YIELD MONITOR DATA ANALYSIS
Contents
1. Duplicates Definition gt Searching for Duplicate Shapes Duplicates saved to gt d sare cotton aj work theme shp gt theme shp has 2 347 records Results The following 4 sets of duplicates were found Set 1 gt ID 780 rec 699 Saved gt ID 781 rec 700 Deleted Set 2 gt ID 768 rec 8687 Saved gt ID 769 rec 688 Deleted ID 770 rec 689 Deleted Set 3 gt ID 765 rec 684 Saved gt ID 766 rec 685 Deleted gt ID 767 rec 686 Deleted Set 4 gt ID 778 rec 8537 Saved gt ID 779 rec 698 Deleted Analysis Began August 9 2 10 00 PM Analysis Complete August 9 2 10 02 PM Time Elapsed 2 seconds Copy to Clipboard Copy and Close Figure 17 Report on duplicates 16 8 30 2005 Purdue Site Specific Management Center 8 30 2005 Spreadsheets Once the dataset has all the necessary GIS work a spreadsheet such as MS Excel is useful for calculating additional variables These variables may include interaction terms dummy variables of differing coding squaring continuous explanatory variables and a unique identifier field if one has not already been created The unique ID field is required by GeoDa and many GIS functions We typically add a column and name it with our initials an underscore and ID so TWG ID is my brand Some analysts use POLYID by convention Then a sequential set2. 506736 16 Northing 4514313 32 4514735 01 Adjust for Moisture oz o HI I Yield Statistics Mean STD CV N Range Clean 213 39 18 92 8 9 6197 128 291 Raw 171 59 84 18 49 1 8469 0 2194 jols Manual Editing Tools SEEE lt lt Advanced Options Rit BB ef o BD inbox Mic E RE vield d E RE yield d E protocol do yield Editor Message C Figure 1 Screenshot of Yield Editor Filtering Mapping and Editing tab Once the analyst is satisfied with the data filtering process and has recorded the parameters either by saving the session or manually recording the parameter values in another document the filtered data can be exported into one of a few file formats The authors typically export the data as space delimited ASCII to facilitate less total steps before the import into ArcView GIS When prompted we place a check next to longitude DD latitude DD and yield under the Save Export File tab as in Figure 2 Some analysts choose to use UTM Easting m and UTM Northing m in meters instead of decimal degree coordinates Other data fields can be selected Assimilate Data with GIS Open the new txt file exported from Yield Editor into WordPad NotePad or Excel If using WordPad or NotePad add a blank line or row and name the column headings The column heading names should be separated with only a space For instance the columns would read lat long yield Save this f
3. diagnostics including Lagrange Multiplier LM values and Robust LM values for both spatial error and spatial lag The diagnostic values with the largest LM and Robust LM values or smallest probability levels is the most appropriate to use In most cases both the LM and Robust LM diagnostics indicate the same model however differing indicators may mean that simultaneously both are appropriate In some cases the Spatial Autoregressive Moving Average SARMA may be the most appropriate as indicated by the diagnostics however estimation is considerably more complicated and no clear interpretation exists In examining the SARMA diagnostic the analyst is cautioned not to compare the LM values directly because SARMA is distributed y chi squared with two degrees of freedom and LM is distributed 7 chi squared with one degree of freedom In addition there is some conceptual evidence that the spatial error model is more appropriate than the spatial lag model however some disagreement by researchers exists as described in the digression on spatial statistical methods below Digression on appropriateness of spatial error and spatial lag models Debates over which spatial model is most appropriate for site specific data are still on going between practitioners and theorists It is our position that the spatial error model is conceptually the most appropriate for field scale data Conceptually the spatial error model tends to be the most appropriate mo
4. Response to Seeding Rate ES EE att t Ze MAMANS MN 80 90 100 110 120 130 140 150 160 170 Seed Population Thousands Acre Avg Elev amp Slope E Low Elev amp Slope High Elev amp Slope gt Low Elev amp High Slope X High Elev amp Low Slope Figure 23 Example graph of results from soybean seeding rate study by regime Disclaimers The purpose of this document is to provide a suggestion on using yield monitor data and spatial analysis methods in evaluation of treatments from farm level field scale experiments The opinions and conclusions expressed here are those of the authors Mention of specific suppliers of hardware and software in this manuscript is for informative purposes only and does not imply endorsement Note on ArcView Extensions I will try to keep a decent list of links to useful Arc View GIS extensions on my website at http web ics purdue edu twgriffi av extensions html 25 Purdue Site Specific Management Center 8 30 2005 References AgLeader SMS http www agleader com sms htm Anselin L 1988 Spatial Econometrics Methods and Models Kluwer Academic Publishers Drodrecht Netherlands Anselin L 1992 SpaceStat Tutorial University of Illinois Urbana Champaign Urbana IL 61801 http www terraseer com Anselin L 1999 Spatial Data Analysis with SpaceStat and ArcView Workbook 3rd Ed Av
5. Table to SpaceStat Data Set SpaceStat can be purchased from TerraSeer http www terraseer com Exploratory Spatial Data Analysis In exploratory spatial data analysis one should not rigidly follow a prescribed sequence of steps but should instead follow one s instinct for explaining anomalies Isaaks and Srivastava page 525 This leads to an underlying assumption in spatial analysis that the analyst either has intimate knowledge of the field or is in close contact with a collaborator who does i e the farmer The results of exploratory spatial data analysis ESDA and steps the analyst takes to arrive at these results are intended to give the analyst a better understanding of the spatial variation of the data Now that the entire dataset is in a single Shapefile ESDA can be performed using GeoDa Open a file using the standard icons and navigate to the folder where the Shapefile is being kept GeoDa asks that a unique identifier be assigned and is referred to as a key variable Figure 18 To perform any ESDA a weights matrix must be specified This can be done by clicking Tools Weights Create The resulting box Figure 19 asks an input file which will probably be the same Shapefile a name for the weights matrix in this case W min and the key variable again In this example we chose to have an Euclidean distance with a cutoff of 7 169765 meters the minimum distance such that each observation has at least one neighbor wh
6. be Purdue Site Specific Management Center 8 30 2005 2 ArcView GIS 3 3 Bae File Edit View Theme Graphics Window Help E SEP KLIN S RSF Ee k To AUKE Scale d BET F View a x A West71 shp 125 904 191 191 514 206 20691 21914 _ West tye txt 125 904 191 191 514 20 20591 2191 219 66 234 234 482 200 T TE RE yield doc Messa T prot R Figure 3 Screenshot of ArcView GIS of yield data exp t orted from Yield Editor included in the data It has been our practice to keep the data in raw point format with the least dense dataset to be the basis for the remaining data layers We caution the analyst not to conduct spatial interpolations via kriging or other geostatistical methods to remedy this dilemma We have avoided using geostatistical interpolation techniques for spatially smoothing a surface because of the problem of introducing a random variable to the data causing a problem in deriving inference Anselin 2001 There are a number of ways to assimilate yield data with the lesser dense soils data Some sort of spatial grid can be assigned to the dataset with each sparse soil data point being attributed to a single grid unit Our preferred method is to create a polygon with given radius with the soil point as the center using the X Tools DeLaune 2001 extension for Arcview GIS and is explained in the folling paragraph A specialized form of grid cel
7. i e soil points 18 Creating buffer areal units for sparse data circle heed esae ect ta ede rec deck to ovas t HA e Nod pay qu ded ERLAS 21 Assigning dense yield data to sparse data points eese esee eene 26 Appending treatment information to the dataset rsrrrrrnrrrronvrrronrrrrnvrrrnrrrrrrnvrrrrnrrrnnnnn 30 Adding the distance from a given attribute c e ee trata teta assu duos a seen ua ae eiu 0d 31 Elevation slope aspect and associated problems eese esee enne 32 Removing duplicate DONIS 2 ea easpudesscc dra caede eda nasa Que aa ode Meade usd dede eve 33 Spreadsheets ninini i ous opone Oxo ee ite te gu onis 35 Exporting data from ArcView GIS for SpaceStat eese enne 39 Exploratory Spatial Data Analysis enne enne enne nennen 39 Spatial Statistical Analysis cete ire oe a e ARES ERAI OUI Ig hoses HAIR E TA M dE ERE ee 46 Definition of regression analysis o n esame eden t aat ode dite aad i eU vct du Chal don d ot dee du ads 47 Digression on appropriateness of spatial error and spatial lag models 49 Interpretation cocos Last honduras Blast odo sibila ever eis pe pns eee ibi tao fee 50 Goodness of fit measurements not useful in spatial models eese 50 Goodness of fit measurements useful in spatial models eese 51 Economic Analysis and Presentation of Results esses 53 Diseamers
8. of spatial autocorrelation To calculate and plot the data for Moran s I go to Space Univariate Moran and select the variable you wish to explore Figure 20 You will be asked to provide a weights matrix to use which you just created Figure 21 The resulting Moran s I scatter plot and value Figure 22 gives indication to the amount of spatial autocorrelation In all the site specific data that we have used we typically expect to have positive values and not negative or zero values at field scales Now that the analyst has a firm understanding of the spatial variation of the dataset the analyst is ready to conduct statistical analyses 19 Purdue Site Specific Management Center 8 30 2005 Variables Settings Select Variables 1st Variable Y 2nd Variable X GID TWG ID AVG YLDO3 YLDO30LD CLAY DPEG Set the variables as default 111978615 i565 111978549 au ness 111900880 E EE 111 370370 1150644 11197814 111476080 5118 718000 z 1 p i 1010908047 39 253000 BEEN sii icy wai i T ETE S222 941000 TOR 7 4 T mamn t im ments cent 053 481000 4886 4030 501000 d 111970149 5187 935000 4279 891000 19 l i 111 978102 5051 215000 4109 161000 fs Select bom He L gal gwt am M 111 978063 490K 252000 axi 436148000 16 alj 4 ati arene Sors orse00 7 v ra 15 I Setas dna E i ausme Soa WO S04 450418000 16 400 T sme 169 010000 456000 16 500 4 n1877901 5032 361000 471342000
9. pop and pop are seeding population and population squared and elev is the elevation The model coefficients are used to calculate yield maximizing soybean population levels or what is commonly known as agronomic maximum However yield maximized levels are not profit maximization levels unless the soybean seed is free an unlikely situation To calculate profit maximization levels or economic optimal levels the profit function must be used z R C where zis profit R is revenue and C is cost The profit function can be expanded to z p y p x where p is the price of the input x So the equation for profit from a soybean population rate study may be m p pop pop elev P pop where 7 is profit from soybean and p is the price of soybean seed Yield maximization and profit maximization levels can be found in the above examples by taking the first derivative and solving for the input For instance the profit maximization level can be solved for the research factor from the above equation by pop a The above examples are only one of a large number of possibilities for models y and research factors Each planned comparison may have a completely different model costs and treatments and the analyst should be prepared to adjust their own protocol accordingly 24 Purdue Site Specific Management Center 8 30 2005 70 65 60 a a Yield Bu Acre a eo 45 40 35 Site Specific Spatial Error Estimated Yield
10. remove data points that are known to have been inaccurately measured Once a dataset is in the appropriate format as per the previous section it can be imported into Yield Editor Figure 1 A user defined or other standard protocol for filtering data can be instated on the yield data but this is not recommended The analyst s intuition experience and skill should guide the procedures The data points are visually displayed so further manual deletions can be made or points added back into the dataset 1f needed thus this is an example of where the analyst s intuition is useful The data filtering protocol may be farmer or field specific The best starting point is most likely zeros for all parameters but this is dependent on how data was managed during the import process in the native software i e if flow delays were allowed to be imposed on the data such as 4 4 and 12 for start stop and flow delays respectively However conscious decisions must be made as to whether the protocols are appropriate for the user s application It is our experience that no single parameter setting structure is universally appropriate for any two fields even with the same harvester and operator Adjusting flow delay start pass delay and end pass delay are the most difficult and may be the most important to the quality of the data End row yield points should be similar to adjacent end row points Differences are due to ramping up and down of the harvester at the be
11. svarSi Eee SS 56 Rae good SS 58 Purdue Site Specific Management Center 8 30 2005 Yield Monitor Data Analysis Data Acquisition Management and Analysis Protocol Version 1 August 2005 Terry W Griffin Jason P Brown and Jess Lowenberg DeBoer Graduate Research Assistants and Professor Department of Agricultural Economics Purdue University Please send comments suggestions and questions to Jess Lowenberg DeBoer lowenbej purdue edu 765 494 4230 or Terry Griffin twgriffi purdue edu 765 494 4257 This document is currently available on line and can be cited as Griffin T W Brown J P and Lowenberg DeBoer J 2005 Yield Monitor Data Analysis Data Acquisition Management and Analysis Protocol Available on line at http www purdue edu ssmc Executive Summary This document serves to share our techniques for managing the analysis of site specific production data for the purposes of analyzing field scale experiments The content of this document is the culmination of over a decade of on farm trial and spatial analysis experience and is still growing The reader is invited to make suggestions and comments that may be incorporated into future versions of this document This document also gives specifics on how to use spatial statistical analyses to analyze site specific yield monitor data rather than using traditional and albeit less efficient analysis In the presence of spatially variable data traditiona
12. that overcomes these limitations of traditional analyses see Anselin 1988 or Cressie 1993 for a thorough treatment of spatial statistical methodologies Definition of regression analysis Regression analysis defined in the traditional sense can be thought of as a model driven functional relationship between correlated variables that can be estimated from a given dataset Regression can be used to predict values of one variable when given values of the others Spatial statistics expands upon traditional regression to address the problems of spatial dependence specifically in the form of spatial autocorrelation and spatial heterogeneity Anselin 1988 Any appropriate statistical analysis of a spatial dataset can be thought of as spatial statistics GeoDa provides an ordinary least squares OLS and two spatial regression methods both using maximum likelihood ML estimation OLS regression is necessary for the purpose of conducting spatial diagnostics on the OLS residuals to determine whether a spatial regression method is justified and which of the two methods is the most appropriate If the diagnostics of 21 Purdue Site Specific Management Center 8 30 2005 the residuals suggests a spatial method is appropriate either a spatial error or a spatial lag model will be indicated that best describes the data From our experience with field scale on farm data the diagnostics indicate a spatial error model most of the time GeoDa presents spatial
13. the yield monitor data SMS and JDOffice both have an automatic export function that exports yield monitor data in the appropriate format AgLeader SMS Software from AgLeader The current version of SMS v5 5 unfortunately crashes when this export is performed however the procedure works in older and hopefully newer versions thus is described here AgLeader support has filed a bug report For the mean time an alternative approach is described in the next section on absence of native files section It should be noted that SMS does not have to be a registered installation in order to perform the necessary procedures Once the yield data has been imported export the data by File Export AgLeader Advanced Export JDOffice Software from John Deere In order for the files to be exported in the appropriate format a one time setting must be made Go to File Preferences Export and click the radio button next to Text comma delimited As with SMS JDOffice does not have to be registered Once the yield layer of the field of interest is active go to File Export Layer Data Files exported by these SMS and JDOffice methods are ready for Yield Editor Using Yield Monitor Data in absence of yld gsy gsd files Whether the data is already in the ArcView Shapefile format combination of shp shx dbf a georeferenced text file or other file format the data can usually be manually converted into the appropriate txt file for Yi
14. Calc After clicking OK you will be prompted to provide a file name and decide whether you want to create a new table or use the existing table We typically accept all the default parameters as shown in Figure 14 This step may take several minutes to a few hours depending on the size and scale of the datasets The new yield averages have been added to the buffer polygon theme Similar steps will need to be conducted to append the soils data to the soils buffered polygon theme These polygon areas need to be converted back into single points This can be done either 1 by using the original coordinates or 2 adding the centroid X and Y coordinate to the dataset opening the dbf of the buffered polygon theme with the Table command in the Project window as described earlier in the section on adding the txt file from Yield Editor to ArcView GIS click Add Table in the Table portion of the Project Window and go to the View click on View from the main menu click Add Event Theme assign X and Y data selecting the theme click Theme Convert to Shapefile Now the data for the dense and sparse data layers are in a point data layer with the same resolution as the original sparse dataset and useful for inferential statistics Remember to convert each joined table to a Shapefile because joins with Point Stat Calc are temporary joins and will be lost when another join is made 13 Purdue Site Specific Management Center 8 30 2005 Choose Fields and Ca
15. This can be done when there are two or more categories When there are three or more dummy variables the convention is to select one treatment to be the reference The process for assigning dummy variables is now to subtract the value of the reference from the remaining categories This method generates a 1 if the observation is of the reference category a 1 for an observation from the category in question and a 0 otherwise When the regression is run the reference category is dropped from the analysis and is captured in the intercept When the dummy variables are coded in this way all coefficients are evaluated at mean conditions When working with large spreadsheets having thousands of rows of data knowing shortcut methods can save a lot of time For instance if the user wants to highlight from the active cell to the last row of data in the spreadsheet press and hold Control and Shift and then press the down arrow Remember when using formulas to fill in data that the formulas need to be saved as values so the resulting dbf or txt files operate properly When working with a dbf and the user wants to create new columns it is easiest to insert a new column in the middle of the data with existing data columns to the right Otherwise the file may not save the new columns if they are to the right of the existing data In addition using a dbf may not save the number of decimal places and revert to an integer causing diffi
16. YIELD MONITOR DATA ANALYSIS DATA ACQUISITION MANAGEMENT AND ANALYSIS PROTOCOL by Terry W Griffin Jason P Brown and Jess Lowenberg DeBoer Graduate Research Assistants and Professor Version 1 August 2005 Department of Agricultural Economics Purdue University Purdue Site Specific Management Center 8 30 2005 Table of Contents Executive Summa sabel TER 5 Overview of Spatial Analysis Steps ccccccccssccssssecesscecssscecssccecssesecssnsecssseecssesesssnsesssneeesaes 7 Yield Monitor Data Preparation sssssssssesseseeeee ener enne enne enne nnns 8 Discussion on using raw yield monitor data rather than filtering erroneous data 8 Using the Yield Monitor s Native Software with yld gsy gsd 10 AgLeader SMS Software from AgLeader eee e eee Linee anat nnne tat an aant eh daa 10 JDOffice Software from John Dre eae dite cr Eq oc NERA Re Seerne DER dua din 11 Using Yield Monitor Data in absence of yld gsy gsd files sussssss 11 Data that has already been exported siis ti via peti ave ieee aiid 12 Using the manual export features of farm level software eese 12 Removing Erroneous Measurements sessi nennen tenente enne 13 Assimilate Data with GIN iocos more eta DIM Uus las ai ab Oan qM i enet ale cds 16 Aggregating the dense data yield to the least dense data
17. ailable on line at http www terraseer com products spacestat docs workbook pdf Anselin L 2001 Spatial Effects in Econometric Practice in Environmental and Resource Economics American Journal of Agricultural Economics 83 705 710 Anselin L 2003 GeoDa 0 9 User s Guide Spatial Analysis Laboratory University of Illinois Urbana Champaign IL http sal agecon uiuc edu geoda main php Cliff A D and Ord J K 1981 Spatial Processes Models and Appplications London Pion Cressie N A C 1993 Statistics for Spatial Data John Wiley amp Sons New York DeLaune Mike Guide to XTools Extension September 2003 Available on line at http www odf state or us divisions management state forests XTools asp Dombroski Mathew ESRI ArcView Extension Point Stat Calc Available on line at http pubs usgs gov of of00 302 Drummond Scott 2005 Yield Editor 1 00 Beta Version User s Manual November 9 2004 http www fse missouri edu ars ye yield_editor_manual pdf JDOffice http www deere com en_US ag servicesupport ams JDOffice html Deere and Company Moline IL Griffin T W D M Lambert and J Lowenberg DeBoer 2004 Testing for Appropriate On Farm Trial Designs and Statistical Methods for Precision Farming A Simulation Approach Forthcoming in 2005 Proceedings of the 7th International Conference on Precision Agriculture and Other Precision Resources Management ASA SSSA CSSA Madison Wisconsin Griffin Terry a
18. chine dynamics and operator behavior These data filtering procedures are by no means a modification or manipulation of the data Data filtering does however increase the quality of the dataset Discussion on using raw yield monitor data rather than filtering erroneous data Removing observations from a dataset without some sort of protocol has not been a commonly accepted practice in statistics Many analysts have omitted outliers by removing 3 standard deviations of the data or by plotting the data on a scattergram and removing obvious erroneous data caused by factors such as human error measurement error or natural phenomena With the case of instantaneous yield monitor data it is widely known that many observations have erroneous yield values due to simple harvester machine dynamics These erroneous observations can be identified by examining harvester velocity velocity change maximum yield and other parameters With harvester yield data errors also arise from start and stop delays for beginning and ending of passes The ramping up and ramping down effects of the harvester yield monitor has adverse effects on yield measurements The flow delay caused by inaccurate assignment of yield measurement to GPS coordinate is the effect of grain being harvested at one location yield measured while harvester is in another location and recorded with GPS coordinates at potentially another location The flow delay must be corrected If this error is not corr
19. culties when dealing with many types of data or even coordinate systems It is a good practice to first save the spreadsheet as the native 17 Purdue Site Specific Management Center 8 30 2005 xls file and then perform a save as to the dbf or txt so that a clean backup is available The analyst should avoid sorting this file unless care is taken to sort the data in a specific manner to be able to resort the data to the original sequence of data rows The best way to sort the data is to have a unique identifier column that has a sequential order The whole dataset except for column headings must be sorted all at once Before saving the dataset file the whole dataset must be sorted back to the original sequence by using the unique identifier column If the rows of data get arranged in an inappropriate manner the GIS software still operates properly however the data does not match the appropriate shape In other words all the data is present but is associated with the wrong location Likewise the analyst must not delete rows of data in the spreadsheet because the GIS software will not accept the Shapefile Exporting data from ArcView GIS for SpaceStat Once the appropriate variables have been created in the spreadsheet added to Arc View GIS and converted to a Shapefile it can be exported in the appropriate format for spatial statistical analysis in SpaceStat by using the SpaceStat extension Anselin 1999 to ArcView GIS by clicking Data
20. d 4 E d 4 111 97700 4436 200000 3067 245000 r 4 i 111 977020 4084 091000 4625 942000 Ma J 1 i 111 977775 3784 000000 3801 000000 Look in C DATA P 1 K LITA SOM NOD 3459 senssimo 13 E 3055 590000 et 20000 PHOT Ed gloieowr 30 080000 tuc suo 14 4 b ow r ausmaz FAD IEI 4671 0700 107870 3539299000 4059 062000 11 577529 5142 950000 14 2111977690 5122 241000 x T I 111 977047 4629 465000 14 po i 4 11977404 2000 b I LITIA 4800 248000 pou 4 anmsmas Fedwe wegine o od x amet b 2 i E 2 JE 411 97740 111 977201 11 977164 nnam 45 amp 4t26555525wut4H uEuWuuWU vt oNS 43s SISS vovven a slalelela ulus elu el E Figure 21 Assigning a weights matrix in GeoDa 20 Purdue Site Specific Management Center 8 30 2005 C GeoDa 0 9 5 Beta Moran W min GWT YLDO2 Te View Edt Toul Table Map Expkre Space Reyes Opies Window Heb a ey velvet a Bele ropes cnfr f res Moran s I 0 8303 2 v W YLD02 a V4 Spatial Statistical Analysis Traditional analyses such as ANOVA and ordinary least squares regression are unreliable in the presence of spatial variability or in other words spatial autocorrelation and spatial heteroskedasticity The assumption of independent observations normality and identically and independently distributed iid errors are all violated Spatial regression analysis is one methodology
21. del when the spatial structure is explained in the residuals of the regression or in other words due to omitting variables that explain the yield variability Yield variability at field scales occurs for several factors and most are not measured and therefore cannot be included in the statistical model When the statistical model is run without the yield variability factors being included the unanswered variability inevitably winds up in the residuals or error term making the spatial error model the most appropriate It is doubtful that researchers and farmers will collect the exact data at the resolution needed to overcome the omitted variable problem even with dense soil data such as electrical conductivity Conversely the spatial lag model is conceptually the most appropriate model when the spatial variability occurs in the predicted dependent variable itself and in our case crop yield In situations where the dependent variables affect each other directly instead of being affected by an underlying mechanism the spatial lag model is appropriate These situations may include any contagion such as property values from regional economics and disease spread in epidemiology These factors affect and are affected by one another It is counterintuitive to suggest that high crop yields in one location cause crop yields in adjoining locations to be high and vice versa However from statistical theory we know that the spatial lag model accounts for spatial aut
22. ected yield values that are otherwise good are at the wrong location Allowing native software Purdue Site Specific Management Center 8 30 2005 packages to impose the 12 second delay may be a good average but we have typically seen appropriate flow delays of six to 18 seconds but rarely exactly 12 Our assertion is that it is dangerous to use raw yield monitor data for analysis whether or not the native software performs the default filtering and flow delay shifts Conscious decision must be made as to the most appropriate handing of the data Some researchers have argued that data filtering is unethical and prefer to accept data as is from the yield monitor and thus from their farm level software regardless of the default filtering settings Using the Yield Monitor s Native Software with yld gsy gsd In packages such as JDOffice and AgLeader SMS the default setting import settings for start delay stop delay and flow delay is typically 4 4 and 12 respectively It is our practice to set these to zeros If there is a minimum and maximum yield we set these to zero and some value near the maximum physical measurement of the yield monitor These settings are chosen so that the native software does not perform its filtering procedures so that more complete control is possible during the filtering protocol We do not perform any data manipulation procedures in these native software packages other than a simple import and export of
23. eld Editor pending having all the necessary data columns The required data columns and arrangement are described in Drummond 2005 Data that has already been exported Using the dbf file portion of the Shapefile as exported from FarmWorks SMS JDOffice EasiSuite MapShots or other software package has been successful Care must be taken to know if the flow rates have been exported in kg per second or Ibs per second etc as required by Yield Purdue Site Specific Management Center 8 30 2005 Editor Drummond 2005 Other measurements with metric or English units must also be identified and converted to English units if necessary Remaining data columns can be deleted Using the manual export features of farm level software We save a export template in SMS and FarmWorks others may work however we do not have extensive experience with other farm level software when we export yield data so we can quickly and easily export yield data in the future for Yield Editor This configuration can be saved by clicking Save Template This template can be easily loaded each time data is to be exported by clicking Load Template Removing Erroneous Measurements Yield Editor 1 01 Beta USDA ARS Drummond 2005 is used to remove erroneous data i e filter the raw yield monitor data Under a certain set of known harvester characteristics the yield monitor is unable to make accurate measurements It is under these conditions that we use Yield Editor to
24. ered before use in inferential analysis thus a digression on data filtering is given Data assimilation and management with a geographical information system GIS is described with several specific treatments of the data illustrated in detail Data preparation for analysis is explained using standard spreadsheets The discussion on exploratory spatial data analysis ESDA precedes the discussion on spatial statistical analysis Finally interpretation and economic analyses are described Yield Monitor Data Preparation This portion of the protocol is for preparing the dataset to be used in Yield Editor USDA ARS Columbia MO Drummond 2005 for removing erroneous data Yield Editor can be downloaded from the USDA ARS website at http www fse missouri edu ars decision aids htm If Yield Editor is not being used the reader may skip directly to the section on GIS According to Drummond s 2005 criteria for importing data a few steps may need to be conducted to ensure the data is ready to be imported into Yield Editor This step is easiest if using the yield monitor s native software package however this is not always possible especially for yield monitors from other than the major manufacturers Both scenarios are described It should be noted that these data preparation procedures may be referred to as data cleaning or data filtering but actually are little more than removing measurements that are known to be erroneous due to harvester ma
25. ginning and end of rows Field experience indicates that it may take as much as 100 feet of harvester travel before accurate yield measurements can be made Adjust the delays until your intuition is satisfied Values for the delays will typically be Flow Delay 8 to 24 Start Pass Delay 0 to 10 End Pass Delay 0 to 16 however variation occurs and parameters are set by trial and error plus intuition Negative values are possible Setting the flow delay is easiest when the operator harvests three to eight passes in one direction and alternates the pattern across the field This allows a visual reference wide enough to be seen on the Yield Editor map Alternating direction between individual passes does not give the needed visual reference Purdue Site Specific Management Center 8 30 2005 Yield Editor Load Import File Filtering Mapping and Editing Save Export File Filter Selection Map and Manual Editor Easting m Nothing m Yield Flow Speed Moist Swath Up Dn Nsecs RmCode Pass Point 508749 4516880 197 54 36 40 489 216 1240 1 1 0 18 255 ag Es o e HH Flow Delay Moisture Delay Start Pass Delay End Pass Delay Max Velocity mph Min Velocity mph Smooth Velocity uM alelalal le Es x x S Minimum Swath in xx 8 kal Maximum Yield Minimum Yield STD Filter Header Down Req dd r3 3 a4 vr eee amp ee ee ee az 1 2 Position Filter To Easting 50532534
26. ich can be determined when the sliding bar is all the way to the left GeoDa Project Setting Input Map shp D SARE cotton DATA data_projected shp Key Variable TWG ID he Figure 18 Selecting a GeoDa project and assigning the key variable 18 Purdue Site Specific Management Center 8 30 2005 CREATING WEIGHTS Input File shp D SARE cotton DATA data_projected shp fa Save output as D SARE cotton DATASW_min GWT Select an ID variable for the weights file TWG ID v CONTIGUITY WEIGHT E C DISTANCE WEIGHT Select distance metric Euclidean Distance v Variable for x coordinates lt X Centroids gt p Variable for y coordinates lt Y Centroids gt E 7169765 Cut off point C k Nearest Neighbors Create Reset Figure 19 Creating a weights matrix in GeoDa In order for GeoDa to display the distance in meters or any other specified unit the Shapefile should be exported in some projection other than decimal degrees This can be done in Arc View GIS by clicking Theme Convert to Shapefile and then select the option to maintain the projection Whatever projection that the View is projected will be the units GeoDa displays Otherwise if the Shapefile is exported without the projection the units will be in decimal degrees and difficult to interpret One statistical measure of spatial variability is Moran s I Anselin 1988 Cliff and Ord 1981 Moran s I is a global indicator
27. ics on the OLS residuals indicated the presence of spatial autocorrelation then the OLS coefficients standard errors and goodness of fit statistics for OLS should be ignored In addition R squared values do not have the same interpretation with a spatial model as the OLS model and are normally assumed to be invalid Anselin 1988 Goodness of fit measurements useful in spatial models A better goodness of fit measurement is the maximized log likelihood The use of traditional measures such as chi squared and mean squared error provide misleading results with spatial models Anselin 1988 The Akaike Information Criterion AIC estimates the expected value of the Kullback Leibler information criterion KLIC which has an unknown distribution Anselin 1988 The ranking of models by AIC is useful although the specific value has little meaning The analyst should examine several goodness of fit measurements and not make judgments based on a single measure With spatial error models the coefficients standard errors z value and probability has similar interpretation as OLS with the z value corresponding to the t value Asymptotically the absolute value of the z value will be greater than or equal to 1 96 to be significant at the 5 confidence level The probability level will be 0 05 for the 5 level meaning that we expect to be wrong 5 of the time Although confidence levels such as 1 5 and 10 are chosen by convention the analyst is able
28. ile as a tab delimited txt file If using Excel open the txt file and specify space delimited if prompted Add a blank row and label the first second and third columns as lat long and yield respectively Save this file as a tab delimited txt file Purdue Site Specific Management Center 8 30 2005 Yield Editor 1 Load Import File Filtering Mapping and Editing Save Export File Export Data Select Output Fields UTM Easting m Moisture I AGL Flag Code UTM Northing m Swath Width in Transect Number v Longitude DD Travel Distance in GPS Time IV Latitude DD Grain Flow Ib s I UTM Zone Save Filter and Configuration Settings IV Yield I Interval Length s AmCode Formatting Point Types to Export Save Config Export CLEAN points Save as Default Configuration Export SELECTED points A Export DELETED points IV Allow Negative Lat Long Export ALL points es Export Data Save Current Yield Editor Session Session Log and Notes Save Session Figure 2 Screenshot of Yield Editor Save Export File tab Add the txt file to your GIS package for instance ArcView GIS or ArcMAP From the Project Window in ArcView GIS select Table and click on Add Navigate to the txt file Go to the View you wish to visualize your data and click View Add Event Theme specify the txt file and assign the X and Y fields Now that the txt file is loaded into the GIS make s
29. l forms of analysis such as analysis of variance ANOVA and least squares regression are unreliable and should be avoided To our knowledge this document provides the most appropriate analysis methods for field scale research with yield monitor data Much of the following text and examples are useful for a wide range of precision agriculture applications but the overall thrust of this document is ultimately for analyzing field scale experiments To conduct spatial analyses of yield monitor data both 1 a good experimental design and 2 a planned comparison must be in place A planned comparison can also be called a testable question or testable hypothesis If there is no hypothesis neither traditional nor spatial analysis can be conducted Although we recommend not using geostatistical interpolation techniques for conducting inferential statistics we do not make any statements on the use of these smoothing techniques for prescription maps defining management zones or other common uses The authors assume the reader has a working knowledge of MS Excel and ArcView GIS Purdue Site Specific Management Center 8 30 2005 Overview of Spatial Analysis Steps The following procedures describe the steps we take in data acquisition management and analysis The first portion of this procedure describes the methods we found that work the best using publicly available software for data acquisition In nearly all cases yield monitor data must be filt
30. lculations Choose the fields you want analyzed and the calculations you want performed Shift Click to select more than one Values Field Calculations Lat aj P Long Maximum Minimum Count Nth Percentile Y Random Include Zeros in Calculations Include Negatives in Calculations s Ignore Dummy Values EE OK Figure 13 Screenshot of Point Stat Calc input fields Create and Join Calculation Table Please provide a file name for the new eee Ge ea e eee butt dat dbf The new table and the original will be joined using an index field in each table containing an unique identifier for each record Either create a new index table or specify a field in the original table to be copied to the new table Create new index item in tables foriginal table Is altered C Use existing index item in table Figure 14 Screenshot of Point Stat Calc Create and Join Calculation Table Appending treatment information to the dataset Treatment information may need to be added to the data file If this information is not already present in the dataset it can be added in a number of ways For instance if the treatments occur in blocks like tillage treatments or split field trials polygons can be created and merged together to form the treatment polygon map From this polygon a specific treatment can be selected Once the treatment is selected from the treatment polygon map a Select by Theme can be done on
31. ls known as Thiessen polygons can be created in GIS or GeoDa University of Illinois Anselin 2003 GeoDa can be downloaded from https geoda uiuc edu and Thiessen polygons created by clicking Tools Shape Points to Polygons Thiessen polygons are a form of nearest neighbor interpolation created by surrounding each input point with an areal unit such that any location within that area is closer to the original point than any other point Thiessen polygons are sometimes called or very similar to Voronoi polygons Delaunay Triangles and Dirichlet Regions A regular grid can also be used but it is difficult to line up irregular spaced data in a one to one format Creating buffer areal units for sparse data With the ArcView GIS View projected with the distance units in the chosen units go to XTools Buffer Selected Features Figure 4 and choose the measurement unit of your choice choose the Purdue Site Specific Management Center 8 30 2005 2 Enhanced Farm Research Analyst x Ele Edit View Theme Analysis Surface Generalize Grid Transform Grid Image Analysis Graphics Data SpaceStat Window Help Research Tools Field Tools PAVector GridAnalyst Image ESAS o E PE s 0 eres Jmepo Exe j FEES FUEZLFIXIZ TEIE vo Batch Overay Scale i56 SA View XTools Log File Clip With Polygon s Cotton aaea shp Erase Features Identity A Soilem shp Intersect Themes Merge Themes v TE Union Polygon Themes Update Polygon Theme Calculate A
32. nd Dayton Lambert 2005 Teaching Interpretation of Yield Monitor Data Analysis Lessons Learned from Purdue s 37th Top Farmer Crop Workshop Journal of Extension 23 3 Isaaks E H and Srivastava R M 1989 An Introduction to Applied Geostatistics Oxford University Press Inc New York NY 26 Purdue Site Specific Management Center 8 30 2005 Jenness J 2005a Distance Matrix dist mat jen avx extension for Arc View 3 x v 2 Jenness Enterprises Available at http www jennessent com arcview dist_matrix htm Jenness J 2005b Find Duplicate Shapes or Records find dupes avx extension for Arc View 3 x v 1 1 Jenness Enterprises Available at http www jennessent com arcview find dupes htm Littell R C G A Milliken W W Stroup R D Wolfinger 1996 SAS System for Mixed Models The SAS Institute Inc Cary North Carolina DeLaune Mike Guide To XTools Extension September 2003 Available on line at http www odf state or us divisions management state_forests XTools asp 27
33. o BE Ro ue o a E ci amp IB 11 02AM Figure 11 Screenshot of showing that the buffered areas do not cross treatments 12 Purdue Site Specific Management Center 8 30 2005 Assigning dense yield data to sparse data points Once polygon areal units have been created around the soils data yield data can be assigned to the soil location The USGS Point Stat Calc Dombroski extension for ArcView GIS is useful in simplifying this step Figure 12 Select the dense yield data theme and the areal unit theme for the less dense soils data as described in the previous discussions on buffered zones Select the value of interest for the point data yield as in Figure 13 and select all the statistics you wish to use We typically only use Average however examining the standard deviation coefficient of variation and other descriptive statistics gives an indication of the appropriateness of the buffered distance for the given spatial variation Point Stat Calc v 2 5 Point Stat Calc selects points within a shapefile by a polygon shapefile calculates one or more statistical values for the points and writes the resulting values to a new table as attributes of their respective polygon s The new table is then joined to the original polygon table Written by Matthew Dombroski United States Geological Survey gt Please send reports of bugs to jgrossman amp usgs gov science for a changing world Figure 12 Screenshot of USGS Point Stat
34. ocorrelation in both the dependent variable and error terms This has caused some theorists to suggest that the spatial lag model is most appropriate This is an open debate and we welcome your thoughts and experiences on this topic Interpretation Spatial regression techniques may someday become commonplace to the farmer or farm consultant but currently university researchers are still developing the methodology For the time being spatial analysts who invest a portion of their time to teach the ultimate end user of this technology the farm manager to interpret analysis results rather than conduct the intricate 22 Purdue Site Specific Management Center 8 30 2005 details may have made considerable contributions to spatial analysis Griffin and Lambert 2005 Goodness of fit measurements not useful in spatial models In traditional analyses the R squared statistic is a common measure of goodness of fit or the adequacy of the model The R squared statistic ranges from zero meaning it explains none of the variability in the data to one meaning the model explains 100 of the data R squared values somewhere between zero and one are expected Although OLS models report R squared even with spatial data the value is meaningless For instance Griffin et al 2004 showed that OLS models were unable to adequately explain spatial datasets under simulation however the R squared values and F statistics were very high If spatial diagnost
35. od involves using the Distance Matrix extension for ArcView GIS Jenness 2005a The output from Distance Matrix can be joined into the existing dataset by the standard table joining techniques in ArcView GIS Elevation slope aspect and associated problems Due to introduction of variability problems associated with geostatistical techniques Isaaks and Srivastava 1989 and imperfect information on proper parameters to assign to interpolation methods spatial interpolation methods such as inverse distance weighting kriging and co kriging have been avoided However if slope or aspect variables are desired the elevation data must be interpolated into a surface In addition the elevation data must be collected at a resolution sufficient to describe the topography and with adequate accuracy Tractors equipped with RTK automated guidance typically provide sufficient data during plating or other field operations Coast Guard and WAAS DGPS do not always provide the needed accuracy Additional data points and resolution are not substitutes for accuracy From this elevation surface a slope aspect or other topographic surface can be calculated The slope surface can be converted into a contour line vector with base of zero and interval of 0 25 percent The yield data can be appended with a value for slope by choosing the closest slope contour line by using the Spatial Join function in Geostatistical Wizard in ArcView GIS The danger in spatial interpola
36. of a different treatments 2 be large enough to have at least one yield observation and 3 be small enough to only include yield data that are comparable with other yield data in buffered zone Figure 11 You now have a new Shapefile layer with circular areal units around each of your sparse soil points and is ready to have the dense yield data points added These circular area units may overlap but that is not of concern gt Buffer Units x Buffer units are Meters To change select new buffer units Cancel Figure 5 Selected meters as the buffer unit 10 Purdue Site Specific Management Center 8 30 2005 2 In Theme x Select input theme to buffer sh Soill_ em Figure 6 Select the Shapefile theme to buffer 2 Out Theme x File Name Directories OK mEENM D SARE cotton work y Cancel clusters 3 cotton aaea 3 cotton aaea2 f cotton aaea3 BOE SIE E Eg ae BE j Scale 1156 RRR Y Jopx i Cotton aaea shp A Soillem shp Bordershp vi Twg dd dbf A Twg_ddshp p _ Twashp _ Treatments A Gwreottonshp LL 2834517 4c 4048 176 4 4803312 54 5415 919 56 IB 50723094 6 Vi Saetshp 1 Soilshp z Themet9 shp Buffer Field E ps P p Buffer Table 4718 501 5t ce 5587 822 64 WM 433 702 81 _ Themet8 shp 7792 18 16 20 3 203 238 238 284 EN 254 316 _ Cotton_aaea shp Themet7
37. of numbers are added to uniquely identify each row of data or record For the purposes of regression analysis some variables must be squared cubed square root natural log or other transformation For most studies the original variable plus a squared term is sufficient if the variable is supporting information If the variable is the experimental treatments cubed and other higher order transformations are needed depending upon the model Once all the main variables are created and exist in the spreadsheet interaction terms of all the explanatory variables should be created that are intended to be used in the full model The most important interaction terms are the factors with each other if there is more than one factor Interaction terms of the factor with other variables such as elevation soil zone dummy variable or other covariates are also useful This allows each major measured yield affecting factor to have its own slope and intercept For categorical treatments and supporting variables such as soil zones hybrids or other discrete choices a binary or dummy variable should be created For instance any observation that is present in soil A has a 1 with other observations having a 0 as outlined in a previous section To make the regression comparable to ANOVA and to have the coefficients presented as the difference from average conditions a restriction on the dummy variables that they sum to zero can be imposed Y dj 0
38. rea Perimeter Length Acres Hectares f Two_dashp Transfer Convert Selected Features 2 View1 Ai Twg dd dbf e Twgshp Convert Polygons To PolyLines Treatments Make One Polygon From Polylines Make One Polygon From Points M Gwrcottonshp Make One Polline From Points E pesti Convert Shapes to Graphics 4903312 54 Convert Graphics To Shapes 5415919 5E x B 6972304 6c Convert Shapes To Centroids Saetshp Batch Import Themes To Shapefiles Ea Manage Shapefiles _ Soilshp Em _ Themet9 shp LL 1635 458 3 1 2863 057 47 4718 501 58 5587 822 64 WM 5255702 81 _ Themet8 shp 203 238 238 264 EH 204 315 _ Cotton aaea shp 1501 886 34 3417876 4 4553 345 5E 553624 554 6548 256 8 _ Themet7 shp _ Twgdata shp BS _ Thies n2 shp 04011 0 035 o 0 047 0 047 0 077 0 077 0 122 Creates buffer around selected features or all features if none selected Requires at least one active theme Press Shift and click this menu item for a description and instructions EM Oe www Figure 4 Screenshot showing ArcView GIS XTools Extension menu most sparse layer you intend to use give the theme a name when prompted choose Buffer Distance assign a buffer distance in your units of choice and select Noncontiguous Figures 5 10 The buffer distance should be chosen as to 1 not overlap into areas
39. shp _ Twgdata shp _ Thiesn2 shp C 0011 0 035 0 047 0 077 0 www Figure 8 SENT the Buffer Distance 11 Purdue Site Specific Management Center 8 30 2005 gt Buffer Distance Soill_em shp Enter buffer distance in Meters Figure 9 Entering the buffer distance as 4 5 meters 2 Output Structure x Select output structure Figure 10 Select Noncontiguous as the output structure 2 Enhanced Farm Research Analyst s x Ele Edi View Theme Analysis Surface Generalize Grid Transform Grid mageAnalysis Graphics Data SpaceStat XTools Window Help Rlesearch Tools Field Tools PAVector GridAnalyst Image aj teens im Je tenes es R 0 FJGEER Ek OME Ie Sj e xTer remremp sep EAX AAS oo EN BE Cites EZ NU WE en AUAM A Ae Er uu ue uc n M i Ruin Buffa shp Cotton aaea shp Soill em shp Border shp Twg dd dbf Tug dd shp Twashp Je ED EY E EY ee Gwrcotton shp 2834517 at I 4048 176 IB 203312 54 IE 5215219 5c I 5072324 6 _ Saet shp _ Soilshp m _ Themet9 shp 1635 459 3E E 2853 057 47 IN 4718 501 se IH 5587 922 5 HB 6433 702 81 Themet shp IH 238 264 EH gt 54 315 _ Cotton_aaea shp 1501 886 34 39417876 4 amp 4563 348 5 5538 24 854 6548 256 84 _ Themet7 shp Twgdata shp x c m uuu SS rg Start
40. sults can be easily communicated with decision makers Once the regression output is available copy and paste the output to a spreadsheet It may be necessary to click Data Text to Columns to nicely fit the data into the spreadsheet cells From the coefficients we calculate the dependent variable typically yield over a range of the covariates such as clay content elevation or other continuous variables for each treatment and or other discrete categories such as soils see Figure 23 for example From these calculations we create a XY Scatterplot These graphs are useful in discussing and interpreting the results of the planned comparison with the farm management decision maker For rudimentary economic analysis of side by side or categorical treatments a partial budget will suffice A partial budget includes only the costs and revenues that differ from alternative to alternative while an enterprise budget is exhaustive For field scale experiments the difference in revenue may only include the difference in revenue for each treatment or R p y where R is revenue p is price of crop and y is the crop yield The difference in costs may include the seed costs if a hybrid trial or the machinery costs if a tillage trial For rate trails such as nitrogen rates or seeding rates the equation derived from the regression model is used For instance with soybean seeding rates the equation may be y pop pop elev where y is soybean yield
41. the yield data points with respect to the selected portion of the treatment polygon map Now that the yield data points associated with the treatment are selected a dummy variable can be added using the SpaceStat Extension to ArcView GIS TerraSeer Anselin 1999 To adda dummy variable click Data Add Dummy and give an appropriate name A 1 is added in this column for selected features and a 0 otherwise These same steps can be done to add a dummy for soil series other regions such as old feedlots pastures homesteads and two existing fields were joined to be one large field A dummy variable should be added for each categorical treatment soil zone and every measurable discrete factor to be included in the statistical model 14 Purdue Site Specific Management Center 8 30 2005 Adding the distance from a given attribute In some cases a distance variable may be useful to help describe variability from isotropic or anisotropic effects In cases of furrow irrigation where plants near the water canal will surely get more water albeit colder water than plants at the other end of the row differing yield responses are expected Distances are also useful in modeling the isotropic effect of flood irrigated rice production where plants near the water source tend to have lower yields due to the colder temperature of the ground water near the well The distance to the given attribute can be added to the dataset in a number of ways One meth
42. tion of a surface is the introduction of variability or in other words introducing a random variable which causes problems with statistical inference Anselin 2001 Removing duplicate points It may be necessary to remove duplicate points in the data For instance GeoDa does not allow points with the same coordinate If this is a problem the Find Duplicate Shapes or Records extension Jenness 2005b can be used in ArcView GIS When using this extension the analyst is asked to give the name of the theme and unique identifier Figure 15 the criteria for defining duplicates Figure 16 and is provided a report of the duplicated points and which points were removed Figure 17 Adding an unique identifier is discussed later in the section on spreadsheets 15 Purdue Site Specific Management Center 42 Select Theme and ID Field x Select Theme Select ID Field Data shp lt No ID Field Ajcotton shp Aj data shp Dpeg A 02 shp Drot Emsoilsdata dbf Dsun MIA iberisk abn gt Dimma gt Cancel OK Figure 15 Screenshot of Find Duplicate Shapes or Records 2 Define Duplicates x Records are duplicated IF Shapes are identical C Attributes are identical C Shapes and Attributes are identical Cancel Figure 16 Selecting duplicate criteria Duplicates Report Analysis Complete Dataset Analyzed Aj shp gt Located at d sare cotton aj work aj shp gt Aj shp has 2 353 records
43. to set their own requirements for confidence The analyst should be cautioned that while the regression results from spatial error models can be directly compared to least squares and ANOVA spatial lag model regression coefficients must be adjusted using an infinite series expansion adjustment A regression model can possess independent variables that are solely dummy variables These models are commonly referred as analysis of variance ANOVA models If the ANOVA coding is used as described in a previous section on Spreadsheets where the restriction that dummy variables sum to zero Y d O is imposed the analyst should be aware that the reported p values represent the model at the average conditions and not at the intercept This is mathematically identical to ANOVA however field scale research typically has a wide range of soils topography and other yield influence factors When ANOVA is used with small plot experiments the average condition of the plots is very similar to any given plot At field scales the average condition probably does not closely describe the majority of locations in the field and the analyst must understand that the p values may differ at differing locations in the field i e soil clay content organic matter level elevation etc 23 Purdue Site Specific Management Center 8 30 2005 Economic Analysis and Presentation of Results It is our practice to take the regression results and graph them so that the re
44. ure it appears correctly in the expected location with expected yield variation patterns similar to the variation in the final Yield Editor map window Figure 3 Depending upon which column variables you selected in Yield Editor to export your dataset will have differing pieces of data At the very least you will have X and Y coordinates and the yield The txt file will need to be converted to a Shapefile format Theme Convert to Shapefile Treatments covariates dummy variables and topographical information will need to be added to this Shapefile in the GIS First to return all inherent information such as elevation back to the new yield data file a spatial join is conducted appending pertinent information from the original yield data Shapefile to the new yield Shapefile The column fields that may be important to keep include elevation treatment information and covariates such as electrical conductivity Aggregating the dense data yield to the least dense data i e soil points Rarely ever does the differing data layers share the same spatial resolution or density so some sort of aggregation of the data is necessary We have chosen the following process to minimize the interference of the statistical reliability Yield data is typically the most dense followed by soils such as Veris EC or other scouting information Soil sampling for chemical analysis tends to be the most sparsely collected data such that it may be too sparse to even
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