Maize Varieties Combination Model of Multi-factor. and Implement

Transcription

1 Maze Varetes Combnaton Model of Mult-factor and Implement LIN YANG,XIAODONG ZHANG,SHAOMING LI Department of Geographc Informaton Scence Chna Agrcultural Unversty No. 17 Tsnghua East Road, Bejng P.R. CHINA Correspondng Author: Xaodong ZHANG Abstract: - The maze varetes combnaton model whch fully uses all knds of resources and strkes a balance between low rsk and hgh yeld has been bult by takng the county as the research unt.it analyzes the factors nfluencng over corn producton so as to determnng the ndexes mpact on corn varetes,profts from the mature nvestment portfolo selecton model and consders from both of qualtatve and quanttatve pont of vew.in addton, under the dea of object-orented programmng, the system of the model, whch has both problem solvng and vsualzaton, has been desgned and mplemented n the.net platform by usng C# language. The result ndcated that the model can provde maze plantng proporton and make useful decson support for seed companes, agrcultural enterprses, agrcultural extenson agents, farmers and so on n accurate popularzaton of maze varetes. Key-Words: - Maze varetes, combnaton, LPM model, markowtz mean - varance model, varetal character,clmate type,terran, pest envronment, farmng arrangements 1 Introducton Wth the development of agrculture and the comprehensve applcaton of nformaton technology, agrculture, nformaton technology has been n the natonal economy and people's lfe occupes an ncreasngly mportant poston. In Chna, due to varous reasons makng the seeds of non-qualty factors from the promoton of accdents occur, f an ntegrated aspect of factors, the use of nformaton technology, the rght mx of varetes, you can accurately promoton, reducton n agrcultural producton, causng a heavy a great loss. At present, at home and abroad on the cultvaton of maze varetes wth relatvely few studes, manly n qualtatve research, such as the theoretcal research and feld trals, and most of them are between the dfferent varetes of maze mxed croppng studes. If SHI voce and from a varety of genetc background, agronomc trats, morphologcal characterstcs, physologcal and ecologcal dfferences and resstance of maze n terms of dfferent knds of sngle cross hybrd, between the knds of cultvaton theory and dscuss techncal ssues; L Xue trees to sut the local good maze varetes cultvated by a certan percentage of tests carred out wth research, the structure than a sngle plant speces yeld more than 11%. In 2008, Wang We-l, who was found n the fnance portfolo research wth maze varetes wth great smlarty, the ntroducton of maze varetes wth ISSN: Issue 6, Volume 7, June 2010

2 ther model, a quanttatve study of ts precedent. However, the complete portfolo model appled, there has been no consderaton of factors wll affect the mx of a comprehensve, rsk-return measurement scales have lmtatons and other ssues. In ths paper, on ts bass, based on the county scale, accordng to the regonal clmate, geographcal envronment, corn trat ndcators and the local farmng arrangements, a varety of factors, qualtatve and quanttatve combned to mprove the model and programmng sutable for cultvaton of maze varetes wth a reasonable n reducng the rsk and ncrease cultvaton of corn producton, for local governments, seed companes, agrculture-related nsttutons to provde decson support. 2 Maze Varetes Combnaton Medol Buldng Corn farmers want the hghest possble yeld and the lowest possble rsk, whle n the process of corn plantng, but nevtably there are many rsks, the rsk of the most mportant nfluence comes from the natural world, such as the accumulated temperature, ranfall, pests and dseases, and most of these rsks have a hdden, and unexpected. Therefore, speces wth the prmary task s to select seeds, gve full play to the technologcal content of seeds. And n accordance wth the regonal clmate, geographcal envronment, sol and farm nfrastructure, plantng varetes reproductve habts, and the length, choose the name wth local characterstcs, superor varetes and the ntroducton of marketable yet famous varetes sutable for local cultvaton. Terms of reducng market rsk, mprove economc effcency n areas such as selecton of speces and varetes wth. Maze varetes wth the process, gve full consderaton to a varety of relatonshps between the envronment, accordng to some smple matchng prncple of varety can be the ntal screenng and matchng, n order to acheve the purpose of quckly and accurately. Frst, the varety of characterstcs - type of clmate to match. Exstng varetes are more resstance has ts own characterstcs, a specfc eco-regons n each of the supportng techncal gudance on cultvaton, there should be some dfferences. When choosng varetes, such as hgher lodgng, selected regons n the maze was born n a larger perod of the wnd, then ths speces and Lodgng varetes wth strong capabltes, and that ts too small wth the rato. Second, the varety characterstcs - Pest envronment match. Selectng speces as the frst dsease, the second dsease, the frst nsect pest, the second nsect pest n the county to partcpate wth the probablty of a small speces. Thrd, the varety of characterstcs - terran match. The cultvaton of maze varetes have a close relatonshp wth the terran, n general, more areas of the county terran types, speces dversty of large speces should be selected to partcpate wth the characterstcs of dfferent apparent. Fourth, the speces growng perod - farmng arrangements to match. Accordng to Varety the length of growng perod to determne the type of cooked of maze varetes.then the choose of varetes s carred out the tme to make combnaton. Ffth, the varety of features - farmer preferences match. Farmers plantng corn n the process, after many years of accumulated experence, for corn plantng sol, fertlzer applcaton s very famlar wth certan characterstcs of the speces have certan preferences wth the varety nvolved n ths factor should be taken nto account. 2.1 Multvarate Qualtatve Analyss 2.2 Multvarate Quanttatve Model Maze Varetes mean - varance model ISSN: Issue 6, Volume 7, June 2010

3 Wang We-l maze varetes wth models n the study found that wth the economcs of the nvestment portfolo has a smlarty to a large extent, to borrow a Markowtz mean - varance model. Markowtz on how to select an optmal portfolo, sad: nvestors, the choce should be made between the objectves of the two mutual restrant to acheve the best balance of reason, that s the rsk n the same context, the hghest expected rate of return; n the same ncome rate levels, the least rsk. Takng nto account maze varetes wth agronomc problems s the ntroducton of the concept of concentraton of the model of mprovement. Selected relef that corn acreage, unstable degrees accumulated temperature and ranfall to determne the degree of nstablty n the degree of concentraton threshold and thus constran the proporton of maze varetes planted. Therefore, the maze varetes mean - varance model s as follows: Target: n n mn x x cov( r, r ) = 1 j= 1 j j (1) max n = 1 b x (2) s.t. n = 1 n = 1 x x x Among the equaton(1)-(3), x for the speces wth the frst -mx wth the proporton of maze varetes; cov (r, rj) for the smlarty between dfferent speces; b for the frst -expected producton of maze varetes. x Model Improvement Maze varetes wth and there are some dfferences n the portfolo, although the model of Wang We-l consderaton of agrculture and mproved, there are Constrants: C (3) stll sgnfcant problems, such as dd not resolve the spatal sequence data to tme-seres data of the converson, does not reflect the varety and the Envronment nteracton and so on. Frst of all, the rsk of a certan tme when the pursut of hgh returns, and ths ncome should be what to say then? Wang We-l model usng the output of maze varetes, as ncome portfolo s to follow the stock prce tme seres and data ntegrty, and a varety of producton data n a certan place ISSN: Issue 6, Volume 7, June 2010

4 does not contnuous n tme. However, each corn varety s the man shape features of the data ntegrty can be judged from a varety of startng ther own varety of benefts. Second, a certan rsk, the rsk for hgh returns as the target constrants, the correlaton coeffcent ρ_j comparson of covarance rsk n terms of more sutable metrc. From ths formula 4 can be seen that wth the rsk of speces are dvded nto systematc rsk and non-systematc rsk of two parts, a macro-systematc rsk s the growng dversfcaton can not be elmnated, but only affectng a varety of mcro-non-systematc rsk can only be through the plantng dfferent varetes n the same area to be weakenng, the correlaton coeffcent determnes the sze of the non-systematc rsk. In the addton, when the proceeds when the pursut of a certan rsk mnmzaton, the ntroducton of benchmarks or reference levels of rsk to replace the varance method, the mean µ, n order to focus on the dstrbuton of proceeds to consder the left, that s, the rsk of loss whle the role of composton, at present the most (4) representatve and whch form a mature theoretcal system s the HALO's LPM () method. LPM s a "Lower Partal Monent" acronym, whch means that only part of the ncome dstrbuton n the left tal was only used for the calculaton of rsk measurement factor (Equaton 5) Compared wth the varance, and low part of the moment method obvously has certan advantages: one, t does not requre varance or standard devaton method as strngent as those assumptons, only some of the assumptons the utlty functon decson-makers (such as the decson maker's preference, etc.); 2, utlty functon type parameters α and target returns T makes the rsk measure, together wth a broader range of (5) sem-varance, when α = 0, LPM_0 merely stated that the probablty of loss; 3, wth LPM_2 more accurate measurement of rsk than varance, more securty. Fnally, consderng the varety and envronmental factors mpact on the model,the constrants s mproved.therefore, maze varetes models wth mproved are: applcatons. When α = 2, T = E ( ), when, LPM_2 Target : ISSN: Issue 6, Volume 7, June 2010

5 (6) (7) Constrants: (8) Among the equaton(6),t s target rate, s the probablty of proft Among the equaton (7) s the proporton of maze varetes wth, characterstc parameters for the maze varetes, s the weght of speces characterstc parameters. 3 Problem Soluton 3.1 Development Envronment Model has been developed by usng the Mcrosoft SQL Server 2000 as a storage medum and n the Vsual C # 2005 programmng language and envronment. 3.2 Database Desgn Maze varetes wth the system accordng to the data requrements, combned wth database desgn, the standardzaton of the prncples of practcal desgn of data tables, the man features of the desgn trats of maze varetes n Table (MazeVaretyTratTable), regonal comparson table (RegonalsmContrastTable), geographcal features of the table (GeoEnvronmentTrat), maze varetes wth the results of the table (CombnatonResult), maze varetes wth detaled nformaton sheet (RateResult) the fve tables. At the same tme, combned wth the database provded by the mathematcal functons, desgned for a specfc query stored procedure. On the one hand can reduce the dffculty of programs to acheve the other hand, can mprove the system speed. ISSN: Issue 6, Volume 7, June 2010

6 3.3 Model mplementaton Based on the analyss of maze varetes wth the model, combned wth the system archtecture, maze varetes wth model selecton and the system s dvded nto two parts wth the structure dsplayed. In both speces - envronment ft at the same tme gves the proporton of maze varetes wth a reasonable and provdes the results of Fg.1 Database Dagram mult-angle dsplay. Model ncludes qualtatve and quanttatve analyss, quanttatve analyss s a two-goal programmng model, commonly used method s to double-objectve to sngle objectve, another objectve as a constrant to solve t. Therefore, the model s desgned to provde three optons approach (Fg.3). User Servce Interface Busness Model Data Access Interfaces Data Servce Bass Data Store Fg.2 GDSS Archtecture ISSN: Issue 6, Volume 7, June 2010

7 Varety-Regonal match:maze varetes wth the county as a unt s carred out, frst of all countes should be makng a choce, choose the envronment. System n accordance wth the two major maze growng areas n north and east to carry out dvson of the Huang-Hua. The man growng areas n accordance wth - Provnce - Cty - County dvson to carry out the tree structure s conducve to the user area selecton. Meanwhle the model provds a map of the regon operaton to select. Then accordng to decson-makers prefer, you can choose varetes. Corn Varety Tral data manly from the Natonal Agrcultural Technology Center, North East, , Huanghuaha Dstrct of New Varetes of Maze regonal tral data. Carred out n accordance wth qualtatve analyss. Quanttatvely match:because of the producton Fg.3 expectatons or tolerance for rsk vares, so ths module desgn wth parameter settngs, allowng users to combne ther own preferences to set (wth a constrant), such as settng an expected output, the system s gven n the The expected level of yeld, wth the smallest cut the rsk of the program; or specfy a rsk tolerance level to arrve at ths level of rsk wth the hghest yeld under the program. 3.4 Applcaton examples In ths paper, Langfang Cty, Hebe Provnce, as specfc examples of the object. Frst select the Langfang Cty area, that s to be selected for plantng of maze varetes wth the envronment. Then select the ,628318,6319, A3303, AY316, as wth speces n the quanttatve model of hgh-rsk target a certan return, and set the rsk-bearng capacty as a general carred out wth (Fg.4). ISSN: Issue 6, Volume 7, June 2010

8 Fg.4 In proporton wth the end, that s, there are "speces wth results" dalog box, n the dalog box, frst select the county you want to vew and varetes, and then clck on "Clck to vew the results table" button, you can see the selected varetes In the countes wth the proporton of data sheets clck on the row headngs to select one or more lnes of data, you can poston the lower rght shows the correspondng results of the graphcal dsplay, swtchng, "Bar", "Column" and " pe chart "opton, you can dsplay the correspondng graphc. As shown Fg.6. Fg.5 Combnaton Result ISSN: Issue 6, Volume 7, June 2010

9 Fg.6 4 Concluson Ths paper ntroduces the LPM model portfolo, consderng the maze varetes wth medum-specfc factors - characterstcs of man characters of maze varetes, weather patterns, terran type, terran undulatng degrees, pest and dsease envronment, farmng arrangements, wll be cross-speces and envronmental analyss, pars of maze varetes mean - varance model has been mproved n order to better comply wth requrements of maze varetes. Accordng to a large number of case studes, reached the followng conclusons: one, the rsk and yeld a postve correlaton between rsk tolerance, the stronger the hgher the expected return; expected yeld more conservatve lower rsks. 2, wth the number of varetes s proportonal to the number and types of terran, wth undulatng terran s also proportonal to the degree. At the same tme mplements a system, gvng local governments, seed companes, agrculture-related nsttutons to provde decson support, and producton practces to provde effectve gudance, not only has theoretcal sgnfcance, but also practcal sgnfcance. References: [1] Zhensheng Sh, The cultvaton technology of mxed cultvaton of dfferent maze varetes, Corn scence, 2006,14(4): [2]Xueshu L,Yubng Jn, Benefts of Several knds of hybrd maze varetes mxed [J],Hebe Agrcultural, 1999,(3): [3]A.GLANNAKOULA,I.ILIAS,PAPASTERGIOU,T he effects of development at chllng temperatures on the functon of the photosynthetc apparatus under hgh and low rradance n leaves of lettuce,proceedngs of the 5th WSEAS Internatonal Conference on Envronment,November 20-22,2006 ISSN: Issue 6, Volume 7, June 2010

10 [4]Luo xnlan,zhou lhong,sprng maze varetes n Northeast Chna wth the decson-makng study of mature-type,journal of Shenyang Agrcultural Unversty, 2002,33(5): [5]Wang wel,zhang xaodong,l shaomng,maze varetes collocton model buldng and mplement,chna Seed Industry, [6]H.V.HAJARE,DR N.S.RAMAN.A new technque for evaluaton of crop coeffcents:a case study,proceedngs of the 2 nd IASME/WSEAS Internatonal Conference on Water Resources,May 15-17,2007 [7]Da lhong,l an,evaluaton of maze dependng on varetes factors,cereal Crops,2005,25(5): [9]Kong xangl,cao lanpu,feng junguo,mult-factor comprehensve evaluaton of maze cultvars,maze Scences,2002,10(3): [10]B.MANOS,TH.BOURNARIS,J.PAPATHANASI OU,CH.MOULOGIANNI,A dss for agrcultural land use,water management and envronmental protecton,proc.of the 3 rd IASME/WSEAS Int.Conf.July 24-26,2007 [11]Markowtz Harry M, Portfolo Selecton.Journal of Fnance,1952,(7):77-91, [12] Markowtz Harry M,Portfolo Selecton: Effcent Dversfcaton of Investment,Cambrdge: Basl BlackWell,1991, [13]S.J.BABALIS,E.PAPANICOLAOU,V.G.BELESS IOTIS,Impact of alternatng dryng-ar flow drecton on the dryng knetcs of agrcultural products,proceedngs of the 3 rd WSEAS Int.Conf. onheat TRANSFER,August 20-22,2005. [14] Chen yunxan, Rsk - return decson analyss, Xnhua Publshng House, ISSN: Issue 6, Volume 7, June 2010