ENVIRONMENT s, CONSERVATION

ISSN 0971-765 X

Vol. 25

(May suppl. Issue )

2019

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ECOLOGY, ENVIRONMENT

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Reflis*,1,2, Fahrurrozie Sjarkowi3, Date: 20.3.2019 Sriati3 and Didik Susetyo4 Ref.No. EEC-F-546 (19)

1Department of Environmental Science, The Graduate School of Sriwijaya University, Indonesia 2Department of Agribusiness, Faculty of Agriculture, University of Bengkulu, Indonesia 3Department of Agribusiness, Faculty of Agriculture, Sriwijaya University, Indonesia 4Department of Economics Development, Faculty of Economics, Sriwijaya University, Indonesia Dear Authors,

The Editor thanks for your manuscript entitled –

- Quality, Quantity and Availability (QQA) Parameter of Water Irrigation Utilization at Upstream Musi River Basin, Kapahiang District, Bengkulu Province, Indonesia

Submitted for publication in –ECOLOGY, ENVIRONMENT AND CONSERVATION. Pl. always quote Ref. No. EEC–F-546 (1), while doing any correspondence with us.

Editor is pleased to inform you that your paper is accepted for publication in ECOLOGY,

ENVIRONMENT AND CONSERVATION, in 2019 May Supplement Issue.

Kindly note the journal has SCOPUS h index 10.0 and NAAS India impact rating 4.89 (www.envirobiotechjournals.com). The journal is in Master Journal List of ISI, Thomson Reuters, U.S.A.

Thanking you & with regards Yours Sincerely

Publisher

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ECOLOGY, ENVIRONMENT AND CONSERVATIONVOL. 25 (May Suppl. Issue) : 2019

CONTENTSS1–S8 Agroecological zones and varietal productivity, profitability, and technical efficiency

differences of small holder dryland mungbean systems in Eastern Indonesia—Damianus Adar, Yosep S. Mau, Fredrik L. Benu, Rao Rachaputi, Feliks Kasman and

Yohanes L. Seran

S9–S13 Effect of copper oxide nanoparticles in some biomolecules content of Vicia faba L. plantstissues—Rana Tariq Yahya

S14–S17 Growth and yield of sesame as influenced by different weed management practices—S. Krishnaprabu

S18–S26 Abiotic stress management by using nanotechnology—Rakhi Mahto, Priyanka Rani, Reshu Bhardwaj, Rajesh Kumar Singh and Saroj Kumar Prasad

S27–S30 Bioherbicides as a sources for weed control, through smothering crops and use of plantextracts—S. Krishnaprabu

S31–S35 GC-MS based determination of different bioactive compounds of diverse extracts of Vitisvinifera L. fruit—Monika Sharma, Ritu Kumari Singh and Chandra Kant Sharma

S36–S40 Enhancing productivity of irrigated direct seeded rice in stable seed bed and weedmanagement practices—S. Krishnaprabu

S41–S44 Seasonal variation in rotifer abundance from few perennial fresh water ponds, NaviMumbai, Maharashtra (India)—Monica Vidhate, Shashank Sarang and Vaishali Somani

S45–S48 Impact of spacing, sources of nutrient and methods of zinc application on yield attributesand yield of greengram (Vigna radiata L.)—S. Krishnaprabu

S49–S53 Soil stabilization using lime and industrial wastes—Obaidurrahman Haqmal, Humaib Nasir and Mandeep Kaur

S54–S60 A survey of algal flora in the water bodies of Kalady, Kerala, India—C.V. Mini and A.K. Bopaiah

S61–S66 Efficiency of experimental scale floating rice farming in flood-prone areas—E.W. Riptanti, Mujiyo, H. Irianto and A. Qonita

S67–S72 Weed management in rice as influenced by nitrogen application and herbicide use—S. Krishnaprabu

S73–S82 Review on risk management in various infrastructure projects in India—Lone Mohsin Nazeer, Humaib Nasir and Mandeep Kaur

S83–S87 Sustainable management of conventional sources of energy—Prabal Anand, Mandeep Kaur and Humaib Nasir

S88–S96 Quality, quantity and availability (QQA) parameter of water irrigation utilization atUpstream Musi river basin, Kapahiang District, Bengkulu Province, Indonesia—Reflis, Fahrurrozie Sjarkowi, Sriati and Didik Susetyo

S97–S101 Management of downy mildew of cucumber by lowering toxic fungicide applications—S. B. Chavan, S. N. Hasabnis, M. B. Kadam and A.V. Bhong

II CONTENTS Eco. Env. & Cons. 25 (May Suppl. Issue) : 2019

S102–S105 Lysimetric studies to estimate the solute and contaminant transport in hydrogeologicalprofile—Arjun Suresh and Tanu Jindal

S106–S110 Preparation of vermicompost in aerobic conditions—Jayanth Chapla, G. Prabhaker, A. Suresh, P. Rajarao and J. Sumathi

S111–S115 Partial replacement of rice husk ash and silica fume as partial replacement in concrete : AReview—Sahil Jaggi, Geeta Mehta, Akshat Mahajan and Akash Aneja

S116–S122 The effect of flipped classroom models and self efficacy on students conceptunderstanding and application of the Hydrology—Retno Kinteki, Punaji Setyosari, Sumarmi and Saida Ulfa

S123–S130 Ingenious feeding sites of Egyptian Vulture (Neophron percnopterus) in some of the Districtsof Uttar Pradesh, India—Shivangi Mishra, Adesh Kumar, Ankit Sinha and Amita Kanaujia

S131–S135 Role of intercropping system for sustainable farming development : an general overview—S. Krishnaprabu

S136–S141 Identification of coral reefs on Mamburit Island at Windward and Leeward sites—Sawiya, Diana Arfiati, Guntur and Ummi Zakiyah and Mohammad Amin

S142–S150 Structure and composition of vegetation for land cover conditions at conservation area—Saptawartono, K. Widen, H. Segah and Yanarita

S151–S155 Experimental study on behavior of coconut and hair fibre in concrete—Geeta Mehta, Sahil jaggi, Akshat Mahajan and Anshul Garg

S156–S165 Irrigation depths in sugarcane crop with drip irrigation system—S. Krishnaprabu

S166–S172 Stochastic soil erosion risk modelling and simulation using Fournier Index—Bernard Moeketsi Hlalele

S173–S180 Bi-hazard assessment for timely and effective disaster management: Free State disasterarea 2015—Bernard Moeketsi Hlalele

S181–S184 Evaluation of Integrated weed management in blackgram—S. Krishnaprabu

*Corresponding author’s email: reflis.ayek@gmail.com

Eco. Env. & Cons. 25 (May Suppl. Issue) : 2019; pp. (S88-S96)Copyright@ EM InternationalISSN 0917–765X

Quality, quantity and availability (QQA) parameter ofwater irrigation utilization at Upstream Musi riverbasin, Kapahiang District, Bengkulu Province,Indonesia

Reflis*,1,2, Fahrurrozie Sjarkowi3, Sriati3 and Didik Susetyo4

1Department of Environmental Science, The Graduate School of Sriwijaya University, Indonesia2Department of Agribusiness, Faculty of Agriculture, University of Bengkulu, Indonesia3Department of Agribusiness, Faculty of Agriculture, Sriwijaya University, Indonesia4Department of Economics Development, Faculty of Economics, Sriwijaya University, Indonesia

(Received 27 January, 2019; accepted 20 March, 2019)

ABSTRACT

Conflict and competition in the utilization of water resources had caused a lack of synchronization of watermanagement in upstream, middle and downstream areas. The Program for Payment of Irrigation WaterResources Services (PIWRS) is a policy for watershed protection as well asquantity, quality, and availability(QQA) for irrigation improvement. The objective of this study was to examine the causal relationship betweenthe QQA parameters in the upstream Musi River basin area (16,116.73 hectares or 26.70% of the total MusiRiver Basin area) towards irrigation water utilization behavior based on the rationality formula fordistribution of upstream-middle-downstream irrigation areas. This study followed the Attribute-BasedMethod (ABM), which is based on farmers’ assessment to attributes or characteristics of goods or Servicesof Irrigation Water Resources (IWRS). The utility values analysis of QQA behavior in the Kepahiang irrigationwater utilization area indicated that irrigation O & M fees were the main priority, as followed by availability(Av) and quality (Ql) of irrigation water.

Key words : Quantity, Quality and Avaibility (QQA), Irrigation water, Attribute-Based Method (ABM), Willingness to Pay(WTP)

Introduction

Rapid population and economic growth have gener-ated the high pressure on land use which caused thereduction of ecological function of watershed area(Muradian and Cardenas, 2015). Ecological water-shed functions and soil and water conservationmanagement is determinant of water and Quantity,Quality and Availability (QQA) system forsustainability of human and all organism life. Wateris one of the essential needs in life, including the

agriculture aspect. The scarcity of water resulted inconflict and competition in owning, utilizing andmanaging water resources. In order to find out thebenchmark of QQA behavior in Kepahiang irriga-tion water use based on the division of irrigationareas of upstream-middle-downstreamor second-ary-tertiary- quaternary irrigation plots, the explora-tion of farmers’ desire and need, water utilizationmechanisms, rights and obligations, funding andconflict resolution associated with The IrrigationWater Resources Services (IWRS) were carried out.

REFLIS ET AL S89

IWRS valuation is an important part in determiningthe sustainability of the QQA behavior model in ir-rigation water utilization area (Hanley et al., 2001;Latinopoulos, 2014a).

The economic value of IWRS is often to be notdefined since the market is not available. Therefore,The IWRS that provides intangible benefits and ser-vices usually misinterpreted as non-market valueproducts and not traded in the real market. Limitedinformation about economic value of environmentalservices causes a lack of appreciation to the servicesprovided by these resources.Therefore, the societymight be unwilling to pay the additional fundsneeded for environmental management. Yeo, et al.(2013) argue that environmental quality is degradedover time due to the absence of prices (moneyvalue). The potential benefits to water users indownstream such as improveming QQA water, re-ducing the risk of severe flooding, and increasinginheritance value of natural resources for futuregenerations (Lapeyre et al., 2015; McElwee et al.,2014). Payment for environmental service is consid-ered as a tool for managing ecosystems related to itsecologic and economic services (Mombo et al., 2014;Rodríguez-de-Francisco and Budds, 2014). Eco-nomically, the payment of environmental servicesof water resources could be running effectively ifthe market mechanism works well.

During the implementation, success and failureof the Payment Environmental Services (PES) pro-gram relates to the role of local community,theamount of compensation received and the broaderdynamics of life (He and Sikor, 2015). The PESemerged as an incentive-based policy instrument tomanage and secure the flow of environmental ser-vices for human welfare (Caro et al., 2015). PES dealswith environmental problems as a consequence ofproduction system failures in internalizing environ-mental costs and regulating the behavior of institu-tions to maximize individual utility (Singh, 2015).In the management context, PES scheme consideredas a management tool for changing the destructiveaction of the people in charge by compensating theirloss and improving their conservation manner(Mombo et al., 2014 ; Hayes et al., 2015). PES is gen-erally arranged voluntarily. Conditional agreementbetween at least one ‘seller’ and one ‘buyer’ duringenvironmental services is well defined or the usedresources will be able to produce environmental ser-vices (Caro et al., 2015). The success of PES dependson the changes of involved people behavior. In fur-

ther stage, it will be connected to the change of com-pensation structure of PES. Thus, local heterogene-ity as a livelihood strategy plays a strong role inachieving the ultimate goal of PES program (New-ton et al., 2012).

Leimona (2015) identified that PES of irrigationwater resource payments is in accordance with thecapabilities and expectations of the communitywhich are very favored and feasible. This type ofpayment is well known as the social economic (so-cioeconomic) investment like mutual cooperationand the role in institutions which is one of the im-portant aspects of the PES and anti-poverty ap-proach. Furthermore, the farming acceptance factordetermines the participation in willingness to paythe PES of irrigation water resource payments. TheWTP can be increased through several efforts whichincrease the acceptance of lowland rice farming. Thehigher level of acceptance of perennial planting ricefarming generates the higher level of farmer partici-pation in the willingness to pay PES (1.641 times).This approach happens as the farmers who have ahigh level of farm acceptance tend to have a higherawareness and willingness to pay for PES of irriga-tion water resource (Bremer et al., 2014; Meyer et al.,2015).

The objective of this study was to examine thecausal relationship between the QQA parameters inthe upstream Musi River basin area (16,116.73 hect-ares or 26.70% of the total Musi River Basin area)towards irrigation water utilization behavior basedon the rationality formula for distribution of up-stream-middle-downstream irrigation areas.

Methodology

Method

This research was conducted in the upstream regionof the Musi River Basin, Kepahiang Regency,Bengkulu Province, Indonesia. This study utilizedthe Attribute-Based Method (ABM) method, which isbased on farmers’ assessment to attributes or character-istics of goods or (IWRS). This approach departs fromthe premise that farmers use IWRS because of itscharacteristics, not solely on irrigation water items.When farmers use irrigation water, what they “buy”is actually the irrigation water characteristics,namely Qt, Ql, AV and the price of the Irrigation wa-ter. One of the ABM methods is conjoint-choicemethod (CCM). The CCM method is a choice ex-

S90 Eco. Env. & Cons. 25 (May Suppl. Issue) : 2019

periment that offers various choices to the respon-dent during the survey. They are asked to maketheir choices. This process is repeated several timesand the attributes changed each time. In the finalstage, one respondent will produce a set of choicepatterns which then become the basis for calculatingthe Value of Water Availability or Willingness(WTP) obtained by each group of water user anduser farmers (upstream-middle-downstream or sec-ondary-tertiary- quaternary) in paying the IWRS feefor changes to one attribute (Yacob et al., 2009).

To estimate the standard of behavior of QQA inthe Kepahiang irrigation water use area based onthe division of upstream-middle-downstream orsecondary-tertiary- quaternary) irrigation areas, itinvolves the following steps

Selection of Attributes and Levels

The initial survey was conducted to identify at-tributes related to QQA behavior involving 100 re-spondents in the study area. This survey identifiesirrigated rice farmers’ opinions on up to date issuesand problems related to QQA behavior in irrigationwater utilization areas. Furthermore, a separate pi-lot survey in the area of irrigation water utilizationis based on the division of upstream-middle-down-stream or secondary-tertiary- quaternary irrigationareas, which aims to improve the selected attributes(Latinopoulos, 2014b). Following this procedure,four attributes were chosen to describe the behaviorof QQA in the area of irrigation water utilization: (1)quantity or water demand “Qt”, (2) water quality“Ql”, (3) water availability for agriculture “Av” and(4 ) costs per planting season for service improve-ments (the value of water availability services ob-tained by each group of water users). The next stepis determining the level of attribute. The level cho-sen must be realistic and relevant to farmers prefer-ences for IWRS. Understanding farmers’ prefer-ences is very important to implement the efficientand effective irrigation water management policyinstruments (Latinopoulos, 2014a).

Water quantity ”Qt“ is defined as the quantity ofwater in one year for three growing seasons ofpaddy rice, consists of: water deficiency (using athermen / rotating system), means that farmers areunable to cultivate rice 3 times (quo situation), asfound in the preliminary survey. Next level: excesswater (cultiavte 3 times per year). “ Ql” water qual-ity is considered a qualitative attribute and two lev-els are chosen: uncertain water quality (status quo

situation) and good water quality. This simple clas-sification was conducted as the difficulty for farm-ers to see the quality of irrigation water based onphysicochemical parameters. Water availability foragriculture “ Av” farming is considered as an avail-ability attribute with two levels of choice: uncertainwater quantity (status quo situation) and good wa-ter quantity.

Coding was used instead of dummy variables torepresent quantitative attributes. The coding of Xvariables consists of 0 and 1 for each attribute oflevel X. The coding of variables for one qualitativelevel is equal to 1 when there is influence at thislevel, and 0 when the level of status quo (reference)(Latinopoulos, 2014a).

Determination of Alternative Attributes (Numberof Attribute Levels and Actual Attribute Values)Presented to Farmers Respondents

After knowing the attributes and its levels, the nextscenario is designing the combination of attributelevels. The combination of attributes levels calledstimuli has a role on the preferences of respondents.This stimulus will influence the farmers decision toconsider the best combination of attribute levelsfrom a set of choices. The complexity of choice setsstimuli design relevance to IWRS (Hensher, 2006).The attribute levels used in research design had twolevels that can be distinguished intuitively.

Selection of the research design form

The study design used the fractional factorial de-sign. The number of attributes used is 4 (four), eachof which has 2 (two) levels. The number of stimuliin this experiment was 24-1= 8 treatments. For thispurpose, the attribute level is allocated to eachchoice according to orthogonal design(Latinopoulos, 2014a). The main step in designingthe study is to illustrate the profiles and choice setswith simple conjunctions of four attributes, namelyQt Ql Av and Irrigation water prices, which is ex-plained in 2 (two) levels. SPSS v.20 software forWindows utilized for the formation of stimuli withorthogonal planning, so that all attributes and levelscan be represented.

Making Choices Set of The Irrigation WaterResources Services (IWRS)

In the conjoined-choice (choice-conjoint) method,farmers are offered a number of choice sets of IWRS

REFLIS ET AL S91

in the form of 2 (two) choices. Respondent farmerswere asked to choose which profile to choose. Num-ber 1 (one) is an additional stimuli as the best com-parison, and the number 0 (zero) is the choice of thecurrent condition (status quo). The set of Irrigationwater environment services that are displayed arechosen randomly using random generators pro-vided by Microsoft Excel. In designing a selection ofWater environmental services, the zone of distribu-tion of irrigation areas is based on the distance be-tween the location of rice fields and rivers as irriga-tion water sources. There are three levels of distanceidentified into the chosen study area, namely Up-stream-Middle-Downstream, or based on the distri-bution of irrigation network plots, namely Second-ary-tertiary- quaternary. All of these areas are lo-cated in the Upper Musi River Basin, KepahiangRegency, Bengkulu–Indonesia.

The distance ratio between the zones of the irri-gation utilization area is based on the interval be-tween the location of rice fields and the river as asource of irrigation, the maximum distance (3,500meters) and the minimum distance (10 meters) di-vided into three classes: Upstream-Middle-Down-stream. Upstream consumption or secondary plotsare areas identified as excess irrigation water, Cen-tral or tertiary plots are areas of adequacy of irriga-tion and downstream water or quaternary plots areareas lacking irrigation water. Farmers respondentsin each division of the region were asked to choosea set of IWRS. Examples of sets of environmentalservices are shown as follows:

Calculating Service Value Availability of WaterObtained by Each Farmer Group Users of IWRS

The service value of water availability in each groupof water-using farmers is analyzed by The Choice-Conjoint Method (CCM), which uses a conditionallogit (CL) regression model consisting of 2 (two)models. The first model is a basic specification thatshows the importance of attributes in explaining thechoice of respondent farmers for two differentchoice options. The second model is expanded toinclude socio-economic and environmental attitudevariables. The inclusion of these variables helps tocorrect heterogeneity in preferences and providesan estimate of the effect of changes in attributes onthe probability that an increase or base option willbe chosen. The CCM is the preference methodstated in the assessment of water availability ser-vices which is generally used to estimate non-mar-

ket values from IWRS (Adamowicz et al., 1998;Latinopoulos, 2014a). The preference choice experi-ment data or data collected in the conjoined studywere analyzed using multiple regression methods(usually OLS regression dummy variables) to esti-mate utility functions for each respondent (or forsubgroups of water-using farmers) (Rao and Pillai,2014). Choice experiment assumes that farmer k willchoose alternative i, from a number of alternatives jbased on the desire to maximize the utility functionU(Uik>Ujk ; i j). Alternative i is composed of a seriesof X attributes, so that the choice of alternative i ver-sus alternative j is the result of comparing Xik to Xjk..Thus, Uik = f(Xik + ik). Alternative opportunities i(Pik) are calculated by number of chosen alternativeiappearances compared to other alternatives. Theopportunity is represented in the general form as:

Pik = f(Uik, Ujk; i j, ) ... (1)

Where: Pik: Opportunities for respondents tochoose alternatives i; Uik: alternative utility i selectedby respondent k; Ujk: alternative utility j chosen byrespondent k; and ,: function parameter obtainedfrom estimating the marginal value of the attributein the choice set.

Pik ... (2)

assuming that it has linear parameters, Xik can bewritten in the equation:

Xik= 1X1ik + 2X2ik +...+ nXnik .. (3)

WTP estimation

The coefficient â can be used to estimate the mar-ginal willingness to pay ((MWTP) for each non-monetary attribute, thus providing a measurementfor increasing benefits in attributes from one level toanother. These values are obtained as the ratio of thecorresponding attribute coefficients and monetaryattributes.

MWTP estimation can be calculated using theratio of attribute coefficients to monetary costs orcoefficients. This ratio is also known as marginalimplicit price (Latinopoulos, 2014a). The implicitprice of MWTP from an attribute reflects the will-ingness of respondent farmers to pay for additionalunits of existing attributes, ceteris paribus, takinginto account the coding attributes (Yacob et al.,2009):

S92 Eco. Env. & Cons. 25 (May Suppl. Issue) : 2019

(k= non monetary at-

tributes) ..................... (4)

where, is a constant depending on the encod-

ing of the attribute k (m = 1, for continuous anddummy variables, and m = 2 for variables with ef-fect codes).

Results and Discussion

Determination of Benchmarks for QQA Behaviorin Irrigation Water Utilization Areas in theUpstream Musi River Basin, Kepahiang RegencyBased on Distribution of Irrigation Areas(Upstream-Middle-Downstream and Secondary-tertiary- quaternary)

Validity and ReliabilityTest

Tests for accuracy with the Pearson’s correlationtest, were carried out on 100 respondent farmers inthe upstream Musi River basin, Kepahiang (Table1).

The results of the correlation values of each at-tribute were compared with r-table with alpha 0.05and N = 100, which is 0.197. All variables are greaterthan r(0,05;100-2) = 0.197. Table 1 showed that all stimuliof QQA behavior attributes in the irrigation waterutilization area in upstream Musi river basin werevalid, and can be accounted with high accuracy inconjoining process. It showed the relationship be-tween estimation and fact were very strong.

After all attribute stimuli declared valid, the reli-ability test was carried out. Statistical test indicatedthat the Alpha Cronbach coefficient value (rcount =0.692> rtable = 0.197 with = 0.05), it couldbe con-cluded that all attributes timuli of QQA behaviorquestionnaire in of irrigation water utilization areain the upstream Musi river basin were reliable.

Utility value on Each Attribute Level Based onRespondents’ Farmers Preferences

The conjoined analysis was carried out in groupsand overall respondents, by ranking the existingstimuli. It obtained the utility value for the attributeof irrigation water QQA benchmark. Each Irrigationwater QQA behavior consisted of 2 (two) attributelevels. Number 1 and 0 which formed the basis forconjoint analysis (Wisanggeni and Putro, 2017).Number 1 showed the attributes that farmers pref-erence, and 0 was not farmers’ preference (Table 2).

The total utility function model for determiningthe benchmark QQA behavior of water of all re-spondents in irrigation water utilization area in theUpstream Musi River basin, Kepahiang, as follow:

U(x) = 6.438 - 0.625 X1 - 1.625X2 - 0.125X3 +1.125X4

Conjoin analysis of QQA behavior bench mark-ing in of irrigation water utilization area in the up-stream-middlestream-downstream region Musiriver basin, Kepahiang, farmers in the upstream-middlestream-downstream farmers region in theproduced model, as follows:

Upstream : U(x) = 6.000 - 0.500X1 - 1.000X2 +0.000X3 + 2.000X4

Middlestream : U(x) = 4.625 - 0.250 X1 - 0.750X2 +0,000X3 - 0,250X4

Downstream : U(x) = 4.375 - 0.500X1 + 0.00X2 -0.250X3 - 0.750X4

Conjoined analysis of the determination of QQAbehavior standards in the area of irrigation waterutilization in the Upper Musi River Basin,Kepahiang Regency based on the distribution of ir-rigation areas (Secondary-Tertiary-Quaternary) re-sulted in the utility model, as follows:

Secondary : U(x) = 4.938 - 0.125X1 - 0.875X2 + 0.375X3

+ 1.125X4

Table 1. Pearson and Kendall coefficients as Test Validity of Research

Attribute Correlation

Person’s R Description Kendall’s tau Description

Water Quantity (Qt) 0,664 Valid 0,779 ValidWater Quality (Ql) 0,248 Valid 0,432 ValidAvailability of Water (Av) 0,931 Valid 0,955 ValidWater Fee 0,430 Valid 0,575 Valid

Source: Research Processed Results, 2017

REFLIS ET AL S93

Tertiary : U(x) = 6.938 - 1.125 X1 - 1.375X2 + 0.375X3

- 0.625X4

Quaternary : U(x) = 3.188 - 0.375X1 + 0.125X2 - 0.375X3

+ 0.125X4

Viewed from Table 2, the results of conjointanalysis show that the utility value of attributes haspositive and negative values. Positive signs of us-ability values indicate that these attributes are moredesirable. Conversely, the negative sign indicatesthat the attribute is less desirable. The more positivethe value of the use of an attribute, then these at-tributes are increasingly in demand.The utilityvalue in Table 2 is a numerical representation of re-spondents’ farmer preferences. The higher the util-ity, the higher the preference. Therefore, we can con-clude that irrigation O & M Fee is the attribute mostconsidered by the respondent’s farmer, followed bywater availability (Av) and water quality (Al). Wa-ter quantity (Qt) is the least considered attribute.The Utility value of respondent farmers in second-ary plots is higher compared to tertiary and quar-terly plots. Likewise, farmer groups in the upstreamare higher in Utility Value than farmers in themiddle and downstream. The results of this conjointanalysis can be considered to calculate the value ofwater availability services in each group of usersand users of farmers IWRS.

Irrigation Water Quantity Attribute (Qt)

The first attribute, namely the quantity of Irrigationwater (Qt). This attribute gets a negative utilityvalue for all respondent farmers, farmers in the up-stream-middlestream-downstream region, andfarmers in the secondary-tertiary- quaternary irriga-tion plot. The negative sign of utility value in con-joined analysis shows, that these attributes are in-creasingly not considered. The quantity of Irrigationwater (Qt) shows water requirements for one year.This attribute consists of two levels, namely lack ofwater (status quo situation), with numbers (0), andexcess water (1). An interesting result is the negativeutility value of the quantity of water (Qt) Irrigationattribute. This, shows that the negative attitude offarmers to the quantity of water for irrigation usecan be minimized. The possible explanation for this,is that there is an influence of survey time and inter-views with farmers. Because at the time of the sur-vey and interview, it coincided with the rainy sea-son, so the quantity of irrigation water (Qt) for low-land rice was not so worrying. Therefore, it is nottoo surprising, that the alternative ‘water require-T

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S94 Eco. Env. & Cons. 25 (May Suppl. Issue) : 2019

ments’ will reduce utility and reduce the possibilityof being chosen.

Irrigation Water Quality Attributes, (Ql)

The second attribute, namely the quality of irriga-tion water (Ql). This attribute has a positive utilityvalue in the downstream irrigation area, which is(0.500). The rest, this attribute gets negative utiliyvalues for all respondent farmers, farmers in theupstream and middlestream region, and farmers insecondary-tertiary-quaternary irrigation plots.Farmers in the downstream region expect waterquality (Ql), that is free from pollution. Negativesigns of utiliy values in this attribute, indicate farm-ers in the upstream-middlestream region, and farm-ers in secondary-tertiary- quaternary irrigation plotsdo not consider the quality of irrigation water. Irri-gation water quality (Ql) in this study consists oftwo levels, namely: water quality is still within thethreshold to be allowed as irrigation water, or thereis a good improvement in water quality (1), anduncertainty or no change in irrigation water quality(status quo situation) (0). Thus, all the selected at-tributes appear to influence the choice of therespondent’s farmers. Regarding the sign of the util-ity value, showing some interesting results found.That is, a positive sign of irrigation water quality(Ql), shows that respondents have a positive prefer-ence for improving water quality, (namely, farmersare more likely to choose alternatives with goodwater quality).

Irrigation Water Availability Attribute, (Av)

The third attribute, namely water availability (Av),this attribute consists of two levels, namely: thelevel of availability of water for agriculture per landof good quantity of water, namely the comparisonof the amount of water available for irrigated ricefields in the Musi River Basin area Upstream withnumbers (1). And the level of uncertainty in water

quantity (status quo situation) number (0). Fromconjoint analysis, this attribute has a positive utilityvalue in the upstream and middle irrigation areas of(0,000), and tertiary plot of (0,375). This attributealso gets negative usability values for all respon-dents (-0.125), farmers in the downstream region (-0.250), in the secondary irrigation plot (-0.375), andquaternary plots (-0.375).

Farmers in the upstream and middle irrigationareas, and in secondary and quaternary plots weremore considerate of water availability (Av). Farmersexpecting an enhancement water availability (Av)and reliability of irrigation water in the future. Inthe enhancement of water availability would reducethe risk of declining agricultural production (due towater deficiency), and could expand the total irriga-tion area. In this condition, the water availability“AV” for agriculture is indirectly used as a quantita-tive attribute.

Conversely, the negative marks of usability val-ues in this attribute indicated that farmers in thedownstream and quaternary plot areas did not re-spond to water availability (Av). It was because ofthe crops cultivated by farmers did not requirelarge amounts of water during dry season. Whilefarmers in secondary irrigation plots were no longerworried about the water availability since it did notdecrease over a period of time.

Cost of Contribution Attribute for Irrigation O andM

The fourth attribute is the fee for irrigation O & M.This attribute obtained the most positive utiliy valueon conjoint analysis of all respondent farmers, in theupstream area, secondary, tertiary and quaternaryirrigation plots, which were 1,125 (all respondents),2,000 (upstream), 1,125 (secondary), 0.625 (Tertiary),and 0.125 (Quaternary). These results indicated thatirrigation contribution cost was more considered bythe respondent farmers. The irrigation contribution

Table 3. Marginal Willingness to Pay (MWTP) for all respondent farmers based on distribution of irrigation in the up-stream-middlestream-downstream region, and secondary-tertiary-quaternary irrigation plot

Attribute The Marginal Willingness To Pay (MWTP)Non All Up- Middle- Down- Secondary Tertiary QuaternaryMonetary Respondents Stream Stream Stream

Water Quality (Qt) -0,556 0,250 -1 -0,667 0,111 1,8 3Water Quality (Ql) -1,444 0,500 -3 0,667 0,778 2,2 1Availability of Water (Av) -0,111 0 0 -0,333 0,333 -0,6 3

Source: Processed Research Results (2017)

REFLIS ET AL S95

cost was indeed a problem for some farmers and alsovery sensitive. The irrigation contribution cost hasbecome a burden of farmers. In order not to imposethe farmers, the amount of irrigation contributioncost must be considered first. Farmers must also takeinto account the benefits of IWRS in farming process.When costs are incurred, farmers will expect benefitsfrom irrigation water. By considering the hopes anddesires of farmers, the right pricing strategy will beable to attract farmers ‘sympathy, and in the end itcan bring farmers’ awareness to pay of IWRS.

The irrigation contribution cost for O & M con-sisted of two levels: paying Rp. 150,000 twice perplanting season (0), and paying Rp. 200,000 once perplanting season (1). Farmers in utilizing IWRS cer-tainly wanted low cost and good quality. It is ex-pected that stakeholders would evaluate the irriga-tion contribution costfor Irrigation O & M and con-sidering the benefits and sustainable irrigation wa-ter and environmental services.

Estimated WTP

The usability value ( coefficient) of each non-mon-etary attribute used to estimate The Marginal Will-ingness To Pay (MWTP), (equation 4). MWTP ispresented in Table 3.

The water quantity attribute (Qt) showed an in-crease in water demand is highly valued by respon-dents. In particular, farmers in Secondary, Tertiaryand Quaternary plots. In accordance with the re-sults of the estimated MWTP calculation, this studyreveals, that the water availability f is important forfarmers. They are willing to pay higher irrigation O& M contributions for better quality and quantity ofirrigation water. MWTP in quaternary irrigationplots was higher compared to secondary and ter-tiary plots. The farther distance between rice fieldsand irrigation sources, the more motivation of farm-ers spend more money to irrigate their fields. Thisfinding was similar with empirical studiy ofMudaca, et al. (2015) who concerned household par-ticipation in ecosystem services payments for inN’hambita in Sofala province, Mozambique. Thedistance between houses and centers of economicactivity plays an important role in the level offarmer participation and they have the opportunityto gain moreincome through the sale of carbon pro-duced from their farms.

Conclusion

The utility value analysis in QQA behavior in the

Kepahiang irrigation water utilization area showedthe irrigation contribution cost attribute for irriga-tion O and M is the main priority, followed by wa-ter availability (Av) and irrigation water quality (Ql).

Acknowledgements

This paper was written when we were studying atthe Environmental Sciences Study Program,Sriwijaya University (UNSRI) - Palembang-Indone-sia. We would like to thank the Director of theGraduate School of Sriwijaya University for provid-ing a very inspiring work environment. The Direc-torate of Research and Community Service, Direc-torate General of Strengthening Research and De-velopment at the University of Bengkulu’s Ministryof Research, Technology and Higher Educationmainly provides financial support for Promotion ofScientific Research. Last but not least, we want tothank the people in the UNSRI candidate room andtheir inspirational atmosphere.

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