Eco‐efficiency of high‐yielding variety rice cultivation after accounting for on‐farm environmental damage as an undesirable output

Eco-eciency of high-yielding variety ricecultivation after accounting for on-farmenvironmental damage as an undesirable output:an empirical analysis from Bangladesh*Noor-E. Sabiha, Ruhul Salim and Sanzidur RahmanThis study computes the eco-eciency of high-yielding variety (HYV) rice productionby including an on-farm environmental damage index (OFEDI) as an undesirableoutput using data envelopment analysis. It then identifies its determinants by applyingan interval regression procedure on a sample of 317 farmers from north-westernBangladesh. Results reveal that the mean level of the OFEDI-adjusted productioneciency (i.e. eco-eciency) is 89 per cent, whereas ignoring OFEDI adjustment (i.e.with OFEDI = 0) reduces the mean level of eciency to 69 per cent, implying that theproduction of undesirable output or on-farm environmental damage induces aneciency loss of 20 per cent with significant dierences across regions. The proportionof farmers income from HYV rice agriculture, land ownership, extension services andsocio-environmental living standard are the significant determinants of improving eco-eciency. Policy implications include investments in extension services and landreform measures to increase land ownership, which will synergistically improve eco-eciency of HYV rice production in Bangladesh.Key words: Bangladesh, data envelopment analysis, eco-eciency, environmentaldamage, high-yielding variety rice agriculture, undesirable output.1. IntroductionIncreasing the level of eciency in agricultural production has been animportant policy objective in most agrarian economies. Agricultural practicesthat aim to increase production eciency are conditional upon limiting theiradverse impacts on the farm environment. This situation arises becausegrowth in agricultural productivity and its sustainability primarily depend onthe quality and ecient management of the natural resource (capital).Korhonen and Luptacik (2004) indicated that the environmental impacts(undesirable output) are jointly produced with the desirable output of a* We gratefully acknowledge the valuable comments from two anonymous referees, theassociate editor and the editor of this journal. These comments and suggestions substantiallyimproved the quality and presentation of this version. However, the authors remain solelyresponsible for any remaining errors.Noor-E. Sabiha is an Assistant Professor at Department of Economics, RajshahiUniversity, Rajshahi, Bangladesh. Ruhul Salim (email: Ruhul.S.au) is anAssociate Professor at Curtin Business School, Curtin University, Perth, Western Australia,Australia. Sanzidur Rahman is Associate Professor at School of Geography, Earth andEnvironmental Sciences, University of Plymouth, Plymouth, UK. 2017 Australian Agricultural and Resource Economics Society Inc.doi: 10.1111/1467-8489.12197Australian Journal of Agricultural and Resource Economics, 61, pp. 247264The Australian Journal ofJournal of the AustralianAgricultural and ResourceEconomics Societyproduction activity. As such, undesirable output should be incorporated intothe economic analysis of the production performance of firms. In this respectfor a natural resource-depleting production activity such as agriculture, it isimportant to evaluate an environmentally adjusted measure of productioneciency at the farm level, that is eco-eciency of agricultural farms.Evaluating eco-eciency in agriculture is challenging. The challenge lies indefining and formulating an indicator that can measure the overall impact onfarm environment (Binder and Feola 2010), because an indicator shouldincorporate agricultural multifunctionality. While addressing agriculturalmultifunctionality, most studies have considered two aspects of agriculture intheir evaluation process, that is farming practice-related indicators and/orfarming system-related indicators (LC19opez-Ridaura et al. 2005). Some studieshave chosen agricultural emission-related environmental attributes only(Rigby et al. 2001). In addition to these, farmers environmental awareness/perception of agricultural pollution has also been indicated as an importantsocio-environmental aspect of agriculture in certain agroecological studies(Rahman 2005). Zhen and Routray (2003) additionally emphasised analysingcountry-specific agri-environmental aspects in addressing environmentalsustainability and highlighted that developing countries would face greaterchallenges in this respect than those faced by developed nations. It is alsoworth noting that incorporating farming system (or state)-related, farmingpractice-related and farmers perception-related environmental impacts into acomposite mode would be useful for evaluating eco-eciency. In previousstudies, the ineciency arising due to on-farm environmental impacts hasrarely been segregated econometrically in farm-level eciency analyses(Picazo-Tadeo et al. 2011; Berre et al. 2015) because of the challenge ofincorporating all attributes into its measurement.Given this backdrop, the principal aim of this study was to evaluate on-farm eco-eciency in relation to high-yielding variety (HYV) rice productionby incorporating an on-farm environmental damage index (OFEDI) as anundesirable output alongside the desirable output to measurement produc-tion eciency of a farm. We achieve these aims by first estimating productioneciency of the farms ignoring any environmental damage (i.e. assigning zerovalues to the OFEDI) and then re-estimating the model by adjusting withnonzero OFEDI values for the same set of farms. Theoretically, the firstmodel is a conventional production eciency model because the OFEDI isassumed to hold zero values which need to be adjusted to obtain the measureof eco-eciency. Technically, the eco-eciency estimate explains trueproduction eciency of a given farm when all of the farms undesirableoutputs are minimised, and the gap between the OFEDI-adjusted productioneciency (eco-eciency) and the production eciency with OFEDI = 0evaluates the environmental impact-induced loss of production eciency fora given farm. The main contribution of our study to the existing agroeco-logical and/or productivity and eciency literatures is that we have applied acomprehensive measure to evaluate on-farm environmental impacts caused 2017 Australian Agricultural and Resource Economics Society Inc.248 N.-E. Sabiha et al.by a given agricultural operation (i.e. HYV rice production) using a set of 17indicators selected and constructed from three main domains of impacts,that is means-based impacts (MBI), eect-based impacts (EBI) andperception-based impacts (PBI) (see Sections 2 and 3.1 for details on thecritique of existing approaches and the construction of our measure,respectively).The remainder of the study is organised as follows. Section 2 provides acomprehensive review of the range of eco-eciency concepts and measures.Section 3 presents the methodology, analytical framework and the data.Section 4 presents the results. Section 5 provides conclusions and drawspolicy implications.2. Eco-eciency: a brief review of concept and measuresA variety of criteria have been used to explain the concept of eco-eciencyonce it was recognised as a useful operational tool for sustainabilityanalysis (Fritsch 1995). Operational research on environmental manage-ment suggests a number of alternative terminologies for defining theenvironmental impact indicators to evaluate eco-eciency. Most of thesestudies on environmental eciency have defined and formulated environ-mental impact indicators as a denominator of measuring eco-eciency. Inagroecological studies on environmental eciency, the notion of eco-eciency is frequently explained in terms of agricultural aspects. In thiscontext, the eco-eciency denominator is usually denoted as the undesir-able output (Seiford and Zhu 2002; Amirteimoori et al. 2006). Picazo-Tadeo et al. (2011) analysed farming practice-related impacts to define theundesirable output variable and incorporated it into their proposed modelof environmental eciency. Graham (2009) considered eect-based (orfarming state-related) environmental impacts to evaluate the eco-eciencyof chemical fertiliser application on ground and surface water. Manystudies have assessed on-farm soil nutrient balance as an indicator ofenvironmental damage in defining undesirable output factor in environ-mental eciency models (e.g. Hoang and Alauddin 2012). Most of thesestudies formulated the undesirable output component using data fromsecondary sources.Pollution data relating to on-farm environmental attributes are oftenunavailable from secondary sources. Consequently, primary data on farm-level environmental impacts are more useful in addressing agriculture-environment issues, particularly for agriculture-based developing countries.Environmental impact indicator accounting usually requires reconcilingrelevant dimensions and aspects of a given production process within thecontext of a given country. Specifically, an eco-eciency measure thatincorporates important aspects of on-farm environmental damage in terms ofan index could be eectively used as an operational tool to assess and addressagricultural sustainability. 2017 Australian Agricultural and Resource Economics Society Inc.Eco-eciency of HYV rice cultivation 2493. Methodology and data3.1 On-farm environmental damage index: the undesirable outputThis study measures the extent of farm-level environmental damage byaggregating several indicative environmental impact variables (Girardin et al.1999; Bockstaller and Girardin 2003) to construct an OFEDI. The proposedindex incorporates three separate types of indicative variable groups: (i)production practice-related (means-based), (ii) system (or state)-related(eect-based) and (iii) farmers perception-related (perception-based) envi-ronmental impacts. A statistical additive aggregation method was utilised tocompile and add these various indicators to produce a composite index asfollows (Sabiha et al. 2016):OFEDIiXnm1MmXke1EeXlp1Pp; 1where OFEDIi= on-farm environmental damage index of the ith farmer/farm; Mm= means-based indicators; Ee= on-eect-based indicators;Pp= perception-based indicators.This study selected indicators that are relevant to the eects of by HYV ricefarming on the environment and are widely recognised in agroecologicalstudies (Alauddin and Hossain 2001; Rahman 2005). A list of means-basedand eect-based environmental impacts was prepared using previous litera-ture on Bangladesh rice agriculture and environmental impacts. To select thePBI, several (nine) focus group discussions (FGD) (with the HYV ricefarmers) were conducted prior to the field survey. We then finalised the PBIby selecting those that were mostly experienced/faced by HYV rice farmerswhile cultivating HYV rice during the previous crop year. Table 1 presentsthe details of the measurement methods and formulas used to construct theseindicators. All raw data were scaled to a normalised score ranging from 0 to 1using an optimal range scoring function (Rahmanipour et al. 2014), wherescores close to 1 imply a stronger environmental impact of a given variable.The OFEDI is then formulated using these normalised values in Equation (1)for each production unit (i.e. HYV rice farms). Thus, the constructed OFEDIis defined as the index of undesirable output produced by HYV rice farmswhich is then subsequently added as a variable to compute eco-eciency. Ahigh OFEDI implies a high level of environmental damage. For more detailson the construction procedure, see Sabiha et al. (2016).We illustrate our approach empirically by utilising data from a survey of317 HYV rice farmers of northern Bangladesh, where, over the past fewdecades, the environment and natural resources of have been aected in partby agricultural pollution caused by the widespread use of HYV seeds incereals, that is rice, wheat and maize (Alauddin and Hossain 2001). Farm-level studies have demonstrated that Bangladesh is experiencing a decline in 2017 Australian Agricultural and Resource Economics Society Inc.250 N.-E. Sabiha et al.Table 1 On-farm environmental damage index (OFEDI): variable descriptionIndicative variables Method FormulaMeans-based impacts (MBI)Crop concentrationindex (CCI)Herfindahl index ofcrop concentrationHI Pa2j; 0C20HIC201; aj= areashare occupied by the jth crop in A.A value of 0 denotes perfectdiversification, and a value of 1denotes perfect concentrationSoil stress factor(SSF)Optimal range scoringfunction: MBFfx0:9x C0236C02C18C190:1Hypothetical threshold range236. (see Appendix I)Nitrogen risk factor(NRF)Applied dose (NA)/recommended dose(NR). Optimal rangescoring function:MBF if NA NRfx0:9xC01:012:0C01:01C18C190:1Hypothetical thresholdrange is 1.012.0Eect-based impacts (EBI)Soil pH (SpH),surfacewater pH (SWpH),ground water pH(GWpH)Optimal range scoringfunction:LBF if pH 7 fx0:9xC07:058:5C07:05C18C190:1Scientific threshold range is7.058.5, if pH 7Soil salinity (SSL) Optimal range scoringfunction: MBFfx0:9xC00:22:0C00:2C18C190:1Scientific threshold range is 0.22.0 ds/mSoil compaction(SCM)Optimal range scoringfunction: MBFfx0:9xC0 100500C0100C18C190:1Scientific threshold range is 100500 psiPerception-basedimpacts (PBI)Soil fertility problem(SFP); soil waterholding capacityproblem (SWH);water logging(WLG); waterdepletion (WDP);soil erosion (SER);pest attack problem(PAP); crop diseasesproblem (CDP); healthimpact; reduction in fishcatch (RFC).Five-point Likert scale using agree disagree approachLikert scale scoring for perception-based indicatorsDisagree AgreeImpactInterpretationNone VerylowLow Medium High VeryhighImpact Weights(Indicatorvalues)0 0.2 0.4 0.6 0.8 1.0Note MBF means more is bad for the environment function; LBF means less is bad for the environmentfunction; x is the indicators actual value; f(x) is the indicators derived impact score, where for everyindicator score, the value range is 0.1 f(x) 1. 2017 Australian Agricultural and Resource Economics Society Inc.Eco-eciency of HYV rice cultivation 251the production eciency of rice over time (Salim and Hossain 2006; Alamet al. 2011) and decreasing returns to scale (Rahman 2011), which could beexplained by the extent of agricultural pollution and environmental factors.In addition, farmers have shown awareness of environmental impacts suchas soil and water problems due to cultivating HYV rice in Bangladesh(Rahman 2005). This provides an opportunity to measure the extent of theenvironmental impacts generated at the farm level that could explain theobservation of a low and/or declining level of technical eciency in HYVrice cultivation.We hypothesise that, in any region or in any farm, if one or severalenvironmental impacts are absent, the associated indicators will hold anormalised score of 0. Other impacts might generate nonzero values of thenormalised scores for these specific regions or farms. We have taken thecumulative form of the normalised scores of these impacts. We did not weightindividual environmental impacts because we assumed that all these impactsare equally important. The farmers during the FGD sessions also assignedhigh importance to these impacts.3.2 Incorporating undesirable output into the eco-eciency modelEco-eciency, which involves the idea of producing maximum outputs usingminimum inputs while reducing on-farm environmental impacts, couldprovide important information for decision-making vis-C18a-vis improvingenvironmental performance. Many researchers have recommended the useof data envelopment analysis (DEA) to measure the eco-eciency of a givenproduction activity (e.g. agriculture) (Poit-Lepetit et al. 1997; Hoang andAlauddin 2012). DEA not only allows for the measurement of environmentaleciency but also examines the nature and causes of environmentalineciencies (i.e. bad environmental performance) (Tyteca 1996). Korhonenand Luptacik (2004) noted that DEA provides an in-depth insight into thecauses of eco-ineciencies when a pollutant is included as an undesirableoutput in an analysis. Cooper et al. (2011) identified that DEA couldsuccessfully reduce errors in ecient frontier estimation. Therefore, estimat-ing eco-eciency by applying DEA can eectively summarise dierentenvironmental impacts and allow decision-making units to arrive at anenvironmentally sustainable production decision, as performed in this study.Generally, three categories of factors, that is desirable outputs, undesirableoutputs (i.e. environmental impacts) and inputs, are considered whenformulating a DEA model intended to evaluate environmental performance(Cherchyey et al. 2013).This study proposes an eciency model that assumes that there are Ihomogeneous farms (i.e. HYV rice farms) consuming J inputs forproducing outputs R (i.e. HYV rice grown in three seasons: Aus(premonsoon), Aman (monsoon) and Boro (dry winter). The outputscorresponding to indices 1.Z are desirable (good) outputs, and the 2017 Australian Agricultural and Resource Economics Society Inc.252 N.-E. Sabiha et al.outputs corresponding to indices Z + 1, Z + 2,. R are undesirable(bad) outputs, that is MBI, EBI and PBI (Table 1). These undesirableoutput indices correspond to the OFEDI when aggregated cumulativelyusing Equation (1). The proposed eciency model (Eqn 2) represents alloutputs as a positive weighted sum and uses negative weights for undesirableoutputs. The model assumes that the ith farm produces ygriunits of desirableoutput (i.e. the HYV rice) and ybsiunits of undesirable output (i.e. the OFEDI)using xjiunits of jth input.First, we estimate the ECE model without adjusting the undesirableoutput as expressed by Equation (2) (i.e. assuming OFEDI = 0 values forall farms). It is worth mentioning that assuming OFEDI = 0 does not meanHYV rice cultivation is releasing zero (no) environmental impact, rather itmeans the model is not identifying a