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Enhancement of cranberry management by quantitative remote sensing techniques Christofferson , Jill Maureen 1992

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ENHANCEMENT OF CRANBERRY MANAGEMENT BY QUANTITATIVEREMOTE SENSING TECHNIQUESByJILL MAUREEN CHRISTOFFERSONB.Sc., The University of British Columbia, 1985A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinTHE FACULTY OF GRADUATE STUDIES(Interdisciplinary Studies, Resource Management Science)The University of British ColumbiaWe accept this thesis as conformingto the required standardTHE UNIVERSITY OF BRITISH COLUMBIAOctober, 1992© Jill Maureen Christofferson, 1992In presenting this thesis in partial fulfilment of the requirements for an advanceddegree at the University of British Columbia, I agree that the Library shall make itfreely available for reference and study. I further agree that permission for extensivecopying of this thesis for scholarly purposes may be granted by the head of mydepartment or by his or her representatives. It is understood that copying orpublication of this thesis for financial gain shall not be allowed without my writtenpermission.Department of Resource Management ScienceThe University of British ColumbiaVancouver, CanadaDateDE-6 (2/88)iiENHANCEMENT OF CRANBERRY MANAGEMENT BY QUANTITATIVEREMOTE SENSING TECHNIQUESABSTRACTCommercial cranberry production involves an intensivemanagement system that requires the close monitoring of the cropthroughout the entire growing season. This is traditionallyaccomplished by ground surveying; however, this method is bothtime consuming and labour intensive and excessive bog traffic canbe damaging to the ground-covering vines. In this study, colournear-infrared photography and quantitative image analysistechniques were used to determine the feasibility of using remotesensing to monitor site conditions within cranberry bogs and torelate these conditions to yield.Colour near-infrared images of four cranberry bogs wereobtained three times over each of two growing seasons.Correlation and regression techniques were used to measure linearrelationships between soil and foliar element concentrations, vinestatus, yield and remote sensing variables. Images were alsoexamined using both supervised and unsupervised classificationtechniques to measure spatial relationships between biophysicaland remote sensing variables.Sample sites containing high levels of chronic weed stresswere also found to have high levels of soil and foliar Al, Fe, andMn, suggesting that excessive levels of these metals had anegative influence on vine status and consequently fruitiiiproduction. This was likely a result of the toxic effects oncranberry vine growth of high levels of Al, Fe and Mn madeavailable by the acidic and wet conditions characteristic of bogs.Higher yielding sites were found to contain lower soil and foliarAl, Fe and Mn concentrations and higher Mg levels.Supervised image classification based on high correlationcoefficients between yield and the NIR/R ratio was effective inidentifying different levels of production within the bog.Unsupervised classification proved to be a fast and effectivemethod of delineating areas containing high levels of weedinfestation as well as areas that were poorly drained. Because ofthe negative impact of these two forms of stress on yield,unsupervised classification was also successful in identifyingdifferent production levels within the bog.The results suggest that remote sensing techniques, when usedin conjunction with field sampling, can be an effective means ofmonitoring conditions within the bog and can be useful inimproving yield forecasts.ivENHANCEMENT OF CRANBERRY MANAGEMENT BY QUANTITATIVEREMOTE SENSING TECHNIQUESTABLE OF CONTENTSABSTRACT 	  iiTABLE OF CONTENTS 	  ivLIST OF TABLES 	 viiLIST OF FIGURES 	  ixLIST OF SYMBOLS 	ACKNOWLEDGEMENTS 	 xiiCHAPTER 1. INTRODUCTION: 	  11.1 Objectives 	  31.2 Thesis Overview 	 4CHAPTER 2. BACKGROUND: 	  62.1 Cranberry Production 	  62.1.1 The Cranberry Plant 	  72.1.2 Commercial Production 	  82.2 Remote Sensing Techniques 	 122.2.1 Basic Concepts 	 122.2.2 Remote Sensing of Vegetation 	 142.2.3 Applications of Remote Sensing Techniques forStudies of Nutrient Stress and WeedInfestations 	 182.2.4 Colour-NIR Aerial Photography and Digital ImageAnalysis 	 21CHAPTER 3. STUDY SITES'	 243.1 Locations and Site Descriptions 	 243.2 Bog Management 	 27CHAPTER 4. METHODS AND MATERIALS•	 284.1 Test Plot Design 	 284.2 Remote Sensing Methods 	 284.3 Field Methods 	 30V4.4 Laboratory Methods 	 314.4.1 Soil Analysis 	 314.4.2 Foliar Analysis 	 324.4.3 Yield Analysis 	 334.4.4 Vine Status 	 334.5 Image Analysis 	 344.5.1 Image Scanning 	 344.5.2 Pixel Extraction 	 344.5.3 Supervised Classification 	 354.5.4 Unsupervised Classification 	 364.5.5 Image Overlay 	 374.6 Statistical Analysis 	 37CHAPTER 5. SPATIAL AND SEASONAL VARIABILITY WITHIN ANDAMONG BOGS: 	 415.1 Within Bog Variability 	 425.1.1 Soil, Foliar, Vine Status andYield Variability 	 425.1.2 Remote Sensing Variability 	 495.2 Among Bog Variability 	 525.2.1 Site Differences Among Individual Bogs 	 525.2.2 Differences in Remote Sensing Variables AmongIndividual Bogs 	 555.3 Seasonal Changes 	 565.3.1 Seasonal Variability in Cranberry FoliarNutrient Content 	 565.3.2 Seasonal Variability in ReflectanceProperties 	 58CHAPTER 6. LINEAR RELATIONSHIPS BETWEEN SITE VARIABLES:. . . . 606.1 Relationships Between Soil, Vine Status andYield Variables 	  606.2 Relationships Between Foliar ElementConcentrations and Yield Variables 	  666.4 Relationships Between Site andRemote Sensing Variables 	  716.5 Summary of Linear Yield Relationships 	  74viCHAPTER 7. SPATIAL VARIABILITY AND RELATIONSHIPS BETWEENBIOPHYSICAL PARAMETERS AS IDENTIFIED BY IMAGECLASSIFICATION•  787.1 Supervised Classification 	  797.2 Unsupervised Classification 	 877.3 Image Overlay 	  94CHAPTER 8. CONCLUSIONS AND RECOMMENDATIONS: 	  978.1 Relationships Between Cranberry Production andSite Variables 	  978.2 Relationships Between Measured Site Propertiesand Pixel Brightness Values 	  998.3 Effectiveness of Image Classification Techniquesfor Measuring Site Properties 	 1018.4 Recommendations 	 102REFERENCES 	 104APPENDIX A: Cranberry tissue critical nutrient levels,B.C. conditions 	 110APPENDIX B: Rainfall and temperature data for the FortLangley and Pitt Meadows sites, 1990 and 1991. . .111APPENDIX C: Bog management for the 1990 and 1991 growingseasons 	 112APPENDIX D: Weed species identified in the four study bogs . 	 116APPENDIX E: Soil and Foliar digest methods 	 117APPENDIX F: Results of ANOVA 	 118APPENDIX G: Data tables 	 123viiLIST OF TABLESTable 4.1: Remote sensing mission dates andgrowth stage represented 	  30Table 5.1: Means and coefficients of variationfor soil data 	 43Table 5.2: Means and coefficients of variationfor foliar data 	 44Table 5.3: Means and coefficients of variationfor vine status 	 46Table 5.4: Means and coefficients of variationfor yield variables 	 47Table 5.5: Means and coefficients of variationfor pixel reflectance data	 50Table 5.6: Significant differences in soil, foliar, vine statusand yield variables among individual bogs 	 54Table 5.7: Significant differences in pixel reflectance valuesamong bogs	 56Table 6.1: Correlations identified between soil and yieldvariables 	 62Table 6.2: Correlations identified between soil variables . . .62Table 6.3: Best regression equations for predictionsof cranberry yield from soil variables 	 63Table 6.4: Best regression equations for predictions ofvegetative cover status from soil data 	 65Table 6.5: Correlations identified between foliar andyield variables 	 67Table 6.6: Best regression equations for predictions of yieldfrom foliar nutrient data 	 70Table 6.7: Correlations identified between site properties andremote sensing variables 	 72Table 6.8: Best regression equations for predictions of sitevariables from pixel reflectance values 	 73viiiTable 7.1: Regression equations used for supervised classificationand class ranges for yield values 	 80Table 7.2: Means of variables exhibiting significant differencesbetween classes (Bog 100; May, 1991) 	 84Table 7.3: Means of variables exhibiting significant differencesbetween classes (Bog 300; June, 1990) 	 84Table 7.4: Means of variables exhibiting significant differencesbetween classes (Bog 300; May, 1991) 	 85Table 7.5: Yield estimates obtained using supervisedclassification	 87Table 7.6: Means of variables exhibiting significant differencesbetween classes (Bog 100) 	 91Table 7.7: Means of variables exhibiting significant differencesbetween classes (Bog 200) 	 92Table 7.8: Means of variables exhibiting significant differencesbetween classes (Bog 300) 	 92Table 7.9: Means of variables exhibiting significant differencesbetween classes (Bog 400) 	 93LIST OF FIGURESFigure 2.1: Spectral signatures for soil, healthy greenvegetation and water 	  13Figure 2.2: Reflectance, absorbance and transmittance froma typical healthy green leaf and dominant factorsaffecting leaf optical properties 	  15Figure 3.1: Locations of the cranberry bogs used forthis study 	  26Figure 4.1: Test plot design 	  29Figure 6.1: Relationships among site and remote sensingvariables 	  77Figure 7.1: Regression model used for supervised classificationand the corresponding classified map,Bog 100; May 31, 1991 	  81Figure 7.2: Regression model used for supervised classificationand the corresponding classified map,Bog 300; June 15, 1990 	  82Figure 7.3: Regression model used for supervised classificationand the corresponding clssified map,Bog 300; May 31, 1991	  83Figure 7.4: Colour-NIR image and unsupervised classificationfor Bog 100; May 31, 1991	  89Figure 7.5: Colour-NIR image and unsupervised classificationfor Bog 300; May 31, 1991	  90Figure 7.6: Areas of Bog 100 containing high levels of weedinfestation in May and June, and changes in thelevel of stress as determined by map overlay . . . 96ixxLIST OF SYMBOLSSymbol	 Variable 	 UnitsSoil:Mg 	 total soil magnesiumCa 	 H 	 11 	 calcium 	 IIK 11	 " 	 potassium	HFe 	 11 	 11 	 iron 	 pg/gMn 	 11 	 11 	 manganese	11Al 	 11 	 11 	 aluminum	11Cu 	 11	 " 	 copper	11Zn 	 11 	 11 	 zinc 	 11Fe:Al 	 iron:aluminum ratio	 (Ag/g)/(Ag/g)Fe:Mn 	 iron:manganese ratio 	 HFoliar:N total foliar nitrogen	 % (air dry tissue)P 11 	 11 	 phosphorus	11Ca 	 11 	 11 	 calcium 	 I/Mg 	 H 	 11 	 magnesium 	 11K 11 	 H 	 potassium	HFe 	 11 	 1/ 	 iron	 Ag/g (air dry tissue)Mn 	 11 	 11 	 managanese	 HAl 	 11 	 11 	 aluminum 	 11Cu 	 H 	 11 	 copper	HZn 	 11 	 H 	 zinc 	 11Mg:Fe 	 foliar magnesium:iron ratio	 (%*100)/(gg/g)Mg:Al 	 foliar magnesium:aluminum ratio	IIVine Status:%Vine 	 percent of plot area that contains	 %healthy vine growth% GL-weeds 	 percent of plot area that contains	 %grass-like weed infestations% Ponding 	 percent of plot area that contains	 %ponded surface waterxiYield:yield100 BWNOBcolU.D.average yield per plot areaweight per 100 berriesnumber of fruit per areafruit colour (anthocyanin)upright densitykg/m2gberries/m2mg/gno. uprights/dm 2Remote Sensing Variables:NIR 	 pixel value of the near-IRsensitive dye layerR		 pixel value of the visible-redsensitive dye layerG		 pixel value of the visible-greensensitive dye layerpixel values arerecorded in arange of 0-255in which0=minimumreflectanceand255=maximumreflectancexiiACKNOWLEDGEMENTSFunding for this thesis was provided by the Science Councilof British Columbia and by Ocean Spray Cranberries Inc.I wish to express my sincere thanks Dr. Sheila Fitzpatrickand Dr. George Eaton, both of whom provided invaluable assistanceand guidance throughout the course of this thesis. Thanks arealso due for their encouragement and support to the other membersof my committee which included Dr. L.M. Lavkulich and Dr. KenHall, and particularly to my thesis supervisor, Dr. Hans Shreier.Sincere gratitude is also extended to Mr. B. von Spindler,Ms. B. Cade-Menum and Ms. L. Toerper for their assistance andguidance in laboratory analysis. Thanks are also due to Dr. J.Davenport and J. Provost of Ocean Spray Cranberries Inc., whoconducted the analysis of foliar samples collected during thefirst year of the study, and to Mr. C. Kinder, also of Ocean SprayCranberries Inc., who helped to initiate the project. Thetechnical instruction and assistance provided by Ms. S. Brown isalso gratefully acknowledged.Finally I would like to thank Mr. S. Ujhazy and Mr. K.Hopkins for allowing me to conduct this study on their cranberrybogs.1CHAPTER 1INTRODUCTIONCommercial cranberry production occurs primarily inMassachusetts, Wisconsin, Oregon, Washington and New Jersey in theUnited States and in British Columbia and Nova Scotia in Canada.The total area under cultivation in North America is about 11,800ha, and total production in 1991 was 3,932,000 barrels (179,000tonnes) (Dr. J. Crooks, Scientist, Ocean Spray Cranberries;personal communication). Commercial plantings of this high-valuedcrop were first established in the Lower Fraser Valley-Deltaregion of British Columbia in the early 1940's (Eaton, 1971a).Moderate climatic conditions and extensive deposits of peat soilsmake this area ideally suited to cranberry cultivation. Totalacreage under cranberry production has more than doubled in B.C.over the past ten years; there are currently 41 growers managing1,100 ha of cultivated bogs located mainly in the Richmond, FortLangley and Pitt Meadows areas. British Columbia's annual crop isapproximately 380,000 barrels (17,000 tonnes), representing about10% of North American production, with a value of approximately$22 to 24 million (Cdn).The management of intensive commercial agriculturalproduction systems is complex and requires the close monitoring ofthe crop throughout the entire growing season. Visual inspectionby ground surveys is the most commonly used method for cropevaluation. However, it is often difficult to estimate at ground2level the extent of damage to a ground-covering vine such ascranberries and extensive surveying may be physically damaging tothe plant. Given the high level of management required forcommercial cranberry production and the high value of this crop,interest has been expressed in using remote sensing to monitorbiophysical conditions within the bog.Remote sensing techniques use quantitative relationshipsbetween energy reflectance values of the canopy and the status ofthe plants as a means of measuring important biophysicalparameters. This technology offers numerous advantages overtraditional methods of surveying including: increased speed ofsurveys; the ability to measure ground conditions directly usingimagery having only minimal spatial distortion; the ability todepict seasonal and long-term changes by using repetitivecoverage; and the fact that remotely sensed data can be integratedinto an existing monitoring system (Myers, 1983). The ability tomonitor long-term change and to create and update a permanentrecord using sequential images is particularly advantageous forcranberry management given the longevity of commercial plantings.The relatively large size of commercial operations in BritishColumbia, coupled with the intensity of commercial productionmethods, makes it necessary for bog managers to allocatefertilizer and other chemical treatments according to when andwhere they will bring the best economic returns. The advantage ofincreased speed and the ability to make direct area measurementsof important ground conditions are therefore especially relevant3to the industry in this province.Monitoring productive verses non-productive area measurementswould also improve the accuracy of yield forecasts. Approximately80% of the North American crop is marketed through aninternational grower cooperative, Ocean Spray Cranberries, Inc.,and hence product development and marketing strategies are largelydependent upon accurate crop forecasts.The focus of this study was to determine the feasibility ofusing large scale colour-IR aerial photography and image analysistechniques to determine site variability, to monitor vine statusand plant stress within the cranberry bogs, and to improve yieldforecasts.1.1 Objectives.The objective of this thesis was to examine the effectivenessof remote sensing techniques as tools for the management andproduction of cranberries. Specifically, this study addressed thefollowing:1. To develop a method of determining the extent and spread ofweed infestations within cranberry bogs;2. To evaluate irrigation and drainage efficiency and fertilizermanagement;3. To determine relationships between the above factors and cropyield and quality; and4. To examine seasonal variations within the bog with regard tofoliar element concentrations and spectral reflectancecharacteristics.41.2 Thesis OverviewThe following section of this thesis, Chapter 2, begins withan introduction to the cranberry species and commercial bogmanagement. An understanding of the plant characteristics andknowledge of ground conditions is necessary in order to relate andevaluate remotely sensed images. Secondly, some of the basicconcepts of remote sensing are reviewed including theelectromagnetic spectrum and the interactions of ground features,in particular vegetative surfaces, with electromagnetic energy.A review of previous studies in remote sensing applications foragricultural purposes is presented. Although a large body ofliterature exists concerning the applications of remote sensingfor the detection and monitoring of stress factors in crops,including insect and disease infestations, the emphasis of thisreview will be on nutrient imbalances and weed infestations.Chapter 3 provides information about the location, physicalenvironment and management of the sites used for this study. Theplot layout, sampling dates and methods, and the methods used foranalysis are presented in Chapter 4. Chapters 5, 6 and 7encompass the results and discussion section of the thesis. Theresults of soil, foliar, vegetation cover status, yield and pixelreflectance value measurements are summarized in Chapter 5together with the degree of variability observed for theseparameters both within and between bogs. Significantrelationships between soil, foliar nutrient, vegetation cover,yield and remote sensing parameters as identified by correlation5analysis are presented and discussed in Chapter 6. This chapteralso lists regression equations for those parameters that aresignificantly correlated. Spatial relationships between siteproperties are discussed in Chapter 7, in which the results ofunsupervised and supervised image classification techniques arepresented together with potential applications of these methods.Also shown in this chapter is an example of an image overlaytechnique that allows temporal changes in bog conditions to beexamined by analysis of spatially referenced sequential images.The conclusions of the current study and recommendations forfuture research are summarized in Chapter 8.6CHAPTER 2BACKGROUND2.1 Cranberry Production2.1.1 The Cranberry PlantVaccinium macrocarpon Ait., the large or American cranberry,is the cultivated cranberry of commerce. The plant is native tothe acidic peat bogs of North America and is described as a low-growing vinelike woody perennial with persistent leaves. It is along-lived plant and established bogs can remain commerciallyproductive for decades (Eck, 1990).The root system is fine, fibrous and shallow, developing inthe top 2.5 to 7.5 cm of soil. The roots have no root hairs butare surrounded by a loose web of fungal mycelium (Addoms andMounce, 1931), and are infected with the mycelium of the endo-mycorrhizal fungus Hymenoscyphus ericae (Read) Korf and Kernan(Shaw et al, 1990; Stribley and Reed, 1980). It is believed thatthe fungus enables root absorption and utilization of organicforms of nutrients, particularly simple organic nitrogencompounds, contained in peat soils (Shaw et al., 1990; Straker andMitchell, 1986; Stribley and Reed, 1980).The thick, leathery leaves are oblong and small, being 2-3 mmbroad and 5-8 mm in length. The upper surface of the mature leafis a dark, glossy green during the growing season and turns to adull reddish brown during the dormant season due to a decline in7chlorophyll content and an increase in red pigment (Dana, 1990).The stomata are located on the underside of the leaf. Guard cellsregulating the stomatal opening operate erratically and as aresult the stomatal apparatus adjusts only slowly or not at all tochanging environmental conditions (Sawyer, 1931, as cited by Eck,1990). The leaves are produced annually but remain on the stemfor at least two seasons.The initial growth of the cranberry plant is in the form ofvines or runners. Runners can reach a length of 2 m and growhorizontally over the floor of the bog forming a dense mat. Rootscan be initiated at any point along the runner or decumbent stemthat is covered with a moist medium (Dana, 1990). Secondarygrowth is in the form of short vertical branches called uprightsthat grow from axillary buds on the runners and older uprights.New vegetative growth, about 5-10 cm annually, develops from theterminal apex of the upright (Dana, 1990; Eck, 1990), and flowersdevelop on the uprights starting in the second year.The apical region of the upright is the point of flower buddevelopment. Terminal buds, destined to produce either leaves orleaves and flowers, are induced in mid-summer and set prior todormancy. The vines remain dormant throughout the winter monthsuntil April. The initial seasonal growth stage is referred to asthe "popcorn" stage due to the appearance of the swelling terminalbuds. Vegetative growth occurs from the terminal during the earlyspring and axillary flowers, 3-5 per upright, appear at the baseof the new shoot about 30 days after growth is initiated (Dana,81990). This growth stage is referred to as the "hook" stage afterthe characteristic appearance of the flowers. The floweringperiod, or bloom, lasts from 2-5 weeks. Insects, especially bees,are needed for pollination and fertilization. Fruit set occurs inJuly and the fruit reaches maturity 60 to 120 days later. Theberry does not separate from the upright at maturity, and harvestof the fruit requires that the pedicel attaching the berry to theupright be broken.2.1.2 Commercial CultivationThe production of cranberries has developed by manipulatingthe sites on which cranberries were native, or on sites having atopography and structure similar to natural sites (Dana, 1990).Individual bogs, or beds, range in size from 0.25 ha in Oregon tolarger than 3.5 ha in British Columbia. Establishment of thefield begins with digging the main drainage canals. Once the boghas been sufficiently drained, planting areas are prepared byscalping and levelling the surface of the bed. Planting in thewestern growing areas is completed in the spring and isaccomplished by inserting cuttings to a depth of 6-10 cm into thebog floor. It normally takes 3 to 4 years from the time cuttingsare planted for the commercial bog to produce a profitable crop(Dana, 1990).An extensive and reliable supply of fresh water is mandatoryfor cranberry management. The water table is controlled at anoptimum depth of 30-38 cm during the growing season (Eck, 1990) by9means of a network of ditches and pumping systems. Sprinklerirrigation is used to protect the vines from freezing temperaturesand desiccation during the winter, to prevent frost damage to thebuds, blossoms and fruit in the early spring and to minimize heatdamage to the vines and crop during the summer months. Sprinklerirrigation is also used to maintain soil moisture during thesummer months and for fertigation. Machine harvesting of berries'on the flood' requires a large supply of water in order to floodthe bogs to a depth of 10-20 cm (Dana, 1990).Commercial production relies on chemical fertilizerapplications to optimize yield and fruit quality. Cranberries aregrown on a combination of peat and muck overlaid with a thatch ofdead cranberry leaves and stems. The physical and chemicalproperties of this medium make it difficult to maintain propernutritional balance and intensity (Chaplin and Martin, 1979).Essential elemental requirements for cranberry vines are generallymuch lower than those for other agronomic crops. Applications aremade according to recommendations based on soil and foliar tests,the growth stage and general appearance of vines and on variationsin climate. Appendix A lists optimum nutrient content forcranberry tissue for B.C. conditions. Excess levels of appliedfertilizer, particularly nitrogen, can overstimulate vine growthand make previously fruitful uprights become vigorous,unproductive runners (Dana, 1990). This decreases yield andpossibly makes the plant more susceptible to stress (Thoma, 1988).Increased vine growth also results in less incident light on the10developing berries causing a reduction in anthocyaninconcentration and consequently poor colour development (Eaton,1971b). Apart from its detrimental effect on the cranberry plant,excess nitrogen can also stimulate weed growth in infested bogs(Dana, 1990). Effective fertilizer management is thereforecritical for commercial cranberry production.In B.C. three insects are of economic significance tocranberry production: the cranberry girdler (Chrysoteuchia topiaria Zeller), the black vine weevil (Otiorhynchus sulcatus Fabr.), and the blackheaded fireworm (Rhopobota naevana Hbn.)(Eck, 1990). Flooding provides an effective means of controllingweevil and girdler, whereas chemical control is recommended forfireworm control. Entomogenous nematodes are also recommended forweevil and girdler (B.C. Ministry of Agriculture, Fisheries andFood, 1992). In recent years, there has been a decreasing trendin insecticide applications due largely to improved monitoringwith visual scanning and sex pheromone traps and improvedintegrated pest management (IPM) schemes.The most important disease affecting cranberry bogs in B.C.is cottonball or tip blight caused by Monilinia oxycocci (Wor)Honey (Eck, 1990). Control of this fungus involves applicationsof fungicides to disease-prone varieties when buds begin to swell(B.C. Ministry of Agriculture, Fisheries and Food, 1992).Weeds are a major management concern as the cranberry plantis a weak competitor (Dana, 1990) and weedy stands are lessproductive than pure stands (Hicks et al., 1968). In addition to11competing directly with vines for water, nutrients and light,weeds can serve as hosts for insects, diseases and nematodes.Cranberry vines shaded by weeds produce longer and fewer uprights(Hicks et al, 1968; Roberts and Struckmeyer, 1942), uprights withfewer flowers, fewer flowers per upright and lower levels of fruitset (Hicks et al., 1968). Flowering weeds can also compete withcranberry blossoms for pollinating insects (Galleta and Himelrick,1990). Water harvesting techniques are less efficient in infestedareas because weeds interfere with the beaters used to removeberries from the vines. Sources of weed infestations includegerminable buried seeds, seeds in the vines used for planting, andweed seeds carried by flood waters from the flora of the ditchbank and adjacent territory (Skroch and Dana, 1965). Wetlandspecies of sedges and grasses are particularly serious invaders(Hall et al., 1981), and ericaceous shrubs and annual andperennial broadleaf wetland species can also become seriousproblems. Chemical weed control methods include blanketapplications of casoron in the early spring and over-the-top wipeapplications of glyphosate (B.C. Ministry of Agriculture,Fisheries and Food, 1992). Hand weeding is used for the controlof some broadleaf species.Berries are machine harvested either dry or "on the flood".Dry-raked fruit is separated from the vines by mechanical rakes,which hold the berries on the tines of the machine while theuprights pass through the tines. Berries collected by dry-pickingare collected into boxes and carried from the field. The methodof water-harvesting involves the bogs being flooded and the12berries mechanically removed from the vines with beaters, gatheredand moved to one end of the bog using floating booms and thenloaded into barrels using conveyor belts. The barrel, the commonunit of measurement for cranberries, equals 100 lbs (45.5 kg) andthe average yield per acre in British Columbia is 160 barrels(B.C. Cranberry Growers Association). Cranberry yield is afunction primarily of the number of flowering uprights per unitarea. The number of flowers per upright and berry set are alsoimportant contributors to yield diversity as is, although to alesser extent, berry size (Eaton and MacPherson, 1978). Becausemuch of the fruit is processed into juice products, the colour ofthe berry, governed by the level of anthocyanin pigments, is animportant criterion of fruit quality (Eck, 1990).2.2 Remote Sensing Techniques2.2.1 Basic ConceptsRemote sensing is a way of measuring the characteristics ofa subject by detecting, recording and analyzing interactionsbetween the subject and electromagnetic energy (light, heat andradio waves) reflected or emitted from it. In most instances,remote sensing techniques record the natural solar energyreflected by an object or surface. The amount of energy reflectedby an object or surface varies with wavelength throughout thespectrum and is governed by the characteristics of the object orsurface being studied. These spectral characteristics arereferred to as signatures. The general spectral signatures forbare soil, green vegetation and water are presented in Figure 2.1.0 1 	 1	 1 	 I400 600 800 1000 	 1400 	 1800WAVELENGTH ( nm )2600220013Figure 2.1: Spectral signatures for soil, healthy greenvegetation, and water.Remote sensing techniques can be divided into image-orientedand digitally oriented systems. Aerial photography represents animage-oriented system in which reflectance, or radiance, data isobtained from the ground surface and recorded in an analog form.Digitally oriented systems record reflectance data in numericalform and are thus ideally suited to computer-aided image analysis.The brightness of objects, as recorded on either photographicor digital images, is proportional to the intensity ofelectromagnetic radiation reflected by that object and isinfluenced both by the sensitivity of the remote sensing systemand the degree of atmospheric scattering. Resolution is a measure14of the degree to which objects that have either similar spectralpatterns or are spatially near to one another can be distinguishedon a photographic or digital image. Spectral resolution is afunction of the number and size of electromagnetic wavelengthbands that are recorded on the image while spatial resolution isa function of scale and the resolving power of the imaging system(Sabins, 1978).2.2.2 Remote Sensing of VegetationRemote sensing studies of vegetation use reflected naturalelectromagnetic energy within the wavelengths ranging from 400 to2500 nm. The visible (400-700 nm) domain is characterized by thestrong absorbance by leaf pigments of energy in this waveband.The near-infrared (700-1300 nm) region, which is characterized bylow absorbance, high reflectance and high transmission, isaffected considerably by the internal structure of the leaf. Themiddle-infrared (1300-2500 nm) region, while somewhat influencedby leaf structure, is largely affected by water levels within theplant tissue (Myers, 1983). Typical reflectance as well astransmittance and absorption characteristics for vegetation withinthese three domains are presented in Figure 2.2. Much of theincident radiation reaching healthy green vegetation istransmitted through the surface of the leaf, with only 2-3% beingreflected by the cuticle (Tucker and Garret, 1977). Oncetransmitted through the cuticle, energy is either absorbed orreflected by the internal leaf structure or is transmitted throughthe leaf.ABSORBANCEREFLECTANCE15Energy associated with the blue and red wavelengths in thevisible part of the spectrum is largely absorbed by chloroplastsin the palisade mesophyll cells (Jensen, 1983). Of the four mainpigments, chlorophyll "a" absorbs at 450 and 660 nm, chlorophyllIlbII absorbs at 450 and 650 nm, and B-carotene and xanthophyllabsorb in the blue to green wavelengths (Curran, 1985).Absorbance of the visible wavelengths is smallest in the regionaround 550 nm, where a reflection peak of about 20% is observed inthe spectral signature of green leaves (Myers, 1983).1- IR FILM --II-zLAUceLAa_LEAFPIGMENTS100 	90 -80 -70 -60 -50 -40 -30 -20 -10-STRUCTURE I-- WATER CONTENTCELL0- 10- 20- 30- 40- 50- 60- 70- 80- 90400 700 1000 1300 1600 1900 2200 2500 2800IVISIBLEI- NEAR IR --I— 	 MIDDLE INFRARED	 —IWAVELENGTH ( n m )Figure 2.2: Reflectance, absorptance and transmittance from atypical healthy green leaf and dominant factors affecting leafoptical properties (adapted from Knipling, 1970 and Jensen, 1983).16Near-infrared reflectance from individual leaves ischaracteristically high and in the case of mature dicotyledons, inwhich the spongy mesophyll is highly developed, accounts for 40 to50% of incident radiation associated with this wavelength band.Near-infrared energy that is not reflected is largely transmitted,with only 5% being absorbed within the leaf (Myers, 1983). Mostof the reflectance results from scattering caused by refractiveindex discontinuities at the hydrated cell wall/air interfaces ofthe spongy mesophyll (Jensen, 1983). Intracellulardiscontinuities involving other cellular constituents, includingstomata, nuclei, cell walls crystals and cytoplasm, account foronly 8% of the near-infrared reflectance from leaves (Gausman,1977; Gausman, 1974; Wooley, 1971). Reflectance in this domain isstrongly affected by leaf anatomical characteristics including thenumber of cell layers, the size of the cells and the relativethickness of the spongy mesophyll. The leaves of dicotyledons,for example, have a higher near-infrared reflectance than do thoseof monocotyledons because the mesophyll structure of the formerdifferentiates into distinct palisade and spongy mesophyll layerswhile that of the latter remains more compact (Myers, 1983).Leaf optical properties in the middle-infrared are largelygoverned by leaf water content (Gausman, 1974; Tucker and Garret,1977). Strong absorption bands occur at 1450, 1950 and 2500 nmand produce leaf reflectance minima in this domain.Remote sensing of a vegetative surface records reflectancefrom a plant canopy that consists of layers of leaves, non-leaf17plant structures, background components and shadows (Curran, 1985;Knipling, 1970). Plant species other than the predominant onewill also contribute to the reflectance (Myers, 1983). Thus,while an understanding of the reflectance properties of individualleaves and how these patterns are altered by various forms ofstress is essential for remote sensing interpretation, plantcanopies are much more complex and spectral patterns identifiedfor individual leaves may not be consistent with those of thecanopy structure (Myers, 1983; Colwell, 1973). This isparticularly true in regard to leaf verses canopy reflectance ofenergy in the near-infrared region. In addition to the highreflectance of near-infrared energy from the leaf structure, thereis also high transmittance. Multiple internal reflections ofenergy transmitted by the top layers of leaves and incident on thelower leaves within the canopy reinforce reflection from the topof the canopy (Colwell, 1973).The geometry of the crop canopy is important as thisdetermines the amount of shadow and the degree of scattering ofradiation among leaves of the canopy and hence the amount ofreflectance (Myers, 1983). Differences or changes in the leaforientation also affect reflectance. For example, a change froma horizontal to a vertical leaf orientation caused by wiltinggenerally decreases the near-infrared reflectance, due to areduction in the total leaf area exposed to the sensors, andincreases the red reflectance, due to increased soil background(Knipling, 1970).182.2.3 Application of Remote Sensing Techniques for Studies ofNutrient Stress and Weed Infestation.Changes in vegetation appearance may be morphological,physiological, or both. Morphological injury refers to changes inthe shape or form of the plant including defoliation, breakage andcellular collapse of plant parts. Examples of physiologicalchanges include alteration of the mesophyll structure, pigmentdeterioration and disruption of nutrient translocation.Physiological damage can induce morphological changes, such aswilting, reduced biomass , top-killing and cell necrosis (Murtha,1982). An understanding of the manner in which the relevant formsof stress cause spectral and reflectance characteristics todeviate from the norm is essential for remote sensinginterpretation of vegetation.The responsiveness of leaf reflectivity in the visible regionto stress conditions is caused by the effect of metabolicdisturbances on leaf chlorophyll (Knipling, 1970). As chlorophylldeteriorates it absorbs energy less efficiently, resulting inincreased reflectance in the visible wavelengths. Al-Abbas et al.(1974) reported higher leaf reflectance in the visible region formaize leaves deficient in N, P, K, S, Ca and Mg then for theunstressed control plants. Similarly, P deficiencies have beenfound to increase reflectance in the green and yellow portions ofthe visible region (Milton et al., 1991), and Gausman et al.(1973) reported that deficiencies of N, K, S, Mg and Fe increasedthe spectral reflectance of Mexican squash in this spectral19region. Nitrogen deficiencies have also been found to increasethe visible reflectance of both cotton (Thomas et al., 1966) andsweet pepper leaves (Thomas and Oerther, 1972).Physiological damage induced by toxic levels of plant-available elements also leads to changes in the visiblereflectance. Horler et al. (1980) reported that high metalconcentrations in plant root zones were associated with increasesin visible reflectance of plant leaves. This was attributedincreases in leaf reflectance in the visible region due to adecrease in chlorophyll content. It has been suggested thatnutrient imbalances or anomalous metal concentrations in the soilresult in physiological conditions at the soil/root interface thatare responsible for the reflectance differences observed in plantsgrowing in metal enriched medias (Milton et al., 1991).The effects of nutrient stress on reflectance in the infraredwavelengths vary considerably according to the element andvegetation type (Horler et al., 1980). Nitrogen, P and Sdeficiencies have been shown to decrease the near-infraredreflectance of maize leaves while deficiencies of K and Caincreased reflectance in this region (Al-Abbas et al., 1974).Nitrogen deficiencies have also been found to decrease the near-infrared reflectance of cotton leaves (Thomas et al., 1966).Conversely, Mexican squash leaves deficient in N, P, K, S and Mghad higher reflectance in this region than did nonstressed controlplants (Gausman et al., 1973), and the near-infrared reflectanceof sweet pepper leaves was also found to increase with N20deficiency (Thomas and Oerther, 1972).Differences in the spectral reflectance patterns of weedspecies from those of agronomic or horticultural crops enableareas of weed infestation to be delineated and quantified usingremote sensing techniques. Photographic differences in weed-cropimages are more directly related to canopy rather than single leafreflectance because the former includes the effects of leaf angleand canopy structure shadows in plant communities (Menges et al.,1985). Colour-IR aerial photography was successful in detectingfalse broomweed (Everitt et al., 1984), broom snakeweed and spinyaster (Everitt et al., 1987) on rangeland. The lower canopy near-infrared reflectance patterns of these weed species, which enabledthem to be differentiated from associated rangeland shrubs andherbaceous vegetation, was attributed to their erect leaf/stemcanopy structure.As well as differences in canopy structure and in chlorophyllcontent, size, intercellular space and surface characteristics ofindividual leaves, detection of weed species in crops is alsoaided by differential stages of inflorescence and senescence(Menges et al.,, 1985). An understanding of the phenologicalstages of the invader species as compared to those of the cropenables images to be obtained when reflectance differences aremaximized and thereby improves quantitative measurements ofinfestation. Computer analysis of positive colour filmtransparencies showed that Chinese tamarisk, an invader species ofriparian sites in the southwestern United States, could be21quantified from associated vegetation in the late fall when thefoliage of the tamerisk began to senesce (Everitt and Deloach,1990). The profuse flowering of ragweed parthenium (Partheniumhysterophorus) and colour changes of Palmar amaranth (Amaranthus palmeri) enabled the discrimination of these weed species fromcarrot and cotton respectively (Menges et al., 1985).2.2.4 Colour-NIR Aerial Photography and Digital Image AnalysisAerial photography remains the most widely used source ofremotely sensed data for crop studies (Blakeman, 1990; Curran,1985). Colour film reacts to those wavelengths that are withinthe visible range; colour-NIR film contains dye layers that reactto reflected energy associated with green (420-500 nm), red (500-600 nm) and photographic near-infrared (600-900 nm) wavelengthbands. Manual interpretation of the image obtained by aerialphotography is limited both by the subjectivity of the interpreterand the inability to detect visually subtle differences in imagetone. Conversely, computer-aided analysis techniques areconsistent and can effectively analyze small differences inspectral reflectance (Jensen, 1986). Computers also provide ameans to store and manipulate detailed quantitative information.Computer-aided image analysis of photographic images requiresthat the analog image be converted into a discrete or digitalformat. A discrete image is composed of a set of individualpicture elements called pixels; each pixel has an intensity valuethat corresponds to the reflectance of each wavelength band and an22address representing its location on a two-dimensional plane(Curran, 1985). A colour or colour-NIR image is thereforeconverted to a set of three two-dimensional planes in which eachplane contains pixel reflectance values for each of the threewavelength bands. Once digitized and loaded into the analysissystem, quantitative information including the mean and standarddeviation of reflectance values for each wavelength band can beextracted for those pixels representing relevant ground features.Computer processing of discreet images provides an effectivemeans of extracting thematic information. Classification ofremotely sensed data serves to reduce multispectral data channelsto a single channel with data grouped into blocks having similarreflectance patterns, or themes, that are of interest to theanalyst. Multispectral classification, using either thesupervised or unsupervised methods, is therefore one of the mostcommonly used means of information extraction (Curran, 1985).Supervised classification requires an a priori knowledge,obtained by way of field work, large scale maps, or personalexperience, of specific sites within the image. These sites arereferred to as training sites because their spectralcharacteristics are used to "train" the algorithm that classifiesthe remainder of the image. Access to ground-truth data obtainedfrom the training sites enables the accuracy of the resultingclassification to be assessed (Jensen, 1986).Conversely, unsupervised classification requires virtually noinput from the analyst. Clustering algorithms are used to search23for "natural" groupings of pixels having similar multispectralreflectance patterns (Jensen, 1986). Following classification,attempts are made a posteriori to assign these natural clusters toground features (Robinove, 1981).CHAPTER 3STUDY SITE3.1 Locations and Site DescriptionsFour water-harvested commercial cranberry bogs were selectedfor this two-year monitoring study. All four bogs had mature,full-bearing vines of the mid-season cultivar Stevens, a detaileddescription of which is provided by Brooks and Olmo (1972). Twobogs were located in each of two growing areas so that differentsoil and climatic conditions could be represented. The two bogslocated in each area represented a range of levels of stressfactors, i.e. one bog contained areas exhibiting symptoms of weed,water or nutrient stress and the other appeared uniformly healthy.Study bogs were located some distance from the main flight path ofthe Vancouver International Airport to allow easy access foraerial photography.Two of the sampling areas (Bogs 100 and 200) were located onadjacent bogs at Fort Langley near the Fraser River. Twodifferent soil series, Triggs and Glen Valley, occur as a complexwithin this study area (Luttmerding, 1980a). Both are classifiedas Typic Fibrisols. The surface, subsurface and subsoil of theTriggs series all consist of relatively undecomposed sphagnum andother mosses; the Glen Valley soils differ in that they consist ofdeep, undecomposed deposits of reeds, sedges, grasses and other2425deciduous plant material rather than mosses. The underlyingmineral material, occurring at a depth greater than 2 m, consistsof medium or moderately fine textured deltaic or floodplainsediments. Soil reaction of the organic layers ranges fromextremely to very strongly acid (Luttmerding, 1980b)The other two sampling areas (Bogs 300 and 400) were locatedon adjacent bogs in the Pitt Meadows area. The organic soilswithin this study area are of the Gibson series and are classifiedas Terric Mesisols (Luttmerding, 1980a). Mesisols differ fromfibrisols in that they are at a more advanced stage ofdecomposition (Canada Soil Survey Committee, 1978). Gibson soilsconsist of 40 to 120 cm of organic deposits composed mainly ofsedge, reed and other deciduous plant remains overlyingmoderately-fine to medium textured floodplain deposits. Thesurface layer consists of 20 cm of well-decomposed organicmaterial and is underlain by between 20 and 100 cm of partlydecomposed material. Soil reaction is extremely acid in thesurface and subsurface layers and strongly acid in the lowersubsoil (Luttmerding, 1980b). The location of these sites isprovided in Figure 3.1.The climate of the Fort Langley and Pitt Meadows sites ischaracterized by warm, rainy winters and cool, dry summers (Hareand Thomas, 1979). Temperatures during the growing season aremoderate and ideal for cranberry production; lower temperaturestend to retard fruit development while higher temperatures cancause the death of flowers and fruits and encourage fruit rot,,' FORTBOGS 100, 200.-' 	 LANGLEY2t 0 2 1 fkm26(Darrow et al., 1924). Of the major cranberry-producing regions,the Pacific Coast has the most moderate temperature and is theleast prone to frost damage (Dana, 1990; Eck, 1990). Commercialcranberry production is particularly susceptible to frost damagebecause cold air tends to be held in the diked, low-lying bogs(Gronwald and Haines, 1982).Figure 3.1: Locations of the cranberry bogs used for this study.Climate data obtained from Atmospheric Environment Service(B.C. Ministry of Environment) stations located near the two sitesindicate that the Fort Langley site had slightly warmer spring andsummer temperatures than did the Pitt Meadows site for both yearsof the study. Rainfall during the 1990 growing season was lower27than in the 1991 season and warmer spring and summer temperatureswere recorded for both sites in the 1990 season.3.2 Bog ManagementChemical fertilizers were applied frequently to all fourbogs; amounts and approximate dates for fertilizer as well aspesticide applications are listed in Appendix B. Because of theextensive use of irrigation during all phases of cranberryproduction and the lack of accurate records on water usage,applications of irrigation water are not presented. Given thefrequency of irrigation, however, it is unlikely that soil waterdeficiencies contribute to production losses in commercialcranberry operations (Dr. M.J. Hattendorf, Washington StateUniversity; personal communication). Of particular importancewere the applications of sand to sections of Bogs 300 and 400 inearly June of 1991, and the blanket application of sand to Bog 100in late June of the same year. Applications in the Pitt Meadowsbogs were for the purpose of levelling depressional areas whichwere damaged by water-logging and extensive weed infestations.Blanket sand application on the Fort Langley site was part of anew management strategy; while not extensively used in BritishColumbian bogs, sand applications for the purposes of stimulatingrooting, reducing frost risk, and controlling insect and fungalinfestations are routine in most cranberry growing regions. Theseapplications unfortunately affected pixel reflectance and soilmoisture measurements as well as the monitoring of vegetativecover type on the treated bogs.28CHAPTER 4METHODS AND MATERIALS4.1 Test Plot DesignIn each of the four bogs 20 sample sites 3x3 m in size werelocated at 15 m intervals in four rows of five sites using asystematic grid. A marker consisting of flagging tape on steelwire was placed at the centre of each site for the duration of thesampling season. White control targets (1x1 m) were placed at thecorners of each of the four study areas to facilitate location ofthe sample sites on the aerial photographs and the ground distancebetween these targets was measured to enable scale calibration ofthe photographic transparencies. Two dipwells per study area wereinstalled, one near the centre of the bog and one near thedrainage ditch, and the water table was measured on each samplingdate. The sampling design for each of the four bogs is presentedin Figure 4.1.4.2 Remote SensingRemote sensing missions were flown three times over each ofthe two years of the study. Colour-IR aerial photographs, 23x23cm format and 1:2,900 scale, were obtained at a flight elevationof 1,500 ft using aerochrome infrared film number 2443 and a WildRC-20 camera. Image acquisition dates were scheduled to coincidewith different stages of the cranberry upright development;110 	 109 	 108 	 107 	 106/	 ■15m105 	 104 	 103 	 102 	 101/ •L 1751.7n	p120 	 119 	 118 	 117 	 116■ ■BOG 100115 	 114 	 113 	 112 	 111■ ■e 65.5m94.5m65.3mACCESS ROADS	 70.3mDRAINAGE DITCHES201 202 203 204 	 205BOG 200206 207 208 209 	 210211 212 213 214 	 215• ■ /15m216 217 218 219 	 220•• • Z-175717117-795.0m15m305 310 315 320■302■307 312 	 317• •ACCESS ROADS2 DRAINAGE DITCHES PUMP HOUSEBOG 400405 410 415 420a 	 • ■ 	 .404 409 414 419■ ■ 	 ■403 408 413 41882.0m402 407 412 41715m1• •401 406 411 41685.5m• •15mBOG 300304 309 314 319■303 308 313 318301 	 306 	 311 	 316■81.5mCOAST CRANBERRIES LTD.;FORT LANGLEY, B.C. 	 NPITT MEADOWS FARMS; 	 NPITT MEADOWS, B.C.29Figure 4.1: Test plot design30however, the first mission in 1990 was delayed due to poor weatherconditions. The date of each mission and the coinciding growthstage is provided in Table 4.1.Table 4.1: Remote sensing mission dates and growth stagerepresented.YEAR IMAGE DATE GROWTH STAGE1990 June 15 hook - early bloomJuly 13 late bloom - fruit setSept 14 fruit development1991 May 31 popcorn - hookJune 22 bloomJuly 21 fruit setAerial photographs were acquired between 10:00 A.M. and 3:00P.M., when the sun is at a high angle above the horizon, in orderto minimize both the areal extent of shadows (Sabins, 1978), andtemporal variations in canopy reflectance that are caused bychanges in the angular sun-object-image relationship (Lillesandand Kiefer, 1979).4.3 Field MethodsSoil samples (0-10 cm) were collected for chemical analysisin May 1990 and April 1991 at all 80 sites. Plant biomass foreach sample site was determined in August of 1990 and 1991 bycounting the number of plant uprights per unit area (composite offour 177 cm2 random samples).Soil samples (0-10 cm) were collected at all 80 sites withinone day of each remote sensing mission and oven dried forgravimetric soil moisture measurement. Foliar samples consisting31of about 40 new-growth uprights were obtained from all 80 sites atthe time of each overflight. Samples were collected randomlywithin 1 m of the centre of each plot.Within 3 days of each remote sensing mission an intensiveground survey was conducted at all 80 sample sites. Infestationsof broadleaf and grasslike weeds, mechanical and chemical damage,surface ponding and areas of healthy vine growth in the 3x3 msites were mapped using diagram sheets. The weed species found atthe four bogs are tabulated in Appendix D. The depth of the watertable was measured using the two dipwells installed in each bog.Fruit samples were collected in September 1990 for 3 of the4 bogs; bog 200 could not be sampled because it was harvestedprior to sample collection. Samples consisted of a composite offive randomly sampled sub-units (total area sampled per plot=885cm2) within each sample site. In September 1991 yield sampleswere collected for all four bogs, samples were collected from fourlx1 m areas at the corners of each sample site to avoid tramplingeffects and areas clipped for foliar analysis at the centre ofeach plot. Yield samples consisted of a composite of two randomlysampled sub-units from each corner for a total of eight sub-samples for each plot (total area sampled per plot=1060 cm 2 ).4.4 Laboratory Methods4.4.1 Soil AnalysisSoil pH in water was determined on field moist samplesimmediately after sample collection. The remainder of the samplewas air-dried and passed through a 2 mm sieve; a subsample was32ground to a 100 mesh size using a Herzog mill grinder inpreparation for carbon determination and total chemical analysis.Total carbon was determined for the 1990 season using the methodof Walkley and Black (Black et al., 1982); carbon content wasconverted to percent organic matter.Soil samples collected in 1990 were digested with a nitric-perchloric acid mixture on an open hotplate. Samples collected inthe 1991 season were digested with reverse aqua regia (nitric andhydrochloric acid) in a microwave oven. Details of theseprocedures are provided in Appendix E. The resulting digestsolutions were analyzed for Ca, Mg, K, Fe, Al, Mn, Cu and Zn byatomic absorption spectrophotometry. The accuracy and precisionof the soil analyses were determined using National Bureau ofStandards reference materials and duplicate digests.4.4.2 Foliar AnalysisTissue samples were rinsed with distilled water in thelaboratory to remove fertilizer residues before being air-driedand ground for analysis. Foliar samples were digested usingconcentrated H 2SO4 ; samples collected during the 1990 season weredigested using an unpublished method developed by Ocean SprayCranberries laboratories, details of which are provided inAppendix E, while samples collected in 1991 were digestedaccording to the method of Parkinson and Allen, 1975. Digestsolutions were analyzed for N (Berthelot reaction) and P(molybdate reaction) using a Technicon auto-analyzer for the 1990samples and a Lechat auto-analyzer for the 1991 samples; Ca, Mg,33K, Fe, Mn, and Zn were measured by atomic absorptionspectrophotometry, Al and Cu were determined only for the 1991season. The accuracy and precision of foliar analyses weredetermined using National Bureau of Standards reference materialsand duplicate digests.4.4.3 Yield AnalysisBerry samples were weighed and yield calculated on a kg/m 2basis. The number of berries per unit area was recorded as anindex of the number of blossoms per area and percent fruit set,and the combined weight of 100 randomly selected berries wasrecorded as a measurement of fruit size. Cranberry pigments(anthocyanins) were extracted using the methanol extraction methodof Lees and Francis (1972). Anthocyanin concentration of theresulting extract was measured using a spectrophotometer.4.4.4 Vine StatusField maps indicating weed infestation, mechanical andchemical damage, the extent of water ponding and areas of healthyvine growth within the 3x3 m sites were digitized into thegeographical information system Terrasoft. Subsequent processingof the linework enabled quantitative measurement of the areaaffected by each form of stress for each plot. Percentages ofeach cover type were regarded as plot attribute data and enteredinto the database for statistical analysis.344.5 Image Analysis4.5.1 Image ScanningEach of the four study bogs were identified on thephotographic transparency positives obtained from the aerialphotography missions. Images with the test sites in the photocentre were selected for scanning so as to minimize spatialdistortion. Transparencies were placed in an Optronics filmscanner and scanned three times using blue, green and red filtersto measure the pixel brightness values of the three individual dyelayers. Scanning was carried out at 100 gm resolution.4.5.2 Pixel ExtractionThe colour-infrared images, after being converted to adigital format, were loaded into the PC-based image analysissystem Earthprobe for pixel extraction, spatial analysis andclassification. The white control targets were readily identifiedon the computer images and image scale and pixel ground resolutionwere measured using pixel counts between targets. There wereslight variations in ground resolution between flights but, onaverage, each pixel represented 28x28 cm. Sample sites werelocated on the image by identifying field markers or, when themarkers were not visible, by ground distance measurements andcorresponding pixel counts from adjacent sites. Sites could belocated within one pixel count, or 28 cm, of their true groundposition. After the sites were identified and marked on theimages, pixel brightness values for the red, green and blue dye35layers (representing reflectance in the near-infrared (NIR), red(R) and green (G) wavelength bands respectively) were extractedas an average of all pixels corresponding to each of the 80 3x3 msample areas. Also recorded for each of the 80 sites was theNIR/R band ratio. Ratios of pixel brightness values, referred toas "vegetation indices", are often better indicators of vegetationstatus because more information is provided by two spectralreflectance bands than by one. The use of a vegetation indexalso serves to reduce unwanted sources of radiometric variation,as ratios have less dependence on radiometric transmission than dothe reflectance values of individual bands (Horler et al., 1980).4.5.3 Supervised ClassificationImages obtained of Bog 300 in June, 1990 and Bogs 100 and 300in May, 1991 were classified in a supervised manner using the"density slice" technique. This technique categorizes individualpixels according to user-defined ranges and class limits.Classifications were based on the strong relationship found toexist between yield and the NIR/R ratio within these bogs; thisrelationship is discussed more fully in Chapter 6. A regressionmodel in which the NIR/R ratio was plotted against yield was usedto arbitrarily divide the training sites into high, moderate andlow yield classes, and the limits of the NIR/R rangescorresponding to the limits of each class were than calculated.The "image divide" function in Earthprobe was then used to createnew images in which pixel values represented the NIR reflectance36values of each pixel in the original image divided by thecorresponding red reflectance value. Finally, the resultingimages were classified by grouping individual pixels into one ofthe three NIR/R ranges using the "density slice" technique, whichin turn represented one of the three production levels.4.5.4 Unsupervised Classification.Unsupervised classification using all three bands was carriedout using the image analysis software package Earthprobe for 23 ofthe 24 images obtained during the study. The image obtained forbog 100 in July 1991 was not included because the bog was sandedshortly before the final remote sensing mission. Sanding alsooccurred on several of the study sites in bogs 300 and 400 priorto the second overflight and these sites were omitted fromstatistical analysis in the image analysis section. The Earthprobesystem clusters pixel value data into spectrally distinct classesusing a migrating means routine which groups similarly valuedpixels around a mean value. Images were classified into 2 or 3groups depending on the variability in reflectance within the bogand class patterns were enhanced using a filtering program withinthe Earthprobe system. The resulting maps were used to separatethe twenty training sites within each bog into the correspondinggroups; soil, foliar, and yield data corresponding to sites withineach class were then compared statistically to determine whichsite variables, other than pixel reflectance values, exhibitedsignificant differences between classes.374.5.5 Image OverlayChanges occurring within Bog 100 between May 31 and June 22,1991, were measured by comparing images obtained on these twodates using an image overlay. The images were first rectified bywarping one image to match the second based on a series ofstationary ground control points common to both images. Groundcontrol points included markers placed at the corners of the studyarea, the corners of the cranberry bogs and powerline poles.Once rectified, the images were overlayed to create acomposite image having six bands: the first three bands consistedof the NIR, red and green pixel values from the May image whilethe second three were comprised of the NIR, red and green bands ofthe June image. Unsupervised classification of the image was thencarried out using bands 1,2 and 3 (i.e., the May image) to createone map and bands 4,5 and 6 (i.e., the June image) to create asecond. Each map represented three different vine status classes:1) healthy vine growth; 2) moderate vine growth with some weedstress; and 3) high levels of weed infestation. Once created, thetwo classified maps were combined within the Earthprobe systemusing the logical operators AND and SUB to create a new mapshowing changes in vine status within the bog.4.6 Statistical AnalysisThe mean and standard deviation were calculated for each ofthe measured parameters on an individual bog basis in order topresent the central tendency and variation of these variables.38Relative variability of the soil, foliar nutrient, vegetationcover type, pixel reflectance and yield properties was assessed bycategorizing these variables according to their coefficients ofvariation. This descriptive statistic incorporates both mean andvariation indices and is calculated as:%CV = (Standard Deviation/Mean) * 100Percent CV is a dimensionless statistic used to comparevariability between different parameters. However, calculatedvalues of %CV are misleading when parameter measurements are closeto laboratory errors, and comparisons in these instances areinappropriate.Significant differences between bogs in regard to measuredsite variables were identified using the two-tailed Mann-WhitneyU-test, p=0.01. This nonparametric analogue to the two sample t-test method does not require the estimation of population means orvariances and makes no assumptions regarding the normality of thesample population (Zar, 1984). It is therefore appropriate when,as was often observed for parameter measurements in this study,the data distribution are skewed rather than normal.Seasonal changes in foliar element content and pixelreflectance values were identified using analysis of variance(ANOVA) on data pooled from all four bogs on each of the threesample dates for the 1990 growing season. This procedure was notrepeated for the 1991 season because sand applied to several ofthe bogs interfered with reflectance value measurements.A correlation analysis was carried out using all soil, foliar39nutrient, vegetation cover type, remote sensing and yieldvariables on an individual bog basis. This analysis expresses themagnitude and direction of linear relationships between any twovariables in the form of a correlation coefficient, or Pearson rvalue. Relationships having r values greater than 0.561 at aprobability level of 0.051 were considered to be significant forn=20 (Zar, 1984).The yield prediction potential of a variable that is linearlyrelated to yield is determined by the magnitude of the correlationcoefficient. Similarly, the magnitudes of significantrelationships involving pixel reflectance variables give anindication of which site properties can be determined using remotesensing methods and which wavelength bands or band ratios are mostappropriate. Regression analysis was used to develop predictiveequations for yield variables and for site variables associatedwith pixel reflectance values when r values indicated a strongrelationship.The effectiveness of remote sensing classification techniquesin differentiating relevant biophysical properties can be assessedby analyzing ground data obtained from sites within each class.Groups of sample sites within each bog that were identified byimage classification as having significantly different reflectancecharacteristics were compared using the Mann-Whitney U-testoutlined previously. Soil and foliar element content, vegetationcover type and yield were all compared between classes todetermine which variables, other than reflectance variables, were40significantly different between classes. The Mann-Whitney U-testwas chosen because it is appropriate for comparisons of smallsample size when the assumptions underlying parametric tests arenot justified (Zar, 1984). A less rigorous significance level(p=0.10) was chosen in order to observe trends in consistencybetween the four bogs and because the sample size of each classswas sometimes quite small.The analysis of variance test used to detect seasonal trendsin foliar nutrient content and pixel reflectance values wasperformed on the mainframe computer at UBC using an unpublishedsoftware program written and provided by Dr. G. Eaton (PlantScience Dept., Agricultural Sciences; University of BritishColumbia). All other statistical analyses were performed usingthe PC-based statistical software package SPSS (StatisticalPackage for the Social Sciences).41CHAPTER 5RESULTS AND DISCUSSION:SPATIAL AND SEASONAL VARIABILITY WITHIN AND BETWEEN BOGSThe thesis objectives covered in this chapter include: 1) tomeasure the degree of variability that exists within each of thefour study bogs by calculating the mean and coefficient ofvariation for soil, foliar nutrient, vine status, yield and remotesensing variables; 2) to determine significant differences betweenbogs with regard to the above listed parameters; and 3) todetermine seasonal changes in foliar nutrient content andreflectance properties within cranberry bogs. In order tosimplify the interpretation, only those soil, foliar and vinestatus variables which were either linearly or spatially relatedto yield are presented. The natures of these relationships arediscussed more fully in Chapters 6 and 7.In a study concerning soil spatial variability, Wilding andDrees (1983), ranked soil properties as being least variable(%CV<15), moderately variable (%CV=15-35), and most variable(%CV>35). These guidelines were used to classify soil propertiesand were also extended to categorize foliar nutrient, yield, vinestatus and pixel reflectance properties with regard to their levelof variability.Between bog variability was determined using Mann-Whitney U-tests. Those properties which are different between bogs at thep=0.01 levels are presented. Seasonal patterns of foliar nutrient42levels and pixel reflectance values were determined with analysisof variance (ANOVA) using data collected on each of the threesample dates for the 1990 growing season. Data from all four bogswere pooled in order to observe general rather than site-specifictrends.5.1 Within Bog Variability5.1.1 Soil, Foliar Vegetative Cover and Yield VariabilityThe mean and %CV for soil variables measured during bothyears of the study are presented in Table 5.1 for all four bogs.Classification of soil variability according to the method ofWilding and Drees, 1983, indicated that total Mg was moderatelyvariable and total Mn was most variable in all four bogs for the1990 season. Individual bogs exhibited different levels ofvariability with regard to soil Fe and Al content: soil Fe wasmost variable in Bogs 100 and 300 but only moderately variable in200 and 400; soil Al was most variable in Bog 100 but onlymoderately variable in the other three bogs.The variability of measured soil properties tended to besomewhat lower in the 1991 season. Soil Mg was least variable inBog 200 but remained moderately variable in the remaining bogs.Similarly, soil Mn was found to be only moderately variable in Bog200 while remaining most variable in the other three bogs. Onlythe variability of soil Al increased, becoming most variable inBog 100 at the Fort Langley site and Bogs 300 and 400 at the FortLangley site.43Table 5.1: Means and coefficients of variation for soil dataBog No.Var.100(n=20)X 	 %CV200(n=20)X 	 %CV300(n=20)X 	 %CV400(n=20)X 	 %CV1990:Tot. Mg 0.114 30 0.083 27 0.056 23 0.069 35Tot. Fe 1453 64 799.3 27 3537 42 3926 34Tot. Mn 12.6 71 7.5 54 13.7 58 19.0 90Tot. Al 2235 45 1537 21 2922 30 2760 37Fe:Al 0.63 20 0.53 26 1.21 26 1.46 29Fe:Mn 131.4 38 137.7 62 304.7 42 258.6 391991:Tot. Mg 0.080 19 0.068 12 0.082 34 0.085 30Tot. Fe 1491 32 957 18 3593 45 3758 26Tot. Mn 18.2 41 13.3 31 22.8 70 27.0 54Tot. Al 2166 38 1712 17 4129 31 3646 41Fe:Al 0.703 16 0.568 20 0.850 21 1.11 31Fe:Mn 87.9 25 78.4 32 190 39 166 40The means and %CV for foliar element levels are presented inTable 5.2 for all four bogs and six sampling dates. The degree ofvariability for foliar element concentrations tended to besomewhat less than that observed for levels of soil elements.Summarizing the foliar data for all four bogs and all six samplingdates indicated that tissue N and Mg were least variable andfoliar Fe and Mn were moderately variable. Foliar Alconcentrations, which were measured only in the 1991 season, werealso moderately variable. Foliar P was categorized as beingmoderately variable in 1990 but only least variable in 1991.44Table 5.2: Means and coefficients of variation for foliar data.1990 Bog No.Var.100(n=20)X 	 %CV200(n=20)X 	 %CV300(n=20)X 	 %CV400(n=20)X 	 %CVJune:N 1.43 15 1.12 16 1.02 18 0.88 11P 0.16 17 0.18 14 0.10 21 0.09 11Ca 0.52 9 0.80 8 0.46 18 0.45 14Mg 0.15 8 0.20 7 0.16 14 0.15 8Fe 67.3 29 72.5 25 80.3 39 60.0 39Mn 72.9 37 66.7 28 71.0 38 106.5 41Mg:Fe 0.23 28 0.29 24 0.23 39 0.29 51July:N 0.90 11 0.88 17 0.92 25 0.84 10P 0.08 13 0.14 19 0.12 12 0.13 11Ca 0.65 12 0.72 14 0.44 17 0.40 35Mg 0.18 9 0.24 12 0.21 12 0.19 18Fe 48.5 23 87.7 22 109.5 16 123.2 15Mn 86.6 43 68.7 25 94.7 33 123.8 27Mg:Fe 0.39 27 0.28 28 0.19 20 0.16 20Sept.:N 0.86 8 0.84 10 0.90 16 0.84 9P 0.16 18 0.21 13 0.13 19 0.10 19Ca 0.78 13 1.09 11 0.68 20 0.69 15Mg 0.23 14 0.27 8 0.261 19 0.29 26Fe 92.1 17 108.5 25 195.8 38 153.8 28Mn 62.0 27 84.9 26 116.0 19 147.7 24Mg:Fe 0.26 23 0.34 22 0.14 25 0.20 3245Table 5.2: (cont'd)1991 Bog No.Var.100(n=20)X 	 %CV200(n=20)X 	 %CV300(n=20)X 	 %CV400(n=20)X 	 %CVMay:N 1.39 13 1.10 15 1.82 14 1.58 10P 0.25 10 0.25 11 0.27 10 0.23 8Ca 0.43 20 0.52 27 0.34 32 0.41 22Mg 0.231 7 0.235 11 0.205 8 0.218 8Fe 78.7 17 81.0 15 107.8 14 110.6 15Mn 44.1 40 46.1 30 74.4 43 86.0 36Al 96.6 20 94.6 30 183.9 12 157.0 22Mg:Al 0.30 20 0.29 13 0.09 13 0.20 18Mg:Fe 0.25 24 0.28 43 0.24 93 0.15 27June:N 1.42 9 1.30 7 1.43 7 1.44 7P 0.20 9 0.22 7 0.20 7 0.20 8Ca 0.41 15 0.51 19 0.27 26 0.36 13Mg 0.17 9 0.19 10 0.16 15 0.18 10Fe 66.5 10 58.6 14 89.8 84 84.7 59Mn 46.3 38 42.6 21 54.1 30 73.7 31Al 51.0 21 68.7 28 149.3 79 108.9 70Mg:Al 0.35 22 0.30 27 0.14 83 0.17 68Mg:Fe 0.42 16 0.40 17 0.20 58 0.30 61July:N 1.34 14 0.97 7 1.53 13 1.39 22P 0.17 15 0.16 6 0.20 11 0.19 9Ca 0.52 17 0.64 15 0.36 20 0.38 16Mg 0.18 10 0.22 9 0.18 15 0.18 16Fe 91.3 42 49.2 30 60.5 41 86.1 20Mn 73.6 25 51.2 17 97.4 24 119.1 28Al 168.0 29 128.7 12 162.0 40 132.5 120Mg:Al 0.12 25 0.17 10 0.12 73 0.15 48Mg:Fe 0.23 34 0.47 24 0.34 39 0.22 32The means and %CV for vine status is tabulated in Table 5.3for all four bogs. These statistics are presented for only theJune, 1990 and May, 1991 sampling dates; changes in vine statuswere marginal throughout the 1990 season while data collected inMay of the following season, when water tables levels are highest,46provides additional information regarding standing water indepressional areas. The percent of area containing unstressedvine growth was highly variable in Bog 100 and least variable inBog 200 while both Pitt Meadows bogs were moderately variable withregard to this measured property. Most of this variabilitystemmed from extensive weed infestations, particularly grasslikeplants, which were observed in Bogs 100 and 300. Appendix D liststhe common and scientific names of the weed species found in thefour study bogs and indicates the species family andclassification; a more detailed description of these plants isprovided by Bitterlich, 1990. It should be noted that much of therecorded variability of vine growth in Bog 400 resulted from thefact that one of the twenty sample sites was highly weed infested.Table 5.3: Means and coefficients of variation for vine statusVariableBog No.100 200 300 400(n=20) (n=20) (n=20) (n=20)X 	 %CV X 	 %CV X 	 %CV X 	 %CVJune: 1990% Vine 55.4 	 42 97.6	 3 82.5 	 24 93.3 	 8% GL weeds 42.3 	 57 2.0 	 125 15.0 	 131 2.1	 128May: 	 1991% Vine 57.2 	 38 97.6 	 3 75.7 	 32 90.0 	 17% GL weeds 41.4 	 52 2.1	 129 5.9 	 150 3.7	 400% Ponding 1.3 	 185 0 	 -- 12.6 	 163 3.2 	 197Table 5.4 lists the means and %CV of yield variables measuredfor the 1990 and 1991 season. As this table indicates, thehighest level of yield variability was observed within Bogs 10047and 300, which also showed the highest degree of variability withregard to percent of healthy vine growth. Yield was also highly,although somewhat less, variable within Bog 400 while productionwithin Bog 200 was relatively uniform. Although yield was highlyvariable in three of the four bogs, fruit size, as indicated bythe 100 berry weight, was shown to be fairly uniform. Most of thevariability in production was explained by the high variability inthe number of fruit per unit area, which in turn indicated thateither the number of blossoms or the percent of fruit set washighly variable in Bogs 100, 300 and 400. In Bogs 300 and 400,the moderate degree of spatial variability observed for uprightdensity also accounted for production variability.Table 5.4: Means and coefficients of variation for yield variablesBog No.Var.100(n=20)X 	 %CV200(n=20)X 	 %CV300(n=20)X 	 %CV400(n=20)X 	 %CV1990:Y 1.59 58 2.28 42 2.45 35100 BW 169.2 8 n/a* 150.1 10 146.7 7NOB 942 57 1516 41 1670 35col 36.4 12 27.3 14 29.0 12UD 274 25 311 12 332 10 311 141991:yield 0.97 55 2.69 18 1.97 72 2.34 38100 BW 131.8 8 145.5 6 122.4 16 125.3 10NOB 672 54 1691 19 1394 71 1698 39col 39.3 19 44.8 11 29.6 21 31.0 18UD 243 24 340 23 342 30 305 33* 1990 yield data for bog 200 was not obtained before commercialharvest.48A comparison of the level of variability found within each ofthe four bogs for all measured site properties indicated that thePitt Meadows site was more variable than was the Fort Langley sitewith regard to foliar element concentrations and, particularly,soil chemical properties. This would suggest that much of thevariability was site related and a function of soil type, bogpreparation and bog management. Of the two Pitt Meadows bogs, Bog300 expressed the highest level of variability while Bog 100 wasthe more variable of the Fort Langley bogs.A high degree of variability within cranberry bogs has beenreported by several researchers (Demoranville and Deubert, 1987;Chaplin and Martin, 1979). Much of this variability is induced byinconsistent drainage and the subsequent decomposition of the peatto more highly decomposed muck soils having a lower C:N ratio anda higher nutrient level (Pitty, 1978). Nonuniform settlingcreates depressions within the bog that are prone to waterlogging,as is indicated by the presence of ponded surface water during Mayin three of bogs. These depressions become areas of accumulationfor mobilized Al, which is highly soluble in the acidicenvironment of the bog, and Fe and Mn, which are reduced tosoluble ionic forms when oxygen levels in the peat soil are low(Tisdale and Nelson, 1975). Year to year variability in thetissue levels of all elements except P has been found to beconsiderable and it has been suggested that critical nutrientlevels may also differ from year to year (Eaton, 1971b). Thishigh degree of foliar variability is a function of the variable49nature of total soil element levels as well as differing levels ofmicrobial activity and reduction-oxidation conditions within thebog, which in turn dictate the portion of the total element poolthat is available for plant uptake (Shaw et al., 1990; Tisdale andNelson, 1975). Differing vine growth rates within the bog alsoaffected measured concentrations of foliar elements as these wereon a dried weight basis rather than a per plant basis; uprightswith a relatively large amount of new growth would therefore havecontained lower tissue levels than those with less vegetativegrowth, although total element uptake may be the same for bothplants, because of the effects of dilution. The high level offoliar nutrient variability inherent within cranberry bogs iscompounded further by management practices. Applications offertilizers, whether by aerial application or direct injectioninto irrigation lines, are not uniform, and hence availability andplant uptake of applied nutrients were variable throughout thebog.5.1.2. Remote Sensing VariabilityThe means and %CV of remote sensing variables obtained forall four bogs for the two years of the study. These descriptivestatistics are not presented for the two Pitt Meadows bogs for theJune and July imagery obtained in 1991, nor for Bog 100 in theFort Langley site for the July, 1991 imagery, because sand appliedto several of the test plots in Bogs 300 and 400 and to all of Bog100 prior to these image acquisition dates interfered too greatly50with the reflectance patterns.	 Percent CV's for all pixelreflectance values for all bands, as well as the NIR/R band ratio,are low. Pixel values corresponding to NIR reflectance containthe least amount of variability while those corresponding toreflectance in the red region contain the highest.Table 5.5: Means and coefficients of variation for pixelreflectance dataBog No.Var.1990:100(n=20)X 	 %CV200(n=20)X 	 %CV300(n=20)X 	 %CV400(n=20)X 	 %CVJune:NIR 176.5 3 158.0 3 162.8 7 169.0 3R 144.9 4 118.6 4 129.6 12 136.9 4G 168.9 3 142.5 3 152.5 7 156.2 4NIR/R 1.220 4 1.333 3 1.267 9 1.235 3July:NIR 178.0 2 182.4 1 168.8 4 165.4 4R 144.9 8 149.8 4 131.3 9 116.6 7G 160.2 4 166.8 3 149.2 5 136.8 5NIR/R 1.235 7 1.219 4 1.294 8 1.4223 5Sept.NIR 172.1 3 166.8 1 168.0 4 169.0 3R 130.3 16 119.2 3 125.6 6 122.8 7G 138.2 7 121.4 3 128.3 6 121.7 8NIR/R 1.324 5 1.401 3 1.341 4 1.380 5Table 5.5: (cont'd)Bog No.Var.1991:100(n=20)X 	 %CV200(n=20)X	 %CV300(n=20)X 	 %CV400(n=20)X 	 %CVMay:NIR 186.4 9 136.0 13 160.2 16 177.9 12R 169.5 12 86.0 14 118.2 14 132.0 13G 176.9 11 97.7 14 119.4 15 134.1 13NIR/R 1.105 8 1.591 8 1.358 11 1.356 9June:NIR 164.9 7 182.4 3R 117.1 14 127.5 4 na naG 123.7 13 135.7 2NIR/R 1.424 10 1.438 3July:NIR 126.7 4R na 70.2 4 na naG 87.4 2NIR/R 1.819 4The highest degree of variability in remote sensing variableswas obtained for Bog 300, while Bog 200 contained the least. Thistrend was similar to that observed for site properties, includingsoil, foliar, vine status and yield variables. This suggests thatvariability in measurements of remote sensing parameters expressesthe variability of features within the bog, and that theapplication of remote sensing techniques to monitor groundconditions is therefore promising.Imagery obtained in May indicated that reflectance was mostvariable during the early spring. Much of this variability wasattributed to extensive areas of ponding, particularly in the Pitt5152Meadows site, as standing water greatly reduces energyreflectance. In addition, variable microclimate conditions withincranberry bogs lead to differences in seasonal development, suchas bud-break, and vegetative growth rates throughout the bog.This variability in site conditions contributes to a highervariability of pixel reflectance values in early season imagery.5.2 Among Bog Variability5.2.1 Site Differences Among Individual BogsSoil, foliar, vine status and yield properties that aresignificantly different among bogs are presented in Table 5.6.Among bog differences in foliar element content were found tofluctuate among sample dates and only those which wereconsistently different for at least three of the six samplingdates are presented.Many of the soil chemical differences appear to be siterelated, with the two Pitt Meadows bogs containing higher levelsof Fe, Mn and Al. Of the two Pitt Meadows bogs, levels of soil Mgand Fe were higher in Bog 400 while Bog 100 contained the highestFe, Mn and Al levels of the two Fort Langley bogs. Differences inconcentrations of foliar Fe, Mn and Al between sites and bogs weresimilar to those observed for total soil levels, suggesting thatplant uptake of these elements is largely governed by soilcontent. In addition to lower concentrations of Fe, Mn, and Al,foliar tissue samples collected from the Fort Langley site alsocontained higher levels of P, Ca and Mg than did samples collectedfrom the Pitt Meadows site. Bog 200 in the Fort Langley site53contained higher foliar concentrations of Mg, Ca and P and lowerfoliar levels of N than did Bog 100. Tissue samples collectedfrom Bog 400 in the Pitt Meadows site contained higher foliarconcentrations of Ca, Mg and Mn as well as a higher Mg:Al ratiothan did Bog 300 while samples collected from the latter containedhigher P levels.Cranberry production in Bog 100 was significantly lower thanthat of Bogs 200 and 400, while yield within Bog 300 wasintermediate. This difference was more pronounced in the 1991season, when sand applications to Bog 100 during the blossom stagecontributed to a marked decline in yield. Fruits produced withinthe two Fort Langley bogs were larger and contained a higheranthocyanin content than those produced in Bogs 300 and 400.These differences were likely due to more vigorous vegetativegrowth in the Pitt Meadows site, which reduced fruit developmentand which effectively shaded the fruits and impeded pigmentdevelopment.Site differences are largely due to differences in the peatparent material and the degree of decomposition as well asdifferences in management strategies including fertilizer andirrigation practices. Differences in soil properties identifiedbetween bogs within a site are possibly due to differences in thelevel of drainage or in the methods of bog preparation. Examplesof the latter include differences in the volume of peat materialscalped from the surface during levelling and differences in thedegree of mixing of subsurface and surface organic layers duringthe installation of drainage and irrigation lines.2Soil: Mg; Fe; AlFoliar: N; P; Ca; Mg;Vine Status: %Vine;%GL-weeds;%Ponding-MayYield: Yield; NOB; BWBog No. 2 31Soil: Mg-90; Fe; AlFoliar: N; P; Ca; Mg;Fe; Mn; Al;Mg:FeVine Status: %Vine;%GL-weedsYield: colourSoil: Mg; Fe; Mn; AlFoliar: N; P; Ca; Mg;Fe; Mn; Al;Mg:FeVine Status: %Vine;%GL-weeds;%Ponding-MayYield: BW; colour3Soil: Mg-90;Fe; Mn; AlFoliar: N; P; Ca; Mg;Fe; Mn; Al;Mg:FeVine Status: %Vine;%GL-weedsYield: Y; NOB; colourSoil: Mg;Fe; Mn; AlFoliar: N; P; Ca; Mg;Fe; Mn; Al;Mg:FeVine Status: %Vine;%Ponding-MayYield: BW; colourSoil: Mg-90;Foliar: N; P; Ca; Mg;Mn; Mg:AlVine Status: %Vine;%GL-weedsYield: 4Table 5.6: Significant differences in soil chemical, foliarnutrient and measured vegetative cover propertiesbetween bogs (p=0.01)54555.2.2 Differences in Remote Sensing Variables Between IndividualBogsSignificant differences in pixel reflectance values and theNIR/R band ratio between all four bogs over the two year study aresummarized in Table 5.7. Differences were most frequentlyobserved between Bog 100 in the Fort Langley site and Bog 400 inthe Pitt Meadows site, while significant differences in spectralproperties were least often found between the two Pitt Meadowsbogs. This would suggest that, as was true of soil, foliar, andyield properties, many of the spectral reflectance properties aresite related. Differences in the pixel reflectance values betweenthe Fort Langley bogs are likely the result of differences in theplant community structure of these two bogs; weed infestation inBog 100 was extensive while cranberry vine growth within Bog 200was uniform and relatively free of weed species.The highest NIR, red and green pixel reflectance values ofthe four bogs were obtained for Bog 100 for all but the July,1990, image. The lowest means for these reflectance variableswere obtained for Bog 200 with the exception of the imageryobtained in July, 1990, when these variables were highest for Bog200. Conversely, the NIR/R ratio, again with the exception of theJuly, 1990 imagery, was the highest for Bog 200.56Table 5.7: Significant differences in pixel reflectance valuesbetween bogs (p=0.01). Bracketed values indicate thepercentage of images obtained throughout the two growingseasons which showed significant differences.Bog No. 1 2 3NIR (100);2 R ( 	 60);G ( 	 80);NIR/R ( 	 60)NIR ( 	 75 ); NIR ( 50);3 R ( 	 75 ); R ( 75 );G (100); G (100);NIR/R ( 	 50) NIR/R ( 75)NIR ( 	 75) ; NIR ( 75) ; NIR ( 0);4 R (100); R ( 75 ); R ( 50);G (100); G ( 75 ); G ( 50);NIR/R ( 	 75) NIR/R ( 75) NIR/R ( 50)5.3 Seasonal Changes5.3.1 Seasonal Variability in Cranberry Foliar Nutrient ContentAppendix F provides details of the analysis of varianceresults for seasonal variability in both foliar element contentand pixel reflectance values. Analysis of the pooled foliarnutrient data for all four bogs in 1990 indicated thatconcentrations of foliar N and K decreased as the seasonprogressed while concentrations of foliar Mg, Ca, Fe and Mnincreased. These patterns conform to results obtained from othercranberry studies (Chaplin and Martin, 1979; DeMoranville andDeubert, 1986), as well as from studies concerning the closelyrelated Vaccinium species highbush blueberry (Chuntanoparb andCummings, 1980; Eaton and Meehan, 1971) and lowbush blueberry(Townsend et al., 1968).57The gradual decrease in foliar N levels from an early seasonhigh of 1.11% in June to 0.86% in September, the final samplingdate for the 1990 season, was likely due to dilution in the plantas a result of vegetative growth and fruit development. A similarlinear trend was observed for K, which ranged from 0.71% in Juneto 0.60% in September. While not a constituent of any majormetabolite in cranberry (Eck, 1990), K is involved in sugarmetabolism and transport and is thus essential during bloom fornectar formation (DeMoranville and Deubert, 1986).The pattern observed for seasonal changes in foliar P levelsin 1990 was not linear. Foliar concentrations decreased from0.13% in June to 0.11% in July and increased to 0.15% inSeptember. The sample obtained in June possibly corresponded tothe "phosphorus peak" observed by other researchers to occurduring bloom. The flowers, fruit and seeds of cranberry are highin phosphorus. The decrease in foliar levels following the latespring peak may be explained by mobilization of P from the leavesand stems into these reproductive structures (DeMoranville andDeubert, 1986).The seasonal patterns of Mg and Ca foliar levels observed in1990 are similar, with the lowest concentrations occurring atbloom (0.16 and 0.55% respectively) and the highest occurringtowards the end of the growing season (0.26 and 0.81%respectively). Calcium and Mg are essential constituents of thecell wall structure (Eck, 1990). Physiological changes in thematuring cranberry leaves, namely a thickening of the cell walls,58would account for the increases in foliar levels observed forthese two nutrients throughout the growing season. Trends infoliar Fe and Mn levels through the 1990 season were similar tothose found for Ca and Mg. The concentration of Fe increased from69.8 to 137 ppm while that of Mn increased from 79.1 to 103.4 ppm.5.3.2 Reflectance PropertiesAnalysis of the pooled pixel reflectance data for all fourbogs in 1990 indicated a significant decrease in green reflectanceover the season. Significant changes in the NIR and redreflectance were not observed; however, the band ratio ([NIR/R])was found to increase through the season. Seasonal changes in thereflectance patterns in the visible wavelengths is a function ofchanges in the leaf chlorophyll content and the consequent changesin absorbance of energy corresponding to these wavelengths (Guyot,1990; Grant, 1987; Curran, 1985). The observed trend towardsdecreasing green reflectance is in agreement with the findings ofDale et al. (1986), who found a similar pattern between spring andautumn imagery of salt-marsh vegetation. The breakdown ofchlorophyll is generally accompanied by an increase in redreflectance (Blakeman, 1990; Dale et al., 1986; Knipling, 1970),as this pigment absorbs 70 to 90% of light in the red part of thespectrum (Myers, 1983). Increases in the anthocyanin content ofleaves and the developing fruit also contributed to a decrease ingreen reflectance as this pigment does not absorb energyassociated with this wavelength (Myers, 1983). In addition, leaf59maturation and the initial stages of senescence are usuallyindicated by an increase in the NIR reflectance as cell walls pullapart and are reoriented, resulting in an increased number of cellwall-air interfaces (Grant, 1987). The lack of significantseasonal changes in NIR and red reflectance in the current studymay be explained by the fact that cranberry leaves remain on theplant for at least two seasons, and the plant canopy is thereforecomposed of leaves of varying levels of maturity throughout thegrowing season. In addition, differences in leaf wetness in thecanopy caused by frequent sprinkler irrigation may have distortedtrue seasonal changes in spectral reflectance as water stronglyabsorbs energy in both the visible and NIR wavelength bands andconsequently causes an overall decrease in reflectance.60CHAPTER 6RESULTS AND DISCUSSION:LINEAR RELATIONSHIPS BETWEEN SITE VARIABLESThe thesis objectives covered in this chapter include: 1) toidentify significant linear relationships that existed betweensoil, foliar, vegetative vine status and yield variables for eachof the four bogs during the two seasons of this study; 2) toidentify significant relationships between the above mentionedbiophysical variables and pixel reflectance variables; and 3) topresent regression equations for the significant relationshipsinvolving yield parameters within each bog.Relationships between all measured parameters were obtainedusing correlation analysis. Relationships were considered to besignificant when analysis yielded a correlation coefficient havingan absolute value greater than 0.561 at the p=0.05 level.Relationships involving yield variables that had the highestcorrelation coefficients, and hence offered the highest potentialfor yield prediction, were selected for regression analysis foreach of the four bogs.6.1 Relationships Among Soil, Vegetation Cover Status and YieldVariablesThe most important relationships between soil elements andcranberry yield are tabulated in Table 6.1. As this tableindicates, soil levels of Al, Mn, Fe and Mg were consistentlynegatively correlated with yield in three of the four bogs; the61strongest relationships occurred in the two Pitt Meadows bogs,Bogs 300 and 400, where concentrations of Fe, Al and Mn were bothhigher and more variable than those of the two Fort Langley bogsand where production, especially within Bog 300, was mostvariable. Conversely, positive correlations between soil elementsand yield were not observed for Bog 200. This bog was unique inthat concentrations of Fe, Al and Mn were the lowest and the leastvariable of the four sites studied and production was consistentlyhigh throughout the bog; the lack of significant relationshipsbetween soil properties and yield in this bog indicated thateither soil levels of Fe, Mn and Al were not high enough to reduceproduction or yield simply was not variable enough for theserelationships to become evident. Soil element data, when highlycorrelated to yield as in the Pitt Meadows bogs, provided a meansof predicting cranberry production using regression. Table 6.3lists regression equations for yield predictions using soilvariables.Relationships among soil concentrations of Al, Fe, Mn and Mgwere found to be consistent and positive; again the strongestrelationships were observed to occur in the Pitt Meadows bogs,particularly in the highly variable Bog 300. Levels of these soilelements were also found to be highly and positively correlated tothe area of standing water, or ponding, occurring within Bogs 300and 400 during the May sampling period. This suggests thatdepressed areas within these bogs became areas of accumulation forAl, Fe, Mn and Mg made soluble by wet conditions within the bogs.62Correlation values indicating significant relationships betweensoil element concentrations are provided in Table 6.2.Table 6.1: Correlations identified between soil variables andyieldYear VariablesBog No.100 200 300 4001990 Al-Yield (-0.47)* (-0.53) (-0.43)Mn-Yield -- na -- --Mg-Yield -- (-0.39) (-0.49)Fe-Yield -- (-0.55) --1991 Al-Yield (-0.50) -- -0.78 (-0.53)Mn-Yield (-0.36) -- -0.70 --Mg-Yield (-0.48) -- -0.61 (-0.55)Fe-Yield (-0.41) -- -0.80 --* Values recorded within brackets are not significant but arepresented for completeness.Table 6.2: Correlations identified between soil variablesYear VariablesBog No.100 200 300 400Ca-Mg -- -- 0.83 0.631990 Ca-Mn (0.36) (0.55) 0.75 (0.49)Mg-Al (0.55) 0.94 (0.44) 0.76Mg-Mn -- -- 0.71 0.71Mg-Fe (0.48) -- (0.49) (0.39)Fe-Mn 0.84 0.65 (0.51) (0.52)Fe-Al 0.97 -- 0.72 0.60Mn-Al 0.76 -- (0.49) 0.60Ca-Mg -- 0.72 (0.40) --1991 Ca-Mn -- 0.59 -- --Mg-Al 0.81 -- 0.87 0.84Mg-Mn 0.67 (0.38) 0.93 0.80Mg-Fe 0.78 0.64 0.79 (0.45)Fe-Mn 0.89 -- 0.81 (0.46)Fe-Al 0.90 -- 0.86 (0.47)Mn-Al 0.75 0.71 0.94 0.67%Pond-Mg -- -- 0.74 (0.48)%Pond-Fe -- -- 0.71 --%Pond-Mn -- -- 0.88 --%Pond-Al -- -- 0.87 (0.37)63Table 6.3: Best regression equations for prediction of cranberryyield from soil variables1990:Bog Var. Equation r2 S.E.300 Al Yield=(-0.00057 A1)+3.94 0.28 0.83Fe Yield=(-0.00035 Fe)+3.51 0.30 0.81400 Mg Yield=(-20.87 Mg)+3.896 0.34 0.711991:Bog Var. Equation r 2 S.E.100 Al Yield=(-0.00033 A1)+1.68 0.25 0.47Mg Yield=(-16.61 Mg)+2.30 0.22 0.48Fe Yield=(-0.00045 Fe)+1.65 0.17 0.50300 Al Yield=(-0.00086 A1)+5.54 0.61 0.91Fe Yield=(-0.00070 Fe)+4.48 0.63 0.88Mn Yield=(-0.06208 Mn)+3.39 0.49 1.05Mg Yield=(-30.71 Mg)+4.50 0.37 1.16400 Al Yield=(-0.00032 A1)+3.49 0.28 0.77Mg Yield=(-19.135 Mg)+3.97 0.30 0.76The negative effect of high soil Al on yield is likely due tothe toxic effects on plants of high levels of Al, which is readilyavailable for plant uptake at the low pH levels characteristic ofcranberry bogs. This is in agreement with the findings of acontainer study which indicated that dry matter yields of newgrowth of cranberry shoots were decreased by high concentrationsof Al in the external solution (Medappa and Dana, 1970). Reducedshoot growth causes a reduction in net photosynthesis, and hencecarbon assimilation, which can subsequently have a negative impacton fruit production.64Also important, although less consistent, were the negativeeffects on yield of total soil Fe, Mn and Mg. Iron plays animportant role in the energy transfer system of metabolicreactions in the cranberry plant. Contrary to the findings of thecurrent study, a survey of the nutritional status of Washingtoncranberry bogs indicated a positive correlation between yield andavailable Fe; high yielding bogs were found to contain at leasttwice as much available Fe as Al (Fisher, 1951) It has beensuggested that higher levels of Fe in the soil may prevent Al fromreaching toxic levels in the plant (Eck, 1990). The negativeresponse observed in the present study may therefore be due to thebalance between Fe and Al, which are positively correlated, ratherthan the actual levels of Fe. The negative relationships observedbetween yield and soil Mn levels is supported by the findings ofDoughty (1984). However, a positive yield response was obtainedwith Mn applications to Washington bogs (Eck, 1990). Eck, 1990,suggests that it is the balance between Mn and Cu and Mn and Bthat determines yield response. Results of another study (Medappaand Dana, 1970) showed that Mn concentration in the shoots wasinversely related to the Fe concentration of the externalsolution, indicating that the ratio of Mn to Fe is also animportant criteria in determining yield response. Apart from thisdirect effect on cranberry yield, toxic levels of soil elementscan also decrease production by making the bog more susceptible toweed infestations.When plant vigour is reduced by stress factors, the ability65of the plant to compete against more hardy species is threatenedwith the ultimate result being a change in the structure of thevegetation community. Levels of soil Al and Mn were found to beconsistently and positively related to the percent of grasslikeweed infestation (%GL-weeds) and, consequently, negatively relatedto the percent of healthy cranberry vine growth (%Vine). Theserelationships indicate that high levels of soil Al and Mn reducevine vigour, making the bog more susceptible to weed infestationand leading ultimately to a reduction in the percent of vine coverand cranberry production. Table 6.4 lists regression equationsfor the prediction of vegetative cover status from soil data.Table 6.4: Best regression equations for predictions of vegetativecover status from soil data.1990:Bog Var Equation r2 S.E.200 Mn % Vine=(-0.505 Mn)+101.4 0.54 1.91% GL weeds=(0.442 Mn)-1.3 0.52 1.75300 Al % Vine=(-0.0126 A1)+119.2 0.32 17.17400 Mn % Vine=(-0.590 Mn)+106.9 0.81 4.98% GL weeds=(0.579 Mn)-7.8 0.81 4.91Al % Vine=(-0.00851 A1)+119.2 0.60 7.24% GL weeds=(0.00812 Al)-19.3 0.57 7.391991:Bog Var. Equation r2 S.E.300 Mn % Vine=(-0.52 Mn)+62.44 0.63 15.1Al % Vine=(-43.5 A1)+7418 0.67 14.3666.2 Relationships Between Foliar Element Concentrations and YieldVariablesThe most significant correlations between foliar elementcontent and yield are presented in Table 6.5. As this tableindicates, tissue concentrations of Mg and Ca were positivelyrelated to yield in three of the four bogs while foliar levels ofN, Fe, and Al were negatively correlated with yield in all fourbogs. Correlations between yield and Al were particularly highfor the two Pitt Meadows bogs, which contained significantlyhigher soil and, consequently, foliar levels of this element.Concentrations of foliar Mn were negatively related to yield inall sites but Bog 200; the relationship in this bog, whichcontained the lowest levels of tissue Mn, was found to bepositive.Concentrations of foliar N were found to be negativelyrelated to yield in all four bogs for at least one of the samplingperiods. While high levels of tissue N can reduce production bystimulating vegetative rather than reproductive development, it ismore likely that this relationship was an indirect result ofreduced vine growth in the stressed areas causing N to beconcentrated in a smaller volume of plant tissue.As was observed for soil-yield relationships, the most andthe strongest foliar-yield relationships were found to occur inBog 300 in the Pitt Meadows site while only a few weak foliar-yield relationships were determined for Bog 200 in the FortLangley site. This was likely due to the differing levels ofvariability found within these bogs; Bog 300 was highly variable67with regard to both foliar and yield variables while productionwithin Bog 200 was fairly uniform and foliar nutrientconcentrations less variable.Table 6.5: Correlations identified between foliar element andyield variables1990:SampleDate VariablesBog No.100 200 300 400June N-Yield -0.67 na (-0.52) --July (-0.48) -0.70 --Sept -- -0.70 --July P-Yield -- -- (0.46)Sept -- -- (0.50)June Ca-Yield (0.41) (0.35) --July (0.36) (0.45) --Sept (0.50) 0.60 (0.34)June Mg-Yield 0.57 (0.42) --July (0.45) (0.49) --Sept (0.48) 0.55 --June Fe-Yield -- (-0.44) --Sept -- -- --July Mn-Yield -- (-0.48) --68Table 6.5: (cont'd)1991:SampleDate VariablesBog No.100 200 300 400May N-Yield (-0.36) -0.62 (-0.50)June (-0.35) -- --July (-0.40) -0.77 --May P-Yield -- (-0.51) (-0.36)June (0.32) -- -0.70 --July -- (0.45) (0.45)May Ca-Yield -- 0.65 --June (0.35) -- -- (0.52)July (0.47) -- -- --May Mg-Yield 0.60 -- 0.90 (0.49)June -- (0.37)July (0.33) -- (0.55) --May Fe-Yield (-0.46) (-0.35) --June (-0.30) -0.68May Mn-Yield -- (-0.44)June (-0.51) (0.45) -- --July 0.58 -0.67 --May Al-Yield (-0.42) (-0.37) -- --June -0.75 -0.60July -- -- -0.62 --Relationships between foliar concentrations of Fe, Mn and Aland yield were similar, although weaker and fewer, to thoseidentified for soil levels of these elements, indicating that thesoil concentrations largely dictated plant uptake. The negativerelationships between tissue Al and yield and tissue Fe and yieldwere supported by the results of research examining the effects ofhigh foliar concentrations of these metals on vegetative growth.69The collapse of growing points is reported as being one of thesymptoms of Al toxicity (Foy et al., 1978) and high foliar Fe, aswell as foliar Cu, levels have been observed to interactnegatively with the growth of terminal buds (Doughty, 1984).Reduced terminal bud growth and shoot development would result ina decrease in upright density and ultimately decreased production.Although yield was found to be negatively correlated withsoil Mg, the relationship between yield and foliar levels of thisnutrient, which is an important constituent of chlorophyll and isthus essential for photosynthesis, was positive. This is inagreement with a cranberry field study in which yield, solublesolids and anthocyanin content were observed to increase withincreasing tissue Mg levels (Eaton and Meehan, 1973). Soil Mgtends to be insoluble and thus unavailable in the acidicconditions common to cranberry bogs (Eck, 1990). As such, plantuptake of this nutrient may have been from fertilizer sourcesapplied throughout the growing season rather than from the soilmedia. The apparent discrepancy between soil and foliar levels ofthis nutrient and their relationship with production may beexplained by the positive correlation between soil Mg and soil Fe,Mn and Al; the negative relationship between yield and soil Mg maybe an indirect result of the negative impact on yield of theseassociated elements.The results suggest that foliar nutrient levels early in thegrowing season give a better indication of yield than do levelsmeasured later in the year. An adequate nutrient status during70this part of the season is critical in order to support vegetativegrowth, floral development, fruit set and berry growth(DeMoranville and Deubert, 1987). This has positive implicationsfor yield forecasts as it suggests that production levels can bepredicted early in the season using measured levels of foliarnutrients. Table 6.6 lists regression equations for theprediction of yield from foliar variables.Table 6.6: Best regression equations for prediction of yield fromfoliar nutrient levels.1990:Bog Var. Equation r 2 S.E.100 Mg Yield=(48.10 Mg)-5.407 0.33 0.71300 CaMgYield=(4.199 Ca)-0.590Yield=(10.503 Mg)-0.4610.360.310.780.811991:Bog Var. Equation r2 S.E.100 Mg Yield=(19.044 Mg)-3.432 0.35 0.44200 Mn Yield=(0.0311 Mn)+1.095 0.33 0.39300 Mg Yield=(70.142 Mg)-12.678 0.80 0.64Ca Yield=(8.664 Ca)-2.678 0.42 1.11Al(Jn) Yield=(-0.00624 A1)+2.908 0.75 0.73Mn(J1) Yield=(-0.03665 Mn)+5.542 0.37 1.15400 Al(Jn) Yield=(-0.00391 A1)+2.769 0.42 0.69Fe(Jn) Yield=(-0.01149 Fe)+3.022 0.41 0.70716.3 Relationships Between Site and Remote Sensing VariablesSignificant relationships between site properties and remotesensing variables are presented in Table 6.7. The strongestrelationships were found to occur between the NIR/R band ratio andthe vegetation cover status and, because production is stronglyand positively related to the percent healthy vine growth,cranberry yield. Percent healthy vine growth and yield werepositively correlated to the NIR/R ratio, which was negativelycorrelated with the percent of grasslike weed infestation. Theserelationships were particularly strong in Bogs 100 and 300, whichcontained the highest levels of weed infestation. Weak butconsistent correlations were also found between the NIR/R ratioand soil levels of Al and Mn, particularly in the Pitt Meadowssite; however, these correlations were likely an indirect resultof the relationships between levels of weed stress and both soiland remote sensing variables.The strongest correlations were identified early in thegrowing season, suggesting that early predictions of yield usingremote sensing techniques might be possible. Detection of weedspecies in crops is aided by physiological differences, and thusdifferences in reflectance characteristics, between the plants.The strong relationships between vine status and pixel reflectancevariables in the May and June imagery indicated that reflectancedifferences between vines and invader species were at a maximumduring the early growing season. Water tables were also highestduring this time, and imagery obtained during the early season,72because of the very low reflectance by water of energy in the NIRregion, served to identify depressional areas within the bog.The best regressions for the prediction of vegetation cover statusand, because of the strong relationship between this status andproduction, cranberry yield based on remote sensing variables areprovided in Table 6.8.Table 6.7: Correlations identified between site properties andremote sensing variables1990:SampleDate VariablesBog No.100 200 300 400June %Vine- -- (0.46) 0.80 (0.33)July (NIR/R) 0.85 -- 0.81 (0.55)Sept (0.49) -- -- 0.64June %GL- -- (-0.34) -0.80 (-0.32)July (NIR/R) -0.86 -- -0.84 (-0.49)Sept (-0.49) -- -- -0.64June Yield- -- 0.82 --July (NIR/R) -- na* 0.67 --Sept -- -- --1991:SampleDate VariablesBog No.100 200 300 400May % Vine- 0.68 -- 0.76 (0.54)June (NIR/R) 0.60 -- na naJuly na* (0.52) na naMay % GL- -0.64 -- (-0.38) -0.62June (NIR/R) -0.60 -- na naJuly na (-0.51) na naMay % Pond.- -- -- -0.69 -0.60(NIR)May Yield- 0.80 -- 0.84 (0.54)June (NIR/R) (0.52) -- na naJuly na -- na naTable 6.8: Best regression equations for prediction of sitevariables from pixel reflectance values1990:Bog Var. Equation r2 S.E.100 %Vine- %Vine=(250.9 	 [NIR/R])-252.4 0.72 14.6[NIR/R] Jl% GL- %GL=(-251.6 	 [NIR/R])+352.8 0.74 14.2[NIR/R] Jl200 %Vine- %Vine=(31.4 	 [NIR/R])+55.9 0.22 2.50[NIR/R] Jn300 %Vine- %Vine=(134.1 	 [NIR/R])-87.4 0.64 12.4[NIR/R] Jl% GL- %GL=(-132.0 	 [NIR/R])+182.2 0.64 12.1[NIR/R] J1Yield- Yield=(6.52 	 [NIR/R])-5.98 0.67 0.56[NIR/R] Jn400 %Vine- %Vine=(100.4 	 [NIR/R])-47.2 0.42 8.36[NIR/R] Se%GL- %GL=(-71.7 	 [NIR/R])+100.6 0.41 6.02[NIR/R] 	 Se7374Table 6.8: (cont'd)1991:Bog Var. Equation r2 S.E.100 %Vine- %Vine=(174.2 	 [NIR/R])-135.3 0.46 16.2[NIR/R] Ma%GL- %GL=(-165.5 	 [NIR/R])+224.3 0.41 17.0[NIR/R] MaYield- Yield=(5.04 	 [NIR/R])-4.60 0.63 0.33[NIR/R] Ma300 %Vine- %Vine=(118.7 	 [NIR/R])-85.5 0.58 16.1[NIR/R] Ma%GL- %GL=(-21.5 	 [NIR/R])+35.1 0.14 8.32[NIR/R] MaYield- Yield=(7.73 	 [NIR/R])-8.53 0.71 0.79[NIR/R] Ma400 %GL weed- %GL=(-79.75 	 [NIR/R])+111.79 0.39 11.9[NIR/R] Ma6.5 Summary of Linear Yield RelationshipsRelationships among soil, foliar, vine status, yield andremote sensing variables are summarized graphically for Bogs 100,300 and 400 in Figure 6.1; Bog 200 was not included becauserelationships in this bog were found to be few and weak. Generalrelationships between site variables and yield were identified inthis study. Of particular importance were the negativerelationships between soil Al, Fe and Mn levels and percenthealthy vine growth and the consequent negative relationshipsbetween these soil variables and cranberry yield. It would appearthat localized depressions within the bog, which result from75inconsistent settling of the peat material, become areas ofaccumulation for these soil elements. High concentrations of Aland Fe in the foliar tissue of cranberry uprights were also foundto have a negative impact on yield. Conversely, a positive andconsistent relationship was observed between foliar levels of Mgand yield.Correlations involving foliar-yield relationships were fewerand lower than those involving soil-yield relationships. Thiswould indicate that soil properties explained a higher degree ofyield variability than did foliar variables and that yieldpredictions using regression equations based on soil data wouldtherefore be more accurate than those based on foliar data. Thereasons for this may in part be due to the high degree ofvariability exhibited by cranberry tissue nutrient levels,particularly Mg and Fe, throughout the season. In addition, giventhe low nutrient requirements of cranberry vines and the frequentapplication of chemical fertilizers, production losses due tonutrient deficiencies are likely uncommon in commercial bogs.Consequently, variability in yield is more likely to be caused bythe inherent variability of soil conditions within the bog, suchas toxic levels of Al, Fe and Mn, which negatively impact vinegrowth and weed competition.While these trends were found to be consistent between bogsand between years, linear regression models were both site-dependent and year-specific and hence prediction equations provedto be inconsistent. It is not likely, therefore, that universal76linear equations can be developed for yield predictions based oneither site data or remote sensing variables. The ability ofremote sensing techniques to detect and evaluate bog variability,however, suggests that these methods would prove useful inassessing bog status in a spatial manner.soil Alsoilsoilsoil MgFol. AlYIELDFol. NFol. Mg+NIR/R7+	2 Vine77BOG 100--------2GL weedBOG 400     Figure 6.1: relationships among site and remote sensing variables:r>0.75 	 ; r=0.56-0.75  	 ; r<0.561 	78CHAPTER 7RESULTS AND DISCUSSION:SPATIAL VARIABILITY AND RELATIONSHIPS BETWEEN BIOPHYSICALPARAMETERS AS IDENTIFIED BY IMAGE CLASSIFICATIONImage 	 classification 	 techniques 	 enable 	 significantrelationships between reflectance variables, measured by remotesensing, and site variables, determined by ground truth sampling,to be identified. While standard correlation and regressiontechniques are effective in identifying linear relationships,these methods fail to identify significant non-linearrelationships which may exist between pixel reflectance and sitevariables. Digital image analysis systems examine data in a morespatial manner, and hence both linear and non-linear relationshipsare identified.The objectives of this chapter are: 1) to use previouslydefined linear relationships to classify bogs in a supervisedmanner and to compare the two classification methods; 2) toexamine spatial variability within the four bogs usingunsupervised classification techniques; 3) to compare linear yieldrelationships identified by correlation/regression techniques withspatial relationships identified by image classification; andfinally 4) to demonstrate a remote sensing method of monitoringchanges in weed infestation by overlaying sequential images.797.1 Supervised Classification.As was discussed in Chapter 6, correlation analysis indicatedthat strong linear relationships existed between the pixelreflectance ratio NIR/R and cranberry yield for Bogs 100 and 300,and that regression equations using pixel value ratios cantherefore be used to predict yield within these two bogs.Graphical representations of these regression equations were usedto separate production values arbitrarily into high, moderate andlow yield categories; the NIR/R ratio limits associated with eachcategory were then identified and used to classify images in asupervised manner. The regression equations used, together withthe yield and associated NIR/R ranges for each of the threeclasses, are presented in Table 7.1; graphical representations ofthese equations and maps created by classifications are shown inFigures 7.1, 7.2 and 7.3.Analyzing the site characteristics of high verses lowyielding areas within a bog allows the factors causing lowproductivity to be more readily determined. Variables which weresignificantly different between yield classes, and which weretherefore associated with yield, are presented in Tables 7.2, 7.3and 7.4.80Table 7.1: Regression equations used for supervised classificationand class ranges for yield and [NIR/R] values.Bog Date Regression Equation Cl YieldRange[NIR/R]Range100 May, Yield=(5.04[NIR/R])-4.60 1 1.40+ 1.19+91 r2=0.63; 	 S.E.=0.33 2 0.75-1.40 1.06-1.193 0-0.75 0-1.06300 Jun, Yield=(6.52[NIR/R])-5.98 1 3.00+ 1.38+90 r2=0.67; 	 S.E.=0.56 2 1.50-3.00 1.15-1.383 0-1.50 0-1.15300 May, Yield=(7.73[NIR/R])-8.53 1 3.00+ 1.49+91 r2=0.71; 	 S.E.=0.79 2 1.20-3.00 1.30-1.493 0-1.20 0-1.30Y=5.04(NIR/R)-4.062R =0.63S.E.=0.33ax811.75E0) 1.250 0.75>-0.251.0 	 rtillW91 1 '0'1. t4 	flit1'41 — 	41.0.	 0-M-_ 	I'.AVM 	 10'174 N	11111 %--Nolpfo" vo' 	 010i-VC2,1":4- 	'1110111r111 	,ofd„. 	•d•	"j14rdTiCLASS 3	CLASS 2 	 CLASS 10.99 	 1.10 	 1.21	 1.32a) 	 NIR/R RATIOb)Class% of mapareaYield(kg/m2)1) green2) grey3) 	 red 4524301.45 ab1.18 b0.56	 cFigure 7.1: Regression model used for supervised classification(a), and the corresponding classified map (b), for Bog 100 (imagedate: May 31, 1991). The table indicates average yield values forplots located within each of the three classes. Yield values whichdo not share a letter are significantly different. (p=0.10).824.03.53 0•CD 2.52.001.501>- 	 •0.5Y=8.52(NIR/R)-5.98R 2 =0.87S.E.=0.56II'a_Ass. 3 	 CLASS 2 	 CLASS0.98 1.085 1.19 1.295 1.40NIR/R RATIOClass% of mapareaYield(kg/m2)1) green2) grey3) 	 red431641	3.33 	 ab	2.77 	 b	1.51 	 cFigure 7.2: Regression model used for supervised classification(a), and the corresponding classified map (b), for Bog 300 (imagedate: June 15, 1990). The table indicates average yield values forplots located within each of the three classes. Yield values whichdo not share a letter are significantly different. (p=0.10).a)b)4.2Y=7.73(NIR/R) -8.532 	 2R =0.71S.E.=0.790_iLL1I---1›-1 .81.20.8CLASS 3 CLASS 2 	 CLASS 13(N 	 .6E\ 3.00)2.4a)1.10 	 1.25 	 1.40 	 1.55NIR/R RATIO83b)Class% of mapareaYield(kg/m2)1) green2) grey3) 	 red4527283.41 ab	2.63 	 b	0.59 	 cFigure 7.3: Regression model used for supervised classification(a), and the corresponding classified map (b), for Bog 300 (imagedate: May 31, 1991). The table indicates average yield values forplots located within each of the three classes. Yield values whichdo not share a letter are significantly different. (p=0.10).84Table 7.2: Means of variables exhibiting significant differencesbetween classes (Bog 100; May 1991). Means within a row that donot share a letter are significantly different. (p=0.10).Variable Class 1 Class 2 Class 3Yield 1.45 ab 1.18 b 0.56 cU.D. 296 a 244 bc 213 c% Vine 78 a 60 b 43 c% GL weeds 21 a 39 b 55 csoil-Fe 1494 ac 1221 b 1670 csoil-Al 1788 ab 1867 b 2576 csoil-Mg 0.07 ab 0.07 b 0.09 cfoliar-N 1.30 ab 1.33 b 1.48 cfoliar-Mg 0.23 abc 0.24 b 0.22 cfoliar-Fe 73.3 abc 73.3 b 85.0 cTable 7.3: Means of variables exhibiting significant differencesbetween classes (Bog 300; June 1990). Means within a row that donot share a letter are significantly different. (p=0.10).Variable Class 1 Class 2 Class 3Yield 3.33 ab 2.77 b 1.51 c% Vine 98 ab 88 b 48 c% GL weeds 1 ab 8 b 51 csoil-Fe 3126 abc 3294 b 5053 csoil-Al 2731 ab 2685 b 4113 cfoliar-N 0.94 ab 0.97 b 1.26 cfoliar-Mg 0.18 a 0.16 bc 0.15 c85Table 7.4: Means of variables exhibiting significant differencesbetween classes (Bog 300; May, 1991). Means within a row that donot share a letter are significantly different. (p=0.10).Variable Class 1 Class 2 Class 3Yield 3.41 ab 2.63 b 0.59 cU.D. 398 ab 395 b 262 c% Vine 84 ab 88 b 59 c% GL weeds 2 ab 5 b 8 c% Pond 8 ab 0 b 28 csoil-Fe 2472 ab 2609 b 5137 csoil-Mn 14.4 ab 14.7 b 35.0 csoil-Cu 11.0 ab 11.6 b 15.5 csoil-Al 3186 ab 3484 b 5246 csoil-Mg 0.07 ab 0.07 b 0.10 cfoliar-N 1.70 ab 1.74 b 2.00 cfoliar-Mg 0.22 ab 0.21 b 0.19 cfoliar-Fe 79.3 a 117.3 be 112.3 cResults obtained from supervised classification wereconsistent with relationships previously determined by lineartechniques. The highest yielding class for each image had ahigher percentage of healthy vine growth and a higherconcentration of foliar Mg. Conversely, the least productiveclass had a higher percentage of weed infestation, higher levelsof both soil and foliar Fe, and higher levels of soil Al, Mn andMg. Low yielding areas of Bog 300 were also shown to be moreprone to surface ponding in the early spring, indicating that poordrainage in this bog is responsible for a substantial loss ofproduction. Levels of foliar N were found to be higher in thosesections of the bogs which were the least productive; however,this was likely a concentration affect due to reduced vegetativegrowth in the stressed vines of low yielding areas rather than adirect effect of N on cranberry yield.86Perhaps the most useful application of supervisedclassification of remotely sensed images for the cranberryindustry lies in its potential to improve yield forecasts.Current yield estimates are based on a composite of 4-5subsamples, approximately 15x15 cm in size, obtained randomly fromwithin each bog. While this sample size is insufficientconsidering the size and level of production variability withinthe bog, time and cost constraints do not allow for more intensivesampling (Dr. J. Davenport, Soil Scientist, Ocean SprayCranberries Ltd.; personal communication). Areas of the bogcontaining large amounts of weed infestation tend to be excludedfrom sampling, and hence forecasts are based on the areal extentof the bog and the yield per unit area of those sections which arehighly productive. This practice generally results in yieldforecasts that are higher than actual production. Rather thanusing completely random sampling, colour-coded classification mapscould be used as guides to make ground sampling more efficient.Samples could be obtained from each of the yield classesrepresented on the map; once the average yield per class has beendetermined, production on a per bog basis could be calculatedusing the percentage of total ground area contained within eachproduction category. An example of this procedure as it comparesto the method of yield forecasting used currently is presented inTable 7.5.87Table 7.5: Yield estimates obtained using supervisedclassification maps.Bog Cl.Meanyield perclass(kg/m2 )Map arearepresented byclass(%) 	 (m2)Calculatedtot. yieldper class(1000 kg)Calculatedtot. yieldper maparea(1000 kg)100 1 1.45 47 	 1970 2.86 4.712 1.18 23 	 970 1.14'91 3 0.56 30 	 1260 0.71 (5.50)*Tot. area=4200 m 2300 1 3.33 43	 2290 7.63 14.982 2.77 41	 2190 6.07'90 3 1.51 16 	 850 1.28 (16.26)Tot. area=5330 m2300 1 3.41 44 	 2350 8.01 12.682 2.63 27 	 1440 3.79'91 3 0.59 28 	 1490 0.88 (16.10)Tot. area=5330 m2* 	 .yield values in brackets indicate production estimates basedonly on the productive sections of the bog (i.e. Class 1 and Class2), as often occurs with current forecast methods.7.2 Unsupervised Classification.Statistical analysis of site conditions between clusterscreated using unsupervised classification showed this method to bea fast and effective means of differentiating vegetation coverstatus within the bog, particularly during the early season.Variable relationships involving these features, as identified byclassification, were consistent with those found using bothcorrelation and regression techniques and the supervisedclassification method. This would suggest that the method of88unsupervised classification, which does not necessitate the aDriori knowledge of bog conditions required for supervisedclassification, would be as effective as the latter for yieldprediction early in the season. Examples of the maps created bythis technique are shown in Figures 7.4 and 7.5. Results of thecomparisons among classes for each bog are represented in Tables7.6 through 7.9.Categorical differences in percent grasslike weeds andhealthy vine growth were consistently significant for three of thefour bogs; image classification of Bog 200 proved less effectivedue to the low level of variability, both in site properties andreflectance characteristics, within this bog. The best resultswere obtained with bogs 100 and 300, both of which had extensiveweed infestations. Soil- and foliar-yield relationshipsidentified by spatial classification were similar to those foundusing linear techniques. Sections of Bogs 100, 300 and 400 thatcontained higher levels of soil Al, Mn and Fe were shown to beless productive, having either lower yield, lower uprightdensities or lower % vine coverage and high % grasslike weedinfestations indicative of vine stress. More productive classestended to be higher in both foliar Mg concentrations and % vinecoverage. As was found for both linear correlation methods andspatial supervised classification, the best results were extractedfrom images obtained early in the growing season.a)89• 	 .411,..1111'b)Class % of mapareaYield(kg/m2)U.D.:(uprights/0.1m2)%Vines%GL-weeds1) green2) grey3) 	 red323731	1.46 	 a	0.88	 b	0.42 	 c279 a231 be211 	 c73 a56 b36 c26 a43 b61 cFigure 7.4: Colour-NIR image (a), and unsupervised classification(b), for Bog 100 (image date: May 31, 1991). The table indicatesseveral of the variables exhibiting significant differences betweenclasses. Variables which do not share a letter are significantlydifferent. (p=0.10).tr-Hov, TsOfli4:1(0 ,.0 	 0in HMH CV0U)a)(d ,Q 00\0 co .cp (----.1 co r-- ■0U) .q.p al ,c1 	 0•• 	 ,.. 	 cm• tp szl'	 N 	 (---(2) -H H co 1-- - 0• ...i 	 • co mQ., 0----0rd (1---(TS .0	 0H co in coW ----. 0 co 0-H 01 • • 	 •>4,,..—•r) c-1 HPAalKia) Ol 	 LI-) 	 tr)4-4 	 ,.•( .7. re) H0 al0\oa) >1a) 	 a) rdU) .-1 	 .•I 	 a)U) Zr tri(0,—i ........,___C..) H (N C)-HO -P• a) a)a)4-) a)al C.) 	 (d• a) 0-H 	 -H4-I R	 4-1w -H• a)•	0 0-)-HH Q (1) U)U4--)	W•0a) a) 4-1 ai-Hja.) • ni.H(1) M• .H▪ (U'1:M-1 - -It5")(1  4 jffil4-) 0•• -Q(I) (I)	0-H01 4-)(0 x• r0 W-HU)PG 0) -HH aiZ 	 •->-H0W ■-1O 0• c,C)) 	 '°(1/• 	a) .H•• 0t31 4i 51▪ W(1..1 	 ••N 	0 	 -1-)•U)a) 4-1 at a)• ul(n 4--(9> 	 4-4-H ,C) 	 H -H4.4	U) U rd0rn91Table 7.6: Means of variables exhibiting significant differencesbetween classes (Bog 100). Means within a row that do not sharea letter are significantly different (p=0.10).Date Variable Class 1 Class 2 Class 3June, 90 Yield 1.76 ab 1.59 bc 1.43 c% Vine 86 ab 55 bc 40 c% GL weeds 13 ab 42 bc 52 cfoliar-P 0.20 ab 0.15 bc 0.15 cfoliar-Mg 0.16 ab 0.15 bc 0.14 cJuly, 90 Yield 1.70 ab 1.90 b 0.80 cU.D. 459 ab 550 b 398 c% Vine 86 a 58 b 29 c% GL weeds 13 a 42 b 71 cSept,90 Yield 1.76 1.28% Vine 67 0.21 --% GL weeds 32 40foliar-Mg 0.24 60May, 	 91 Yield 1.46 a 0.88 b 0.42 c% Vine 73 a 56 b 36 c% GL weeds 26 a 43 bc 61 csoil-Mg 0.07 ab 0.08 bc 0.09 csoil-Fe 1262 ab 1510 bc 1783 csoil-Cu 9.4 a 11.2 bc 11.0 csoil-Al 1749 a 2220 bc 2664 cfoliar-Mg 0.24 ab 0.23 bc 0.22 cfoliar-Al 84 ab 98 bc 107 cJune, 91 Yield 1.21 ab 1.07 b 0.48 c% Vine 73 ab 59 b 38 c% GL weeds 27 ab 40 b 62 csoil-Mg 0.06 ab 0.08 bc 0.09 csoil-Cu 9.3 ab 10.4 bc 11.6 csoil-Al 1778 ab 2028 bc 2906 cfoliar-Mg 0.18 ab 0.17 bc 0.17 c92Table 7.7: Means of variables exhibiting significant differencesbetween classes (Bog 200) (p=0.10).Date Variable Class 1 Class 2June, 90 U.D. 448 414% Vine 99 95% GL weeds 0 3soil-Mn 8.7 5.3soil-Cu 6.6 2.9foliar-Mn 71 58June, 91 U.D. 359 294soil-Mn 11 17soil-Al 1578 2022July, 91 Yield 3.0 2.5foliar-Cu 55 48Table 7.8: Means of variables exhibiting significant differencesbetween classes (Bog 300) (p=0.10).Date Variable Class 1 Class 2 Class 3June, 90 % Vine 92 64% GL weeds 5 35 --soil-Al 2659 3479Sept, 90 Yield 2.70 2.56% Vine 92 64 --% GL weeds 5 35foliar-Fe 73.9 81.1May, 91 Yield 3.08 ab 1.85 bc 1.03 cU.D. 384 ab 372 b 261 c% Vine 88 ab 74 b 67 c% Pond 5 ab 11 b 23 csoil-Al 3606 ab 3907 b 4948 csoil-Mn 18.5 ab 19.2 b 31.7 cfoliar-Mg 0.22 a 0.20 bc 0.19 cfoliar-Fe 61.5 ac 73.3 b 53.3 cJune, 91 Yield 3.60 a 2.10 b 0.80 csoil-Al 3078 a 3921 b 4967 cfoliar-Mn 45 a 57 bc 56 cfoliar-Al 66 a 173 b 430 c93Table 7.9: Means of variables exhibiting significant differencesbetween classes (Bog 400) (p=0.10).Date Variable Class 1 Class 2July, 90 Yield 2.54 2.38% Vine 92 89% M/C damage 6 11foliar-Mn 119 167Sept, 90 soil-Mn 12.5 21.8soil-Al 2410 2910May, 91 Yield 2.60 2.10U.D. 362 257% Pond 0 6Classification of images obtained in May, when dip wellmeasurements indicated the highest water table elevations of thegrowing season, identified areas of ponding in Bogs 300 and 400that were indicative of poor drainage in depressional areas.Comparisons of test sites located within these depressional areasto the remaining sites within each bog showed the former to havelower upright densities, much fewer fruit per area and a smallerberry size, all of which contributed to a reduction in yield.Yield reduction was likely a consequence of reduced carbonassimilation caused by a decrease in leaf area; which in turn wasa result of flooding in the early season when vines are most proneto damage (Crane and Davies, 1989). Furthermore, high waterlevels in spring may have delayed the development of shoots androots resulting in an inadequate root system for uptake ofnutrients and water later in the growing season (Hall, 1971).Classification of images obtained later in the growing seasonindicated these areas to be prone to weed infestations, most94likely as a result of reduced plant vigour causing cranberry vinesto be less resistant to competition from weed species.7.3 Image Overlay.The map combine option of the Earthprobe system enables newmaps to be created by applying logical operands to existing mapsthat have been registered to a common plane. When this method isapplied to sequentially obtained images, the resulting mapsrepresent temporal changes in the image object. This techniquewas used to measure changes in the level of weed infestation inBog 100 between May and June of 1991. The original maps showingthe extent of weed infestation in May and June and the combinedmap showing the dynamics of weed stress within this bog arepresented in Figure 7.6.Three classes of change were represented in the new map. Thefirst class, indicated as class 1, indicated areas having highweed stress in both May and June of 1991. This class representedchronic weed infestation where control measures had provenineffective and hence where cranberry vines had not been able tocompete with invader species. The second class represented areasof recovery, that is areas which were weed infested in May but byJune contained healthy vine growth. Vine recovery in these areasindicated a delayed effect of herbicide (casoron) application onthis bog in April of 1991; recovery was seen to occur mostlyaround the margins of weed areas where cranberry vine runners havegrown into the patches of dying grass. The final class was small,95containing only 2% of the map area, and represented areas ofrecent infestation, that is areas that were classified as havinghealthy vine growth in May but which by June had deteriorated toa high level of weed stress.Classification of overlayed sequential images, byrepresenting the dynamic nature of various forms of stress both ona seasonal and yearly bases, can have several managementapplications. Different formulations and application methods offertilizers can be evaluated to determine the most effective meansof optimizing cranberry vine growth and fruit production. Variousforms of stress, including ponding and infestations of weeds,insects and diseases can be monitored to determine which, if any,bog features are associated with stress. An example of this wouldbe outbreaks of fungal diseases which are more common to areas ofthe bog having exceedingly high moisture levels. Monitoring andrecording changes in various forms of stress levels also providesa means to determine the effectiveness of control measures.Finally, an increasing percentage of chronic weed stress mayindicate that the vines are no longer able to compete with weedspecies and that partial or complete bog renovation is warranted.96a) 	 b)c )Class % of map area1) red: chronic weed stress 182) green: vine recovery 93) blue: recent infestation 2Figure 7.6: Areas of Bog 100 containing high levels of weedinfestation in May (a), and June (b), and changes in the level ofstress between these two dates as determined by map overlay (c).97CHAPTER 8CONCLUSIONS AND RECOMMENDATIONSThe objectives of this thesis were to examine therelationships between measured site parameters and cranberry yieldand to determine the efficiency of using large scale colour-IRaerial photography and digital images analysis techniques tomonitor and evaluate plant stress within cranberry bogs. Althoughconsiderable within bog variability has been observed both by thepresent study and by previous cranberry research, applications offertilizers and biocides, with the notable exception of wipe-onglyphosate treatments, are routinely applied in a blanket manner.Yield forecasts, which are particularly important given the natureof the industry's marketing infrastructure, also fail to accountfor this spatial variability and are hence often inaccurate. Thefollowing conclusions can be drawn from the thesis research:8.1 Relationships Between Cranberry Production and Site VariablesThe Al, Fe, Mn and Mg content in soils were found to beconsistently and positively correlated to grasslike weedinfestation and, by corollary, were negatively correlated to thepercentage of healthy cranberry vine growth in three of the fourbogs. This would suggest that high levels of these elements inthe soil have a negative impact on the cranberry plant's abilityto compete against other species which are adapted to the bogenvironment. These soil variables were also consistently98correlated to each other, suggesting a level of interaction andcodependence. Bog 200, for which the above relationships were notfound to occur, was unique in that it contained very low levelsof weed infestation and concentrations of soil Fe and Al that wereboth lower and less variable than those of the other three bogs.Because of the strong relationship between fruit productionand the percentage of healthy vine coverage, high levels of soilFe and Al were also observed to have a negative impact on yield.Levels of foliar Fe and Al were found to be negatively related toyield in three of the four bogs, indicating that plant uptake ofthese elements is related to total soil levels. Decreased yieldis likely the result of the negative interaction of Fe with thegrowth of terminal buds and the collapse of growing points whichis a symptom of Al toxicity. Conversely, foliar levels of Mg werefound to be positively related to yield and, given the weak butconsistent positive correlation between yield and the foliar Mg:Aland Mg:Fe ratios, there is evidence that this element serves tocounteract the negative effects of high foliar Al and Feconcentrations. Relationships between foliar elements and yieldwere observed to be the strongest during the early season, when anadequate nutrient status is critical to support vegetative growth,floral development, fruit set and berry growth. This has positiveimplications with regard to production forecasts in that enablesyield responses to be predicted early in the growing season.Correlations involving soil-yield relationships proved to behigher and more consistent than those involving foliar-yield99relationships, indicating that soil properties explain a higherdegree of yield variability than do foliar nutrient variables.Production losses resulting from nutrient deficiencies areuncommon in cranberry bogs due to the low nutrient requirements ofthe crop and the frequent applications of chemical fertilizers.Consequently, variability in yield is more likely to be caused bythe inherent variability of conditions within the bog, such astoxic levels of Al, Fe and Mn, which negatively impact vine growthand the plant's ability to compete against invader species. Thissuggests that better production forecasts can be obtained usinglinear regression equations involving soil variables rather thanfoliar variables.Although relationships between yield and the above-mentionedsite variables are consistent for three of the four bogs and forboth growing seasons, the linear regression equations whichdescribe these relationships are site and year specific. Whilesoil and foliar data would be useful for identifying spatialtrends in production within the bog, accurate yield forecastsbased only on soil or foliar nutrient data are unlikely.8.2 Relationships Between Measured Site Properties and PixelBrightness ValuesThe best prediction equations for yield using remote sensingvariables were obtained with the NIR/R band ratio. Linearrelationships were strong for three of the four bogs, withregression equations accounting for up to 67% of the variabilityin production. The strongest relationships were observed using100images obtained during the early season, indicating that earlyyield predictions can be made using remote sensing techniques.The relationship between yield and remote sensing variablesis likely indirect, being the result of high correlations betweenreflectance values, yield and the percent of healthy vine growthcoverage. In Bog 200, which had a very low incidence of weedinfestation and no surface ponding during the early spring andwhich exhibited very little spatial variability with regard toproduction, no correlations were found between yield and the NIR/Rratio. The effectiveness of using remote sensing variables topredict yield is therefore largely governed by the degree of weedinfestation and early season ponding in the bogs and theconsequent affect that these stress factors have on fruitproduction.As was determined for yield relationships involving soil andfoliar content variables, linear regression equations involvingyield-pixel value relationships are site-dependent and year-specific. Thus, while remote sensing variables provideinformation as to the spatial variability of production within thebog, accurate yield forecasts based only on pixel reflectancevalues are limited. The best application for remote sensing andimage analysis techniques in regard to yield forecasting lies inthe ability of these techniques to differentiate production levelsin the bog.1018.3 Effectiveness of Image Classification Techniques for MeasuringSite VariablesSupervised classification maps based on the positiverelationship between yield and the NIR/R band ratio was effectivein differentiating levels of production. A comparison of soil andfoliar nutrient levels between classes confirmed relationshipspreviously identified by linear correlation/regression techniques.High levels of soil Al and Fe were found to be associated withhigher foliar concentrations of these metals, high levels of weedinfestation and decreased cranberry production. In addition to Aland Fe, high levels of soil Mn and Mg were also associated with areduction in yield.Unsupervised classification, which unlike supervisedclassification does not require calibration using ground truthdata, proved to be a fast and effective means of highlightingweeds infestations and, using images obtained in May, depressionalareas prone to surface ponding in the early spring. Becauseproduction was highly influenced by these two forms of stress inthree of the four bogs studied, significant differences in yieldwere also found between classes differentiated using thistechnique. Improved yield estimates could, therefore, be realizedby using colour-coded maps created by image classification asguides for ground sampling. Each production class represented onthe map could be sampled separately and the total yield thencalculated according to the percentage of total area containedwithin each class.Because maps created by image classification provide a means102of differentiating levels of stress factors within cranberry bogs,this technique could also be useful for targeting applications offertilizers and biocides in a more selective manner that would bemore cost effective and pose less risk of soil and watercontamination. The effectiveness of control measures andfertilizer practices could be monitored by comparing spatiallyrectified sequential images allowing classes of chronic stress,recovery and recent infestation to be identified. Thisinformation would be useful for measuring the effectiveness ofmanagement techniques and for making decisions regarding thepartial or complete renovation of bogs.8.4 RecommendationsThe effectiveness of remote sensing techniques as amanagement tool to improve cranberry bog management would beimproved by minimizing the time lag between image acquisition andinterpretation. Recommendations for future research includeassessing the usefulness of video colour-IR systems which,although having a much lower spatial resolution than the aerialphotography method used in the current study, can provide near-real-time information for agricultural producers and extensionworkers. The effectiveness of remote sensing techniques inidentifying and measuring other forms of stress within bogs shouldalso be examined to determine the overall usefulness of suchsystems.Relationships between soil and foliar elements and cranberry103yield are complex and interrelated. In the current study foliarMn and Fe were found to result in decreased yields at levels belowthose listed as being excessive in Appendix A. Similarly, nodeficiencies in foliar Mg were found according to the rangeslisted in this appendix, yet higher yielding areas containedhigher foliar concentrations of this nutrient, indicating apositive response. Further research, using a more controlledenvironment in which yield is not influenced by outside sources ofnutrients, is needed to identify and more fully understand therelationships between soil and foliar variables and cranberryyield. While similar trends were observed regarding relationshipsamong soil and foliar nutrient status and cranberry production forthe bogs studied in the current thesis, these relationships may beunique to the soil types and climate of the Pacific Northwest.Similar studies conducted in other growing regions would indicateif these relationships are consistent among a range of growingconditions and if they are influenced by management practices suchas sanding.104REFERENCESAddoms, R.M., and F.C. Mounce. 1931. Notes on the nutrientrequirements and the histology of the cranberry (Vacciniummacrocarpon Ait) with special reference to mycorrhiza. PlantPhysiol. 6: 653-668.Al-Abbas, A.H., R. Barr, J.D. Hall, F.L. Crane and M.F.Baumgardner. 1974. Spectra of normal and nutrient-deficientmaize leaves. Agron. J. 66: 16-20.Allison, L.E. 1965. Organic matter determination: Walkley-Blackmethod. pages 1372-1376. In: C.A. Black (ed). Methods of SoilAnalysis. Part 2: Cemical and microbiological properties. ASAmonograph #9. Madison, Wisc.B.C. Ministry of Agriculture, Fisheries and Food. 1992. Berryproduction guide for B.C. growers. B.C. Min. of Agricultureand Fisheries.Bitterlich, I. 1990. Weeds of British Columbia cranberry bogs.British Columbia Cranberry Growers Association. 138 pages.Blakeman, R.H. 1990. The identification of crop disease and stressby aerial photography. pages 229-255. In M.D Steven and J.A.Clark (Eds) Application of remote sensing in agriculture.Butterworths, London, England. 427 pages.Brooks, R.M. and H.P. Olmo. 1972. Register of new fruit and nutvarieties. 2nd Ed., University of California Press, Berkley.Canada Soil Survey Committee, Subcommittee on Soil Classification.1978. The Canadian System of Soil Classification. Can.Dep. Agric. Publ. 1646. Supply and Services Canada, Ottawa,Ont. 164 pages.Chaplin, M.H. and L.W. Martin. 1979. Seasonal changes in leafelement content of cranberry, Vaccinium macrocarpon Ait.Commun. in Soil Sci. Plant Anal. 10: 895-902.Chuntanaparb, N. and G. Cummings. 1980. Seasonal trends inconcentration of nitrogen, phosphorus, potassium, calcium andmagnesium in leaf portions of blueberry, grape and peach. J.Amer. Soc. Hort. Sci. 105: 933-935.Colwell, J.E. 1973. Vegetation canopy reflectance. Remote Sens.Environ. 3: 175-183.Crane, J.H. and F.S. Davies. 1989. Flooding responses of Vacciniumspecies. HortScience 24: 203-210.105Curran, P.J. 1985. Principles of remote sensing. Longman GroupLimited, Essex, England. 282 pp.Dale, E.R., K. Hulsman and A.L. Chandica. 1986. Seasonalconsistency of salt-marsh vegetation classes classified fromlarge-scale colour infrared aerial photographs. Photogram.Eng. Remote Sens. 52: 243-250.Dana, M.N. 1990. Cranberry management. pages 334-362. In G.J.Galletta and D.G. Himelrick (Eds) Small fruit management.Prentice Hall, Anglewood Cliffs, N.J. 602 pages.Darrow, G.M., H.J. Franklin, and O.G. Malde. 1924. Establishingcranberry fields. U.S. Dept. Agr. Farmers Bull. no. 1401.DeMoranville, C.J. and K.H. Deubert. 1986. Seasonal changes ofnitrogen, phosphorus, potassium, calcium and magnesium in theleaves of the Massachusetts cranberry. Commun. Soil Sci.Plant Anal. 17: 869-884.DeMoranville, C.J. and K.H. Deubert. 1987. Effect of commercialcalcium-boron and manganese-zinc formulations on fruit set ofcranberries. J. Hort. Sci. 62: 163-169.Doughty C.C. 1984. Some effects of minor elements on cranberry(Vaccinium macrocarpon Ait) growth. Can. J. Plant Sci.64: 339-348.Eaton, G.W. 1971a. Effects of NPK fertilizers on the growth andcomposition of vines in a young cranberry bog. J. Amer. Soc.Hort. Sci. 96: 426-429.Eaton, G.W. 1971b. Effect of N, P, and K fertilizer applications oncranberry leaf nutrient composition, fruit colour and yield ina mature bog. J. Amer. Soc. Hort. Sci. 96: 430-433.Eaton, G.W. and E.A. MacPherson. 1978. Morphological components ofyield in cranberry. Hort. Res. 17: 73-82.Eaton, G.W. and C.N. Meehan. 1971. Effects of leaf position andsampling date on leaf composition of eleven highbush blueberrycultivars. J. Amer. Soc. Hort. Sci. 96: 378-380.Eaton, G.W. and C.N. Meehan. 1973. Effects of N, P, and Kfertilizer on leaf composition, yield, and fruit quality ofbearing 'Ben Lear' cranberries. J. Amer. Soc. Hort. Sci.98: 89-93.Eck, P. 1990. The American Cranberry. Rutgers State UniversityPress, 420 pp.106Everitt, J.H. and C.J. Deloach. 1990. Remote sensing of Chinesetamarisk (Tamarix chinensis) and associated vegetation. WeedSci. 38: 273-278.Everitt, J.H., S.J. Ingle, H.W. Gausman, and H.S. Mayeux, Jr. 1984.Detection of false broomweed (Ericameria austrotexana) byaerial photography. Weed Sci. 32: 621-624.Everitt, J.H., R.D. Petit and M.A. Alaniz. 1987. Remote sensing ofbroom snakeweed (Gutierrezia sarothrae) and spiny aster (Asterspinosus). Weed Sci. 35: 295-302.Fisher, R.A. 1951. Soil data on nutrition in Washington bogs.Cranberries 16: 8-10.Foy, C.D., R.L Chaney and M.C. White. 1978. The physiology of metaltoxicity in plants. Ann. Rev. Plant Physiol. 29: 511-566.Galletta, G.J. and D.G. Himelrick. 1990. Factors that influencesmall fruit production. pages 14-83. In G.J. Galletta and D.G.Himelrick (Eds) Small fruit management. Prentice Hall,Anglwood Cliffs, N.J. 602 pages.Gausman, H.W. 1974. Leaf reflectance of near infrared.Photogrammetric Engineering, 40: 183-191.Gausman, H.W. 1977. Reflectance of leaf components. Remote Sensingof the Environment. 6: 1-9.Gausman, H.W. and W.A. Allen. 1973. Optical parameters of leaves of30 plant species. Plant Physiol. 52: 27-62.Gausman, 	 H.W., 	 D.E. 	 Escobar and R.R. 	 Rodriquez. 	 1973.Discriminating among plant nutrient deficiencies withreflectance measurements. Pages 13-27 In Proceedings of theFourth Biennial workshop on Aerial Colour Photography in thePlant Sciences, July 10-12, University of Maine, Orono, Maine.Grant, Lois. 1987. Diffuse and specular characteristics of leafreflectance. Remote Sens. Environ. 22: 309-322.Gronwald, R.F. and W. Haines. 1982. Cranberry frost protectionusing sprinklers. Pages 284-289 In Proceedings of thespecialty conference on environmentaly sound water and soilmanagement: Orlando, Fla., July 20-23, New York: AmericanSociety of Civil Engineering.Guyot, G. 1990. Optical properties of vegetation canopies. pages19-43. In M.D Steven and J.A. Clark (Eds) Application ofremote sensing in agriculture. Butterworths, London, England.427 pages.107Hall, I.V. 1971. Cranberry growth as related to water levels in thesoil. Can. J. Plant Sci. 51: 237-238.Hall, I.V., R.A. Murray, C.R. Blatt, C.L. Lockhart, R.W. Delbridge,G.W. Wood and C.J.S. Fox. 1981. Growing cranberries. Agric.Canada Public. 1282/E, 29 pages.Hare, F.K., and M.K. Thomas. 1979. Climate Canada. 2nd Edition.John Wiley and Sons Canada Limited, Toronto.Hicks, J.L, I.V. Hall, and F.F. Forsyth. 1968. Growth of cranberryplants in pure stands and in weedy areas under Nova Scotianconditions. Hort. Res. 8: 104-112.Horler, D.N.H., J. Barber, and A.R. Barringer. 1980. Effects ofheavy metals on the absorbance and reflectance spectra ofplants. Int. J. Remote Sens. 1: 121-136.Jensen, J.R. 1986. Introductory digital image processing: A remotesensing perspective. Prentice-Hall, Englewood Cliffs, NewJersey. 379 pagesJensen, J.R. 1983. Biophysical remote sensing. Ann. Assoc. Amer.Geog. 73: 111-1132.Knipling, E.B. 1970. Physical and physiological basis for thereflectance of visible and near-infrared radiation fromvegetation. Remote Sens. Environ. 1: 155-159.Lees, D.H., and F.J. Francis. 1972. Standardization of pigmentanalyses in cranberries. HortScience 7: 83-84.Lillesand, T.M. and R.W. Kiefer. 1979. Remote sensing and imageinterpretation. John Wiley and Sons, 612 pages.Luttmerding, H.A. 1980a. Soils of the Langley-Vancouver map area.Vol. 1. Soil map mosaics. RAB Bulletin 18, Report No. 15, B.C.Soil Survey. British Columbia Ministry of Environment,Victoria.Luttmerding, H.A. 1980b. Soils of the Langley-Vancouver map area.Vol. 3. Description of the soils. RAB Bulletin 18, Report No.15, B.C. Soil Survey. British Columbia Ministry ofEnvironment, Victoria.Medappa, K.C. and M.N. Dana. 1970. Tolerance of cranberry plants tomanganese, iron and aluminum. J. Amer. Soc. Hort. Sci. 95:107-110.108Menges, R.M., P.R. Nixon, and A.J. Richardson. 1985. Lightreflectance and remote sensing of weeds in agronomic andhorticultural crops. Weed Sci. 33: 569-581.Milton, N.M., B.A. Eiswoerth, and C.M. Ager. 1991. Effect ofphosphorous deficiency on spectral reflectance and morphologyof soybean plants. Remote Sensing of the Environment, 36: 121-127.Murtha, P.A. 1982. Detection and analysis of vegetation stress. InC.J. Johannsen and J.L. Sanders (Eds) Remote sensing forresource management. Soil Concervation Society of America,Ankley, Iowa.Myers, V.I. 1983. Remote sensing applications in agriculture. pages2111-2228. In R.N. Colwell; J.E. Estes; and G.A. Thorley (Eds)Manual of remote sensing; Second Ed, Volume 2. AmericanSociety of Photogrammetry, Falls Church, Virginia. 2440 pages.Parkinson, J.A. and S.E. Allen. 1975. Wet oxidation proceduresuitable for the determination of nitrogen and mineralnutrients in biological material. Commun. in Soil Sci. andPlant Anal. 6: 1-11.Pitty, A.F. 1978. Geography and soil properties. Methuen and CoLtd., London, England. 287 pages.Richardson, A.J. 1984. Interception of light by a plant canopy.Tenth International Symposium on Machine Processing ofRemotely Sensed Data. June 12-14., Purdue University, WestLafayette, Indiana. 378-382.Roberts, R.H, and B.E. Struckmeyer. 1942. Growth and fruiting ofthe cranberry. Proc. Amer. Soc. Hort. Sci. 40: 373-379.Robinove, C.J. 1981. The logic of mutispectral classification andmapping of land. Remote Sen. Environ. 2: 231-244.Sabins, F. F. 1978. Remote sensing: Principles and interpretations.W.H. Freeman and Company. 426 pp.Sawyer, W.H., Jr. 1931. Stomatal apparatus of the cultivatedcranberry, Vaccinium macrocarpon. Amer. J. Bot. 19: 508-513.Shaw, G., J.R. Leake, A.J.M. Baker and D.J. Read. 1990. The biologyof mycorrhiza in the Ericaceae. XVII. The role of mycorrhizalinfection in the regulation of iron uptake by ericaceousplants. New Phytol. 115: 251-258.Skroch, W.A. and M.N. Dana. 1965. Sources of weed infestation incranberry fields. Weeds 13: 263-267.109Stieber, T. and L.A. Peterson. 1987. Contribution of endogenousnitrogen toward continuing growth in a cranberry vine.HortScience 22: 463-464.Straker, C.J. and D.T Mitchell. 1986. The activity andcharacterization of acid phosphatases in endomycorrhizal fungiof the Ericaceae. New Phytol. 104: 243-256.Stribley, D.P. and D.J. Reed. 1980. The biology of mycorrhiza inthe Ericaceae. VII. The relationship between mycorrhizalinfection and the capacity to utilize simple and complexorganic nitrogen sources. New Phytol. 86: 365-371.Thoma, N.N. 1988. The use of remote sensing in an integratedapproach for stress detection on the cultivated cranberry.MSc. thesis, Oregon State University.Thomas, J.R., V.I. Myers, M.D. Heilman and C.L. Wiegand. 1966.Factors affecting light relectance of cotton. Pages 305-312 InProceedings of the Fourth Symposium on Remote Sensing of theEnvironment, Ann Arbor, Michigan.Thomas, J.R. and G.F. Oerther. 1972. Estimating nitrogen content ofsweet pepper leaves by reflectance measurements. Agron. J. 64:11-13.Tisdale, S.L. and W.L. Nelson. 1975. Soil fertility andfertilizers, Third Ed. MacMillan Publishing Co., Inc. NewYork, New York. 694 pages.Townsend, L.R., I. V. Hall and L.E. Aalders. 1968. Chemicalcomposition of rhizomes and associated leaves of the lowbushblueberry. Proc. Amer. Soc. Hort. Sci. 93: 248-253.Tucker, C.J. and M.W. Garratt. 1977. Leaf optical systems modeledas a stochastic process. Applied Optics 16: 635-642.Wilding, L.P and L.R.Drees, 1983. Spatial variability and pedology.Pages 83-116 In L.P. Wilding, N.E. Smeck and G.F. Hall (Eds)Pedogenesis and soil taxonomy I. concepts and interactions.Developments in soil science II A. Elsevier Publishing Co.,Wageningen.Wooley, J.T. 1971. Reflectance and transmittance of light byleaves. Plant Physiol. 47: 656-662.Zar, Jerrold H. 1984. Biophysical analysis, 2nd edition. Prentice-Hall, Inc., Englwood Cliffs, New Jersey. 718 pages.APPENDIX A:Cranberry tissue critical nutrient ranges, B.C. conditions.Element Low Sufficient High ExcessNitrogen (N) 0-0.80 0.81-0.95 0.95-1.40 >2.00Phosphorus (P) 0-0.06 0.07-0.12 0.13-0.28 >0.50Potassium (K) 0-0.20 0.21-0.25 0.26-0.45 >1.00Calcium (Ca) 0-0.30 0.31-0.60 0.61-1.60 >3.00Magnesium (Mg) 0-0.08 0.09-0.12 0.13-0.23 >0.50Sulphur (S) 0-0.06 0.07-0.10 0.11-0.15 >0.50Boron (B) 0-15 16-25 26-60 >80Iron (Fe) 0-50 51-60 61-250 >300Manganese (Mn) 0-30 31-50 51-250 >300Zinc	 (Zn) 0-10 11-15 16-55 >100Copper (Cu) 0-5 6-10 11-25 >50N, P, K, Ca, Mg and S as % ;B, Fe, Mn, Zn, Cu as ppmSource: Dr. J. Davenport; Cranberry Mineral Nutrition WorkingGroup.110111APPENDIX B: Rainfall and temperature data for the Fort Langley andPitt Meadows sites, 1990 and 1991.Fort Langley:Year Month Precip.(mm)Temp. 	 °CMean Max. Min.1990 March 122.8 7.3 19.0 -2.5April 103.0 11.2 28.0 0.0May 84.2 13.2 27.0 5.5June 120.0 16.0 31.0 8.5July 25.2 20.1 34.5 10.5Aug. 73.2 19.4 34.0 7.5Sept. 44.8 17.2 30.0 4.01991 March 107.2 5.6 19.9 -2.0April 178.4 9.8 24.0 0.5May 123.0 12.6 24.0 3.0June 104.0 14.6 28.5 5.5July 43.2 18.7 35.0 9.5Aug. 202.8 18.8 30.5 9.0Sept. 15.0 16.2 28.5 6.0Pitt Meadows:Year Month Precip.(mm)Temp. 	 °CMean Max. Min.1990 March 131.3 7.0 18.5 -4.5April 108.8 10.7 27.0 -0.5May 66.1 12.6 25.5 4.0June 123.4 15.3 30.0 8.0July 18.0 19.7 34.0 10.0Aug. 62.0 19.1 35.5 6.5Sept. 37.2 16.3 31.5 4.01991 March 128.6 5.4 20.0 -4.0April 159.7 9.2 23.0 -1.5May 132.9 12.0 24.0 2.5June 88.3 14.3 30.0 4.0July 37.0 18.3 35.0 8.0Aug. 216.0 18.5 30.5 8.0Sept. 19.3 16.0 29.0 6.5Source: B.C. Ministry of Environment; Atmospheric Services Branch.APPENDIX C: Bog management for the 1990 and 1991112growing seasonsFort Langley: Bogs 100 and 200.1990Fertilizer Rate Date Growth stagemix:0-45-021-0-0-24-1 250 kg/ha (bog 100) May 1 popcorn0-0-22-22-17mix:(bud-break)0-45-0 200 kg/ha (bog 200) May 8 popcorn0-0-22-22-17 (bud-break)25-10-10 100 kg/ha June 5 hookgypsum 100 kg/ha June 28 bloom0-23-25 100 kg/ha June 29 bloomcalcium-boron 1.9 L/ac July 2 late bloom0-23-25 100 kg/ha July 16 fruit set46-0-0 20 kg/ha (bog 100)10 kg/ha 	 (bog 200)July 31 fruit set/developmentpotash 150 kg/ha (bog 100) Nov 20 dormant100 kg/ha	 (bog 200)Pesticide Rate Date Reason appliedguthion 1.7 L/ac May 8 firewormcasoron 75 kg/ha (bog 100) June 5 weeds40 kg/ha (bog 200)diazonon 1.1 L/ha (bog 100) June 30 fireworm1.1 Ljha (bog 200) July 2Fort Langley:1991Bogs 100 and 200.113Fertilizer Rate Date Growth stageiron sulphate 100 kg/ac April 19 early popcornCopperoxichloride1.6 kg/ac(foliar spray)April 19 early popcorn0-22-17 200 kg/ha April 26 early popcorn+2Mg+9S0-0-22-17(Mg) 200 kg/ha June 14 hookcalcium-boron 2.4 L/ac July 10 late bloomurea 5 kg/ha July 10 late bloom0-23-25 200 kg/ha July 19 fruit seturea 5 kg/ha July 30 fruit set/development6-25-25 100 kg/ha Aug 10 fruit set/developmentPesticide Rate Date Reason appliedcassaron 100 kg/ha (bog 100) (April) weeds60 kg/ha 	 (bog 200)diazonon 1.4 L/ac May 8 firewormdiazonon 1.4 L/ac 	 (bog 100) June 14 firewormfalpan 4.1 kg/ac July 8 fungaldiseasefalpan 4.1 kg/ac July 18 fungaldiseasediazonon 1.4 L/ac July 30 firewormSanding 	 4 cm applied by June 25hydraulic hose (bog 100)114Pitt Meadows: Bogs 300 and 400.1990FertilizerCopper sulphate0-0-30copper: (5%)magnesium: (3%)calcium-boron:(5% Ca; 0.5% B)15-10-106-24-244-25-35carbohydrate:molasses0-0-60Rate 	 Date 	 Growth stage2 kg/ha 	 April 25 	 early popcorn2 gal/ac 	 June 30 	 hook/early bloom0.3 L/ac 	 June 24 	 bloom0.5 L/ac 	 June 24 	 bloom3 L/ac 	 June 24 	 bloom45 kg/ha (bog 300) June 28 	 bloom100 kg/ha 	 July 5 	 late bloom5 kg/ha 	 July 15 	 fruit set2 L/ha 	 July 15 	 fruit set120 kg/ha 	 July 25 	 fruit set/developmentPesticide 	 Rate 	 Date 	 Reason appliedcasoron 	 110 kg/ha 	 March 25- 	 weedsApril 1guthion 	 1.7 L/ac 	 (May) 	 firewormglyphosate 	 applied as a wipe-on treatment throughout thegrowing season 115Pitt Meadows:1991Bogs 300 and 400.Fertilizer Rate Date Growth stage2-16-36 5 kg/ha May 13 popcorn5-15-30 150 kg/ha July 12 late bloom5-20-30 5 kg/ha II IIsolubor 1 kg/ha II IIcarbohydrates: 5 kg/ha 11 11(molasses)8-32-16 	 300 kg/ha 	 July 19 	 earlyfruitsetsolubor 	 1 kg/ha 	 n 	 1121-0-0-24 	 70 kg/ha 	 July 28 	 allfruitset5-20-30 	 5 kg/ha+humic acidsolubor 	 1 kg/ha 	 II 	 16-24-24 	 200 kg/ha	 Aug 8 	 fruitenlargement2-18-36 	 5 kg/hasolubor 	 1 kg/ha0-0-60 	 150 kg/ha2-18-36 	 5 kg/hasolubor 	 1 kg/ha11 	 1(mid-late Aug) 	 budset11 	 1Pesticide 	 Rate 	 Date 	 Reason applied1.7 L/ac 	 May 14 	 fireworm2 L/ac 	 Aug 3 	 firewormapplied as a wipe-on treatment throughout thegrowing season guthionsevinglyphosate116APPENDIX D: Weed species identified in the four study bogs.Common name 	 Scientific name Family 	 ClassificationGrasslike:Sweet 	 Anthoxanthum 	 Grass 	 Herbaceousvernalgrass 	 odoratumGrey 	 Carex 	 Sedge 	 Herbaceoussedge 	 canescens Slough 	 Carex 	 Sedge 	 Herbaceoussedge	 obnupta Chamisso's 	 Eriphorum 	 Sedge 	 Herbaceouscotton-grass 	 chamissonis Common 	 Juncus 	 Rush 	 Herbaceousrush 	 effusus Broadleaf:Sheep 	 Rumex	 Buckwheat 	 Herbaceoussorrel 	 acetosellaSpreading 	 Rorippa 	 Mustard 	 Herbaceousyellow cress 	 curvisiliqua Watson's 	 Epilobium 	 Evening Primrose Herbaceouswillow-herb	watsonii Bog	 Ledum	 Heath 	 Woodylabrador-tea 	 groenlandicumBog 	 Vaccinium 	 Heath 	 Woodyblueberry 	 uliginosumPacific 	 Rubus 	 Rose 	 Woodyblackberry 	 ursinus 117APPENDIX E: Soil and foliar digest methodsSoil Digest: 1990;Nitric-Perchloic Digest- Add 10-20 ml. HNO 3 to 1.00 g soil in digestion vessel- Place vessel on hotplate @ 150 C., with coverglass- Allow to reflux for ca. 30 minutes- Remove coverglass and allow brown fumes to clear, reduce volumeto ca. 5 ml- If solution is heavily coloured or contains oil or grease residueadd 10 ml. HNO3 and continue heating uncovered- Once a lightly coloured sol'n is obtained add 5 mis. HNO 3 andenough HC1O4 to give a final concentration of 2% plus anadditional 2 ml. to be consumed in the digest- Continue heating, uncovered , until white HC10 4 fumes areproduced- Cover, raise temp. to 250 C. and reflux until the digestate iscolourless- Remove from heat, add ca. 20 ml. D.I. water then enough HC1 tomake the final concentration 2%- Volumize with D.I.Soil Digest: 1991;Reverse Aqua Regia- Samples digested as recieved in teflon digestion vessels withreverse aqua regia (3 parts HNO3 to 1 part HC1) in a microwave oven- Digests diluted to 50.0 ml with D.I.Foliar Digest: 1990;Ocean Spray Method- 0.1800-0.2500 g tissue weighed into 60 ml teflon digestionvessels- Concentrated H2SO4 (5m1) was added to sample, vessels were cappedand tightened to 180 inch lbs with a torque wrench- Nine samples were concurrently digested in a microwave oven atfull power for 3 minutes- Vessels were cooled in a water bath for 5-10 minutes, uncappedand cleared with 2m1 50% H202- Samples were transferred to 50 ml volumetrics with distilledwater and brought to volumeAPPENDIX F: Results of ANOVAFoliar Data.foliar-NSource D.F. S.S. Mean SQ Error F ProbSite 1 0.600 0.600 Bog/Site 2.05 0.29Bog/site 2 0.585 0.292 M*B/S 1.98 0.25Month 2 3.08 1.542 M*B/S 10.45 0.026M*S 2 1.39 0.693 M*B/S 4.70 0.089M*B/S 4 0.59 0.148 6.80 0.4E-4Error 228 4.94 0.0217Total 229 11.19Bog: 	 100 	 200 	 300 	 400	n: 	 60 	 60 	 60 	 60	Mean: 	 1.06 	 0.95 	 0.95 	 0.87Month: 	 June 	 July 	 Sept.	n: 	 80 	 80 	 80	Mean: 	 1.11 	 0.89 	 0.86Linear: Q2 2.568; F-Value 17.41; Prob 0.014foliar -PSource D.F. S.S. Mean SQ Error F ProbSite 1 0.101 0.1009 Bog/Site 2.68 0.243Bog/site 2 0.075 0.0376 M*B/S 7.29 0.046Month 2 0.041 0.0203 M*B/S 3.94 0.113M*S 2 0.104 0.0522 M*B/S 10.13 0.027M*B/S 4 0.021 0.0052 10.92 0.4E-7Error 228 0.108 0.0005Total 229 0.449Bog: 	 100 	 200 	 300 	 400	n: 	 60 	 60 	 60 	 60	Mean: 	 0.13 	 0.18 	 0.12 	 0.11Month: 	 June 	 July 	 Sept.	n: 	 80 	 80 	 80	Mean:	 0.13 	 0.12 	 0.15Dev: Q2 0.0304; F-Value 5.90; Prob 0.0721.118foliar -KSource D.F. S.S. Mean SQ Error F ProbSite 1 0.271 0.2714 Bog/Site 3.13 0.22Bog/site 2 0.174 0.0868 M*B/S 2.77 0.18Month 2 0.612 0.3058 M*B/S 9.75 0.03M*S 2 0.215 0.1072 M*B/S 3.42 0.14M*B/S 4 0.126 0.0314 3.46 0.009Error 228 2.065 0.0091Total 229 3.461Bog: 100 200 300 400n: 60 60 60 60Mean: 0.63 0.65 0.74 0.67Month: June July Sept.	n: 	 80 	 80 	 80	Mean: 	 0.71 	 0.70 	 0.60Linear: Q2 0.507; F-Value 16.2; Prob 0.016.foliar-MgSource D.F. S.S. Mean SQ Error F ProbSite 1 0 0 Bog/Site 0 naBog/site 2 0.0817 0.0409 M*B/S 10.33 0.026Month 2 0.4035 0.2017 M*B/S 51.00 0.001M*S 2 0.0248 0.0124 M*B/S 3.13 0.152M*B/S 4 0.0158 0.0040 3.52 0.008Error 228 0.2564 0.0011Total 229 0.7822Bog: 	 100 	 200 	 300 	 400	n: 	 60	 60 	 60 	 60	Mean: 	 0.18 	 0.24 	 0.21 	 0.21Month: 	 June 	 July 	 Sept.	n: 	 80 	 80	 80	Mean: 	 0.16 	 0.20 	 0.26Linear: Q 2 0.398; F-Value 100.6; Prob 0.00056.119foliar-CaSource D.F. S.S. Mean SQ Error F ProbSite 1 3.396 3.396 Bog/Site 4.52 0.17Bog/site 2 1.502 0.751 M*B/S 8.22 0.038Month 2 3.467 1.734 M*B/S 18.99 0.009M*S 2 0.042 0.021 M*B/S 0.23 0.805M*B/S 4 0.365 0.009 9.85 0.2E-6Error 228 2.115Total 229 10.89Bog: 	 100 	 200 	 300 	 400	n: 	 60 	 60 	 60 	 60	Mean: 	 0.65 	 0.87 	 0.53 	 0.52Month: 	 June 	 July 	 Sept.	n: 	 80 	 80 	 80	Mean: 	 0.54 	 0.56 	 0.81Linear: Q2 2.56; F-Value 28.1; Prob 0.006.foliar-FeSource D.F. S.S. Mean SQ Error F ProbSite 1 98881 98881 Bog/Site 9.38 0.092Bog/site 2 21088 10544 M*B/S 1.85 0.27Month 2 0.2E+6 93708 M*B/S 16.41 0.012M*S 2 55984 27992 M*B/S 4.90 0.084M*B/S 4 22849 5712 5.89 0.2E-3Error 228 0.2E+6 970Total 229 0.6E+6Bog: 	 100 	 200 	 300 	 400	n: 	 60 	 60 	 60 	 60	Mean: 	 69.3 	 89.6 	 128.5 	 111.5Month: 	 June 	 July 	 Sept.	n: 	 80 	 80 	 80	Mean: 	 69.8 	 92.2 	 137.0Linear: Q2 180721; F-Value 31.64; Prob 0.005.120foliar -MnSource D.F. S.S. Mean SQ Error F ProbSite 1 82648 82648 Bog/Site 4.85 0.16Bog/site 2 34105 17053 M*B/S 7.66 0.043Month 2 23974 11987 M*B/S 5.39 0.073M*S 2 17635 8817 M*B/S 3.96 0.113M*B/S 4 8903 2226 2.66 0.033Error 228 0.2E+6 836Total 229 0.4E+6Bog: 	 100 	 200 	 300 	 400	n: 	 60 	 60 	 60 	 60	Mean: 	 73.8 	 73.5	93.9 	 127.6Month: 	 June 	 July 	 Sept.	n: 	 80 	 80 	 80	Mean: 	 79.1 	 94.1 	 103.4Linear: Q 2 23544; F-Value 10.6; Prob 0.031.121Remote Sensing Data.Pixel values for the Green sensitive dye layerSource D.F. S.S. Mean SQ Error F ProbSite 1 4736 4736 Bog/Site 1.8 0.31Bog/site 2 5217 2609 M*B/S 1.5 0.33Month 2 38288 19144 M*B/S 10.8 0.0024M*S 2 4173 2087 M*B/S 1.2 0.396M*B/S 4 7093 1773 35.4 0.14E-3Error 228 11416 50Total 229 70925Bog: 	 100 	 200 	 300 	 400	n: 	 60 	 60 	 60 	 60	Mean: 	 155.8 	 143.6 	 143.3 	 138.3Month: 	 June 	 July 	 Sept.	n: 	 80 	 80 	 80	Mean: 	 155.3 	 153.3 	 127.4Linear: Q2 30533.7; F-Value 17.2; Prob 0.0142.[NIR/R] band ratio.Source D.F. S.S. Mean SQ Error F ProbSite 1 0.071 0.0714 Bog/Site 0.87 0.44Bog/site 2 0.163 0.0817 M*B/S 1.50 0.33Month 2 0.402 0.201 M*B/S 3.70 0.09M*S 2 0.283 0.142 M*B/S 2.61 0.19M*B/S 4 0.218 0.054 10.9 0.004Error 228 1.138 0.005Total 229 2.276Bog: 	 100 	 200 	 300 	 400	n: 	 60 	 60 	 60 	 60	Mean:	 1.260 	 1.318 	 1.300 	 1.346Month: 	 June 	 July 	 Sept.	n: 	 80 	 80 	 80	Mean: 	 1.264 	 1.293 	 1.361Linear: Q 2 0.381; F-Value 7.00; Prob 0.052.122123APPENDIX F: Data filesSoil Data: 1990	plot 	 Ca 	 K 	 Mg Fe Mn Cu Zn Al 	 ratios	no. 	 pH O.M.     Ag/g 	 Fe:Al Fe:Mn101 3.31 80.70 0.239 0.078 0.117 1107 4 14 21 2163 0.512 275102 3.34 81.58 0.447 0.063 0.098 1590 31 11 39 1809 0.879 52103 3.47 80.30 0.372 0.086 0.077 1098 11 5 21 1457 0.753 100104 3.51 80.06 0.353 0.074 0.109 1505 8 16 29 1936 0.777 188105 3.30 79.98 0.343 0.075 0.110 1195 10 17 34 1972 0.606 120106 3.37 79.35 0.371 0.086 0.111 1249 9 5 26 2023 0.617 132107 3.40 81.39 0.266 0.070 0.086 701 5 5 15 1632 0.429 140108 3.55 83.53 0.354 0.078 0.108 900 8 6 20 1980 0.455 113109 3.33 79.71 0.289 0.092 0.113 1004 8 13 24 2038 0.493 125110 4.03 81.37 0.397 0.110 0.117 1394 11 15 29 2092 0.667 127111 3.36 80.96 0.334 0.072 0.092 1204 9 4 26 1705 0.706 133112 3.25 81.94 0.327 0.081 0.117 1196 11 6 27 2124 0.563 109113 3.43 81.95 0.419 0.070 0.119 1396 9 6 28 2084 0.670 156114 3.35 80.89 0.418 0.089 0.111 1196 17 18 33 2014 0.594 71115 3.35 80.28 0.336 0.087 0.103 1049 9 7 24 1863 0.563 111116 3.51 73.25 0.338 0.089 0.110 4738 39 13 43 5746 0.825 121117 3.45 76.45 0.328 0.107 0.249 3293 25 8 26 4371 0.753 132118 3.48 82.89 0.390 0.059 0.107 995 9 24 19 1940 0.513 111119 3.51 79.39 0.402 0.073 0.120 1157 13 7 29 2064 0.561 92120 3.47 78.55 0.313 0.080 0.098 1096 5 4 43 1693 0.647 220X 3.44 80.23 0.352 0.081 0.114 1453 13 10 28 2235 0.629 131S.D. 0.16 2.20 0.051 0.013 0.033 906 9 6 7 982 0.121 48%CV 5 3 15 16 29 62 70 54 26 44 19 37201 3.48 82.38 0.303 0.069 0.152 849 5 6 18 2594 0.327 180202 3.45 80.74 0.396 0.093 0.076 1106 10 6 24 1377 0.803 110203 3.36 82.84 0.301 0.096 0.112 1095 9 5 19 1920 0.570 122204 3.53 82.31 0.327 0.089 0.079 900 7 2 16 1420 0.634 129205 3.41 80.08 0.267 0.089 0.096 502 4 2 13 1677 0.299 125206 3.44 75.44 0.328 0.093 0.082 706 9 3 19 1502 0.470 78207 3.45 78.70 0.321 0.072 0.059 504 3 1 12 1118 0.450 167208 3.51 77.57 0.354 0.105 0.094 857 6 9 19 1668 0.514 142209 3.43 79.11 0.339 0.070 0.088 803 9 10 18 1596 0.503 89210 3.39 80.15 0.280 0.063 0.074 598 2 13 15 1296 0.462 300211 3.40 79.11 0.297 0.050 0.059 704 3 11 11 1107 0.636 233212 3.37 81.88 0.259 0.052 0.077 804 2 1 12 1437 0.559 400213 3.35 78.39 0.394 0.054 0.075 706 8 1 17 1403 0.504 88214 3.37 80.85 0.258 0.050 0.081 606 6 1 13 1444 0.420 100215 3.36 82.03 0.236 0.050 0.084 704 7 2 14 1548 0.455 100216 3.42 82.17 0.320 0.050 0.104 1000 17 6 32 1870 0.535 59217 3.40 78.52 0.394 0.080 0.087 1300 15 7 53 1660 0.783 87218 3.38 81.10 0.299 0.058 0.057 997 9 6 17 1446 0.690 111219 3.35 79.83 0.382 0.059 0.063 746 12 7 19 1224 0.610 62220 3.49 79.77 0.298 0.054 0.055 499 7 6 20 1437 0.347 71X 3.42 80.15 0.318 0.070 0.083 799 8 5 19 1537 0.528 138S.D. 0.05 1.85 0.046 0.018 0.022 213 4 4 9 320 0.134 83%CV 2 2 15 26 27 27 52 67 47 21 25 61124plotno. pH%O.M. 	Ca K%Mg Fe Mn Cu ZnAg/g  Al ratiosFe:Al Fe:Mn301 3.71 88.01 0.229 0.071 0.051 6439 10 10 10 3924 1.641 640302 3.34 87.03 0.197 0.100 0.052 4200 30 11 20 4040 1.040 140303 3.33 85.60 0.185 0.059 0.050 2808 8 8 17 2813 0.998 350304 3.42 84.25 0.245 0.107 0.086 4217 22 10 26 5060 0.833 191305 3.34 84.80 0.144 0.064 0.050 3386 9 9 8 2998 1.130 378306 3.57 79.06 0.350 0.108 0.089 8135 31 12 17 4464 1.822 265307 3.55 86.38 0.151 0.095 0.050 3618 15 8 12 2995 1.208 240308 3.35 84.92 0.164 0.090 0.050 3704 8 11 13 3063 1.209 462309 3.29 86.31 0.169 0.076 0.050 3920 11 8 11 3196 1.226 355310 3.36 85.90 0.183 0.115 0.050 2803 13 10 16 3043 0.921 215311 3.43 86.28 0.211 0.097 0.050 2716 10 11 19 3008 0.903 270312 3.47 86.81 0.179 0.099 0.050 3190 9 10 15 2463 1.296 356313 3.31 82.54 0.197 0.097 0.050 2480 13 9 13 2331 1.064 192314 3.42 84.38 0.170 0.090 0.050 2305 6 9 13 1954 1.179 383315 3.55 85.57 0.128 0.055 0.050 1608 5 7 12 2181 0.737 320316 3.38 88.96 0.151 0.065 0.050 4096 10 8 11 2818 1.453 410317 3.44 86.72 0.196 0.090 0.050 3393 16 9 20 2156 1.574 212318 3.31 89.56 0.179 0.100 0.050 2087 5 9 13 2187 0.955 420319 3.46 84.80 0.347 0.134 0.073 2605 28 14 30 2084 1.250 93320 3.59 84.70 0.261 0.065 0.071 3030 15 10 18 1657 1.829 200X 3.43 85.63 0.202 0.089 0.056 3537 14 10 16 2922 1.213 305S.D. 0.11 2.20 0.058 0.020 0.012 1457 8 2 5 861 0.305 125%CV 3 3 29 23 22 41 57 17 34 29 25 41401 3.58 69.52 0.402 0.120 0.151 6461 87 23 31 6113 1.057 74402 3.52 86.52 0.271 0.089 0.067 1984 20 16 22 1905 1.042 100403 3.53 85.42 0.275 0.097 0.068 3366 21 10 22 2678 1.257 160404 3.61 83.12 0.229 0.101 0.060 2880 9 7 13 2224 1.295 322405 3.81 85.66 0.154 0.069 0.050 1688 10 7 15 2145 0.787 170406 3.50 82.76 0.268 0.093 0.093 5321 27 12 29 4237 1.256 196407 3.49 85.64 0.239 0.069 0.067 3119 14 14 18 2173 1.435 221408 3.69 87.07 0.224 0.073 0.063 3085 10 11 12 3204 0.963 310409 3.94 90.19 0.200 0.080 0.053 2712 9 9 12 2271 1.194 300410 3.39 87.44 0.202 0.052 0.050 4108 16 10 12 1884 2.181 256411 3.55 83.03 0.274 0.091 0.084 5231 20 17 37 3471 1.507 260412 3.54 85.43 0.333 0.079 0.065 2792 17 14 35 1974 1.414 165413 3.30 85.22 0.183 0.071 0.050 5210 13 14 20 2525 2.063 400414 3.43 86.80 0.208 0.077 0.050 4418 18 8 14 2289 1.930 244415 3.40 87.34 0.247 0.075 0.061 2312 5 10 16 2121 1.090 460416 3.58 87.87 0.284 0.106 0.083 5706 18 15 21 2753 2.073 317417 3.54 91.19 0.195 0.062 0.050 3904 10 14 12 2202 1.773 390418 3.33 85.92 0.217 0.082 0.062 5305 18 19 26 3073 1.726 294419 3.49 83.48 0.258 0.086 0.099 4391 24 16 18 3603 1.219 183420 3.51 86.72 0.266 0.086 0.059 4526 13 12 19 2362 1.916 350X 3.54 85.32 0.247 0.083 0.069 3926 19 13 20 2760 1.459 259S.D. 0.15 4.20 0.054 0.016 0.024 1315 17 4 8 987 0.405 99%CV 4 5 22 19 34 33 88 31 38 36 28 38Soil Data:plotno. 	 pH 	1991Ca K%Mg Fe Mn Cu ZnAg/g  Al ratiosFe:Al Fe:Mn101 4.15 0.210 0.074 0.079 1531 18 9 42 1800 0.851 87102 3.97 0.233 0.076 0.081 1465 20 10 32 1709 0.857 75103 4.02 0.224 0.085 0.085 1426 24 13 33 1740 0.820 60104 4.10 0.208 0.089 0.066 1625 21 12 40 1864 0.872 77105 3.73 0.164 0.080 0.049 1295 19 9 43 1653 0.783 68106 4.15 0.240 0.082 0.084 1427 20 10 32 1870 0.763 73107 4.19 0.240 0.085 0.079 1288 11 12 29 2204 0.584 113108 4.20 0.226 0.082 0.075 1346 13 10 30 2385 0.565 108109 4.12 0.173 0.077 0.073 974 8 8 23 1540 0.633 117110 4.16 0.206 0.097 0.087 1557 17 12 37 1915 0.813 92111 4.15 0.208 0.058 0.082 1079 15 8 23 1914 0.564 72112 4.06 0.151 0.082 0.064 1065 7 8 23 1889 0.564 144113 4.20 0.236 0.097 0.092 1581 19 12 31 2806 0.563 84114 3.92 0.158 0.081 0.069 1204 17 9 27 1750 0.688 72115 4.08 0.178 0.080 0.069 1351 15 10 30 1931 0.700 88116 4.36 0.177 0.104 0.128 3299 41 13 69 5279 0.625 81117 4.10 0.224 0.087 0.089 1623 26 13 38 2839 0.572 62118 4.12 0.217 0.095 0.085 1793 20 11 42 2271 0.789 90119 4.20 0.192 0.094 0.080 1651 22 11 37 2179 0.758 76120 4.16 0.161 0.084 0.087 1250 11 9 43 1788 0.699 118X 4.11 0.201 0.084 0.080 1491 18 10 35 2166 0.703 88S.D. 0.13 0.029 0.010 0.015 465 7 2 10 792 0.110 21%CV 3 14 12 19 31 39 17 29 37 16 24201 4.14 0.198 0.094 0.076 1290 14 9 39 1548 0.833 93202 4.07 0.169 0.072 0.073 943 11 8 27 1387 0.680 83203 3.97 0.128 0.085 0.077 1060 9 7 21 1753 0.604 122204 3.98 0.143 0.065 0.060 900 9 7 19 1373 0.655 106205 3.88 0.136 0.092 0.067 791 15 7 23 1536 0.515 53206 4.05 0.190 0.099 0.080 1081 13 8 25 1720 0.628 82207 4.15 0.199 0.091 0.080 1202 13 8 27 1510 0.796 89208 4.05 0.123 0.083 0.055 765 11 6 16 1491 0.513 67209 3.87 0.158 0.076 0.064 821 10 7 19 1350 0.608 82210 3.98 0.128 0.068 0.054 728 6 5 15 1592 0.457 125211 4.03 0.188 0.077 0.075 1010 20 9 25 1990 0.507 50212 3.95 0.166 0.085 0.065 879 17 8 24 1895 0.464 53213 3.94 0.145 0.081 0.064 900 8 8 21 1411 0.638 119214 3.93 0.133 0.092 0.065 933 11 7 21 1763 0.529 83215 3.85 0.128 0.077 0.061 783 15 9 20 1585 0.494 53216 4.02 0.151 0.090 0.063 1270 16 9 27 2266 0.560 81217 3.90 0.203 0.077 0.076 859 17 7 48 2224 0.386 50218 4.17 0.148 0.086 0.060 730 14 9 20 1780 0.410 54219 4.01 0.182 0.076 0.063 1044 14 8 17 1898 0.550 73220 4.00 0.202 0.086 0.074 1143 23 9 56 2157 0.530 50X 4.00 0.161 0.083 0.068 956 13 8 26 1711 0.568 78S.D. 0.09 0.028 0.009 0.008 170 4 1 10 278 0.113 24%CV 2 17 11 12 18 31 14 40 16 20 31125plotno. pH 	Ca K%Mg Fe Mn Cu ZnAg/g  Al ratiosFe:Al Fe:Mn301 4.14 0.100 0.163 0.095 6967 36 16 25 5428 1.284 191302 3.90 0.099 0.182 0.108 4991 31 15 40 4182 1.194 163303 3.92 0.052 0.152 0.138 5441 64 22 47 7127 0.763 85304 4.01 0.056 0.152 0.115 4715 45 19 39 5806 0.812 104305 3.89 0.046 0.138 0.082 3137 25 16 23 4421 0.710 125306 4.24 0.081 0.170 0.118 7092 37 14 26 5736 1.236 193307 4.13 0.101 0.176 0.137 5754 50 17 30 6429 0.895 116308 3.86 0.041 0.133 0.051 2996 8 11 18 3625 0.827 381309 3.88 0.053 0.118 0.062 2820 19 12 15 3515 0.802 150310 3.73 0.052 0.163 0.064 2598 9 12 21 4020 0.646 294311 3.93 0.116 0.179 0.089 2896 14 12 30 3620 0.800 214312 3.89 0.058 0.144 0.053 3137 10 11 14 3639 0.862 325313 3.73 0.072 0.146 0.055 2338 10 10 17 2700 0.866 223314 3.84 0.059 0.145 0.063 2277 14 9 19 3245 0.702 160315 3.67 0.048 0.125 0.048 1881 9 8 14 2881 0.653 211316 3.91 0.088 0.124 0.071 2933 17 11 26 3696 0.793 175317 3.78 0.091 0.124 0.078 2810 19 14 33 3285 0.855 145318 3.76 0.088 0.133 0.075 2036 14 10 22 2964 0.687 143319 3.82 0.055 0.128 0.060 2675 12 10 18 3424 0.781 229320 3.89 0.091 0.136 0.082 2358 13 12 21 2839 0.830 185X 3.90 0.072 0.146 0.082 3593 23 13 25 4129 0.850 191S.D. 0.14 0.022 0.019 0.027 1579 15 3 9 1253 0.177 72%CV 4 31 13 33 44 68 26 36 30 21 38401 3.73 0.076 0.159 0.122 5253 39 19 38 6276 0.837 133402 3.98 0.069 0.167 0.130 4718 73 19 51 6342 0.744 65403 4.15 0.133 0.160 0.118 3927 42 15 37 4358 0.901 93404 3.96 0.065 0.205 0.129 4379 33 16 33 7763 0.564 131405 3.82 0.087 0.167 0.094 2529 34 14 36 3903 0.648 74406 3.81 0.104 0.134 0.099 3992 41 13 33 3698 1.080 98407 4.01 0.139 0.144 0.112 4143 34 16 43 3486 1.189 121408 3.87 0.076 0.145 0.067 2140 14 10 16 2879 0.743 157409 3.90 0.080 0.160 0.081 3044 11 12 20 3104 0.981 277410 3.90 0.115 0.124 0.063 4748 17 12 18 2645 1.795 272411 3.82 0.080 0.127 0.079 3398 21 14 26 3417 0.994 159412 3.76 0.079 0.103 0.062 2018 20 12 26 1811 1.114 103413 3.76 0.096 0.144 0.093 5118 27 16 40 3169 1.615 193414 3.92 0.100 0.141 0.067 3681 24 9 21 2505 1.469 154415 3.70 0.083 0.119 0.065 2913 12 12 30 3136 0.929 250416 4.06 0.082 0.099 0.057 3349 18 11 20 2962 1.131 187417 3.90 0.100 0.125 0.064 4945 21 11 20 2967 1.667 233418 3.88 0.105 0.114 0.073 4077 29 15 36 2939 1.387 142419 3.88 0.104 0.125 0.073 2992 13 12 23 2529 1.183 238420 3.81 0.078 0.099 0.050 3786 16 12 19 3029 1.250 244X 3.88 0.093 0.138 0.085 3757 27 13 29 3646 1.111 166S.D. 0.11 0.020 0.026 0.025 932 14 3 10 1447 0.334 65%CV 3 21 19 29 25 54 21 32 40 30 39126Foliar Data: June, 1990Fe 	 Mn 	 Zn 	 K	  Ag/g 	Mg Ca P% 	N101 46.0 46.0 20.0 0.77 0.15 0.55 0.21 1.40102 61.9 41.3 41.3 0.71 0.16 0.58 0.18 1.40103 72.2 72.2 20.0 0.73 0.14 0.48 0.19 1.72104 62.6 62.6 20.0 0.77 0.15 0.50 0.18 1.54105 120.5 72.3 20.0 0.67 0.17 0.63 0.21 1.11106 67.5 90.0 22.5 0.70 0.14 0.52 0.15 1.44107 63.0 42.0 20.0 0.69 0.13 0.48 0.14 1.50108 63.2 63.2 20.0 0.74 0.13 0.48 0.15 1.48109 85.1 56.7 20.0 0.75 0.14 0.51 0.15 1.45110 75.1 75.1 20.0 0.76 0.14 0.48 0.15 1.70111 47.1 70.6 20.0 0.73 0.16 0.59 0.13 1.16112 66.9 66.9 66.9 0.69 0.16 0.56 0.14 1.13113 42.6 63.8 20.0 0.71 0.14 0.47 0.13 1.26114 89.6 89.6 44.8 0.70 0.14 0.48 0.13 1.25115 85.4 64.0 21.3 0.82 0.16 0.52 0.16 1.19116 76.1 101.4 50.7 0.77 0.14 0.55 0.17 1.52117 59.5 158.8 39.7 0.65 0.14 0.47 0.12 1.20118 74.6 49.8 24.9 0.77 0.14 0.49 0.16 1.70119 42.7 106.8 21.4 0.76 0.14 0.50 0.15 1.70120 43.7 65.6 21.9 0.73 0.14 0.47 0.13 1.67X 67.3 72.9 27.8 0.73 0.15 0.52 0.16 1.43S.D. 18.7 26.3 13.1 0.04 0.01 0.05 0.03 0.20%CV 28 36 47 6 7 9 16 14201 65.7 65.7 21.9 0.75 0.20 0.76 0.14 1.33202 64.7 107.9 43.2 0.71 0.22 0.83 0.15 1.25203 50.3 75.5 25.2 0.70 0.21 0.79 0.18 1.26204 63.5 63.5 21.2 0.77 0.18 0.62 0.17 1.32205 54.3 54.3 27.1 0.69 0.22 0.77 0.19 1.41206 54.1 54.1 27.0 0.72 0.21 0.93 0.18 1.05207 81.8 61.3 40.9 0.70 0.20 0.89 0.13 1.00208 98.9 49.4 49.4 0.74 0.20 0.85 0.18 1.31209 55.6 55.6 55.6 0.76 0.18 0.79 0.16 1.18210 47.9 47.9 71.8 0.72 0.19 0.73 0.16 1.03211 63.8 42.5 21.3 0.78 0.20 0.75 0.18 1.00212 69.6 46.4 46.4 0.80 0.20 0.80 0.21 1.12213 85.4 85.4 42.7 0.71 0.24 0.86 0.22 1.17214 67.5 45.0 22.5 0.71 0.20 0.80 0.21 1.25215 70.9 70.9 47.3 0.68 0.19 0.74 0.19 1.19216 99.9 99.9 99.9 0.72 0.20 0.83 0.17 0.95217 94.2 94.2 70.6 0.68 0.20 0.86 0.22 1.02218 114.9 69.0 46.0 0.64 0.20 0.82 0.21 0.92219 71.5 71.5 23.8 0.58 0.20 0.80 0.18 0.79220 74.7 74.7 24.9 0.59 0.20 0.86 0.19 0.80X 72.5 66.7 41.4 0.71 0.20 0.80 0.18 1.12S.D. 17.8 18.3 20.4 0.05 0.01 0.07 0.02 0.17%CV 25 27 49 8 7 8 14 16127plot Fe Mn Zn K Mg Ca P Nno. 	 Ag/g 	 % 	301 154.6 51.5 51.5 0.72 0.15 0.50 0.10 0.93302 104.0 104.0 41.6 0.78 0.17 0.55 0.13 1.19303 87.8 131.6 43.9 0.67 0.13 0.38 0.11 1.47304 73.6 122.7 49.1 0.78 0.17 0.50 0.12 1.29305 78.2 52.1 26.1 0.80 0.17 0.46 0.11 1.15306 100.8 50.4 25.2 0.70 0.14 0.34 0.10 1.01307 -- 40.1 20.1 0.60 0.12 0.29 0.09 0.96308 113.1 45.2 22.6 0.70 0.13 0.37 0.10 1.08309 102.5 82.0 20.5 0.63 0.14 0.38 0.08 0.86310 67.0 67.0 22.3 0.70 0.17 0.50 0.08 0.87311 47.0 70.5 20.0 0.84 0.17 0.53 0.08 0.90312 64.2 42.8 21.4 0.69 0.15 0.40 0.09 0.91313 48.2 48.2 48.2 0.81 0.19 0.57 0.09 0.96314 64.8 64.8 21.6 0.64 0.18 0.49 0.08 0.79315 77.0 51.3 20.0 0.84 0.20 0.55 0.13 1.26316 134.3 67.2 22.4 0.75 0.18 0.46 0.16 0.99317 39.7 59.5 39.7 0.58 0.14 0.40 0.07 0.71318 54.6 81.9 27.3 0.68 0.14 0.41 0.10 0.99319 51.7 103.4 25.9 0.77 0.17 0.49 0.11 0.98320 63.1 84.1 42.1 0.81 0.18 0.57 0.12 1.05X 80.3 71.0 30.6 0.72 0.16 0.46 0.10 1.02S.D. 31.3 26.1 11.2 0.08 0.02 0.08 0.02 0.18%CV 39 37 36 11 14 17 21 17401 46.0 91.9 23.0 0.83 0.17 0.55 0.10 1.19402 84.1 140.1 28.0 0.77 0.14 0.42 0.11 1.05403 76.6 76.6 25.5 0.71 0.14 0.41 0.11 0.93404 86.7 86.7 21.7 0.73 0.11 0.28 0.11 1.06405 75.3 100.4 25.1 0.79 0.14 0.45 0.09 0.87406 51.0 102.0 51.0 0.66 0.16 0.56 0.09 0.91407 101.8 101.8 25.5 0.71 0.15 0.53 0.10 0.78408 62.1 82.9 41.4 0.72 0.15 0.50 0.11 0.78409 98.4 24.6 24.6 0.63 0.14 0.42 0.10 0.78410 25.4 127.0 25.4 0.70 0.15 0.48 0.09 0.71411 51.1 102.2 25.6 0.61 0.14 0.43 0.08 1.01412 41.9 104.7 20.9 0.59 0.15 0.46 0.08 0.90413 25.2 226.5 25.2 0.61 0.15 0.43 0.09 0.85414 41.8 62.6 20.9 0.62 0.15 0.46 0.09 0.91415 28.3 169.8 28.3 0.61 0.16 0.51 0.08 0.87416 47.0 93.9 23.5 0.73 0.15 0.49 0.09 0.84417 51.2 153.7 25.6 0.65 0.14 0.46 0.08 0.80418 74.3 99.1 24.8 0.63 0.13 0.37 0.10 0.94419 52.4 104.7 52.4 0.71 0.15 0.45 0.10 0.89420 65.1 65.1 43.4 0.70 0.15 0.43 0.09 0.86X 60.0 105.8 29.1 0.69 0.15 0.45 0.09 0.90S.D. 22.3 41.8 9.4 0.07 0.01 0.06 0.01 0.11%CV 38 39 32 10 8 14 11 12128Foliar Data:	plot 	 Fe	no. 	July,MnAg/g 	1990Zn 	 K Mg Ca P N101 47.9 71.8 23.9 0.61 0.19 0.62 0.09 0.89102 45.1 22.5 45.1 0.58 0.17 0.50 0.07 0.85103 41.5 83.0 41.5 0.64 0.17 0.60 0.09 0.99104 46.4 69.6 46.4 0.65 0.15 0.51 0.08 1.10105 43.3 65.0 43.3 0.67 0.22 0.69 0.10 0.81106 45.1 67.6 45.1 0.57 0.17 0.72 0.08 0.93107 61.4 61.4 40.9 0.55 0.17 0.63 0.08 0.91108 69.8 116.3 46.5 0.55 0.17 0.63 0.06 0.88109 48.2 72.3 24.1 0.64 0.20 0.82 0.07 0.99110 46.8 93.7 23.4 0.65 0.18 0.63 0.08 1.11111 67.3 134.6 44.9 0.61 0.19 0.74 0.07 0.78112 60.5 100.9 40.4 0.58 0.20 0.79 0.07 0.72113 60.4 80.5 20.1 0.63 0.16 0.64 0.08 0.86114 40.1 80.3 20.1 0.57 0.18 0.68 0.07 0.78115 42.7 85.4 42.7 0.58 0.17 0.60 0.07 0.85116 41.7 20.8 41.7 0.68 0.17 0.69 0.09 0.97117 21.7 65.0 21.7 0.61 0.16 0.67 0.07 0.85118 51.8 155.5 51.8 0.57 0.19 0.70 0.06 0.90119 45.5 159.4 22.8 0.67 0.17 0.57 0.08 0.85120 42.2 126.5 42.2 0.67 0.17 0.63 0.08 0.94X 48.5 86.6 36.4 0.61 0.18 0.65 0.08 0.90S.D. 10.7 36.2 10.7 0.04 0.02 0.08 0.01 0.10%CV 22 42 29 7 9 12 13 11201 49.1 73.6 24.5 0.71 0.24 0.81 0.08 0.79202 40.2 60.4 40.2 0.71 0.19 0.60 0.08 0.86203 81.0 81.0 27.0 0.68 0.24 0.76 0.12 0.91204 109.5 43.8 21.9 0.79 0.15 0.46 0.15 1.42205 86.5 64.9 21.6 0.71 0.24 0.69 0.12 1.00206 80.6 53.7 20.0 0.71 0.21 0.75 0.12 0.86207 82.6 61.9 20.6 0.71 0.23 0.72 0.13 0.84208 87.8 65.8 21.9 0.68 0.24 0.92 0.15 0.87209 83.5 83.5 20.9 0.71 0.23 0.73 0.14 0.89210 88.6 66.5 22.2 0.65 0.24 0.82 0.15 0.81211 82.7 62.1 62.1 0.70 0.23 0.70 0.16 0.90212 88.0 66.0 44.0 0.74 0.24 0.73 0.17 0.92213 111.7 111.7 22.3 0.70 0.27 0.83 0.17 0.83214 119.3 47.7 23.9 0.69 0.24 0.76 0.15 0.89215 104.0 62.4 41.6 0.69 0.27 0.81 0.16 0.99216 82.4 61.8 20.6 0.69 0.27 0.70 0.16 0.84217 84.9 63.7 21.2 0.68 0.21 0.62 0.13 0.78218 82.0 54.6 20.0 0.67 0.25 0.63 0.15 0.75219 107.6 107.6 21.5 0.69 0.26 0.69 0.18 0.77220 102.7 82.1 20.5 0.64 0.25 0.74 0.14 0.72X 87.7 68.7 26.9 0.70 0.24 0.72 0.14 0.88S.D. 18.6 16.9 10.9 0.03 0.03 0.10 0.03 0.14%CV 21 25 40 4 12 13 19 16129plotno. 	Fe MnAg/g 	Zn K Mg Ca P N301 116.9 116.9 20.0 0.74 0.19 0.44 0.13 1.10302 89.3 89.3 22.3 0.71 0.20 0.49 0.11 0.79303 118.2 165.5 23.6 0.79 0.17 0.33 0.12 1.27304 103.7 155.5 20.0 0.79 0.21 0.49 0.12 1.03305 104.4 83.5 20.0 0.73 0.19 0.38 0.12 0.93306 121.1 72.6 24.2 1.07 0.15 0.24 0.15 1.71307 141.5 94.3 23.6 0.84 0.21 0.45 0.13 0.84308 108.8 130.6 21.8 0.89 0.20 0.52 0.11 0.84309 151.7 126.4 20.0 0.85 0.23 0.46 0.16 0.98310 106.7 80.0 26.7 0.72 0.19 0.48 0.11 0.71311 119.1 95.3 23.8 0.73 0.21 0.52 0.11 0.75312 107.7 107.7 21.5 0.79 0.24 0.43 0.13 0.83313 124.8 74.9 20.0 0.85 0.20 0.47 0.12 0.90314 107.3 107.3 26.8 0.73 0.24 0.54 0.13 0.76315 112.1 67.2 20.0 0.65 0.25 0.47 0.11 0.79316 95.5 71.6 20.0 0.68 0.24 0.43 0.12 0.90317 97.6 73.2 24.4 0.65 0.20 0.39 0.10 0.90318 80.9 60.7 20.0 0.71 0.18 0.32 0.14 0.78319 80.7 60.5 20.2 0.65 0.22 0.40 0.12 0.69320 102.2 61.3 20.4 0.71 0.22 0.45 0.11 0.88X 109.5 94.7 22.0 0.76 0.21 0.44 0.12 0.92S.D. 17.2 30.2 2.26 0.10 0.02 0.07 0.01 0.23%CV 16 32 10 13 12 17 12 25401 124.4 174.2 24.9 0.94 0.20 0.45 0.12 1.28402 124.8 124.8 25.0 0.68 0.20 0.42 0.12 0.85403 132.1 158.5 20.0 0.74 0.18 0.34 0.13 1.02404 132.7 88.5 22.1 0.72 0.15 0.27 0.14 1.02405 151.3 100.9 25.2 0.82 0.20 0.40 0.12 0.84406 107.1 150.0 21.4 0.77 0.21 0.41 0.12 0.84407 128.7 180.1 20.0 0.70 0.23 0.39 0.11 0.78408 137.0 191.8 20.0 0.73 0.25 0.41 0.13 0.82409 126.5 151.8 25.3 0.77 0.25 0.40 0.15 0.86410 121.4 97.1 20.0 0.67 0.22 0.56 0.12 0.70411 112.4 157.4 20.0 0.72 0.18 0.49 0.12 0.82412 99.4 99.4 19.9 0.68 0.18 0.50 0.12 0.74413 157.5 105.0 26.2 0.62 0.18 0.55 0.12 0.71414 126.8 147.9 21.1 0.68 0.21 0.55 0.15 0.84415 116.4 116.4 20.0 0.64 0.19 0.56 0.13 0.75416 73.7 98.3 20.0 0.63 0.17 0.49 0.17 0.92417 109.3 87.5 21.9 0.78 0.13 0.42 0.14 0.85418 -- 102.1 25.5 0.72 0.13 0.00 0.13 0.82419 127.9 106.6 20.0 0.74 0.19 0.28 0.14 0.90420 132.1 88.1 20.0 0.74 0.20 0.20 0.14 0.81X 123.2 126.3 21.9 0.72 0.19 0.40 0.13 0.86S.D. 18.6 33.2 2.3 0.07 0.03 0.13 0.01 0.13%CV 15 26 11 10 17 33 11 15130Foliar Data:	plot 	 Fe	no. 	Sept., 	 1990Mn 	 Zn 	 KAg/g 	Mg Ca% 	P N101 89.6 67.2 22.4 0.46 0.22 0.81 0.15 0.81102 101.5 50.8 20.0 0.53 0.20 0.63 0.18 0.87103 91.8 68.9 23.0 0.52 0.25 0.80 0.17 0.81104 89.2 66.9 22.3 0.57 0.22 0.62 0.16 0.86105 108.4 65.0 21.7 0.49 0.26 0.74 0.17 0.76106 74.6 74.6 20.0 0.61 0.20 0.60 0.15 0.93107 111.0 66.6 22.2 0.50 0.22 0.78 0.18 0.91108 101.1 60.7 20.2 0.48 0.20 0.73 0.16 0.83109 68.3 45.6 22.8 0.46 0.18 0.77 0.16 0.78110 68.0 45.3 22.7 0.49 0.27 0.88 0.17 0.81111 82.6 55.0 27.5 0.45 0.25 0.94 0.11 0.80112 114.2 85.7 28.6 0.51 0.26 0.89 0.19 0.96113 67.8 67.8 45.2 0.57 0.25 0.81 0.20 0.86114 101.6 61.0 40.7 0.60 0.24 0.73 0.18 0.87115 107.5 86.0 43.0 0.56 0.28 0.92 0.18 0.91116 70.4 93.9 46.9 0.55 0.21 0.75 0.11 0.82117 90.1 -- 45.1 0.67 0.27 0.74 0.16 1.03118 114.7 45.9 45.9 0.50 0.18 0.71 0.11 0.90119 92.3 46.1 46.1 0.55 0.18 0.74 0.12 0.77120 96.3 24.1 48.2 0.54 0.22 0.92 0.12 0.96X 92.1 62.0 31.7 0.53 0.23 0.78 0.16 0.86S.D. 15.4 16.8 11.2 0.05 0.03 0.10 0.03 0.07%CV 17 27 35 10 14 12 18 8201 121.8 73.1 24.4 0.51 0.29 1.07 0.17 0.79202 90.7 90.7 45.3 0.54 0.25 0.93 0.17 0.85203 92.2 92.2 46.1 0.52 0.28 1.11 0.19 0.88204 102.1 51.1 25.5 0.64 0.23 0.72 0.17 0.92205 111.8 67.1 20.0 0.53 0.29 1.07 0.23 0.78206 201.0 67.0 22.3 0.51 0.27 1.07 0.20 0.84207 89.1 89.1 22.3 0.52 0.27 1.07 0.22 0.83208 97.7 73.3 24.4 0.56 0.27 1.20 0.22 0.87209 94.3 141.5 23.6 0.55 0.26 1.18 0.21 0.57210 90.8 90.8 22.7 0.49 0.30 1.23 0.22 0.87211 132.7 66.4 20.0 0.55 0.27 1.04 0.22 0.83212 77.2 77.2 25.7 0.58 0.28 1.08 0.24 0.95213 89.6 112.0 22.4 0.54 0.31 1.12 0.22 0.86214 132.9 79.7 26.6 0.57 0.29 1.20 0.26 0.82215 119.8 71.9 24.0 0.52 0.29 1.17 0.23 0.85216 105.1 78.8 0.0 0.57 0.24 1.02 0.22 1.00217 115.8 69.5 23.2 0.56 0.25 1.04 0.22 0.79218 102.2 76.6 20.0 0.54 0.26 1.05 0.23 0.86219 81.9 109.2 27.3 0.56 0.27 1.12 0.26 0.88220 120.8 120.8 24.2 0.46 0.24 1.26 0.17 0.83X 108.5 84.9 24.5 0.54 0.27 1.09 0.21 0.84S.D. 26.4 21.2 8.9 0.04 0.02 0.12 0.03 0.08%CV 24 25 36 7 8 11 13 10131plotno. 	Fe MnAg/g 	Zn K Mg Ca% 	P N301 136.9 91.3 20.0 0.00 0.14 0.41 0.17 1.21302 188.5 107.7 20.0 0.67 0.19 0.73 0.13 0.92303 123.3 148.0 24.7 0.81 0.20 0.52 0.11 1.00304 135.1 135.1 20.0 0.75 0.23 0.68 0.21 1.01305 157.6 112.6 20.0 0.66 0.25 0.70 0.11 0.87306 152.4 87.1 20.0 1.15 0.20 0.41 0.12 1.05307 206.2 103.1 20.0 1.11 0.28 0.62 0.10 0.90308 218.4 145.6 24.3 1.06 0.27 0.80 0.14 0.94309 150.2 125.2 20.0 0.98 0.25 0.63 0.14 0.90310 162.9 116.3 23.3 0.70 0.26 0.77 0.13 0.76311 135.1 157.7 20.0 0.64 0.29 0.97 0.12 0.71312 154.9 132.8 20.0 0.92 0.27 0.62 0.15 0.99313 285.7 95.2 47.6 0.95 0.26 0.67 0.15 0.58314 262.2 131.1 26.2 0.67 0.31 0.81 0.15 0.84315 209.8 104.9 26.2 0.71 0.31 0.73 0.13 0.82316 186.4 116.5 23.3 0.57 0.30 0.68 0.14 1.01317 451.6 132.8 53.1 0.55 0.35 0.85 0.10 1.03318 231.1 92.4 23.1 0.60 0.25 0.62 0.13 0.84319 202.2 89.9 22.5 0.56 0.29 0.70 0.13 0.80320 164.9 94.2 47.1 0.59 0.31 0.73 0.12 0.78X 195.8 116.0 26.1 0.73 0.26 0.68 0.13 0.90S.D. 72.6 21.0 10.0 0.25 0.05 0.13 0.02 0.14%CV 37 18 38 34 19 19 18 15401 115.9 208.6 46.4 0.67 0.28 0.83 0.08 0.84402 153.5 131.6 21.9 0.63 0.24 0.55 0.08 0.93403 265.8 155.1 22.2 0.52 0.29 0.66 0.09 0.94404 220.8 132.5 22.1 0.57 0.27 0.53 0.10 1.01405 211.6 132.3 26.5 0.53 0.34 0.82 0.09 0.90406 176.9 227.4 25.3 0.63 0.33 0.78 0.08 0.90407 141.8 165.5 23.6 0.59 0.31 0.59 0.09 0.77408 148.2 197.6 24.7 0.52 0.42 0.84 0.09 0.83409 160.3 137.4 22.9 0.71 0.41 0.69 0.14 0.81410 193.5 120.9 48.4 0.55 0.41 0.73 0.11 0.83411 120.6 217.1 20.0 0.63 0.36 0.63 0.09 0.75412 107.4 134.2 20.0 0.66 0.38 0.59 0.10 0.70413 150.8 100.5 25.1 0.60 0.20 0.60 0.07 0.85414 132.4 185.4 20.0 0.48 0.29 0.85 0.11 0.78415 107.1 133.8 20.0 0.52 0.21 0.72 0.10 0.79416 105.2 131.5 20.0 0.67 0.24 0.74 0.07 0.85417 105.3 105.3 20.0 0.65 0.18 0.61 0.08 0.82418 138.4 138.4 20.0 0.60 0.18 0.60 --419 142.2 142.2 20.0 0.54 0.26 0.78 0.12 0.79420 140.4 117.0 23.4 0.61 0.26 0.82 0.11 0.87X 153.8 150.7 24.6 0.59 0.29 0.70 0.10 0.84S.D. 42.0 36.2 7.9 0.06 0.07 0.10 0.02 0.08%CV 28 24 32 10 25 15 19 9132133Foliar Data: May, 	 1991plot 	 N 	 P 	 Ca 	 Mgno.   % 	K Fe Cu 	 Zn 	 Mn 	 Alpg/g 	101 1.39 0.244 0.470 0.23 0.61 73 9 39 35 103102 1.51 0.250 0.470 0.23 0.61 60 7 35 23 63103 1.39 0.267 0.590 0.25 0.56 72 5 36 38 90104 1.18 0.259 0.377 0.21 0.48 73 16 32 29 103105 0.94 0.185 0.443 0.23 0.43 99 27 29 46 103106 1.40 0.245 0.363 0.21 0.53 60 0 29 23 103107 1.27 0.239 0.393 0.22 0.51 87 17 29 31 77108 1.36 0.235 0.390 0.23 0.51 86 17 33 42 90109 1.30 0.272 0.390 0.24 0.53 72 4 35 34 63110 1.50 0.275 0.497 0.27 0.56 60 12 35 43 90111 1.29 0.220 0.523 0.24 0.53 59 2 33 57 77112 1.39 0.268 0.443 0.25 0.53 73 8 33 58 90113 1.46 0.289 0.203 0.24 0.56 99 11 33 31 90114 1.31 0.269 0.430 0.24 0.53 73 4 31 26 130115 1.42 0.257 0.337 0.23 0.59 87 5 36 45 117116 1.62 0.284 0.417 0.22 0.61 72 11 37 57 103117 1.79 0.261 0.283 0.20 0.75 100 8 41 92 143118 1.51 0.243 0.497 0.23 0.64 100 17 37 56 90119 1.26 0.212 0.635 0.24 0.56 73 5 37 31 117120 1.50 0.248 0.398 0.21 0.61 87 9 40 39 90X 1.39 0.251 0.430 0.23 0.56 79 10 35 44 97S.D. 0.17 0.025 0.082 0.02 0.07 14 6 3 16 20%CV 12 10 20 7 12 17 65 10 22 20201 1.05 0.237 0.573 0.25 0.56 82 16 39 68 83202 1.02 0.240 0.373 0.21 0.51 68 12 32 46 83203 1.10 0.275 0.480 0.25 0.53 68 8 31 47 70204 1.05 0.278 0.387 0.22 0.51 82 13 33 28 110205 0.56 0.253 0.267 0.15 0.29 55 13 28 18 97206 1.22 0.242 0.530 0.23 0.53 82 7 36 32 110207 1.24 0.231 0.543 0.25 0.51 95 4 35 38 83208 1.18 0.251 0.560 0.24 0.51 82 11 36 54 83209 1.36 0.265 0.533 0.25 0.53 68 4 39 63 57210 1.18 0.239 0.640 0.25 0.48 82 11 36 39 83211 0.97 0.266 0.387 0.20 0.43 68 11 35 44 110212 1.21 0.267 0.653 0.26 0.51 68 15 36 44 123213 1.02 0.304 0.560 0.26 0.53 82 35 36 70 123214 1.13 0.274 0.667 0.27 0.51 108 9 37 38 137215 1.20 0.265 0.613 0.26 0.56 95 17 37 42 150216 1.14 0.167 0.573 0.23 0.43 82 5 39 66 123217 1.13 0.266 0.480 0.24 0.48 82 5 35 60 83218 0.97 0.209 0.427 0.21 0.48 82 4 28 34 30219 1.15 0.246 0.573 0.23 0.51 95 7 35 42 57220 1.16 0.245 0.680 0.23 0.56 95 8 36 39 97X 1.10 0.251 0.518 0.24 0.5 81 11 35 46 95S.D. 0.16 0.028 0.138 0.03 0.06 12 7 3 14 29%CV 15 11 27 11 12 15 63 9 30 30plotno. 	N P Ca%  Mg K Fe Cu 	 Zn 	 Mn 	 AlAg/g 	301 1.66 0.246 0.333 0.18 0.77 135 7 35 83 213302 1.97 0.275 0.320 0.20 0.88 108 7 36 66 160303 2.51 0.307 0.227 0.18 0.75 82 7 40 93 147304 2.19 0.291 0.240 0.19 0.80 95 5 37 78 173305 1.72 0.289 0.293 0.19 0.72 108 4 32 74 160306 1.71 0.245 0.293 0.19 0.80 122 5 31 76 200307 2.16 0.283 0.093 0.18 0.91 122 5 32 56 173308 1.77 0.262 0.307 0.20 0.80 108 5 33 68 200309 2.12 0.295 0.227 0.20 0.88 108 7 37 70 160310 1.89 0.287 0.400 0.22 0.77 108 5 33 65 187311 1.70 0.266 0.547 0.22 0.77 108 4 35 90 173312 1.75 0.257 0.307 0.20 0.75 95 7 33 69 173313 1.68 0.296 0.507 0.23 0.77 108 4 32 77 213314 1.81 0.277 0.400 0.21 0.80 108 4 37 72 200315 1.59 0.261 0.387 0.22 0.67 122 4 32 68 200316 1.75 0.236 0.267 0.21 0.64 135 4 29 70 227317 1.76 0.235 0.320 0.22 0.61 108 4 29 74 173318 1.53 0.211 0.373 0.22 0.53 108 5 29 90 200319 1.56 0.243 0.453 0.23 0.59 72 4 31 65 173320 1.59 0.227 0.440 0.24 0.61 95 5 33 85 173X 1.82 0.264 0.337 0.21 0.74 108 5 33 74 184S.D. 0.24 0.026 0.103 0.02 0.1 15 1 3 31 21%CV 13 10 32 8 13 14 21 9 43 12401 1.98 0.278 0.520 0.21 0.85 117 7 43 169 155402 1.80 0.274 0.320 0.20 0.75 117 5 35 43 155403 1.76 0.244 0.447 0.23 0.69 130 7 36 108 168404 1.43 0.215 0.280 0.18 0.53 143 4 32 89 195405 1.56 0.232 0.520 0.23 0.67 117 5 35 83 182406 1.58 0.238 0.507 0.23 0.72 117 7 36 153 195407 1.52 0.211 0.293 0.19 0.64 90 5 29 91 128408 1.43 0.217 0.440 0.22 0.61 117 5 31 67 155409 1.46 0.234 0.373 0.22 0.64 90 4 31 57 88410 1.50 0.241 0.373 0.22 0.64 77 5 31 53 102411 1.67 0.222 0.307 0.21 0.69 117 7 32 96 168412 1.52 0.246 0.373 0.22 0.64 103 5 31 91 155413 1.63 0.219 0.280 0.21 0.61 90 5 31 57 115414 1.53 0.225 0.413 0.24 0.64 90 8 35 99 155415 1.49 0.238 0.413 0.24 0.61 103 7 31 75 115416 1.72 0.241 0.373 0.22 0.69 130 5 33 65 128417 1.50 0.209 0.347 0.20 0.61 117 7 28 64 195418 1.64 0.230 0.360 0.21 0.64 103 8 32 92 182419 1.50 0.232 0.587 0.26 0.61 130 9 32 87 182420 1.32 0.236 0.467 0.22 0.59 117 8 32 80 222X 1.58 0.234 0.405 0.22 0.65 111 6 33 86 157S.D. 0.15 0.018 0.091 0.02 0.07 17 1 3 30 34%CV 10 8 22 8 10 15 22 10 36 22134Foliar Data: June, 1991plot 	 N 	 P 	 Ca 	 Mgno.   % 	K Fe Cu 	 Zn 	 Mn 	 Ag/g 	Al101 1.30 0.193 0.511 0.18 0.66 85 10 32 33 70102 1.43 0.194 0.460 0.18 0.74 65 10 32 32 40103 1.58 0.226 0.430 0.18 0.82 65 9 33 37 40104 1.40 0.209 0.370 0.16 0.78 65 5 25 26 30105 1.39 0.193 0.500 0.20 0.72 65 12 31 39 50106 1.37 0.180 0.359 0.17 0.74 65 7 28 77 40107 1.35 0.186 0.350 0.16 0.66 75 6 26 48 50108 1.36 0.217 0.400 0.17 0.70 75 5 24 46 60109 1.37 0.200 0.460 0.18 0.72 65 7 28 43 50110 1.35 0.181 0.320 0.14 0.74 75 7 26 44 50111 1.47 0.237 0.530 0.20 0.92 75 7 31 48 70112 1.51 0.239 0.380 0.18 0.84 65 7 27 38 50113 1.12 0.174 0.320 0.14 0.72 65 4 22 54 50114 1.26 0.219 0.470 0.18 0.78 65 5 29 40 70115 1.43 0.201 0.400 0.17 0.80 65 4 26 38 40116 1.70 0.213 0.420 0.16 0.78 55 5 32 60 60117 1.51 0.190 0.400 0.18 0.78 65 7 28 75 50118 1.58 0.205 0.420 0.17 0.78 65 7 28 29 50119 1.54 0.188 0.360 0.17 0.78 55 3 31 91 50120 1.41 0.187 0.370 0.15 0.76 65 6 30 28 50X 1.42 0.202 0.412 0.17 0.76 67 7 28 46 51S.D. 0.13 0.018 0.060 0.02 0.06 10 2 3 17 10%CV 8.8 9.083 14.5 9.07 7.73 10 33 10 37 20201 1.16 0.238 0.700 0.24 0.70 70 14 30 52 90202 1.22 0.200 0.320 0.15 0.78 50 4 25 50 30203 1.35 0.233 0.520 0.19 0.82 70 7 28 40 50204 1.38 0.235 0.440 0.18 0.74 60 6 25 34 70205 1.18 0.229 0.480 0.19 0.78 50 8 27 46 70206 1.36 0.239 0.620 0.20 0.76 60 7 32 35 60207 1.34 0.224 0.600 0.20 0.72 50 4 28 34 80208 1.38 0.241 0.660 0.21 0.76 60 6 32 36 80209 1.19 0.210 0.560 0.18 0.70 50 4 27 55 60210 1.30 0.207 0.560 0.19 0.74 60 4 29 33 50211 1.47 0.229 0.400 0.18 0.80 63 5 29 33 67212 1.45 0.240 0.467 0.21 0.80 50 5 33 39 67213 1.25 0.238 0.467 0.20 0.80 50 11 31 51 67214 1.29 0.236 0.533 0.18 0.77 63 7 31 36 107215 1.29 0.209 0.453 0.17 0.80 77 7 33 40 67216 1.41 0.224 0.493 0.19 0.80 50 7 35 45 80217 1.18 0.189 0.387 0.16 0.72 50 4 32 33 40218 1.27 0.238 0.520 0.19 0.77 63 9 29 40 93219 1.28 0.227 0.427 0.19 0.77 63 5 31 60 53220 1.24 0.201 0.560 0.19 0.72 63 5 32 59 93X 1.30 0.224 0.508 0.19 0.76 59 6 30 43 69S.D. 0.09 0.015 0.092 0.02 0.04 8.1 2 3 8.8 19%CV 7 7 18 9 5 17 38 9 21 27135plotno. 	N P Ca% 	Mg K Fe Cu 	 Zn 	 Mn 	 Al	 	 Ag/g 	 	301 1.63 0.220 0.280 0.15 0.80 82 4 29 77 246302 1.49 0.193 0.307 0.18 0.91 148 4 31 60 259303 1.42 0.212 0.240 0.16 0.85 202 9 31 72 284304 1.58 0.194 0.253 0.19 0.77 135 7 32 93 247305 1.38 0.185 0.280 0.16 0.77 122 5 24 51 262306 1.31 0.175 0.200 0.14 0.80 242 3 25 63 256307 1.46 0.173 0.133 0.11 0.72 295 7 21 44 334308 1.42 0.173 0.213 0.13 0.75 82 4 24 71 183309 1.30 0.193 0.200 0.13 0.77 95 3 23 40 204310 1.42 0.196 0.333 0.16 0.83 42 5 28 47 83311 1.35 0.209 0.413 0.18 0.83 42 3 29 60 70312 1.36 0.194 0.267 0.14 0.75 15 4 25 52 83313 1.57 0.202 0.267 0.15 0.91 15 1 27 40 43314 1.29 0.201 0.333 0.18 0.85 28 3 25 60 57315 1.39 0.210 0.307 0.14 0.75 42 5 28 40 70316 1.66 0.224 0.160 0.11 0.75 82 4 23 25 80317 1.40 0.197 0.280 0.15 0.85 28 5 24 39 70318 1.40 0.195 0.280 0.17 0.77 28 5 25 40 48319 1.38 0.186 0.333 0.18 0.80 28 5 31 60 48320 1.39 0.188 0.373 0.18 0.77 42 5 28 47 57X 1.428 0.196 0.273 0.16 0.8 90 5 27 54 149S.D. 0.103 0.014 0.068 0.02 0.05 77 2 3 16 117%CV 7.205 7.082 24.76 14.7 6.5 68 38 11 29 79401 1.63 0.211 0.413 0.18 0.91 107 4 35 153 217402 1.53 0.186 0.267 0.14 0.80 147 5 28 63 250403 1.40 0.204 0.293 0.15 0.85 187 7 31 71 200404 1.36 0.191 0.440 0.15 0.67 147 13 27 53 215405 1.35 0.185 0.357 0.17 0.77 53 9 28 57 67406 1.54 0.231 0.371 0.17 0.85 40 8 33 97 53407 1.51 0.222 0.347 0.17 0.75 93 9 33 56 93408 1.45 0.198 0.347 0.18 0.77 107 7 32 80 103409 1.19 0.180 0.329 0.18 0.78 120 7 34 64 99410 1.34 0.209 0.387 0.19 0.77 40 8 31 56 70411 1.36 0.193 0.307 0.17 0.77 80 7 29 79 113412 1.50 0.224 0.347 0.19 1.04 173 15 35 71 103413 1.39 0.192 0.320 0.17 0.75 107 7 29 51 190414 1.41 0.186 0.333 0.18 0.75 53 7 29 79 68415 1.36 0.187 0.400 0.19 0.75 27 7 31 72 73416 1.51 0.205 0.333 0.17 0.77 40 8 29 51 48417 1.38 0.191 0.320 0.16 0.77 40 7 29 80 42418 1.50 0.194 0.360 0.17 0.72 53 5 31 80 49419 1.60 0.227 0.427 0.21 0.85 40 5 29 93 73420 1.46 0.213 0.427 0.21 0.80 40 8 33 67 55X 1.44 0.202 0.356 0.18 0.79 85 8 31 74 109S.D. 0.10 0.015 0.046 0.02 0.08 48 2 2 22 92%CV 7.08 7.579 12.92 9.56 9.63 57 33 8 31 75136Foliar Data: July, 1991plot 	 N 	 P 	 Ca 	 Mgno.   % 	K Fe Cu 	 Zn 	 Mn 	 Ag/g 	Al101 1.33 0.173 0.616 0.20 0.67 53 15 31 96 147102 1.33 0.170 0.683 0.22 0.64 93 8 31 88 160103 1.43 0.183 0.573 0.19 0.67 93 7 29 67 147104 1.40 0.175 0.595 0.20 0.64 93 7 28 104 173105 1.45 0.185 0.440 0.18 0.80 53 5 29 63 93106 1.21 0.146 0.480 0.16 0.67 67 4 23 108 133107 1.50 0.181 0.440 0.16 0.69 67 5 25 71 147108 1.16 0.138 0.333 0.14 0.45 200 4 21 49 307109 1.18 0.149 0.493 0.17 0.64 187 5 25 71 293110 1.45 0.177 0.507 0.18 0.75 93 5 28 75 173111 1.11 0.136 0.493 0.17 0.56 93 5 25 57 147112 1.14 0.151 0.453 0.17 0.59 80 5 25 56 147113 1.30 0.172 0.533 0.18 0.67 80 5 27 45 160114 1.29 0.165 0.507 0.18 0.61 93 4 27 68 160115 1.16 0.148 0.693 0.21 0.53 93 5 32 61 173116 1.51 0.192 0.493 0.19 0.75 67 5 33 96 160117 1.93 0.250 0.400 0.18 0.85 107 5 31 93 133118 1.31 0.165 0.507 0.18 0.64 93 4 28 73 160119 1.37 0.183 0.533 0.19 0.67 53 5 28 57 160120 1.19 0.134 0.547 0.19 0.56 67 4 25 73 187X 1.34 0.169 0.516 0.18 0.65 91 6 28 74 168S.D. 0.18 0.026 0.085 0.02 0.09 38 2 3 18 48%CV 14 15 17 10 14 41 40 11 24 29201 0.94 0.162 0.680 0.23 0.53 40 8 31 52 120202 0.92 0.164 0.467 0.18 0.59 27 3 27 57 107203 0.92 0.162 0.733 0.23 0.51 53 4 29 47 133204 0.88 0.159 0.360 0.15 0.61 40 4 23 35 107205 0.91 0.169 0.560 0.22 0.53 40 5 27 52 120206 0.99 0.165 0.733 0.22 0.56 40 3 31 43 120207 0.98 0.151 0.747 0.22 0.51 53 5 29 52 133208 0.98 0.151 0.667 0.21 0.53 53 3 28 47 107209 0.89 0.153 0.693 0.20 0.53 40 3 31 56 107210 0.91 0.172 0.733 0.22 0.56 40 3 29 51 147211 1.00 0.141 0.640 0.22 0.53 53 3 31 47 133212 1.15 0.160 0.680 0.22 0.61 40 4 32 47 133213 1.02 0.147 0.667 0.22 0.51 40 3 32 57 133214 0.96 0.171 0.573 0.23 0.56 53 4 28 51 147215 0.91 0.162 0.587 0.21 0.59 80 3 29 39 120216 0.95 0.156 0.720 0.22 0.51 93 3 32 56 160217 1.07 0.156 0.720 0.23 0.53 40 3 36 52 147218 1.02 0.157 0.627 0.22 0.53 53 3 33 45 147219 1.01 0.181 0.613 0.23 0.59 53 3 31 77 120220 0.91 0.158 0.653 0.22 0.59 53 1 31 60 133X 0.97 0.16 0.643 0.22 0.55 49 3 30 51 129S.D. 0.07 0.009 0.095 0.02 0.03 15 1 3 8.7 15%CV 7 6 15 9 6 30 42 9 17 12137plotno. 	N P Ca Mg K Fe Cu 	 Zn 	 Mn 	 Ag/g 	Al301 1.95 0.208 0.347 0.17 0.93 40 5 27 140 189302 1.48 0.183 0.427 0.19 0.85 27 5 29 133 168303 1.75 0.197 0.387 0.19 0.91 53 7 28 131 207304 1.87 0.190 0.333 0.17 0.85 53 4 27 144 177305 1.80 0.192 0.373 0.19 0.91 53 5 25 100 187306 1.53 0.193 0.253 0.14 0.88 133 3 20 88 260307 1.43 0.159 0.253 0.13 0.77 93 1 19 96 247308 1.36 0.155 0.240 0.11 0.72 107 3 19 84 260309 1.64 0.198 0.360 0.18 0.83 53 4 25 99 197310 1.46 0.193 0.413 0.19 0.85 53 4 28 92 157311 1.45 0.233 0.440 0.20 0.91 53 4 27 111 157312 1.57 0.195 0.387 0.18 0.88 53 3 24 100 143313 1.44 0.220 0.373 0.19 0.88 53 4 25 67 180314 1.38 0.239 0.520 0.23 0.83 80 4 25 93 170315 1.52 0.232 0.347 0.17 0.83 53 5 24 64 103316 1.63 0.195 0.280 0.18 0.85 53 4 28 81 113317 1.38 0.203 0.333 0.19 0.72 53 4 23 72 93318 1.39 0.235 0.373 0.21 0.69 53 3 23 73 93319 1.13 0.197 0.333 0.18 0.67 40 4 28 92 83320 1.35 0.191 0.453 0.21 0.69 53 5 28 88 57X 1.53 0.201 0.361 0.18 0.82 61 4 25 97 162S.D. 0.19 0.022 0.069 0.03 0.08 24 1 3 23 71%CV 13 11 19 15 10 40 30 12 24 44401 1.47 0.188 0.427 0.17 0.88 80 11 32 160 257402 1.55 0.177 0.387 0.18 0.80 107 5 29 143 317403 1.86 0.204 0.387 0.17 0.88 93 7 27 160 198404 2.17 0.212 0.348 0.18 1.09 97 6 26 97 224405 1.46 0.189 0.400 0.18 0.72 93 3 25 84 89406 1.18 0.187 0.440 0.17 0.69 80 4 31 192 78407 1.68 0.191 0.400 0.19 0.88 80 4 31 135 95408 1.19 0.167 0.480 0.23 0.67 53 4 27 145 123409 1.25 0.176 0.413 0.21 0.77 80 4 28 111 98410 1.20 0.182 0.413 0.22 0.67 80 5 29 97 95411 1.54 0.176 0.373 0.20 0.85 93 5 28 167 136412 0.94 0.166 0.280 0.14 0.56 93 5 29 116 132413 0.89 0.155 0.240 0.11 0.51 93 12 27 77 137414 1.18 0.190 0.333 0.17 0.56 53 5 25 115 87415 1.43 0.180 0.440 0.21 0.61 80 4 27 93 96416 1.34 0.183 0.347 0.16 0.67 80 7 25 93 98417 1.53 0.170 0.387 0.18 0.69 80 7 27 97 95418 1.56 0.195 0.400 0.19 0.67 80 7 29 125 87419 0.97 0.222 0.293 0.13 0.45 133 7 27 93 112420 1.41 0.217 0.427 0.20 0.80 93 7 27 81 102X 1.39 0.186 0.381 0.18 0.72 86 6 28 119 133S.D. 0.30 0.017 0.058 0.03 0.15 17 2 2 32 159%CV 22 9 15 16 20 19 37 7 27 120138plotno.Vine Status:%VINJune,%GL1990%BL %M/Cplotno. 	 %VIN %GL %BL139%M/C101 94.9 4.6 0.6 0.0 301 	 77.4 12.3 0.0 10.3102 85.1 14.7 0.2 0.0 302 	 90.5 7.2 0.1 2.3103 46.7 14.4 0.0 0.0 303 	 44.7 55.2 0.1 0.0104 81.7 17.0 1.3 0.0 304 	 68.7 28.6 0.7 0.0105 90.9 9.1 0.0 0.0 305 	 65.2 33.6 1.1 0.0106 34.1 65.9 0.0 0.0 306 	 31.2 68.8 0.0 0.0107 25.1 74.9 0.0 0.0 307 	 62.8 12.2 0.0 25.0108 75.3 24.4 0.2 0.0 308 	 71.1 28.9 0.0 0.0109 47.0 53.0 0.0 0.0 309 	 86.7 13.3 0.0 0.0110 49.4 50.3 0.4 0.0 310 	 98.9 0.4 0.7 0.0111 81.6 18.4 0.0 0.0 311 	 97.6 0.0 1.1 1.3112 58.0 41.9 0.2 0.0 312 	 73.0 27.1 0.0 0.0113 25.3 73.9 0.8 0.0 313 	 94.3 4.1 1.6 0.0114 57.0 42.8 0.2 0.0 314 	 95.3 4.7 0.0 0.0115 66.0 33.8 0.2 0.0 315 	 96.5 0.0 3.6 0.0116 57.6 42.4 0.0 0.0 316 100.0 0.0 0.0 0.0117 34.8 61.7 3.6 0.0 317 	 99.8 0.3 0.3 0.0118 33.4 66.2 0.4 0.0 318 	 99.0 1.0 1.0 0.0119 38.0 65.2 1.0 0.0 319 	 99.0 1.0 1.0 0.0120 27.0 72.2 0.8 0.0 320 	 97.8 2.2 2.2 0.0X 55.4 42.3 0.5 0.0 X 	 82.5 15.0 0.7 1.9S.D. 22.5 23.3 0.8 0.0 S.D. 	 19.6 19.2 0.9 5.8%CV 40 55 162 %CV 	 24 127 136 296201 99.4 0.0 0.6 0.0 401 	 49.9 48.5 1.7 0.0202 95.4 4.2 0.2 0.0 402 100.0 0.0 0.0 0.0203 96.7 4.3 0.2 0.0 403 	 100.0 0.0 0.0 0.0204 99.8 2.6 0.8 0.0 404 	 99.5 0.5 0.0 0.0205 99.8 0.0 0.2 0.0 405 	 99.4 0.0 0.6 0.0206 99.6 0.0 3.9 0.0 406 	 99.9 0.0 0.1 0.0207 100.0 0.0 0.0 0.0 407 100.0 0.0 0.0 0.0208 99.2 0.0 0.8 0.0 408 	 97.0 3.0 0.0 0.0209 94.7 4.8 0.5 0.0 409 	 89.9 10.1 0.0 0.0210 99.6 0.0 0.4 0.0 410 	 97.5 0.0 3.1 0.0211 99.6 0.0 0.4 0.0 411 	 91.8 0.0 0.0 8.2212 99.8 0.0 0.2 0.0 412 	 99.1 0.9 0.0 0.0213 99.9 0.0 0.1 0.0 413 	 99.8 0.0 0.2 0.0214 99.8 0.0 0.2 0.0 414 	 100.0 0.0 0.0 0.0215 99.4 0.0 0.6 0.0 415 	 97.9 0.0 2.1 0.0216 93.8 4.7 1.5 0.0 416 	 97.2 0.0 0.0 2.8217 91.8 7.2 1.0 0.0 417 	 99.1 0.0 0.9 0.0218 93.7 5.6 0.8 0.0 418 	 99.2 0.0 0.8 0.0219 97.2 1.8 1.0 0.0 419 	 99.0 0.0 1.0 0.0220 93.7 4.4 2.2 0.0 420 	 99.0 0.0 1.0 0.0X 97.6 2.0 0.8 0.0 X 	 95.8 3.2 0.6 0.6S.D. 2.7 2.4 0.9 0.0 S.D.	 10.8 10.6 0.8 1.9%CV 3 121 113 %CV 	 11 338 146 338140Vineplotno.Status: July, 1990%V 	 %GL 	 %BL 	 %MCplotno. %V %GL %BL %MC101 95.9 4.1 0.0 0.0 301 77.0 6.2 0.0 16.8102 91.0 9.0 0.0 0.0 302 85.3 4.4 0.0 10.3103 49.2 50.1 0.7 0.0 303 44.6 55.2 0.2 0.0104 84.0 16.0 0.0 0.0 304 70.5 28.6 0.8 0.0105 93.9 5.9 0.2 0.0 305 65.1 33.6 1.3 0.0106 43.8 55.6 0.7 0.0 306 31.2 68.8 0.0 0.0107 31.9 67.9 0.2 0.0 307 62.9 31.1 0.0 6.0108 74.3 25.2 0.4 0.0 308 71.1 26.8 0.0 2.1109 66.8 33.2 0.0 0.0 309 76.5 17.4 0.0 6.1110 75.4 22.3 2.2 0.0 310 98.7 0.4 0.9 0.0111 85.3 14.7 0.0 0.0 311 97.4 0.0 1.3 1.3112 59.7 39.9 0.4 0.0 312 71.1 28.1 0.0 0.7113 19.0 80.1 0.9 0.0 313 94.1 4.1 1.9 0.0114 70.8 29.2 0.0 0.0 314 95.3 4.7 0.0 0.0115 66.7 32.4 0.9 0.0 315 95.9 0.0 4.1 0.0116 47.7 51.7 0.7 0.0 316 100.0 0.0 0.0 0.0117 18.1 79.9 2.0 0.0 317 94.5 0.0 0.3 5.2118 36.9 63.1 0.0 0.0 318 98.9 0.0 1.1 0.0119 7.8 92.2 0.0 0.0 319 98.9 0.0 1.2 0.0120 32.4 67.1 0.4 0.0 320 97.4 0.0 2.6 0.0X 57.5 42.0 0.5 0.0 X 81.3 15.5 0.8 2.4S.D. 26.4 26.2 0.6 0.0 S.D. 19.2 19.8 1.1 4.4%CV 46 62 127 -- %CV 24 128 136 179201 99.9 0.0 0.0 0.0 401 48.7 34.2 1.9 15.2202 99.8 0.0 0.0 0.1 402 97.0 0.0 0.0 3.0203 99.8 0.0 0.0 0.0 403 100.0 0.0 0.0 0.0204 99.9 0.0 0.0 0.0 404 92.1 0.0 0.6 7.3205 99.9 0.0 0.0 0.0 405 99.3 0.0 0.7 0.0206 99.9 0.0 0.0 0.0 406 95.6 0.0 0.6 3.8207 99.9 0.0 0.0 0.0 407 90.3 0.0 0.5 9.2208 99.8 0.0 0.0 0.1 408 92.8 0.0 0.0 7.2209 99.8 0.0 0.0 0.0 409 89.5 0.0 0.0 10.5210 99.9 0.0 0.0 0.0 410 97.2 0.0 2.8 0.0211 99.9 0.0 0.0 0.0 411 85.6 0.0 0.0 14.4212 99.9 0.0 0.0 0.0 412 93.8 0.0 0.0 6.2213 99.9 0.0 0.0 0.0 413 92.8 0.0 0.0 7.2214 99.8 0.0 0.0 0.0 414 89.7 0.0 0.0 10.3215 99.9 0.0 0.0 0.0 415 94.6 0.0 1.7 3.7216 99.9 0.0 0.0 0.0 416 90.0 0.0 0.0 10.1217 99.8 0.1 0.0 0.0 417 93.4 0.0 0.4 6.2218 99.9 0.0 0.0 0.0 418 93.5 0.0 1.0 5.6219 99.9 0.0 0.0 0.0 419 94.0 0.0 1.2 4.8220 99.9 0.0 0.0 0.0 420 97.5 0.0 0.9 1.7X 99.9 0.0 0.0 0.0 X 91.4 1.7 0.6 6.3S.D. 0.0 0.0 0.0 0.0 S.D. 10.4 7.5 0.8 4.3%CV 4 115 92 162 %CV 11 436 126 68141Vine Status:plotno. 	 %VSept., 	 1990%GL 	 %BL 	 %MCplotno. %V %GL %BL %MC101 95.9 4.1 0.0 0.0 301 77.0 6.2 0.0 16.8102 91.0 9.0 0.0 0.0 302 85.3 4.4 0.0 10.3103 49.1 50.1 0.8 0.0 303 44.6 55.2 0.2 0.0104 84.0 16.0 0.0 0.0 304 70.5 28.6 0.8 0.0105 93.8 5.9 0.3 0.0 305 65.1 33.6 1.3 0.0106 43.6 55.6 0.8 0.0 306 31.2 68.8 0.0 0.0107 31.8 67.9 0.3 0.0 307 62.9 31.1 0.0 6.0108 74.2 25.2 0.5 0.0 308 71.1 26.8 0.0 2.1109 66.8 33.2 0.0 0.0 309 76.5 17.4 0.0 6.1110 75.0 22.3 2.7 0.0 310 98.7 0.4 0.9 0.0111 85.3 14.7 0.0 0.0 311 97.4 0.0 1.3 1.3112 59.6 39.9 0.5 0.0 312 71.1 28.1 0.0 0.7113 18.8 80.1 1.1 0.0 313 94.1 4.1 1.9 0.0114 70.8 29.2 0.0 0.0 314 95.3 4.7 0.0 0.0115 66.5 32.4 1.1 0.0 315 95.9 0.0 4.1 0.0116 47.5 51.7 0.8 0.0 316 100.0 0.0 0.0 0.0117 17.7 79.9 2.4 0.0 317 94.5 0.0 0.3 5.2118 36.9 63.1 0.0 0.0 318 98.9 0.0 1.1 0.0119 7.8 92.2 0.0 0.0 319 98.9 0.0 1.2 0.0120 32.4 67.1 0.5 0.0 320 97.4 0.0 2.6 0.0X 57.4 42.0 0.6 0.0 X 81.3 15.5 0.8 2.4S.D. 26.4 26.2 0.7 0.0 S.D. 19.2 19.8 1.1 4.4%CV 46 62 127 %CV 24 128 136 179201 100.0 0.0 0.0 0.0 401 48.7 34.2 1.9 15.2202 99.9 0.0 0.0 0.1 402 97.0 0.0 0.0 3.0203 99.9 0.0 0.0 0.0 403 100.0 0.0 0.0 0.0204 99.9 0.0 0.0 0.0 404 92.1 0.0 0.6 7.3205 100.0 0.0 0.0 0.0 405 99.3 0.0 0.7 0.0206 100.0 0.0 0.0 0.0 406 95.6 0.0 0.6 3.8207 100.0 0.0 0.0 0.0 407 90.3 0.0 0.5 9.2208 99.9 0.0 0.0 0.1 408 92.8 0.0 0.0 7.2209 99.9 0.0 0.0 0.0 409 89.5 0.0 0.0 10.5210 99.9 0.0 0.0 0.0 410 97.2 0.0 2.8 0.0211 100.0 0.0 0.0 0.0 411 85.6 0.0 0.0 14.4212 100.0 0.0 0.0 0.0 412 93.8 0.0 0.0 6.2213 100.0 0.0 0.0 0.0 413 92.8 0.0 0.0 7.2214 100.0 0.0 0.0 0.0 414 89.7 0.0 0.0 10.3215 100.0 0.0 0.0 0.0 415 94.6 0.0 1.7 3.7216 100.0 0.0 0.0 0.0 416 90.0 0.0 0.0 10.1217 99.9 0.1 0.0 0.0 417 93.4 0.0 0.4 6.2218 100.0 0.0 0.0 0.0 418 93.5 0.0 1.0 5.6219 100.0 0.0 0.0 0.0 419 94.0 0.0 1.2 4.8220 100.0 0.0 0.0 0.0 420 97.5 0.0 0.9 1.7X 100.0 0.0 0.0 0.0 X 91.4 1.7 0.6 6.3S.D. 0.0 0.0 0.0 0.0 S.D. 10.4 7.5 0.8 4.3%CV 0 115 92 162 %CV 11 436 126 68Vine Status: May, 1991plotno. %V %GL %BL %M/C %POND142plotno. %V %GL %BL %M/C %POND101 81.0 18.3 0.6 0.0 0.0 301 65.0 7.7 0.0 0.0 27.3102 73.3 26.0 0.6 0.0 0.0 302 88.6 5.0 0.0 2.4 4.0103 52.1 47.9 0.0 0.0 0.0 303 13.2 11.9 0.4 0.0 74.4104 66.3 32.6 1.0 0.0 0.0 304 71.3 3.2 0.2 0.0 25.2105 91.3 8.4 0.2 0.0 0.0 305 49.4 39.4 0.6 10.4 0.0106 54.8 41.8 0.1 0.0 3.3 306 27.0 4.9 0.0 34.0 34.2107 33.3 57.3 0.0 0.0 9.3 307 52.6 2.7 0.0 0.0 44.8108 77.0 18.4 0.0 0.0 4.4 308 78.9 6.4 0.0 4.0 10.8109 69.0 27.9 0.0 0.0 3.1 309 63.8 4.9 0.0 0.0 31.3110 79.7 20.2 0.1 0.0 0.0 310 96.3 0.0 0.3 3.3 0.0111 81.7 18.2 0.0 0.0 0.0 311 94.8 4.7 0.6 0.0 0.0112 66.0 33.3 0.2 0.0 1.1 312 76.4 8.9 0.0 14.7 0.0113 27.9 72.1 0.0 0.0 0.0 313 93.9 1.3 1.0 3.9 0.0114 66.0 30.1 0.2 0.0 3.8 314 85.4 12.3 0.3 1.8 0.0115 62.0 38.0 0.0 0.0 0.0 315 96.0 3.3 0.7 0.0 0.0116 31.2 68.7 0.2 0.0 0.0 316 97.9 0.0 0.0 2.0 0.0117 35.8 64.2 0.0 0.0 0.0 317 89.7 0.0 0.0 10.3 0.0118 32.9 67.0 0.2 0.0 0.0 318 84.4 0.6 0.6 15.0 0.0119 16.8 83.2 0.0 0.0 0.0 319 97.2 0.4 0.4 2.4 0.0120 45.9 54.1 0.0 0.0 0.0 320 91.3 0.3 0.3 8.3 0.0X 57.2 41.4 0.2 0.0 1.2 X 75.7 5.9 0.3 5.6 12.6S.D. 21.0 21.1 0.3 0.0 2.4 S.D. 23.5 8.5 0.3 8.1 20.0%CV 37 51 163 -- 189 %CV 31 145 106 144 159201 93.2 6.8 0.0 0.0 0.0 401 32.6 66.8 0.7 0.0 0.0202 96.8 3.2 0.0 0.0 0.0 402 98.9 0.8 0.3 0.0 0.0203 98.8 0.7 0.5 0.0 0.0 403 90.1 0.0 0.0 0.0 9.9204 97.8 2.1 0.1 0.0 0.0 404 85.0 0.0 0.0 0.0 15.0205 98.8 1.2 0.0 0.0 0.0 405 99.6 0.0 0.4 0.0 0.0206 99.9 0.0 0.1 0.0 0.0 406 98.6 1.0 0.4 0.0 0.0207 99.7 0.0 0.3 0.0 0.0 407 84.7 0.7 0.2 0.0 14.4208 96.1 3.0 1.0 0.0 0.0 408 98.1 0.0 0.0 1.9 0.0209 98.7 0.0 1.3 0.0 0.0 409 75.4 4.4 0.0 0.0 20.1210 98.0 1.6 0.4 0.0 0.0 410 99.1 0.0 0.9 0.0 0.0211 99.8 0.0 0.2 0.0 0.0 411 96.1 0.0 0.2 0.0 3.7212 100.0 0.0 0.0 0.0 0.0 412 86.2 0.0 1.1 12.7 0.0213 100.0 0.0 0.0 0.0 0.0 413 89.3 0.0 0.2 10.6 0.0214 100.0 0.0 0.0 0.0 0.0 414 87.6 0.0 0.2 12.2 0.0215 99.7 0.0 0.3 0.0 0.0 415 89.9 0.0 1.1 9.0 0.0216 98.8 0.8 0.4 0.0 0.0 416 97.9 0.0 0.1 2.0 0.0217 91.1 8.9 0.0 0.0 0.0 417 99.4 0.0 0.5 0.0 0.0218 96.3 3.2 0.4 0.0 0.0 418 99.1 0.0 0.9 0.0 0.0219 95.4 3.5 1.1 0.0 0.0 419 93.6 0.0 0.4 6.0 0.0220 93.0 7.0 0.1 0.0 0.0 420 99.4 0.0 0.6 0.0 0.0X 97.6 2.1 0.3 0.0 0.0 X 90.0 3.7 0.4 2.7 3.2S.D. 2.6 2.6 0.4 0.0 0.0 S.D. 14.8 14.5 0.4 4.5 6.1%CV 3 125 123 %CV 16 394 84 164 194143Vine Status:plotno. 	 %VJune,%GL1991%BL %M/Cplotno. %V %GL %BL %M/C101 81.8 17.2 1.0 0.0 301102 74.6 24.4 1.0 0.0 302103 52.1 47.9 0.0 0.0 303104 67.1 31.3 1.6 0.0 304105 92.1 7.6 0.3 0.0 305106 54.8 45.1 0.1 0.0 306107 34.1 65.9 0.0 0.0 307108 78.5 21.5 0.0 0.0 308109 70.9 29.1 0.0 0.0 309110 80.9 19.0 0.1 0.0 310 - -111 82.9 17.1 0.0 0.0 311 94.7 4.7 0.7 0.0112 67.6 32.4 0.0 0.0 312113 25.2 74.8 0.0 0.0 313 93.6 1.3 1.2 3.9114 69.2 30.5 0.3 0.0 314 85.5 12.3 0.4 1.8115 64.0 36.0 0.0 0.0 315 95.9 3.3 0.8 0.0116 30.3 69.4 0.3 0.0 316117 39.6 60.4 0.0 0.0 317 89.7 0.0 0.0 10.3118 36.7 63.0 0.3 0.0 318 83.8 0.6 0.7 15.0119 18.7 81.3 0.0 0.0 319 96.6 0.4 0.5 2.4120 45.1 54.9 0.0 0.0 320 90.9 0.3 0.4 8.3201 94.2 5.8 0.0 0.0 401202 96.8 3.2 0.0 0.0 402203 98.4 0.7 0.8 0.0 403204 97.8 2.1 0.1 0.0 404205 98.8 1.2 0.0 0.0 405 99.5 0.0 0.5 0.0206 99.9 0.0 0.1 0.0 406 98.5 1.0 0.5 0.0207 99.5 0.0 0.5 0.0 407208 95.5 3.0 1.5 0.0 408 98.1 0.0 0.0 1.9209 97.9 0.0 2.1 0.0 409210 97.7 1.6 0.7 0.0 410 98.9 0.0 1.1 0.0211 99.7 0.0 0.3 0.0 411 96.6 0.0 0.5 3.1212 100.0 0.0 0.0 0.0 412 88.2 0.0 1.5 10.3213 100.0 0.0 0.0 0.0 413 90.4 0.0 1.0 8.6214 100.0 0.0 0.0 0.0 414 87.5 0.0 0.3 12.2215 99.5 0.0 0.5 0.0 415 90.7 0.0 0.3 9.0216 98.5 0.8 0.7 0.0 416 96.7 0.0 1.3 2.0217 94.1 5.9 0.0 0.0 417 99.9 0.0 0.1 0.0218 96.1 3.2 0.7 0.0 418 99.4 0.0 0.6 0.0219 94.8 3.5 1.8 0.0 419 92.9 0.0 1.1 6.0220 94.8 5.0 0.1 0.0 420 99.5 0.0 0.5 0.0144Vineplotno.Status:%VJuly,%GL1991%BL %M/Cplotno. %V %GL %BL %M/C101 80.6 18.3 1.1 0.0 301 68.0 25.3 0.0 6.7102 72.9 26.0 1.1 0.0 302 89.3 8.4 0.0 2.3103 52.1 47.9 0.0 0.0 303104 65.6 32.6 1.9 0.0 304 75.2 20.8 0.5 3.5105 91.3 8.4 0.3 0.0 305 49.3 39.4 0.8 10.4106 54.7 45.1 0.2 0.0 306 30.2 23.5 5.6 40.7107 33.3 66.7 0.0 0.0 307108 77.1 22.9 0.0 0.0 308109 69.0 31.0 0.0 0.0 309 70.5 26.8 0.0 2.7110 79.6 20.2 0.2 0.0 310 97.4 0.0 1.8 0.8111 81.8 18.2 0.0 0.0 311 94.6 4.7 0.8 0.0112 65.6 34.4 0.0 0.0 312 76.4 8.9 0.0 14.7113 27.9 72.1 0.0 0.0 313 93.4 1.3 1.4 3.9114 65.8 33.9 0.3 0.0 314 85.4 12.3 0.5 1.8115 62.0 38.0 0.0 0.0 315 95.7 3.3 0.9 0.0116 31.0 68.7 0.3 0.0 316117 35.8 64.2 0.0 0.0 317 89.7 0.0 0.0 10.3118 32.7 67.0 0.3 0.0 318 83.7 0.6 0.8 15.0119 16.8 83.2 0.0 0.0 319 96.5 0.4 0.6 2.4120 45.9 54.1 0.0 0.0 320 90.9 0.3 0.5 8.3201 93.2 6.8 0.0 0.0 401202 96.8 3.2 0.0 0.0 402 98.8 0.8 0.5 0.0203 98.3 0.7 0.9 0.0 403 100.0 0.0 0.0 0.0204 97.7 2.1 0.2 0.0 404 --205 98.8 1.2 0.0 0.0 405 99.4 0.0 0.6 0.0206 99.8 0.0 0.2 0.0 406 98.4 1.0 0.6 0.0207 99.4 0.0 0.6 0.0 407208 95.3 3.0 1.7 0.0 408 98.1 0.0 0.0 1.9209 97.6 0.0 2.4 0.0 409210 97.6 1.6 0.8 0.0 410 98.8 0.0 1.3 0.0211 99.7 0.0 0.3 0.0 411 99.7 0.0 0.3 0.0212 100.0 0.0 0.0 0.0 412 85.8 0.0 1.6 12.7213 100.0 0.0 0.0 0.0 413 89.1 0.0 0.3 0.6214 100.0 0.0 0.0 0.0 414 87.5 0.0 0.3 12.2215 99.4 0.0 0.6 0.0 415 89.5 0.0 1.6 9.0216 98.4 0.8 0.8 0.0 416 97.9 0.0 0.1 2.0217 91.1 8.9 0.0 0.0 417 99.3 0.0 0.7 0.0218 96.0 3.2 0.8 0.0 418 98.8 0.0 1.3 0.0219 94.5 3.5 2.0 0.0 419 93.4 0.0 0.6 6.0220 92.8 7.0 0.2 0.0 420 99.2 0.0 0.8 0.0145Harvest Data: 1990100 B 	 no.	plot Yield	 wt. 	 fruit col	n . kg/m2 	g	 ldm2 	mg/gU.D.ldm2plot 	 Yieldno. 	 kg/m2100 B 	 no.wt. 	 fruit col U.D.g 	 ldm2 	 mg/g 1dm2101 1.66 149.0 11 42.7 345 301 0.88 93.6 9 27.5 363102 1.23 153.8 8 41.9 291 302 2.45 141.8 17 36.4 328103 1.28 175.9 7 38.2 283 303 0.29 145.5 2 30.1 321104 1.26 151.9 8 38.1 360 304 1.08 142.2 8 25.2 316105 2.26 145.5 16 42.2 291 305 1.93 156.4 12 22.9 283106 0.91 166.5 5 40.2 290 306 0.72 152.2 5 25.1 367107 0.57 152.4 4 37.1 291 307 1.84 164.7 11 27.8 380108 2.13 163.0 13 39.5 326 308 2.49 152.6 16 27.9 304109 1.96 185.0 11 30.6 332 309 2.69 145.5 18 31.1 287110 1.19 160.9 7 39.1 363 310 2.66 147.5 18 26.3 350111 3.85 190.2 20 33.0 326 311 3.23 161.6 20 33.6 316112 2.61 175.1 15 32.3 296 312 2.96 163.7 18 25.6 344113 1.07 188.9 6 33.3 196 313 4.23 153.6 28 27.1 406114 2.53 174.3 15 31.1 282 314 2.65 156.3 17 24.6 343115 3.08 176.8 17 33.0 280 315 2.18 155.1 14 22.2 316116 0.51 160.3 3 35.2 137 316 2.92 146.3 20 25.7 328117 1.23 180.2 7 36.9 146 317 2.42 158.6 15 25.8 359118 0.93 181.8 5 37.9 180 318 2.69 151.1 18 22.4 278119 0.17 176.3 1 40.2 219 319 3.08 159.0 19 30.9 335120 1.39 175.8 8 26.6 250 320 2.12 155.2 14 26.5 324X 1.59 169.2 9 36.4 274 X 2.28 150.1 15 27.2 333S.D. 0.90 13.4 5 4.3 65 S.D. 0.92 14.5 6 3.6 32%CV 57 8 54 12 24 %CV 41 10 38 13 10201 -1 -1 -1 -1 279 401 0.69 156.3 4 28.2 216202 -1 -1 -1 -1 313 402 1.94 131.3 15 30.1 406203 -1 -1 -1 -1 321 403 2.58 143.2 18 33.2 319204 -1 -1 - 1 -1 313 404 2.56 143.9 18 26.0 299205 -1 -1 -1 -1 343 405 2.14 133.1 16 31.7 342206 -1 -1 - 1 -1 352 406 1.74 150.8 12 28.9 309207 -1 -1 -1 -1 313 407 2.22 137.3 16 27.0 319208 -1 -1 -1 -1 318 408 2.08 152.9 14 25.3 315209 -1 -1 -1 -1 284 409 3.04 145.8 21 32.8 330210 -1 -1 - 1 -1 279 410 1.97 143.7 14 30.8 374211 -1 -1 -1 -1 288 411 1.47 132.9 11 28.7 296212 -1 -1 -1 -1 243 412 2.53 140.6 18 33.4 275213 -1 -1 -1 -1 290 413 2.68 151.0 18 31.7 267214 -1 -1 -1 -1 292 414 4.99 156.9 32 27.7 241215 -1 -1 -1 -1 301 415 2.92 151.0 19 27.1 290216 -1 -1 -1 -1 350 416 2.08 136.2 15 18.2 355217 -1 -1 -1 -1 288 417 2.62 145.8 18 28.4 338218 -1 -1 -1 -1 305 418 2.37 145.5 16 30.8 349219 -1 -1 -1 -1 408 419 2.88 177.0 16 29.4 293220 -1 -1 -1 -1 345 420 3.52 158.0 22 29.7 294X na na na na 311 X 2.45 146.7 17 28.9 311S.D. na na na na 35 S.D. 0.83 10.5 5 3.4 43%CV na na na na 11 %CV 34 7 31 12 141461991:plot Yieldno. kg/m2100 B 	 no.wt. 	 fruit colg 	 1dm2 	mg/gU.D.1dm2plot 	 Yieldno. 	 kg/m2100 B 	 no.wt. 	 fruit col U.D.g 	 ldm2 	 mg/g ldm2101 1.45 135.5 11 29.8 324 301 0.11 85.4 1 -1 261102 1.92 139.6 14 44.3 317 302 0.76 152.7 5 29.8 271103 1.25 142.8 9 29.8 247 303 0.14 120.1 1 29.0 291104 1.19 129.5 9 37.8 271 304 1.11 142.8 8 22.7 273105 1.27 155.0 8 35.3 287 305 1.22 117.7 10 24.9 317106 0.54 139.4 4 45.3 303 306 0.32 92.2 3 24.7 190107 0.61 122.5 5 38.2 282 307 0.04 85.9 0 23.7 118108 0.66 123.0 5 36.1 215 308 0.48 98.3 5 31.2 248109 1.13 128.2 9 30.5 289 309 1.41 137.6 10 16.0 303110 1.43 129.0 11 33.4 280 310 2.53 122.8 21 31.5 412111 1.44 116.2 12 43.1 259 311 2.52 118.7 21 21.8 351112 1.73 140.8 12 47.4 229 312 1.81 135.2 13 29.5 441113 0.95 135.3 7 39.9 229 313 3.18 122.7 26 28.6 509114 0.99 132.0 7 54.2 278 314 2.52 125.7 20 28.1 381115 1.35 122.8 11 46.5 229 315 2.90 119.9 24 33.9 435116 0.28 10.7 3 48.6 130 316 3.25 154.7 21 34.6 356117 0.02 130.5 1 -1 141 317 2.90 126.5 23 33.9 398118 0.46 120.2 4 -1 204 318 4.48 141.0 32 39.2 418119 0.46 141.7 3 29.5 178 319 4.27 125.2 34 39.7 463120 0.22 140.9 2 -1 162 320 3.49 122.7 28 37.3 405X 0.97 131.8 7 39.4 243 X 1.97 122.4 15 29.5 342S.D. 0.52 10.4 4 7.4 56 S.D. 1.38 19.2 11 6.1 96%CV 54 8 51 19 23 %CV 70 16 69 21 28201 2.56 143.2 18 40.7 396 401 0.08 101.5 1 20.1 102202 2.77 140.5 20 39.9 567 402 2.39 132.8 18 29.0 256203 2.91 140.5 21 44.0 367 403 1.78 115.3 15 22.3 227204 2.40 134.5 18 44.8 263 404 1.34 136.3 10 30.7 129205 3.33 143.2 23 50.8 264 405 2.22 118.8 19 31.0 282206 1.72 135.2 13 43.6 342 406 2.61 123.8 21 28.6 423207 3.03 150.0 20 43.6 379 407 2.11 144.3 15 38.5 266208 2.66 152.8 17 41.1 326 408 3.18 132.2 24 36.8 470209 3.07 152.1 20 39.9 372 409 1.81 132.8 14 31.0 171210 2.18 139.1 16 50.6 448 410 3.39 134.2 25 39.7 395211 2.07 150.3 14 51.3 361 411 1.92 132.5 15 26.9 226212 2.45 170.3 14 36.8 291 412 1.02 120.5 8 30.5 248213 2.49 161.1 15 36.5 351 413 2.06 139.7 15 27.3 164214 2.30 151.3 15 53.2 379 414 3.29 122.0 27 37.8 301215 2.62 139.7 19 40.7 270 415 2.83 123.9 23 25.9 359216 3.30 139.2 24 44.8 282 416 2.55 116.5 22 36.5 407217 3.13 135.1 23 44.0 293 417 2.68 122.5 22 30.0 317218 2.61 147.2 18 46.9 211 418 2.85 117.4 24 34.4 331219 3.65 140.4 26 46.5 368 419 3.90 121.4 32 24.9 448220 2.48 145.0 17 53.5 278 420 2.87 117.5 24 37.3 569X 2.69 145.5 16 44.7 340 X 2.34 125.3 19 31.0 305S.D. 0.46 8.9 10 5.0 77 S.D. 0.86 9.9 7 5.4 119%CV 17 6 19 11 23 %CV 37 8 38 18 39Pixel Reflectance Values:June, 1990plotno.NIR R G NIR/Rplotno.NIR R G NIR/R101 174.09 136.30 161.27 1.277 301 148.26 119.35 142.12 1.242102 176.67 142.28 165.80 1.242 302 144.59 115.05 139.46 1.257103 177.23 160.43 175.28 1.105 303 165.96 167.41 175.94 0.991104 175.73 137.27 164.83 1.280 304 163.48 149.27 165.26 1.095105 168.85 141.07 160.77 1.197 305 169.11 127.31 154.19 1.328106 178.02 136.60 168.20 1.303 306 159.90 164.38 171.79 0.973107 169.31 149.79 168.28 1.130 307 132.98 107.20 133.95 1.240108 172.49 140.49 163.47 1.228 308 168.32 133.42 155.30 1.262109 179.67 149.28 172.54 1.204 309 148.40 116.01 138.99 1.279110 177.51 141.67 168.11 1.253 310 170.05 122.30 153.85 1.390111 177.19 142.14 165.22 1.247 311 178.05 133.14 159.44 1.337112 180.73 149.74 171.37 1.207 312 176.26 131.73 157.28 1.338113 179.86 147.47 172.54 1.220 313 179.12 126.40 159.89 1.417114 180.81 143.98 171.70 1.256 314 171.79 127.54 155.09 1.347115 179.10 150.02 171.65 1.194 315 171.09 126.38 152.94 1.354116 167.30 138.93 164.01 1.204 316 158.16 125.19 145.23 1.263117 170.91 151.12 169.56 1.131 317 163.54 126.54 147.44 1.292118 181.63 145.19 174.09 1.251 318 162.86 130.51 148.61 1.248119 182.30 144.19 174.78 1.264 319 164.06 121.90 146.75 1.346120 181.38 150.17 174.04 1.208 320 160.51 120.46 145.88 1.332X 176.54 144.91 168.88 1.22 X 162.82 129.57 152.47 1.27S.D. 4.48 5.93 4.45 0.05 S.D. 11.49 14.69 10.50 0.12%CV 3 4 3 4 %CV 7 11 7 9201 161.11 121.90 148.43 1.322 401 178.81 150.89 171.83 1.185202 149.84 122.17 145.23 1.226 402 167.98 131.64 151.06 1.276203 157.14 117.31 140.75 1.340 403 169.69 130.59 152.21 1.299204 154.83 108.99 140.26 1.421 404 169.05 134.11 154.43 1.261205 157.00 114.98 139.25 1.365 405 170.65 132.91 153.75 1.284206 163.80 119.05 145.75 1.376 406 172.59 133.04 157.23 1.297207 158.95 119.25 143.38 1.333 407 161.48 128.68 149.00 1.255208 161.14 119.67 143.09 1.347 408 154.38 126.53 144.67 1.220209 163.15 123.68 146.53 1.319 409 172.66 139.06 159.68 1.242210 158.86 122.21 143.01 1.300 410 170.73 136.35 157.81 1.252211 152.93 115.44 138.75 1.325 411 162.70 131.17 152.52 1.240212 158.78 115.47 140.93 1.375 412 165.33 135.94 155.44 1.216213 152.60 115.05 138.94 1.326 413 167.49 140.65 155.41 1.191214 151.90 111.16 135.54 1.366 414 165.96 141.75 155.16 1.171215 151.58 114.23 138.31 1.327 415 169.22 141.60 157.70 1.195216 162.42 119.32 143.75 1.361 416 171.28 142.79 159.33 1.200217 161.96 122.68 147.00 1.320 417 173.10 143.03 159.83 1.210218 160.14 124.11 144.32 1.290 418 174.01 142.58 160.23 1.220219 158.69 119.38 140.37 1.329 419 172.66 139.06 159.68 1.242220 162.86 126.31 146.05 1.289 420 170.73 136.35 157.81 1.252X 157.98 118.62 142.48 1.33 X 169.03 136.94 156.24 1.24S.D 4.23 4.39 3.37 0.04 S.D. 5.14 5.85 5.33 0.04%CV 3 4 2 3 %CV 3 4 3 3147148July,plotno.1990NIR R G NIR/Rplotno. NIR R G NIR/R101 179.58 131.47 155.31 1.366 301 166.70 121.02 141.05 1.377102 180.86 133.57 155.54 1.354 302 158.53 126.89 141.57 1.249103 179.69 151.40 164.06 1.187 303 167.67 157.44 157.10 1.065104 171.06 130.99 149.80 1.306 304 168.56 139.63 149.15 1.207105 170.56 130.52 149.36 1.307 305 171.80 131.02 151.41 1.311106 180.36 151.62 165.20 1.190 306 159.54 160.80 157.65 0.992107 172.02 147.54 155.47 1.166 307 156.89 121.26 137.09 1.294108 177.25 133.04 153.91 1.332 308 167.58 128.72 146.74 1.302109 180.21 144.95 162.47 1.243 309 161.04 124.74 142.83 1.291110 183.17 130.74 157.05 1.401 310 175.64 133.09 155.69 1.320111 180.67 141.41 162.49 1.278 311 177.73 130.97 157.28 1.357112 178.94 147.01 162.09 1.217 312 180.52 139.16 162.09 1.297113 177.85 168.63 171.85 1.055 313 178.50 126.94 153.16 1.406114 181.52 155.10 167.33 1.170 314 173.83 131.35 153.84 1.323115 179.17 141.91 160.60 1.263 315 173.70 127.25 150.73 1.365116 175.28 142.32 156.48 1.232 316 166.32 129.30 144.90 1.286117 173.09 146.36 155.17 1.183 317 168.04 125.04 145.10 1.344118 182.00 157.57 169.15 1.155 318 169.56 132.27 150.35 1.282119 178.10 164.62 170.31 1.082 319 167.49 119.43 143.10 1.402120 178.60 146.27 160.51 1.221 320 166.98 119.26 142.68 1.400X 178.00 144.85 160.21 1.24 X 168.83 131.28 149.18 1.29S.D. 3.61 10.95 6.35 0.09 S.D 6.44 10.79 6.66 0.10%CV 2 8 4 7 %CV 4 8 4 8201 184.28 149.05 168.40 1.236 401 182.33 143.85 162.07 1.268202 179.72 144.20 162.54 1.246 402 167.01 115.14 135.93 1.450203 184.11 153.28 169.53 1.201 403 166.14 116.01 137.99 1.432204 185.73 149.40 167.63 1.243 404 161.27 112.17 131.63 1.438205 186.49 156.22 172.33 1.194 405 172.04 118.94 142.44 1.446206 181.26 136.68 159.79 1.326 406 170.41 110.19 137.44 1.547207 179.35 142.28 161.01 1.261 407 160.49 116.02 133.16 1.383208 183.04 145.91 165.88 1.254 408 156.57 107.35 128.70 1.459209 183.48 152.81 169.78 1.201 409 157.56 109.70 131.10 1.436210 183.09 159.11 172.42 1.151 410 163.65 115.94 137.60 1.412211 181.01 146.56 163.11 1.235 411 161.28 112.79 131.41 1.430212 183.04 145.64 164.95 1.257 412 162.41 120.65 134.83 1.346213 179.98 147.47 165.44 1.220 413 158.63 120.59 131.95 1.315214 181.89 153.31 168.16 1.186 414 163.94 123.78 139.31 1.324215 185.07 158.05 171.57 1.171 415 166.28 123.13 140.61 1.350216 180.28 141.42 161.48 1.275 416 167.44 117.83 138.73 1.421217 180.52 148.85 164.54 1.213 417 164.44 112.60 131.73 1.460218 179.81 151.93 165.90 1.184 418 167.10 114.41 135.58 1.461219 180.52 151.43 167.70 1.192 419 168.67 110.90 137.81 1.521220 184.83 163.21 174.21 1.132 420 169.80 109.80 136.80 1.546X 182.38 149.84 166.82 1.22 X 165.37 116.59 136.84 1.42S.D. 2.17 6.31 3.98 0.04 S.D. 5.72 7.70 6.79 0.07%CV 1 4 2 4 %CV 3 7 5 5149Sept,plotno.1990NIR R G NIR/Rplotno. NIR R G NIR/R101 172.72 119.98 127.11 1.440 301 166.60 114.04 126.38 1.461102 172.60 119.06 129.84 1.450 302 155.65 112.31 117.83 1.386103 173.07 129.30 141.98 1.339 303 158.27 116.22 120.10 1.362104 175.05 128.23 137.00 1.365 304 169.94 128.10 135.00 1.327105 160.51 119.05 114.54 1.348 305 168.86 120.68 127.88 1.399106 174.17 138.40 146.79 1.258 306 157.47 113.11 122.21 1.392107 159.63 122.37 121.84 1.304 307 167.84 123.31 128.31 1.361108 170.07 122.78 129.79 1.385 308 160.54 120.98 119.96 1.327109 175.67 138.91 146.17 1.265 309 163.16 117.34 120.14 1.390110 177.69 136.22 144.81 1.304 310 166.93 130.16 128.96 1.282111 176.61 142.12 144.13 1.243 311 176.77 130.84 140.21 1.351112 169.90 122.27 129.96 1.390 312 179.62 141.00 149.89 1.274113 167.78 128.09 135.68 1.310 313 174.69 131.14 138.26 1.332114 175.62 137.91 147.11 1.273 314 169.59 128.07 131.56 1.324115 178.48 136.25 145.75 1.310 315 169.85 124.83 126.54 1.361116 169.12 121.00 135.37 1.398 316 170.23 135.21 127.74 1.259117 171.86 129.57 138.74 1.326 317 171.99 132.01 127.26 1.303118 172.19 137.24 148.21 1.255 318 170.27 134.83 124.16 1.263119 174.01 142.62 153.71 1.220 319 171.96 129.04 128.69 1.333120 174.46 135.36 145.15 1.289 320 169.89 128.53 124.21 1.322X 172.06 130.34 138.18 1.324 X 168.01 125.59 128.26 1.340S.D. 4.84 7.98 9.83 0.063 S.D. 6.13 7.88 7.60 0.050%CV 3 6 7 5 %CV 4 6 6 4201 168.74 125.51 129.86 1.344 401 178.51 149.69 155.58 1.193202 167.87 116.77 124.69 1.438 402 169.81 119.74 118.25 1.418203 165.80 122.02 122.41 1.359 403 163.73 123.23 115.21 1.329204 167.35 116.59 123.23 1.435 404 161.47 118.56 111.36 1.362205 168.14 121.57 123.12 1.383 405 167.89 120.38 115.93 1.395206 169.06 119.56 122.91 1.414 406 173.69 124.19 128.21 1.399207 167.53 118.95 121.23 1.408 407 167.77 123.72 117.83 1.356208 166.57 114.16 120.22 1.459 408 162.85 109.85 112.47 1.482209 169.67 118.58 123.41 1.431 409 163.48 109.52 114.25 1.493210 164.90 120.26 118.41 1.371 410 163.21 111.30 112.42 1.466211 167.77 119.00 119.73 1.410 411 170.88 128.38 128.77 1.331212 170.16 118.38 121.23 1.437 412 172.00 132.09 126.01 1.302213 165.19 119.36 121.10 1.384 413 168.86 125.17 118.22 1.349214 164.31 115.54 117.00 1.422 414 170.36 126.36 121.46 1.348215 164.59 118.25 119.40 1.392 415 170.85 126.72 122.79 1.348216 164.48 116.63 117.16 1.410 416 174.02 127.70 124.53 1.363217 167.22 119.56 126.74 1.399 417 168.91 119.15 117.83 1.418218 162.40 121.74 116.73 1.334 418 169.20 118.98 119.72 1.422219 165.67 114.78 116.75 1.443 419 170.86 122.14 126.21 1.399220 169.26 125.99 123.00 1.343 420 171.77 119.74 126.69 1.435X 166.83 119.16 121.42 1.401 X 169.01 122.83 121.69 1.380S.D. 2.05 3.06 3.36 0.035 S.D. 	 4.22 8.51 9.43 0.067%CV 1 3 3 3 %CV 	 2 7 8 5150May,plotno.1991NIR R G NIR/Rplotno. NIR R G NIR/R101 185.64 157.61 165.14 1.178 301 144.19 111.53 107.14 1.293102 176.11 133.33 137.64 1.321 302 136.03 105.72 103.08 1.287103 191.92 176.64 179.42 1.087 303 136.69 128.78 120.92 1.061104 203.31 184.61 191.36 1.101 304 161.36 135.44 128.08 1.191105 184.19 167.56 168.64 1.099 305 163.31 132.58 133.86 1.232106 157.33 159.06 164.39 0.989 306 124.44 103.53 106.64 1.202107 155.42 157.42 163.11 0.987 307 94.36 83.72 77.25 1.127108 169.89 151.92 158.94 1.118 308 162.53 133.67 134.42 1.216109 186.19 173.06 183.25 1.076 309 152.50 108.86 110.28 1.401110 200.92 166.92 173.33 1.204 310 154.92 101.19 108.17 1.531111 170.97 143.25 153.50 1.194 311 199.31 148.89 153.39 1.339112 179.61 152.56 159.61 1.177 312 168.44 113.17 118.33 1.488113 185.97 178.17 185.22 1.044 313 195.92 137.03 146.78 1.430114 197.69 174.22 184.86 1.135 314 185.50 131.00 141.53 1.416115 185.89 158.64 170.58 1.172 315 181.97 129.03 134.64 1.410116 179.39 167.92 175.08 1.068 316 173.14 121.89 122.14 1.420117 188.06 176.86 189.89 1.063 317 164.53 110.14 105.44 1.494118 191.25 191.03 196.97 1.001 318 171.58 118.33 118.50 1.450119 219.22 212.83 222.03 1.030 319 175.44 104.53 114.17 1.678120 218.28 206.47 214.36 1.057 320 157.28 104.72 103.56 1.502X 186.36 169.50 176.87 1.105 X 160.17 118.19 119.42 1.3584S.D. 16.27 19.07 19.65 0.082 S.D. 24.14 15.63 17.59 0.1506%CV 9 11 11 7 %CV 15 13 15 11201 155.97 91.22 116.22 1.710 401 172.97 167.47 162.25 1.033202 168.53 107.86 128.17 1.562 402 169.58 122.33 122.19 1.386203 138.86 78.58 95.11 1.767 403 158.44 119.06 119.25 1.331204 129.94 73.78 91.19 1.761 404 119.92 94.92 96.28 1.263205 127.67 74.33 83.92 1.718 405 159.50 118.72 117.75 1.343206 153.06 98.06 100.56 1.561 406 191.33 131.14 141.08 1.459207 148.67 96.97 107.25 1.533 407 183.50 123.03 125.03 1.492208 136.47 79.47 97.08 1.717 408 183.19 124.36 129.14 1.473209 140.17 85.11 96.67 1.647 409 152.69 99.92 107.78 1.528210 111.47 65.83 77.39 1.693 410 188.67 122.94 133.17 1.535211 131.70 86.27 96.30 1.527 411 185.36 145.78 140.31 1.272212 150.47 91.53 101.11 1.644 412 168.97 126.72 122.47 1.333213 132.42 77.28 96.14 1.714 413 158.61 128.89 119.36 1.231214 104.25 65.31 72.00 1.596 414 189.19 135.17 141.53 1.400215 132.50 88.56 95.83 1.496 415 193.31 141.83 143.36 1.363216 152.33 97.92 109.33 1.556 416 203.19 147.78 150.78 1.375217 150.03 111.42 122.44 1.347 417 198.75 153.42 154.08 1.295218 107.94 84.72 83.83 1.274 418 189.39 145.83 149.22 1.299219 117.00 79.94 88.53 1.464 419 192.36 148.64 155.11 1.294220 130.56 85.25 94.03 1.531 420 199.72 142.22 151.42 1.404X 136.00 85.97 97.66 1.591 X 177.93 132.01 134.08 1.3554S.D. 16.65 12.10 13.68 0.130 S.D. 19.91 17.23 17.15 0.1135%CV 12 14 14 8 %CV 11 13 13 8151June,plotno.1991NIR R G NIR/Rplotno. NIR R G NIR/R101 178.86 124.56 133.64 1.436 301102 173.81 108.17 117.97 1.607 302103 173.67 129.47 134.97 1.341 303104 165.42 106.11 114.31 1.559 304105 136.67 97.00 97.69 1.409 305106 173.61 131.00 136.86 1.325 306107 165.92 145.75 147.53 1.138 307108 150.61 103.67 110.58 1.453 308109 173.53 126.08 133.25 1.376 309110 159.83 94.25 102.39 1.696 310 --111 169.75 110.19 116.72 1.541 311 205.44 138.31 159.22 1.485112 166.17 109.56 118.97 1.517 312 --113 167.97 121.11 126.89 1.387 313 188.00 118.53 131.94 1.586114 166.14 114.81 122.47 1.447 314 180.11 114.22 126.22 1.577115 141.11 92.17 97.72 1.531 315 175.31 114.00 120.25 1.538116 149.44 115.50 118.39 1.294 316 --117 162.00 101.28 115.39 1.600 317 169.33 107.14 109.31 1.580118 174.11 148.00 150.83 1.176 318 168.61 117.78 115.06 1.432119 174.92 137.44 143.28 1.273 319 183.97 106.92 120.83 1.721120 173.97 126.36 133.25 1.377 320 158.61 98.14 98.47 1.616201 186.97 131.00 141.14 1.427 401202 194.69 142.11 148.67 1.370 402203 195.94 147.64 153.36 1.327 403204 195.92 149.39 155.44 1.311 404 --205 197.86 157.75 160.92 1.254 405 145.81 92.56 90.61 1.575206 169.75 113.44 122.31 1.496 406 176.94 94.89 113.72 1.865207 183.56 123.31 133.03 1.489 407208 185.56 130.14 140.81 1.426 408 175.83 109.44 117.53 1.607209 186.00 128.17 137.83 1.451 409210 183.94 130.39 138.89 1.411 410 161.81 99.47 103.31 1.627211 179.06 116.83 127.67 1.533 411 177.78 116.03 124.03 1.532212 177.61 114.33 122.50 1.553 412 161.42 130.53 131.03 1.237213 177.58 126.50 133.39 1.404 413 168.33 123.06 125.81 1.368214 179.61 126.39 133.81 1.421 414 163.53 91.39 98.31 1.789215 182.17 126.47 135.17 1.440 415 159.56 94.97 98.50 1.680216 180.25 113.83 122.94 1.584 416 183.61 98.25 112.86 1.869217 180.78 121.53 130.78 1.488 417 163.22 100.17 104.36 1.629218 159.61 109.58 115.69 1.457 418 159.36 90.92 95.28 1.753219 175.00 113.89 124.31 1.537 419 168.36 92.22 99.89 1.826220 175.83 127.53 134.42 1.379 420 177.36 82.08 100.64 2.161152July,plotno.1991NIR R G NIR/Rplotno. NIR R G NIR/R101 301 143.17 97.28 116.89 1.472102 302 137.44 86.75 106.50 1.584103 303 --104 304 148.31 84.86 104.28 1.748105 - - 305 144.06 66.33 86.92 2.172106 306 111.06 99.17 104.19 1.120107 307 --108 - - 308 --109 309 124.75 77.47 88.94 1.610110 310 128.78 57.61 79.17 2.235111 311 162.53 58.00 88.19 2.802112 312 129.28 80.17 102.67 1.613113 313 149.86 52.94 79.83 2.831114 314 132.75 60.69 83.44 2.187115 315 146.56 64.89 89.28 2.259116 316 --117 317 152.72 66.08 91.94 2.311118 318 146.03 84.36 107.47 1.731119 319 148.83 65.06 93.83 2.288120 320 152.39 68.36 94.78 2.229201 149.97 88.83 110.89 1.688 401 --202 143.11 79.97 103.61 1.790 402 170.17 77.31 105.28 2.201203 132.11 79.19 97.61 1.668 403 157.36 82.22 102.92 1.914204 119.08 72.81 85.39 1.635 404 --205 119.67 74.11 83.56 1.615 405 151.64 75.56 102.64 2.007206 131.75 65.58 80.22 2.009 406 186.39 84.08 119.19 2.217207 129.47 68.97 87.25 1.877 407 --208 128.86 71.28 89.19 1.808 408 177.06 95.14 128.97 1.861209 136.61 71.92 96.75 1.899 409 --210 122.50 65.89 82.44 1.859 410 175.14 80.92 118.89 2.164211 133.78 62.03 84.00 2.157 411 186.28 96.83 129.31 1.924212 129.75 53.22 75.00 2.438 412 183.83 94.81 126.69 1.939213 121.06 71.61 84.92 1.691 413 186.92 95.17 127.19 1.964214 98.22 55.06 62.14 1.784 414 181.78 81.83 120.86 2.221215 116.53 61.06 74.94 1.908 415 184.64 93.97 131.81 1.965216 145.42 72.72 99.31 2.000 416 194.03 92.39 134.42 2.100217 127.53 73.89 97.86 1.726 417 188.06 97.50 132.94 1.929218 119.03 73.17 87.81 1.627 418 179.08 85.08 117.44 2.105219 121.36 72.14 88.58 1.682 419 182.33 88.92 125.69 2.050220 108.86 71.50 77.36 1.523 420 171.19 70.08 104.64 2.443

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