Empirical Analysis of the Main Factors Influencing Rice Harvest Losses Based on Sampling Survey Data of 10 Provinces in China

Transcription

1 Emprcal Analyss of the Man Factors Influencng Rce Harvest Losses Based on Samplng Survey Data of 10 Provnces n Chna Lnha Wu, 1,2,* Qpeng Hu, 1 Dan Zhu, 3,4 and Janhua Wang 1 1 Food Safety Research Base of Jangsu Provnce, School of Busness, Jangnan Unversty, No.1800, Lhu Road, Bnhu Dstrct, Wux, Jangsu , PR Chna. 2 Synergetc Innovaton Center of Food Safety and Nutrton, Jangnan Unversty, No.1800, Lhu Road, Bnhu Dstrct, Wux, Jangsu , PR Chna. 3 Department of Economcs, School of Dongwu Busness, Soochow Unversty, No. 50, Donghuan Road, Pngjang Dstrct, Suzhou, Jangsu , PR Chna. 4 School of Food Scence and Technology, Jangnan Unversty, No.1800, LhuRoad, Bnhu Dstrct, Wux, Jangsu , PR Chna. Contrbuted Paper prepared for presentaton at the 90th Annual Conference of the Agrcultural Economcs Socety, Unversty of Warwck, England 4-6 Aprl 2016 Copyrght 2016 by Lnha Wu. All rghts reserved. Readers may make verbatm copes of ths document for non-commercal purposes by any means, provded that ths copyrght notce appears on all such copes * Lnha Wu (Address: , Jan Kang Cun, Wux, Jangu, Provnce, Chna. Post Code: ;E-mal: wlh6799@126.com) 28 * Correspondence author: Lnha Wu, Tel: ; fax: ; E-mal: wlh6799@126.com -1-

2 Acknowledgments: Ths paper s a staged achevement of the 2015 specal project on nonproft gran ndustry research, Research on nvestgaton and assessment technques for post-harvest gran losses and waste (Project Number: ), and the project Food safety rsk control n Chna (Project Number: ) run by the outstandng nnovaton team of humantes and socal scences from unverstes n Jangsu Provnce. Of course, the authors take sole responsblty for ther vews Abstract Gran securty should be a prorty for the Chnese government when managng state affars. The total rce producton needs to reman stable at more than 200 mllon tons. However, there have been serous rce harvest losses, especally the harvest stage. In ths study, the meanng of rce harvest losses was defned based on prevous research fndngs on the defnton of gran harvest losses and the realtes n Chna. The current rce harvest losses n dfferent areas n Chna were analyzed based on samplng survey data from 957 farmers n 10 provnces n Chna. On ths bass, the man factors nfluencng rce harvest losses and ther margnal effects were analyzed usng the ordered multnomal logstc model. The survey found that 56.22% of respondents beleved that rce harvest losses were 4% or lower n Chna, though there were dfferences among the provnces. The proporton of famly rce-farmng ncome, sze of producton area, level of mechanzaton, tmely harvest, and operatonal metculousness had negatve effects on rce harvest losses. On the other hand, farmers experence of employment as mgrant workers had a postve effect on rce harvest losses. In addton, bad weather and short handedness durng harvest sgnfcantly ncreased rce harvest losses. Keywords: rce, harvest losses, ordered multnomal logstc model, margnal effect JEL code: Q18-2 -

3 Introducton For 11 years n a row, ran output ncreased n Chna and was estmated to be mllon tons n 2014 *. Ths was a record hgh yeld, a 0.9% (5.16 mllon tons) ncrease over Another hstorc breakthrough occurred n 2014 when Chna's gran output surpassed the prevously hgh level of 600 bllon klograms. The contnuous ncrease n gran output of Chna not only plays an mportant role n ts own smooth economcal operaton, but also contrbutes to world food securty. However, studes ndcate that Chna's gran supply wll face serous challenges due to tght resources, frequent extreme weather events, populaton growth, rgd growth of gran demand for feed and processng use, and uncertanty n the world gran market (Aulakh and Regm, 2013; L, 2014). The Food and Agrculture Organzaton of the Unted Natons (FAO) estmated that the average annual growth rates of gran producton and supply/consumer demand would be 1.7% and 1.9%, respectvely, between 2013 and 2022 n Chna representng a gradually wdenng gap between gran supply and demand (Lu et al., 2013). The Development Research Center of the State Councl of Chna reported that Chna s gran mports exceeded 90 mllon tons n 2014, accountng for 15% of domestc gran producton. Ths ncluded mllon tons of cereal mports (an ncrease of 33.8% over 2013), and 71.4 mllon tons of soybean mports (an ncrease of 12.7% over 2013). Gran losses and waste are serous outstandng problems n Chna's gran securty. However, n terms of gran securty, Chna has placed great emphass on pre-harvest nput and management of producton factors, whle payng serously nsuffcent attenton to reducng post-harvest gran losses and waste. Chna s annual post-harvest gran losses are estmated to be 50 bllon klograms. Ths amount s equvalent to the gran output of 200 mllon mu of * Natonal Bureau of Statstcs of the People's Republc of Chna: Announcement on gran output n 2014 by the Bureau of Statstcs, Natonal Bureau of Statstcs of the People's Republc of Chna: Announcement on gran output n 2014 by the Bureau of Statstcs, Ths gran producton and consumpton data n Chna and other data presented n ths paper refer to manland Chna, excludng Tawan, Hong Kong, and Macao. Chna Agrcultural Outlook Prospect Report ( ),

4 arable land. In addton, Chna s post-harvest gran loss rate * s approxmately 10%, much hgher than the world average of 3% to 5% and that of developed countres (Guo et al., 2014). Post-harvest gran losses represent a waste of manpower, fresh water, arable land, fertlzers, and other resources expended durng gran producton (Rdoutt et al., 2010; Gustavsson et al., 2011). In addton, greenhouse gas emssons, arsng from prevous producton and subsequent waste treatment of lost gran, can exert tremendous pressure on the envronment (WRAP, 2011; Dorward, 2012; Kummu et al., 2012). The post-harvest system of gran has a rch meanng. The levels and causes of post-harvest gran losses vary wth the dfferent post-harvest stages and gran varetes. In Chna, the post-harvest perod of gran can be generally dvded nto seven stages, harvest, transport, dryng, storage, processng, dstrbuton, and consumpton (Cao and Jang, 1999). As the frst stage of the post-harvest perod, harvest has a specal status n reducng post-harvest gran losses. Rce, for example, s one of the most mportant gran crops n Chna, wth an output accountng for approxmately 33.83% of the domestc gran output n Along wth the urbanzaton process, a large number of young farmers have mgrated to the ctes. As a result, women and older male farmers have become the man labor force for rce producton. Ths stuaton has not only led to extensve ntal post-harvest processng by tradtonal small-scale farmers, but also has further ncreased rce harvest losses (Zhang et al., 2009). Therefore, an emprcal analyss of the key nodes and man nfluencng factors of rce harvest losses can provde a reference for the government for gudng farmers n controllng post-harvest rce losses Lterature revew and concept defnton Post-harvest food losses can be further dvded nto food losses and food waste. Based on the dfferent factors causng food losses, Aulakh and Regm (2013) defned those n two ways. * Post-harvest gran loss rate s the rato of post-harvest gran losses to total gran output. Source: The webste of Natonal Bureau of Statstcs of Chna (

5 Those based on objectve factors (such as natural condtons and techncal equpment) were defned as as food losses, and those caused by decson-makng mstakes of the supply chan players as food waste. Prefer et al. (2013) regarded food waste as a subset of food losses. Food losses nclude all food that runs off the supply chan, whle food waste refers a partcular part of food losses caused by human factors. Zhang et al. (1998) and Song et al. (2015) made no dstncton between losses and waste n ther analyses of post-harvest food losses n Chna. They beleved that waste fell nto the moral category, rather than an economc or techncal category, and was just a judgment on post-harvest food losses. Based on the exstng lterature, the authors beleved that food losses referred to a reducton n the quantty and qualty of edble food n the post-harvest supply chan, and that losses caused by human factors were called food waste *. In the early 1990s, Zhejang Academy of Agrcultural Scences (1991) subdvded the post-harvest gran losses of Chna nto nne sub-systems,.e., harvest, threshng, transport, cleanng, dryng, storage, processng, dstrbuton, and consumpton. Teshome et al. (1999) dvded the post-harvest gran losses of Afrcan countres nto seven stages, ncludng harvest, transport, dryng, threshng, storage, processng, and consumpton. However, most researchers dvded the post-harvest gran losses based on the condtons of developng countres wth a low mechanzaton level. At present, the mechanzaton level n rce harvestng has been ncreasng rapdly n Chna. Accordng to statstcs, 73.59% of rce was harvested by machne n 2012 throughout Chna (Lu et al., 2014). In addton, the area of rce harvested va the combne harvestng method accounts for an ncreasng proporton of rce plantng area. Unlke tradtonal segment harvestng, reapng and threshng are completed n one operaton through the combne harvestng method. Hence, t s dffcult to make a strct dstncton between the actual losses of reapng and threshng n practce. Therefore, rce harvest losses can be defned as a reducton n quantty or qualty of rce due to natural condtons, techncal * As there are no unform defntons for food losses and food waste, and prevous studes dd not make a strct dstncton between them due to practcal factors, gran losses dscussed n ths paper cover gran waste, n order to mprove the comparablty between Chna's gran harvest loss data and those n other countres

6 equpment, management sklls, and farmers decson-makng from reapng and threshng to baggng (loadng). The man factors nfluencng rce harvest losses have been analyzed from dfferent angles. Tmely harvest s crucal to reducng the loss of rce quantty and qualty durng a harvest. It has been noted that the perod from 10 to 15 days after physcal maturty s the best tme to harvest rce (Akar et al., 2004). Lantn (1999) suggested that premature harvestng led to ncluson of a large amount of mmature rce wth a hgh mosture content, whle delayed harvestng exposed mature rce to rsks of beng attacked by nsects, brds, anmals, and mcroorgansms; tmely harvest not only reduces the mpact of bad weather on output, but also decreases the crack rato *. It was also demonstrated that harvestng too early led to a lower gran weght. Tmely harvest based on rce maturty and local clmatc condtons can not only mprove rce yeld, but also provde a hgher mlled rce rate and a better cookng qualty (Chen et al., 2006). Weather condtons durng harvest also have a close relatonshp wth rce harvest losses. Akar et al. (2004) ndcated that rany weather durng harvest would exacerbate pest problems and premature senescence, resultng n a decreased maturaton rate, and thus yeld losses. Moreover, prolonged exposure of mature rce to hgh temperatures and humd envronments would ncrease pershablty, resultng n reduced yeld and qualty of rce (World Bank et al., 2011). Contnuous rany weather would not only lead to a sharp drop n the bologcal producton of rce, but also result n mldew of unhusked rce spread on the ground due to untmely sun-dryng (Fe et al., 2013). Furthermore, stormy weather wll ncrease the lodgng area of rce and harvest dffculty, resultng n shatterng and pre-harvest sproutng durng reapng and threshng, thus ncreasng harvest losses (Zhang et al., 2013). Rce harvest losses are drectly related to feld management as well as the metculousness of farmers harvestng operatons. World Bank et al. (2011) found that pre-harvest management and decsons, such as plantng densty, feld management (weedng, * The occurrence of transverse cracks n rce gran s termed crackng, whch not only reduces gran qualty, but also ncreases the broken rce rate n post-harvest processng

7 dsnsecton, fertlzaton, etc.), and tmely harvestng, had an mpact on fnal rce harvest losses. In addton, Hodges and Martme (2012) beleved that non-metculous harvestng operatons would sgnfcantly ncrease the quantty loss of rce durng harvest, and that random placement of rce ears would make the rce more vulnerable to mcroorgansms, thus causng a greater qualty loss. Appah et al. (2011) reached a smlar concluson that rce harvest losses n dfferent plots were closely related to feld weed control, farmers harvest experence and sklls, and the metculousness of harvestng operatons. Harvestng methods * also nfluence rce harvest losses. Lantn (1999) ndcated that, compared to combne harvestng, segment harvestng nvolved more stages, and each stage nevtably caused quantty and qualty losses of rce. However, Akar et al. (2004) ponted out that harvesters mght substantally ncrease the operaton speed of machnes to ncrease the harvest area per unt tme durng combne harvestng, thus ncreasng the harvest loss rate. L et al. (1991) suggested that due to unreaped rce and harvest shatterng losses, rce losses durng combne harvestng were much greater than those durng segment harvestng. Feng and Sun (2014) also beleved that the effect of combne harvestng was susceptble to mechancal propertes and operator sklls, whle segment harvestng allowed more metculous harvest of lodged rce and provded a threshng effcency of 99.5%, as well as comprehensve loss rates of 2% or lower. In addton, the causes of rce harvest losses have also been analyzed from the perspectve of economc and socal development. Grethe et al. (2011) noted that soco-economc factors and agrcultural technology were the man causes of rce harvest losses n developng countres. Buchner et al. (2012) found that rce losses at the front end of the post-harvest supply chan were sgnfcantly hgher n developng countres than n developed countres, and that the man reason was related to the fact that small-scale labor-ntensve agrcultural producton n developng countres was neffcent due to the lmtaton of captal, technology, * There are two man harvestng methods: combne harvestng and segment harvestng. The latter ncludes reapng, bundlng, stackng, pckng, threshng, and cleanng

8 and management. Prefer et al. (2013) suggested that rce harvest losses were ncreased by farmers poor harvestng operaton sklls, nsuffcent government management, and a lack of relevant polces. Lu (2014) found that nadequate nfrastructure, poor awareness of gran savng and loss reducton, lag n harvestng operaton technology, and small-scale scattered producton were common factors affectng post-harvest rce losses n Chna and other developng countres. The exstng research results serve as an mportant reference for ths study. After summarzng and reflectng on the prevous research results, the authors found two sgnfcant defcences n exstng studes. Frst, most exstng studes focus on the assessment of rce losses n all post-harvest stages, whle rce losses n a partcular stage and the nfluencng factors have rarely been analyzed usng quanttatve tools. Second, most exstng studes focus on post-harvest rce losses n backward developng countres, whle rce harvest losses n Chna durng ts agrcultural transton to modernzaton have rarely been analyzed. To ths end, on the bass of the exstng lterature, the man factors nfluencng rce harvest losses and ther margnal effects were analyzed usng the ordered multnomal logstc model based on samplng survey data from 957 farmers n 10 provnces n Chna Survey desgn and sample analyss 3.1. Survey desgn In ths study, data were collected usng a mult-stage samplng method from 10 provnces/regons n Chna, ncludng Helongjang, Jangsu, Zhejang, Guangdong, Hube, Hunan, Anhu, Jangx, Schuan, and Guangx. Most of these are major rce producng provnces n Chna. The rce producton of the 10 provnces/regons accounted for 78.96% of the natonal rce producton n 2013 *. The samplng area not only nvolves four major regons of Chna,.e., the eastern (Jangsu, Zhejang, and Guangdong), central (Hube, Hunan, Anhu, * Calculated based on the relevant data from the Chna Statstcal earbook 2014 (Natonal Bureau of Statstcs of Chna, ed., Chna Statstcs Press, 2014)

9 and Jangx), western (Schuan, and Guangx), and northeast regons (Helongjang), but also stretches across the fve major rce areas of Chna,.e., the south, central, north, southwest, and northeast regons of Chna *. Therefore, the samplng area s hghly representatve n terms of spatal dstrbuton. On ths bass, fve countes were selected from each of these provnces/regons accordng to rce harvestng methods, topographc features, rce plantng proporton, and rural resdents ncome. Fve admnstratve vllages were then randomly selected from each of the selected countes. In the actual survey, house numbers were randomly selected, and then correspondng farmer households were vsted by traned nvestgators. The questonnare was answered drectly by the respondents. The rce harvest loss rate was dvded nto sx levels, lower than 3%, 3%-4%, 4%-5%, 5%-6%, 6%-7%, and hgher than 7% based on the exstng research results, as well as farmers feedback from the pre-survey. A total of 1000 copes of the questonnare was dstrbuted n the above 10 provnces/regons. After careful screenng, 957 copes of vald questonnares were collected, representng a response rate of 95.7%. The survey was carred out n July and August, Sample analyss Demographcs of respondents Table 1 lsts the basc demographcs of the respondents. Of the 957 respondents, men comprsed a slghtly hgher proporton (54.96%) than women. Most respondents were aged "46-55 years" and "56-65 years", accountng for 41.27% and 29.68% of the total sample, respectvely. In terms of educaton, famly sze, and annual household ncome, most respondents fell nto the category of mddle and hgh school, 3-4, and 60,000 yuan and less, respectvely, accountng for 65.20%, 67.71%, and 69.80% of the total sample. In addton, 47.23% of respondents had experence of workng n the cty. * Chna generally ncludes sx major rce areas,.e., the south, central, north, southwest, and northeast sngle croppng rce areas, and the northwest ard area. Rce harvest loss rate = rce harvest losses per mu/rce yeld per mu. A relatvely consstent concluson on rce harvest losses n exstng lterature s that rce harvest loss rates generally range from 3% to 7%

10 Overall estmates of rce harvest loss rate As shown n Tables 2 and 3, 26.96% and 29.26% of respondents beleved that the rce harvest loss rate was lower than 3% and 3%-4%, respectvely, 18.29% and 13.06% suggested that t was 4%-5% and 5%-6%, respectvely, and 5.64% and 6.79% estmated that t was 6%-7% and hgher than 7%, respectvely. As to the man cause of rce harvest losses, 45.46% of respondents attrbuted the losses to changeable weather, whle 19.65%, 18.18%, and 10.55% suggested that t was due to outdated equpment, dseases and pests, and shatterng durng harvest, respectvely Estmates of rce harvest loss rates n dfferent regons As shown n Tables 2 and 3, respondents n dfferent regons had dfferent estmates of rce harvest loss rates. In the survey sample, 50.13% of respondents from the eastern regon and 56.71% from the central regon beleved that the rce harvest loss rates n ther regons were lower than 3% or 3%-4%. In the western regon, 61.92% of respondents estmated the rce harvest loss rates n ther regon to be 3%-4% and 4%-5%, and 64.93% of respondents from the northeast regon estmated the rce harvest loss rate to be lower than 3%. In addton, the respondents beleved that changeable weather was a major factor for rce harvest losses n all regons, followed by dseases and pests and outdated equpment Theoretcal model and varable settngs 4.1. Theoretcal model of rce harvest losses Intutvely, farmers are not pleased to see losses. However, as an economc person, a farmer ams to maxmze hs/her net ncome. The reducton of rce harvest losses wll nevtably ncrease costs. If the ncrease n cost exceeds the ncrease n ncome, the net ncome of the farmer wll be reduced. The net ncome of the farmer can be maxmzed only when the margnal cost of reducng harvest losses equals the margnal ncome. In ths regard, t s 251 assumed that MC s the ncrease n subjectve cost for farmer to reduce harvest losses

11 The judgment of subjectve cost s affected by many factors,.e., MC X (1) 254 In equaton (1), X s the vector of factors affectng the subjectve cost judgment of 255 farmer, s the vector of the coeffcent to be estmated, and s an ndependent and dentcally dstrbuted random dsturbance. As the farmer ams to maxmze net ncome, the ncrease n cost for the farmer to reduce harvest losses should theoretcally equal the ncrease 258 n ncome. Therefore, snce t s dffcult to observe subjectve cost, rce loss was selected as a dsplay varable and takes on the values n [1, n ]. =1 represents lower than 3%, =2 represents 3%-4%, =3 represents 4%-5%, =4 represents 5%-6%, represents 6%-7%, and =6 represents hgher than 7%. A larger value of =5 ndcates a greater loss. The followng classfcaton framework was constructed: 1, MC 1 2, 1 MC 2 n, n MC (2) 264 In equaton (2), n s the crtcal pont for changes n the farmer s subjectve cost and satsfes 1 2 n. As the ordered multnomal logstc model does not requre a normal dstrbuton or equal varances, t can be used to assess the relatonshp between multnomal dependent varables and ther nfluencng factors, and thus to quanttatvely assess the factors nfluencng dfferent levels of rce harvest losses. In general, the dstrbuton functon of s assumed to be Fx ( ), and the probablty for the dependent varable take each value can then be calculated as below: to

12 p( 1)= F( 1 X ) p( 2)= F( 2 X ) F( 1 X ) p( n)=1 F( n X ) Snce follows a logstc dstrbuton, then: p( 0) F( U 0) F( X ) F( X ) exp( X 1) 1 exp( X ) 4.2. Composton and varable selecton for rce harvest losses 1 1 (3) (4) Rce harvest losses can be subdvded nto harvest shatterng loss, unreaped loss, wndrowng loss, unthreshed loss, spatter loss, and entranment loss (L, etc., 1991; Aulakh and Regm, 2013). Among them, harvest shatterng loss, unreaped loss, unthreshed loss, 279 spatter loss, and entranment loss consst mostly of gran weght losses (volume and quantty losses), and are predomnantly affected by the mature perod of rce, lodgng, feld sze and shape, harvestng methods, manpower adequacy, and harvestng technques. The qualty loss (reduced nutrton and ncreased deteroraton) durng wndrowng s closely related wth weather condtons durng harvest and wndrowng duraton (see Fgure 1). In fact, there are many factors nfluencng rce harvest losses, and ntensve studes have been carred out from several dfferent perspectves. Table 4 provdes a prelmnary summary of the man conclusons of these studes. Based on prevous research fndngs, factors affectng rce harvest losses are summarzed as 15 varables n three categores, demographcs, producton characterstcs, and harvestng operaton characterstcs, as shown n Table Model estmaton results and dscusson Estmaton results

13 In ths study, factors affectng rce harvest losses were estmated usng SPSS The model estmaton results are shown n Table 6. Eght ndependent varables, ncludng employment as mgrant workers, proporton of famly busness ncome, plantng scale, level of mechanzaton, tmely harvest, manpower adequacy, operatonal metculousness, and harvest weather, passed the sgnfcance test. The producton characterstcs and harvestng operaton characterstcs had a greater nfluence on rce harvest losses Interpretaton of estmaton results Influence of demographcs The estmated coeffcent of employment as mgrant workers was 0.386, whch was sgnfcant at the level, ndcatng that the employment of respondents as mgrant workers ncreased rce harvest losses. Ths s consstent wth the argument of L (2010) that the transfer of rural labor force to ctes and towns has exacerbated the extensve land management. However, ths study argues that the ncrease of rce harvest losses s actually a result of the ncreased opportunty cost of rce cultvaton due to the employment of farmers as mgrant workers. When the ncome obtaned by reducng harvest losses s nsuffcent to make up for the explct and opportunty costs, the wllngness of farmers to reduce harvest losses wll be reduced. In addton, the estmated coeffcent of the varable proporton of famly busness ncome was , whch was sgnfcant at the level, ndcatng that the rce harvest losses were sgnfcantly reduced wth the ncrease n the proporton of famly busness ncome. Ths may be because the hgher dependency of famly ncome on rce, the greater the cost of rce harvest losses for farmers, and the hgher ther wllngness to control harvest losses Influence of producton characterstcs The estmated coeffcent of plantng scale was , whch was sgnfcant at the level, ndcatng that rce harvest losses were reduced by the ncrease of plantng scale. Ths s n agreement wth the conclusons of Basavaraja et al. (2007). The possble reason for ths s

14 that large-scale rce farmers can also effectvely reduce the cost of rce harvest losses n the entre producton process by usng advantages n captal avalablty and advanced equpment. The analyss of large-scale rce cultvaton by Huang et al. (2014) ndcated that large-scale agrcultural operatons could reduce land fragmentaton and help ncrease post-harvest workng effcency, thus reducng harvest losses. The estmated coeffcent of mechanzaton level was , and was sgnfcant at the level, ndcatng that rce harvest losses were effectvely reduced by the ncrease n harvestng mechanzaton level. Ths s smlar to the conclusons of Buchner et al. (2012). Ths may be because, wth the contnuous decrease of mechanzed harvestng costs and rapd mprovement of technology, farmers can reduce harvest losses at a lower cost by usng mechancal harvestng equpment Influence of harvestng operaton characterstcs The estmated coeffcents of metculousness level 3 and tmely harvest were and , respectvely, and both were sgnfcant at the level, ndcatng rce harvest losses were reduced by tmely harvest and metculous harvestng operatons. The wllngness of farmers to reduce rce harvest losses depends on ther subjectve judgment of the costs of loss reducton and the resultng ncome. The hgher the farmers subjectve judgment of ncome from reducng rce harvest losses, the hgher ther motvaton to perform tmely harvest and mprove the metculousness of harvestng operatons, and the smaller the rce harvest losses. In addton, the estmated coeffcent of harvest weather 1 was 1.612, and t was sgnfcant at the level, ndcatng that adverse harvest weather sgnfcantly ncreased rce harvest losses. Ths s n accordance wth the fndngs of Abass et al. (2014). Ths may be because adverse harvest weather ncreases the rce lodgng area, and thereby ncreases the harvestng dffculty; when the ncome obtaned by reducng harvest losses s nsuffcent to make up for the costs, farmers wll gnore such losses. In addton, the model estmaton results of ths study reveal that, although nadequate manpower may ncrease rce harvest losses, adequate manpower does not effectvely reduce the losses. The possble reason for ths s that the margnal effect of unt manpower on reducng rce harvest losses wll

15 347 be decreased wth the ncrease n manpower Margnal effect analyss Although the estmated coeffcents n Table 5 reflect the nfluences of dfferent factors on rce harvest losses, they cannot accurately reflect the degree of nfluence of these factors. To ths end, margnal effects of nfluencng factors were calculated usng crtcal pont estmates and related estmated coeffcents to perform further analyss. Snce the method of calculatng margnal effects for conventonal contnuous varables does not apply to dummy varables, all other varables were assumed to be zero n the calculaton of margnal effect of a sngle dummy varable (see Greene, 2003), and the followng equaton was used (Newell and Anderson, 2003): E xk 1 E xk 0 F cn xk F cn (5) In equaton (5), Table 7. cn s the crtcal pont, and n = 1, 2, 3, 4, and 5. The results are shown n The followng fndngs were obtaned by analyzng the margnal effects of the varables n Table 7. Frst, the margnal effect of employment as mgrant workers was less than zero when = 0 and = 1. Ths ndcated that ceters parbus, there was a hgher probablty for the rce harvest loss rate to be hgher than 4% f farmers had experence as mgrant workers. The margnal effects of metculousness level 1 and metculousness level 3 were also less than 367 zero when = 0 and = 1, whle that of metculousness level 4 was greater than zero when = 0. Ths ndcated that farmers operatonal metculousness dd not sgnfcantly reduce the rce harvest losses, and there was stll a hgh probablty for the rce harvest loss rate to be hgher than 4%. Only wth a hgh rate of operatonal metculousness, can the probablty for a rce harvest loss rate hgher than 3% be sgnfcantly reduced

16 Second, the margnal effects of proporton of famly busness ncome, plantng scale, level of mechanzaton, tmely harvest, harvest weather 1, harvest weather 2, and 374 manpower adequacy 1, were greater than zero when = 0. Ths ndcated that ceters parbus, there was a hgher probablty for the rce harvest loss rate to be lower than 3% for farmers wth a hgh proporton of famly busness ncome n total ncome, large rce plantng scale, tmely harvest, and a hgh level of mechanzaton. Moreover, t s more lkely to keep a rce harvest loss rate lower than 3% wth favorable harvest weather condtons and approprately tght manpower, compared wth adverse harvest weather condtons and shortage of manpower Man conclusons As the frst stage of the post-harvest rce processng system, harvest s related to the post-harvest quantty and qualty of rce. The current rce harvest losses n dfferent regons n Chna, as well as the man nfluencng factors, were analyzed usng an ordered multnomal logstc model based on samplng survey data from 957 farmers n 10 provnces/regons n Chna. Survey results revealed that, compared wth the cereal harvest loss rate of around 2% n Amercan and European countres, the rce harvest loss rate n Chna was not only hgher, but also had regonal dfferences. Accordng to statstcs, the average rce harvest loss rate n Chna was 4% or lower. The rce harvest loss rate n the eastern and central regons was close to the natonal average; that n the western regon was generally 3% to 5%, whch was hgher than the natonal average, and that n the northeast was generally 3% or lower, representng a lower than average level. Further analyss revealed that the proporton of famly busness ncome, rce plantng scale, tmely harvest, level of mechanzaton, and operatonal metculousness had a negatve mpact on rce harvest losses, whle employment as mgrant workers had a postve mpact. Moreover, although nadequate manpower and adverse weather condtons ncreased rce harvest losses, adequate manpower and favorable weather

17 condtons had no sgnfcant mpact on the reducton of rce harvest losses. Ths study has some notable lmtatons. For example, qualty loss, as part of rce harvest losses, was not fully nvestgated, as t s dffcult to measure by survey. In addton, rce harvest losses of large-scale rce farmers, famly farms, and specalzed cooperatves for rce producton were not nvestgated, as ths survey focused on ordnary farmers. These wll be mportant ssues to be nvestgated n follow-up research

18 References Abass, A. B., Ndunguru, G., Mamro, P., Alenkhe, B., Mlng, N. and Bekunda, M. Post-harvest Food Losses n a Maze-based Farmng System of Sem-ard Savannah Area of Tanzana, Journal of Stored Products Research, Vol. 57, (2014) pp Akar, T., Avc, M. and Dusuncel, F. Berley: Post-harvestng operatons (Food and Agrculture Organzaton of the Unted Natons, Turkey, 2004). Appah, F., Gusse, R. and Dartey, P. K. Post-harvest Losses of Rce from Harvestng to Mllng n Ghana, Journal of Stored Products and Postharvest Research, Vol. 2, (2011) pp Aulakh, J. and Regm, A. Post-harvest Food Losses Estmaton-Development of Consstent Methodology (paper submtted to Agrcultural & Appled Economcs Assocaton s 2013 AAEA & CAES Jont Annual Meetng, Washngton DC, 2013). Basavaraja, H., Mahajanashett, S. B. and Udagatt, N. C. Economc Analyss of Post-harvest Losses n Food Grans n Inda: A Case Study of Karnataka, Agrcultural Economcs Research Revew, Vol. 20 (2007) pp Bokusheva, R., Fnger, R., Fschler, M., Berln, R., Marín,. and Pérez, F. Factors Determnng the Adopton and Impact of a Postharvest Storage Technology (IAAE, Brazl, 2012). Buchner, B., Fschler, C., Gustafson, E., Relly, J., Rccard, G., Rcord, C. and Verones, U. Food Waste: Causes, Impacts and Proposals (Barlla Center for Food & Nutrton, 2012). Cao, B. M. and Jang, D. B. Post-harvest gran losses n Jangsu Provnce, Chna, and ther causes and countermeasures, Journal of Nanjng Unversty of Economcs, Vol. 1 (1999) pp Chen, W. J., Zhou, Q. F. and Huang, J. J. Estmatng pgment contents n leaves and pancles of rce after mlky rpenng by hyperspectral vegetaton ndces, Chnese Journal of Rce Scence, Vol. 20 (2006) pp Dorward, L. J. Where Are the Best Opportuntes for Reducng Greenhouse Gas Emssons

19 n the Food System (ncludng the food chan)? A comment, Food Polcy, Vol. 37 (2012) pp Fe,. C., Chen, L., Peng, G. Z., Rong, R. and Zhu, K. The characterstcs of autumn contnuous ran and rce harvest weather sutablty n Schuan Provnce, Chna, Jangsu Agrcultural Scences, Vol. 41 (2013) pp Feng, G. and Sun, C. C. Segment harvestng technque ncludng reapng, sun-dryng and pckng, Rural Scence & Technology, Vol. 04 (2014) pp Greene, W. H. Econometrc Analyss (Prentce Hall, New Jersey, 2003). Grethe, H., Dembele, A. and Duman, N. How to Feed the World's Growng Bllons: Understandng FAO World Food Projectons and Ther Implcatons (Study for WWF Deutschland and the Henrch-Böll-Stftung, Berln, 2011). Guo,. Z., Chen, R. and Guo, J. L. Analyss of gran losses n the whole ndustry chan from farm to table n Chna and countermeasures, Agrcultural Economy, Vol. 01 (2014) pp Gustavsson, J., Cederberg, C., Sonesson, U., Otterdjk, R. V. and Meybeck, A. Global Food Losses and Food Waste: Extent, Causes and Preventon (Food and Agrculture Organzaton of the Unted Natons, Rome, 2011). Halloran, A., Clement, J., Kornum, N., Bucataru, C. and Magd, J. Addressng Food Waste Reducton n Denmark, Food Polcy, Vol. 49 (2014) pp Hodges, R. J., Buzby, J. C. and Bennett, B. Postharvest Losses and Waste n Developed and Less Developed Countres: Opportuntes to Improve Resource Use, The Journal of Agrcultural Scence, Vol.149 (2011) pp Hodges, R. J. and Martme, C. Post-harvest Weght Losses of Cereal Grans n Sub-Saharan Afrca (Natural Resources Insttute, Unversty of Greenwch, 2012). Huang,. X., Zhang, H.., L, W.. and Lu,Q. Rural land transfer: survey and reflectons, Issues n Agrcultural Economy, Vol. 32 (2015) pp.4-9. Huang, Z. H., Wang, J.. and Chen, G. mpacts of off-farm employment, land transfer, and

20 land fragmentaton on techncal effcency of rce farmers, Chnese Rural Economy, Vol.11 (2014) pp Kummu, M., Moel, H. D., Porkka, M., Sebertd, S., Varsa, O. and Wardb, P. J. Lost food, Wasted Resources: Global Food Supply Chan Losses and Ther Impacts on Fresh Water, Cropland, and Fertlser Use, Scence of the Total Envronment, Vol. 438 (2012) pp Lantn, R. Rce: Post-harvestng operatons (Internatonal Rce Research Insttute, Phlppnes, 1999). L, G. X. Analyss of gran producton capacty and natonal food securty assurance of Chna n 2020, Chnese Rural Economy, Vol. 5 (2014) pp L. W. Peasants wllngness to rce plantng and ts nfluental factors: Based on data of Zxng, Hunan, Journal of Hunan Agrcultural Unversty (Socal Scence), Vol. 11 (2010) pp L, Z. F., Xa, P. K., Wang, Z. H., Wan, S.. and He,. Composton of post-harvest gran losses and ther preventon measures, Journal of Zhejang Agrcultural Unversty, Vol. 4 (1991) pp Lu, G. Food Losses and Food Waste n Chna: A Frst Estmate, OECD Food, Agrculture and Fsheres Papers, Vol. 66 (2014) pp Lu, H., Chen, W. L., Zou, S.. and Zhou, H. L. Applcaton status and development trend of Rce Harvestng machnery n South Chna, Modern Agrcultural Equpments, Vol. 1 (2014) pp Lu, J., Folberth, C., ang, H., Röckström, J., Abbaspour, K. and Alexander, J. B. A Global and Spatally Explct Assessment of Clmate Change Impacts on Crop Producton and Consumptve Water Use. Plus One, Vol. 8 (2013) pp Newell, R. G. and Anderson, S. Smplfed Margnal Effects n Dscrete Choce Models, Economcs Letters, Vol. 81 (2003) pp Parftt, J., Barthel, M. and Macnaughton, S. Food Waste wthn Food Supply Chans:

21 Quantfcaton and Potental for Change to 2050, Phlosophcal Transactons, Vol. 365 (2010) pp Prefer, C., Jörssen, J. and Bräutgam, K. R. Technology Optons for Feedng 10 Bllon People. Optons for Cuttng Food Waste (Insttute for Technology Assessment and Systems Analyss, 2013). Rdoutt, B. G., Julano, P., Sanguansr, P. and Sellahewa, J. The Water Footprnt of Food Waste: Case Study of Fresh Mango n Australa, Journal of Cleaner Producton, Vol. 18 (2010) pp Song, H.., Zhang, H. C., L, J. and Wu, Z. G. Study of post-harvest gran losses n Chna: The case of wheat n Henan Provnce, Journal of Huazhong Agrcultural Unversty (Socal Scence), Vol. 4 (2015) pp.1-6. Teshome, A., Kenneth, T. J., Bernard, B., Lenore, F. and Lambert, D. H. Tradtonal Farmers Knowledge of Sorghum Landrace Storablty n Ethopa, Economc Botany, Vol. 53 (1999) pp World Bank, FAO and NRI. Mssng Food: The Case of Post-harvest Gran Losses n Sub-Saharan Afrca (World Bank, Washngton DC, 2011). WRAP. New Estmates for Household Food and Drnk Waste n the UK (Waste and Resources Acton Program, Bradbury, UK, 2011). Zhan,. E., Chu, Q. Q. and Wang, H. G. Gran securty stuaton and countermeasures durng urbanzaton n Chna, Research of Agrcultural Modernzaton, Vol. 30 (2009) pp Zhang, H.., Lu,. M. and Chen, X. L. Causes and preventon measures of hgh-yeldng rce lodgng, Nongmn Zhfuzhyou uekan, Vol. 6 (2013) pp Zhang, J., Fu, Z. T. and L, D. L. Formaton of gran losses and current stuaton of gran losses n Chna, Journal of Chna Agrcultural Unversty (Socal Scence), Vol. 4 (1998) pp Zhejang Academy of Agrcultural Scences. Gran Post-producton System Analyss n Chna:

22 Termnal Report (Zhejang Academy of Agrcultural Scences, Postharvest Development Research Center, Hangzhou, Chna, 1991)

23 Table 1 The basc demographcs of respondents Characterstc Classfcaton n % Gender Male Female years years Age years years years Prmary or lower Junor hgh school or lower Educaton Hgh school or lower ( ncludng vocatonal hgh school) College and above members Famly sze 3 members members members yuan Annual household yuan ncome yuan yuan Experence of workng es n the cty No

24 Table 2 Percentage of respondents selectng each rce harvest loss rate range(%) Harvest loss Lower than Hgher than rate 3%-4% 4%-5% 5%-6% 6%-7% 3% 7% Regon Natonwde Eastern regon Central regon Western regon Northeast regon

25 Table 3 Percentage of respondents selectng each rce harvest loss nfluencng factor(%) Influencng Changeable Outdated Inadequate Dseases factor Weather Equpment Manpower and Pests Regon Shatterng Others Natonwde Eastern regon Central regon Western regon Northeast regon Note: The percentage data for the entre country were calculated by dvdng the total sample sze by the frequency of each opton. The percentage data for each regon were calculated by dvdng the sample sze n that regon by the frequency of each opton

26 Table 4 Typcal studes on post-harvest losses and ther nfluencng factors Lterature Country ( regon) Varety Influencng Factors Akar et al.(2004) Afrca Rce Harvest weather, rce varetes wth dfferent mature perods, and varety maturty Basavaraja et al. (2007) Inda Rce Respondents age and educaton, rce plantng area, and number of famly laborers Zhang et al.(2009) Chna Rce Sources of household ncome, and proporton of gran ncome n total household ncome Hodges et al. (2011) Southeast Respondents educaton, and gran-savng and Cereals Asa loss-reducng awareness Parftt et al. (2010) EU Cereals Rce market prce, farmers' sklls, power grd nstallaton, and rrgaton condtons L we(2010) Chna Rce Employment of famly member(s) as mgrant workers or not, and rce harvestng methods Respondents gender, plantng years, famly age Appah et al. (2011) Ghana Rce structure, and level of mechanzaton of rce harvestng Gustavsson et al. Household ncome, power grd nstallaton and road Afrca Cereals (2011) facltes, and gran-savng awareness Farmers harvestng sklls, gran harvestng Bokusheva et al. (2012) Central Cereals equpment, and metculousness of farmers harvestng Amerca operatons Respondents age and educaton, famly sze, annual Prefer et al. (2013) EU Cereals household ncome, farmers sklls, clmatc condtons, rural nfrastructure Aulakh and Regm Mechanzaton level of cereal harvestng, clmatc Afrca Cereals (2013) condtons, and harvest weather Tmely harvest or not, harvest weather, farmers sklls, Abass et al. (2014) Tanzana Cereals mechanzaton level of rce harvestng, and gran-savng awareness Halloran et al. (2014) Denmark Cereals Metculousness of farmers harvestng operatons, and gran-savng and loss-reducng awareness Guo et al.(2014) Chna Rce Manpower adequacy, rce harvestng equpment, and farmers harvestng sklls Huang et al.(2015) Chna Rce Acceptance of land transfer or not, and rce harvestng methods

27 643 Table 5 Names, meanngs, and statstcal characterstcs of model varables Varable Varable Mean Standard Name Meanng Devaton Demographcs Gender Male=1; female= Age Actual age (years) Educaton Specfc years of schoolng (years) Employment as mgrant workers or not es=1; no= Annual household ncome Famly net ncome value (10 thousand) Proporton of famly busness Famly busness ncome accountng for the proporton of ncome total household ncome Producton characterstcs Plantng scale Household per capta rce cultvaton area (mu) Proporton of mechancally harvested area n the total Level of mechanzaton harvested area Land transfer es=1; no= Rce prces Satsfacton Satsfacton =1; Not satsfacton = Harvestng operaton characterstcs Harvestng methods Segment harvestng =1; combne harvestng = Tmely harvest or not Tmely harvest =1; Not tmely harvest = Includng fve categores: very crude, crude, moderate, Operatonal metculousness metculous, very metculous (wth very crude as the reference) Operatonal metculousness 1 Operatonal metculousness s crude (yes=1, no=0) Operatonal metculousness 2 Operatonal metculousness s moderate, (yes=1, no=0) Operatonal metculousness 3 Operatonal metculousness s metculous (yes=1, no=0) Operatonal metculousness 4 Operatonal metculousness s very metculous (yes=1, no=0) Includng fve categores: very adverse, adverse, Harvest weather moderate, favorable, very favorable (wth very adverse as the reference) Harvest weather 1 Harvest weather s adverse (yes=1, no=0) Harvest weather 2 Harvest weather s moderate (yes=1, no=0) Harvest weather 3 Harvest weather s favorable (yes=1, no=0) Harvest weather 4 Harvest weather s very favorable (yes=1, no=0) Includng fve categores: very nadequate, nadequate, Manpower adequacy moderate, adequate, very adequate (wth very nadequate as the reference)

28 Manpower adequacy 1 Manpower adequacy s nadequate (yes=1, no=0) Manpower adequacy 2 Manpower adequacy s moderate (yes=1, no=0) Manpower adequacy 3 Manpower adequacy s adequate (yes=1, no=0) Manpower adequacy 4 Manpower adequacy s very adequate (yes=1, no=0)

29 Table 6 Model estmaton results of man factors nfluencng rce harvest losses Varable Coeffcent Standard Error Wald Value P Value Gender Age Educaton Employment as mgrant workers or not *** Annual household ncome Proporton of famly busness ncome *** Plantng scale *** Level of mechanzaton ** Rce prce satsfacton Land transfer Harvestng methods Tmely harvest or not *** Manpower adequacy *** Manpower adequacy Manpower adequacy Manpower adequacy Operatonal metculousness *** Operatonal metculousness Operatonal metculousness *** Operatonal metculousness *** Harvest weather *** Harvest weather *** Harvest weather Harvest weather Crtcal pont Crtcal pont Crtcal pont * Crtcal pont *** Crtcal pont *** Crtcal pont *** Nagelkerke R Cox & Snell R test *** Note: * represent p< 0.1, ** represent p< 0.05, *** represent p<

30 Table 7 Margnal effects of sgnfcant ndependent varable on rce harvest losses (ceters parbus) Sgnfcant Independent Varable Employment as mgrant workers or not Proporton of famly busness ncome =0 =1 =2 =3 =4 = Plantng scale Level of mechanzaton Tmely harvest or not Manpower adequacy Operatonal metculousness Operatonal metculousness Operatonal metculousness Harvest weather Harvest weather

31 Fgure 1. Composton of rce harvest losses