The Impact of Agricultural Extension on Farmer Nutrient Management Behavior in Chinese Rice Production: A Household-Level Analysis

Transcription

1 Sustanablty 214, 6, ; do:1.339/su Artcle OPEN ACCESS sustanablty ISSN The Impact of Agrcultural Extenson on Farmer Nutrent Management Behavor n Chnese Rce Producton: A Household-Level Analyss Dan Pan Insttute of Poyang Lake Eco-economcs, Jangx Unversty of Fnance and Economcs, Nanchang 3313, Chna; E-Mal: pandan@jxufe.edu.cn; Tel.: ; Fax: External Edtor: Marc A. Rosen Receved: 13 August 214; n revsed form: 16 September 214 / Accepted: 18 September 214 / Publshed: 29 September 214 Abstract: Agrcultural nutrents play a crtcal role n food producton and human nutrton n Chna. Aganst ths backdrop, agrcultural extenson servces are essental for provdng farmers wth knowledge and nformaton about nutrent management. By usng a propensty score-matchng (PSM) approach, ths study examnes the mpact of agrcultural extenson on farmer nutrent management behavor. Survey data about rce farmers n seven provnces of rural Chna are used. The emprcal results ndcate that partcpaton n agrcultural extenson has a postve mpact on ratonalzng farmer nutrent management behavor. However, ths mpact s trval. Compared wth non-partcpatng farmers, the reduced rato of total fertlzer use and total norganc fertlzer use by partcpatng farmers s only 1.7% to 3.7%, and the mproved rato of the total organc fertlzer use and the level of sol-testng-based fertlzer use by partcpatng farmers s only 1.8% to 1.173%. Addtonally, the causal mpacts of agrcultural extenson partcpaton on nutrent management behavor tend to be hgher for more educated, rsk-lovng and larger-scale farmers. Ths study reveals that Chna faces great challenges n mplementng mproved nutrent management practces for hundreds of mllons of farmers through extenson servces. The fndngs also have mportant mplcatons for Chna s extenson system to meet the objectves of mprovng nutrent management. Keywords: agrcultural extenson; nutrent management; propensty score matchng; Chna; rce producton

2 Sustanablty 214, Introducton Nutrents, such as ntrogen (N), phosphorus (P), potassum (K), mcronutrents, and others, are essental for plant growth, food producton and, ultmately, for adequate human nutrton [1]. It has been estmated that the survval of nearly half of the world s populaton depends on the use of agrcultural nutrent nputs [2], whereas lack of access to nutrents n most Afrcan countres s a prmary cause of low crop yelds and food shortages [3]. Over the past 5 years, Chna has successfully acheved food self-suffcency for ts rapdly growng populaton. Chna s now feedng approxmately 22% of the global populaton wth only 7% of the global arable land area. Ths accomplshment was acheved prmarly by ncreasng the use of chemcal fertlzer nutrents, especally N and P. Chna s now the world s largest producer, consumer and mporter of chemcal fertlzers, consumng over 1/3 of the world s chemcal fertlzers and accountng for approxmately 9% of the ncrease n global fertlzer consumpton snce 1981 [4]. However, Chnese agrculture uses far more chemcal fertlzers per unt of crop producton than comparable systems n Europe or North Amerca [3]. In 21, Chnese agrculture consumed 28.1 Tg N as synthetc fertlzer, exceedng consumpton n North Amerca (11.1 Tg N) and the European Unon (1.9 Tg N) combned [5]. Numerous agronomc and economc studes under both expermental condtons and on farm felds provde conclusve proof that the overuse of chemcal fertlzers has become wdespread across Chna. For example, the average amount of N fertlzer used n the major rce producng regons of Chna s 195 kg ha 1, whch s 47% hgher than the recommended rate [6]. The oversupply of nutrents or an mbalance between nutrents reduces the effcency of nutrent use. As a consequence, the mean N-use effcency n crop producton n Chna has decreased drastcally from 32% n 198 to 26% n 25 and s much lower than the effcency acheved n many developed countres [7]. Nutrent losses from agrculture have resulted n serous envronmental stress by ncreasng greenhouse gas (GHG) emssons and by pollutng ground and surface water through N leachng [8]. Accordng to the offcal report from the Mnstry of Envronmental Protecton of Chna n 21, the annual loadngs of N and P from the agrcultural sector nto the naton s water bodes reached 2.7 and.3 Tg, whch contrbuted to approxmately 6% of the total N and P loads. The hgh rate of N fertlzer use has led to large N losses n the form of ammona (NH 3 ) volatlzaton and N leachng nto groundwater and lakes [9]. Furthermore, the manufacture and use of N fertlzers are estmated to have contrbuted to approxmately 3% of agrcultural GHG emssons and more than 5% of Chna s total GHG emsson n 27 [1]. To address the country s wdespread water qualty and other nutrent-related envronmental ssues (e.g., sol acdfcaton, N deposton, and clmate change), drastc mprovements n nutrent management that wll allow the Chnese food producton ndustry to smultaneously feed the growng human populaton and decrease the envronmental mpacts of food producton are one of the great challenges Chna faces n the 21st century. In an effort to address these food securty and envronmental challenges related to agrcultural nutrent use, Chna has mplemented wde-rangng nutrent management practces to ncrease the effcency of N and P use [11]. However, most of these nutrent management technologes, programs, and recommendatons have not been adopted by farmers. The prmary reason for ths problem s rooted n the lack of knowledge and nformaton by end users, because the majorty of the hundreds of mllons of farmers have receved lmted educaton about the value and effcent use of plant nutrents [12].

3 Sustanablty 214, Hu et al. [13] found that, wth approprate N fertlzer applcaton technology, N fertlzer use could be reduced by more than 3% wthout lowerng (and potentally even ncreasng) rce yelds. Cu et al. [14] found that usng mproved nutrent management technologes could reduce N fertlzer use by 4% wthout lowerng maze yelds, compared wth current farmng practces. Therefore, the tmely delvery of scence-based fertlzer recommendatons through educaton, tranng and extenson servces s essental for mprovng nutrent use effcency and for reducng the over-applcaton of nutrents [15]. However, gven the mportance of agrcultural extenson servces for proper nutrent management, lttle emprcal work has been conducted to examne ths area of farm management n Chna. To the best of our knowledge, the only two exceptons can be found n [1,16]. Usng data collected on the North Chna Plan, Huang et al. [1] showed that through tranng and scentst-guded on-farm plot experments, N-fertlzer use could be reduced by 22% n maze producton wthout compromsng yelds. Usng data from 813 maze farms, Ja et al. [16] found that mproved N management tranng could sgnfcantly reduce farmer N fertlzer applcaton by 2%. A major drawback of the above studes s that they do not properly control for potental dfferences between partcpants and farmers n the comparson group (non-partcpants), makng t dffcult to draw defntve conclusons. To dentfy the mpacts of agrcultural extenson partcpaton, an evaluaton must construct a credble counterfactual outcome; that s, a study must estmate the nutrent management behavor of partcpants f they had not partcpated n the agrcultural extenson programs. Falure to do ths wll bas the correspondng mpact estmates. To fll ths gap, we employ a propensty score matchng (PSM) method to overcome ths unobserved counterfactual problem. We use the PSM model because t can create expermental condtons n whch partcpants and non-partcpants are randomly assgned, provdng an unbased estmaton of the treatment effects, and t can be used to dentfy a causal lnk between agrcultural extenson partcpaton and farmer nutrent management behavor. To the best of our knowledge, ths s the frst study to use the PSM method to evaluate the mpact of agrcultural extenson partcpaton on farmer nutrent management behavor. Rce producton s selected for ths study for two reasons. Frst, rce s the number one crop n terms of the unt per area yeld n Chna, reachng t ha 1 n 212, whch s and tmes greater than the unt per area yeld for wheat and maze. Second, as dscussed above, there s suspected overuse of agrcultural nutrents n rce producton. The rest of the paper s organzed as follows. The next secton presents an analytcal framework and methodology, followed by a presentaton of the data and descrptve statstcs n Secton 3. The emprcal results and fndngs are dscussed n Secton 4. The last secton concludes wth key fndngs and polcy mplcatons. 2. Analytcal Framework and Methodology 2.1. Decson to Partcpate n Agrcultural Extenson Followng [17,18], the economc ratonale that drves the analytcal framework underlyng farmer partcpaton n agrcultural extenson s the maxmzaton of perceved utlty. The decson about whether to partcpate n an agrcultural extenson program depends on the utlty the farmer expects to derve from partcpaton. Farmer partcpaton only occurs when the expected utlty of partcpaton

4 Sustanablty 214, (U P ) s greater than the utlty wthout partcpaton ( U N ),.e., UP UN. The dfference between * the utlty wth and wthout partcpaton may be denoted as a latent varable D, such that D * * ndcates that the utlty wth partcpaton exceeds the utlty wthout partcpaton. Therefore, the D s not observable, but can be expressed as a functon of the observed characterstcs and attrbutes denoted as Z n a latent varable model as follows: D * Z (1) and D * 1, f D (2), otherwse where D s a bnary ndcator varable that equals 1 f a farmer partcpates n an agrcultural extenson program and s otherwse zero; s a vector of the parameters to be estmated; Z s a vector of explanatory varables, ncludng the household and farm-level characterstcs; and s the error term, whch s assumed to be normally dstrbuted. The probablty of partcpaton n an agrcultural extenson program by a farmer based on observable characterstcs can then be estmated usng ether a bnary probt or a logt model: Pr( D * 1) Pr( D ) Pr( Z ) 1 F( Z ) (3) where F s the cumulatve dstrbuton functon for, whch s commonly assumed normally dstrbuted n the probt model or extreme value dstrbuted n the logt model. The extreme value dstrbuted error gves the functon ts logstc dstrbuton. In can be noted that the decson by a farmer to partcpate or not n an agrcultural extenson program s dependent on the farm, as well as farmer characterstcs; therefore, t reles on each farmer s self-selecton rather than on random assgnment Impact of Agrcultural Extenson Partcpaton on Farmer Nutrent Management Behavor A commonly used approach to evaluate the mpact of partcpaton n an agrcultural extenson program on the outcome of farmer nutrent management behavor s to nclude a dummy varable equal to the one n the outcome equaton ndcatng whether the farmer partcpated n an agrcultural extenson program, but otherwse equalng zero, and then applyng an ordnary least squares (OLS) regresson. Ths may be expressed as follows: Behavor X D u (4) where Behavor represents the nutrent management behavor of farmer, X s a vector of farm-level and household-level characterstcs, such as the age and educaton of the farmer, the farm sze, the farmer rsk atttude, and sol qualty varables; D s a dummy varable, D 1 for partcpaton n an agrcultural extenson program and D otherwse. The coeffcent n the specfcaton captures the mpact of agrcultural extenson partcpaton on farmer nutrent management behavor. Ths approach, however, s lkely to generate based estmates because t assumes that partcpaton n an agrcultural extenson program s exogenously determned; however, t s potentally endogenous. Partcpaton n

5 Sustanablty 214, agrcultural extenson programs s not random and s strongly correlated wth unobservable household and farm characterstcs (e.g., manageral skll, motvaton, and so on) that may be correlated to nutrent management behavor. Ths may arse from farmer self-selecton for partcpaton n an agrcultural extenson program or from strategc program placement. The ssue of selecton bas occurs f unobservable factors nfluence both the error term of the partcpaton equaton n Equaton (1) and the error term of the nutrent management behavor u n Equaton (4), resultng n correlaton between the two error terms. Therefore, estmatng Equaton (4) wth ordnary least squares wll lead to based estmates. Researchers have proposed varous methods to avod selecton bas [19]: (1) an expermental study n whch partcpants can be randomly assgned to ether control or treatment groups, but ths s not possble for ex post studes; (2) the nstrumental varables (IV) approach, n whch a major lmtaton s that t normally requres a vald nstrument that determnes the treatment status but not the outcome varable, whch s an arduous task n emprcal studes [2]. Moreover, the IV procedure assumes that the treatment varable only nduces a parallel shft (ntercept effect) on the outcome varable, mplyng that the nteractons between extenson partcpaton and other covarates does not exst; (3) Heckman s two-step method; however, ths two-step procedure depends on the restrctve assumpton that the unobserved varables are normally dstrbuted [21]; (4) a dfference-n-dfferences estmaton, whch examnes the effect before and after a treatment and between treated and untreated groups; therefore, ths method s lmted to studes wth longtudnal data; and (5) a propensty score-matchng method, whch, unlke the methods mentoned above, requres no assumpton about the functonal form specfyng the relatonshp between outcomes and outcome predctors. Therefore, the dffculty of fndng vald nstrumental varables can be avoded, and cross-sectonal data collected at one pont n tme can be used [22,23]. Based on these attrbutes and the data avalablty, we chose the PSM method to control for selecton bas n our analyss The Propensty Score-Matchng (PSM) Method Average Treatment Effect (ATE) The objectve of ths study s to estmate the average treatment effect (ATE) of agrcultural extenson partcpaton on farmer nutrent management behavor. An deal stuaton to estmate the ATE s to smply compare two outcomes for the same unt: when the unt s assgned to the treatment and when t s not [24]. In the context of ths study, for example, the ATE could be estmated by comparng nutrent management behavor when the farmer s enrolled n an agrcultural extenson program and when not enrolled. In the absence of expermental data, the bggest challenge to estmatng an ATE s that we do not know what the nutrent management behavor would have been f the farmer had not partcpated n the agrcultural extenson program. Therefore, constructon of an unobserved counterfactual remans the basc problem of the evaluaton of ATEs [25]. Rosenbaum and Rubn [26] developed the PSM approach, whch s most commonly used n non-expermental settngs to overcome the unobserved counterfactual PSM constructs of a statstcal comparson group by matchng every ndvdual observaton of partcpants wth an observaton havng smlar characterstcs

6 Sustanablty 214, to the group of non-partcpants. In essence, the PSM model creates the condtons of an experment n whch partcpants and non-partcpants are randomly assgned, provdng an unbased estmate of treatment effects, and t can be used to dentfy a causal lnk between agrcultural extenson partcpaton and farmer nutrent management behavor. Accordng to [26], the ATE ( ) n a counterfactual framework can be defned as follows: where 1 and 1 (5) denote the nutrent management behavor of farmer who partcpates n the agrcultural extenson program and farmer who does not partcpate n the agrcultural extenson program, respectvely. Estmatng the mpact of agrcultural extenson partcpaton on the th farmer from Equaton (5) would be msleadng due to the problem of mssng data. Normally, we can only 1 observe ether outcome or for one farmer at a tme, not both. The normally observed outcome can be expressed as follows: 1 D ( 1 D ) (6) where D s a dummy varable that ndcates agrcultural extenson partcpaton. The average effect of the treatment on the treated (ATT) s defned as the dfference between the expected value of the outcome by partcpants whle partcpatng n the agrcultural extenson program and the expected value of outcome they would have receved f they had not partcpated n the program. Followng Smth and Todd [23], the ATT, whch s the parameter of nterest n ths emprcal research, can be defned as follows: 1 1 ATT E( 1) E( 1) E( - 1) (7) Data on ( 1 E 1) are avalable from the program partcpants, but data on E ( 1), whch s the counterfactual outcome, are not observable for a gven farmer. Therefore, what we can usually observe s the ATE, whch can be expressed as follows: ATE E( 1 ATE [ E( 1 ATE ATT E( 1) E( 1) E( 1) E( ) 1)] [ E( ) 1) E( )] If partcpaton n agrcultural extenson s randomly assgned, the partcpaton dummy varable D 1 s statstcally ndependent of the outcome (, ), and the mean outcome of untreated ndvduals E ( ) can be used as a proxy for E ( 1). However, n non-expermental surveys, the treated and untreated groups may not be the same before recevng treatment. Therefore, E ( ) cannot be used as a proxy for E ( 1). E ( 1) E( ) ndcates the extent of selecton bas that arses when the ATE s used to examne the mpact of a treatment n non-expermental studes. Therefore, gven the non-random partcpaton n agrcultural extenson, usng Equaton (8) to estmate the mpacts of agrcultural extenson would yeld based estmators (.e., due to selecton bas). The basc objectve of the mpact analyss s to fnd ways to make the selecton bas zero ( E ( 1) E( ) ) so that the ATT=ATE. The PSM model can be employed to account for ths selecton bas. (8)

7 Sustanablty 214, The valdty of the PSM method depends on two condtons: (1) the assumpton of unconfoundedness or condtonal ndependence (CIA); and (2) the assumpton of common support (CSA). The CIA assumpton states that gven a set of observable covarates X, the respectve treatment outcomes, are ndependent of the actual partcpaton status D. In notaton, as follows: 1 (, ) P X (9) / Hence, after adjustng for observable dfferences, the mean of the potental outcome s the same for D 1 and D ( E ( 1) E( ) ). The CIA assumpton permts the use of matched non-partcpatng farms to measure how the group of partcpatng farms would have performed had they not partcpated. Under the CIA, the propensty score n ths study s context, whch can be defned as the condtonal probablty that a farmer wll partcpate n an agrcultural extenson program, gven ts pre-partcpaton characterstc, s gven as follows: h( ) p( X ) Pr( D 1 X ) E( D X ); p( X ) F X (1) where F can be the normal or logstc cumulatve dstrbuton and X s a vector of pre-treatment characterstcs. On the other hand, the CSA assumpton rules out the phenomenon of perfect predctablty by ensurng that every ndvdual has a postve probablty of ether beng a partcpant or a non-partcpant n an agrcultural extenson program. The CSA can be expressed as follows: Pr( D 1 X ) 1 (11) Under the assumptons of CIA and CSA, the ATT effect can then be estmated as follows: 1 ATT E( 1 E(( 1 E{ E[( E{ E[( ) E( - ) ) 1)) 1) 1, p( X )]} 1, p( X )] E[(, p( X )] 1} (12) Matchng Algorthm Varous matchng algorthms are avalable to match partcpants wth non-partcpants of smlar propensty scores, dependng on the dstrbuton of the covarates n the matched treatment and control groups. In all matchng algorthms, each treated ndvdual s pared wth some group of comparable non-treated ndvduals j and then the outcome of the treated ndvdual, s lnked wth the weghted outcomes of hs neghbors j n the comparson (control) group. Asymptotcally, all matchng methods should yeld the same results. However, n practce, there are trade-offs n terms of bas and effcency wth each method [27]. The most commonly used approaches are nearest neghbor matchng (NNM), kernel-based matchng (KBM), and radus calper matchng (RM) [28]. The NNM nvolves choosng ndvduals from the partcpants and non-partcpants that are closest n terms of propensty scores as matchng partners. It s usually appled wth replacement n the control groups. In the KBM, all treated subjects are matched wth a weghted average of all controls, usng weghts that are nversely proportonal to the dstance between the propensty scores of treated and comparson groups.

8 Sustanablty 214, RM uses a tolerance level on the maxmum propensty score dstance between a subject n the treatment group and all ndvduals n the control group who are wthn that dstance Matchng Qualty Because the man purpose of PSM s to reduce selecton bas by ncreasng the balance between the partcpants and non-partcpants [29], there should be no systematc dfferences n the dstrbuton and overlap of covarates between the two groups after matchng. It s mportant to check f the matchng procedure s able to balance the dstrbuton of the relevant varables across groups of partcpants and non-partcpants. Ths balancng test s normally requred after matchng to ascertan whether the dfferences n the covarates n the two matched sample groups have been elmnated, n whch case, the matched comparson group can be consdered plausbly counterfactual [2]. There are several covarate-balancng tests can be used to test the balance of the PSM results. In ths study, we used the followng methods to check the balance of the scores and covarates. Frst, we calculated the standardzed bas before and after matchng and checked for a sgnfcant dfference n the covarates of both groups usng a two-sample t-test. After matchng, there should be no sgnfcant dfferences [3]. Secondly, we run a logt model usng the after-matchng sample to compare the pseudo-r 2 wth the R 2 obtaned from the logt estmaton usng the before-matchng sample. After matchng, there should be no systematc dfferences n the dstrbuton of covarates between both groups, so the low value of a pseudo R 2 ndcates that the balancng property s satsfed [31]. Fnally, the balancng property was checked usng the mean absolute standardzed bas (MASB) between partcpants wth non-partcpants, as suggested by Rosenbaum and Rubn [32], who recommend that a standardzed dfference of greater than 2% should be consdered too large and an ndcator that the matchng process has faled Senstvty Test Despte the fact that PSM tres to compare the dfference between the outcome varables of partcpants wth non-partcpants wth smlar nherent characterstcs, t cannot correct unobservable bas because PSM only controls for selecton bas that s specfcally due to observable varables ( selecton on observables ). If there are unobserved varables that smultaneously affect the partcpaton decson and the outcome varables, a hdden bas or selecton on unobservables bas mght arse and the PSM estmator may no longer be consstent. There s the need to check for senstvty of the ATT to hdden bas after matchng. Rosenbaum [3] has suggested the use of a senstvty analyss called boundng approach to address ths problem. The purpose of the senstvty analyss s to ask whether nferences about partcpaton effects may be changed by unobserved varables. The senstvty analyss nvolves calculatng upper and lower bounds wth a Wlcoxon sgn-rank test to test the null hypothess of no partcpaton effect for dfferent hypotheszed values of unobserved selecton bas [33].

9 Sustanablty 214, Data and Descrpton Statstcs 3.1. Samplng Procedure and Data The data used for ths paper were collected n a nearly natonally representatve household survey n seven provnces of rural Chna, and the collecton took place between January and March 213. A three-stage stratfed random-samplng desgn was chosen to ensure the representatveness of the sample. Frst, seven provnces were selected from Chna s major agro-ecologcal zones from a lst of provnces arranged n descendng order based on ther gross value of ndustral outputs (GVIO). The GVIO was used on the bass of the concluson from [34] that the GVIO s one of the best predctors of the standard of lvng and development potental and s often more relable than the net rural per capta ncome. The seven representatve provnces ncluded: Jangsu, representng southeastern coastal rce producton areas (Jangsu, Shangha, Zhejang, Fujan, Guangdong and Hanan); Shandong and Henan, representng northern rce producton areas (Bejng, Tanjn, Hebe, Shanx, Shandong and Henan); Schuan, representng southwestern rce producton areas (Schuan, Chongqng, Guzhou, Guangx, unnan and Tbet); Helongjang, representng northeastern rce producton areas (Jln, Laonng, Helongjang and Inner Mongola); and Hebe and Jangx, representng the central rce producton areas (Anhu, Hube, Jangx and Hunan). Second, n each selected provnce, three countes were randomly selected, one from each quntle of a lst of countes arranged n descendng order of GVIO. Thrd, wthn each selected county, three vllages were chosen. Fnally, twenty rce producton households were then randomly sampled from a lst of farmng famles n each vllage. As a result, a total of 125 rce producton households n 63 vllages from 21 countes were surveyed usng a standardzed survey nstrument. The survey nstrument was a closed-ended questonnare that was modfed from the baselne survey nstrument. It was feld-tested durng a three-day tranng exercse wth the enumerators and local researchers n each of the seven provnces. Data were checked usng a data-cleanng syntax that checked for errors. Data cleanng was then performed at the country level by data assstants. The household survey used a structured questonnare to collect data from the selected households on the demographc characterstcs of the household, farm-level characterstcs, ndvdual features, farmer partcpaton n agrcultural extenson programs, as well as farmer nutrent management behavor. In addton to the household survey, we also conducted a vllage survey to collect valuable nformaton about the soco-economc characterstcs and the agrcultural extenson program characterstcs of the vllage Varable Selecton The mplementaton of matchng requres the choce of a set of varables that credbly satsfy the assumpton of unconfoundedness. The choce of covarates to be ncluded n the frst step (propensty score estmaton) was an ssue. Heckman et al. [21] ndcated that omttng mportant varables wll ncrease the bas n the resultng estmaton. Bryon et al. [35] noted that ncludng extraneous varables n the partcpaton model would reduce the lkelhood of fndng common support. In prncple, only varables that smultaneously nfluence the choce to partcpate n an agrcultural extenson program and the outcomes of partcpaton, whch are not affected by partcpaton, should be ncluded n the PSM when matchng s performed [27]. Meanwhle, the choce of varables should be guded by

10 Sustanablty 214, prevous research, economc theory, and the nsttutonal settng wthn whch the treatment and outcomes are measured. Under those prncples, the varables employed n ths study can be dvded nto three groups: the household characterstcs (age, educaton, farmng experence, rsk atttude, extenson contact, vllage leader, household ncome, off-farm ncome rato, and dstance to the nearest fertlzer shop); farm characterstcs (farm sze and sol qualty) and vllage characterstcs (extent of agrcultural extenson partcpaton, vllage ncome and off-farm ncome rato) Summary Statstcs Summary Statstcs of Independent Varables Table 1 presents the defntons and dfferences n the characterstcs of partcpants and non-partcpants wth ther t-values. The t-values ndcate that there are sgnfcant dfferences n some of the varables used n the emprcal analyss. Specfcally, the partcpants were younger and were closer to the nearest fertlzer shop than non-partcpants. However, the educaton level, rsk atttude, proporton of vllage leaders, farm sze, sol qualty and extent of agrcultural extenson partcpaton n ther vllage were all sgnfcantly hgher factors for partcpants than for non-partcpants. The dfferences n the mean characterstcs between partcpants and the non-partcpants that could have affected partcpaton ndcated a potental source of bas, hence, the need for matchng and selecton bas tests. Table 1. Varables defnton and dfferences n means of partcpants and non-partcpants. Varables Descrpton Household characterstcs Partcpants (N = 396) Non-partcpants (N = 854) T-test Mean SE Mean SE Age of household head ear ** Educaton of household head Farmng experence of household head Rsk atttude of household head Extenson contact Vllage leader dummy 1 = year; 2 = less than 6 years; 3 = 6 9 years; 4= 9 12 years; 5= more than 12 years 1 = less than 3 years; 2 = 3 1 years; 3 = 1 15 years; 4 = more than 15 years 1 = rsk averson; 2 = rsk neutralty; 3 = rsk lovng Number of household head s contact wth the agrcultural extenson agent one year 1 = the household head s a vllage leader, = no ** ** *** *** Household ncome Ln (household ncome) Off-farm ncome rato Dstance to the nearest fertlzer shop The proporton of off-farm ncome to the total ncome (%) Klometers *** Farm characterstcs Farm sze Ha ** Sol qualty 1 = poor; 2 = moderate; 3 = good **

11 Sustanablty 214, Varables Extent of vllage agrcultural extenson partcpaton Descrpton Table 1. Cont. Vllage characterstcs The proporton of agrcultural extenson partcpants n vllage (%) Partcpants (N = 396) Non-partcpants (N = 854) T-test Mean SE Mean SE *** Vllage ncome Ln (vllage ncome) Vllage off-farm ncome rato The proporton of off-farm ncome to the total ncome n vllage (%) Note: ***, **, and * ndcate statstcal sgnfcance at 1%, 5% and 1%, respectvely Summary Statstcs of Dependent Varables In accordance wth prevous studes, farmer nutrent management behavor s measured n terms of the total amount of fertlzer used, the total amount of norganc fertlzer used, the percentage of organc fertlzer used and the percentage of sol-testng-based fertlzer used [1,36]. Table 2 reports the nutrent management behavor of partcpants and non-partcpants n rce producton. The nutrent management behavor appears to be more ratonal among the partcpants. Frst, partcpatng farmers used much less fertlzer and norganc fertlzer than non-partcpatng farmers. Non-partcpatng farmers appled an average of 717 kg ha 1 of fertlzer and 642 kg ha 1 of norganc fertlzer, whch was more than 1.648% and 19.19% of partcpatng farmers, respectvely. Second, the percentages of organc fertlzer used and sol-testng-based fertlzer used by partcpatng farmers were much hgher than those of non-partcpatng farmers. For non-partcpatng farmers, the percentages of organc fertlzer used and sol-testng-based fertlzer used were 6.834% and 3.626%, whch were lower than the correspondng amounts of 3.66% and 2.71% used by partcpatng farmers. Table 2. Nutrent management behavor of partcpants and non-partcpants. Nutrent management behavor Partcpants Non-partcpants Dfferences The total amount of fertlzer used (kg ha 1 ) The total amount of norganc fertlzer used (kg ha 1 ) The percentage of organc fertlzer used (%) The percentage of sol-testng-based fertlzer used (%) The uncondtonal summary statstcs n the above tables generally suggest that agrcultural extenson may have a role n mprovng farmer nutrent management behavor, but because agrcultural extenson partcpaton s endogenous, a smple comparson of the nutrent management behavor ndcators of partcpants and non-partcpants has no causal nterpretaton. That s, the above dfferences may not be the result of agrcultural extenson but nstead may be due to other factors. Therefore, we need to use a PSM method to control for ths self-selecton problem to test the mpact of agrcultural extenson partcpaton on farmer nutrent management behavor.

12 Sustanablty 214, Results and Dscusson In ths secton, we outlne the common steps used to mplement the PSM method. Frst, a probablty model for partcpaton n agrcultural extenson programs s estmated to calculate the probablty (or propensty scores) of partcpaton for each observaton. In the second step, each partcpant s matched to a non-partcpant wth a smlar propensty score to estmate the ATT Factors That Affect Partcpaton n Agrcultural Extenson The factors that affect the decson to partcpate n agrcultural extenson programs are estmated usng a logt model. Table 3 presents the results. The last column of Table 3 ndcates changes n the probablty of partcpaton n agrcultural extenson programs gven one unt of change n the explanatory varables; these are computed from the means of all of the explanatory varables. The lkelhood rato statstcs of suggested that the estmated model s statstcally sgnfcant at the 1% level and that the pseudo-r 2 value ndcates that the equaton explans 25.39% of the varance n decson-makng about whether to partcpate n an agrcultural extenson program. Table 3. Logt regresson estmates of propensty scores for partcpaton n agrcultural extenson programs. Varable Coeffcent Standard error Margnal Probablty (dy/dx) Household characterstcs Age of household head.262 ** Educaton of household head.895 *** Farmng experence of household head Rsk atttude of household head Extenson contact.821 ** Vllage leader dummy.7214 *** Household ncome Off-farm ncome rato Dstance to the nearest fertlzer shop Farm characterstcs Farm sze.4251 ** Sol qualty Vllage characterstcs Extent of vllage agrcultural extenson partcpaton.568 *** Vllage ncome Vllage off-farm ncome rato Constant Log lkelhood = ; Pseudo R 2 =.2539; Prob > ch 2 =.; Number of observatons = 125 Note: (1) ***, **, and * ndcate statstcal sgnfcance at 1%, 5% and 1%, respectvely; (2) The standard errors of the coeffcents are estmated from bootstrap method wth 1 replcatons.

13 Sustanablty 214, The results ndcates that older farmers were less lkely to partcpate n agrcultural extenson programs, whereas farmers that are more educated have a hgher probablty of partcpaton. As expected, farmers that have more contact wth agrcultural extenson agents are more lkely to partcpate n agrcultural extenson programs. Beng a vllage leader and havng larger farm sze also ncreased the probablty of agrcultural extenson partcpaton. The hgher the proporton of agrcultural extenson partcpants n a vllage, the more lkely farmers are to partcpate n agrcultural extenson programs Treatment Effects of the PSM Methods The results modelng the mpact of agrcultural extenson partcpaton on farmer nutrent management behavor wth KBM, RM and NNM are presented n Table 4. The three matchng methods ndcate that partcpaton n agrcultural extenson programs has a postve mpact on farmer nutrent management behavor. The mpact of agrcultural extenson partcpaton on reducng fertlzer use and norganc fertlzer use are postve and sgnfcant for all the matchng algorthms. For the amount of fertlzer used, the ATT ranges from 11 to 24 kg ha 1, mplyng that on average partcpants used 11 to 24 kg ha 1 less fertlzer than matched non-partcpants, and/or the amount of norganc fertlzer used ranges from 1 to 18 kg ha 1. Agrcultural extenson partcpaton also led to clear and sgnfcant mprovement n organc fertlzer use and sol-testng-based fertlzer use. Farmers that partcpated n agrcultural extenson programs mproved ther percentage of organc fertlzer use by 1.8% to 1.75%. They also had a hgher percentage of sol-testng-based fertlzer use than non-partcpants by an average score of 1.96% and 1.173%, respectvely. However, although agrcultural extenson partcpaton has an mpact on ratonalzng farmer nutrent management behavor, ths mpact s trval. Based on our study, partcpatng farmers total fertlzer use was reduced by only 1.7% to 3.7%, and ther norganc fertlzer use s reduced by only 1.9% to 3.3%. The mproved percentage of organc fertlzer use and sol-testng-based fertlzer use due to agrcultural extenson partcpaton are also small, rangng from 1.8% to 1.173%. The reasons for ths are as follows: frst, there are many complex barrers to effectve knowledge and technology transfer to farmers n Chna. Most of the more than 2 mllon farmers n Chna are poorly educated, are relatvely old, and operate very small holdngs (an average.1.5 ha of agrcultural land per farm) [11]; second, Chna has lacked a wde-reachng and functonal extenson system. Accordng to one report, there were only 11 techncans provdng servces for 2, farmers n one county; at the townshp level, the extenson personnel, f any, have become fertlzer salesmen or have become engaged n other unrelated actvtes (e.g., famly plannng) [15]; thrd, the extenson system n Chna generally takes a top-down approach, determnng what technologes should be transferred at the central, provncal or county level wthout the suffcent nvolvement of local farmers [13,36]; fourth, ncreasng agrcultural producton and food securty have been the prmary objectves of the agrcultural extenson system. Extenson offcers usually only promote programs ntended to ncrease crop yelds, as do most governmental ncentves [37]. However, snce the end of the 2s, government polces have broadened to nclude, not only food securty, but also envronmental sustanablty. For example, n 25 the Mnstry of Agrculture began a sol- and plant-testng program called the Natonal Sol-Testng and

14 Sustanablty 214, Fertlzer-Recommendaton Program (STFR). By 29, more than 25 countes were nvolved and had receved 1.5 bllon uan of fnancal support from the central government to establsh sol-testng laboratores and demonstrate the use of sol-testng and fertlzer recommendatons for a dverse range of croppng systems. However, agrcultural bureaus lack the knowledge, traned staff, and nstruments (e.g., taxes and subsdes, regulatory authorty, extenson servces, educaton and demonstraton, and polluton standards) to mplement such a polcy wth the concurrent goals of envronmental sustanablty and food securty [11]. Table 4. Estmates of the average treatment effect on treated (ATT). Outcome varable Matchng algorthm Treated Controls ATT T-stat The total amount of fertlzer used (kg ha 1 ) The total amount of norganc fertlzer used (kg ha 1 ) The percentage of organc fertlzer used (%) The percentage of sol-testng-based fertlzer used (%) Kernel-based matchng * Radus calper matchng ** Nearest neghbor matchng ** Kernel-based matchng * Radus calper matchng * Nearest neghbor matchng *** Kernel-based matchng * Radus calper matchng ** Nearest neghbor matchng ** Kernel-based matchng ** Radus calper matchng * Nearest neghbor matchng ** Note: (1) ***, **, * denote statstcal sgnfcance at the 1%, 5% and 1%, respectvely; (2) T-values are calculated usng bootstrap wth 1 repettons. To gan further understandng of the mpact of agrcultural extenson partcpaton on dfferent groups of partcpants, we also examned the dfferental mpact of partcpaton by dvdng households nto dfferent categores based on educaton level, rsk atttude, ntal applcaton level and farm sze. The stratfcaton was made based on matched samples obtaned from the nearest neghbor-matchng estmator. (Results are reported n Tables 5 8.) As observed n Table 5, the mpact of partcpaton on total fertlzer use and norganc fertlzer use decrease wth educatonal level, whle the relatonshp between partcpaton and organc fertlzer use and sol-testng-based fertlzer use are postve. Ths s consstent wth the expectaton that better educated farmers are more adept at acqurng and processng nformaton from varous sources, and then adoptng and mplementng recommendatons and solutons relevant to ther specfc problems [38]. Table 5. Dfferental mpact by educaton level. Category Outcome varable ATT T-stat Low ( 6 years) The total amount of fertlzer used (kg ha 1 ) ** The total amount of norganc fertlzer used (kg ha 1 ) ** The percentage of organc fertlzer used (%) * The percentage of sol-testng-based fertlzer used (%) **

15 Sustanablty 214, Table 5. Cont. Category Outcome varable ATT T-stat Mddle (6 9 years) Hgh (more than 9 years) The total amount of fertlzer used (kg ha 1 ) * The total amount of norganc fertlzer used (kg ha 1 ) ** The percentage of organc fertlzer used (%) ** The percentage of sol-testng-based fertlzer used (%) * The total amount of fertlzer used (kg ha 1 ) *** The total amount of norganc fertlzer used (kg ha 1 ) ** The percentage of organc fertlzer used (%) *** The percentage of sol-testng-based fertlzer used (%) ** Note: (1) ***, **, * denote statstcal sgnfcance at the 1%, 5% and 1%, respectvely; (2) T-values are calculated usng bootstrap wth 1 repettons. Table 6 presents results for the causal mpacts of partcpaton on nutrent management behavor for dfferent categores of rsk atttude. The results generally reveal that the partcpaton of agrcultural extenson exerts a postve and statstcally sgnfcant mpact on nutrent management behavor among the rsk-lovng farmers and rsk-neutralty farmers, but nsgnfcant effects on the rsk-averson farmers. It may be that rsk averson leads farmers to want to avod the possblty of applyng too lttle fertlzer, and are less concerned about applyng too much fertlzer. Gven that farmers n Chna, lke rural households n many developng countres, have lmted access to formal nsurance and credt markets, they are generally rsk-averse and more rsk averson can lead to more ntensve fertlzer use, provdng crop nsurance would be a benefcary polcy to help allevate farmers fertlzer use. Table 6. Dfferental mpact by rsk atttude. Category Outcome varable ATT T-stat The total amount of fertlzer used (kg ha 1 ) Rsk averson The total amount of norganc fertlzer used (kg ha 1 ) The percentage of organc fertlzer used (%) The percentage of sol-testng-based fertlzer used (%) The total amount of fertlzer used (kg ha 1 ) ** Rsk neutralty The total amount of norganc fertlzer used (kg ha 1 ) ** The percentage of organc fertlzer used (%) ** The percentage of sol-testng-based fertlzer used (%) ** The total amount of fertlzer used (kg ha 1 ) *** Rsk lovng The total amount of norganc fertlzer used (kg ha 1 ) ** The percentage of organc fertlzer used (%) ** The percentage of sol-testng-based fertlzer used (%) ** Note: (1) ***, **, * denote statstcal sgnfcance at the 1%, 5% and 1%, respectvely; (2) T-values are calculated usng bootstrap wth 1 repettons. The relatonshp between partcpaton and ntal applcaton level are shown n Table 7. The results generally reveal that wthn the dfferent ntal applcaton level groups, the mpacts of partcpaton on nutrent management behavor are all very trval. The reason for ths result may be that farmers n Chna had been overusng fertlzer n the past and they are becomng too used to relyng on chemcal

16 Sustanablty 214, fertlzer. As a result, farmers become locked nto unsustanable agrcultural systems once fertlzers are adopted. As Tsdell [39] demonstrates, when chemcal agrcultural systems are adopted, agrcultural yelds or returns become dependent on them despte the very hgh costs, and thus mpose an economc barrer to swtchng to organc systems. In short, agrcultural practces tend to become nclned towards such systems once they are adopted despte beng unsustanable. Table 7. Dfferental mpact by ntal applcaton level. Category Outcome varable ATT T-stat The total amount of fertlzer used (kg ha 1 ) ** Low The total amount of norganc fertlzer used (kg ha 1 ) * The percentage of organc fertlzer used (%) ** The percentage of sol-testng-based fertlzer used (%) * The total amount of fertlzer used (kg ha 1 ) ** Mddle The total amount of norganc fertlzer used (kg ha 1 ) * The percentage of organc fertlzer used (%) ** The percentage of sol-testng-based fertlzer used (%) * The total amount of fertlzer used (kg ha 1 ) * Hgh The total amount of norganc fertlzer used (kg ha 1 ) ** The percentage of organc fertlzer used (%) * The percentage of sol-testng-based fertlzer used (%) ** Note: (1) ***, **, * denote statstcal sgnfcance at the 1%, 5% and 1%, respectvely; (2) T-values are calculated usng bootstrap wth 1 repettons. Results from the causal mpacts of partcpaton on nutrent management behavor for dfferent categores of farm sze are presented n Table 8. It s sgnfcant to note that agrcultural extenson partcpaton exerts a postve and statstcally sgnfcant mpact on nutrent management behavor among the medum and large farmers, but nsgnfcant effects on the small-scale farmers. Ths result s consstent wth Zhou et al. [4], who found an nverse relatonshp between farm sze and fertlzer ntensty n a study n Hebe Provnce, ndcatng that smaller farms are more lkely to have hgh ntenstes. The reason for the nsgnfcant effects of the small-scale farmers may be that farmers wth less farm land wll fnd t more dffcult to spread the rsks across famly plots and, thus, could possbly use fertlzer more ntensvely to stablze the crop yelds. Table 8. Dfferental mpact by farm sze. Category Outcome varable ATT T-stat The total amount of fertlzer used (kg ha 1 ) Small The total amount of norganc fertlzer used (kg ha 1 ) The percentage of organc fertlzer used (%) The percentage of sol-testng-based fertlzer used (%) The total amount of fertlzer used (kg ha 1 ) ** Medum The total amount of norganc fertlzer used (kg ha 1 ) ** The percentage of organc fertlzer used (%) ** The percentage of sol-testng-based fertlzer used (%) ***

17 Sustanablty 214, Table 8. Cont. Category Outcome varable ATT T-stat The total amount of fertlzer used (kg ha 1 ) *** Large The total amount of norganc fertlzer used (kg ha 1 ) ** The percentage of organc fertlzer used (%) ** The percentage of sol-testng-based fertlzer used (%) ** Note: (1) ***, **, * denote statstcal sgnfcance at the1%, 5% and 1%, respectvely; (2) T-values are calculated usng bootstrap wth 1 repettons Assessng the Qualty of the Matchng Process The matchng process s checked to determne whether t balances the dstrbuton of the relevant covarates n both the treatment and control groups usng dfferent methods. The results of the covarate-balancng tests are presented n Tables 9 and 1. Frst, the propensty score test ndcates a sgnfcant reducton n bas after matchng, and most mportantly, there are no sgnfcant dfferences n matched non-partcpants and partcpants for any of the covarates (Table 9). Varable Table 9. Tests for selecton bas after matchng. Matched sample Treated (N = 396) Control (N = 854) Bas % Bas % Bas reducton T-test p-value Household characterstcs Age of household head Educaton of household head %.358 Farmng experence of household head %.172 Rsk atttude of household head %.616 Extenson contact %.216 Vllage leader dummy %.238 Household ncome %.228 Off-farm ncome rato %.172 Dstance to the nearest fertlzer shop %.425 Farm characterstcs Farm sze %.16 Sol qualty %.273 Vllage characterstcs Extent of vllage agrcultural extenson partcpaton %.345 Vllage ncome %.772 Vllage off-farm ncome rato %.298 Second, there s a substantal reducton n bas as a consequence of matchng. The estmates ndcate that the standardzed mean bas before matchng s 28.71%, whereas the standardzed mean bas after matchng s reduced to between 6.79% and 13.65%. The percentage reductons n the absolute bas are 65.62%, 76.35% and 52.46% wth KBM, RM and NNM matchng methods, respectvely. Because the

18 Sustanablty 214, percentage reducton n bas by all three matchng methods s greater than 2%, a value recommended by Rosenbaum and Rubn [32] as a suffcently large enough reducton n standardzed bas, t s determned that the matchng substantally reduced the selecton bas. Smlarly, the pseudo-r 2 of the estmated logt model was hgh before matchng and low afterwards for all matchng algorthms. The p-value of the lkelhood rato test was always rejected after matchng, whereas t was never rejected at any sgnfcance level before matchng, suggestng that there s no systematc dfference n the dstrbuton of covarates between partcpants and non-partcpants after matchng (Table 1). Matchng algorthm Table 1. Statstcal tests to evaluate the matchng. Before matchng Mean bas After matchng % bas reducton Pseudo-R 2 p-value of LR Unmatched Matched Unmatched Matched Kernel-based matchng Radus calper matchng Nearest neghbor matchng Testng for Hdden Bas wth Senstvty Analyss Mght endogenety drve our results? As noted above, the effectveness of our matchng estmators n controllng for selecton bas are dependent on the untestable dentfyng assumpton that we are able to observe confoundng varables that smultaneously affect farmers decsons to partcpate n agrcultural extenson programs and to adopt or not to adopt the nutrent management practces that serve as our outcome varables. That s, we essentally assume that endogenety s not a problem [17]. We calculate Rosenbaum bounds to check the senstvty of our results wth the falure of ths assumpton. Gven that the senstvty analyss of nsgnfcant effects s not meanngful, the Rosenbaum bounds were calculated only for the treatment effects that are sgnfcantly dfferent from zero [41]. As Duvendack and Palmer-Jones [42], and DPrete and Gangl [43] noted, f the crtcal value s less than two, one may assert that the lkelhood of such unobserved characterstc s relatvely hgh; therefore, the estmated mpact s rather senstve to the exstence of unobservables. As shown n Table 11, n our results, the lowest crtcal value of γ s 2.8, whereas the largest crtcal value of γ s Therefore, our senstvty tests suggest that even large amounts of unobserved heterogenety would not alter the nference of the estmated effects. In other words, endogenety s unlkely to drve our results. Table 11. Senstvty analyss wth Rosenbaum bounds. Matchng algorthm Kernel-based matchng Radus calper matchng Outcome Varable Crtcal level of hdden bas( ) The total amount of fertlzer used (kg ha 1 ) The total amount of norganc fertlzer used (kg ha 1 ) The percentage of organc fertlzer used (%) The percentage of sol-testng-based fertlzer used (%) The total amount of fertlzer used (kg ha 1 ) The total amount of norganc fertlzer used (kg ha 1 ) The percentage of organc fertlzer used (%) The percentage of sol-testng-based fertlzer used (%)