Yield Mapping and Use of Yield Map Data

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Yield Mapping and Use of Yield Map Data

SF-1176-3 (Revised)Site-specificFarming3Crop raw yield monitor data.Yield Mapping andUse of Yield Map DataDave Franzen, NDSU Extension Soil SpecialistFrancis Casey, Associate ProfessorNathan Derby, Research AssociateYield maps…may be predictive of yield potential and soilnutrient variability for future crop management decisionsCombine yield monitors are growing in popularity. Dataare utilized to build fi eld yield maps in certain yearsor yield frequency maps during multiple years. Mapsserve as location-year record of management andmay be predictive of yield potential and soil nutrientvariability for future crop management decisions.Yield MonitorsYield monitors are available for purchase from thecombine manufacturer or an independent yield monitormanufacturing company. Most U.S. grain yield monitorsmeasure grain fl owing through the clean grain augerinto the hopper and grain moisture continuously in thegrain fl ow. Data generated with a yield monitor areonly as good as the correct installation, calibration andmaintenance of the unit and its components.For a current list of suppliersand their links, seehttp://mpac.missouri.edu/links/yieldmonitor.htmFargo, North Dakota 58108DECEMBER 2008Even though yield monitor manufacturers normallysell computer software to help analyze yield data,growers may want to conduct their own analyses usingcommonly available data management software andgeographic information software (GIS) programs. Beingable to export your data into useful fi les is importantin analyzing yield data in data management software.Choose a yield monitor capable of exporting data in .txtformat because this format is imported easily into mostdatabase spreadsheets and GIS software programs.A number of yield monitor data errors are associatedwith each data set generated within a fi eld. Theseinclude partial combine header passes, the lag betweenthe beginning of grain cut and when the monitormeasures the grain yield, and the time the header takesto move from the down position during the completion ofa pass to the up position that terminates the data fl ow.The grain fl ow within the combine may bunch up andspurt on occasion insteadof fl owing continuously.For tips on correct installation andoperation of yield monitors, see themanufacturer’s recommendations and thesesuggested publications:“Elements of precision agriculture: Basicsof yield monitor installation and operation,”by Shearer et al., publication PA-1,University of Kentucky. “Precision Farming Tools: Yield Monitor” byGrisso et al., publication 442-502, VirginiaCooperative Extension. Considerable smallscalevariability occurswithin any yield monitordata set because of theseand other sources oferror. One of the easiestdata-cleaning operationsis to eliminate outliersin the data set using aspreadsheet, such asMicrosoft Excel. Outliersare data that, becauseof gaps or spurts in the


Yield Mapping and Use of Yield Map DataSite-specificFarming3grain stream, are unreasonably low or high yields. Forexample, in a fi eld of spring wheat that varies from 20 to50 bushels per acre (bu/acre), some yield points may beas high as 150 bu/a. These actual yields most likely donot exist, so choosing a more reasonable maximum yieldis desirable.In fi elds with soluble salt problems, zero yield ispossible. Cleaning the lower end of the yield sort is morediffi cult. In this case, the area of actual zero yield wouldneed to be identifi ed within the data set and only thosedata not within the problem area should be consideredfor cleaning.In fi elds where actual grain yields are not zero,choosing some lower limit of yield also would berecommended. A data sort from high to low within aspreadsheet then can be conducted, and yields aboveand below the limits can be deleted from the dataset. Some precision-ag consultants have their ownproprietary cleaning software. A public arena downloadof yield data-cleaning software is available from theUSDA-ARS in Missouri (Suddoth, 2007) at www.ars.usda.gov/services/software/download.htm?softwareid=20.Since small spatial errors occur among yieldmeasurements, yield monitor data usually are displayedas maps in larger zones of varying yield ranges. Theseyield maps are useful to producers because they visuallyshow that yields vary throughout the fi eld and where theyields are high, low and in between without the clutterof individual dots that vary widely in value. For visualdisplay, choosing yield increments that make sense ishelpful. For example, in a corn fi eld, choosing displayranges of 30 bu/acre in a fi eld that averages 200 bu/acreis probably appropriate. Displaying ranges of 10 bu/acre increments would create a map that would be too“busy” and hard to interpret. In contrast, a spring wheatfi eld that averaged 40 bu/acre would need incrementsof about 10 bu/acre or less to be meaningful. Thirty bu/acre increments may not show important variation fromthe average.Although the yield map itself is a useful tool thatillustrates the management and climate results forthe current growing season, yield maps also may bepredictive of future yield potential and might be relatedto the availability of certain soil nutrients, such asnitrogen. Single-year yield maps for predictive purposeshave not been nearly as useful as multiyear yield maps.A multiyear yield map also is called a “yield frequencymap.”Whether a fi eld has had a history of a single cropor a diverse crop rotation, the same general procedureshould be followed to create a yield frequency map. Afi eld that has been in wheat continuously for 10 yearsmight average 80 bu/acre one year and 20 bu/acreanother year. The actual bushels for the fi eld thereforecannot be used when the data sets are combined. Ifthe fi eld was corn one year, soybeans the next, wheatthe next and then sunfl owers the year after, these yieldsobviously cannot be added to each other spatially withany meaning. The range of yields in any year thereforemust be “normalized.”Normalization is a simple mathematical exercise thatconverts bu/acre into relative yield. In the example year ofhigh wheat yield with highest cleaned yield of 80 bu/acre,divide each yield by 80. The range of yields is thereforefrom 0 to 1. If the next year is canola and the highestcanola yield is 3,500 pounds/acre, divide each yield by3,500. The range of yields is from 0 to 1.Another way to normalize yield data is to normalizeit at the end of analysis and not at the beginning. Usingthe following fi gures as examples, fi rst impose a grid onthe yield data that makes some sense. A fi eld shouldhave at least 40 grids to produce a meaningful map at theend of the exercise. For example, a user might chooseto superimpose 40 1-acre grids over a 40-acre fi eld. Aquarter-section might have 2-acre grids. A 20-acre fi eldmight have ½-acre grids.To create the grids and the average yield within agrid, use a software program such as Surfer (GoldenSoftware Co., Golden, Colo.) or ArcGIS (ESRI GISSoftware Co., Redlands, Calif.) that can import spatialdata and then convert them to estimated values. Thisestimation feature usually is used for taking less densedata and estimating values at small distances. However, italso can be used to take densely sampled data, such asthe thousands of points of yield data, and average themwithin a less dense grid of your choosing. In Surfer, theresulting grid fi le can be saved in an ASCI text fi le andthen uploaded into a spreadsheet.Within the spreadsheet, the grid is given a +1, -1 or0 value, depending on whether the average of the grid isgreater than the fi eld average, less than the fi eld averageor within ½ bu/acre of the fi eld average. Giving the +1, -1or 0 is a normalization procedure. Then these normalizedgrids can be exported into a spreadsheet and summedby grid with other years of data that have been treated


An example of raw yield monitor datafrom a field of spring wheat in bu/acre.During the normalization process, agrid is superimposed over the data. Thesoftware recognizes the set boundaries foraveraging the data inside each grid box.The data are averaged within eachgrid box independently of the otherdata.If the data average within the grid is higher than average, the grid isgiven a +1. If average, it is given a value of 0. If the average within thegrid is lower than average, the grid is given a -1. Afterward, this datacan be exported into a spreadsheet for summing multiple years of yielddata that have been handled similarly. The resulting summation can beimported into mapping software to construct the yield frequency map.YEAR 1YEAR 2YEAR 3YEAR 4Each year’s data aresuperimposed withthe same grid. Yieldsare averaged withineach grid. +1 is givento grids greater thanaverage for the field, -1for grids lower than theaverage and 0 for therare grid that is within ahalf-bushel of average.These grids then aresummed individuallywithin a spreadsheetand remapped into themapping software.YEAR 5A series of five years of crop raw yieldmonitor data of the same field. Missing datacan be normalized by weighting yields byyears of data so the large area of missingdata in the third yield map is divided onlyby 4 instead of 5, or normalized by thesummation multiplied by 1.25, to comparefavorably with data from five years of mapsinstead of the four it has available.A visual depictionof the four yearsof individuallynormalized yieldssummed and mappedas a yield frequencymap (bottom map).


A yield frequency map depicting acorn and soybean rotation in Illinoisin a five-year period (from Franzen,2008). Each individual year did notshow all of these patterns. Meaningfullong-term trends in yield were revealedwith accompanying relationshipsto soil phosphorus, potassium andpH (acidity or alkalinity) levels onlyfollowing the combination of years.in the exact same manner. In this way, two years, threeyears or 20-plus years of data can be combined tocreate a more meaningful yield frequency map.The yield frequency map has been used as a zonelayer for revealing residual soil nitrate and other nutrientsin North Dakota. The yield frequency map also can helpreveal areas that require additional management, suchas a change in nitrogen application timing or a change indrainage if permitted. It can reveal the yield drag due tohedgerows, saline spots, compacted areas or locationscontaining harmful levels of sodium.The yield frequency map also can be usedconceivably to predict the relative performance of farmsand evaluate future production potential for renting orpurchasing decisions. If the soils on a farm that youmanage have a certain level of productivity, this potentialproductivity probably also is possible on farms withinseveral miles of that farm. This information can helpdetermine whether a farm is worth renting or buying.The yield maps are a valuable archive of fi eldperformance and the changes that management mighthave had on the fi elds. These archived maps and datacan be shared with bankers to help secure loans andwith future renters or purchasers of the land. They canbe passed down to the next generation so that the thingsgrowers learn about their fi elds will not be lost to theheirs.Many growers also use a yield monitor to helpdevelop nutrient strategies on specifi c farms. Differentrates of nitrogen, for example, can be applied in stripsacross soils within a fi eld, and the yields can help thegrower determine what the best rate might be for a soilor the fi eld. In addition, new products can be tested ona small strip within the fi eld and the effi cacy of their useYield frequencymap at Williston,N.D. (top), withlower yields onthe ridge slope(southeast tonorthwest direction)and higher yieldin the more levelareas above andbelow the ridgeslope (southwestand northeast)(elevation image onthe bottom).can be much more easily seen this way than by using aweigh wagon. The many data points of each strip alsolend themselves to the use of statistics offered withinspreadsheets to provide confi dence to the growerthat the differences seen were statistically real or not.Variability within a fi eld can mask differences or createdifferences unless statistics are used to help eliminatethe clutter.ReferencesSuddoth, 2007. Yield editor software. USDA-ARS. www.ars.usda.gov/services/software/download.htm?softwareid=20Franzen, 2008. Summary of forty years of grid sampling in twoIllinois fi elds. NDSU Technical Report.The NDSU Extension Service does not endorse commercial productsor companies even though reference may be made to tradenames,trademarks or service names.This publication may be copied for noncommercial, educational purposesin its entirety with no changes. Requests to use any portion of thedocument (including text, graphics or photos) should be sent to NDSU.permission@ndsu.edu. Include exactly what is requested for use andhow it will be used.For more information on this and other topics, see: www.ag.ndsu.eduCounty commissions, North Dakota State University and U.S. Department of Agriculture cooperating. North Dakota State University does not discriminateon the basis of race, color, national origin, religion, sex, disability, age, Vietnam Era Veterans status, sexual orientation, marital status, or public assistancestatus. Direct inquiries to the Vice President for the Division of Equity, Diversity and Global Outreach, 205 Old Main, (701) 231-7708. This publication will bemade available in alternative formats for people with disabilities upon request, (701) 231-7881.1M-12-08

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