Not every farmer aquaculture, while the northern zone for

was able to fully utilizing avaialable resources conservation purposes (Martono, 2007).

and technology to maximize yield production or Melon has cultivatied for many years by few

in other words that not all farmers were able to farmers in Kulon Progo regency, but has

take amount of the minimum input required to spread widely in last decade. Central melon

produce the quantity of the desired output with production in Kulon Progo scattered in five

the available technology. Farmers play important districts, namely: Lendah, Galur, Temon,

role because mostly farmers act as managers as Panjatan and Wates. Melon farming

well as workers in their farming. Differences in condition for last 5 years is presented in Table

structural factors and characteristics of

1. managerial among farmers are able to caused Tabel 1. Harvested Area, Yield and

level of productivity and efficiency among Productivity of Melon Farming in Kulon

farmers in Kulon Progo melon varied. Suratiyah Progo Regency, 2008-2012

(2014) revealed that farmers' ability to detect the

Year Harvested

Yield

Producti

main and additional problems still low, while the

Area (Ha)

(ku)

vity

success of farming was determined by timely and

appropriate decisions. To increase productivity 2008

(ku/Ha)

of melon, farmers are faced with a problem of the 2009

use of capital and appropriate technologies. To 2010

overcome its conditions, selection of technology, 2011

combination usage of capital such seeds, 2012

fertilizers, medicines and the skilled labor will be Source: Dinas Pertanian & Kelautan

basis for making appropriate decissions. Van Kabupaten Kulon Progo (processed).

Passell et al ., (2006) explained that characteristics of managerial and structural

technical inefficiency. in Kulon Progo was increasing every year. This

Based on Table 1, melon’ harvested area

factors

affected

Managerial characteristics associated with age, indicates that melon cultivation has high

education, experience, access to education, appeal for farmers, mainly because of the

credit, and markets, while structural factors poten tial melon farm’ income is higher than

type of farming, other commodities. Farmers in Kulon Progo

included

location,

environmental characteristics, finance and cultivated watermelon firstly before swicth to

technology. Many studies have been done melon commodity. Its result was in line with

related to the characteristics of managerial and previous study by Martono (2007), that melon

structural factors on technical efficiency farming more viable financially than

(Fauziyah 2010; Bare, 2012; Nahraini et al., 2013;

JEJAK Journal of Economics and Policy Vol 10 (1) (2017): 12-29 15

Adenuga et al., 2013; Abiola and Daniel, 2014; higher profits and motivated them to cultivated Amoah, 2014). They have come to the

in wet season, although they have known that it conclusion that some

had higher potential of failure because melon characteristics and certain managerial

of structural

cultivation more appropriate technically characteristics influenced positively or

agronomic in dry season. A part of it, profit is not negatively on technical efficiency of farming.

only determined by the high selling price. Prices of agricultural products always

Optimal production is needed to obtain fluctuated any time, due to changed in

maximum profit. The profit is determined by number of yield and marketing related to the

amount of farm’ income and production costs, seasonal nature of agricultural commodities.

where farm’ income is the product of the sale Majority of horticultural commodities are

price and a number of yield. In order to achieve cultivated during dry season therefore price of

optimal production, farmers should be run their agricultural commodities has tended to be

business efficiently. By knowing the technical more expensive in wet season than dry season

efficiency index could be assessed whether due to the availability of fewer production.

farmers have used its resources optimally to Empirical evidence was shown by Pranata and

achieved the purpose of farming (Guestami et al., Umam (2015), that amount of onion

production fluctuated by season and yield at Technology was one of the decisive factors harvest time depended on climate. Thus the

in the production process (Nicholson, 1998). season is one of the important factors for

Application of new technology will lead to horticultural farmers to determine planting

improvements in the use of one or more inputs season in the pursuit of maximum benefit.

in the production process, so it can bring Setiadi and Sarimin (2006) explained that the

possiblity to achieve farm efficiency. Problem is melon could be cultivated in dry and wet

not only about the use of factors production, but season. Eventhough melon cultivation in wet

managerial skills possessed by farmers also can season required outpouring of concern higher

lead to an inefficient farming. Thus and more intensive caredry season.

enhancement of technical efficiency does not Opportunities to succeed cultivation in dry

only involve technological improvements and season is higher than wet season, because its

resource allocation, but also involves enhanced agro-climatic conditions accordance with

ability and capability farmers’ managerial. terms of plant growth. This condition is also

Theoretically there were three sources of provedby the empirical fact that cultivation in

productivity growth, which were technological dry season is more prevalent than wet season.

change, an increase in technical efficiency, and Seasonal nature of agricultural product

economies of scale (Coelli et al., 2005). The provides an opportunity for farmers to

linkage between technical efficiency and achieve higher profits by performing off-

productivity have been studied by several season cultivation. In the early years of melon

researchers. Kumbakhar (2002) purposed that cultivation, farmers have growth only in dry

production of a commodity was affected by season. But through more experienced and

allocation of input efficiently, presenced or improvement technologies, has encouraged

absenced of technical inefficiency problems farmers to tried cultivation in wet season. The

related to the managerial capability of farmers, higher selling price of melons in wet season

and risk factors of production in farming. This has been seen as an opportunity to achieved

was confirmed by Bokusheva and Hockmann

16 Ananti Yekti, et al., Technical Efficiency of Wet Season Melon Farming

(2006), stated one of the factors causing the decline in productivity was technical inefficiency. Thus melon’ farmers need to encourage for fully utilizing available resources and technology to maximize yield. So this study has been designed to: 1) identify the factors that affect melon production in wet season 2) determine technical efficiency of melon cultivation in wet season and 3) identify factors responsiable for various level of melon farming technical efficiency in wet season.

Measurement of Technical Efficiency

Efficiency was an important indicator to measuring the overall performance of business unit activities (Sutanto, 2015), and important to knew as a first step towards saving resources and the allocation of resources appropriately (Rahman et al., 2012). The concept of efficiency refers to the writings of Farrell (1957) in Coelli et al., (2005) suggested that efficiency was composed of two components, namely technical efficiency and allocative efficiency. Technical efficiency was associated with the ability of a company to get the maximum output from the use of a set (bundle) input, while allocative efficiency reflected farm ability to use input to optimal portion at a certain level and price. In other word allocative efficiency was the ability to generated a number of output under conditions minimizing input cost ratio. Technical efficiency gave an idea of how far a deviation of business unit operated from frontier production function at a particular technology. If the production closed to the maximum potential of a production process on the available technology (the best practice) in the same environmental conditions, it can

be said that farmers had manage their farming efficiently. Khan and Ali, (2013) mentioned technical inefficiency reflected the deviation

from the position frontier of isoquant line, while allocative inefficiency associated with deviation from the minimum ratio of input costs. Efforts to enhanced efficiency is generally associated with

a smaller cost production to obtain a certain result, or with certain cost earned more results. All the things that made it possible to reduced these costs was done for efficiency (Susanto & Imaningati, 2014).

Measurement of efficiency can be done in two methods, parametric methods and non- parametric methods. Parametric methods are divided into stochastic and deterministic approach. These approach are widely used in various studies and literature to measure the technical efficiency. Parametric method that most frequently used is stochastic frontier analysis (SFA) while non-parametric methods using Emvelopment Data Analysis (DEA). The nature of the agricultural production process, particularly crops are highly affected by the disruption that can be controlled (structural factors and managerial) and distruption that can not be controlled by the farmer (pest-deases attack, climate, fluctuations in input prices, fluctuations in output prices). It makes stochastic frontier approach more appropriateto

be applied in this study.

Stochastic Frontier Approach

Stochastic frontier production function was firstly purposed by Aigner et al., (1977) and Meeusen and Broeck (1977). It postulated existence of technical inefficiency in the production process of a bussines unit when producing a specific output (Battese & Coelli, 1995). Coelli et al. (2005) in detail explained the concept of stochastic frontier production function. The essential concept was the error term of stochastic frontier production function was divided into two categories, namely errors due to factors that could be controlled by farmer (inefficiency) and errors caused by factors

JEJAK Journal of Economics and Policy Vol 10 (1) (2017): 12-29 17 beyond the farmer’ control (such as climate,

Where qi is the i-th output; xi reflects the pest attack). Stochastic frontier approach

k x 1 vector of (transformations) input to the i-th entered factors related to random error

in a production- process; β is a vector of measurement in which the output of a farm

parameters to be estimated; vi is a random error was a function of random noise (disturbance

is assumed to be identically independenly term), measurement error and exogenous

distributed as N (0, σv 2 ) which captures the effect shocks that were beyond out of f armers’

of stochastic out of farmers and u i is a non- control. The output was assumed to be

negative random variable which is assumed to be limited by a stochastic production function

caused by technical inefficiency in production that was known as stochastic frontier

and distributed as iid ~ N+(0,σu 2 ), ui component production (SFP). Because it required a

is assumed the one hand, it is a non-negative certain production functions in the analysis,

random variables, describing the achievement therefore it could be used to test the

shortage of farm output i-th production hypothesis directly (Dijk & Szirmai, 2006).

frontiernya (Guestami et al., 2012). Thus u = 0 for The production function that widely used to

output which lies on its line frontier, and u < 0 measuring technical efficiency in agriculture

for output under the frontier line (Amaza et al., research was Cobb-Douglas production

2006). The model in equation (2) is called function and Translog.

stochastic frontier production function for the Coelli et al., (2005) described the

output values are limited by stochastic variable frontier production function as a production

(random), namely exp (X i β+v i ). Random error, function of the maximum output that could

v i , can be positive or negative. Thus the

be achieved on any use of certain inputs. In stochastic frontier output varies around the other word, frontier production function is a

deterministic part of the model frontier, namely function that indicates the possibility of

exp (Xiβ). u i component affirms that farmers are maximum production in which the

always under its frontier and it strongly production process is highly technical

associated with the characteristics of farmers. efficient and there is no attempt to produce a

Stochastic frontier model is illustrated in the higher output without using more inputs.

form of two-dimensional as shown in Figure 1. Frontier production function model gave

Figure 1 illustrates that the input X on the possibility to estimated relative efficiency of a

horizontal axis and the output Y on the vertical particular farm by relationship between

axis. The results of the production function in actual production and potential production

the form of a deterministic approach frontier (Green, 1993). General form of stochastic

models, exp (X i β), assumed at the diminishing frontier production function, purposed by

return scale. The results of observation output Aigner et al., (1977) and Meeusen and van

and input from two farm, i and j have been Broeck (1977) was:

determined.

Farming-i used inputs to produced outputs ln q i = x i β+v i -u i

Y i . Observed input-output marked by X. Output Following Cobb-Douglas production

stochastic frontier Y i * = exp (X i β + v i ), located function, the stochastic frontier function can

above the deterministic production function. It

be written in the form: was occured because production process was

Ln q i =β o +β 1 lnx i +v i –u i (2)

influenced by the favorable conditions that

q i = exp(β o +β 1 lnx i +v i –u i )

random errors (vi) was positive. Conservely, X j

18 Ananti Yekti, et al., Technical Efficiency of Wet Season Melon Farming

farming using X j input and produce output Y j .

exp(⁡𝑥 ′ 𝑖 𝛽+𝑣 −𝑢 ⁡)

Frontier output Y * = exp (X β+v ) was under

exp(⁡𝑥 ′ 𝛽+⁡𝑣 )

= exp(-u i )

production function deterministic. It was occured because production process was

Maximum likelihood estimation method affected by unfavorable circumstances which

on stochastic frontier production function v j variable was negative. Note that the outputs

produces β, λ and σ 2 parameters where σ 2 = σ 2 u + stochastic frontier Y i * and Y j * was not

σ 2 v and ⁡λ = σ u /σ v . Jondrow et al. (1982) showed observable due to random error v i and v j could

technical efficiency of each farm unit could be not be observed. But the deterministic part of

calculated from the expected value of u i on stochastic frontier model had to lie between

condition ɛ i as follows:

stochastic frontier output. In both cases, the

production was

production function Y = exp (X i β). This

specification allows non-negative random Where f (•) is the standard normal density component in the error term to produced a

function while F (•) is the standard normal measure of technical inefficiency, or the

distribution function. Therefore N(μ i ,σ 2 ) is non- actual ratio for maximum output expected at

negative, the magnitude of the technical certain inputs and existing technologies

efficiency index is between 0 - 1 or 0 ≤ 1 ≤ ET. (Kompas, 2001).

Battase and Coelli (1992) and Coelli et al. (2005) suggested that in order to examined

determinants of technical efficiency and at the same time technical efficiency could be done by two methods. First method used a two-stage procedure, the first stage was estimating efficiency using frontier production function, second stage was estimated using a regression model where efficiency was defined as a function of socio-economic variables. The second method was a one step or simultaneous procedure, where the effects of the inefficiency in the stochastic

Source: Coelli et al., (2005) frontier production function was modeled using Figure 1. Stochastic Frontier Production

relevant variables to explained technical Function

inefficiency.

Some researchers (Coelli, 1995; Battese Stochastic frontier production function

and Coelli, 1996; Amaza et al ., 2006; Bozoğlu and was being introduced by Aigner et al., (1977)

Ceyhan, 2007) proposed stochastic frontier pointed out that the ui were the components

production function once included effects of of the specific error term ( ɛ i ) which ɛ i =v i -u i .

inefficiency which formulated as a linear Farm technical efficiency that used in this

function of independent variables representing study refers to Coelli et al., (2005) as follows:

characteristics

social economi and

demographics and random error. Following TE = exp⁡(𝑥 ′ 𝛽+𝑣 )

JEJAK Journal of Economics and Policy Vol 10 (1) (2017): 12-29 19

Battese and Coelli (1995), technical district of Kulon Progo regency. Gulurejo was inefficiency effect model was measured by

selected as a village production centers melon. equation:

Melon’ farmers selected using quota sampling ui = δ0 + δ1Z1 + δ2Z2 + ... + δiZi + ɛi (5) technique, i.e. 45 farmers who had cultivated melons during wet season. Primary data were

which: collected during April-June 2014, through

ui = Technical inefficiency δ interview techniques guided by a questionnaire

= parameters that has been prepared. Data were gathered on

Z = Management variable which various information on demographics, farm

supposed effecting technical

inputs and outputs as well as price.

inefficiency

Measurement of technical efficiency in this Coelli et al., (2005) stated that

study refers to the technical inefficiency effects estimation of stochastic frontier production

model developed by Coelli (1995), Battese and function

using Maximum

Likelihood

Coelli (1995) and Coelli et al., (2005). This model Estimation (MLE) was more efficient than

has been widely applied in various fields. using Corrected Ordinary Least Squared

Measurement of technical efficiency using a (COLS) methods. Empirical studies showed

model of Cobb-Douglas stochastic frontier by that level of significance MLE better than

incorporating effect inefficiency simultaneously COLS when contributions from technical

on agriculture has applied some researchers inefficiency effect to total variants were large.

β, and δ was the coefficient of the parameters (Abedullah et al., 2006; Bozoglu and Ceyhan, 2007; Nahraeni et al., 2012; Khan and Ali, 2013;

in the equation (1) and (3) estimated Rizkiyah et al., 2014; Navky et al., 2014). Cobb-

simultaneously using MLE, along with the Douglas stochastic frontier used in this study is a

variance parameters (Coelli & Battese, 1996; function of melon production per farm,

Amaza et al., 2006) as:

modeled:

σ 2 = σ 2 + 2 v 2 σ u dan γ= σ u

ln Y = β 0 +β 1 lnX 1 +β 2 lnX 2 +β 3 lnX 3 +β 4 lnX 4 +β 5 Where σ 2 is the total variance of error

lnX 5 +β 6 lnX 6 +β 7 lnX 7 +β 8 lnX 8 + (v i -u i ) term. Parameter γ describe contribution of

t echnical efficiency to total residual (ε), with

Keterangan:

Y i = Yield (kg)

value between 0 -1. If the value of the

X 1 = Land area (m 2 ) parameter γ close to zero indicates that the

X 2 = Seed (gr)

X 3 = Nitrogen fertilizer (kg) residual effects (error), whereas if the γ value

deviation from the frontier tends to the

X 4 = Phosfor fertilizer (kg) close to one indicates that the deviation leads

X 5 = Kalium fertilizer (kg) to technical inefficiency effects.

X 6 = Organik fertilizer (kg)

X 7 = Pesticide (kg)

RESEARCH METHODS

X 8 = Labor (Man day) Research location had been determined

To determine influence of managerial and using multistage cluster sampling technique.

characteristics ontechnical Districts Lendah was chosen under

structural

inefficiency, single-stage procedure is used, with consideration of its territory simultaneously

the following equation:

represented a zone of central and south u i =δ 0 +δ 1 lnZ 1 +δ 2 lnZ 2 + δ lnZ 3 +δ 4 D 4 (8)

20 Ananti Yekti, et al., Technical Efficiency of Wet Season Melon Farming

Which: land area of 1.385m 2 . According melon’ farmers, u i

= Technical inefficiency dominant variety cultivated in wet season was Z 1 = Age (years)

Action, because it was more resistant to pest- deases attack and still produced good quality

Z 2 = Formal education (years) fruit on unfavourable environmental conditions.

Z 3 = Farm experience (years) The amount of anorganic fertilizer is measured

D 4 = dummy acces to credit in terms of the macro nutrients nitrogen (N),

(1=acces; 0=no) Phosfor (P) and Kalium (K). They are sourced Cobb-Douglas stochastic frontier and

from a single fertilizer: ZA, SP-36, KCl, KNO3 and technical inefficiency effects model estimated

NPK compound fertilizers. Melon farmers used simultaneously using Frontier 4.1c software

organic fertilizers sourced from manure and package written by Coelli (1996). Technical

manufacturized organic fertilizer (petroganik). inefficiency effects model can only be

Petroganik dose recommendation is 2 tons / ha, estimated if technical inefficiency effects is

much lower than non-manufacturer which is 20 stochastic and has a particular distribution, so

tons / ha. Average of organic fertilizer was it required the test about presence of

obtained from calculation of manufacturer and technical inefficiency. According Coelli et al.,

non-manufacturer organic fertilizers usage. (2005), one-sided generalized likelihood ratio

Farmers used 3 to 7 types of trademarks (LR) test was the appropriate test for

pesticides. For sake of labor saving, farmers estimation using MLE method. LR test

mixed more than one type of pesticides in obtained from equation:

sprayer tank. Pesticides were sprayed once every LR = -2 {ln [L (H0) - ln [H1)]} (9)

two days, but it could turn into every day if conditions of the plants or the environment was

Where L (H0) and L (H1) is the value of unfavourable. Main source of labor is family

likelihood function on the null hypothesis labor. Hire labors were needed at land

(Ho) and alternative hypothesis (H1). preparation, installation of mulch, bamboo

The hypothesis is: H0: γ = 0, there is no technical inefficiency trellis (stake) and planting of seedlings.

Nurseries and maintenance such: supplementary effects

H1: γ> 0, there is technical inefficiency effects fertilization, pest and disease control, selection Because the value of γ is always positive, of flowers and fruit, binding plants on trellis, farmland sanitation were done by family labors.

then used LR one-sided test. LR test is compared with the value χ2mix table (Table Farmers has sold melon before it was harvested,

so transportation cost and harvesting cost were Kodde and Palm, 1986).

responsibility of buyer.

Melon is harvested about 60-65 days after

RESULTS AND DISCUSSION

transplanting. All melon cultivations applied

Farmer’ Chataretistics and Melon Farming

black silver plastic mulch and trellis system. Each Descriptive statistics of the variables

plant only kept one fruit to be harvested. This used in testimation of stochastic frontier

condition was quite different from melon production function is presented in Table 2. It

cultivation during dry season, which some shows the average melon production per farm

farmers cultivatied melons without trellis (vine during wet season was 3.890kg with average

system).

JEJAK Journal of Economics and Policy Vol 10 (1) (2017): 12-29 21

Average of farmers was 44 years old Estimation of Cobb-Douglas stochastic melon, catagorized in productive age, with a

frontier and the factors that contributed to range of 24-63 years. Average formal

carried out education was found 9 years (graduated

technical

inefficiency,

simultaneously. The results of the analysis are junior high school) with a range of education

discussed and presented separately in Table 3 levels from did not finish elementary school

and Table 5.

to college graduation. Based on Table 3, were found four independent variables significantly affected Table 2. Summary Statistics of Variables

melon production in wet season: land area,

Item

Average Min

Max

seeds, N fertilizer and K fertilizer. Four other

Production & Inputs:

variables: P fertilizer, organic fertilizer, Yield (kg)

1.000 11.50 pesticides and labor did not significantly affect

0 production of melon in wet season. Land area (m 2 )

4.00 Maximum likelihood method provides a

0 variant parameter σ 2 and γ. Sigma-squared (σ 2 ) Seed (g)

described distribution of the error term, which N fertilizer (kg)

was 0.1213, significantl y different at α = 5%. It K fertilizer (kg)

P fertilizer (kg)

3 90 means that production was really varied, so there Organic fertilizer (kg) 612

was no evidence that all farming was already Pesticide (kg)

100% efficient. Parameter gamma (γ) of 0.999 Labor (Man day)

and significant at 99% confidence level, give

Socio-economic charateristics:

meaning that 99.9% of output variation among Age (years)

24 63 44 farmers due to technical inefficiency effects and Education (years)

1 17 9 only remaining 0.1% are caused by external Farm

experience 4 22 12 influences that could not be controlled by (years)

farmers. It revealed variations in the output of Dummy credit acces: the production frontier could be considered as a

Have acces (man)

result of the level of technical efficiency gain

No acces (man)

related to managerial problems in farm Source: Data processed, 2015.

management.

LR test one-side error was 18.43, exceeded Average farm experience was 12 years. It

χ was total farm melon experience as a whole, 2 mix at α = 1% (Table Kodde and Palm, 1986) not specific experience melon cultivation in

was 16.074, significantly rejected the hypothesis wet season only. Melon production cost is

there was no inefficiency effect. It indicates stochastic frontier production function can

more expensive than crop commodities. It explain the existence of efficiency and technical

became logical if the majority of farmers have inefficiencies in production process. LR test

access to credit. Farmers in the study area had reject the null hypothesis (Ho), also described

good access to financial institutions, melon farming activities affected by technical

particularly banks and pawnshops. Not found any access to the stalls of agricultural inputs

efficiency. Log-likelihood was 12.28, higher than and moneylenders.

OLS estimation of 3.06. It can be interpreted that production function with MLE method was good

Stochastic Frontier Production Function

22 Ananti Yekti, et al., Technical Efficiency of Wet Season Melon Farming

and could represent actual conditions than production (Sukiyono, 2005; Saptana et al., 2010; estimated by OLS.

Nwaru et al., 2011; Khan and Ali, 2013). Coefficient of seed was 0.2863, had Table 3. Maximum Likelihood Estimation of

positive sign and significantly effected on level of Cobb-Douglas Stochastic Frontier

convidence 99%. It implies additional amount of

Variabel Coefficient Std.

t-ratio

seed by 1% would increase production of melon

0.2863%. Many studies reported same Constanta

Error

conclusion to the findings of this study, that Land area

seeds contributed significantly increasing Seeds

production (Banani et al., 2013; Amoah, 2014;

Nahraeni et al., 2012; Darmansyah et al., 2013; N fertilizers

0,0463 -4,2668

Abiola and Daniel, 2014). Melon plantation P fertilizers

0,0788 -0.5606

always uses hybrid seeds, which has high Organic

K fertilizers 0.2561***

response to fertilization and potential to gain a fertilizers

high production. Increasing the number of seeds Peticedes

implied the more fruits produced. Labor

Coefficient of N fertilizer was -0.1975, σ²

mean has negative sign and significantly take γ

0.9998*** 0,0003 3034,467 effect on the production of melon during wet

2 season. Increasing N fertilizer as much as 1% will Loglikelihood

12,2801 reduce melon production in wet season to

LR-test one- 18,4359

0.1975%. Theoretically, extra inputs are expected

side error

increasing production, but in this study was Source: processed data, 2015

Note: ***

found to differ. It could be occurred because N , ** , * indicate significant at 1%, 5% and fertilizer was applied excessive its dosage

10% levels, respectively recommendation showing a negative effect as a

result. Assumption that more provision of input This

production could lead to produced more output, production function, so regression coefficient

motivated farmers to applied excessive doses also showed elasticity of input. Regression

fertilizers (Setiawan and Prajanti, 2011). coefficient of land area was 0.5821, significant

Application of N fertilizer reached 191 kg / and positively impact on melon production

ha, while the dosage recommendations based on during wet season. It meaned additional 1%

Standard Operating Procedures (SOPs) Melon land area would increase 0.58% production of

(Directorate General of Horticulture, 2004) was melons. Melon production is highly

112.5 kg/ha. An excess of N fertilizer up to 70% of dependent on plant populations, because only

recommended doses. As known, N fertilizer was one fruit is maintenanced each plants.

a macro nutrient for plant growth, which in Addition of land area will increase number of

general was very necessary for formation or populations, thus amount of fruit produced

growth of vegetative parts of the plant, such as are multiplied, then output quantity gains

leaves, stems, and roots. However, excess N heavier weight. Many studies have reported

fertilizer in plant might stimulate vegetative that land area significantly increased

growth (leaves, roots, and stems), increased

JEJAK Journal of Economics and Policy Vol 10 (1) (2017): 12-29 23

synthesis of carbohydrates which were then Organic fertilizer had positive sign but not converted into protein, and improved cell

significant increased melon production during formation, as well as increasing the size of the

wet season. This was presumably because cells that caused cells to be succulent

application of organic fertilizer was still below (Buckman and Bradey, 1982). Melon

the recommended dose of 20 tons/ha, which was cultivation during wet season caused plants

only 4.5 tons/ha caused effect on the production were susceptible to failured due to

was very slight so it was not significant different unfavourable

statistically. But it could also be caused by some Excessive N fertilizer would cause plant to

agro-climatic

conditions.

area implementated became more vulnerable, and tend to

farmers at

study

manufactured organic fertilizers (petroganik), vegetative growth than generative growth.

wherein the dose per hectare was relatively lower The addition of N fertilizer showed a negative

than nature organic fertilizer. Several studies influence and significantly affected the

have found that organic fertilizer did not production of red chilli (Saptana et al., 2010).

significantly affect production (Abedullah et al., Similarly, Darmansyah et al., (2013) reported

2012; Rizkiyah et al., 2014).

addition of urea and NPK fertilizer caused Pesticides have not significantly effected decreasing production of cabbage. As it was

increasing melon production during wet season. known that urea fertilizer and NPK fertilizer

A number of studies support its finding contained N nutrient was relatively high.

(Nahraeni et al., 2012; Banani et al., 2013; Thus melon farmers in the study area needs to

Adenuga et al., 2013; Abiola and Daniel, 2014; reduce dose of N fertilizer to increase their

Rizkiyah et al., 2014). Horticultural farmers production.

applied pesticides for curative and preventive Coefficient of P fertilizer is -0,04 and

purposes (Saptana et al., 2010; Zuhriyah and found to be insignificant. This was similar to

Happy, 2013). It made sense that farmers would Saptana et al., (2010) and Suharyanto et al.,

spraied pesticides periodically, regardless of the (2013). Provision of P nutrient through SP-36

presence or absence pest-deases attack. fertilizer and compound fertilizer which

Pesticides application in study area was caused excessive P nutrient was not absorbed

excessive, did not consider the type and intensity by plants and only accumulated in the soil

of pest-deases attack thus addition of pesticide eventually.

doses did not provide significant effect on Regression coefficient of K fertilizer is

production. Excessive in pesticide applications 0.2561, significant at the 99% level of

woud not decrease production, but would cause convidence. This implies that 1% increase in K

wasted of costs and had negative effects on fertilizer will increas farm yield by 0.2561%. K

environment and human health. Several studies nutrient serves a role in improving process of

support the findings that labor force did not photosynthesis,

significantly effected in melon production maintained turgor pressure, forming rod

during wet season (Fauziyah 2010; Abiola and stronger, activators of various enzyme

Daniel, 2014: Ibrahim et al., 2014; Rizkiyah et al., systems, strengthen plant roots and improve

2014). Horticultural cultivation required plant resistance to disease. This was in line

outpouring labor higher than other crops. Melon with research by Saptana et al., (2010) that the

cultivation needs intensive care during growing K fertilizer (K 2 O) significantly increased

process, so farmers would always give the production of red chilli.

maximum outpouring of labor.

24 Ananti Yekti, et al., Technical Efficiency of Wet Season Melon Farming

Technical Efficiency of Melon Farming

cabbage in Rejang Lebong regency producesd Distribution of technical efficiency

technical efficiencies among 0.78 to 0.99, with an index can give an idea of managerial

average value of 0.91. While Abiola and Daniel capabilities, and breaks it down so it is useful

(2014) examined melon technical efficiency in for agricultural extention and formulated

Nigeria provided indices between 0.43 to 0.97, other activities that aim to improve the

with an average of 0.84. Studies conducted managerial capacity of farmers in accordance

Baree, (2012) on the onions in Bangladesh with target group.

produces technical efficiency index ranged from Based on distribution of technical

0.58 to 0.99 with average of 0.83. efficiency index in table 4 was known that

Referring to the previous study (Bravo- efficiency of melon farming in wet season

uretra and Penheiro, 1997; Nwaru et al., 2013) ranged from 0.40 to 0.99, with the average of

which categorized level of technical efficiency ≥

0.70 has been efficient, majority of melon average 23% of potential production lost due

0.77. These results gave a meaning that on

farmers in Kulon Progo regency were catagorized to technical inefficiency.

in already technically efficient because 66.67% of the farmers had technical efficiency index above

Table 4. Frequency Distribution of Technical

0.70. However, it still leaves 33.33% of farmers Efficiency Indeces

who have not technically efficient yet, so they

TE index Farmer (man)

need efforts to enhanced their technical 0,40 – 0,49

Percent (%)

efficiency through improving technical skills and 0,50 – 0,59

managerial capability of farmers in aspects of 0,60 – 0,69

melon cultivation, respectively. Usage of input 0,70 – 0,79

production efficiently can be increased to 0,80 – 0,89

achieve its production frontier. 0,90 – 1,00

Farmers who have achieved average Total

technical efficiency index and want to reach Maximum

maximum efficiency likely have opportunity to

increase production by 22% i.e. (1- 0.77 / 0.99). Source: Data processed, 2015.

Average 0.7691

The same calculation if the most inefficient farmer wants to achieve maximum efficiency,

Study of technical efficiency, especially then there is an opportunity to increase in the horticultural commodities have

production by 40.5% i.e. (1 to 0.40 / 0.99). resulted good technical efficiency indeces

This study revealed there was room to relatively. Fauziah (2010) on tobacco in

improve the gap on melon production in wet Madura provided technical efficiency index

season by utilizing resources and technology between 0.56 to 0.99 with an average of 0.78.

available. Efforts to improved technical Saptana et al., (2010) found that average

efficiency of farmers can be made by applying the technical efficiency was 0.90 for red chili in

technology of cultivation and allocate resources Central Java. Study by Banani et al., (2013) on

as has done by the most efficient one. Action was the onions in Brebes found levels of technical

a variety that most widely cultivated in study efficiency ranged from 0.65 to 0.99, with an

area. Based on information released by seed average of 0.80. Darmansyah et al., (2013) on

producers through the company's official

JEJAK Journal of Economics and Policy Vol 10 (1) (2017): 12-29 25 website (www.tanindo.com ), Action’ variety

Abid et al., (2012) as well as Abiola and Daniel had potential productivity of 31.5 to 42

tons/hectare. While based on calculation of technical efficiency in study area, potential

Table 5. Determinant of Technical Inefficiency productivity that could be achieved with

Effect

existing technology was 36.5 tons/ha (1 / 0.77

Variable

Coefficient Std. t-ratio

x 21704 kg). Average productivity achieved at

Error

the study time was 28.01 tons/hectare.

Potential productivity in study area was in the

0.1269 range of maximum productivity test results by 0.9744

seed producers, so there are still

Dummy acces 0.1379

opportunities to improved productivity of

to credit

8.49 tons/ha. An effort to increasing Source: processed data, 2015

production could be done through improving Note: *** , ** , * indicate significant at 1%, 5% and 10% allocation of usage input production and

levels, respectively.

enhanced technical efficiency index

Determinat of Technical Efficiency

Coefficient of education showed positive Factors affecting farmers' technical

sign and did not significantly affect technical efficiency was analyzed simultaneously with

inefficiency, which meaned the technical technical inefficiency effects model in

inefficiency effect increased aligned with equation (7). In model of technical

increasing levels of education. These results were inefficiency, a positive sign of regression

not as expected. However, this was likely coefficient implies that its variables

happened if the majority of farmers who were increasing technical inefficiency, or in other

well educated had an alternative source of words giving effect of to reduce technical

income, and they were not completely efficiency, and vice versa.

dependent on agriculture for their livelihoods Table 5 presents regression coefficients

(Baree, 2012). Thus there would be lack of of the predictor variables of technical

attention by well educated farmers than less inefficiency model, i.e. age, formal education,

educated workers where they relied on melon farm experience and access to credit. One

farming as main source of income for their variable found to significantly affect the

family. Findings Fauziyah (2010) and Ibrahim et technical inefficiency during wet season,

al ., (2014) supported results of this study. w hich was farmer’ experience.

Coefficient of farm experience was -0.1002, Age variable had positive sign and found

significant at 99% confidence level. The negative insignificant to technical inefficiency of

that more experienced farmer would be more more resistant to adopted technological

technically efficient. In other words, the innovations (Navqi and Ashfaq, 2014) as well

experience could be a factor to improving as be more traditional and conservative, and

achievement of technical efficiency. Farmer who less willing to adopted cultivation practices

Technical Efficiency of Wet Season Melon Farming