u mull m: llHHHIJTHIIVllllllllflfll MR 3 12 293 00654 4708 ' . £1.51“ ' M'r' fl—d‘ w— :3- . .‘ - Q - . -.._. I, ‘ ’ {.- n_._,1 I... -' i ‘ - 3' '4“..- U “y '— _.. *0 '0‘ " — - ”' _ — 9 I O . 4 .. { vi. . 1‘91””- 7“ ' I ‘ subvn-v-t—J This is to certify that the dissertation entitled DETECTION, INFLUENCE AND ECONOMICS OF ANNUAL GRASS INTERFERENCE ON SOYBEAN [CLYCINE MAN (L.) MerrJ presented by Dale Robert Mutch has been accepted towards fulfillment of the requirements for PhD degreein £1er and Soil science Date {1%ng [73% MS U it an Affirmative Action/Equal Opportunity Institution 0-12771 MSU RETURNING MATERIALS: Place in book drop to LjBRARJES remove this checkout from .—::—-. your record. FINES will be charged if book is returned after the date «oxfi“” stamped below. _ i) W13 a 10:? DETECTION, INFLUENCE AND ECONOMICS OF ANNUAL GRASS INTERFERENCE ON SOYBEAN [GLYCINE MAX (L.) MerrJ By Dale Robert Mutch A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Crop and Soil Sciences 1986 ABSTRACT DETECTION, INFLUENCE, AND ECONOMICS OF ANNUAL GRASS INTERFERENCE ON SOYBEAN [GLYCINE MAx (L.) MerrJ By Dale Robert Mutch The development of selective postemergence grass herbicides has enhanced the implementation of Integrated Weed Management systems. A knowledge of the influence of different weed species, their density, and allowable duration in a field, must be known for effective and economical postemergence herbicide applications. An ecological quadrat sampling method provided the most efficient scouting method for prediction of weed infestations requiring postemergence herbicide treatments in row crops. Giant foxtail (Setaria faberii Herm.) and fall panicum (Panicum dichotomiflorum Michx.) infestations resulted in significant soybean [Glycine max (L.) Merr.] yield reductions. Soil type and moisture influenced the degree of soybean yield loss. Generally, annual grass interference were greater on sandy loam soil as compared to loam soil. 0n loam soil, dry conditions resulted in greater yield loss, while annual grass interference on sandy loam soil was greater during a high moisture season. In 1983 at annual grass weed densities of 14, 28. 56 plants/M2 as well as the natural population of weed reduced soybean yield as percent of weed free control 52, 51, 51, and 51% on sandy loam Dale R. Mutch soil high moisture, respectively. At the same weed densities on loam soil with high moisture, soybean yield reductions were 19, 20, 26 and 26% respectively. In 1984 weed densities of 14, 28, 56 and 70 plants/M2 on sandy loam soil with low moisture, reduced soybean yield 46, 44, 41, and 55%, respectively. At these same weed densities on loam soil with low moisture soybean yield reductions were 34, 54, 38, and 46%, respectively. Soybean yield response curves were plotted by a non-linear model to predict annual grass interference by density and duration on soybean yield. Based on these yield response curves, tables on a computer spreadsheet were developed which predict net profit or loss from postemergence grass herbicide application at several points during the soybean growing season. Input variables to this procedure include expected yield goal bu/A, soybean price $/A, and herbicide application cost $/A. DEDICATION TO MY SONS NICOLAS ABRAM MUTCH AND LUCAS AVERY MUTCH SPECIAL DEDICATION TO BENJAMIN TORREY MUTCH UNDERSTANDING "I'll lend you for a little time a child of mine, He said, "For you to love the while he lives, and mourn for when he's dead. It may be six or seven years, or twenty two or three; But will you, ’til I call him back, talee care of him for Me? He’ll bring his charms to gladden you, And should his Stay be brief; You'll have his lovely memories as solace for your grief. I cannot promise he will Stay, since all from earth return, But there are lessons taught down there I want this child to learn. I 've looked this wide world over in my search for teachers true, And from the throngs that crowd life's lanes, I have selected you. Now, will you give him all your love, nor thinl: the labor vain, Nor hate Me, when I come to call to take him back again?" I fancied that I heard them say, "Dear Lord, Thy will be done, For all the joy Thy child shall bring, the risk of grief we'll nm. We’ll shelter him with tendcmess, We'll love him while we may, And for the happiness we've known, forever grateful Stay. But should the Angels call for him much sooner than we planned, We'll brave the bitter grief that comes, and try to understand. " ' Written by Edgar Guest l l ACKNOWLEDGEMENTS The undertaking of this degree has been long, yet very satisfying. Much has been learned and many people need to be recognized for their support, assistance, and guidance. First and most important are my three major professors. Dr. Jim Kells, is acknowledged for his assistance, knowledge, friendship, and guidance through my later stages of my research, and production of this document. Dr. Bill Meggitt for his guidance, sharing experiences, and support. Lastly, Dr. Michael Barrett who accepted me as a graduate student and remains a good friend. I also want to express my gratitude to my three other committee members. Dr. George Bird, for his leadership, friendship and for being an exceptional boss. Dr. Donald Penner for his friendship, knowledge, and availability during tough times. Lastly, Dr. Alan Putnam for his inputs, guidance, and leadership. I want to thank the Michigan Cooperative Extension Service for allowing me to pursue this degree while working. In particular the Agriculture and Marketing Staff in 11 Ag Hall, and the field staff agents are recognized for tremendous support and putting up with me. Special thanks is given to Dr. Oran Hesterman, Dr. Bruce Branham, Roch Gussoin, Mark Swartz, and Pat Michalak for their technical assistance. A Special acknowledgement is given to Nancy Busch for her dedication and accuracy in typing this dissertation. iii TABLE OF CONTENTS INTRODUCTION ................................................... 1 CHAPTER 1 - REVIEW OF LITERATURE ............................... 2 NEED BIOLOGY ............................................... 2 NEED SEEDS ...... .... ....................................... 3 NEED COMPETITION/INTERFERENCE .............................. 6 Soybean density and row widths ........................ 7 Annual grass density on interference to soybean.......8 Annual grass duration on interference to soybean ...... 9 Allelomediation (indirect sources) ................... 10 Allelopathy .......................................... 10 Methods to study interference ........................ 12 SCOUTING FOR WEEDS... ..................................... 17 POSTEMERGENCE HERBICIDES IN SOYBEAN ....................... 19 ECONOMIC THRESHOLDS IN SOYBEANS ........................... 20 LITERATURE CITED .......................................... 25 CHAPTER 2 - A COMPARATIVE ANALYSIS OF THREE NEED SAMPLING METHODS FOR RON CROPS .................... 31 ABSTRACT. ................................................ .31 INTRODUCTION ....... . ...................................... 32 MATERIALS AND METHODS ..................................... 33 General site description ............................. 33 iv Heed infestation assessment studies .................. 34 RESULTS AND DISCUSSION .................................... 39 M-pattern ............................................ 4O Quadrat method ....................................... 46 Five-row method ...................................... 46 CONCLUSIONS ............................................... 49 The M-pattern ........................................ 49 The Quadrat method ................................... 49 The S-row method ..................................... 49 LITERATURE CITED....... ...... . ...... . ..................... 52 CHAPTER 3 - THE INFLUENCE OF ANNUAL GRASS INTERFERENCE ON SOYBEAN PERFORMANCE ............................... 53 INTRODUCTION .............................................. 55 MATERIALS AND METHODS.. ................................... 57 General field procedures ............................. 57 Influence of annual grass density and duration on soybean performance .................................. 58 RESULTS AND DISCUSSION .................................... 60 CONCLUSIONS ............................................... 76 LITERATURE CITED .......................................... 88 CHAPTER 4 - APPLYING ECONOMICS TO HEED INTERFERENCE DATA ...... 89 ABSTRACT .................................................. 89 INTRODUCTION .............................................. 90 MATERIALS AND METHODS... .................................. 92 RESULTS AND DISCUSSION .................................... 93 CONCLUSIONS ............................................... 98 LITERATURE CITED ......................................... 102 LIST OF TABLES Table Page CHAPTER 2 1 Relative weed abundance as determined by Quadrat sampling method ...................................... 39 2 Comparison of time, cost, and efficiency of three weed infestation sampling methods .................... 45 CHAPTER 3 1 Precipitation at East Lansing ........................ 59 2 Effect of annual grass density and duration on soybean performance, at site I (sandy loam) 1983 ..... 62 3 Effect of annual grass density and duration on soybean performance, at Site II (loam) 1983 .......... 63 4 Effects of annual grass density and duration on soybean performance, at site I (sandy loam) 1984 ..... 65 5 Effects of annual grass density and duration on soybean performance, at site II (loam) 1984 .......... 67 6 Soybean yield as influenced by annual grass density and full season duration in 1983 and 1984....68 CHAPTER 4 1 Predicted soybean yield values as influenced by annual grass density and duration .................... 94 2 Influence of weed density and duration on economic return from postemergence grass herbicide application for different soil type and moisture conditions ...................... . .................... 96 vi Influence of weed density return from postemergence application for different conditions...... .......... Influence of weed density return from postemergence application for different conditions........ ........ Influence of weed density return from postemergence application for different conditions...... .......... vii and duration on economic grass herbicide soil type and moisture ...97 and duration on economic grass herbicide soil type and moisture ....... ....................99 and duration on economic grass herbicide soil type and moisture ........... ...............100 LIST OF FIGURES Figure Page CHAPTER 1 1 Experimental plot where weed species was established in the center of the row in uniform soybean stand....16 2 The sphere of influence of a Single weed species on neighboring soybean plants ........................... 16 CHAPTER 2 1 M-pattern sampling method ............................ 36 2 Quadrat sampling method .............................. 38 3 Five row sampling method ............................. 42 4 Distributions of velvetleaf and hemp dogbane when sampling the field with a M-pattern ........... . ...... 44 5 Contour map of hemp dogbane abundance, using the 5-row weed sampling method.. ......................... 48 6 Contour map of velvetleaf abundance, using the 5-row weed sampling method ........................... 51 CHAPTER 3 1 Influence of annual grass interference on soybean yield in21983. Annual grass population was 14 plants/M ............................................ 71 2 Influence of annual grass interference on soybean yield in21983. Annual grass population was 28 plants/M ....... ...... ...............................73 3 Influence of annual grass interference on soybean yield in21983. Annual grass population was 56 plants/M . ............................... . ........... 75 viii Influence of annual grass interference on soybean yield in 1983. Annual grass natural population ...... 78 Influence of annual grass interference on soybean yield in21984. Annual grass population was 14 plants/M ............................................ 80 Influence of annual grass interference on soybean yield in21984. Annual grass population was 28 plants/M ............................................ 82 Influence of annual grass interference on soybean yield in21984. Annual grass population was 56 plants/M ............................................ 84 Influence of annual grass interference on soybean yield in 1984. Annual grass natural population ...... 86 ix INTRODUCTION "Cursed is the ground for thy sake; in sorrow shalt thou eat of it all the days of thy life; thorns and thistles shall it bring forth to thee; and thou shalt eat the herb of the field" (Genesis III:17-18). Needs were recorded as early as the biblical times. Hence, the interference from weeds on desirable crops is not a new field of study. In the 1800's and early 1900's, weeds were managed principally by various non-chemical methods such as crop rotation, cultivation, and hand hoeing. The development of organic herbicides has resulted in a change in weed management strategies. In a report on Michigan pesticide use in soybeans [Glycine max (L.) Merr.] (71), it was reported that in 1978, 96% of the soybean acres were treated with herbicides for weed control as compared to 64% in 1970. In the same report, herbicides were used to control weeds on 67% of the acres of five field crops as compared to 24% and 1.5% for insecticides (insect control) or disease control, respectively. Therefore, herbicides have become an accepted practice in agriculture. Heed interference with soybean is a complex subject requiring the consideration of numerous variables. For example, weed biology, weed seed production and dormancy, weed competition, selective herbicide availability, and the economic threshold of Specific weeds on soybean performance. 2 The objective of this literature review is to identify previously reported research and desirable future research concerning annual grass interference in soybean. CHAPTER 1 REVIEW OF LITERATURE NEED BIOLOGY Needs have been grouped according to their similar life cycles. The three major classifications of plants, annuals, biennials, and perennials, are also applicable to weeds. Annuals complete their life cycles in one growing season. Annual weeds which complete their life cycle during spring to fall are referred to as summer annuals. Annual weeds which complete their life cycle during fall to Spring are referred to as winter annuals. The majority of problem annual weeds in soybean are summer annuals. Biennial weeds require two growing seasons to complete their life cycle. A rosette is usually formed the first year, and the second year a flower stalk with viable seed is produced followed by death. Perennial weeds live for 3 or more years. Reproduction is commonly by propagation and spread by asexual means. Seed can be produced in all years, however, it is not uncommon for most of the seed to be nonviable, for example, quackgrass [AgrOpyron repens (L.) Beauv.] In contrast, johnsongrass [Sorghum halpense (L.) Pers.]seeds remain viable. 3 The understanding of a weed life cycles can enhance weed control in soybean. Generally, summer annual weeds are predominant in Michigan. Most herbicide usage in Michigan is directed for the control of annual weeds. Biennial weeds are not as common in soybean and therefore, not a major concern in soybeans in Michigan. Perennial weeds are a problem in Michigan soybeans. Furthermore, perennial weeds are difficult to control, due to their mechanisms of reproduction. HEED SEEDS The longevity of weed seeds can be influenced by dormancy, depth of burial, or tillage. Harper 1977 (37) concluded that 1) long lived seeds are characteristic of disturbed habitats, 2) most long lived seeds are annuals or biennials, 3) small seeds tend to have much greater longevity than large ones. Beal (7) and Duval (34) established long term burial studies. Both studies concluded that seeds that retained the ability to germinate the longest were from weeds Species. Odum (63) found viable seed in common lambsquarters (Chenopodium album L.) and corn spurry (Spergula arvensis L.) seed dated to be 1700 years old. The natural decline in weed seeds in buried soil could be by soil pathogens, predation, or desiccation. Radosevich and Holt (68) reported that possibly the best way to handle weed seeds in the soil is to leave them buried in order to maintain dormancy and to allow their eventual death by predation or senescence. 4 Therefore, increased use of no-tillage systems in soybean could impact weed seed pOpulations in the soil. However, extremely proficient weed control (100%) would be required to prevent the reestablishment of weed seeds in the upper 2.5 cm of the soil surface. A comparative study was reported by Palmbald (65) where nine weed species were evaluated for seed production per pot as influenced by weed population density. The highest density 200- fold seed input never resulted in even as much as a 2 fold output of seed. It can be concluded from this experiment that plant density—dependent mortality and plasticity together regulate the seed output of a population. Therefore, given the proper conditions, very low pOpulations of weeds have the ability to produce massive quantities of seeds. It is known that annual weeds are capable of prolific seed production. In 1932, Stevens (80) reported that Single plants of green foxtail [Setaria viridis (L.) Beauv.] produced 34,000 seeds, barnyardgrass[Echinochloa crus-galli (L.) Beauv.] produced between 2,000 to 4,000 seeds (41), 1977, common lambsquarters produced between 13,000-500,000 seeds (41), 1977, and redroot pigweed (Amaranthus retroflexus L.) 117,400 seeds (80). Schweizer and Zimdahl (74) reported in a 6-year continuous corn (Zgg_mgy§_L.) study that annual application of atrazine (2- chloro-4 ethylamino-G-isopropylamino-§;triazine) at 1.7 kg/ha resulted in very few weed seeds produced during a 5-year period and no weed seeds produced during a sixth year. In this same study however, 3 years of no atrazine applications resulted in a 5 weed seed reserve in soil of 648 million seeds/ha, half the population of the start but a sufficient reserve to reestablish weed populations. Roberts (69) and Dunham et al. (32) reported that tillage practices alone failed to reduce the number of weed seeds in soil. Herbicides, tillage, and crop rotations however, could maintain weed seeds in soil at a level of 25 million or less/ha (69). Robertson (70) reported that the reservoir of seeds in ’agricultural soils at four locations in Minnesota ranged from 9 to 430 million/ha. Chancellor (19) reported 32 locations in England had reservoir of weed seeds in soil that ranged from 15 to 237 million/ha. Even though weed seed production can be managed, the weed seed pool in the soil remains (68). Needs have many mechanisms of dispersal. Under natural field situations, weeds are disseminated by wind, water (flooding), animals, and humans (machinery). Therefore, it has become difficult for the vast majority of farm land to eliminate their weed seed pool in the soil with traditional herbicide applications. Dormancy of weed seeds is well documented, however, the mechanisms of dormancy are not well understood. Certainly, a complete understanding of these Specific dormancy mechanisms would enhance the feasibility of eradication of a weed seed pool. Heed seed reserves in agricultural soils are well documented. Additionally, these reserves are extremely difficult to eradicate. The accurate identification of weed species in 6 combination with weed interference research data could enhance the growers ability to make economic weed control decisions without increasing their seed reserve in the soil. NEED COMPETITION/INTERFERENCE Needs have been recognized as a pest in cultivated crops since ancient times. Some early observations of weed competition were reported by Decandolle (28) in 1832. He reported crop rotation decisions were based on the current planted crop not being inhibited by toxic substances left by the preceding crop. Today, in weed science this phenomenon would be evaluated for the potential of allelOpathic toxins. Brenchley (13) in 1920 reported that specific weed species could be associated with certain cultivated crops, while other weed species were common to all cultivated crops. She concluded that weeds compete with crops mainly in three ways, above ground for light, and below ground for nutrients and moisture. Putnam reported (67) weed competition (allelospoly) can be defined as the depletion of one or more limiting resources such as light, nutrients or water. Allelopathy is defined as the production of chemicals by living or decaying plant tissue which interferes with the growth of a neighboring plant. Allelomediation (indirect sources) is defined as effects of physical or biological environment that interfere with growth of a neighboring plant. Interference is a term which combines all these mechanisms and can be defined as the effect the presence of 7 a plant has on the environment of its neighbor plant. This effect can be positive (additive) or negative (subtractive) and sometimes neutral (no effect) (67). Competitive Interference (Allelospoly) surveys conducted in 1971 indicated that the average United States soybean yield in 28 states was reduced by 12% due to weed interference (3). A more recent survey in 1984 indicates the soybean yield in the Lake States was reduced annually by 14% (20). It was concluded from these studies that known weed densities left in the field throughout the growing season will reduce soybean yield by a predictable amount. Soybean Density and Row Hidths. It has been determined that both soybean plant pOpulation, and the width of soybean row influences weed interference. Hhen soybean stands were less than 30 to 49 plants/M or row, soybean yield reductions were increased (81). Yield reductions were ten-fold when soybean plant populations were reduced from 30 to 49 plants/M of row to 10 plants/M of row (81). Soybeans planted in narrower row spacing than 102 cm resulted in increased yield from mixed annual grass and broadleaf weed populations due to increased early shading by the crop (17). Burnside concluded this yield response was due to earlier shading, more optimal distribution of plants and greater efficiency in use of light, nutrients, and moisture. Yield increases were reported from 76, 51, 25 cm wide rows of 10, 18, and 20% as compared to 102 cm row widths (80). Needs emerged for 7 weeks for 40 inch(102 cm) row as compared to 6 weeks for 20 inch(51 cm) row spacing (18). Soybean yield was less for 8 40 inch(102 cm) row spacing while weed seed yields were increased as compared to 20 inch(51 cm) rows(18). Several researchers have reported differences in soybean cultivar competitiveness to weeds (14, 15, 40, 58, 59). In contrast, Staniforth (77) reported four cultivars with different maturity dates, all demonstrated equivalent responses to annual weed competition. Hinson and Hanson (40) reported the primary factor involved in the soybeans competitive ability was the photoperiodic response. Annual Grass Density on Interference to Soybean. Knake and Slife reported on several studies conducted in Illinois on giant foxtail (Setaria faberi Herrm.) (50, 51, 52, 53, 54). They reported giant foxtail densities 54, 12, and 6 plants/ft. (30.5 cm) of row decreased soybean yield 28, 18, and 10% respectively. They concluded giant foxtail interference resulted in fewer pods per soybean plant with little effect on beans/pod or bean Size. Staniforth and Weber (79) reported yellow foxtail [Setaria viridis (L.) Beauv.] at densities of 6 and 12 plants per foot of soybean row reduced soybean yield 3 and 11% respectively when left in the field all season. They reported weeds delayed maturity about 1 day, decreased height about 2 inches (5 cm) and increased lodging of soybeans 2 to 6%. Staniforth (78) evaluated three foxtail weeds for their interference on soybean. He concluded giant foxtail was more competitive than either yellow foxtail or green foxtail due to more vigorous growth and increased dry matter production. 9 Annual Grass Duration on Interference to Soybean. Knake and Slife (51, 52) studied the effect of giant foxtail seeded the same day as the soybean, 3, 6, 9, 12 weeks later compared to a weed-free control on soybean yield. The average giant foxtail plant density was 45 plants/ft (30.5 cm). Soybean yield was reduced only when foxtail seed was planted with soybeans. Staniforth and Weber (79), evaluated yellow foxtail effect on soybean yield when removed at different soybean growth stages. They concluded foxtail infestations prior to soybean stage 5 (nine to ten trifoliolate leaves unrolled) did not cause severe yield reductions. Soybean yield reductions increased when weeds were left in the field to stage 7 (pods plainly evident in tops of plants) of the soybean were greatest from weeds left in the field until stage 9 (top pods almost fully develOped with beans approaching "green bean" stage) or soybean maturity. Dawson (26) reported annual weeds which emerged soon after field bean planting caused Significant yield reductions in field beans, but those emerging 5 to 7 weeks later had no effect on yield. The duration period before weed interference affecting soybean yield may be related to soil moisture. Hammerton (36) reported giant foxtail interference was greatest when soil moisture was adequate early in the season. Staniforth (76) concluded soybean yield reduction was least from Setaria Spp. when soil moisture was a) adequate over the whole season, b) limiting over the whole season, or c) limiting to the end of vegetative growth stage and then adequate to soybean maturity. Young, Hyse, and Jones (83) reported minimum density of quackgrass required to reduce soybean 10 yield is not static, but may be related to soil moisture supply. Zimdahl (84) concluded the greatest soybean yield reductions occurred when water was limiting during the reproductive period, or when total soil moisture was limiting for the whole season. ‘ Allelomediation (indirect sources). Heed densities in the field all season increased soybean harvesting losses from a combine harvester by 3.5% (66). Smooth pigweed,(Amaranthus hybridus L.) caused greater soybean harvest loss as compared to giant foxtail (61,62). Harvesting soybean prior to weed desiccation due to frost, increased soybean threshing and separating losses when forward speed increased from 1 to 3 mph (61). The author is unaware of any literature citing an association between insects or diseases with annual grasses which decrease soybean yield. Allelopathy. Giant foxtail has been reported to have allelopathic potential. Schreiber and Williams (73) reported that decaying roots of giant foxtail greatly inhibited corn root growth. Bell and Koeppe (8) reported that giant foxtail inhibited corn growth 35% by an allelopathic mechanism while a 90% reduction resulted from competition and allelopathy. Bhowmik and Doll (11) supported these data and found aqueous extracts of giant foxtail residues reduced radicle and coleoptile growth of corn. Incorporation of these residues in the soil inhibited seedlings of soybean and corn height and fresh weight. Fall panicum (Panicum dichotomiflorum Michx.) has been reported to have allelopathic potential. Bhowmik and Doll (10) reported water extracts of fall panicum plant residues inhibited 11 growth of soybean hyopcotyl. They also reported that fall panicum plant residues mixed in sand resulted in inhibition of height, growth, and fresh weight of soybean Shoots and roots. Fuerst and Putnam (35) outlined the requirements to prove competitive interference and allelopathic interference. Their steps to prove competitive interference are 1) Identification of the symptoms of interference, 2) Demonstration that the presence of the agent is correlated with reduced utilization of resources by the susceptible weed, 3) Demonstration of which resources depleted by the agent are limiting resources and 4) Simulation of that interference in the absence of the agent by reduction of the supply of resources to levels that occur during interference. They described proof of allelopathic interference as 1) Identification of the symptoms of interference, 2) Isolation, assay, characterization and synthesis of compounds, 3) Simulation of the interference by supplying the toxin as it is supplied in nature and 4) Quantification of the release, movement, and uptake of the toxin. Without these determinations, the researcher must classify the weeds influence on the crop as interference. Review of the literature indicates annual grass weeds can reduce soybean yield. The literature reports giant foxtail competition against soybean to be ordinarily caused by limiting water. Allelopathic potential has been reported for giant foxtail and fall panicum to soybean. From the literature reviewed, the term interference appears more appropriate in both cases. AS described previously by Fuerst and Putnam, neither competition nor allelopathy were proven. 12 Methods To Study Interference: The first method to be discussed is called an additive design. These designs usually involve two species, a crap and a weed which are grown together. Generally, the crop density is held constant while weed densities are varied. Therefore, a bioassay is established to interfere with the crap species. The evaluation is usually determined by crop seed production as compared to weed density. This approach is used widely because of the relevance to many field Situations where one species is established in an area at a fixed density, the area is then "invaded" by the other (68). They reported the value of the additive approach is the ability to determine directly the cost (crop loss) that is associated with the absence of weed control. A substitutive experiment is a method or replacement series experiment which was introduced by DeHit 1960. The main characteristic of this method is that the proportions of two species I and J in mixture are varied while overall density I and J is maintained constant (37). Four different predictive models have been proposed of these interactions by Harper (37). Harper proposed a relative crowding coefficient (RCC) when yield in mixtures can be determined: Relative Crowding Mean yield per plant Mean yield per plant Coefficient of A = A in the mixture Of A in pure stand__ with respect to 3 Mean yield per plant - Mean yield per plant B in the mixture of B in pure stand and a Relative Yield (RY) when combined yield cannot be predicted: RY Yield of A in mixture Yield of A in pure stand 13 A large RCC value indicates a high degree of aggressiveness of one species relative to the other. Calculation of both species (RY) added together give the relative yield total (RYT). RYT values of about 1.0 indicates that the same resource is being utilized by both species. RYT values less than 1.0 imply mutual antagonism and greater than 1.0 imply species avoid competition, make different demands on resources or maintain a symbiotic relationship (36). Radosevich and Holt reported the substitutive method may create a more accurate assessment of competitiveness than the additive method. However, the series appears ideally suited for greenhouse studies, but artificial under field conditions. The systematic design method concept was introduced by Nelder. His designs evaluated single species interference, and consist of a grid of points with each point representing the position of the plant. Bleasdale modified Nelder designs for row crop interference evaluation. Two designs can be used, a "fan" design and “parallel row" design (68). Huxley and Maingu (42) utilized a systematic design for evaluation of intercropping systems. The systematic design Shows promise for interference studies however, establishment of the experiment may be time consuming and difficult. Coble (24) at North Carolina State University has develOped a new approach to studying weed interference called the sphere of influence. This method evaluates the influence of a Single weed Species on neighboring soybean plants. Soybeans are planted in non-weed 3-Mz plots and thinned to create a uniform stand. Heed 14 seeds are planted or weed transplants are placed in the middle of the row in between the soybean plants. It is essential that the weed plant emerges prior to or at the same time as the soybean plant. Each 3 M2 plot is replicated 10 times. Individual soybean plants in the plot are measured for yield. Therefore, the influence of the Single weed species in the soybean row can be evaluated for its direct effect on its neighboring soybean plants. This effect is measured for distance from the weed within the soybean row and the weeds effect on neighboring soybean rows (Figure 1). A sphere of weed influence can then be established for a single weed on soybean yield (Figure 2). A single weed will have greater influence on its direct neighbor, and therefore have a decreasing effect on the soybean plants in the row as distance from the weed Species increases, and in neighboring rows. When the sphere of the individual weed species is known, a competitive index of that weed species can be calculated and compared to other weed species. The advantages of this method are a) minimal Space is required, b) the experiment can be replicated several times, c) the experiment can be conducted with current field equipment, and d) several weed Species can be evaluated in the same experiment. Some disadvantages may be a) prevention from other weed interference is essential and b) the experiment is time consuming and labor intensive. 15 Figure 1. Experimental plot where weed species was established in the center of the row in uniform soybean stand. Figure 2. The Sphere of influence of a single weed species on neighboring soybean plants. 16 ................................ xb-_--_----_--_-----___--_----_-_ 3 m a = uniform soybean stand established b = weed species placed in the center of soybean row IOO % Yield 50- Reduction 17 SCOUTING FOR HEEDS In Michigan and throughout the United States, Integrated Pest Management (IPM)/CrOp Pest Management (CPM) programs evolved around insect pest scouting after crop emergence. Hence, much research was initiated addressing insect sampling, pOpulation, and life cycles of insects in crop production. Michigan State University (MSU) pioneered much of this research and hired pest scouts who monitored pests (diseases, insects, weeds and nematodes) in the Six regions of the state. In 1981, the MSU IPM program reorganized and developed Extension program leader positions to intensify scouting efforts in field crops, forestry, fruit, and vegetable. Since 1981, scouted field crop acreage has increased seven-fold and approximately 70,000 acres were monitored in 1985. The MSU Cooperative Extension Service Field Crop IPM program utilizes a computerized data base management system known as Cooperative Crop Monitoring System (CCMS) to summarize pest and crop information collected by scouts. Needs are the number one pests in field crops, representing approximately 80% of the recorded observations by the scouts. The availability of postemergence herbicides and cultivation in row craps, has increased the importance of early weed detection in field crops. For this reason, many IPM/CPM programs have introduced weed monitoring to their list of pests. Montana State University has developed a sampling method for wild oat (Avena fatua L.) in which they use a M-pattern and 20 samples 18 (4). The University of Kentucky scouts monitor a 30 foot strip in a marked 75 foot spot in the field in 5 locations of a 50 acre field (39). These same 75 foot strips are monitored throughout the season. The University of Illinois (9) reports accurate and thorough coverage of a field for weed scouting, without mention of a specific sampling procedure. The University of Missouri utilizes 5 samples for 60 acres or less for the weed scouting methods (29). Purdue University (46) reports observations should be made, preferably, at five locations in the field to provide a representative sample of the vegetation. In North Carolina, Cable (23) reports weed monitoring should occur 14 to 17 days after the soybean planting date, with one representative sample of the weed Species and their density recorded from a 30 cm band over a 10 step sampling area and repeated every 2 ha of the soybean field. Sampling methods for weeds vary and no clear cut information has been available to identify the best method of weed monitoring. However, weed scientists have reported (23, 60) that more samples, greater than 5 (the commonly used insect sample size) is needed to monitor weeds. Previously, the weed mapping discussed was for enhancement of early current season grower weed control decisions. Most IPM/CPM programs additionally provide late season weed maps. These maps highlight weed escapes and assist growers on evaluation of their herbicide program. A specific sampling procedure for this map does not appear to be as critical since it is an accumulation of seasonal scouting efforts. 19 POSTEMERGENCE HERBICIDES IN SOYBEAN Acifluorfen (sodium 5-(2-chloro-4-(trifluoromethyl)- phenoxy)-2-nitrobenzoate), and bentazon (3-iSOprOpyl-1H-2,1,3- dioxide) are commercially available to selectively control broadleaved weeds in soybean (48). Fomesafen (5-(2-chloro-4- (trifluoromethyl) phenoxy)-N-(methylsulfonyl)-2-nitrobenzamide), is a selective broadleaved herbicide in soybean expected to be available in 1986 or 1987 (43). Many selective postemergence grass herbicides are available in soybean. Diclofop- methyl(methyl 2-(4-(2,4-dichlorophenoxy) phenoxy) propanoate, a restricted use pesticide (1), fluazifop-P-butyl(R)-2(4-((5- (trifluoro-methyl)-2-pyridinyl) oxy) phenoxy) propanoate (14), and sethoxydim 2-(1-(ethoxyimino) butyl)-5-(2-(ethylthio) propyl)-3-hydroxy-2-cyclohexen-l—one (6) have been available to soybean growers since 1983. New selective grass postemergence herbicides which may become available for use in soybean in the future are DPX-Y6202 2-(4((6-chloro-2-quinoxalinyl) oxy) phenoxy) propionic acid, ethyl ester (33), and haloxyfop-methyl, methyl 2- (4-((3-chloro-5-(trifluoromethyl)-2-pyridinyl) oxy) phenoxy) propanoate (31). Oliver et al. (64) compared several postemergence herbicides for control of annual grasses in soybean. They reported the order of phytotoxicity was haloxyfop-methyl, sethoxydim, fluazifop-butyl. Sethoxydim efficacy on 25 cm giant foxtail was reported excellent by Lueshchen (57). Different tillage systems 20 were evaluated for total postemergence weed control by Kinsella and Burdick (49). They reported sethoxydim provided an advantage in grass species, giant foxtail, barnyardgrass, and volunteer corn control over alachlor (2-chloro-2',6'-diethyl-N- (methoxymethyl) acetanilide (2-chloro-N-(2,6-diethylphenyl)-N- methoxy-methyl acetamide) in no-till and conservation tillage or trifluralin a,a,a,-trifluoro-2,6-dinitroiflzfl-dipropylig-toluidine in conventional tillage soybeans. Harvey and Fawcett (38) reported phytotoxicity of sethoxydim, haloxyfop-methyl, fluazifop-butyl to wild prosso millet (Panicum milaceum L.) was greater when plants were 20 to 30 cm tall than 8 to 13 cm tall. Chaney and Kapusta (21) reported variation in the height of giant foxtail between locations greatly affected the control afforded by fluazifop-butyl, however, sethoxydim control was unaffected by weed height. The results presented have only touched a small proportion of data available on these new selective grass herbicides. Generally, haloxyfop-methyl and DPX-Y6202 have been reported to be the most effective new herbicides. Sethoxydim has provided greater annual grass control to taller weeds than fluzaifop-butyl. The first selective grass herbicide, dichlofop- methyl, is primarily used for wild oat control in the North Central region because of lack of activity on perennial grasses, such as quackgrass. ECONOMIC THRESHOLDS IN SOYBEANS Knake (55) reported that unlike insects, diseases and 21 nematodes, weeds will occur in cultivated row crops every year at pOpulation threshold levels that will cause severe crOp losses unless controlled. Chandler et al. (20) reported annual losses due to weeds in soybean in the Lake States (Minnesota, Wisconsin and Michigan) to be 142 million dollars. Shaw (75) reports more than 1800 weed species cause serious economic losses and each year, 10 to 50 different species of weeds infest each of our major food crops. Knake (55) reported, because there are many kinds of weeds with varying periods of germination and with highly divergent life cycles, they obviously cannot be managed by a single method. Therefore, weed scientists have initiated Integrated Weed Management System (IWMS). Shaw (75) reports an IWMS approach utilizes cultural, mechanical, biological, ecological and chemical methods in a directed agroecosystem approach. Blair and Parochetti (12) reported the use of widespread, weed-science IPM strategies require establishment of damage thresholds for weeds in various crOpping Situations. The development of selective postemergence herbicides in soybean has enhanced the success of the IWMS and the economic threshold concept in soybean. Application of these compounds, integrated with improved weed scouting methods and weed interference data, give growers alternate weed management strategies. Increased grower awareness for the total system and IPM was reported in a recent article in Agrichemical Age (47). Iowa estimates 10-15% of its field crop base, Nebraska 10% Wisconsin 8%, Indiana 7% and Illinois 5% of field crop land are scouted by 22 formal pest management programs (47). Michigan field crop acreage scouted still remains less than 1% (56). Anderson (2) reports IPM is a very key idea in most states because of the economic crunch in agriculture. A symposium on Economic Thresholds of Weeds was conducted at the Weed Science Society of America national meeting in 1985. Schreiber (72) reported environmental conditions such as temperature and moisture can significantly alter threshold levels by influencing crop and weed growth. He also concluded the economics of maintaining zero weed levels in the field is questionable in todays agriculture systems. Dawson (27) reported the concept of period thresholds. He reported lack of any weed control practice would result in annual weed "saturating populations" in almost all fields where annual crops are grown in North America in the 1980's. The "saturating population" of an annual weed can be defined as the numerical weed density that causes harmful effects to crop plants. In most row crOp situations, weeds can grow with the crap for a certain period of time before yield loss occurs. Therefore, a period threshold for postemergence weed control exists for many weed Species. Dawson (27) reported this to be at 5 weeks. Weeds that damage crops before or soon after emergence have a zero period threshold and must be controlled immediately. Fields which remain weed-free for an extended period of time after crop emergence can tolerate late weed emergence with no weed interference. Dawson reports this period threshold to be 10 weeks after crOp emergence. He concluded a critical period for 23 weed removal therefore, to be between 5 weeks and 10 weeks after crap emergence. A period concept could be different for different weed Species and crap grown. The period threshold concept therefore, given sufficient weed interference data could be utilized for postemergence herbicide applications on soybean. Researchers have utilized interference data to determine the economics of herbicide applications. Cable (22) reported a competitive index (CI) for 34 weed species. Each Species is given a competitive value from O to 10 where 10 represents the highest interference to soybean. CI = bl/b x 10 where CI is the competitive index value for the Species in question, b1 is the SlOpe of the linear regression line for the Species, b is the Slope for common cocklebur (Xanthium pensylvanicum Wallr.) (North Carolina's most interfering weed species in soybean) and the multiplier converts the index to a 0 to 10 scale. Through monitoring the field, a competitive load (CL) can be determined. The CL is the average number of species per 10 meter of soybean row. By totaling the CL for each Species, a total competitive load (TCL) can be determined for the weed species in the field (23). Each TCL accounts for 0.5% yield loss and the economics for postemergence herbicide application can be determined. Researchers at the University of Illinois have determined the effects of five weed Species in soybean (5). They reported predetermined yield reductions based on the densities of 2 anhual grasses and 3 annual broadleaf species in a field to calculate the economics from postemergence herbicide applications. Mutch 24 et al. (60) reported an equation to predict economic return from postemergence herbicide application for annual grass control in soybean. They reported Economic return = (yield with treatment) - (yield without treatment) x (soybean price) - herbicide cost). This process could be applied to any weed species or mixed population where adequate weed interference data exists. Unlike other predictive methods, this procedure allows for consideration of the economics of late postemergence herbicide treatments. Evaluation of weed thresholds are based on certain assumptions (22, 23). Cable reports 1) weed and soybean emerge at the same time, 2) no intraspecific competition among weed present, 3) soybean are grown in 76 cm rows, 4) weather is normal for crop growth. Mutch et al. (60), add potential harvesting losses from remaining vegetation and weed seed production on subsequent crops, as additional limitations. Weed interference data still needs to be collected (22, 23, 60). Weed scientists have initiated methods to implement weed thresholds into pest management programs (5, 22, 23, 60). Proper sampling by scouts can provide growers with the economics of postemergence herbicide applications. 10. 11. 12. 13. 14. 25 LITERATURE CITED American Hoechst Corp. 1976. HOE-23408, technical information bulletin. American Hoechst Corp., Agric. Chem. Dept., Somerville, N.J. pp. 7. Anderson, W.P. 1977. Weed Science: principles. West Publ. Co. New York. pp. 30-108. Anonymous, 1971. Weed losses in soybeans. 1971 National Soybean Loss Survey. Elanco Products Co., Chicago, Ill. Anonymous, 1984. Wild oat staging card. Montana State University. Anonymous, 1986. How many soybean weeds does it take to lose money? Agrichemical Age. January. pp. 42. BASF Wyandotte Corp. 1980. Poast, technical information bulletin. BASF Wyandotte Corp., Agricultural chemicals division. Parsippany, N.J. pp. 4. Deal, W.J. 1911. The vitality of seeds buried in the soil. Proc. Sec. Promot. Agric. Sci. 31:21-23. Bell, D.T., and D.E. Koeppe. 1972. Non competitive effects of giant foxtail on the growth of corn. Agron. J. 64:321-325. Beuerman, R.A., and J.P. Downs. 1982. Field Crop Scouting Manual. University of Illinois. Urbana. 117 pp. Bhowmik, P.C. and J.D. Doll. 1979. Evaluation of allelopathic effects of selected weed species on corn and soybeans. Proc. North Centr. Weed Conf. 34:43-45. Bhowmik, P.C. and J.D. Doll. 1982. Corn and soybean response to allelopathic effects of weed and crop residues. Agron. J. 74:601-606. Blair, B.D. and J.V. Parochetti. 1982. Extension implementation of integrated pest management systems. Weed Sci. Supp. 1 Vol. 30:pp. 48-53. Brenchley, W.E. 1920. Weeds of farmland. Longmans, Green and Co., London. pp. 159-174. Burnside, 0.C. 1972. Effects of weed competitiveness and twice normal herbicide rates on ten soybean varieties. Weed Sci. Soc. Amer. Abstr. No. 104. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 26 Burnside, 0.C. 1972. Tolerance of soybean cultivars to weed competition and herbicides. Weed Sci. 20:294-297. Burnside, 0.C. 1973. Influence of weeds on harvesting efficiency in soybeans. Weed Sci. Soc. Amer. Proceedings No. 6. Burnside, 0.C. and W.L. Colville. 1964. Soybean and weed yields as affected by irrigation, row spacing, tillage and amiben. Weeds 12:109-112. Burnside, 0.C., G.A. Wicks, P.D. Warnes, B.R. Somerhaler and S.A. Weeks. 1969. Effects of weeds on harvesting efficiency in corn, sorghum and soybeans. Weed Sci. 17:438-441. Chancellor, R.J. 1965. Weed seeds in the soil. Rep. A.R.C Weed Res. Org. (1960-4), pp. 15-19. Chandler, J.M., A.S. Hamill, and A.G. Thomas. 1984. Crop losses due to weeds in Canada and the United States. Weed Sci. Soc. Amer. Spec. Report 22. Chaney, D. and G. Kapusta. 1983. Postemergence soybean herbicides. North Centr. Weed Cont. Conf. Proc., 38. pp. 25. Cable, H.D. 1984. Multi-species number thresholds for soybeans. Weed Sci. Soc. Amer. symposium on weed thresholds. pp. 1-2. Coble, H.D. 1985. Weed index minimizes spraying guesswork. Soybean Digest, December 18-19. Coble, H.D. 1986. Personal communications. Cox, G.W. 1976. Laboratory manual of general ecology, pp. 32-37. Wm. C. Brown Co., Dubuque, Iowa. 232 pp. Dawson, J.H. 1964. Competition between irrigated field beans and annual weeds. Weeds 12:206-208. Dawson, J.H. 1984. The concept of period thresholds. Weed Sci. Soc. Amer. symposium on weed thresholds. pp. 1-2. DeCandolle, M.A.P. 1832. "Physiologie Vegetale" Tome III, pp. 1474-1475. Bechet Jeune, Lib, Fac. Med., Paris. Dierker, W.W. 1985. Field crop pests in Missouri. University of Missouri-Columbia. Manual 122. 241 pp. Dewit, C.T. 1960. On competition. Verslagen van landbouwkundige ondersoekingen. no. 66.8. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 27 Dow Chemical Co. 1982. Dowco 453 ME, technical information bulletin. Dow Chemical Co., Midland, MI. pp. 4. Dunham, R.S., R.G. Robinson, and R.N. Anderson. 1958. Crop rotation and associated tillage practices for controlling annual weeds in flax and reducing the weed seed pOpulation of the soil. Minn. Agric. Exp. Stn. Tech. Bull. 230,20 pp. DuPont Company. 1983. Assure, technical information bulletin. E.I. du Pont de nemours & Co.(Inc.).. Wilmington, De. pp. 5. Duval, J.W.T. 1902. Seeds buried in soil. Science 17:872-873. Fuerst, E.P., and A.R. Putman. 1983. Separating the competitive and allelopathic components of interference: Theoretical principles. J. Chem. Ecology. Vol. 9. No. 8 Hammerton, J.L. 1972. Effects of weed competition, defoliation and time of harvest on soybeans. Exp. Agr. 8:333-338. Harper, J.L. 1977. The population biology of plants. Academic Press, London. pp. 237-276. Harvey, R.G. and J.A. Fawcett. 1983. Wild prosso millet control in soybeans. North Centr. Weed Cont. Conf. Proc. 38:22. Herron, J., J. Martin and W.W. Witt. 1983. Kentucky integrated pest management scout manual. Univ. of Kentucky. Lexington. 100 pp. Hinson, K. and W.D. Hanson. 1962. Competition studies in soybeans. Crop Science 2:117-123. Holm, L., D.L. Plunknett, J.V. Pancho, and J.P. Herberger. 1977. The world's worst weeds: distribution and biology. University Press of Hawaii, Honolulu. Huxley, P.A. and Z. Maingu. 1978. Use of a systematic Spacing design as an aid to the study of inner-cropping: some general considerations. Exp. Agric. 14:49-56. ICI Americas Inc. 1984. Fomesafen, technical information bulletin. ICI Americas Inc., Agricultural chemicals division. Goldsboro, N.C. pp. 41. ICI Americas Inc. 1981. Fusilade technical information bulletin. ICI Americas Inc., Agricultural chemicals division. Goldsboro, N.C. pp. 41. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 28 Johnson, G. 1971. Results of a national soybean weed loss Survey. Weed Sci. Soc. Amer. Abstr. No. 134. Jordan, T.N. 1982. Field crop IPM decision-making guide. Purdue University, IPM-2. 187 pp. Kavazanjian, N. 1986. Is IPM still alive? Agrichemical Age January. pp. 38E. Kells, J.J. 1986. Weed control guide for field crops. Michigan State Univ. Ext. Bull. E-O434. pp. 58. Kinsella, J. and B. Burdick. 1983. Utilizing post- emergence chemicals for total weed control in soybeans. North Centr. Weed Cont. Conf. Proc., 38:16. Knake, E.L. and F.W. Slife. 1962. Competition of Setaria faberii with corn and soybeans. Weeds 10:26-29. Knake, E.L. and F.W. Slife. 1964. The effect on corn and soybeans from giant foxtail that begins growth at various times. Weed Science Society of America Abstract, p.26. Knake, E.L. and F.W. Slife. 1965. Giant foxtail seeded at various times in corn and soybeans. Weeds 13:331-334. Knake, E.L. and F.W. Slife. 1968. Effect of time of giant foxtail removal from corn and soybeans. Proceedings of the North Centr. Weed Cont. Conf., p.66. Knake, E.L. and F.W. Slife. 1969. Effect of time of giant foxtail removal from corn and soybeans. Weed Science 17:281-283. Knake, E.L. 1978. The weed science phase of pest management. Dep. Agron., Univ. of Illinois, Urbana. pp. 8. Lichey, J. and D.R. Mutch. 1985. Evaluation of non-profit grower associations in Michigan. Unpublished survey: 40 pp. LUeschen, W.E. 1983. A comparison of time and rate of application of Sethoxydim with soybean oil and petroleum oil for weed control in soybeans. North Centr. Cont. Conf. Proc. 38:11. McWhorter, C.G. and E.E. Hartwig. 1968. Effect of johnsongrass and cocklebur on the growth and yield of several soybean varieties. Weed Sci. Soc. Amer. Abstr., p.107-108. McWhorter, C.G. and E.E. Hartwig. 1972. Competition of johnsongrass and cocklebur with six soybean varieties. Weed Science 20:56-59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 29 Mutch, D.R.. G.W. Bird, J.J. Kells, P. Hart, 0.8. Hesterman, and R. Ruppel. 1985. Field crop scouting manual. Michigan State Univ. pp. 245. Nave, W.R. and L.M. Wax. 1971. Effect of weeds on soybean yield and harvesting efficiency. Weed Science 19:533-535. Nave, W.R. and L.M. Wax. 1971. Harvesting efficiency in soybean as influenced by weeds. Weed Sci. Soc. Amer. Abstr. No. 194. Odum, S. 1965. Germination of ancient seeds: floristical observations and experiments with archaeologically dated soil samples. Dan Bot. Ark. 24:2. Oliver, L.R.. D.G. Mosier and 0.W. Howe. 1982. A comparison of new postemergence herbicides for control of annual grasses. Weed Sci. Soc. Amer. Abstr.. p.17. Palmbald, 1.6. 1968. Competition in experimental studies on populations of weeds with emphasis on the regulation of population size. Ecology 49:26-34. Park, J.D. and B.K. Webb. 1959. Soybean harvesting losses in South Carolina. South Carolina Agriculture Experiment Station Circulation 123, 8 pp. Putnam, A.R. 1985. Weed allelopathy. Ifl_Weed physiology. 5.0. Duke. (Ed.) Vol. 1 Chapter 5. CRC Press. pp. 131- 156. Radosevich, S.R. and J.S. Holt. 1984. Weed ecology implications for vegetation management. John Wiley and Sons, Inc. New York, pp. 265. Roberts, H.A. 1968. The changing population of viable weed seeds in arable soil. Weed Res. 8:253-256. Robinson, R.G. 1949. Annual weeds, their viable seed population in the soil and their effects on yields of oats, wheat, and flax. Agron. J. 41:513-518. Ruppel, R.F., T.A. Dudek, C. Tiedgen, D.J. Fedewa, M.L. Holko, A.R. Budge. 1978. Pesticide use on Michigan field crops. Michigan State Univ. pp.11. Schreiber, M.M. 1984. Variation in weed number thresholds in corn and soybeans. Weed Sci. Soc. Amer. symposium on weed thresholds. pp. 1-2. Schreiber, M.M. and J.L. Williams. 1967. Toxicity of root residues of weed grass species. Weeds 15:80-81. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 3O Schweizer, E.E. and R.L. Zimdahl. 1984. Weed seed decline in irrigated soil after six years of continuous corn (Zea mays) and Herbicides. Weed Sci. 32:76-84. Shaw, W.C. 1982. Integrated weed management systems technology for pest management. Weed Sci. Supp.1 Vol., 3032-12. Staniforth, D.W. 1958. Soybean-foxtail competition under varying soil moisture conditions. Agron. J. 50:13-15. Staniforth, D.W. 1962. Responses of soybean varieties to weed competition. Agron. J. 54:11-14. Staniforth, D.W. 1965. Competitive effect of the foxtail species on soybeans. Weeds 13:191-193. Staniforth, D.W., and C.R. Weber. 1956. Effects of annual weeds on the growth and yield of soybeans. Agron. J. 48:467-471. Stevens, 0.A. 1932. The number and weight of seeds produced by weeds. Am. J. Bot. 19:784-794. Wax, L.M. and J.H. Pendleton. 1968. Effect of row Spacing on weed control in soybeans. Weed Science 16:462-465. Weber, C.R. and D.W. Staniforth. 1957. Competitive relationships in variable weed and soybean stands. Agron. J. 49:440-444. Young, F.L., D.L. Wyse, and R.J. Jones. 1982. Influence of quackgrass (Agropyron repens) density and duration of interference on soybeans (Glycine max.) Weed Sci. 30:614-619. Zimdahl, R.L. 1980. Weed-crop competition, a review. International Plant Protection Center, Oregon State University. Corvallis. p.198. CHAPTER 2 A COMPARATIVE ANALYSIS OF THREE WEED SAMPLING METHODS FOR ROW CROPS ABSTRACT Three unique weed monitoring methods were compared in 1984 in a 20 hectare irrigated, reduced-till corn (Egg may; L.) field at the Kellogg Biological Station in southwest Michigan, for evaluation of the field distributions of velvet leaf (Abutilon theophrasti medic.) and hemp dogbane (Apocynum cannabinum L.). The monitoring methods were compared for accuracy and efficiency, and consisted of: 1) walking through the field in an "M-pattern" (quick survey); 2) quadrat sampling (ecological survey); and 3) walking every fifth row (management survey). The quadrat sampling method was given the highest efficiency rating. The five-row sampling method was the most accurate, however, extremely labor intensive. The M-pattern required the lowest total labor, however, provided insufficient information on weed distribution, and therefore, was given a low efficiency rating. 31 32 INTRODUCTION Weed sampling or the early detection of weeds in row crOpS such as corn or soybean [Glycine max (L.) Merr.] provides a cornerstone for postemergence weed management in Integrated Pest Management (IPM)/Crop Pest Management (CPM) programs. These programs train scouts to help growers maximize profits by monitoring the growers fields and alerting him to potential pests (weed, insect, nematode, disease and crap physiological disorders) outbreaks. The pest information is used by the grower to enhance timely pesticide applications or to prevent unnecessary treatments. Initially, IPM/CPM programs evolved around insect pest scouting after crop emergence. With the availability of post emergence herbicides and cultivation in row crops, the benefits of early weed detection became apparent. For this reason, many IPM/CPM field crop programs have introduced weed monitoring to their lists of pests scouted. Sampling methods vary and no clear cut information has been available to identify the best method of weed monitoring. Montana State University reported a sampling method for wild oat (Avena fatua L.) in which they use a M-pattern with 20 samples (1). The University of Kentucky monitors a 30 foot strip in a marked 75 foot row in 5 different field locations for a 50 acre 33 field (5). These same 75 foot areas are monitored throughout the season. The University of Missouri and Purdue University take 5 weed samples for 60 acres or less (4, 2). The objectives of this research were to a) evaluate the M- pattern for post emergence weed control decisions, b) to compare the M-pattern sampling method with two other weed sampling methods and c) to determine which sampling method is the most cost effective for labor and accuracy. MATERIALS 8 METHODS General Site Description. Field studies were conducted in 1984 in a 20 hectare irrigated corn field at the Kellogg Biological Station (KBS) in Hickory Corners, Michigan. The site is characterized by a Kalamazoo sandy loam soil type with gentle slopes and has been chisel plowed (reduced-tilled) for five consecutive years. On May 3, 1984, Great Lakes field corn varieties 542 and 547 were planted at approximately 23,000 plants/acre. A three way herbicide tank mix of alachlor (2-chloro-2',6'-diethyl-N- (methoxy-methyl) acetanilide (2-chloro-N-(2',6'-diethylphenyl)-N- methoxymethylacetamide) at 3.36 kg/ha, plus atrazine (2-chloro-4- (ethylamino)-6-(isopropylamino)-S-triazine) at 1.12 kg/ha, plus cyanazine (2-((4-chloro-6-(ethylamino)-5-triazin-2-yl)amino)-2- methylpropionitrile (2-chloro-4-(l-cyano-I-methylethylamino)-6- ethylamino-S-triazine) at 1.12 kg/ha was applied preemergence on May 4. 34 Three weed infestation sampling methods were evaluated in corn. This research focused on velvetleaf (Abutilon theophrasti Medic.), hemp dogbane (Apocynum cannabinum L.) and quackgrass [Agrogyron repens (L.) Beauvg]. Weed Infestation Assessment Studies. The M-pattern sampling method allows for 5 samples taken in the 20 hectare field (figure 1). The method has traditionally been used for insect scouting. On June 7, the density and distribution of weeds was determined by quickly walking the site and recording sections of the field that were densely populated with weed species. These weeds were recorded on a field map. . The quadrat method (ecological survey) allows for 20 samples per 20 hectares taken at random (figure 2). On July 7, the weed population was surveyed with a random quadrat technique to determine weed dominance. A stick was thrown into the field, landing at point 1. Beginning at point 1, all weeds in 10 meters of row, and 38 cm to either Side of the row were recorded. This procedure was repeated at twenty random points in the field. Relative density (RD), relative frequency (RF), and prominence values (PV) of velvetleaf, hemp dogbane, and quackgrass were determined (table 1). RD, RF, and PV were determined in reference to Cox (3), where R0 = density of a species x 100 total density for all species RF = frequency of a species x 100 sum frequencies all Species and the PV = RD + RF. 35 Figure 1. M-pattern sampling method. 36 37 Figure 2. Quadrat sampling method. 38 39 Table 1. Relative weed abundance as determined by Quadrat sampling methoda, Relative Value/m2 Species Densityb Frequencyc Promineace Value Velvetleaf 52.8 42.3 95.1 Hemp dogbane 25.7 38.5 64.2 Quackgrass 21.5 19.2 40.7 aCox, G.W. 1976. Laboratory Manual of General Ecology, pp. 32-37. Wm. C. Brown Co., Dubuque, Iowa. 343 pages. bRelative Density density of a species x 100 total density for all species cRelative Frequency frequency of a species x 100 sum frequency all species dProminence Value relative density + relative frequency 40 The five row method allows for every fifth row of the 20.23 hectare field to be monitored for weeds (figure 3). On July 18- 20, a detailed weed map of the field was conducted. For this method, the scout walked every fifth row of corn and weed populations were summarized at 100-pace intervals (approximately 91 meters for the length of the field). Each weed species was given a population density rating of zero, low (1-5 plants/100 paces), medium (5-10), or high (>10). The summary of these weed levels were entered into a computer and contour weed maps of velvetleaf and hemp dogbane were generated for the field. RESULTS AND DISCUSSION M-Pattern. The M-pattern sampling method provided a very general weed infestation assessment (figure 4). This method is the least accurate of all methods evaluated, as no attempt was made to quantitatively evaluate weed densities or their location in the field. This method was originally developed for IPM/CPM insect scouting. Unlike insects, however, non-transient weeds have definitive boundaries which can be treated as unique Sites within a larger field. This method took one scout approximately 2 hours of field time and 1 hour office time at an approximate cost of $18.00 (Table 2). The M-pattern provided insufficient data for effective post emergence weed control decisions. Weed distributions were only recorded at the five sample Sites with incomplete field representation. 41 Figure 3. Five row sampling method. .NJ C. 43 Figure 4. Distributions of velvetleaf and hemp dogbane when sampling the field with a M-pattern. 44 u o_ "m ago “a “cococc_c x_ucau_c_cm_n go: at» gouge. use" as“ xa.uoxo__oc can—cu a c.cu_2 manure me u “mo_ cm mm u com 05 ow u omoN ~mv _m a ~mm~ m_m mm a New" mm fie a mm~_ mm ow u n-~ mm ~m a mom" om cm u mom mN vv 3 v-_ mw ON a om- mm “m n "—o~ aw on o nmwu v“ ov a moo" a" a" n ugnw w~ um a cam" v_ o a 0mm” o o o v~o~ o o o nvmw o o a nmwn o ARV “aa\mxv A~z\aaaasv Any Aaa\mxv A~:\aaoaav any Aaa\axv A~:\aaoozv .nv A.a\aav . .~z\aaaasv xu.mcuu x8_mcou xu_mcou au_mcov co_uu:noc u_o_x "meta co_.u:voc v.0.» «meta co.uu=noc u—o.x mango co_uu=uoc v—o.a «auto u.o.» comnxom .o:cc< v.0.» caoaxom .a:cc< v.9.» cooaxom .oacc< u.u.» econxom _~=cc< vmm~ p.0m snow .ovmm_ can nmm_ c. no.0ocau common ..ac can xu_m=ou cam" —_0m soo~ xvcom nmog __0m Eco; manta _o=cca xn caucus—cc. no u—o_x mam" ~.om soc. macaw cooaxom .u o_aoh 69 as compared to 1984, In contrast, season long annual grass weed infestations on loam soils during low moisture conditions (1984) resulted in much greater yield loss when compared to 1983. These data suggest that the effect of annual grass interference to soybean yield are influenced by soil type and soil moisture supply. Data trends are consistent with report findings of Young et al. (9). They reported that when soil moisture is abundant, denser weed stands are required to cause a reduction, whereas in dry years, moderate densities can compete effectively with soybean for available soil moisture and cause yield reductions. On both soil types natural weed densities were greater in 1983 as compared with 1984, however, greater yield loss resulted from dry conditions. This trend does, however, contrast with the observation of Knake and Slife (5). They reported giant foxtail, did not reduce soybean yield as much during dry growing seasons as with years with adequate moisture. Additional research is needed to evaluated the influence of soil type and soil moisture on the interference of annual grasses on soybean. A non-linear regression model was used to compare annual grass interference data from this research. In 1983 reduction in yield occurred at an earlier duration period at the sandy loam Site as compared to the loam soil. At 14 plants/M2 a steep decrease in yield resulted 4 weeks after weed emergence on sandy loam soil as compared to a Slight decrease in yield at 6 weeks after weed emergence at 14 plants/M2 on loam soil (Figure 1). Similar regression curves resulted at weed densities 28 and 56 plants/Mz in 1983 (Figure 2, 3). The greatest decrease in 70 Figure 1. Influence of annual grass interference on soybean yield in21983. Annual grass population was 14 plants/M . 71 Fm.nnnm wow H mm $204 >oz0 zozozOzoz0 z> 28.3ij m w M. m o . u 2.3.. 52% I am am . 353 .l . mum .. mm 1 tom 33 >ozQz0z0 z0 z0 z20 plants/ftz, respectively. In contrast, soybean yield prediction on loam soil with high moisture at full season weed duration was 80, 80, 74, and 72% at 5, 10, 20, >20 plants/ft2 weed densities, respectively. Therefore, these data suggest during growing seasons with high soil moisture, annual grass interference was greater on sandy loam soils as compared to loam soils. Under low soil moisture conditions, the predicted soybean yield on sandy loam soil was 58, 56, 58, and 44% of soybean yield as weed-free control at weed densities 5, 10, 20, >20 plants/ftz, respectively, at full season weed duration. 0n loam soil with low moisture soybean yield was 65, 47, 62, and 53% at weed densities of 5, 10, 20, >20 plants/ftz. respectively, at full season weed duration. Similar to data from high soil moisture conditions, these data suggest generally, under low moisture, annual grass interference was greater on sandy loam soil compared to loam soil. The comparison of sandy loam soil, under different soil moisture conditions, —'I 94 Table 1. Predicted soybean yield values as influenced by annual grass density and duration. Soil Period after Soybean yielda Type Moisture weed emergence WeédSiper foot of row 5 10 20 )20 (wk) ------------ (%) ------------ Sandy loam High 1 100 100 100 95 2 100 100 100 85 3 98 97 98 77 4 93 92 93 69 5 87 87 86 63 6 79 80 80 57 7 71 73 73 53 8 63 65 64 51 Full Season 46 49 47 51 Sandy loam Low 1 97 97 96 92 2 94 92 93 87 3 91 86 87 81 4 88 79 80 72 5 84 74 72 63 6 79 68 64 55 7 75 64 59 48 8 69 59 57 44 Full Season 58 56 58 44 Loam High 1 100 100 100 96 2 100 100 100 95 3 100 100 99 93 4 100 100 98 91 5 98 99 98 88 6 96 97 96 86 7 92 94 94 83 8 87 91 91 81 Full Season 80 80 74 72 Loam Low 1 100 100 100 100 2 100 100 100 97 3 100 98 99 93 4 100 97 98 88 5 97 94 97 81 6 91 91 94 72 7 85 84 91 65 8 79 76 85 59 Full Season 65 47 62 53 aPercent of weed free control 95 generally resulted in greater soybean yield reductions at full season weed durations and high soil moisture. In contrast, weed interference on soybean yield was greater during dry seasons on loam soils and full season weed durations at all weed densities. The data were then used to predict economic return from postemergence grass herbicide applications. Generally, the longer a grower waits after weed emergence, the lower the net economic return, regardless of herbicide effectiveness (Table 2). The grower inputs were a) expected yield 40 bu/A, b) soybean price 6 $/A, and c) herbicide application cost 25 $/A. On a sandy loam soil with high moisture, herbicide application was profitable at 8 week duration for weed densities 5, 10, and 20 plants/ft2 and at 5 week duration for >20 plants/ft2 weed density. In contrast, herbicide application on sandy loam soil with low moisture was profitable at weed densities 10, 20, and >20 plants/ft2 at 6, 5, and 6 weeks after weed emergence, respectively. At weed density 5 plants/ftz, however, economic return from a herbicide occurred even at 8 weeks after weed emergence. 0n loam soil with high moisture, herbicide application was profitable at 8 weeks after weed emergence at weed densities 10 and 20 plants/ft2 and at 7 weeks after weed emergence for 5 and >20 plants/ft2 weed densities. Low soil moisture on loam soil resulted in profitable herbicide application at 8 weeks after weed emergence for weed densities 5, 10, and 20 plants/ft2 and at 7 weeks after weed emergence for >20 plants/ft2 weed density. With the expected yield goal changed to 25 bu/A, differences 96 Table 2. Influence of weed density and duration on economic return from post- emergence grass herbicide application fOr different soil type and moisture conditionsa. Soil Period after Economic return *Type MoiSture weed emergence Weeds per foot of row 5 10 20 >20 (wk) ----------- ($/A) ----------- Sandy loam High 1 105 97 102 81 2 105 97 102 57 3 100 90 97 37 4 88 78 85 18 5 73 66 69 4 6 54 49 54 -11 7 35 33 37 -20 8 16 13 16 -25 Sandy loam Low 1 69 73 66 90 2 61 61 59 78 3 54 47 45 64 4 47 30 28 42 5 37 18 9 21 6 25 4 -11 1 7 16 -6 -23 -15 8 1 -18 -27 -25 Loam High 1 23 23 37 33 2 23 23 37 30 3 23 23 35 25 4 23 23 33 21 5 18 21 33 13 6 13 16 28 9 7 4 9 23 1 8 -8 1 16 -3 Loam Low 1 59 102 66 88 2 59 102 66 81 3 59 97 64 71 4 59 95 61 59 5 52 88 59 42 6 37 81 52 21 7 23 64 45 4 8 8 45 30 -11 aExpected yield goal 49.8u/A Expected soybean price 6 $l8u Herbicide application cost ‘25'3/A 97 Table 3. Influence of weed density and duration on economic return from post- emergence grass herbicide application for different soil type and moisture conditions . Soil Period after Economic return [Type Moisture weed emergence Weeds per foot of row 5 10 20 >20 (wk) ----------- ($IA) ----------- Sandy loam High 1 56 52 54 41 2 56 52 54 26 3 53 47 52 14 4 46 40 44 2 5 36 32 34 -7 6 24 22 24 -16 7 12 11 14 -22 8 0 -1 O -25 Sandy loam Low 1 34 36 32 47 2 29 29 28 40 3 24 20 18 30 4 20 10 8 17 5 14 2 -4 4 6 6 -7 -16 -9 7 0 -13 -24 -19 8 -8 -20 -26 -25 Loam High 1 5 5 14 11 2 5 5 14 10 3 5 5 12 . 6 4 5 5 11 4 5 2 4 11 -1 6 -1 0 8 -4 7 -7 -4 5 -8 8 -14 -8 0 -12 Loam Low 1 28 54 32 46 2 28 54 32 41 3 28 52 30 35 4 28 50 29 28 5 23 46 28 17 6 14 41 23 4 7 5 30 18 -7 8 -4 18 10 -16 aExpected yield goal 25 Bu/A Expected soybean price '_6'$/Bu Herbicide application cost _'2__5' $/A 98 resulted in the economic return from herbicide application (Table 3). Under all moisture and soil conditions, economic return was lower as compared to 40 bu/A expected yield goal (Table 2). A lower expected soybean price $5.50/bu (Table 4) resulted in greater economic return from early herbicide application as compared to a decrease in soybean yield (Table 3). Economic return potential at expected soybean price $5.50/bu were decreased, however similar trends resulted when compared to soybean price $6.00/bu (Table 2). In contrast, the greatest economic return/A resulted when herbicide application costs were reduced to 20 $/A (Table 5). These data could allow a grower to compare the difference in economic return/A from hiring a commercial applicator as compared to using his own equipment. Economic loss, however, resulted at the same weed infestation duration periods even though economic return potential was higher under reduced application costs. CONCLUSIONS The predictive model developed for evaluation of post emergence herbicide application can be applied to any weed species or mixed weed population where adequate weed interference data exist. This research supports and other weed research has demonstrated that early post emergence herbicide applications can reduce weed interference and increase profitability. However, this procedure allows growers the opportunity for evaluation of economic return or loss associated with soybean yield, price, 99 Table 4. Influence of weed density and duration on economic return from post- emergence grass herbicide application for different soil type and moisture conditions . ¥_ Soil Period after Economic return Type Moisture weed emergence ‘ Weeds per foot of row 5 10 20 >20 (wk) ----------- (s/A) ----------- Sandy loam High 1 94 87 97 72 2 94 87 92 50 3 89 81 87 32 4 78 70 76 15 5 65 59 61 1 6 48 43 48 -12 7 30 28 32 -21 8 12 10 12 -25 Sandy loam Low 1 61 65 59 81 2 54 54 52 7O 3 48 41 39 56 4 41 26 23 37 5 32 15 6 17 6 21 1 -12 -1 7 12 -7 -23 -16 8 -1 -18 -27 -25 Loam High 1 19 19 32 28 2 19 19 32 26 3 19 19 30 21 4 19 19 28 17 5 15 17 28 10 6 10 12 23 6 7 1 6 19 -1 8 -10 -l 12 ~5 Loam Low 1 52 92 59 78 2 52 92 59 72 3 52 87 56 63 4 52 85 54 52 5 45 78 52 37 6 32 72 45 17 7 19 56 39 1 8 6 39 26 -12 alExpected yield goal 5g Bu/A Expected soybean price 5.50 $/Bu Herbicide application cost :ZETSYA 100 Table 5. Influence of weed density and duration on economic return from post- emergence grass herbicide application for different soil type and moisture conditions . Soil Period after Economic return Type . Moisture weed emergence Weeds per foot of row 5 10 20 >20 (wk) ----------- ($/A) ----------- I Sandy loam High 1 110 102 107 86 2 110 102 107 62 3 105 95 102 42 4 93 83 9O 23 5 78 71 74 9 6 59 54 59 -6 7 40 38 42 -15 8 21 18 21 -20 Sandy loam Low 1 77 78 71 95 2 66 66 64 83 3 59 52 50 69 4 52 35 33 47 5 42 23 14 26 6 30 9 ~6 6 7 21 -1 -18 -1O 8 6 -13 ~22 ~20 Loam High 1 28 28 42 38 2 28 28 42 35 3 28 28 40 30 4 28 28 38 26 5 23 26 38 18 6 18 21 33 14 7 9 14 28 6 8 -3 6 21 2 Loam Low 1 64 107 71 93 2 64 107 71 86 3 64 102 69 76 4 64 100 66 64 5 57 93 64 47 6 42 86 57 26 7 28 69 50 9 8 14 50 35 ~6 aExpected yield goal 4O Bu/A Expected soybean price _6'$/Bu Herbicide application cost 2:0 $/A 101 application costs and later season applications (a common situation faced by many soybean producers). Limitations to these data are as follows, a) weed density is often variable throughout a field, b) most weed interference data assume Simultaneous crop and weed emergence, and c) this analysis considers crop yield in the year of application only and does not address potential harvesting problems or the potential impact of weed seed , production on subsequent crops. 51 Research is needed to evaluate the interference of other E4; weed species in row crops. Although this procedure has some limitations, the principles and predictive model discussed here should enhance growers abilities to utilize economics in their weed control decisions. This method can act as a foundation for the incorporation of further interference data in the future. .44- 102 LITERATURE CITED Blair, 8.0. and J.V. Parochetti. 1982. Extension implementation of integrated pest management systems. Weed Sci. Supp. 1:48-53. Chandler, J.M., A.S. Hamill, and A.G. Thomas. 1984. Crop losses due to weeds in Canada and the United States. Weed Sci. Amer. Spec. Report 22. Coble, H.D 1985. Weed index minimizes spraying guesswork. Soybean Digest, Dec. pp. 18-19. l1 Knake, E.L. 1978. The weed science phase of pest management. Dept. Agron., Univ. of Illinois, Urbana. pp. 8. Ruppel, R.F. 1986. Projecting losses by European corn borer in field corn. Pest Profiles, pp. 17, ISSN: 0736-9689. pp. 5. Ruppel, R.F. and 0.8. Rajer. 1986. Estimating benefits from alfalfa weevil control. Michigan State University, COOperative Extension Service, East Lansing, In Press. Shaw, W.C. 1982. Integrated weed management systems technology for pest management. Weed Sci. Supp. 1:2-12.