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1. 740 1745 370 755 1380 775 1350 1770 780 1360 1060 1810 1820 53 Type of farming 8 groups TF8 Code Name 1 Fieldcrops 2 Horticulture 3 Wine 4 Other permanent crops 5 Milk 6 Other grazing livestock 7 Granivores 8 Mixed Type of farming 14 groups TF14 Code 13 Specialist COP 14 Specialist other fieldcrops 20 Specialist horticulture 31 Specialist wine 32 Specialist orchards fruits 33 Specialist olives 34 Permanent crops combined 41 Specialist milk 44 Specialist sheep and goats 45 Specialist cattle 50 Specialist granivores 60 Mixed crops 70 Mixed livestock 80 Mixed crops and livestock 54 OTEB type of farming 8 groups no mixed production group Name OTEB aggregates the following OTE 1310 1320 1330 1410 1420 1430 1441 1442 1443 6020 Fieldcrops A 1 6030 6040 6050 8110 8130 8210 Milk C 2 4110 4120 4310 7110 8120 Grazing livestock D 3 4210 4220 4320 4410 4420 4430 4440 7120 8140 5011 5012 5013 5021 5022 5023 5031 5032 7210 7220 Granivores E 4 7230 Quality wine 5 3110 Ordinary wine FCPVO 6 3120 3130 3141 3142 3143 Other permanent crops FAUCP 7 3211 3212 3213 3220 3230 3300 3400 6062 8220 2011 2012 2013 2021 2022 2023 2031 2032 2
2. Stop Selected Server Local YW Analyze Program Export Send To Create 8 Properties Jf 6 96 9e e e e e ke e e ke e e e ee e e e e de e ie a see e a a e a a a sa e se b e se a a sa se ee e ee e ee e he e be he e e he e e f weve STEP 1 ENTER HERE THE LOCATION OF THE PARAMETERS FILE S89 9 e he e le e ke e e ke e ke e e de e e e e le e ie e e be e e a b a e e be e e B br a sa e e be b e e be br e e b ke be be he v a b e e e e e e e filename com C FACEPA parameters file txt a e e e de de e ke e Ra v STEP 2 get back the parameters file w de a e e e de e e e e de e de e Ra SIDATA DT1 COM Then go to branch structure to run process flow There are several ways to proceed which may vary slightly in different versions of Enterprise Guide Here are the two ways to proceed using version 4 3 of Enterprise Guide 42 1 EFS E Open Export 4 Save extract parameters Save extract parameters As extract parameters on Local z Run Branch from extract parameters Select Server Condition X Create Stored Process Send To Link extract parameters to
3. BASE i 2 Run 1 Stop Export Schedule Run extract parameters Run Branch From extract parameters Run Flux de processus Zoom Run Project ae extract SAS base 1 Right click on first code on the left then click Run Branch from extract parameters 2 Single click on extract parameters to select it click on from extract parameters nme then select Run Branch HE COEF E prese COEF_BY_G Wait for the model to finish running The SAS tables which allow you to generate output tables are located at the end after the code export to the right of the process flow if the SAS tables format has been selected in the output options It is also possible to open the HTML pages if the option is selected directly in Enterprise Guide 43 Caution Microsoft EXCEL must be installed on the computer if the EXCEL checkbox is ticked otherwise the files cannot be generated Furthermore the name generated for the output file depends on the country and the year If there is a results table from a same country or same year already open the table cannot be overwritten by the model It is therefore strongly recommended that you empty the folders contained in the results directory before running the model 44 Appendix 1 For experienced users Some information about the different process flow codes Extract pa
4. 19200 81 16641 62 4915 55 2682 47 1400 78 sum 2007 2 mid output 60805512 00 5741814 00 17587407 00 28795784 00 3279114 00 2238880 00 8577800 00 8759 sum 2007 2 mid quanti 3372359 00 224887 00 1072021 00 1547983 00 203223 00 10207 80 597792 00 3423 2007 2 mid coef 0 86 0 96 0 92 0 86 1 76 0 53 0 75 sum 2007 3 high area 34030 10 3745 74 13070 81 10302 37 3449 13 2216 57 1276 76 2007 3 high output 34766106 00 3189803 00 10329456 00 15650136 00 1863013 00 1599846 00 6869285 00 5869 sum fra 2007 3 high quanti 2044233 00 152045 00 656814 00 911150 00 124723 00 7214 30 521647 00 2334 fra 2007 3 high coef 1 14 1 53 1 23 1 12 2 11 0 79 0 97 This option also generates costs per hectare quintals or tons livestock unit and head if these options are selected in Additional results see the explanation above Finally if the SAS table option is selected from the Output formats menu the Allocation of residuals option generates a SAS table TAB COEF GRP containing all the farm holdings in the selected sample along with a certain amount of information about these holdings such as the productions Yi costs Xj defined in the form the values of the estimated coefficients th Y1Xj and production cost Yi associated with each product Including or excluding home grown consumption The option Including
5. 4532614 6 468395 112141 8214012 30 85375 12 91907 17 55899 8945472 32 9458 1015534 6 595274 8 257504 13 08663 8 499704 6 760591 1 09181 2 697387 6 127659 6 139417 1307529 1 396208 6 333587 4215149 14 4203 7 411508 8 932106 26 96202 20 37163 15 30218 2 409866 0 49353 1 494366 7752327 8 255438 11 8881 0 895788 8531572 4 45832 25 4348 8 461619 DAD Ee Hrt RadjustedWHEAT DWHEA BARLE MAIS 27 25391 35 11776 18 31131 30 53837 21 93565 13 65969 0 756467 13 62724 1452414 19 39695 6 113155 1355164 9 639205 34 0553 13 05271 5 55087 10 7025 6 59072 7 75100 313783 430143 0 67195 5 04444 8 15438 9 53437 4 72287 5 53563 1 76004 14 934 1 72551 If you select several years or several countries with the EXCEL option you also have access to an additional option by ticking Output by production and by year only in Excel which enables the user to obtain an EXCEL document containing output by production and by year presenting one production per EXCEL sheet FERTIL a MOTFUE 4 4 OENERG 4 MACHUK 7 OTHSIC LANDCO 4 INTERE 4 DEPREC 4 TAXES 4 0211459 0 381813 0 503218 0 03328 019519 2 48255 0 020874 0 05831 4057734 0 274099 0 069886 0529213 0 18446 2 609886 0 07228 0 041431 0 196
6. 929 55 72016 529 931 2750 18 1327481 580 45 2197 50 649 76 Tota costs 2007 97392 104195 81061 1482 04 1139 24 435 670 1492 691 2258 60 855 8 2725 91 1054 891 Net income 2007 60147 60135 485 23 74655 10 01 5054611 1529 33 357 08 204 88 153358 60 54 Total cost by group in per ha for 2007 Country Year GRP__ WHEAT DWHEAT BARLEY MAIS OTCER DRYPU POTAT SUGAR RAPE OCROP CATTL 2007 1 low 566 36 56650 500 09 943 05 613 76 208 955 2014 53 1768 49 568 76 15485 436 84 2007 2 mid 880 16 1008 75 77224 1369 31 1083 45 369 203 3902 97 2311 57 820 22 810 26 1059 00 2007 3_high 1230 83 1516 34 1102 86 1941 90 1764 87 699 372 8416 15 3024 85 1157 25 22292 07 2159 82 Only if the Allocation of residuals option is selected The above two three tables are also available in Euros per quintal per livestock unit or per head 23 You must now choose between several options Outlier deletion gt value 5 by default _ Weighted _ Allocation of residuals _ Including home grown consumption cassiteaton variae Choose i Outlier deletion It is possible to delete outliers By ticking the Outlier deletion box you have a choice of three values for the size criterion level 1 5 or 10 defining the tail area of the distribution that the program will use in the outlier deletion process By default all produ
7. F Offermann W Kleinhanss Comparison of cost estimates based on different cost calculation methods and or different databases Deliverable D3 3_vTI Mars 2011 In the folder D FACEPA SAS_Pgm Morobe Program which allows user to validate the parameters SERE aggregated variables choice of options etc Program which calculates estimated costs and generates Model odel egp required results In the folder D FACEPA Misc FADN_CSV_to_SAS_files sas If you do not already have the FADN database in the SAS format this program enables you to convert the CSV file into SAS tables compatible with the model There is also an example available to help the user with his her first steps In the folder D FACEPA example parameters_file txt Text file containing parameters required by the model this file is generated by the Java form results Folder which contains the output tables generated by the model There are three required steps to estimating production costs with the FACEPA model Definition of the parameters with the help of the form component called Form jar Verification validation of the parameters using the program Inter_tab egp Estimation of production costs per production using Model egp The model The model estimates input output production cost coefficients using EU FADN data The production cost coefficients fx are estimated by us
8. 764 765 766 kE 1995 2008 ire Ireland Irlande 380 14 1995 2008 ita Italie 221 222 230 24 1 242 243 244 250 260 270 281 282 291 29 15 2004 2008 Lithuania Lituanie 775 16 1995 2008 lux Luxembourg Luxembourg 350 E 2004 2008 Iva Latvia Lettonie 770 18 2004 2008 mit Malta Malte 780 19 1995 2008 ned Netherlands Pays Bas 360 20 1995 2008 ost Austria Autriche 660 21 2004 2008 pol Poland Pologne 785 790 795 800 22 1995 2008 por Portugal Portugal 610 620 630 640 650 23 2007 2008 rou Romania Roumanie 840 841 842 843 844 845 846 847 24 1995 2008 suo Finland Finlande 670 680 690 700 25 1995 2008 sve Sweden Suede 710 720 730 26 2004 2008 svk Slovaquie 810 27 2004 2008 svn Slovenia Slovenie 820 28 1995 2008 United Kingdon Royaume Uni 411 412 413 421 431 441 29 To add a country go to the region tab and complete the file Be sure to include all the information such as the first year and last year available the country key country id and the key for the region s separated by a hyphen B G D francais crop idlenglish 2 O 120 120 Wheat common 120 Bl tendre 3 O 121 121 Wheat durum 121 Bl dur 4 O 122 122 Rye 122 seigle 15 123 123 Barley 123 Orge 6 _126 126 Grain maize 126 Mais grain 7 124 124 Oats 124 Avoine 8 _125 125 Summer cereals 125 C r ales d t _9 127 127 Rice 127 Riz 10 0_128 128 Cereals oth
9. File Checking General Crops Livestocks Costs This option allows you to keep only individuals with positive production for the selected variable s select the variables used for the removal of outliers WHEAT 1120 Wheat comm M vj DWHEAT i 121 Wheat durum Mig L BARLEY 123 IPIE 7 O mas 126 Grain maize 122 Rye gt 24 oats 125 Summer cere 121 Rice z Cereats tathart lw le v See appendix 2 for list of available crop products 31 Livestock File Checking General Crops Livestocks Costs This option allows you to keep only individuals with positive production for the selected variable s select the variables used for the removal of outliers L cam 5 v Ir SHEEP 15 sheep ir L Pourr sn Poultry Iz r L CMILK 162 Cows milk 163 Cows milk prod v X lal OMILK 164 Buffalo and sh w m 1167 Sheeps milk p In L gcc 1169 Hens eggs 7 CONTR 1711 Contract rearinal w
10. Open j Welcome Welcome FACErA Please select the data file EXCEL XML OR Click on XML and a file explorer will appear The XML file is in the same directory is the form which is why the file explorer is located in the same directory as Form jar Select data_file xml and click Open 13 Open p sjela 3 data_file xml bid Welc rename FACErA mesolpe Please select tl e A new window opens with a choice of language At present the form is only available in English and French but new languages can easily be added by modifying the XML file Choose the language and click OK to open the form Menus 14 The File menu contains the following options New Clears form of data in order to start a new blank form Open Opens a parameters file in form enabling it to be completed or modified Go to File gt Open or press Ctrl O then select your file and click on Open It may take a little time to load Save Saves the parameters file keeping its initial name and location i e when it was opened If the file has never been saved when you click Save the program saves the parameters file in the directory where form is located and name it parameters_file txt Warning This command will cause the loss of the initial information if the user has made changes af
11. code of the variable label of the variable in variable different languages You must then add as many lines as there are modalities for the added variable Each of these lines must contain the following information about these modalities The first column must contain the term grouping the code of the variable for example grouping TF8 or grouping A24 The second line must remain blank The next line contains the code of the modality and the following columns contain the label of the modality in different languages The code of the variables is also rewritten here in brackets before the label of the modalities and of the variable This allows the code and the formulation of the variables and modalities to appear in the form It is not obligatory however If you add a variable it is recommended that you fully complete the file however if you wish you can delete one of the languages from the form 50 You are strongly advised not to use special characters in the formula as this may result in problems when opening the form If a grouping variable is added it must be declared in the model s SAS code 51 Appendix 2 Dictionary of Variables Countries Countries Code Year s Austria ost from 1995 Belgium bel from 1995 Bulgaria from 2007 from 2004 Czech Republic cze from 2004 Denmark dan fr
12. holding in the sample by multiplying the number of annual family work units AWU by the wage rage AWU employed in the region These calculated costs can be useful notably for comparing individual farm holdings with incorporated farms for which various costs are outsourced Do not forget to save the file when you have finished in order to create the parameters file required for the FACEPA model to work 34 Example of a parameters file When you save your file using the Save or Save as option the Java form creates a parameters file containing the information required for the model to run This file is in a text format and looks like this one NOT 1 PARAMETERS FILE SSSSSAHESASHHSHUHSSUHHS42 The Cost of Production Model 8H HH84 Y CAL Y to stall otherwose N lt PS Y for weighted N otherwise N CST Y for constant N otherwise Y INTRA Y with intra N otherwise 09 NV Number of crop variables 2 positions 09 lt NA Number of livestock variables 2 positions 07 cv Number of variable costs 2 positions 3 lt CA of which number of livestock variable costs 1 position 09 lt CF Number of fixed costs 2 positions 3 SUB Number of subsidies 1 position 1 b
13. is calculated from the total production INTRAP F69 OC I A_56 the area attributed to pig is equal toA_CER INTRAP These areas are then set at 0 if the INTRAP coefficient is less than 0 and A_56 A CER if INTRAP gt 1 67 Appendix 4 Exchange rate used in FADN Databases to convert national units into euros year 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 bel 42 35358 4215573 4095097 4043596 3920909 3864957 39 75310 40 70471 40 46743 40 33990 dan 7 88284 7 92465 7 59946 762781 745044 7 30935 741614 7 52064 745340 7 44297 deu 2 05678 2 04706 1 97325 1 92806 189320 1887906 193692 197424 1 95952 1 95583 ell 0 20141 0 22522 0 24698 0 26857 0 28802 030299 030555 0 30936 0 33073 0 32576 esp 129 31560 128 46850 132 51270 149 12400 158 91880 163 00000 160 74750 165 88670 167 18400 166 38600 fra 6 91416 697334 684881 663368 6 58273 652505 6 49300 661260 6 60141 6 55957 ire 0 76777 0 76781 0 76074 0 79995 0 79361 081552 0 79345 0 74752 0 78625 0 78756 ita 1 52194 1 53324 1 59529 1 84123 1 91483 213014 1 95896 1 92930 1 94365 1 93627 lux 42 35358 42 15573 40 95097 4043596 39 20909 38 64957 39 75310 40 70471 40 46743 40 33990 ned 2 31710 2 30662 2 23835 2 16994 213489 2 10485 216490 222180 221150 2 20371 ost 13 18239 13 43447 13 82400 13 85450 13 76030 por 181 10760 178 61410 174 69800 188 36990 196 90580 196 10470 195 76150 198 58890 201 69500 200 48200 SUO 5 70855 5 82816 5 88064 5 98201 5 94573 s
14. lw See appendix 2 for the list of available livestock and other products Costs The three cost tabs are similar to the tabs for crop and livestock productions and are constructed in the same way except that the first checkbox is not available The Fixed costs sub tab has an extra part for subsidies The user must choose one of the four following options the fourth one being selected by default Option 1 total subsidies SE605 Option 2 crop subsidies SE610 and livestock subsidies SE615 Option 3 crop subsidies SE610 livestock subsidies SE615 and other subsidies SE620 SE630 Option 4 crop subsidies SE610 livestock subsidies SE615 decoupled subsidies SE630 and other subsidies SE620 32 File Checking General Crops Livestocks Costs Livestock costs Other variable costs Fixed costs Subsidies option 1 total subsidies SE605 option 2 crop subsidies SE610 and livestock subsidies SE615 option 3 crop subsidies SE610 livestock SE615 and other subsidies SE620 SE630 C option 4 crop subsidies SE610 livestock subsidies SE615 decoupled subsidies SE630 and other subsidies SE620 the fixed cost F83 taxes and other dues is imposed in the model select the variables used for the removal of outliers loENERG F79 Electricity v lt 80 Heating fuels co
15. permanentes 8 grouping TF8 5 5 Milk 5 Lait 9 grouping TF8 6 6 Other grazing livestock 6 Autres herbivores 10 grouping TF8 rd 7 Granivores 7 Granivores 11 grouping TF8 8 8 Mixed 8 Mixtes 12 grouping 1995 TF14 TF14 Type of farming 14 TF14 Orientation technico economic 13 grouping TF14 13 13 Specialist COP 13 Sp cialiste COP 14 grouping TF14 14 14 Specialist other fieldcrops 14 Sp cialiste autres cultures de plein 15 grouping TF14 20 20 Specialist horticulture 20 Sp cialiste horticulture 16 grouping TF14 31 31 Specialist wine 31 Sp cialiste vin 17 grouping TF14 32 32 Specialist orchards fruits 32 Sp cialiste vergers fruits 18 grouping TF14 33 33 Specialist olives 33 Sp cialiste olives 19 grouping TF14 34 34 Permanent crops combine 34 Cultures permanentes combin es 20 grouping TF14 41 41 Specialist milk 41 Sp cialiste lait 21 grouping TF14 44 44 Specialist sheep and goat 44 Sp cialiste ovins et caprins 22 grouping TF14 45 45 Specialist cattle 45 Sp cialiste bovins 23 grouping TF14 50 50 Specialist 50 Sp cialiste M crop livestock livest cost crop cost_ fixed cost grouping 4 Pr t mr To add a variable go to the bottom of this table without leaving a line blank and complete it as follows First line Column A Column B Column C Following columns grouping first year of
16. to reduce the size of the sample by selecting a filter based on one or more European FADN variables Selected farm holdings can meet certain modalities for qualitative type variables Selecting a filter or thresholds for numerical variables Selecting a sample Selecting a filter For qualitative type variables select the one from the proposed list the same one as the classification list and define the modalities of this variable you wish to save or delete You can create up to eight filters and thus filter through eight variables 26 T pt File Checking General Crops Livestocks Costs General Options Filters selecting a fitter eser esso gt viene X p messi v einesantcom y X Selecting a sample el IL rn en T2 Bur She Selecting a sample This second option allows the user to choose a sample from one or more numerical variables production or cost in Euros The first step is to select the type of variable i e crop production livestock production variable livestock costs variable crop costs fixed costs The drop down menu will then give you a list of related variables Now define the selected criterion by choosing one of the operators the value to be compared and whether you want to save or delete the individual
17. 033 2034 Horticulture 8 6010 6061 8231 8232 Organisational form A18 Code Name 1 Individual family farms 2 Partnerships 3 Other Warning this variable is available only from 2002 onwards Economic Size Class A26 Code Grouping size in ESU 01 2 ESU 02 2 4 ESU 03 4 6 ESU 04 6 8 ESU 05 8 12 ESU 06 12 16 ESU 07 16 40 ESU 08 40 100 ESU created by the French team 55 09 100 250 ESU 10 gt 250 ESU ESU European size units Organic farming A32 Code Definition 1 does not apply organic production methods 2 applies only organic production methods 3 is converting to organic production methods or applies both organic and other production methods Warning this variable is available only from 2000 onwards Less favoured area A39 Code Definition 1 not in less favoured areas i e in normal areas 2 in less favoured not mountain areas 3 in less favoured mountain areas 4 no significant areas in the member state or region Principal type of farming A29 Code 13 Specialist COP 14 Specialist other fieldcrops 20 Specialist horticulture 31 Specialist wine 32 Specialist orchards fruits 33 Specialist olives 34 Permanent crops combine
18. 40 33990 0 69620 0 42990 2 20371 13 76030 4 02300 200 48200 5 94573 9 28220 38 59900 239 56810 0 68225 0 26427 0 78756 1 93627 3 45280 40 33990 0 69620 0 42900 2 20371 13 76030 3 89500 200 48200 5 94573 9 25400 37 23400 239 64000 0 67861 0 25135 0 78756 1 93627 3 45280 40 33990 0 70010 0 42930 2 20371 13 76030 3 78370 200 48200 3 33280 5 94573 9 25010 33 77500 239 64000 0 69828 0 25174 0 78756 1 93627 3 45280 40 33990 0 70263 0 42930 2 20371 13 76030 3 51511 200 48200 3 68402 5 94573 9 61688 33 77500 239 64000 0 82150 FACEPA Farm Accountancy Cost Estimation and Policy Analysis of European Agriculture www ekon slu se facepa
19. 54 0 32319 1 77056 0 174776 0 21599 1 49741 0 22265 0 05445 0 316103 0 04494 0 242984 0 868296 0 361501 0 198183 0 36723 1 46577 0 02657 0 110149 1 962 0 418784 0 02119 0 29649 0 21091 1 422358 0 378924 0 351812 0 10414 0 21635 0 04147 0 30588 0 57556 0 97374 0 303675 0 264833 0 26992 0 007075 WHEAT CONTEA ARLE OTCER POT 3 603224 0 26554 0 11445 0 06432 0 28609 1 386626 0 10582 0 48831 1 75299 0 03412 0 370452 0 28914 0 030128 0 320928 0 460724 0 0914 0 01022 0 047506 1 044682 0 02608 0 089921 0 484144 0 146465 0 580998 1 114819 0 21845 D E Ed H 9 year 1999 2000 2001 year_ 2002 year_ 2003 year_ 2004 year_200 50 2114 0 11597 0 0239 0 00635 0 74921 0 18994 0 17530 0 35751 1 1552 0 22331 0 0320 0 1023 0 44628 Note If SAS is not selected there will be no OUTPUT window when the model will be finished There will be only files in the library Results 22 Additional results The model generates the three following main tables Production coef Tests and Descriptive statistics this output will be detailed later But it is also possible to obtain additional results per product with monetary costs in Euros per hectare per quintal 100 kgs per livestock unit and per head With eac
20. 64 APPENDIX 3 FORAGE AREA ALLOCATED TO LIVESTOCK 66 EXAMPLE FOR SHEER MILK er ni i i s E REO RU Da s athe 66 EXAMPLE FOR PIG o sey Meds asia s a ada A 67 APPENDIX 4 EXCHANGE RATE USED IN FADN DATABASES TO CONVERT NATIONAL UNITS INTO EUROS tC 68 Abbreviations and Acronyms AWU EU FACEPA Agriculture FADN INRA LU SFP vTI WP Annual work unit European Union Farm Accountancy Cost Estimation and Policy Analysis of European Farm Accountancy Data Network National Institute for Agricultural Research Livestock Unit Single Farm Payments Johann Heinrich von Th nen Institut Work Package Introduction In the European FADN costs detailed by category seeds fuel etc are available for all agricultural holdings The FACEPA model is designed to allocate these costs to different productions or enterprises The starting point to develop the software was a model built by INRA in the early 2000 s Then the software was established from the works conducted by vTI in work package WP 3 of the FACEPA project For further information on the methodology and the implications on the econometric results the reader may consult the WP3 documents written by the vTI team The FACEPA model is programmed in SAS language and runs on the Enterprise Guide SAS module The user chooses the costs he she wishes to target along with the livestock and crop productions to which t
21. FACEPA Farm Accountancy Cost Estimation and Policy Analysis of European Agriculture FACEPA USER GUIDE The FACEPA Model Software Estimating Costs of Production using the EU FADN database FACEPA Deliverable No D4 2 September 2011 INRA AgroParisTech France Butault Jean Pierre Zardet Guillaume Mathias Loic Delame Nathalie Desbois Dominique Rousselle Jean Marc VTI Braunschweig Germany Kleinhanss Werner Offermann Frank The research leading to these results has received funding from the European Community s Seventh Framework Program FP7 2007 2013 under grant agreement n 212292 Executive summary In the European FADN costs detailed by category seeds fuel etc are available for all agricultural holdings The FACEPA model is designed to allocate these costs to different productions The starting point to develop the software was a model built by INRA in the 2000 years Then the software was established from the works conducted by the in work package WP 3 of the FACEPA project The FACEPA model is programmed in SAS language and runs on the Enterprise Guide SAS module The model estimates input output or production cost coefficients from EU FADN data The SAS PROC SYSLIN procedure is used to estimate the FACEPA model Once model coefficients have been estimated it is possible to obtain production costs per quintal 100 kgs cost per hectare or cost per animal The model runs for a si
22. NwoR SE350 Contract wo E Upkeep land i M v MACHUK 61 Upkeep of mac v See appendix 2 for the list of available costs The production cost model does not create an other costs variable related to costs not included in the model However it does create a NetVal variable which is the difference between the total revenues and total costs NetVal sum of output values sum of costs including taxes subsidies The NetVal variable can be considered to be value added if the user does only take intermediate consumption into account It is thus possible to choose the type of margin gross or net by including depreciation or not The user may also wish to include in total costs the expenses associated farm owned factors of production such as faire valoir direct equity capital or family labor To do this it is possible to add the following calculated cost items 33 Calculated land rent Calculated for each farm holding in the sample by multiplying the number of hectares directly owned by the farmer by the unit per hectare land rent prevailing in the region Calculated interest cost Calculated for each farm holding in the sample by multiplying the interest rate prevailing in the region multiplied by the value of the equity capital to which estimated land rents have been taken away Calculated family labor cost Calculated for each farm
23. Other variable costs Fixed costs Insert a compulsory Now that general options have been selected the productions crop and livestock along with the costs variable and fixed must be defined and then be entered into the estimable production cost model The Crops and Livestock tabs function in the same way the Costs tab is divided into three sub tabs Livestock costs Other variable Costs and Fixed costs To add a product or a cost click on this icon 0 Note new product will be inserted after product where cursor is positioned If no product is selected the new line is inserted at the end of the list To delete a product or cost select the line and click on this icon Ka 29 File Checking General Crops Livestocks Costs This option allows you to keep only individuals with positive production for the selected variable s Wheat 1 1120 wheat comm 121 wneat durum z Rye 122 Rye vj x O O Barley i 123 Barley z L oats 124 Oats x n L Summer 125 Summer cere Grain 126 Grain maize NA x sa lt L 127 Rice hs In the Crops and Livestock tabs the green bu
24. Output Data Click on Export then Export name of the table RCRG Modifi le Le dossier est vide SAS Data Es v7 Long Name sas bdat SAS Microsoft Access 2002 2003 Databases dBASE Files dbf Lotus 1 2 3 Files wk4 Lotus 1 2 3 Files wk3 Lotus 1 2 3 Files wk 1 Paradox Files db Text Files Comma csv Tex Files 54 Text Files Tab delimited tab Tex Files ap bt All HTML htm html 40 Errors to avoid Here are the most frequently encountered errors which prevent the inter_tab egp program from producing results 1 2 3 4 The path indicated for the parameters file is not valid The path indicated for storing the SAS databases is not valid The SAS databases are incomplete The sample defined by the user is empty too many limitations for the variables 41 Model for estimating production cost coefficients the FACEPA model To activate the model go to the FACEPA SAS_Pgm directory and double click on the SAS Enterprise Guide model egp program The first thing to do is to open the first code named extract parameters on the left of the process flow Correct the path in single quotes after the filename indicating the path to the directory with the parameters file that you wish to test Example 4 Program 7 Log Output Data Save b Run
25. akaka 35 VALIDATION OF PARAMETERS AND INTERMEDIATE TABLES a000000000000000000000000000000000 37 ERRORS TO AVOID E A e da dd A dd 41 MODEL FOR ESTIMATING PRODUCTION COST COEFFICIENTS THE FACEPA MODEL on e io sa 42 APPENDIX 1 FOR EXPERIENCED USERS 0000000000000 0000000000000 0000 0000000000 sensns tatus eta asusta suns tn an 45 SOME INFORMATION ABOUT THE DIFFERENT PROCESS FLOW CODES 45 Extract parameters P 45 45 live input MISE x ie so nie ate desna er e CHE va ence qucd 45 A Jr 45 Extract for SYSLIN pro ERE GR dO ER Pace agenda Pa A 45 Pro SYSLIN TE 45 46 EX POPE zv rom ee mese 46 THE DATA FILE M OG vd E ee 47 Data file E Coat 47 Data file XIS d RD ee oo 48 APPENDIX 2 DICTIONARY OF VARIABLES cccccccscssssssssccsccssssscsceccccssscssssseccccessssssscececescees 52 COUNTRIES NER P V 52 AVAILABLE VARIABLES USED FOR CLASSIFICATION OR IN THE FIRST FILTERS eee 53 ROP NR M mcm E 61 oi ba dei EE S S a aa A E EA ES A E 63 INPUTS isch sees odes en tate drs ae a ee Dve ete S rain pi m
26. ania Slovakia Slovenia Spain Sweden United Kingdom The two options are incompatible By selecting one of the two the user is prevented from accessing the control commands of the other option In the above image example you will see that the option for a country has been selected The user must then choose a country and the year or years of the desired results with the option of checking the constant sample box to work on a balanced panel farm holdings present for all the selected years It is also possible to pool several years to be treated by the model as a single year Simply click on the green box and line will appear indicating first the code of the new year being created not to be modified To the right of the code there is a drop down menu containing the years as well as a second drop down menu containing the term OR Select the year in the first drop down menu then click on the second drop down menu and choose Insert to make another drop down menu which appears with the remaining years It is possible to add as many years as desired from the years that are available see appendix 2 18 C Constant sample balanced panel Awvailahla vaare Select The red cross box F3 enables the user to delete the whole line constant sampie Darancea panei To delete a single year click drop down menu containing term OR to the right of the year an
27. between the herbivores in proportion to the number of livestock units The cost per hectare of granivores is not calculated as they do not use fodder areas In the version of the model without home grown consumption the grain area corresponding to this home grown consumption is allocated to herbivores and granivores in proportion to the number of livestock units see appendix 3 forage area allocated to livestock Fertilizer use to produce for animal feed is allocated to livestock productions 11 The main options of the model Different options are available It is possible to delete outliers Estimation results can be weighted not with the SYSO2 variable which corresponds to the weight of each holding in the sample being used The Allocation of residuals option allows the user to edit the breakdown of individual costs The residual difference between the estimated and observed costs for each farm holding is re distributed over the different products proportionally to the gross output including home grown consumption or the gross product excluding home grown consumption This option also creates three groups of farms which differ in terms of intensity in the use of inputs depending upon the levels of specific costs obtained for each product The files include imputed costs for family owned factors It is possible to include these costs in the model such as family labor farm owned land or capital But this p
28. centrated feedingstuffs for grazing livestock F64 Coarse fodder for grazing livestock F65 Feedingstuffs for pigs F66 Feedingstuffs for poultry and small animals F67 Feed for grazing livestock home grown SE315 Feed for pig home grown F69 Feed for poultry and small animals home grown F70 Other livestock specific costs SE330 Other variable costs Motor fuels and lubricants F62 Seeds and seedlings purchased F72 Seeds home grown SE290 Fertilizers SE295 Crop protection SE300 Fixed costs Rent paid SE375 Wages paid SE370 Depreciation SE360 Contract work SE350 Forestry specific costs SE331 Other crop specific costs SE305 Interest paid for land and buildings F90 Interest paid for working capital and creditors F92 Taxes on land and buildings F88 Insurance for farm buildings F87 Other farming overheads F84 Insurance F82 Water F81 Heating fuels F80 Electricity F79 64 Upkeep of land improvements and buildings F78 Car expenses F63 Upkeep of machinery and equipment F61 Fixed costs imposed by the model Taxes F83 Other fixed costs Computed wage 701 Computed rent Z02 Computed interest 703 Subsidies considered as negative input Total subsidies on crops SE610 Total subsidies on livestock SE615 Other subsidies SE620 Decoupled payments SE630 The Taxes variable is imposed by the model it can t be chosen in the Java form list but it wi
29. ction and cost variables will be cleaned up However the user can limit the selection of productions and or costs to be treated in the CROPS LIVESTOCK and COSTS sheets by unticking the box followed by the symbol next to each of the products not intended to be cleaned up THIS LU ACCP VINNY HIUurvirgudr WILT PUSTUVOC pruuuuvuuH IVE UIT SCIZUUCU select the variables used for the removal of outliers S_WHEAT 120 Wheat comm w D ge nes L v RYE 122 Rye v D 1 INATS 1 Mate Weighting of estimation results If you tick the Weighted option the estimation results will be weighted with the SYSO2 variable which corresponds to the weight of each farm holding in the sample being used A word of caution for balanced panels the weight remains the same and therefore does not take into account the possible effects of farm holdings dropped from the selected data sample Allocation of residuals option The residual difference between the estimated and observed costs for each farm holding is distributed over the different products in proportion to gross output including home grown consumption or gross product excluding home grown consumption The Allocation of residuals option allows you to edit the breakdown of individual costs 24 The printed results are
30. d 41 Specialist milk 42 Specialist cattle 43 Mixed milk rearing and fattening 44 Specialist sheep and goats 50 Specialist granivores 56 60 Mixed crops 71 grazing livestock 72 granivores 81 field crops and grazing livestock combined 82 Mixed crops and livestock Type of farming at recording A30 Code 1310 Specialist COP other than rice 1320 Specialist rice 1330 COP and rice combined 1410 Specialist root crops 1420 Cereals and root crops combined 1430 Specialist field vegetables 1441 Specialist tobacco 1442 Specialist cotton 1443 Various field crops combined 2011 Specialist market garden vegetables outdoor 2012 Specialist market garden vegetables under glass 2013 Specialist market garden vegetables outdoor and under glass combined 2021 Specialist flowers and ornamentals outdoor 2022 Specialist flowers and ornamentals under glass 2023 Specialist flowers and ornamentals outdoor and under glass combined 2031 General market garden cropping outdoor 2032 General market garden cropping under glass 2033 Specialist mushrooms 2034 Various market garden crops combined 3110 Quality wine 3120 Wine other than quality 3130 Quality amp other wine combined 3141 Specialist table grapes 3142 Specialist raisins 57 3143 Mix
31. d the term Insert is replaced by the term Delete When you click Delete the year to the right of the Or menu will be deleted It is therefore not possible to delete the first year selected unless you delete the whole line In the image example shown below the other option has been selected The user must choose a year and the countries he she wishes to work on with the select all key enabling the user to select or drop all the countries with a single click 19 Available countries YEAR S zo C Constant sample balanced panel Available years Select all C Austria _ Belgium _ Bulgaria C Cyprus C Czech Republic C Denmark C Estonia _ Finland France Germany Greece _ Hungary Ireland C Italy C Latvia _ Lithuania Luxembourg Malta C Netherlands C Poland _ Portugal C Romania C Slovakia Slovenia C Spain C Sweden 1 United Kingdom It is possible to pool several countries which the model will then treat as a single country by following the same procedure as for pooling years tUACIHTVUIY Lj rweunerrunmuo jm ruya Spain Sweden United Kingdom v grouping all countries Austria v OR France Germany OR gt ler Ireland w Yv There is also another available option The checkbox grouping all countries allows user to group all the countries toget
32. e the product aggregate it with a product with similar characteristics or revise the classification variable by aggregating some of the modalities Finally if the user has selected the OUTLIER option in the Java form two other tables will be generated in order to assess the impact of deleting outliers First the number of farm holdings considered as outliers then a table showing part of the data from the OUTPUT table but this time from the sample before deletion of outliers in order to be able to compare results 39 NB of OUTLIER by outputs for fra YEAR Semple Label Unite WAIEAT BARLEY MAIS OTCER POTAT SUGAR RAPE outputs WITH OUTLIERS for fra YEAR Sample Label Units WHEAT DWHEAT BARLEY MAIS OTCER DRYPU POTAT SUGAR RAPE 2007 WITH mean 3585723 31304 21 14280 83 33635 69 5071 00 8041 80 71793 33 27652 97 18058 14 2008 WITH mean j 34769 49 32397 28 16550 46 24296 30 4962 16 9314 70 71623 92 3097936 25372 01 2007 ALL FARMS mean j 2037355 209205 573406 9699 62 1151 00 793 04 3208 37 2929 82 5092 19 2008 ALL_FARMS mean 20018 09 2145 34 655124 7386 60 1136 11 744 18 3091 54 2861 23 658108 2007 ALL ARMS shar 1089 112 3 5 os oa 157 272 2008 ALLFARMS shar s 1038 1n 345 383 05 039 160 148 341 If desired it is very easy to export these output tables from the SAS table format
33. ed livestock granivores amp non dairy grazing 7230 Mixed livestock granivores with various livestock 8110 Field crops amp dairying 8120 Dairying amp field crops 8130 crops amp non dairy grazing 8140 Non dairy grazing amp field crops 8210 Field crops amp granivores 8220 Permanent crops amp grazing livestock 8231 Apiculture 8232 Various mixed holdings Altitude zone A41 Code Definition 1 at less than 300 metres 2 at from 300 to 600 metres 3 at above 600 metres 4 data not available Environmental constraints Area A45 Code Definition 0 data not available 1 Ino environmental restrictions 2 environmental restrictions 59 General type of farming 8 A28 Code Definition 1 Field crops 2 Horticulture 3 Permanent crops 4 Grazing livestock 5 Granivore 6 Mixed cropping 7 mixed livestock 8 Mixed crops livestock Classification UAA CLUAA Code Definition 1 lt 5 Ha 2 5 10 Ha 3 10 20 Ha 4 20 30 Ha 5 30 50 Ha 6 gt 50 Ha Structural Funds area A44 Code Definition 0 data not available 1 none before 2000 2 Objective 1 area before 2000 3 Objective 5b area before 2000 4 Objective 6 area before 2000 5 none since 2000 6 Objective 1 area since 2000 7 Objective 2 area since 2000 8 area elig
34. ed vineyards 3211 Specialist fresh fruits other than citrus 3212 Specialist nuts 3213 Fresh fruits other than citrus and nuts combined 3220 Citrus fruits 3230 Fruits amp citrus fruits combined 3300 Olives 3400 Various permanent crops combined 4110 Milk 4120 Milk amp cattle rearing 4210 Cattle rearing 4220 Cattle fattening 4310 Dairying with rearing amp fattening 4320 Rearing amp fattening with dairying 4410 Sheep 4420 Sheep amp cattle combined 4430 Goats 4440 Various grazing livestock 5011 Specialist pig rearing 5012 Specialist pig fattening 5013 Pig rearing and fattening combined 5021 Specialist layers 5022 Specialist poultry meat 5023 Layers and poultry meat combined 5031 Pigs and poultry combined 5032 Pigs poultry and other granivores combined 6010 Market gardening amp permanent crops 6020 Field crops amp market gardening 6030 Field crops amp vineyards 6040 Field crops amp permanent crops 6050 Mixed cropping mainly field crops 6061 Mixed cropping mainly market gardening 58 6062 Mixed cropping mainly permanent crops 7110 Mixed livestock mainly dairying 7120 Mixed livestock mainly non dairy grazing 7210 Mixed livestock granivores amp dairying 7220 Mix
35. er 128 Autres c r ales _360 360 Peas 360 Pois 12 _361 361 Lentils 361 Lentils 13 _330 330 Other protein crops 330 Autres prot agineux 14 130 130 Potato 130 Pommes terre 15 _131 131 Sugar beets 131 Betteraves sucri res 16 331 331 Rape 331 Colza 17 _332 332 Sunflower 332 Tournesol 18 _333 333 Soya 333 Soja 19 334 334 Oil seed other 334 Autres ol agineux 26A Lin AGA I264 Claw region crop livestock livest cost cost fixed crop and livestock tabs and the three types of costs livest cost crop cost and fixed cost are done on the same model If you wish to add a production put it at the end indicating its label in the first column and the code key in the following columns Caution If you add a variable you must modify the SAS code in Enterprise Guide declaration of the variable 49 The last tab grouping contains the variables which can be used as classification variables and as filter variables selecting a filter B C D E year grouping id english francais 2 grouping 1995 A1 A1 Region A1 Region 3 grouping 1995 TF8 TF8 Type of farming 8 TF8 Orientation technico economiqt 4 grouping TF8 1 1 Fieldcrops 1 Cultures de plein champ 5 grouping TF8 2 2 Horticulture 2 Horticulture 6 grouping TFS 3 3 Wine 3 Vin 7 grouping TF8 4 4 Other permanent crops 4 Autres cultures
36. esecesncsdeses 3 CONTENTS O N 5 ABBREVIATIONS AND ACRONYMG ccssscsscscsssssssscccsccsscsssscecsccccssssessececccccssssscscccescesscsssscescccsses 7 INTRODUCTION o ES 8 MODEL PC Ip 10 WARNINGS Hn 11 THE MAIN OPTIONS OF THE MODEL w cccsssscssssscscssssccsessscccscssccccssseccesssccccessceccesseccsesssesces 12 THE FORM COMPONENT isiisessccccscssossesniciactecccssedeuccsexacescssseceusdantecectesteeesdascesaceesesessveseseassoveseacters 13 OPEN Ee E i g ak s aan dak S ad ba gain jaaa 13 MENUS o sekako o BE EA OER E E SE DB BE SA EA 14 THE io de 16 COMPLETING THE FORM THE GENERAL TAB cceccseeesesesesssesesesesecesereserssesesesesecesecesesesereseresereseseeerens 16 The 16 The Options Sub t b es RERO CR HURTS RUE C EEUU HOO EO Eee ri IN CHE 21 The Filters a I 26 COMPLETING THE FORM SELECTION OF CROP AND LIVESTOCK PRODUCTIONS AND DIFFERENT TYPES OF COSTS 29 Go 31 e e AN O i A i E Oe mee ene ae ee 32 K A A m 32 EXAMPLE OF A PARAMETERS d
37. f Type vw Came C Finland 1 France Germany Now click on the second Browse button next to the Results directory and in the same way select the directory where the model will save the results files When you click Open the Java form automatically creates a directory named Results in the selected directory The Results directory contains three sub directories HTML SAS and EXCEL If you have already created a Results directory be careful the form will not delete it but will add the three sub directories if they are not already present If those three sub directories have already been created files can be erased by new results of the FACEPA Model if the files results have the same name 17 We now arrive at the selection of data used by the model The first thing to do is to decide whether you want the estimation results 1 for a single country over a period of several years 2 for asingle year for one or more countries 2 Available countries YEAR S from to Available years Select all Austria Belgium Bulgaria Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Rom
38. h of these tables there is an associated table detailing the main types of costs along with a second table which gives the total costs per intensity group if the Allocation of residuals option is selected see the explanation below Costs of production in per ha for 2007 County YeerNPUT UY WHEAT UY OWHEAT UY BARLEY UY MAIS UY OTCER UY DRYPU fa 2007TOTAL mezos 111802 913232 175640 695 202 829 700 fa meme po op pop u COS ra 2007 SEED 5 1038649 5727 1204 1 0854 2007FERTL 3 180 101 22465 19545 76045 152 06 115294 112 9345 14618 2007 MOTFUE 5695 8254 560 8649 59013 4453 2007 OENERG 244 ra 2007 CONWOR 4509 4825 30992 17618 95885 25 124 fa porem su 328 r 1023 18005 fa 2007 INNTINTHSIC RRA 137 R 921 Summary in ha for 2007 Label YEAR WHEAT DWHEATIBARLEY OTCER DRYPUI POTATI SUGAR RAPE OCROP CATT Production prce 2007 1163 08 1116 02 91323 175040 695 20 020700 6043 85 2133 98 803 73 422248 8084 mes POTOP 3629 E 1668 125 23M ZM asi 942 4 737 Subsides O07 4085 047 39143 160 95 46529 13755 70 90 53040 261 34 438 68 Basic price 2007 1575 39 1643 30 1295 84 2228 69 1149 25 941 131 6022 02 615 58 1060 66 4259 49 1115 43 Variable costs 2007 45447 46201 40832 55249 419 07 4262 174250 93112 275 34 52841 405 14 579 9 402 29
39. her with a single click In this way the user can obtain results for the EU 15 EU 25 or EU 27 Important When working on several countries it is no longer possible to choose the region variable A1 in the classification variables presented below Please find the list of countries and years available in appendix 2 20 The Options sub tab eneral Crops Livestocks Costs eneral Options Filters Complementary outputs Output formats Test Costs of production in per ha SAS table Standard error Costs of production in per q EXCEL sheet T value Costs of production in per LU HTML document P value Costs of production in per head Output by production and by year only on EXCEL RES SI Test The model estimates the production cost coefficients with the SAS Syslin procedure It is possible to select here the test statistics used by the Syslin procedure to assess the statistical significance of the coefficients You can choose between the following options Standard error editing the standard error of the estimated coefficients T value editing the T statistic value estimated coefficient standard error P value editing the probability level of the T statistic value in a Student distribution Output formats The model generates output tables in SAS format by default but it is also
40. hese costs will be attributed There are also a number of options which allow the user to define the sample of agricultural holdings chosen for the estimation of the model according to several criteria such as country year type of farming economic size etc In short the user must select at the outset the parameters in order to define the model This manual explains to uninitiated users how the model works and how to use it There are many files stored in the FACEPA directory Some of these files interact with each other we recommend that new users copy the directory directly into their hard drives C or D in order to limit potential errors and make their first trials easier File Name Comments In the folder D FACEPA Form_xml Form jar Form for defining parameters used by the model File which contains the names of the fields in the form and daa the labels of the available variables In the folder D FACEPA Form_xls Form jar Form for defining parameters used by the model File which contains the names of the fields in the form and smjeni the labels of the available variables F Offermann Implementation validation and results of the cost of production model using national FADN databases FACEPA Deliverable D3 1_vTI January 2011 W Kleinhanss Implementation validation and results of the cost of production model using the EU FADN FACEPA Deliverable D3 2_vTI April 2011
41. home grown consumption enables the user to work on gross production add home grown production used directly on the agricultural holding 25 Be careful however do not forget to add the cost variables representing expenses devoted to home grown feeds by herbivores SE315 pigs F69 poultry F70 and home grown seeds and plants 5 290 Breakdown of cost estimates with a classification variable It is also possible to add a classification variable to the list of selected variables in order to obtain cost results per sub group for instance per region or per type of farming By selecting a classification variable two buttons appear on the left of the drop down menu The green plus sign enables the user to add a group of variables and the red cross box to delete one It is thus possible to group two or more modalities categories of the selected classification variable into one modality Warning Using a classification variable requires a lot of system resources due to the large number of tables that are edited Furthermore the amount of output results that are generated is multiplied by the number of categories for the selected classification variable these output results are therefore more difficult to read and interpret Comment a single classification variable can be used to avoid Too many tables Too long calculation time Misleading information stemming from too small samples The Filters sub tab It is possible
42. ible to transitional support since 2000 60 Crops Description Formula EU FADN name Wheat common K120TP Wheat durum KI21TP Rye K122TP Barley K123TP Oats K124TP Summer cereal mixes K125TP Grain maize K126TP Rice K127TP Other cereals K128TP Peas K360TP Lentils K361TP Other protein crops K330TP Potatoes K130TP Sugar beet K131TP Rape K331TP Sunflower K332TP Soya K333TP Other oil seed K334TP Flax K364TP Hops K133TP Tobacco K134TP Other industrial crops K135TP Fresh vegetables open field K136TP Fresh vegetables market gardening K137TP Fresh veg under glass excl tomatoes K138TP K337TP Tomatoes K337TP Mushrooms K139TP Flowers open air K140TP Flowers protected K141TP Grass seed K142TP 61 Other seeds K143TP Fodder roots and brassicas K144TP Other fodder plants K145TP Fallow land K146TP Temporary grass K147TP Other arable crops K148TP Permanent pasture K150TP Rough grazing K151TP Pome fruit excl table grapes K349TP Stone fruit excl olives K350TP Nuts K351TP Small fruits and berries K352TP Tropical fruits K353TP Citrus oranges K354TP Citrus tangerines mandarines clementines K355TP Citrus lemons K356TP Citrus other K357TP Olives table K281TP Olives for oil production K282TP Olive oil K283TP Olive b
43. imations The combination of filters classification variables and deletion of outliers can rapidly diminish the number of observations Furthermore the model can take a long time to generate production cost coefficients especially if many countries or years are selected It is therefore strongly recommended to start by using the inter tab egp program before running the model This program which also runs with Enterprise Guide enables the user to generate intermediate tables which will inform the user on the variables defined in the form average costs number of farm holdings production share etc In so doing the user can get a quick idea of the selected sample and can if need be return to the java form to make changes in case that problems such as non existing production wrong choice of options etc crop out The program inter tab egp can be found in the FACEPA SAS_Pgm directory Double click to open this program ERATE OSD PET RNA NN AMETE PSR OP RTE SR File Edit View Tasks Program Tools Help Br g Ba M mv bes Process Flow 3 eg Process Flow gt Run E Stop Export Schedule Zoom 52 Project Log Programs i E inter tab E inter tab The process flow only contains one code Double click to open so as to indicate the path of the directory containing the parameters file to be tested 37 Tools Help 4 73 v begFluxde processus inte
44. ing the SAS PROC SYSLIN procedure K gt Ty k l where xis the total cost of input i paid by farm f including income is the total value of output k produced farm f fix is the unknown coefficient of production It is defined as the average for all farms expenditure on input i required to produce one unit on value of output value is the error term specific to each input i and farm f K Du 1 k l and net farm income being considered as an input with 10 Warnings It is not essential that the production list chosen by the user be exhaustive As all of the productions are included in the model a residual term is automatically calculated This residual term is obtained for both crop production OCROP and livestock production OLIST This distinction is made because crop costs are restricted to zero for livestock production Fodder is not given a value in the European FADN database Similarly costs related to livestock are also equal to zero for crop production Please note that crop production includes forestry and that livestock production includes other productions contract rearing income from occasional rental of grazing areas custom work farm tourism honey and products from apiculture The model distinguishes between costs related to livestock costs related to crops and fixed costs Total cost however is not given This is justified on the grounds that it allows the
45. les for crops and livestock which will be used by the model The Costs tab consists of three sub tabs Livestock costs Other variable costs crops costs and File Checking Fixed costs allowing the user to define which gt General Livestocks Costs kinds of costs will be taken into consideration by Livestock costs Other variable costs Fixed costs the model o ccc diee diee a mec adi oesie Completing the Form The general tab It is recommended that you complete the form in the order presented here The General sub tab First of all you must choose the location of the SAS format database to be used by the model as well as the location of the directory where the estimation results coefficients tests statistics etc will be saved 16 File Checking General Crops Livestocks Costs General i Options Filters Define your locations SAS databases Results directory Click on the Browse button next to SAS databases to open the file explorer situated in the user s My documents folder and scroll to find the directory with the European FADN database in SAS format then click open to validate File Checking General Crops Livestock Costs General Options Filters Define your locations SAS database Browse 4 Open Look In FACEPA FileName D SAS bases Files o
46. ll appear in the results tables This choice is imposed in order to calculate the output at basic prices output coupled subsidies taxes The average price of AWU by region multiplied by the family AWU per farm 201 X SE370 X SE020 by region SE010 SE020 The average price of the rent by region multiplied by the UAA in owner occupation per farm 202 SE375 gt SE030 B50 by region B48 The average price of the interest by region multiplied by SE436 total assets minus loans SE490 SE495 minus computed rent Z02 If computed interest is negative then it is set as zero 203 X SE380 x SE490 5 495 Z02 if 203 lt 0 then 203 0 65 by region S E436 S E490 S E495 Appendix 3 forage area allocated to livestock The areas of fodder crops and pastures temporary or permanent are allocated to livestock productions equines cattle sheep and goats cow milk ewe milk goat milk according to the number of LU For cattle sheep and goats the allocation of milk and dairy products is done by quantity Example for sheep milk Forage area to be allocated SHERB K144AA K145AA K147AA K150AA Number of herbivore LUs UHERB LU_51 LU_52 LU_55 LU_54 Ewes are considered as dairy sheep if milk production is over 5 of the production of the sheep farm If K164TP K167TP E54TO 164 K167TP g
47. ment 8 Please wait during the load Veuillez patienter 9 10 menu menu 11 File Fichier 12 Parameter Parametre 13 Configuration Configuration 14 Checking Verification 15 Open Ouvrir 16 Exit Quitter 17 Save Enregistrer 18 Save as Enregistrer sous 19 exit application quitter l application 20 open a file ouvrir un fichier parametres 21 save a parameters file enregistrer un fichier param tres 22 save a parameters file as enregistrer un fichier param tres sous 23 Check fields V rifier n Itis possible to modify the labels of the Java form To add a language the model must be copied in the following column and all labels indicated including in the other tabs otherwise the form will not be able to run 48 B D first yearllast year country id english francais reg id ral 1995 2008 bel Belgium Belgique 340 341 343 EA 2007 2008 bgr Bulgaria Bulgarie 831 832 833 834 835 836 4 2004 2008 Cyprus Chypre 740 E53 2004 2008 cze Czech Republic Republique Tche 745 6 1995 2008 dan Denmark Danemark 370 7 1995 2008 deu Germany Allemagne 10 20 30 50 60 70 80 90 100 112 113 114 115 116 8 1995 2008 ell Greece Grece 450 460 470 480 Foi 1995 2008 esp Spain Espagne 500 505 510 515 520 525 530 535 540 545 550 555 560 56 10 2004 2008 est Estonia Estonie 755 11 1995 2008 fra France France 121 131 132 133 134 135 136 141 151 152 153 162 163 16 12 2004 2008 hun Hungary Hongrie 760 761 762 763
48. ngle country over a period of several years or for a single year for one or more countries It is possible to add a classification variable to the list of given variables in order to obtain cost results per sub group per region or per type of farming It is also possible to obtain more specialized cost results The model takes into account total production automatically The user defines a list of crop and livestock outputs Residual terms corresponding to the remaining productions and maintaining the balance between revenues and costs are automatically calculated Variable and fixed costs are distinguished and calculated Depending on the user s choice of costs determines the type of income indicator estimated by the FACEPA model For instance if the user takes only into consideration costs associated with intermediate consumption the income indicator is value added The model could include imputed costs for family owned factors such as family labor farm owned land and family owned assets However this procedure to calculate these imputed costs was not tested by the vTI team More generally it is necessary to be careful in interpreting the model results on fixed costs In the European FADN databases outputs are valued at the producer price levels In the FACEPA model subsidies are considered as negative costs It is possible to select coupled or total subsidies In the final model printing results subsidies are included in the basic p
49. nly if the parameters file is incomplete 2 number of countries 00 because the per year option is selected 3 path of the directory containing the SAS databases 4 path of the directory where results will be saved 5 list of variables that have to be positive so that the farm observation is kept in the sample 0 otherwise 6 productions and costs included by the outlier option YAi relating to crop and livestock production to costs 0 otherwise 7 line generated by the filter with string type variables blank line otherwise 8 line generated by the filter with numerical variables blank line otherwise 9 line generated if there is an aggregation of classification variables 0 otherwise 10 selected classification variables otherwise 11 line generated if the aggregation of countries if the option per country is selected or years if the option per year is selected 12 formula to calculate of crop variables crp and livestock variables liv 13 formula for the calculation of different costs 14 labels of crop variables livestock variables and different costs 36 Validation of parameters and intermediate tables The form ensures that the user will obtain a parameters file avoiding the syntax errors in the model However the user must take certain precautions to obtain the most reliable estimation results as possible The sample being used must contain enough observations to back up the est
50. now all allocated except the significance tests for the value of the coefficients estimated before the Allocation of residuals This option also allows the user to create three groups which sort out the farm holdings according to three levels low medium and high of intensity in inputs used to produce a given product The low level input 1 low group represents 30 of the farm holdings in terms of weighted area and has the lowest production cost coefficients The Mid level input 2 mid group represents the 40 in the middle range and high level input 3_high the 30 with the highest production cost coefficients The output result table generated by this option gives average production cost coefficient for each group and for each production as well as the sums of the area the productions and the corresponding quantities Coefs for total cost by group for 2007 Country Year GRP__ Label DWHEAT amp MAIS OTCER DRYPU sum sum 2007 1 low 38411 66 4278 07 13464 07 13424 80 4291 60 1943 15 2007 1 low output 51446244 00 5952404 00 13603750 00 26441027 00 3329316 00 1897769 00 7289197 00 6500 sum 2007 1 low quanti 2657593 00 194704 00 77448200 1357917 00 193613 00 7999 10 459228 00 2460 2007 1 low coef 0 59 0 75 0 75 0 70 1 55 0 37 0 63 2007 2 mid sum area sum 51678 10 5244 19
51. ns that have been selected by the user in the form The first table named OUTPUT contains information about crop and livestock production defined in the form number of farms and average production values The Sample column indicates whether the data concern all farm holdings in the defined sample ALL FARMS or just those with a positive value for the production of interest WITH This table also shows each share of each production with respect to the total production value OUTPUTS for fra YEAR Sample___ Label_ Units WHEAT DWHEAT BARLEY MAIS OTCER DRYPU POTAT SUGAR RAPE 2007 ALL FARMS total nb farms 7362 00 736200 7362 00 736200 736200 7362 00 736200 736200 7362 00 2008 FARMS total nb farms 7460 001 7460 00 7460 00 746000 746000 7460 00 7460 00 7460 00 2007 WITH number farms 418100 49200 2950 00 211700 1660 00 719 00 32700 78000 2076 00 2008 WITH number farms 428200 49300 297300 223700 167700 57600 31800 68700 1935 00 2007 35857 23 31304 21 14280 83 33635 69 5071 00 8041 80 71793 33 27652 97 18058 14 2007 ALL FARMS mean 20373 65 2092 05 573406 9699 62 1151 00 793 04 3208 37 2929 82 5092 19 2007 ALL FARMS share amp 1089 11 306 518 05 oa 1r 15 272 2008 ALL FARMS share 1038 1 345 383 05 0 180 148 Gross production for the with home grown option gro
52. om 1995 Estonia est from 2004 Finland suo from 1995 France fra from 1995 Germany deu from 1995 Greece ell from 1995 Hungary hun from 2004 Ireland ire from 1995 Italy ita from 1995 Latvia from 2004 Lithuania ltu from 2004 Luxembourg lux from 1995 Malta mlt from 2004 Netherlands ned from 1995 Poland pol from 2004 Portugal por from 1995 Romania rou from 2007 Slovakia svk from 2004 Slovenia svn from 2004 Spain esp from 1995 Sweden sve from 1995 United Kingdom uki from 1995 52 Available variables used for classification or in the first filters Region A1 Countries with several regions BEL BGR DEU ESP FRA HUN 500 SVE UKI 340 831 010 450 500 121 1760 221 785 610 1840 1670 710 411 341 832 020 1460 1505 1131 761 222 1790 620 841 680 720 412 343 833 030 470 1510 132 1762 230 795 630 1842 690 730 1413 834 1050 480 515 1133 1763 241 800 1640 1843 1700 421 835 1060 520 1134 1764 242 650 844 431 836 070 525 135 1765 243 845 441 080 530 1136 1766 244 846 090 535 141 250 847 100 540 1151 260 112 545 152 270 113 550 153 281 114 555 162 282 115 560 163 291 116 565 164 292 570 182 301 575 183 302 580 184 303 192 311 193 312 201 320 203 330 204 Region A1 suite Countries with only one region CZE DAN EST IRE LTU LUX LVA NED OST SVK
53. ot total production meat milk Extract for SYSLIN proc Creates the database used by the SYSLIN procedure containing he information that is strictly necessary in terms of variables for greater efficiency For each variable defined in the form by the user the program generates the associated variables containing production area quantities livestock units number of heads and the label defined in the form by the user Proc SYSLIN The SYSLIN procedure estimates parameters in an interdependent system of linear regression equations Tables that are generated are as follows 45 Temp gt coefficients estimated by the proc SYSLIN t_syslin_fitstatistics gt Goodness of fit statistics including Adjusted R square t_syslin_parameterestimates gt tests t_syslin_modelvarsl gt names and labels of the model Output SYSLIN Retrieves and treats tables created by the SYSLIN procedure and generates the following tables from the results 1 2 3 4 5 6 7 8 9 T_SUM_F gt descriptive statistics TABI gt production coefficients TAB2 gt Tests TAB3 gt Costs per hectare gt Costs per quantity TABS gt Costs per LU TAB6 gt Costs per head RECAP_TAB3 gt Recap per hectare RECAP_TAB4 gt Recap per quantity 10 5 gt Recap per LU 11 RECAP TAB6 gt Recap per head 12 SUM GRP Coefficients and other information per adjustment group 13 T MEAN AA Total cost per hectare
54. per adjustment group 14 T MEAN gt Total cost per quantity per adjustment group 15 T MEAN LU Total cost per LU per adjustment group 16 T MEAN NB Total cost per head per adjustment group Export The last treatment of output tables before exporting results tables in the desired format 46 The data_file This file contains the information required to open the Java form such as the available productions and costs the variables and their labels the countries and years that are available This data file comes in two formats XML and EXCEL and in different languages English and French at present Data_file XML To open the XML file in order to modify it Notepad is recommended free software Here are some screen captures Java form labels data_file xml 9 0 KE Ta ip D lt english gt lt accueil chmp Welcome gt lt accueil chmp Choose your language gt lt accueil chmp 0K gt menu chmp File gt lt menu chmp Parameter gt lt menu chmp Configuration gt menu rhmn Chenkina gt It is possible to modify the Java form labels Be sure to choose the concerned language english or francais To add another language this model must be copied and all the labels must be indicated otherwise the form will not be able to run Data available 172 173 174 J O Oc H HH O CD N H i 2 b C l
55. possible to obtain this output as an Excel table and or an HTML page To generate output results in EXCEL form h must h Microsoft EXCEL install n th m r If n Enterpri SAS Table j coef for fra sas7bdat FEEDPC FEEDHC VETCOS SEED 0 0482168 0 0669046 FERTIL 0 1379386 0 1002876 0757440 0 1062227 0 1258185 2737299 0 1638667 0 2904318 MOTFUE 0 0562480 0 0504580 0 0760358 0 0697895 0 1028122 OENERG 0 009116 0 0333143 0 0099268 0 0415301 0 0345491 CONWOR 0 0415923 0 022143 0 0083857 0 1531950 0 1949287 BUILUK 10 0145911 0 0134485 0 0062281 0 0014490 0 0074858 MACHUK 0 0555209 0 0670541 0 0709144 0 0572933 0 1233400 OTHSIC 0 0723480 0 1195190 0 1343450 0 1085341 0 3551349 0 0 CRPROT _ 0 1978563 10 1357603 0 1766616 0 0729843 0 16527701 0 0 21 EXCEL Table 2006 FEEDPC Value 2006 FEEDHC Value 2006 VETCOS tValue 2006 SEED 2006 FERTIL tValue 2006 CRPROT 2006 MOTFUE 2006 OENERG 2006 CONWOR Value 2006 BUILUK tValue 2006 MACHUK Value 2006 OTHSIC tVelue 2006 LANDCO Value 2006 INTERE Value 2006 DEPREC Value 2006 TAXES 2006 SUB_ TOT Value 2006 Netval Value 0 945 0 46506 0 83499 0 84678 091574 0 92596 0 90202 0 73525 0 75024 0 38107 0 8146 0 87655 0 89782 0 73615 0 87644 0 65711 0 94263 0 79372 10 50543 25 10301 42 15444 20 56975 4 088953
56. ppendix for the complete list If you select insert in the second drop down menu a new drop down menu appears with a list of remaining productions This is so as to be able to concatenate these productions into a single variable You can add as many productions as you wish from the list but it is not possible to use the same production twice 30 There are limits however to the number of variables being created It is thus possible to define 47 crop production variables 21 livestock production variables eight variable livestock cost variables five variable crop cost variables and 18 fixed cost variables not including the variable F83 taxes which is included in the model and subsidies which are defined separately from fixed costs Certain other types of productions have been created to avoid duplicates For example oil seed crops are broken down into rapeseed sunflower soya and others To add the oilseed crop output variable you must select the sum of the above mentioned productions as explained above It is easy to aggregate the variables in the form The cost of production model automatically creates two variables OCROP Other Crops and OLIST Other Livestock They respectively correspond to the residual crop and livestock productions not included by the user in the java form This is so as to include all productions of farm holdings in the regression estimations This operation is not done for the costs Crops
57. r_tab 3 Stop Selected Server Local Connected YW Analyze Program Export Send Create We le e he le e her f ER HERE THE LOCATION OF THE PARAMETERS FILE 89 e e e lu e e a e e e e a he lu a e e e e e e e e be e he bu e e e V e bu e be e a e e t bu e ie e e e du e e eu A filename com C FACEPA parameters file txt g 8 9 B e he hu e de she lu e lu d be bu e e de be de e e be de d e du e e bu e lo he e d e e dee f f STEP 2 get back the parameters file Be e lu e e e hu he e he de e e be e e be de le e du e e be e lu e e e be e de ske f EDATA DT1 INFILE COM LRECL 1000 INPUT ama 1 G1 Tampa C1 Correct the path in single quotes after the filename and indicate the path of the directory where the parameters file you wish to test is stored After you have modified the path click on Run to execute the inter tab program This program generates two main tables or more depending on the optio
58. rameters Extracts the parameters file containing the options chosen by the user the variables defined with their labels and calculation formulae As for output the COM table contains in a single line all information from the parameters file required for the model to work Extract SAS bases This part of the program extracts the databases in SAS format as many databases to extract as there are countries or years selected If several countries or years are selected these databases will be concatenated so there will be only one table in output BASE Crop live input other misc crop is for data relating to crops live for data relating to livestock input for data relating to costs other concerns the other variables and misc other general variables Each of these five codes retrieves variables required by the model renames them and adds their labels It also creates a variable called IDENT which is a concatenation of variables Al A2 and A3 Merge Merges the five previously created databases Forage areas are allocated proportionally between meat production and milk production from different herbivores Livestock units and heads are allocated proportionally between meat and milk production Caution after running this part of the program LU and NB 52 NB 54 et NB 55 which correspond respectively to livestock units and the number of heads of cattle sheep and goats will be associated with meat production and n
59. rices This solution is in theory only valid for coupled subsidies but it is possible to include single farm payments SFP and the second pillar payments Taxes on products are also automatically deducted in the calculation of basic prices It is possible to obtain information to assess the statistical significance of the estimated production cost coefficients Standard errors t statistics and p values Different options are possible It is possible to delete outliers The results could be weighted or not with the SYSO2 variable which corresponds to the weight of the holding in the sample used Output value for crop and livestock could include or not on farm use of feeds and seeds If the user includes home grown production used on farms it is necessary to adjust the used inputs accordingly The Allocation of residuals option allows the user to print breakdown of individual costs The residual difference between the estimated and observed costs for each farm holding is distributed over the different products in proportion to the gross output with home grown consumption or the gross product without home grown consumption This option also creates three groups of farms which differ in terms of intensity in the use of inputs depending upon the levels of specific costs obtained for each product Contents EXECUTIVE SUMMARY ssscc sisssscadcessssocsssesssseseessnsedoascadesosssossbonccsdesbuccesesendesduesdecesabeosesaeseues
60. rocedure was not validated by the vTI team More generally it is necessary to be careful when the model includes fixed costs 12 The Form component The form component called hereafter either form or the form is a graphic interface written in Java language which enables the user to create the parameters file parameters file txt This form was developed to help uninitiated users avoid unwanted errors It aims to be as user friendly and exhaustive as possible The parameters file created by form contains all information concerning the choices and options defined by the user Form comprises two elements Form jar which is the program itself and data_file xml which contains all the data needed by form labels menus variables and terms available These data are not contained in Form jar so as to enable the user to add new languages or to modify the variables or years without having to modify the Java code There is also a version of the form associated with an Excel file data file XLS instead of XML which allows for new variables to be added more easily To open form double click on Form jar First you must indicate where the file xml file used by form is located This user guide will use the XLM version of form as its reference The procedure for the Excel version is the same apart from this first step where the user must indicate the location of file xls instead of data file xml
61. s farm holdings that meet the selected criterion 27 Selecting a sample ee Ir o s mE ww For Crops Livestock the variables used for filtering the output values gross production if the option Including home grown consumption In the example above we have saved only the farm holdings that have a production of barley greater than ten Euros In order to select a sample of farm holdings with a selection criterion other than gross production or a physical quantity this is possible with a direct modification of the corresponding instruction in the parameters file Example a selection of farms with at least 10 hectares of barley is desirable Select barley when selecting the sample see above When the parameters file is completed save it and open it in the bloc note Then replace the command if O_123 gt 10 LE DIS by the command if A 123 10 kai 5 It is possible to do the same with other outputs O_xxx for gross output or gross product values A_xxx for areas Q xxx for gross output or gross product quantities or volumes with xxx the code for the list of crops or livestock see appendix 2 28 Completing the form selection of crop and livestock productions and different types of costs File Checking General Crops Livestocks Costs Livestock costs
62. ss product for without home grown option 38 The INPUT table contains the average value of the costs defined by the user for all of the farm holdings in the sample ALL FARMS or for those with a positive value WITH concerned by these expenses INPUTS for fra ALL FARMS 23319 71 6924 91 11481 LE 2007 WITH mean 34519 74 6895 04 5416 04 3631741 11400 82 2008 WITH mean 4116542 7136 19 7210 03 383182 12046 19 If you have chosen a classification variable Organic Farming in the example below a supplementary table is generated showing the number of farms per type of production for the different modalities of this classification variable Number of farm by year and by Organic Farming D WEIGHTED nb of farm 4035 479 2859 2069 1580 306 756 2025 WITH NOT WEIGHTED nb_of farm 63 2 8 4 10 1607 WITH NOT WEIGHTED nb of farm 57 39 26 nb of farm 2008 2 nb of farm 2008 3 nb_of_farm The preceding table can provide weighted or unweighted results depending on whether the user has selected the Weighted option in the form The indication WEIGHTED or NOT WEIGHTED in the Sample column reminds the user of the option that has been selected in the form If the number of holdings is too small for a product it is preferable to return to the Java form to modify the parameters Delet
63. t 0 05 then LU ewes D40AV 0 1 With K164TP gross ewe milk product K167TP gross ewe dairy product E54TO gross product sheep meat mutton D40AV number of sheep U0410L LU dairy sheep LU_meat LU_sheep LU_ewes Area allocated to sheep meat A_54 SHERB LU meat UHERB The area allocated to dairy sheep is divided between milk and cheese Milk A 164 SHERB LU ewes UHERB 16400 K164QQ Q167Q0Q Dairy product A 167 SHERB LU ewes UHERB 016700 16400 016700 66 To ensure coherence in the model the forage area SHERB is reduced from the allocated areas A_54 164 167 Comment Pig poultry and eggs have no area allocated if option Including home grown consumption is selected If this option is not selected the cereal growing area corresponding to this home grown consumption is allocated according to the number of livestok units With this option the cereal growing areas are also allocated to herbivores corresponding to the area of home grown consumption from the total production according to the number of livestock units Example for pig O CER the sum of the cereal productions K120TP K121TP K1227TP K123TP K124TP K125TP K126TP K128TP A CER the sum of the cereal growing areas KI20AA K121AA K122AA K123AA K124AA K125AA K126AA K128AA If O_CER is positive then home grown production INTRAP of granivore feed
64. t donnee gt lt langage gt francais english lt langage gt lt date_end id 2007 gt lt pays id bel gt lt date_ start gt 1995 lt date start code postal 340 341 343 code postal lt francais gt Belgique lt francais gt lt english gt Belgium lt english gt lt pays gt lt pays id dan gt date start 1995 date start code postal gt 370 lt code_ postal 4frannaia hanemark franoceaia 47 349 lt vegetaux id 0 120 gt 350 lt francais gt Ble tendre lt francais gt lt english gt Wheat common lt english gt F lt vegetaux gt E lt vegetaux id 0 121 gt lt francais gt Bl dur lt francais gt lt english gt Wheat durum lt english gt lt vegetaux gt E lt vegetaux id 0 122 gt lt francais gt seigle lt francais gt lt enalish gt Rve lt enalish gt in w N t9 WwW W ih X T O O It is possible to modify this list of data and also to add or delete productions or costs or to add available countries and years Be sure to always follow the model with signposts per type of variable and per language Data_file XLS To open the Excel file in order to modify it Microsoft Excel is recommended Here are some screen captures The Java form labels D 5 2 3 home home 4 Welcome Bienvenue 5 Choose your language Choix de la langue 6 Valider Loading Charge
65. ter opening the file Save as Saves the file A window opens to choose the name and location of the file There are a number of fields of information see below required for the model to use the parameters file but it may be saved at any time However an incomplete parameters file cannot be read by the model and such files when saved will be labeled as incomplete in order to limit ambiguities Exit Allows you to exit the application In the Checking menu the Check fields option shows the fields in the form Required fields which have not been filled in will appear in red along with an alert message for each incomplete compulsory field L 1 JL CRPROT Crop protection E E Motor fuel and lubric w SEED Pc i Crop costs fields are not all filled Correct the errors recheck if desired and save your parameters file 15 The Tabs The General tab is divided into three sub tabs General Options and Filters with each one containing general options of the model such as amp File Checking j addresses where databases are to be found General Crops Livestocks Costs T countries and years being selected weighting General Options Filters options sampling options deletion of outliers etc The Crops and Livestock tabs enable you to define the variab
66. tton with a sign brings up a line with five fields The first checkbox preceded by the symbol is an option which allows the user to save only those farm holdings with a positive value for the selected variable For example only farm holdings with a positive production a gross product for the version excluding home grown consumption for the wheat variable sum of variables K120TP and K121TP will be entered into the database used by the model Important This option can considerably reduce the number of farm holdings especially if it is applied to uncommon productions or several productions at the same time It is therefore necessary to check that the obtained database is not empty before running the production cost model The second checkbox followed by the symbol has already been mentioned It is associated with the Outlier deletion option and enables the user to identify which variables he she wishes to use in the deletion of outliers dialog box allows the user to label the variable being created This is essential The label is limited to 14 characters and special characters are not accepted Only letters and underscores are accepted In order to help the user the form automatically generates a name corresponding to the first selected variable but this name is not definitive and can be easily changed The drop down menu enables the user to choose a variable from a list of available variables see a
67. user greater flexibility notably the possibility to select the income indicator The user must however verify that the list of costs is exhaustive in order to ensure the coherence of the results In particular if the home grown consumption option is selected then the variables SE315 F69 and F70 home grown feeds for herbivores pigs and poultry respectively must be added to the list of livestock related costs and the variable SE290 home grown consumed seeds and plants must be added to the list of crop related costs Remember that in the FADN database production is calculated at the producer price level Subsidies are considered as negative costs It is possible to select coupled or total subsidies In the final output subsidies are included in the basic prices This solution is in theory only valid for coupled subsidies but it is possible to include Single Farm Payments SFP and the second pillar pillar payments Taxes on products are also automatically deducted in the calculation of basic prices In addition to estimating production cost coefficients it is also possible to obtain cost per quintal 100 kg per hectare per livestock unit and per head The cost per quintal is obtained by multiplying the production coefficient by the output price It is not given for certain activities where the quantities are unknown which is the case with for some livestock productions As to costs per hectare the fodder area is divided
68. ve 9 33192 8 51472 8 65117 8 91593 8 77460 uki 0 71047 0 70262 0 75273 0 77121 0 78289 0 83666 0 79427 0 68228 0 68142 0 64623 year 2000 2001 2002 2003 2004 2005 2006 2007 2008 bel 40 33990 40 33990 40 33990 40 33990 40 33990 40 33990 40 33990 40 33990 40 33990 bgr 1 95580 1 95580 cyp 0 58192 057683 0 57500 0 58263 0 58263 cze 31 90567 29 78400 28 34200 27 76600 24 95900 dan 7 45380 7 45058 743052 7 43114 743990 745180 7 45910 7 45060 7 45595 deu 1 95583 195583 195583 195583 195583 1 95583 1 95583 1 95583 1 95583 ell 0 33664 0 34075 0 34075 0 34075 034075 034075 0 34075 0 34075 0 34075 esp 166 38600 166 38600 166 38600 166 38600 166 38600 166 38600 166 38600 166 38600 166 38600 est 15 04660 15 64660 15 64600 15 64600 15 64660 fra 6 55957 6 55957 6 55957 6 55957 6 55957 6 55957 6 55957 6 55957 6 55957 68 ost pol por rou suo sve svk svn uki 0 78756 1 93627 40 33990 2 20371 13 76030 200 48200 5 94573 8 45489 0 61438 0 78756 1 93627 40 33990 2 20371 13 76030 200 48200 5 94573 9 25269 0 61853 0 78756 1 93627 40 33990 2 20371 13 76030 200 48200 5 94573 9 16110 0 63689 0 78756 1 93627 40 33990 2 20371 13 76030 200 48200 5 94573 9 17367 0 69559 0 25178 0 78756 1 93627 3 45286 40 33990 0 66508 0 42794 2 20371 13 76030 4 53224 200 48200 5 94573 9 12430 40 03283 239 06690 0 68018 69 0 24805 0 78756 1 93627 3 45280
69. y products K284TP Table grapes K285TP Grapes Wine quality K286TP Grapes wine other than quality K287TP Misc vine products K288TP Wine quality K289TP Wine other than quality K290TP Raisins K291TP Other vine products K155TP K285TP K286TP K287TP K288TP K289TP K290TP K291TP Permanent crops K156TP Nurseries K157TP Other permanent crops K158TP 62 Young plantations 159 Processed products from crops K160TP By products from crops K161TP Woodland K174TP K175TP K176TP K173TP not available woodland is included in crops outputs Livestock Description Formula EU FADN name Livestock outputs Horses E51TO Cattle E52TO Sheep E54TO Goats 55 Pigs E56TO Poultry E57TO Other animals E58TO Cows milk K162TP Cows milk products K163TP Sheep s milk K164TP Goat s milk K165TP Wool K166TP Sheep s milk products K167TP Goat s milk products K168TP Hens eggs K169TP Other animal products K170TP Contract rearing K171TP Occasional letting forage K172TP Contract work for others K177TP Receipts from tourism K179TP Honey amp bee products K313TP other outputs included in livestock outputs 63 Inputs Description Formula EU FADN name Livestock costs Con
70. yprod excel output by prod and year takes O or 1 fra COUNT Selected country 00 lt NBCOUNT Number of countries 2 2007 FYEAR First year 2008 LYEAR Last year ST TESTB Type of test PV ST or TV 1 parea Costs per ha takes the values O or 1 1 lt pqtl Costs q takes the value 0 or 1 0 lt plvst Costs per LU takes the value 0 or i 0 lt phead Costs per head takes the value 0 or 1 1 lt sasform SAS output takes O or 1 1 xlsform EXCEL output takes 0 or 1 1 htmlform HTML output takes O or 1 1 OUTLIER Deletion of outliers takes 0 or 1 A PVO pvalue outlier C Users FACEPA BASES SAS 3 C USers E ACEPANresuTtS li viv3 6 if A26 in 01 02 10 then delete 7 if SE300 lt 10 4 then delete 8 if a a OR TF8 6 then TF8 10 if TF8 7 OR TF8 8 then TF8 11 9 TFS if 24 est OR 24 sve then 24 cti 11 crpOi o 120 crp02 0_121 crp03 0_123 04 0_126 crp05 0_122 0_124 0_125 0_127 0_128 crp06 0_360 0_361 0_330 crp07 0_130 12 131 9 331 livOi o 52 liv02 0 54 0 0 56 liv04 0 57 liv05 0 16240 163 liv06 0 164 0 167 q4 on7 60 For ACC MI ve mu 13 fixed cost09 F83 subsidies01 SE610 subsidies02 SE615 subsidies03 SE620 SE630 WHEAT DWHEAT BARLEY MAIS OTCER 14 DRYPU 1 line generated o

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