WHITE CUTWORM (EUXOA SCANDENS [RILEY]): "SAMPLING AND BIOLOGY IN ASPARAGUS IN MICHIGAN Thesis for the Degree of M. S. MICHIGAN STATE UNIVERSITY EMMEI‘T PHILIP LAMPERT 1976 ABSTRACT WHITE CUTWORM (EUXOA SCANDENS [RILEY]): SAMPLING AND BIOLOGY IN ASPARAGUS IN MICHIGAN By Emmett Philip Lampert Several non-destructive sample methods were evaluated with barrier-baited plots being the best for quantitative samples and open-baited plots being best for detection purposes. Movement rates of overwintering larvae were calculated and a FORTRAN model was used to simulate the effect of treatment spacing on expected mortality. It shows that mortality can be selected by varying the between treatment spacing. Adult flight behavior as measured by a blacklight shows better synchronization when time is changed from chronological to physiological time (degree-day--°DSO). weather parameters were evaluated in the fluctuations in within year flight activity. Temperature estimation at a field site was accomplished through regression analysis between a thermograph operated in a commercial field and the weather station in Hart, Michigan. Developmental information was used to allow calculations of weighted mean instars. This allowed aging of a population and Emmett Philip Lampert when weighted mean instars are between 2.0 and 4.0 insecticide applications should be made if densities require treatment. WHITE CUTWORM (EUXOA SCANDENS [RILEY]): SAMPLING AND BIOLOGY IN ASPARAGUS IN MICHIGAN By Emmett Philip Lampert A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Department of Entomology 1976 ACKNOWLEDGMENTS To my major professor, Dr. Donald Cress, I'd like to extend my thanks for his friendship and many hours of consultation through- out this project. Dr. Dean Haynes has also been a major influence in my training and I'd like to thank him for his discussions on science and philosophy. He also made many facilities available to me during this project. To my committee members, Dr. Richard Sauer and Dr. Alan Putnam, I'm grateful for their many important inputs. The asparagus growers of Oceana County, especially Lyle and Evelyn Sheldon, have been very cooperative and their cooperation has been greatly appreciated. Many other people have been helpful to me throughout this project. Dr. Haynes' students, Dick Casagrande, John Jackman, Winston Fulton, and Alan Sawyer, have given me many ideas and helped in solving problems. These have been appreciated and incorporated into my training. Vivian Napoli's editorial assistance has been grately appreciated in the assimilation of this thesis. To Deb, my wife, I'd like to thank for her patience, understanding, and encouragement throughout this period of training. 11 TABLE OF CONTENTS ACKNOWLEDGMENTS . LIST OF TABLES LIST OF FIGURES . LIST OF APPENDICES . INTRODUCTION . LITERATURE REVIEW METHODS AND MATERIALS . Questionnaire Survey . Adult Sampling . Yearly Samples Hourly Samples Larval Sampling Baited-Barrier Plots Open- -Baited Plots Pitfall Traps . Pitfall Trap Evaluation . Oceana County White Cutworm Survey Asparagus . . . . . . Larval Feeding Behavior . Nocturnal Observations Laboratory Experiments Larval Movement Rates Movies . . Pitfall Traps RESULTS AND DISCUSSIONS Questionnaire Survey. . Field Age and Size Distribution Special Distribution of Fields . Fertilizers Used in Asparagus Pesticide Use in Asparagus Degree of Tillage in Fields . Iii Page ii , vii ix —l O‘DmmVNN \l (A) Adjacent Crops Methods of Harvest . Drainage and Irrigation Adult White Cutworm . Yearly Sampling for Adults . . Weather Parameters and Flight Activity . Sex Ratio of Blacklight Collected Moths . Hourly Sampling for Moths . Larval White Cutworm . . Instar and Population Age Determination . Larval Sampling with Barrier-Baited Plots Larval Sampling with Open-Baited Plots Larval Sampling with Pitfall Traps Comparison of Larval Sampling Methods Probability of Larval Detection Oceana County White Cutworm Survey Asparagus Spear/Butt Ratio Larval Feeding Behavior . Field Feeding Behavior . Laboratory Feeding Behavior . Larval Movement Rates . Diffusion Coefficients Obtained from Pitfall Traps Diffusion Coefficients Obtained from Movies Evaluation of Larval Movement . Estimation of Field Degree-Day Accumulation CONCLUSION BIBLIOGRAPHY . APPENDIX iv Table 10. 11. LIST OF TABLES Asparagus field age distribution for 1975 as reported from questionnaires (A zero entry indicates no responses for that category) Asparagus field size distribution for 1975 as reported from questionnaires (A zero entry indicates no responses for that category) . . Asparagus acreage and number of fields by townships and cutworm damage as reported from questionnaires (A zero entry indicates no response for that category) . Number of acres, percent of acres with cutworm damage, and number of fields for the primary and secondary field soil type for each of Oceana county's soil types as reported from questionnaires (A zero entry indicates no response for that category) . . . Insecticides used for cutworm control and acres and number of fields for each insecticide as reported from questionnaires (A zero entry indicates no response for that category) . . . . . . . . Season of year when cutworm insecticides applied and acres and number of fields for each season as reported from questionnaires (A zero entry indicates no response for that category) . . Correlation coefficients for I975 daily white cutworm moth catches for Hawley's and Sheldon's blacklight traps with several measured environmental factors Summary of total moths caught per hour from the Sheldon blacklight trap for July 9, IO, l8, and 25, l975 . Results of the baited barrier soil plots for quantitative .samples for white cutworms . . . . . . Results of small open-baited plots (3 feet by 6 feet) . Results of the experiments for determination of white cutworm attraction or repulsion to pitfall traps . V Page 23 24 28 32 33 41 47 54 57 59 Table Page 12. Results of the use of baited and unbaited pitfall traps as a white cutworm detection tool . . ,, . 61 13. Probability of detecting at least one white cutworm larva with one asparagus row foot samples for various larvae densities and sample sizes. N95 is the number of samples required to obtain a probability of detection of .950 . . . . . . . . . . . . . . . . 65 14. Correlation coefficients between nightly spears damaged and white cutworms caught with environmental parameters (Ray Wybenga Farm) . . . . . . . . . . . . . 68 15. Results of the laboratory random feeding tests for white cutworms on asparagus spears or butts . . . . . . 75 16. Observations on white cutworm movement in 1974 at the . M.S.U. Botany farm . . . . . . . . . . . . . 77 17. Movement observations of white cutworms based on movie on May 19, 1975. 1 Frame = 8 sec. (.0022 hr) . . . . . 79 l8. Regression equations for estimation of field degree- day accumulations at the Lyle Sheldon farm (S) from degree-day accumulation from the Hart, Michigan, weather station (H) for 1975 . . . . . . . . . 84 V1 Figure 10. 11. 12. 13. 14. LIST OF FIGURES Known distribution of white cutworms in North America . Known white cutworm distribution in Michigan and counties where asparagus is commercially grown 15' by 15' white cutworm larval plots in asparagus . 3' by 6' white cutworm larval plots in asparagus--4(a) between two rows, 4(b) perpendicular to a row, 4(c) parallel to a row . . . . . . . . . Test area for determination of attraction or repulsion of white cutworms to pitfall traps . . Asparagus cutworm survey pitfall trap plot design Test area for calculating movement rates for white cutworms The townships of Oceana County showing the location of Hart, Shelby, the blacklight traps, the public land and asparagus fields with and without cutworm damage Weekly blacklight trap catches of white cutworms from Oceana County . . . . . . Blacklight trap catches of white cutworms per degree-day from Oceana County. . . . . . . . Number of male and female moths caught per week at the Sheldon blacklight trap for 1974 . . . . Number of male and female moths caught per week at the Sheldon blacklight trap for 1975 . . . . Total hourly moth catches from the Sheldon blacklight trap for July 9, 10, 18, and 25,1975 . . . Frequency distribution of head capsule width measurements from laboratory reared (n = 2923) and field collected (n = 1053) white cutworm larvae vii Page 11 13 15 19 27 37 39 43 44 46 49 Figure Page 15. Weighted Mean Instar for field collected larvae for 1974 and 1975 . . . . . . . . . . . . . . 53 16. White cutworms caught per trap per day and number of spears damaged per plot for four field plots . . . . 66 17. Percentage of food units that are spears for Hart 1975(a) and Sodus farm 1975(b) . . . . . . . . . . . 74 18. Mean percentage of food units that are spears for the 9 varieties of asparagus grown at the M.S.U. Horticultural Research Farm, Sodus, for 1975. Butts palatable for 10 days . . . . . . . . . . 75 .19. Effects of treatment spacing and diffusion coefficients on expected mortality for 15 hours simulation. Rows are 5 feet apart . . . . . . . . . . . . . 84 20. Effect of simulation time on expected mortality with a constant diffusion coefficient (0 = 1.6). Rows are 5 feet apart . . . . . . . . . . . . . . . 86 viii LIST OF APPENDICES Appendix A. D O o o Im-nrn Host Range of the White Cutworm Asparagus Grower Questionnaire Packet Asparagus Cutworms Survey Data Sheet . Computer Listings of Strip Bait Model Questionnaire Results . Yearly Blacklight Trap Catches for Oceana County . Degree-Day Accumulation for Hart, Michigan . Diet Used for Rearing White Cutworms . White Cutworm Developmental Times . Instars and Weighted Mean Instar of Field Collected Larvae . . . . . . . . . . . . . . Small Open-Baited Plot Results . Percent of Food Units That are Spears Degree-Day Accumulation for the Thermograph Operated at .. the Farm of Mr. Lyle Sheldon, Shelby, Michigan . ix Page 92 94 101 103 106 111 113 117 119 121 124 126 130 INTRODUCTION. On May 10, 1971 the initial report of significant cutworm damage in commercial asparagus was received from Oceana county, Michigan (Cress and Wells unpublished). Specimens were collected and identified as white cutworms, Euxoa scandens (Riley), and bristly cutworms, Lacinipolia renigera Stephens. Since 1971 the damage caused by the bristly cutworms has decreased to an insignificant level. White cutworms, however, have been gradually increasing and by 1975 were present in eco- nomically damaging numbers in most of the commercial asparagus growing region of Oceana county. Since the larvae are nocturnal feeders, feeding is not frequently observed. Larval feeding begins about one hour after sunset with the larvae climbing the asparagus spear and feeding on the tender spear tips and spear sides. This direct feeding results in an unmarketable spear due to insect damage and/or termination of normal spear growth. Commercial asparagus plantings are most productive when planted in deep, loose, and light soil types (Commercial Growing of Asparagus 1971). Good examples of such soil types are mucks and loamy sands. The larvae of the white cutworm are also most commonly found in sandy soils (Hudson and Wood 1930, Hardwick 1970, and Bierne 1971). Because of this overlap in soil types it becomes more important to understand more of the biology and behavior of the white cutworm in an effort to reduce its damage to the aSparagus industry. I Asparagus is an important vegetable crop in Michigan, with the 1975 production valued at 4.7 million dollars (1975 Crop Reporting Board). Acreage of asparagus in Michigan was reported at 18,493 acres in 1972, with commercial asparagus being grown in 22 counties (1972 Michigan Asparagus Survey). Unpublished blacklight trap records of Mr. John Newman show ten (45.45%) of these counties to have white cutworms present. However, only Oceana county has reported them as economically important. There is, therefore, a potential economic problem with the white cutworm to the asparagus industry. Since asparagus is a perennial crop which requires several hundred dollars investment per acre before it can be harvested, a destructive larval sampling technique would not be tolerated by most farmers. Due to the unique nature of asparagus, a non- destructive larval sampling technique had to be developed. It was with these factors in mind that a study was under- ‘taken to: (l) more fully understand the biology of the white cutworm; (2) investigate feeding behavior of adults and larvae; (3) develop a non-destructive larval sampling technique; and (4) calculate movement rates to investigate various strip baiting strategies. LITERATURE REVIEW The white cutworm, Euxoa scandens, was first taxonomi- cally categorized in 1869 by C. V. Riley (Riley 1869) upon the successful rearing of a previously unidentified moth. The larvae had been collected from mixed orchards of apples, pears, (peaches, and cherries near Calumet, Illinois (Riley 1869). Dr. Riley designated it as the Climbing Rustic (Agrotis scandens); chaosing the specific name scandens, which means to climb, because of the climbing tendencies exhibited by the larvae. After going through a series of generic changes, scandens has been placed in the genus Euxoa, Hardwick (1970) has summarized the synonomy through 1970 with a brief abstract of each paper. Slingerland (1895) proposed the present common name of white cutworm. He reasoned cutworms are commonly named by color or habit. Since many other equally common-cutworms have exhibited a larval climbing tendency, he believed "climbing rustic" was too general for a common name. Therefore, owing to its pale color and white markings, he proposed "white cutworm" as a more appro- priate common name. Many keys are available for the larvae (Crumb 1932, Walkden 1950, Frost 1955) and for the adults (Forbes 1954, Hardwick 1966 and 1970) of E. scandens. Each contains a brief description of biology and damage. The most comprehensive biological work has been done by Hudson and Wood (1930). They identified twenty of the known food hosts (Appendix A). From Appendix A it can be seen that the white cutworm larvae are omnivorous feeders, feeding on whatever is available. Of particular interest is the fact that most of the literature describes E, scandens as a fruit pest rather than a vegetable pest. Parasites of the larvae include Copidosoma bakerii (Howard) (Hudson and Wood 1930) and Poecilanthrax willistonii (Coq.) which has been recovered from E, scandens in the western extremes of its range (Painter 1960). Parasitism by g, bakerii reached a maximum rate of about 20% in Oceana county in late May and early June in 1974 and 1975. g, scandens is a northern univoltine species and is distributed from the Rocky Mountains east to the Atlantic Ocean, and from Nebraska and Colorado north to two specimens taken in the Northwest Territories (Hardwick 1970) (Figure l). Hardwick (1970) presents an extensive list of moth collection records from the United States and Canada. In Figure 2 the known Michigan distribution of white cutworms from the personal records of Mr. John Newman is shown. The Michigan counties where asparagus is commercially grown are also shown in Figure 2. / _— curwoams III I I ASPARAGUS COUNTY IMDEX l. Berrien 24. Lake 2. Cass 25. Osceola 3. St. Joseph 26. Manistee 4. Lenawee 27. Missaukee 5. Monroe 28. Roscomon 6. Van Buren 29. losco 7. Kalamazoo 30. Benzie 3. Uashtenaw 31. Grand Traverse 9. Wayne 32. Crawford 10. Allegan 33. Leelanau 11. Barry 34. Anterim 12. lnghal 35. Otsego 13. Oakland 36. Alpena 14. Ottawa 37. Charlevoix 15. lonia 38. Cheboygan 16. St. Clair 39. Preque Isle l7. Muskegon 40. Menominee 13. Montcall 41. Dickinson l9. Oceana 42. Delta 20. Mewoygo 43. Schoolcraft 21. Mecosta 44. Mackinac 22. Midland 45. Chippewa 23. Mason 46. Baraga O I. 00 W Figure 2. Known white cutworm distribution in Michigan and counties where asparagus is commercially grown. METHODS AND MATERIALS Questionnaire Survey A list of the asparagus growers in Oceana county was obtained from the county extension agent. Edgar Strong. The growers on this list were then sent an asparagus grower's packet (Appendix 8). Items included in this packet were: 1. An introductory letter of explanation and objectives of the questionnaire; 2. An "Asparagus Insect Identification and Control for 1975" fact sheet, which included a brief biology, identification characteristics, and control measures for the three main problem asparagus insects; 3. An asparagus questionnaire; 4. A self-addressed stamped return envelope. This packet was then mailed to 327 Oceana county asparagus growers. A follow-up questionnaire was mailed to a random sample of 50 non-reSpondents. Adult Sampling Yearly Samples Adult white cutworms were collected with two ElliscoR general purpose, 15 watt blacklight insect traps in Oceana county. One trap was operated on the farm of Mr. Lyle Sheldon, eight miles west of Shelby; the second trap was operated on the farm of 7 Mr. Francis Hawley, two miles northeast of Shelby. Both traps were in operation from 1972 through 1975. CyanogasR (American Cyanamid Corp.) was used as a killing agent. It was placed in small paper bags in the bottom of the blacklight trap and changed on two-day intervals. On one- or two-day intervals the moths were collected from the trap, dated, and allowed to dry. Once a week the collections were then mailed to M.S.U. for sorting and identification. Hourly Samples Information on hourly moth flight activity at the black- light trap was obtained by collecting the trap contents on hourly intervals from 9:00 p.m. to 6:00 a.m. This was done on four separate occasions (July 9, 10, 18, and 25, 1975). The collections were labeled and stored in plastic bags until the following day when the white cutworm moths were sorted, sexed, and the collection time recorded. Larval Sampling, A larval sampling technique was developed which incorpo- rated a 5% apple-pomace bait formulation of Carbaryl (SevinR). SevinR was selected since it was the only insecticide registered for cutworm control in asparagus in Michigan. The insecticide was used in three sampling designs: 1. Baited-barrier plots; 2. Open-baited plots; 3. Pitfall traps. Baited-Barrier Plots In the enclosed soil plots, seven circular plot sizes were used. These plot areas were 3, 6, 12, 24, 48, 96, and 192 square feet. The circumference was calculated for each of these areas and strips of four inch steel lawn edging were cut to form each circle. The ends of the lawn edging were then stapled together. centered over an asparagus crown, and then pushed one inch into R the soil. Sevin bait was spread evenly throughout the enclosed area and the number of dead cutworms were recorded the following days. Due to the rapid breakdown of SevinR, bait was reapplied on two-day intervals in all experiments. Open-Baited Plots Two sizes of open plots were evaluated. The largest plot was 15 feet by 15 feet and encompassed three asparagus rows (Figure 3). SevinR bait was spread evenly throughout the plot and dead larvae were collected from the central square yard for the four following days. The remainder of the plot acted as an insecticide barrier about the desired sample area. The small plots, three feet by six feet, were used with three different placements within the rows: (1) between-two rows (Figure 4a); (2) perpendicular-to a row (Figure 4b); and (3) parallel-to a row (Figure 4c). SevinR bait was spread evenly 10 godmdmmd -------..5'-------- J mDodmdmmd magmdamd 15' by 15' white cutworm larval plots in asparagus. ["2 '/2 Figure 3. 11 ASPARAGUS O a s- _.L ASPARAGUS 33 i I“""“ 6; ASPARAGUS E I i ___I 63 t _ 1 I I ASPARAGUS i L I Figure 4. 3' by 6' white cutworm larval plots in asparagus - ' (4a) between two rows, (4b) perpendicular to a row, (4c) parallel to a row. 12 throughout the plot; the entire plot was examined for dead larvae on each of the following days. Pitfall Traps Two pitfall traps, two-cup plastic containers, were placed in each of three adjacent asparagus rows in holes made with a golf course hole cutter. The traps were set in the holes with their lips flush with the soil surface. Each trap site consisted of six traps, alternating a baited trap (1/4 inch SevinR bait) with an unbaited trap in each of the three rows. Pitfall Trap Evaluation To evaluate pitfall traps as a sampling tool, a test was conducted at the M.S.U. Botany Farm Research asparagus plots to check for larval attraction or repulsion to the pitfall traps. Four concentric circles with radii of one, two, three, and four feet respectively (Figure 5) represented the trap site. As close to 20% as possible of the first three circles were pitfall traps, whereas the fourth circle had pitfall traps two inches apart with barriers between them to prevent the white cutworms from escaping. Since no white cutworms were present in this site, no marking was necessary. Small groups of cutworms were released every hour in the center of the test area. Whenever a cutworm tumbled into a pitfall trap, the time and pitfall trap number were recorded and the specimen removed. Figure 5. 13 (D 8 4 a: E (I) < Test area for determination of attraction or repulsion of white cutworms to pitfall traps. 14 Oceana County_White Cutworm Survey An Oceana county white cutworm survey was taken in ten growers' asparagus fields. Growers were selected from the questionnaire responses; selecting those growers who (1) had agreed to cooperate; (2) had white cutworms in their asparagus; and (3) used few insecticides. Cooperators were given nine pitfall traps, col- lection vials, vial labels, forceps, and data sheets. A trap site consisted of nine traps, with three traps placed 12 feet apart in a row in three alternate rows (Figure 6). TWo inches of soapy water, which reduced surface tension and facilitated drowning of the captured insects, were placed in the traps. Cooperators were requested to check the traps daily, remove the captured white cutworms, and place them in labeled vials filled with FAA (50 parts H20, 47 parts 95% ETOH, 2 parts Formaldehyde, 1 part Glacial Acetic Acid). Weather information for the previous night was recorded on the data sheets (Appendix C). This information correspondence with the weather conditions present when the larvae were collected. On this sheet the cooperators were also asked to record field information for the present day, i.e. harvest, application of insecticides, etc. On days when harvest occurred, the cooperators were re- quested to record the number of damaged spears in 40 of each of the three rows. Damaged spears were to be removed from the field. Asparagus Asparagus yield information was obtained from Dr. Hugh Price, M.S.U. Horticulture Department, and N. J. Fox and Sons 15 ROW ROW 2 ROW 3 ROW 4 ROW 5 12' <> <9 4H I<——Ir>——>I Figure 6. Asparagus cutworm survey pitfall trap plot design. 16 Processing, Shelby, Michigan. Dr. Price provided yield information on nine experimental varieties grown at the Sodus Experimental Farm, Sodus, Michigan (M.S.U. 1, Mary Washington, U.C. 66, U.C. 72, U.C. 309, U.C. 711, N.J. 44X22, N.J. 51x22, N.J. Improved). N. J. Fox and Sons provided information on the number of spears in a 50, 100, 150, or 200 ounce sample based on sales receipts for the 1975 asparagus crop from a commercial field. Larval Feedinngehavior Nocturnal Observations The locations of feeding larvae were recorded as feeding on spears or butts (unharvested portion of spears). Also recorded were all the spears and butts within a one foot radius of the observed larva. This allowed the calculation of a ratio of the number of cutworm larvae on spears and butts which could then be compared to the overall spear-butt ratio in the field. LaboratorygExperiments Freshly harvested spears were cut into two three-inch sections, the tip of the spears being considered an experimental spear and the lower section considered an experimental butt. The basal ends of both were then dipped in melted beeswax to prevent subsurface feeding and moisture loss. Combinations of one spear and one butt, one spear and two butts, and two spears and one butt were then placed in a two-cup plastic container filled with one inch of moist sand. One larva was released in each container and feeding damage evaluated the following morning. 17 Larval Movement Rates Diffusion coefficients, as described by Pielou (1969), were used by Casagrande (1975) in the development of a strip spray model for the cereal leaf beetle. Modifications in Casagrande's model were made such that the model could be used to evaluate movement of the white cutworm larvae. The model (Appendix 0) functions on the following assumptions: (1) a bait insecticide (SevinR) would be used and its band of application was limited to one foot in width; (2) any larva, which came within this treated band would stop, feed, and ultimately die; (3) the cutworms were actively moving about the fields for five hours per night. This assumption was based on field observations and movie evaluations which indicate larval activity for about five hours per night. Diffusion coefficients (0) were calculated by (Pielou 1969): r2 D=— (1) 4T where r = distance in feet larvae moved T = hours required to move r distance. Estimates of D were obtained in two manners: (1) from movies in the field and (2) from pitfall trap movement experi- ments. 18 Owl-2'2 A Minolta Autopak-8 010 was used for nocturnal observations of larvae. The camera was equipped with an intervalometer, which allowed for time-lapse photography, and an AC rechargeable flash. The intervalometer allowed for selection of time between frame exposures (T). Thus, the only necessary variable was r, which could be measured by direct observation of the film. The movie camera was nnunted on a tripod and focused on approximately one square yard of asparagus row in a commercial asparagus field. The mean distance the larvae moved between frames was calculated, from which individual larval diffusion coefficients were obtained. Diffusion coefficients obtained in this way were averaged and a mean diffusion coefficient was calculated for each night. Pitfall Traps A four-foot radius circle of 60 pitfall traps placed 1/2 inch apart was constructed at the M.S.U. Botany Farm asparagus research plots and served as the test area (Figure 7). Wooden one-foot stakes, placed between the pitfall traps, served as barriers to prevent the larvae from leaving the test areas. A single group of larvae (75 on June 18, 25 on June 19) was released at 10:00 p.m. in the center of the circle per night and the traps monitored until 1:00 a.m. Whenever a larvae tumbled into a trap, trapped larvae were removed and the trap number and time captured were recorded. In this fashion, an estimate for T was obtained since r was fixed at four feet. Individual larval diffusion i e Figure 7. Test area for calculating movement rates for white cutworms. 20 coefficients were again calculated and their mean obtained for the experiment for each night. RESULTS AND DISCUSSIONS Qpestionnaire Survey 0f the 327 questionnaires initially mailed out, seven were returned to the sender due to various postal problems, i.e. no forwarding addresses, improper signatures, etc. Of the 320 ques- tionnaires which reached the growers, 107 were returned for a response of 33.44%. The response to the follow-up 50 questionnaires was very poor; only seven responded for 14.00%. Due to the extremely poor response to the follow-up questionnaire, the normal statistical analyses for significant differences between first respondents and non-respondents were not performed since little faith could be placed in the results. Therefore, the two responses will be treated as one response of 35.63% reporting 3334 acres of asparagus. Extrapolation of 100% response estimates 1974 asparagus acreage in Oceana county as: 3334/35.63% = 9357.28 acres (2) Since the statistical analyses were not performed, no further extrapolations to county totals will be attempted and the following discussion will deal only with the responses to the questionnaire. Of the 114 responses, five or 4.39% were from counties other than Oceana (Mason and Mecosta) and ten or 8.77% had no asparagus. These responses will be omitted from the following discussion. 21 22 Field Age and Size Distribution The 1975 age distribution of asparagus fields in Oceana county as reported from the questionnaires (Table 1) indicates the recent trend toward increased asparagus planting. This is shown by the fact that 41.15% of the total number of asparagus fields and 38.42% of the total acres are less than five years old. Asparagus reaches its maximum production by the age of 15 years (Price personal communication) and since about 84% of both acreage and number of fields were less than 15 years old, most of the asparagus fields were or soon will be of a highly productive age. It is interesting to note that the one-year fields make up a large percent of total fields (10.29) but a rather small percent of the total acres (4.74), thus indicating that many small fields were planted in 1974. According to Table 2, 54.73% of the total number of fields are less than ten acres but only 21.99% of the total acres. The importance of this is if a given number of acres are to be planted then by planting many small fields rather than a few large fields the county's density of fields will increase. As the field density increases, the probability of planting a field in or near a population of white cutworms increases. Since crops such as corn, potatoes, and others are planted too late in the spring for the larvae to feed on, the larvae tend to feed on weeds and other economically unimportant vegetation present in those fields. 23 Table 1. Asparagus field age distribution for 1975 as reported from questionnaires (A zero entry indicates no responses for that category). Field % of Acres % of Age No. of Total Cumulative in Age Total Cumulative (Years) Fields Fields % Class Acres % l 25 10.29 10.29 158 4.74 4.74 2 12 4.94 15.23 203 6.09 10.83 3 21 8.64 23.87 347 10.41 21.24 4 19 7.82 31.69 233 6.99 28.22 5 23 9.47 41.15 340 10.20 38.42 6 23 9.47 50.62 228 6.84 45.26 7 17 7.00 57.61 229 6.87 52.13 8 16 6.58 64.20 236 7.08 59.21 9 6 2.47 66.67 66 1.98 61.19 10 16 6.58 73.25 353 10.59 71.78 11 3 1.23 74.49 71 2.13 73.91 12 8 3.29 77.78 118 3.54 77.44 13 6 2.47 80.25 91 2.73 80.17 14 l .41 80.66 10 .30 80.47 15 7 2:88 83.54 137 4.11 84.58 16 3 1.23 84.77 31 .93 85.51 17 3 1.23 86.01 41 1.23 86.74 18 2 .82 86.83 25 .75 87.49 19 0 0 00 86.83 0 0.00 87.49 20 9 3 70 90.53 106 3.18 90.67 21 1 41 90.95 35 1.05 91.72 22 O O 00 90.95 0 0.00 91.72 23 2 82 91.77 11 .33 92.05 24 2 .82 92.59 71 2.13 94.18 25 8 3.29 95.88 138 4.14 98.32 26 2 82 96 71 20 .60 98 92 27 O O 00 96 71 O 0.00 98 92 28 l 41 97 12 4 .12 99 O4 29 0 O OO 97 12 O O 00 99 O4 30 4 1 65 98 77 20 60 99 64 >30 3 l 23 100 00 12 36 100 00 24 Table 2. Asparagus field size distribution for 1975 as reported from questionnaires (A zero entry indicates no responses for that category). Field % of Acres % of Size No. of Total Cumulative in Age Total Cumulative (Acres) Fields Fields % Class Acres % 2 22 9.05 9 05 32 .96 .96 4 29 11.93 20 99 103 3.09 4.05 6 38 15.64 36 63 207 6.21 10.26 8 14 5.76 42 39 101 3.03 13.29 10 30 12.35 54 73 290 8.70 21.99 12 15 6.17 60.91 176 5.28 27.26 14 13 5.35 66.26 174 5.22 32.48 16 7 2.88 69.14 107 3.21 35.69 18 9 3.70 72.84 157 4.71 40.40 20 13 5.35 78 19 259 7.77 48.17 22 5 2.06 80.25 108 3.24 51.41 24 4 1.65 81.89 94 2.82 54.23 26 10 4.12 86.01 250 7.50 61.73 28 4 1.65 87.65 109 3.27 65.00 30 6 2.47 90.12 176 5.28 70.28 32 2 .82 90.95 62 1.86 72.14 34 3 l 23 92 18 101 3.03 75.16 36 4 1.65 93.83 140 4.20 79.36 38 2 .82 94.65 76 2.28 81.64 40 2 .82 95.47 80 2.40 84 O4 42 2 .82 96.30 83 2.49 86.53 44 0 0.00 96.30 0 0.00 86.53 46 2 .82 97.12 90 2.70 89.23 48 l .41 97.53 47 1.41 90.64 50 2 .82 98.35 100 3.00 93.64 >50 4 1.65 100.00 212 6.36 100 00 25 Special Distribution of Fields Benona, Elbridge, Grant, Hurt, and Shelby--the five top asparagus growing townships--reported cutworm damage on 77.05%, 48.23%, 59.13%, 69.92% and 74.20% of their acres respectively (Table 3). These five townships contain 66.67% of the county's asparagus fields and 69.08% of the acres (Figure 8). It was apparent that most of the available farm land in Oceana county was limited to these five townships plus Clay Banks and Golden townships. The rest of the land in Oceana county was mostly state parks or Manistee National Forest. Therefore, by increasing the number of fields in the county, the density of fields in these townships will increase. As this happens, the probability of white cutworm presense will increase once again adding to its importance to the county. The acres of asparagus with and without cutworm damage and number of fields in each of the county's soil types is given in Table 4. The primary soil was defined as the soil type which occupies most of the field and the secondary soil was the second most predominant soil type in the field. Of 243 fields, 102, or 41.98%, are on Emmet loamy sand or sandy loam; and 23, or 9.47%, are on Rubicon sand. Cutworm damage was reported on 58.96%, 73.12% and 84.49% of their reported acres respectively. For the primary soil type, 95.06% of the fields were on loamy sands, sandy loams, or sandy soils, and for the secondary soil 78.19% of the fields were on these soils. Since sandy soils are the preferred soil types for white cutworms (Hudson and Wood 1930, Hardwick 1970, 26 meN emmm OF ooF Pm 4N0, Nm_ oo_N msqeoe mN omN _ 0 ¢_ mN_ N mmp meow: mm mem o o N am 0N omN »n_mcm o o o o o o o o Empazucwa __ em 0 o P m o_ mm 0550 N Pm o o o o N _m e_m_c3mz P m o o o o _ m uuw>mm4 _m mmm m _N N NN FN _mN “an: N o_ o o N o_ o o noozcmmew NN Nom 0 o N Om, ¢_ N_N pence mp map 0 o 0 mm m ¢N_ cau_ou F_ oF_ o o 0 _4 m mN sccma 0N Nme o 0 NF emN __ N_N mmuwca_m a mm, o o m NN e m_F _mpmsau o o o o o o o o xme_ou N mm o o e co m NN mxcmm smpu cm CNN 0 MN m 00” mm Foo ecocmm mupwwu mmLu< mupmwu mmcu< mupmwm mmcu< mUmem mmgu< co .oz eo.oz e0 .02 eo.oz mo .02 co.oz co .0: $0.02 .Hmmmm .NMMNm.mmz omgc0¢mm acoz .mmmummmm maHImzzoe mwmmou N m.~m 0 ON NF m 0.0 0 Po 0 team m:_w cmEochm o 0.0 o o o _ o.oo_ o o «N Ego. ucm_mcmm o 0.0 o o o N m.Nm o N_ 0N Emop Xucmm warm umcme< c N.¢N m o mN m a.mm 0 um um ucmm AEoo— umcmc< _ o.oo_ o o m N o.oo_ o o oo ENQP Aucmm Ewcuc< IJN d N a no JJN d N 8 Nu [.0 a )0 )3 )3 L.O a )0 )3 )8 a. J 91+ QdN 9d a. J 91. PdN 9d mo N nenmw mm mo N we “mm mm mezss. 51+. U 3L. 31.3 31+ 51: U 3L. 31.3 31+ 1 SA 53 $3 1.- SA 53 $3 .Axgommumo wasp Low mmcoqmmg o: mmpauPucw xgucm ocmN 0<0Z0NNN 0.0N >002000 .000000000 .0 00000 30 --_—-0-—~—.. F"_‘_——‘—‘—.-”‘-"""’ m —.00 mm mm m 0.0 o 00 o 0 0.0 0 0 . 0 o 0.0 o o o N o.oo_ o 0 mm o 0.0 o o o 0 o.mm 0 mm mmp F 0.0 o 00 o o 0.0 o o o N 0.0 00 o o 0 N.om o 00 00 o 0.0 o o o o 0.0 o o o o 0.0 o o o o 0.0 o o o 0 o.om o 00 OF 0 m.mn o om mo 00 m.mN 0 mm om— JJN d N a no [.0 a \10 )8 )8 a. J p.04. PdN 9.0 I. 3 3 300 30 pO 8 J9 JJU JJ S 1. u a L. a 1.8 .0.4 1+ SA 53 53 (W (p (p 000m >mo o 0.0 o o o 0000 000000 >000 00>o o 0.0 o o o 00:0 000000 0 0.000 o o 00 00:0 0000000 0 0.0 0 0 0 0000 00000000 0 0.000 o 0 mm 00:0 00000000 0 0.0 0 N0 0 0000 00000 o o.o o o o 0000 0co3000go o o.o o o o 0000 0000 0000: o 0.0 o o o 0000 00000 300000003 o 0.0 o o o 0000 0000003 o 0.0 o o o 0000 00000003 o 0.0 o o o 0000 000» 0000003 o 0.0 o o o 0000 0000— 000000 0 0.0 m o o 0000 x0000mz00 00 0.00 0 0N 0N0 00mumm0mwmfim00 N0 0.Nw 0 mm 00— 0000 0000000 Wm M Mm mmw mm W w. W WW0. WWW WW 0mm: :00 Jaom >m.750). Pesticide Use in Asparagus Insecticides used for cutworm control (Table 5) indicate dieldrin was the most frequently applied insecticide (2412 acres or 72.35%) while formulations of SevinR were second (l665 acres or 49.94%). Dieldrin, a pre-emergence insecticide, must be applied in the early spring before any asparagus has emerged and Table 6 shows that 2295 acres (68.84%) were treated in the spring whereas only 93 acres (2.79%) were treated in the fall. Insecticides used to control other insects (Appendix E2) include: SevinR formulations which were used on 2632 acres (78.94%), dieldrin spray used on 314 acres (9.42%), chlordane wettable powder used on 186 acres (5.58%), and methoxychlor used on 23 acres (.69%). SevinR formulations were the most frequently used insecticide for insects other than cutworms, of which the primary use was to control the two types of asparagus beetles (asparagus growers personal communication). 32 m0 mmm m 0 NF mom mm NFM =m>ww ~02 N mm o o — _m P we mcmugopgu m mm o o P m m mm Lopcuxxocpwz MQF Npcm m Fm Fm Non mop amm— 0m :_20_mwo mm mmmp m Fm o_ mam cu moo, macaw cm>mm 5 “PF N 00 o o m mu pwmm c_>mm mm mum m mp m mm NP mom umao 0P>0m mayor; mmco< mcpmwu 00L0< mcpmwu mmgo< mufimwm m¢gu< $0 .02 $0.02 m0 .mmlll.wmw02 $0 .02 $0.02 $0 .02 $0.02 uoHuHhummzH aEPEH :95 0.02 038002 252 3 L00 P000» mw$w 002 mc$20m S N: 0 «<2 8 SN 00 :m 0 :0“. 02 300 2 00 2 000 02 $2 052% m 8 o o m 00 0 mm :00 00P0$2 m020< 00$0$2 m020< 00$0$2 m020< mu~0$2 m020< $0 .02 $0.02 $0 .02 $0.02 $0 .02 $0.02 $0 .02 $0.02 (I .llll 20200 $000$ c0>$w 002 00020002 0002 u0p20m0m mu .100). Field experiments by Dr. Putman (personal conmunication) have indicated a significant inverse relationship between weed presence and tillage. This was apparently due to stirring of the weed seed reservor in the soil and exposing seeds to germinating conditions. No significant relationships were observed between these weeds and the presence of cutworm damage (Appendix E5; x; = .43, p = .750). Since no significant relationships were found, one can conclude weeds and cutworm damage were not related and the decrease in damage in the tilled field was probably due to a combination of mechanical and physical factors--exposure to predators, larval injury, or incorporation of the insecticides in the soil. Adjacent Crops Woods and shrubs, grasslands, fence rows and neighbors' asparagus were the most common asparagus field borders; bordering 45.27%, 40.74%, 22.22% and 21.81% of the fields respectively (Appendix E6). Apple, cherry, peach, and pear trees bordered 16.46%, 12.35%, 7.41% and 6.17% of the fields respectively. The importance of these adjacent crops is that they provide the moths with diurnal hiding locations other than the fields. Diurnal hiding places were sought on several occasions, but no significant numbers of adults were ever found. Very few were found in the asparagus fields relative to those collected in the blacklight 36 traps which makes these border fields the apparent diurnal hosts for the resting moths. Methods of Harvest Of the 3334 acres reported, the following summarizes the harvest methods given in Appendix E7: Harvest Method Acres % of Acres Hand 2814 84.40% Mechanical 291 8.73 Not Harvested 57 1.71 Not Given 172 5.16 Due to all the land labor used in harvesting asparagus, the overhead incurred by the growers was very high and losses must be kept minimal to insure an operational profit. Drainage and Irrigation No significant differences were found between cutworm damage on the field considered well drained and those poorly drained, 84.77% and 4.94% of the fields respectively (Appendix E8, x? = .35, p > .500). Most of the fields were not irrigated (90.54%, Appendix E9). Again no significant differences were found between cutworm damage in irrigated and non-irrigated fields (x? = .33, p > .500). It appears as though moisture level has no effect upon cutworm damage, even though larvae are always found in well-drained sandy acres. Adult White Cutworm YearlygSampling for Adults In Figure 9 the blacklight trap catches from Oceana county from 1973 to 1975 are shown (Appendix F). The initial moth catches NO. MOTHS CAUGHT PER WEEK 37 2KKND« C 1973 Shelby 1000« A 1974 Sheldon o 1975 Sheldon 500. I 1975 Howhy O O 100« ' 50< 10< ' 51 O 152025505 .6 11520215310; 9 {41934 JUNE JULY AUGUST Figure 9. Weekly blacklight trap catches of white cutworms from Oceana county. 38 were taken on June 18, 1973; June 24, 1974; June 19, 1975 for the Hawley trap; and June 20,1975 for the Sheldon trap. When plotting catch against chronological time, there was considerable variation in the flight curves. In 1975 the peak blacklight trap catches were about ten days earlier than in both 1972 (Insect Alerts)1 and in 1974. The peak catch for 1973 was unknown due to a campus postal strike in July. This prevented all but first-class mail from reaching campus, which included the blacklight collections. When postal services resumed, the samples had deteriorated and could not be identified; therefore, the flight records for 1973 are incomplete. By changing the time axis to degree-days (°D) (Baskerville and Emin 1969), which measure physiological time rather than chronological time, much of the variations were removed from the flight curves causing better synchronization (Figure 10). Degree- day accumulations were calculated from the Hart, Michigan weather station for 1973 to 1975 (Appendix 01). Since the actual flight threshold was unknown, 50°F was chosen as the base temperature from which to accumulate °D (Thompson 1966). On a physiological time scale, the range of initial catch was from 625° 0 > 50°F to 675° 0 > 50°F, which was not that much better than chronological time. However, there was better synchronization of the flight curves with degree-days rather than chronological time. 1Insect Alerts are published by the Cooperative Extension Service of Michigan State University. 39 EIKND‘ <3 1973 swung / A 1974 Sheldon 1000- / ‘ o 1975 Sheldon ’." II 1975 linflqy 5m: ’2' O O ” o O O C HE chrd ’ '— 9 5°‘ ' <1 (J U) I 25 . :E K) 0' Z v I V l V I ' I ' l ' V GEN) SKID ICKND IZIX) 14(XD IGCKJ IEKX) ’0 > 50°F (Hort) Figure 10. Blacklight trap catches of white cutworms per degree- day from Oceana County. 40 Weather Parameters and Flight Activity, Since a blacklight trap is passive, movement of the insect to the trap is required for the insect to be collected. Flight activity and movement of the moths then become important when evaluating blacklight trap catches. Williams (1940) and King (1962) have stated that the number of insects caught on a given night was not a function of population or activity, but rather both. Weather factors were the major reasons for flight activity fluctuations in their studies. In an effort to relate the importance of weather parameters to white cutworm moth activity, a series of correlations were computed between adjusted daily catch and weather parameters (Table 7). Daily catch was calculated by dividing catch during a time interval by the number of days in that interval. A significant positive correlation was found between the two trap's catches. This was not surprising, since the traps were only seven miles apart and in similar habitats. The Sheldon trap showed a significant positive correlation with relative humidity, but no correlation was shown for the Hawley trap. This was quite unexpected, since other authors (Cook 1921 and Hanna 1968) have shown increasing relative humidity to increase flight activity of certain Lepidoptera. For the Hawley trap, there was a significant inverse correlation between daily catch and barometric pressure, but again, no correlation was shown by the other trap. (This inverse cor- relation would indicate that as the barometric pressure increases, 41 Table 7. Correlation coefficients for 1975 daily white cutworm moth catches for Hawley's and Sheldon's blacklight traps with several measured environmental factors. BLACKLIGHT EQEEESEQL, Sheldon Hawley Sheldon's Trap 1.000 .380* Relative Humidity (Muskegon) .336* -.245 Percent Sky Cover (Muskegon) .016 .065 00 per day (Hart) .051 -.299 Minimum Air Temperature (Hart) -.0001 -.193 Maximum Air Temperature (Hart) .200 -.188 Average Air Temperature (Hart) .112 -.211 Barometric Pressure (Muskegon) .014 -.620** Rainfall (Hart) -.114 .011 * significant at 5% level ** significant at 1% level n == 43 42 the catch decreases.) Williams (1940) stated that the effects of barometric pressure were complicated and difficult to under- stand, but nay partially be explained by the fact that as the barometric pressure rises, the air generally becomes warmer and drier. Hanna (1968) has shown that low temperatures and high relative humidities favor flight activity of the black cutworm Agrotis jpsilon (Hufnagel), thus possibly explaining the inverse correlation between barometric pressure and daily moth catch. ' Sex Ratio of Blacklight Collected Moths Sexing of the moths collected at the Sheldon blacklight trap for 1974 (Figure 11) and 1975 (Figure 12) revealed that more males than females were collected. The ratio of females to males for 1974 and 1975 was 1 to 3.46 and 1 to 2.99, respectively. Possible explanations for this include: (1) the male population was greater than the female population; (2) males were more at- tracted to blacklight traps; or (3) once a female was trapped, she emitted pheromones and attracted males. From the data available, there was no way to determine if males were more attracted to the blacklight trap or if a trapped female attracted males. From sexing pupae, (Cheng 1970) which were laboratory-reared from field collected females, the sex ratio was 1 to 1.05 females to males. If this was an indication of the true field population, then the sex ratios were approximately equal. As a check on the hypothesis that males were more attracted to the blacklight trap, moths were collected on milkweed blossoms 43 1 A. 197w. Iooo« “a.“ _"°_"F.me 500 ‘ f, \\ ------o--...- Md. :1 I. ’ /‘\,\ u: , , \‘ “J : f 3 l" / \,\‘ a: , \\ 3! KM) f ix/ i; t / “\ 3 ; \ (I) J l/ ‘ g , .92 / J I 660 abo 1600 1500 1400 16230 °o >50°F (HART) Number of male and female moths caught per week at the Figure 11. Sheldon blacklight trap for 1974. 44 ,x’q‘.‘ — "°—-- Female 1000‘ x," ‘x‘ ------- o- ------ "0|. /\ K '0‘ " ‘ “\ / \- NOL NKHTESlmNUGHW PER “Eflfl( \ 660 860 1600 1230 moo K300 °D >50°F (HART) Figure 12. Number of male and female moths caught per week at the Sheldon blacklight trap for 1975. 45 and at the blacklight trap on July 9, 1975. Females were more abundant than males at the milkweed blossoms (ratio of 1 to .58), whereas males were more abundant at the blacklight trap (ratio of 1 to 1.90). It appears as though males were more active at the blacklight trap, whereas females were actively feeding. Hourly Sampling for Moths A relative estimate of hourly moth activity, as measured by a blacklight trap (Figure 13), indicates males to have a unimodal and females a bimodal flight activity. Male activity gradually increased to a peak of about 30% (Table 8) of the total males collected from 1 a.m. to 2 a.m. Female activity increased rapidly to a first peak between 10 p.m. and 11 p.m. of about 24% than slowly declined to about 6% caught between 2 a.m. and 3 a.m. The second peak occurred between 4 a.m. and 5 a.m. with about 18% of the females caught during that hour. Females became more active about the blacklight trap earlier in the evening than males. A possible explanation for this difference in flight activity could be that moths coming into the fields from their daily hiding sites were attracted to the blacklight trap. Females, once they had reached the fields, were more attracted to feeding and ovipositing. Males, however, were continually attracted to the blacklight. Once the females had finished feeding or. ovipositing, they started leaving the fields, returning to their daily hiding places and were again collected in the trap, thus explaining the second peak. 46 70‘ _.-0—--Femole ----o---- Male .GOJ IQ\ —O—T0101 \ 1' \ I \ sod ’ I ‘~ m ' ‘ :> ' ‘ (D i l I I 4 \ a: 40i I ‘\ u I \ 02 I \ l- I “ I ' ‘l g 301 t, \ 4 d ‘\ 0 I, \ 2 cf,” \\ 5 2°‘ /\ I’ o >’ v"4 :5 I; P“ ‘\\‘ —£k 5" \ .0— ‘ \ 0° . ’ b” \ '/ ‘1‘ z 10‘ ',’ ‘\ I /’ s‘ 4/ \ ’1’ v” ‘ I (3 ' . _ . 222 - - - 22! 153 2!} l 2 13 4» 55 IS TIME TRAP CHANGED Figure 13. Total hourly moth catches from the Sheldon blacklight trap for July 9, 10, 18, and 25, 1975. 47 Table 8. Summary of total moths caught per hour from the Sheldon blacklight trap for July 9, 10, 18, and 25, 1975. Percent of Percent of Total Hourly Catch Nightly Catch Catch per Hour 322:5 Female Male Both Female Male Female Male Both 21-22 1 0 1 100.00 0.00 1.14 0.00 .34 22-23 21 15 36 58.33 41.67 23.86 7.35 12.33 23-24 13 21 34 38.24 61.76 14.77 10.29 11.64 24-1 14 27 41 34.15 65.85 15.91 13.24 14.04 1-2 9 61 70 12.86 87.14 10.23 29.90 23.97 2-3 5 45 50 10.00 90.00 5.68 22.06 17.12 3-4 7 16 23 30.43 69.57 7.95 7.84 7.88 4-5 16 18 34 47.06 52.94 18.18 8.82 11.64 5-6 2 l 3 66.67 33.33 2.27 .49 1.03 88 204 292 48 Another possible explanation could be that early in the evening both males and females are attracted to external stimuli, i.e., blacklight traps, milkweed blossoms, etc. After they have aggregated at these stimuli, mating occurs. Once mated, the females became less interested in external stimuli and more interested in feeding and ovipositing. After oviposition and feeding, the females were once again attracted to external stimuli and were again caught in the blacklight trap. Since no data is available to support either of these statements, no definite conclusions can be drawn. Larval White Cutworm Instar and ngulation Age Determination Figure 14 shows the frequency distribution of headcapsule widths of field-collected and laboratory-reared white cutworm 1arvae'(fed on a diet obtained from Drs. Dupre and McLeod of Agriculture Canada, Appendix H). Larvae were measured with an ocular micrometer in a WildR microscope, with the small larvae measured at 50x and the large at 25x. Overlap in headcapsules increased as the instars increased, and was the greatest between the laboratory fourths and the field collected fifth instars. Due to this overlap, exact separations into instars were impossible. Based on this data, the most probable ranges of instar headcapsules are given below. 49 (031333100 0131:!) 'ON .00>20p 2203000 00023 Ammop u :v 000000000 000$$ 0:0 Ammmm u :v 002002 2200020200 202$ 000020230002 2000: 00000000002 $0 000002020000 200000022 .00 020002 25.5 1.523 0.530604% 8.» 8.0 0..., E. 000 ,8. , 0.0. .02 _ .0... . _ .00 $20-6, l 1‘; w, 021 K1 . a . 0.0 . m. .. a . .12 1. om «0 .2 . V020. $2“... 0 . cc _ . m 2. . 2.. . .. fl 0 22 w . :2 a E. 2:. :2 . J .E 02 0: 0.. .20}. E... _ .2 To .00 : _ 2. . .. 0 ., N m 0.. . . . .w J 3 2. 00. M 2. _ 1....2, _ .00 .O . . _ _ _ . .- 00 .2 mN. .. mum“ .1...“ .... K .00. ) 02.. E2: ... a." m 2 w 00. w... u. 2 . . .00. 8 .. .__. 00 00. a .. 00. 3 2 . v 00. .00. mm .00. {fl . .oou ommdwm 50.20.09: Incl CNN 0955.300 0.5.“. 2:0.-- 208 50 Instar Head Capsule Width (mm) 1 Less than .39 2 .39 to .52 3 .53 to .78 4 .79 to 1.12 5 1.13 to 1.86 6 1.87 to 2.70 7 ‘ Greater than 2.70 Once the instars have been determined, the population can then be aged. Fulton (1975) describes a method for determining weighted mean instars (WMI) for the cereal leaf beetle; also in- cluded was a discussion on calculation of WMI for insects with unequal instar durations. WMI can then be calculated by: 7 1 1 Pi Ni / 1:] Pi Ni (3) WMI = 1 "MN where Pi = proportion of the duration of larval development represented by instar i. Exact developmental times for the white cutworm were un- known; however, information on development (Appendix I) indicates' that developmental time was not equal for all instars. Values of P would than have to be calculated for each instar. Since the developmental time for seventh instars was combined with pupation time, they would first have to be separated before Pi could be calculated. The two were separated as follows. A linear regression was calculated between temperature and time required for seventh 51 instar and pupal development. Only those temperatures with equal photoperiods were used in the regression. Y = -.027 + .00071 X (4) where Y = percent development per day X = rearing temperature in °F r2 = .935 The total number of days required for seventh instar and pupal development at 80°F was then obtained from the reciprocal of the percent development per day (33.88 days). From this the known time for pupal development at 80°F was subtracted (16.92 days 1 5.49 [S2], n = 25) to obtain a seventh instar developmental time of 16.96 days. A proportion of seventh instar developmental time (16.96) to total developmental time (33.88) was then calculated (.50). The seventh instar and pupal developmental times were then multiplied by this proportion, thus yielding seventh instar develop- mental time. Once the seventh instar developmental time had been calculated, P values were calculated for each instar and are listed below: Instar P .087 .080 .086 .110 .152 .165 .349 —l \IO‘U‘l-th 52 WMI from field collected larvae (Appendix J) were then calculated for the different collection dates and are shown in Figure 15. Larval Sampling with Barrier-Baited Plots The expected row area was calculated for each of the seven plots, based on their radius, average row spacing (5 feet) and average row width (15 inches). Since no significant differences were found between the expected row areas and the observed areas (x2 = 10.75, p > .05), the rows selected were representative of the field and as such could be used in the experiments (Table 9). Looking at Table 9, one will see that the number of larvae decreased rapidly through the test interval, with 82.54% being recovered after the first night. Due to a rain storm on the night of September 7, the larvae were less active. This, coupled with a reduction of the bait's efficacy due to deterioration by the rain, accounts for the decreased larval recovery on September 8. Distribution of the dead larvae showed that 92.06% of all recovered larvae were within one foot of the center of the asparagus row. Since only about 25% of the asparagus.fie1d is actually row, little effort need be expended in sampling between rows to get an estimate of larval densities present in the field. Estimates for the mean number of larvae per square foot of plot, per square foot of observed asparagus row, and per square foot of expected asparagus row after one day were calculated as .41 :_.52 (S), 1.26 :_1.26 (S), and .86 :_.83 (S), respectively. 53 .0202 0:0 0202 20$ 00>202 000002200 02022 202 200022 :00: 0002020: .02 02:022 Foo Hamm .5354 >43... MZD... >42 2.0 2... 2 2.0 2... 2. 0m 0.. 0 0w. 0.. 0 0.0 m. 0 00 0.... 0. b D b b l M B 9 V m 2.0. .4 .N 3 0 ohm. . o W .n W. N M . .c m... V a . l. .0 4 80 o. 0 r0 ‘ d o 00 00. o. o o o. 000 o o -0500... .0 0F 54 m0 0 0 0 N0 0<202 .uwmu 0 0 0 NF m_.mN 0N.00 00.20 00.0m N00 N0.N N 00 N 0 0 mp N0.¢N N0.MN 00.0N 00.00 00 mm.m 0 0 0 0 0 m N0.00 00.0 00.00 00.2 00 00.m m 2 0 P 0 0 00.2N NN.0 20.0 mm._ 0N 02.N e m 0 F 0 N 00.0m 02.0 mN.0m mn.m N0 00.0 m 00 N m 0 0 20.00 0m.m 2m.00 00.0 0 0m.0 N N 0 0 0 N 00.02 mm.N 00.n0 No.0 m 00. 0 00>200 00\0 0\0 0\0 N\0 003mw2< MWH<0MW2 003mw2< 0Mwm ”MW 0wwu ”MN; 0Mwwwm 0000 00002 . 00002 2 00002000< F0002 2 00002000< 0000 002000000 0<>2<0 02<0 00200020 00>20000 .02202000 00023 20$ 0000200 0>$0002000 20$ 00000 0000 2002200 000002 020 $0 0000002 .0 00202 55 Unfortunately, no replications of these plots were made and no significant differences were observed between the means. From the mean number of larvae per square foot of asparagus row (i) and its variance ($2), the appropriate sample size (N) needed to bring the standard error of the mean within a fixed percent of the mean can be calculated by (Nelgeson 1972): N = sZ/si2 -.(5) Larval Sampling with Open-Baited Plots A feature present in the open-baited plots not available in the barrier-baited plots is that larvae are capable of movement into the plot. Samples taken in this fashion include both the larvae present in the soil at the time the plot was treated plus those that move into the plot. In an effort to use open-baited soil plots as a quantifiable sample method, large plots (15 feet by 15 feet) were tested. The entire plot was treated with SevinR bait but only the central square yard was used as the sample unit. The six feet on each side thus acted as an insecticide barrier and prevented larvae from moving to the central sample yard. 0f the 67 larvae collected from these sample plots, only three were collected from the central square yard. The remaining larvae were collected in the six-foot insecticide barrier, of which 28 (77.78%) were collected on the first night. All larvae collected on subsequent nights were in the first three feet, thus indicating that the insecticide barrier was effective in preventing 56 larvae from moving into the central square yard. However, the baited-barrier soil plots were mOre satisfactory for quantitative sampling since no assumptions about movement had to be made. The advantage to the open-baited soil plots is that larvae moving through a baited area will stop and feed upon the bait and this makes an excellent larval detection technique. The large plots, however, were so large that examination of the plot for larvae required considerable time and almost all of the dead larvae were found within the outer three feet of the plot. There- fore, as a detection technique, this plot size was much too large and a smaller plot size (six feet by three feet) was considered. The results of the small open-baited plots (Appendix K) are summarized in Table 10. The low density plots (1-5) received an additional fall insecticide application in 1974 which accounts for fewer larvae being collected. When the daily catches for the high and low density between-row plots (Figure 4a) are combined and compared to the perpendicular to a row (Figure 4b) daily catches, a highly significant difference (t67 = 3.72, p = .00041) was found. However, if one adjusts the catch according to the number of asparagus row feet per plot (six in the between-row plots and three in the perpendicular to row plots), no significant difference (t7 = 1.68, p = .10) was found. This indicates that ' the number of dead white cutworms found was more related to the number of asparagus row feet in the plot rather than the area of the plot itself. 57 Table 10. Results of small open-baited plots (3 feet by 6 feet). _ . . Mean Larvae Standard Number of P1°t R0" Relationship per Plot Deviation Observation Between Low Density 1.20 1.23 10 High Density 7.27 5.96 15 Combined (Low 8 High) 4.84 5.52 25 Perpendicular 1.48 1.58 44 Parallel 1.40 .55 5 Five sets of paired plots were set up in the same location to test the importance of the asparagus row location in the plot to the number of dead larvae found. Each pair consisted of one between-row plot and one parallel to a row plot (Figure 4c). These plots both contained six asparagus row feet; however, the between-row plot consisted of two row segments each three feet long. Daily catches from these two plots were not significantly different from one another (t18 = .25, p = .81), which indicates the location of the asparagus row to the plot was not a factor in determining the number of larvae found. Therefore, when using small open-baited plots for detection purposes, maximization of resources occurs when the asparagus row area in a plot is at a maximum. 58 Larval Sampling with Pitfall Traps Pitfall traps were tested to determine white cutworm larvae attraction or repulsion at the M.S.U. Botany Research Farm (Table 11). The expected larval catch for a given radius (ECR) was calculated by: R-l EC = PCR (TT 2 EC.) (6) R i=1 where PCR is the proportion of the circumference that was pitfall traps for that radius and T is the total number of white cutworms released. Stated explicitly, the expected catch for any radius is the product of the proportion of the circumference that was occupied by pitfall traps and the total number of cutworms left to be trapped. No significant deviations were found between the observed catch and the expected catch (xg = 6.296, P = .098). As one can see from Table 11, much of the total x2 comes from the final radius. The exact cause of this deviation from the expected catch is unknown, but is probably due to two causes. First, since the area of the test circles are increasing with the square of the radius, the area was increasing much faster than the radius. When this happens, the probability of a catch becomes more dependent upon area and less dependent upon the percent of the circumference that was occupied by pitfall traps. Second, there were also Undoubtedly repellant effects caused by the barriers between the pitfall traps since they were quite reflective to the moonlight and the larvae are photonegative. This deviation was 59 00N 00 000 00 000<0002 0<202 00N.0 mup mm mm 00 002020<0 0<202 000.0 00.02 2m 20 NN 00 00.02 20 02.00m 00 moo. m0.0m 0m m 00 N0 00.0N FF 0N.0NN mm 000. 00.00 00 0 0 NF 00.0N N 00.000 0N 000. Pm.Nm 00 00 00 NN 00.mN 0 00.02 N_ r0002 0 00022 N F0022, 2120022 00022 200002020200 20000 2200<0 0<>2<0 20 .0200 00022 20020200 20020000 Nx 000000xu $0 a Pp0$0$0 2 002020$2002$0 000002 .00020 220$000 00 000000002 20 0000002000 2203000 0002: $0 0000000220000 20$ 000020200x0 020 $0 0000002 .00 00202 60 of little concern since no barriers were used in conjunction with pitfall traps in the field. Since no significant deviations were observed, this experi- ment has shown that pitfall traps could be used quite successfully as collection and detection tools for white cutworm larvae. This Justified the further use of pitfall traps for sampling larvae. Both unbaited and baited pitfall traps were used for larvae trapping. Pitfall traps were baited by placing one-fourth inch of SevinR bait in the trap. The results of the pitfall traps (Table 12) show that a significant difference was observed between the number of cutworms caught per day per baited pitfall trap (.54 : .89[S]) versus unbaited pitfall trap (.16 :_.39[S]) (t172 = 3.69, p = .0003). Thus, the baited pitfall trap was significantly ' better for detection of white cutworms than was the unbaited pitfall trap. Comparison of Larval Sampling Methods For quantitative samples the barrier-baited plots were more satisfactory than the open-baited plots. With open-baited plots the effects of weather parameters and larval age upon movement would have to be fully understood. However, with the barrier- baited plots no assumptions about movement were necessary to evaluate the results. The barrier-baited plots consisting of six square feet appeared to be the most desirable plot size. Plots larger than this incorporate too much row area and require the examiner to enter the plot to examine it for dead larvae, which causes much unproductive searching effort and plot disturbance. 61 Results of the use of baited and unbaited pitfall traps Table 12. as a white cutworm detection tool. NUMBER OF WHITE CUTNORM LARVAE PER PITFALL TRAP Pitfall - 9/10 9/11 9/12 9/l3 9/l4 TOTAL 9/9 Plot Trap 042624916532131. 000000001101000 000000000000000 000010300000000 012300201101100 ,030111212130010 00020320220002] 666LL—F888999.00.0. Baited 49 16 12 14 .TOTAL 00012041001203 00000010001100 00000001000110] 00000000000001- 00001000000000 00000010000000 00011102000000] 44444444444444 66677788899900. 1] Unbaited l4 5 TOTAL 62 Since white cutworms are distributed within rows rather than between rows (over 90%) and over 80% of the larvae are recovered after one night, a sample taken with a plot of size 2 would estimate approximately 70% of the larval density in asparagus, Thus providing a relatively simple method of obtaining quantitative samples. For larval detection purposes pitfall traps (both baited and unbaited) and open-baited soil plots were compared. Plots six through ten each consisted of three baited and unbaited pitfall traps, a parallel to row open-baited plot, and a six square foot barrier-baited plot. The estimate of the mean number of larvae per square foot of asparagus row obtained from the barrier- baited plots was l.73 :_l.lO (S) for this area. This was not sig- nificantly different from the mean obtained from the open-baited plots (l.73‘:_.85[S]) (t9 = .0003, p > .99). From this it appears as though with one night of operation an open-baited plot will estimate the same mean as will a barrier-baited plot over five days. The movement into the open baited plot in one night approxi- mately equals the number of larvae which remain in the soil for more than one night, thus explaining the equalization of these means. Baited pitfall traps caught an average of .54 :_.89 (S) cutworms per day per trap whereas an unbaited pitfall trap caught an average of .16 :_.39 (8). Since pitfall traps require larval movement before the larvae can be detected, larval detection is dependent upon factors which affect movement. The open-baited 63 plots, on the other hand, indicate the number of larvae present when the plot was initiated as well as those that move into the plot. Taking this into consideration, the open-baited plots. were more satisfactory for larval detection than were the pitfall traps (either baited or unbaited). Probability of Larval Detection If one assumes the sample means fit a Poisson distribution, then probabilities can be placed on detection of low density white cutworms (less than one per asparagus row foot). This assumption can be made for two reasons: (1) the mean number of larvae recovered per row foot of asparagus in the baited-barrier plots was .74 after one day of operation. This was approximately equal to the variance (.82) which fits the definition of a Poisson distribution given by Ruesink and Haynes (1973) (2). The observed frequency distribution of the number of cutworms collected from the perpendicular to row small plots did not differ significantly from that predicted by a Poisson distribution (Kolmogorov-Smirnov test, 011 é .l35, p > .20). When using the Poisson distribution, the probability of finding r individuals (Pr) per sample can be calculated by (Pielou. 1974): p = — e'x (7) where: x is the expected mean density e is the base of the natural logarithm 64 However, for detection purposes, one is interested in the probability of finding at least one organism (P). Probabilisticly, this can be expressed as one minus the probability of finding zero organisms and when N samples are taken becomes (Ruesink and Haynes 1973): A P = i-e'XN (8) Using this equation, the probability of finding at least one larva, given an expected mean, (2), was computed for densities from .01 to 1 white cutworm per asparagus row foot and for one to twenty samples (Table 13). By solving equation 8 for N, the total number of samples (Np) one would have to take to obtain a given probability of a find for any expected density can be calculated by: Np = -ln(l-Pf)/; (9) where in is the natural logarithm of the quantity (l-Pf). These values have been calculated for densities of .01 to l larva per asparagus row foot and are also shown in Table l3. Oceana County White Cutworm Survey A total of five plots were monitored from which data was obtained for the entire survey. The number of white cutworms caught per day per trap (Figure 16) gradually decreased throughout the season, with a large peak on June 2. On June 1 two of the five fields were chopped to ground level. This was done because Table 13. 65 Probability of detecting at least one white cutworm larva with one asparagus row foot samples for various larvae densities and sample sizes. to 0bta1n a N95 is the number of samples required probability of detection of .950. Number of Samples Taken x 1 2 3 4 5 10 15 20 N95 .010 .010 .020 .030 .039 .049 .095 .139 .181 300 .020 .020 .039 .058 .077 .095 .181 .259 .330 150 .030 .030 .058 .086 .113 .139 .259 .362 .451 100 .040 .039 .077 .113 .148 .181 .330 .451 .551 75 .050 .049 .095 .139 .181 .221 .393 .528 .632 60 .060 .058 .113 .165 .213 .259 .451 .593 .699 50 .070 .068 .131 .189 .244 .295 .503 .650 .753 43 .080 .077 .l48 .213 .274. .330 .551 .699 .798 37 .090 .086 .165 .237 .302 .362 .593 .741 .835 33 .100 .095 .181 .259 .330 .393 .632 .777 .865 30 .110 .104 .197 .281 .356 .423 .667 .808 .889 27 .120 .113 w .213 .302 .381 .451 .699 .835 .909 25 .130 .122 .229 .323 .405 .478 .727 .858 .926 23 .140 .131 .244 .343 .429 .503 .753 .878 .939 21 .150 .139 .259 .362 .451 .528 .777 .895 .950 20 .160 .148 .274 .381 .473 .551 .798 .909 .959 19 .170 .156 .288 .400 .493 .573 .817 .922 .967 18 .180 .165 .302 .417 .513 .593 .835 .933 .973 17 .190 .173 .316 .434 .532 .6l3 .850 .942 .978 16 .200 .181 .330 .451 .551 .632 .865 .950 .982 15 .250 .221 .393 .528 .632 .713 .918 .976 .993 12 .300 .259 .451 .593 .699 .777 .950 .989 .998 10 .350 .295 .503 .650 .753 .826 .970 .995 .999 9 .400 .330 .551 .699 .798 .865 .982 .998 .999 7 .450 .362 .593 .741 .835 .895 .989 .999 .999 7 .500 .393 .632 .777 .865 .918 .993 .999 .999 6 .550 .423 .667 .808 .889 .936 .996 .999 .999 5 .600 .451 ,.699~ .835 .909 .950 .998 .999 .999 5 .650 .478 .727 .858 .926 .961 .998 .999 .999 5 .700 .503 .753 .878 .939 .970 .999 .999 .999 4 .750 .528 .777 .895 .950 .976 .999 .999 .999 4 .800 .551 .798 .909 .959 .982 .999 .999 .999 4 .850 .573 .817 .922 .967 .986 .999 .999 .999 4 .900 .593 .835 .933 .973 .989 .999 .999 .999 3 .950 .613 .850 .942 .978 .991 .999 .999 .999 3 1 000 .632 .865 .950 .982 .993 .999 .999 .999 3 66 SAVO 0M1 NI 1.01:! 83d OBS-WWW SHVBdS 'ON _ .mpopq umem Lsom com popa cmn ummmsmu mgmmnm mo cmnE:c ucm any Lon amp“ cmq “guano macozpzu maps: .op meamwu UZD... N. n o. . o a .v y m o... #0.. H m .. T a . W M 8.. .8 m n 1 m S 0 on; :0”. w m a u .A W I. 8.. TO? £960 necesso 0 39255 833 Q 67 high temperatures had caused the asparagus to grow beyond a marketable length between harvests and the chopping brought the field back under the grower's control. As a result of this chopping, more larvae were collected than was expected. This indicated that larvae were still present in about equal numbers as at the beginning of the survey and the gradual decline was probably not due to mortality but rather to decreased activity. The increased availability of food would be the main reason for this decline in activity since less searching would be required by the larvae in order to find food. The number of spears damaged per plot increased until May 12, then decreased throughout the season. This increase until May ll and l2 was due to those days being the first day of harvest f0r most of the fields. Prior to this date only three fields were monitored daily for damage. Also, spears were constantly emerging throughout this period making more spears available. There are two main explanations for the decrease in damage after May 12. First, mortality could be reducing the number of cutworms in the area or second, there was a change in feeding behavior of the larvae. As stated earlier, mortality doesn't seem to be responsible for the reduction in feeding. Therefore, the second hypothesis, a change in feeding behavior, must be explained. In an effort to evaluate feeding damage and feeding behavior, correlations were made between the number of spears damaged per day and the number of white cutworm larvae caught per day with environmental parameters (Table 14). 68 Table 14. Correlation coefficients between nightly spears damaged and white cutworms caught with environmental parameters (Ray Wybenga Farm). SPEARS DAMAGED CUTNORMS CAUGHT Cutworms caught .203 l.000 Number of previous harvests -.540** -.495** Air maximum temperature .026 -.l35 Air minimum temperature -.235 -.058 Soil maximum temperature -.205 -.342* Soil minimum temperature -.4l8** -.329* Rainfall -.249 -.182 Percent sky cover (Muskegan) .OOl -.05l *Significant at .05 level. **Significant at .Ol level n = 37 A highly significant inverse correlation was found between the number of spears damaged per day and the number of white cut- worm larvae caught. This is due to the increasing amount of food units (spears and butts) being available to the larvae; less movement was required by the larvae to find food and therefore less larvae were collected. A highly significant inverse correlation was also found between the number of damaged spears and the number of previous harvests. This relationship implies that less damage occurred as the number of butts (unharvested spear portions) remaining in the field increased. 69 Significant correlations were also found between soil maximum temperature and the highly white cutworm catch and between soil minimum temperature and both spears damaged per night and white cutworms caught per night. These three correlations are merely spurious correlations. Since soil temperatures slowly increase through harvest due to advancement into spring and the number of spears damaged and the number of cutworms caught decrease due to reasons already explained, a non-real negative correlation was observed between these parameters and the survey results. Asparagus Spear/Butt Ratio When asparagus spears are harvested, the sections of the asparagus spears below the height at which the spears were harvested (the butts) remain in the field. As the asparagus season proceeds, more butts are added to the field with each successive harvest. White cutworms will also feed on these butts as long as they are palatable, with the length of butt palatability dependent upon temperature and relative humidity. Butts generally become unpalatable due to dessication. To calculate the percent of spears in the field, the yield information was used to determine spears and butts in the field. Prior to a harvest, the amount of spears in the field is approximately equal to the harvested spear yield. Since spears grow extremely fast, those below harvest height will be ignored for this discussion. Thus prior to the first harvest the only food available for the white cutworms to feed upon were spears. 70 However, prior to the next harvest, there were the last harvest‘s butts plus those spears that had emerged which gave the larvae two types of food. The percent of spears (P5) in the field at harvest T can be calculated by: 51 Ps = -——————— (l0) T H 2 S. i=H-P ‘ where ST = number of spears in harvest at harvest T P = number of days of butt palatability. H = number of days since first harvest. There was error involved in this method since new spears are constantly being added to the field due to emergence. However, considering the frequency of harvests (up to thirty per two months), this error was relatively low. The percent of spears in the field has been calculated for varying days of palatility for a commercial field at Hart, Michigan (Appendix Ll) and for a Mary Washington variety at the Horticulture Experimental Farm at Sodus (Appendix L2) and are presented in Figures l7A and 178, respectively. The difference in the early part of the graph is due to the difference in initial harvest date and the frequency of harvest. The commercial field was harvested more frequently than the experimental field. Ten days was the most realistic average length of butt palatability and will be used from this point in this paper. The percent of HI!) uwns PRDR ‘KDFflRWEfi EflfARS I“; PENMHHIGE 0F 71 1006 sun maumuw (A) q a 700): 004 o @0013 A I400” 4 304 4 ‘0: q :01 4 n v1VTVIVIIVIVVVrI'V'V'vrv'I'vjf‘VVT'IVVV'I'Vv'IvrTV'VV'VI 5 IO I5 20 25 so 4 9 l4 IS 24 29 MAY JUNE IOO“ . (B) .0- fi 604 40" 1 20" d o V v V 1 I v V I 'T“ V V r" V V T V I V V v V ' V V v V ' t ' I V I V " V V I V V V V ' V v V ' ' ' v ' T—' 5 .0 IS 20 25 30 4 9 l4 IS 24 29 MAY JUNE Figure 17. Percentage of food units that are spears for Hart l975(a) and Sodus farm 1975(b). 72 spears for the nine Sodus varieties of asparagus (Appendix L3) has been averaged and graphed in Figure 18. In all these graphs one will notice that with ten days butt palatability, the graphs all tend to plateau off at about 20% of the food units as spears. This is about what was expected if all harvests were of equal size and harvested at two day intervals. One will notice that the percent spears in Figure 18 and the number of damaged spears per day in Figure 16 both decline similarly through the season. If larval feeding was completely random with respect to spears and butts, then one would expect these two graphs to be similar in form. Linear regression analysis was performed between the per- cent spears and the reciprocal of the number of harvests for the mean of the nine varieties of asparagus at the Sodus Experimental Farm and for a commercial field near Hart, Michigan. The following equations were obtained: 16 (ll) 12.80 + 92.42 RH r Sodus Ps .900, N Hart Ps ll.54 + 81.37 RH r .9l0, N 26 (12) where Ps - percent of food units that are spears. RH reciprocal of the number of harvests. The 95% confidence intervals on both the slope and y-intercept of these equations overlapped so they were not significantly different at the .05 significance level. 73 .mzmu op com mFampMFMQ mpasm .mnmp coc .mzcom .Ecmd :ucmmmmm FacsppauPpco: .=.m.z mg» pm :zocm mammcmnmm we mm_umwcm> m on“ com mammam mgm page wave: uoom mo mmopcmucma cam: wzaa >42 0 0 0» am ON 9 O. n bb-b-bp-LPbb-LPbpbbbbbPrpbprLbb-b-bo you r .o¢ r .0m .00 .OO. .w_ 6236.2 n NS 1d 3 SV d 8% 0 8a.. m% 86. V 8.: Am 3 S I; 74 Larval Feeding Behavior Field Feeding Behavior On May 29 and June ll, 20 observations on the location of feeding larvae were recorded. Larvae were recorded as feeding on a spear or butt. The total number of spears and butts within one foot of the larval feeding site were also recorded. In this fashion, 29 larvae were observed with eight feeding on spears and 2l feeding on butts. A total of 73 Spears and 2l0 butts were recorded as present near the larval feeding sites. A chi square test showed no significant difference between the number of larvae feeding on spears or butts and the expected number to be feeding on spears and butts (x? = .049, p = .82). This implies that larvae feed without preference on spears or butts. It was also observed that several larvae could be found feeding on the same food site when ample food was available. Therefore, it would appear as though there was no intraspecific competition between larvae for food. Laboratory Feeding Behavior Results from the laboratory test for randomness of larvae feeding (Table l5), revealed one test of the eight to deviate significantly from random. An overall chi square significance 2 test for a series of individual test significance levels (x2 [n-l] = n -2 ln 2 oi) indicated this series of tests did not deviate i=l ' 75 Table 15. Results of the laboratory random feeding tests for white cutworms on asparagus spears or butts. .- Oi. . # Spears # Butts 2 Significance Tr1al # Spears Damaged # Butts Damaged X Level 1 8 5 l6 3 2.835 .09 2 10 5 20 5 .919 .34 3 l4 » 3 4 1.313 .25 4 l4 5 7 5 .027 .87 5 6 2 6 4 .333 .56 6 6 4 6 2 .333 .56 7 7 5 7 2 1.143 .29 8 5 5 5 0 6.400 .01* *Significant at .05, df = 1. 2 significantly from random (x16 = 24.43, p = .081) and no significant preference for feeding sites was observed. Since feeding damage was random, the amount of resulting damage was then a function of the percent of spears present in the field. Larval Movement Rates Casagrande (1976) developed an insecticide strip spray model for the cereal leaf beetle. Casagrande's model was modified with the necessary assumptions to make it appropriate for evaluating white cutworm movement (Appendix 0). These assumptions were: (1) A bait insecticide would be used (SevinR) and its band of application 76 could be controlled; (2) Any larvae that came within this baited band would stop and feed resulting in 100% mortality; (3) The toxicity of the bait would last for three days. This was chosen because this is the mean time interval between rains for Hart, Michigan (Climatological Data); (4) The cutworms were actively moving about the fields for five hours per night. This value was obtained from field observations and movies of larvae; most activity takes place from about 10:30 p.m. to 3:30 a.m. during the spring months. Diffusion Coefficients Obtained from Pitfall Traps Diffusion coefficients (0) from the M.S.U. Botany Farm experiments are presented in Table 16. In these experiments the only variable that had to be measured was time (T) since the distance (r) was fixed at four feet. 0 could then be calculated for each larvae by the simplified equation: D = -- (l3) The nightly diffusion coefficients for the two nights were found to be significantly different (t36 = 3.16, p = .003). This difference in movement rates was probably the result of the differences in air temperature and relative humidity for the two nights. On June 19, D was higher than on June 18 as were both temperature and relative humidity. 77 Table 16. Observations on white cutworm movement in 1974 at the M.S.U. Botany Farm. JUNE 18 JUNE 19 Time Released 10 p.m. 10 p.m. No. Released 75 25 Temperature 56°F 62°F Hours No. Hours No. to Move Caught D to,Move Caught D 4' (t) 4 (t) .58 4 6.90 .20 3 20.0 .80 6 5.00 .32 l 12.62 1.00 4 4.00 .40 l 10.00 1.23 1 3.25 .47 2 8.57 1.42 l 2.82 .60 2 6.67 1.53 2 2.61 .70 1 5.71 1.97 3 2.03 .83 l 4.80 2.30 l 1.74 .92 l 4.36 1.00 l 4.00 1.42 l 2.82 1.93 1 2.07 2.25 l 1.78 0 4.215 8.665 5 1.707 6.324 Si .364 1.581 n 22 16 95% Confidence Interval 3.502- s65 4.928 5.566 56: 11.764 478 Diffusion Coefficients Obtained from Movies From the movies taken of larvae in a commercial asparagus field on May 19, 1975, estimates of D were obtained for each of the larvae observed (Table 17). Since the time between frames was fixed at eight seconds (.0022 hours), the equation for dif- fusion coefficients simplifies to: r2 D - (l4) - .0088 where r equals the mean distance the larvae moved per frame. The mean value of 0 obtained from the movie (1.629 square feet per hour) was significantly different from the lowest mean 0 estimated from the pitfall traps (4.215 square feet per hour) (t3] = 4.51, p < .0001). This is probably due to two main factors. (1) The larvae in the movies were in a natural field condition and no pre-experimental handling or stresses were present. (2) The soil in Oceana County is sandier than the soil at the Botany Farm. Therefore the Botany Farm soil may have been artificial to the larvae and the higher 0 values probably reflect the larvae searching for a more familiar soil texture--sandy. Evaluation of Larval Movement Once D had been calculated, the model could then be implemented and treatment strategies simulated. In Figure 19, the effect of treatment spacing and diffusion coefficients on expected percent 79 Table 17. Movement observations of white cutworms based on movie on May 19, 1975. 1 Frame = 8 sec. (.0022 hr.). Mean Distance Cutworm Moved in .0022 hr (r) 0 1 .1978 4.45 2 .0616 .43 3 .1166 1.54 4 .0958 1.04 5 .1239 1.74 6 .0375 .16 7 .1372 2.14 8 .1154 1.51 9 .1506 2.58 10 .1083 1.33 11 .0930 .98 O-= 1.629 S = 1.166 Si .352 n 11 95% Confidence Intervals .940 $155 2.318 80 100 80" ). [33155 1: . a 0:10 .I— 8 °°" 5 . 0:5 34‘ o 40‘ 0:25 1'." 0 d 31' 0:1 ‘ if, 20- 0:0.5 0% T 1 I W O I 2 3 4 NO. ROWS UNTREATED Figure 19. Effects of treatment spacing and diffusion coefficients on expected mortality for 15 hours simulation. Rows are 5 feet apart. 81 mortality is shown. The range of D values simulated is about equal to the range obtained from the movement experiments. One can readily see that through manipulation of the treatment spacing a desired mortality can be selected for which is very useful in a pest management framework where a certain number of white cutworms present may be desirable. Probably the most realistic 0 value to use is the value obtained from the field observations since the larvae were under a natural condition and no prior handling was necessary. This 0 value (approximately 1.6 square feet per hour) was used in a simulation to show the effects of duration of insecticide efficacy on expected percent mortality (Figure 20). Over 60% of the expected mortality occurred within three days (15 hours) of bait application. Since the average frequency of rainfall was equal to three or four days for Oceana county in May, this was desirable because the bait's efficacy decreases once it hasbeen exposed to water. A solution to this would be the development of an insecticide bait that is more moisture-resistant. Estimation of Field Degree-Day Accumulation A three sensor Wilk-Lambrecht thermograph was operated in a commercial asparagus field on the farm of Mr. Lyle Sheldon from October 1974 through November 1975 (Appendix M1). The sensors were placed at three strata, ten inches above, one inch below, and six inches below the soil surface. These were the strata where the larvae spent most of their time. This field was a typical asparagus 82 100 80 )- .1: .J <1 E 60‘ 35 Hrs. .,\° 30 Hrs. 25 Hrs. a 4’0 20 Hrs. '63 I5 its. m 10 Hrs (1. fi 2° 5 Hrs. 0 . 6 { fl 0 l 2 3 4 NO. ROWS UNTREATED Figure 20. Effect of simulation time on expected mortality with a constant diffusion coefficient (0 = 1.6). Rows are 5 feet apart. 83 field in Oceana county with respect to soil type and drainage. It was located 7 1/4 miles southwest of the Hart weather station. Regression analysis between the degree-day accumulations at the Sheldon thermograph (S) and the Hart weather station (H) produced the regression equations in Table 18. The equations for each month proved to be significantly different from one another at .05 signifi- cance level; therefore, a more accurate prediction can be made through the use of individual monthly regression equations rather than the yearly regression equations. These regression equations enable estimations of degree-day accumulations in the field based on readily accessible weather information from the Hart weather station. 84 co_666m 6656663 666: u I camcooscmzu coupmcm n m 666. u *mem._ + mom._ u 666. n 22606., + mma.ma n 666. u _mem._ + 6Fo.66 u »_666> 656. u 52666. + amm.oq_m u 666. u 22mpo. + omm.mmom u F66. u .2NN_.P + NF6.N6m u 66ne6>oz ~66. u .Lmom. + 6F.Noop n F66. u TamoP.P + mmm.mom u 666. n saakm.F + NN6.~66- n 6666660 666. u _L~FM.P + 6NN.6N n N66. u .2m66.F + moo.om u 666. u .26N6.P + 66m.66N- u 66626666m ~66. u 2266m._ + moq.mp u 666. u .mem.~ + mmm.~o_ u 666. u 2266N.P + mNN.N_ n 66=6=< 666. u 22~6M.P + mm6.~m- u 666. u .2N06.P + 606.66 u 666. u .2mo_._ + P66.mm_ n »_66 666. u T.mm~._ + NM6.66 n 666. u _2mmm.F + “P6.mo_ " 666. u _+m6~._ + mom.6fi_ u 6:66 666. u 226N6.P + Fma.mm- u mam. u 226mm.P + mmw.m u 666. u .2666.P + mmo.mm u >62 F56. n saomm. + NR6.- u 666. u .226N.F + mkm.P- H _mm. H *meo.m + mm_.6 u F266< 66:62 on F1 op+ .mump LO$ sz cowpmum cwgummz .cwmmsuwz .ucm: 65p soc; cowumpassuom amulmmcmmu soc» Amy scam coupmcm mpap 6;» p6 mcoppmpssauum amuuwmcmmv upwwm mo comumeppmw com meowumzcm cowmmmsmmm .mp 6Pnoh CONCLUSION This study has been an effort to design a larval sampling method for white cutworms and to describe their biology and behavior. Early in this study it became apparent that standard de- structive soil samples were not applicable to asparagus. Therefore a non-destructive sampling technique was developed which incorpo- rated SevinR bait in the sampling. Barrier-baited six square feet plots were the most desirable for quantitative samples and small open-baited plots were most effective for larval detection. Treatments for control of larvae should be applied in the fall when the larvae are small and soil temperatures are still high. Harris (1968, 19718, 1975) has shown in laboratory experiments with Euxoa messoria (Harris), Agrotis igsilon (Hufnagel), Pseudaletia unipuncta (Hawthorn) that larger instars are more tolerant to an insecticide than are smaller instars. If one assumes this to be true for white cutworms, then treating when the weighted mean instar is between 2.0 and 4.0 good control could be expected. This range of WMI was selected because smaller instars tend to be skeletonizers of asparagus seedlings and are less affected by the baits. Soil temperatures are also higher in late August or September than in May and O'Brien (1967) and Harris (1971b) have shown that many organochlorines and other 85 86 insecticides' toxicities are directly related to temperature. Therefore, an insecticide applied in May would be less toxic than if it were applied in early September, thus making late August or early September the ideal time for larval control. A simulation of the effects of treatment spacing on expected mortality has shown that by varying the treatment spacing a specific mortality can be selected. This is instrumental in a pest manage- ment system where the pest population must be kept at a desired level rather than be eliminated. More work should be done on validation . of the assumptions of the model, especially on the efficacy of the insecticide and its length of toxicity. Research should be continued in locating the diurnal resting ' sites for the moths, since no significant numbers of adults were ever found during the day. This would be instrumental in under- standing the biology and could aid in control. BIBLIOGRAPHY 87 BIBLIOGRAPHY Baskerville, G. L. and P. Emin. 1969. Rapid estimation of heat accumulation from maximum and minimum temperature. Ecology. 50: 514-517. Bierne, B. P. 1971. Pest insects of annual crop plants in Canada, I. Lepidoptera, II. Diptera, III. Coleoptera. Mem. Entomol. Soc. Can. 78: 17. Buetenmuller, W. 1901. Descriptive catalogue of the Noctuidae found within fifty miles of New York City. Part 1. Bull. Am. Mus. Nat. Hist. 14: 229-312. Casagrande, R. A. 1975. Behavior and survival of the adult cereal leaf beetle, Oulema melangpus (L.). Ph.D. Thesis, Michigan State University. 174 pp. Cheng, H. H. 1970. Characteristics for distinguishing the sex of pupae of the dark-sided cutworm, Euxoa messoria (Harris), (Lepidoptera: Noctuidae). Can. J. Zool. 48: 587-588. Cook, W. C. 1921. Studies on the flight on nocturnal Lepidoptera. 18th. Rep. Minn. State Entomol. Agr. Exp. Stn. 43-56. Crumb, S. E. 1932. The more important climbing cutworms. Bull. Brooklyn Entomol. Soc. 27: 73-98. Forbes, W. T. M. 1954. Lepidoptera of New York and neighboring states. Part 3. Mem. Cornell Univ. Agric. Exp. Stn. 329: 433 pp. Frost, S. W. 1955. Cutworms of Pennsylvania. Penn. Bull. 596: 29 pp. Fulton, W. C. 1975. Monitoring cereal leaf beetle larval popu- lations. M.S. Thesis, Michigan State University. 108 pp. Gibson, A. 1912. Cutworms and army-worms. Entomol. Bull. Can. Dept. Agric. 3: 29 pp. Gibson, A. 1915. Cutworms and their control. Entomol. Bull. Can. Dept. Agric. 10: 31 pp. Hague, W. 1898. Notes on insects of the year, Division No. 1, Ottawa District. 29 th. Ann. Rept. Entomol. Soc. Ontario. 87. 88 89 Hanna, H. M. and I. E. Atries. 1968. On the time of flight of certain nocturnal Lepidoptera as measured by a light trap. Bull. Soc. Entomol. Egypte. 52: 535-545. Hardwick, D. F. 1966. A synopsis of the westermanni group of the genus Euxoa an. (Lepidoptera: Noctuidae) with descriptions of two new species. Can. Entomol. 98: 760-768. Hardwick, D. F. 1970. The genus Euxoa (Lepidoptera: Noctuidae) in North America. Part 1. Mem. Entomol. Soc. Can. 67: 177 pp. Harris, C. R. and H. J. Svec. 1968. Toxicological studies on cut- worms. III. Laboratory investigations on the toxicity of insecticides to the black cutworm, with special reference to the influence of soil type, soil moisture, method of application, and formulation on insecticide activity. J. Econ. Entomol. 61: 965-969. Harris, C. R. and F. Gore. 1971a. Toxicological studies on cut- worms. VIII. Toxicity of three insecticides to the various stages in the development of the darksided cutworm. J. Econ. Entomol. 64: 1049-1050. Harris, C. R. 1971b. Influence of temperature on the biological activity of insecticides in soil. J. Econ. Entomol. 64: 1044-1049. Harris, C. R., H. J. Svec, S. A. Turnbull, and W. W. Sans. 1975. Laboratory and field studies on the effectiveness of some insecticides in controlling the armyworm. J. Econ. Entomol. 68: 513-516. Helgeson, R. G. and D. L. Haynes. 1972. Population dynamics of the cereal leaf beetle, Oulema melanopus (Coleoptera: Chrysomelidae): a model for age specific mortality. Can. Entomol. 104: 797-814. Hudson, H. F. and A. A. Wood. 1930. The life-history of the white cutworm, Euxoa scandens (Riley). Rep. Entomol. Soc. Ont. (1929): 67-70. King, E. W. 1962. The use of weather in the estimation of field populations of insects. South Carolina Agric. Stn. Tech. Bull. 1008: 1-12. Knutson, H. 1944. Minnesota Phalaenidae (Noctuidae): the seasonal history and economic importance of the more common and destructive species. Minn. Tech. Bull. 165: 18. 90 Michigan Crop Reporting Service. 1972. Michigan asparagus survey: acreage, production and marketing 1972. October 1972. 22 pp. Michigan Crop Reporting Service. 1974. Michigan agricultural statistics. 77 pp. Middleton, M. S. 1913. Cutworms and their control. Proc. Entomol. Soc. British Columbia. 3: 36-37. O'Brien, R. D. 1967. Insecticides: Action and metabolism. Academic Press. New York. Painter, R. H. and J. C. Hall. 1960. A monograph of the genus Poecilanthrax (Diptera: Bombyliidae). Agric. Exp. Stn. Kansas State Univ. Tech. Bull. 106:123-125. Pielou, E. C. 1969. An introduction to mathematical ecology. Wiley-Interscience, New York. 286 pp. Pielou, E. C. 1974. Population and community ecology: principles and methods. Gordon and Breach Science Publishers, New York. 424 pp. Riley, C. V. 1869. First annual report on the noxious, beneficial and other insects, of the state of Missouri. Jefferson City, Missouri. 76-79. . Ruesink, W. G. and D. L. Haynes. 1973. Sweepnet sampling for the cereal leaf beetle, Oulema melanopus (L.). Environ. Entomol. 2(2): 161-172. Saunders, W. 1883. Insects injurious to fruit. J. B. Lippincott Co., London. 436 pp. Slingerland, M. V. 1895. Climbing cutworms. Bull.Corne11 Univ. Agric. Exp. Stn. 104: 553-600. Thompson, R. E. 1966. Seasonal appearance of selected species of Noctuidae in Michigan. M.S. Thesis, Michigan State Uni- versity. 61 pp. . Tietz, H. M. 1951. A manual of the Lepidoptera of Pennsylvania. Pennsylvania Agricultural Station. State College, Pennsylvania. 194 pp. U. S. Department of Agriculture. 1971. Commercial growing of asparagus. Farmers Bull. 2232: 22 pp. Walkden, H. H. 1950. Cutworms, armyworms and related species attacking cereal and forage crops in the central Great Plains. Circ. U. S. Dept. Agric. 849. 91 Williams, C. B. 1935. The times of activity of certain nocturnal insects, chiefly Lepidoptera, as indicated by a light trap. Trans. Roy. Entomol. Soc. Lond. 83: 523-556. Williams, C. B. 1940. An analysis of four years capture of insects in a light trap. Part II. The effects of weather condi- tions on insect activity; and the estimation and forecasting of changes in the insect population. Trans. Roy. Entomol. Soc. Lond. 90: 227-308. APPENDIX A HOST RANGE OF THE WHITE CUTWORM 92 Table A3. published literature. COMMON NAME 5. Morgue CroBa Sweet Clovwr Q;_Fru1£§ Apples Cherries Grapes Painlfhvs Pears Pnnldu‘rriwfi [Tuuhfl luivns) Rhubarb C. Stimulant} Tobacco Table Al. (cont’d) SCIENTIFIC NAME Host range of the white cutworm as reported in REFERENCES T53§9lxyn sp- Halus Eumilg Prunus ccrasus Vitis Vinitvra L. Prunus p-rSica Pvrus comxuris hi...— Dubus 3p. Rhoum rharontivum Nicotiuna tabu‘um L. COMMON NAME SCIENTIFIC NAME Hudson L Hood Halkdun (1950). Beirnn (1971). Riley (1869), Bouton- mullor (l90l), Gibuon (19121. 1915). Middle- ton '1913), Cruflb 11932). Tiotz (1951), Forbes (1954). Riley (l869), Bouton- mullvr (1901). Crumb (1932). Rilry 11869), Slinqurlund (1893). Bcutcnmuller (1901), Crumb (1932). Tietz (19611. Pilny (1969), Slinq- CI land 119(35), BHU‘ tvnmullur (1901). Gibson (1912. 19151, Crumb [1932), Tlftz 11331). Rilny (18691, Tietz 1001), Gibson (1912, 1915!. (19391, Crumb (19521. Hudnon L Hood (1910). Hudson L Hood noirnv (1971). NFLOOd . SVI‘C" . REFERENCES P. Hoods Canada Thistle Couch Grass Evening Primrose Green Fox Tail Hornetail Larqe Flowered Dock Milkweed Pigeon Grass Russian Thistle Miscellaneouu Bush Fruits Fruit Buds L Leaves Nuraory Stock Shnde Trees Shrubbcry Garden Vegetable. L Plants ClTSlHE arvensg IL) Bfiauv. Agrogyron [cggpa 1L1 Beauv. ggtaria erldlfi (L) Beauv. ESUisotum 5p. Rumex venosus Pursh noclegiag syriaca L. Setaria 112252 (Ll Beauv. §al§ola kali var. Traaus Hudson L Hood (l910). HudSOn L Hood (1910!. Hudson L Hood (1930). Hudson L Hood (1930). Hudson L Hood (1930). Halkdon (1950). Hudson L Hood (1910). Hudson L Hood (1910). Hudson L Hood (1910) Crumb (1932). TlOt! (19511. Saundvrs (1883), Slinquland (1895), Gib'xcn (1912, 19151, Fnutoon (1944), Tivtz (l9511. Frost (1933), Hardwick (1970). Hardwick (19701. Slinqorland (189$). Riloy 11869). Haouo (1898), Gibson (1912. l9lS). Middle- ton (19131, Knutson (1944), Frost (1955). Beirne (1971). . (19501. Table Al. (cnnt'dl COMMON NAME SCIENTIFIC NAME PFFFRENCES g; Trvya and Scrub} Elm Evcroroons Honeysurklu Oak OVUr’CUp Oak White Oak Hillou sprouts E- 3191931119: Asparaaus 80' a n S Ducts Cabhaqc Carrots Corn Onion Penn Potatoes Radishcs Table A1. lcont‘d) Crumb (1932). Hudson L Hood (10111. Crumb (I932). Crumb 119321, Tier? 119811. Knutson (I944). Ulmus amoricana Cymnonyvrn EQHLCPFJ Sp. gunffus 5p. Quercus 5p. Knutson (19441. Tuitz (193)). qurcus Alba Silix 5p. Salkdcn (1950). K§E°I9BU5 oflis: M.S.U. (1971).. inalis Hudoon L Hood (1710). Beirnu (1971). Hudson L Hood 11110), Deirnu (1971! Hudson L Hood (1950). Tlvtz (1951). Hudson L Hood (111”), Beirno (l97l). Hudson L Hood (1930), Boirnc (1°71). Beirno (19711. Hudson 4 Hood (1910'. Reirm‘ (19711. Hudson L Hood (191”). Brirnv (1971). CHESQOIJS 5p. %i9YUWPU§ draggica oluracfa ”£2532 carat} 299 99:9 L- .‘\_1 L1 LII-'11 1‘.“ [ml Pisum Sirivum Snlanum tubvrosun COMMON NAME “igrnnufi nativa Gibson (1912. 19'“), Hudsnu L Hood 1‘. ‘11‘, Tint: 11931), n... v (19711. SCIENTIFIC NAM? REFERENCES Miscelluncgug lcont'd) Low Plants RoadSide L Irrigation Ditch Wasteland Succulent Plants I Unpublished .Unpublishcd Tiot: (1951). Crurb (19321. Crumb (19321. Aqriculturc Canada Data M.S.U. data APPENDIX B ASPARAGUS GROHER QUESTIONNAIRE PACKET 94 February 18, 1976 Dear Asparagus Grower: The enclosed questionnaire is designed to aid in cutworm research and act as a preliminary damage survey. It is being sent to all the asparagus growers in Oceana County and should give us an esti- mate of acreages, location and damage. The first question is very important in that we will be able to locate the asparagus fields and plot them on a soil type map. We can then study the relationship between each fields soil type and the other information found in the following questions. Please draw in the location, as close as possible, of your fields in the model sections, number them, and give township. All this is very important in locating that field on the soil map. Please do this for each of your asparagus fields. If you have more than one field on a section, please draw them on separate model sections. Also if you have more than five fields, we would appreciate including them and answers on another sheet of paper. The additional enclosure "Asparagus Insect Identification and Control 1975" is intended for your personal reference. Be sure to take note of the changes in recommended chemicals from 1974. Oceana County is presently the only county that has reported problems with white cutworms. They have been collected in 10 of the 22 asparagus growing counties but are not problems there. We are, therefore, trying to learn what is the unique factor or factors in Oceana County which has allowed this buildup. The white cutworms overwinter a few inches below the soil surface and wait there for the first asparagus spears to emerge in the spring. There is evidence of a preference for sandy soil. The cutworms feed until mid June when they pupate--go underground and change to a moth. This takes about 2 weeks and the male moths begin flying about the end of June, with female emergence about 1 week after males. The adults feed on milkweed, this is the basis for the question on herbicides and uncontrolled weeds. The females lay up to 600 eggs which hatch in about two weeks. The young cutworms begin feeding about the middle of July or early August on the ferns and on November lst were observed feeding on ferns at a height of about 2 feet. This may be an important factor in fall treatment. A parasite has also been reared from larvae and more research needs to be conducted on it. 95 96 February 18, 1976 Page 2 Your cooperation in filling out this questionnaire has been greatly appreciated and will be very valuable in our research. All responses will be confidential and used only for cutworm research. The figure below gives an indication of the number of moths caught in the past three years and indicates the coming year may be much worse. There were about three times more moths collected than in the two previous years. Sincerely, Donald C. Cress Emmett P. Lampert Extension Specialist Graduate Student In Entomology 1974 1973 3500 J , .. .__ [ 1972 3000 ‘ 7 I I I 11 2500 J i \‘ 2000 . \ 1500 . ' l 1000 4 500 - 97 Asparagus Insect Identification and Control for 1975 Prepared by: Edgar L. Strong Donald C. Cress Emmett P. Lampert There are three insects that cause economic damage to asparagus in Michigan. These are the common asparagus beetle, the twelve-spotted asparagus beetle, and cutworms. Common Asparagus Beetle Description: The adults are about 1/4 inch in length, with a bluish black head, a red thorax (back), and dark blue wing covers marked with lemon yellow and margined with red. The larvae (immature beetles) are olive gray with black heads, and the brownish eggs are elongate and attached by one end to the foliage. There are several generations per year. Damage: Both adults and larvae of these beetles cause feeding damage. The adults congregate in early spring and feed upon the tender new spears. They eat out and cause a brownish discoloration of the tissue. The larvae feed on both tender young spears and foliage. Twelve-spotted Agparagus Beetle Description: This beetle is slightly larger than the common asparagus beetle. The adults are red orange in color with black antennae and six black spots on each wing cover. They lay their eggs with a side attached to the plant rather than on end. Damage: The adults of this beetle cause some damage in early spring by eating the buds of the new tender spears and some foliage. The larvae cause little damage because they feed on the insecide of the berries. Cutworms W Description: There are several types of cutworms that cause damage to asparagus. The white cutworm is most important in Oceana County. Cutworms are the immature stages of moths and can be identified by being soft bodied worm-like insects. pair of hind legs. 98 can vary considerably. Damage : They have three pairs of front legs and four or five They have a dark, distinct head and their body color Damage by cutworms can be caused by either climbing the spear and feeding on the tip and sides, cutting the spears at the ground level and feeding on it, or feeding below the soil on the spears. Insect Control on Asparagus for 1975 When to Apply Amount of Active Chemical per acre and formulation Warning Pre-emergence: For cutworms only: dieldrin, 1 pound WP. Apply in spring before the first spears emerge. Avoid Soil and Spears: (During harvest) For Asparagus beetles: Sevin, 1 pound WP or SC. drift. Follow all label directions. 1 day. Space treatment 3 days apart. or Methoxychlor, 1 pound 3 days. Unless washed and WP or D. blanched. or Malathion, 1 1/4 pound 1 day. EC. For Cutworms only:* Sevin, 2 pounds B 1 day. Repeat treatment as needed. Fall treatment: Sevin 2 pounds B Consult County Extension Service for timing. WP=wettable powder; SC=suspension concentrate; D=dust; B=bait; EC=emulsifiable concentrate *Please note changes from 1974 recommendations. 99 COOPERATIVE EXTENSION SERVICE Michigan State University Cooperative Extension Service U. S. Department of Agriculture Entomology Department Cooperating East Lansing. Michigan 48824 NAME PHONE N0. l. If y0u are renting out your land, please indicate the renter and return. Renter 2. In the model sections below please draw in your asparagus fields. list the section number. and the township name. I T T T T I T Field I Field 2 Field 3 Field 4 Field 5 Sec#«___ Sec! Sect Secs -__.__> Seca Township Township_ Township Township _____*.-_'_' Township Acreage Acreage ‘~_‘ Acreage Acreage_ Acreage —_—_——‘"' Agefi Age_ Age Age ‘___ Age 3. Cutworm damage present in field. Field l Field 2 Field 3 Field 4 Field 5 yes yes yes yes .______ yes no no no no no 4. Chemicals used for cutworm control and active ingredient per acre used. A) Sevin Dust F) Methoxychlor Dust B) Sevin Bait G) Chlordane (HP) C) Devin Spray (HP) H) Chlordane (EC) 0) Dieldrin (HP) 1) Chlordane Granule E) Methoxychlor (HP) J) Other Field l Field 2 Field 3 Field 4 Field 5 Chem. A.I./acre Chem. A.I./acre Chem. A.I.lacre Chem. A.I./acre Chem. A.I.Iacre 5. Time control measures taken. A = Fall 8 = Spring Field l Field 2 Field 3 Field 4 Field 5«__. 6. Other insecticides used for other insects and the active ingredient per acre used. A) Sevin Dust Methoxychlor HP F) B) Sevin WP G) Malathion D C) Chlordane NP H) Malathion HP D) Chlordane Dust l) Malathion EC E) Dieldrin J) Other Field 1 Field 2 Field 3 Field 4 Field 5 Chem. 'A.I./acre Chem. A.I./acre Chem. A.I./acre Chem. A I./acre Chem. A I./acre 100 7. Are your fields on a till or no-till cultivation system? A = till Field l 8. Herbicides and fungicides used and active ingredient/acre used. A) Zineb B) Polygram C) Maneb D) Manzate 200 E) Dithane M-45 Field 1 Pesticide A.I./acre 9. Need problems not A) Milkweed B) Sandbur C) Other (please list) Field l 10. Field irrigated or not irrigated. Field 1 ll. Field well drained. Field 1 12. Fertilizers used. A) Nitrogen 8) Phosphorus C) Magnesium D) Pot Ash E) Lime Field l 13. Crops adjacent to your field. A) grassland B) woods or shrubs C) fence rows 0) corn Field l l4. Type of harvest procedure. Field 1 B = no-till Field 3 Field 4 F) Princep (Simazine) G) Karmex H) Dowpon I) 2.4-0 J) Other Field 3 Field 4 B Pesticide A.I./acre .——--_____ A Field 2 _ A) handpicked l5. Time of year when you chop old fern. Field l 16. Could I contact you for further information? 17. Comments, if any. Pesticide A.I./acre Field 3 = Irrigated B = Not irrigated Field 3 no Field 3 Field 3 neighbor's asparagus field apples peaches pears other Field 3 B) sled harvested Field 3 A) Fall B) Winter C) Spring Field 3 Please check: yes Pesticide A.I.Iacre Field 4 Field 4 Field 4 Field A Field 4 Field 4 Field 4 no Field 5 Field 5 Pesticide A.I.lacre Field 5 Field 5 ____ Field 5 __“_‘a Field 5 Field 5 Field 5 Field 5 Thank you for your cooperation in completing this questionnaire. Please return to D.C. Cress in the envelope provided. Sincerely yours. Donald C. Cress Extension Specialist In Entomology APPENDIX C ASPARAGUS CUTNORMS SURVEY DATA SHEET lOl '102 .m .N .P ”czotu can msgozuzo an ummmsmu mtmwam mo guess: .m .N .F "exoco can mammam mo tmaszz onhmcoz s_ptaa zoaemmd cauaappou mama conssz "Lazaro >m>m2m 44wmomc msuoe on use mmpmswe use mmpms do Sam new gasped Page» cmmzumn mmucwcmmewoa m_o>cmpcp m~qsmm mmmcp cow apgmaoca um>wmumc yo: mmpasmm .1 momp o o o c o o enmp e a a vomp NP Npum\w Fwo— mp p P m F F mmep mm m— up mmmp mop m\anM\n mmep pm mm em op Nump mmp mop an ewcp .emp mNum~\n emmp em mm om mom amp we umpp ommp Nam wmc mmmp now Nuump\m umpp app mm mp Pmm mum vmp N¢op Pomp mmmp mom mwpp mom mpim\n coop sup mm Nc mmmp “opp Pam mww mum com me vac, smcp mim\n emm me cm e wmm mpm mw— mum mp mp N mmm omm P\uucm\o Pm“ Np m N em mm m mpm o o o omn om mmicp\m SA ’30... co m ~32. :o m 97 ~30... b a Q? p38. 38 no . no Do >m43m4mzm - mnmp emmp mnmp .mccmuo ease mEL03u=o dawn: do mogupmu amen acmwpxumpn z—cmm> .pm mpamh APPENDIX G DEGREE-DAY ACCUMULATION FOR HART, MICHIGAN .113 114 Table 61. Degree-day accumulations for Hart, Michigan. 1973 °D >50°F DAY' FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1 1 1 42 135 334 885 1549 2256 2635 2884 2892 2 1 1 42 142 354 907 1566 2287 2650 2884 2892 3 1 1 42 142 372 929 1582 2315 2664 2884 2892 4 1 1 42 144 390 950 1598 2341 2675 2884 2892 5 1 1 42 146 408 968 1626 2363 2683 2884 2892 6 1 5 43 154 426 987 1654 2381 2689 2884 2892 7 1 10 43 161 442 1015 1682 2392 2697 2884 2892 8 1 10 43 169 460 1044 1712 2401 2707 2884 2892 9 1 10 43 176 480 1068 1740 2415 2725 2884 2892 10 1 10 43 184 502 1092 1762 2425 2745 2884 2892 11 1 17 43 189 529 1107 1784 2441 2767 2884 2892 12 1 17 43 194 551 1123 1804 2450 2785 2884 2892 13 1 17 43 194 563 1151 1821 2458 2798 2885 2892 14 1 24 43 194 579 1174 1843 2472 2808 2888 2892 15 1 3O 49 196 598 1189 1861 2482 2820 2889 2892 16 1 3O 54 198 622 1202 1881 2487 2824 2889 2892 17 1 3O 55 200 641 1218 1902 2490 2824 2889 2892 18 1 3O 62 204 661 1240 1924 2495 2825 2889 2892 19 1 3O 77 212 684 1266 1948 2500 2829 2889 2892 20 1 3O 92 219 702 1289 1971 2503 2834 2889 2892 21 1 30 108 228 719 1310 1985 2505 2837 2891 2892 22 1 30 117 236 734 1333 1995 2514 2843 2892 2892 23 1 31 119 247 750 1355 2007 2524 2851 2892 2892 24 1 32 119 257 766 1379 2023 2533 2861 2892 2892 25 1 32 122 270 780 1405 2043 2549 2877 2892 2892 26 1 35 125 280 804 1429 2073 2571 2882 2892 2892 27 1 36 126 288 826 1452 2105 2593 2883 2892 2892 28 1 38 127 298 842 1470 2137 2604 2883 2892 2892 29 1 40 127 305 851 1484 2169 2617 2883 2892 2892 30 1 41 128 310 866 1508 2199 2625 2884 2892 2892 31 1 41 128 318 866 1528 2228 2625 2884 2892 2892 115 Table 6]. Continued. 1974 °D>-50°F DAY FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1 0 O 5 125 323 729 1374 1943 2224 2352 2376 2 0 O 7 133 333 751 1392 1949 2224 2361 2376 3 0 4 13 141 341 779 1410 1954 2224 2368 2376 4 O 4 17 143 359 803 1423 1960 2230 2368 2376 5 0 4 17 144 381 817 1436 1968 2240 2368 2376 6 0 4 17 144 403 833 1454 1978 2248 2368 2376 7 0 5 17 144 424 856 1474 1990 2248 2368 2376 8 O 5 17 144 438 883 1493 2006 2249 2371 2376 9 0 5 17 146 458 913 1513 2026 2250 2373 2376 10 0 5 18 149 472 941 1533 2046 2255 2374 2376 11 O 5 19 153 477 958 1556 2070 2261 2374 2376 12 0 5 27 157 484 974 1574 2092 2268 2374 2376 13 O 5 34 157 494 1002 1595 2104 2270 2374 2376 14 O 5 36 162 508 1032 1610 2110 2273 2374 2376 15 0 '5 36 165. 522 1042 1628 2122 2273 2374 2376 16 O 5 36 167 529 1058 1650 2129 2275 2374 2376 17 0 5 40 175 531 1079 1668 2141 2276 2374 2376 18 O 5 43 182 540 1107 1687 2145 2276 2374 2376 19 0 5 43 193 557 1133 1709 2156 2276 2375 2376 20 O 5 51 205 575 1153 1735 2163 2276 2375 2376 21 0 5 65 223 597 1169 1763 2167 2276 2375 2376 22 O 5 70 239 609 1187 1789 2167 2279 2375 2376 23 0 5 70 251 616 1203 1809 2168 2284 2376 2376 24 0 5 70 258 625 1221 1823 2172 2287 2376 2376 25 O 5 70 262 634 1241 1836 2178 2291 2376 2376 26 0 5 78 266 645 1263 1858 2188 2293 2376 2376 27 0 5 90 271 659 1283 1882 2204 2298 2376 2376 28 O 5 108 279 674 1302 1896 2217 2308 2376 2376 29 0 5 117 293 689 1322 1908 2224 2318 2376 2376 30 O 5 123 304 709 1338 1919 2224 2328 2376 2376 31 0 5 123 314 709 1354 1933 2224 2340 2376 2376 116 Table 61. Continued. 1975 °D>'50°F DA!’ FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1 0 O 2 53 418 934 1581 2165 2403 2590 2661 2 O 0 2 58 427 958 1611 2181 2403 2593 2661 3 0 0 2 66 435 986 1635 2193 2407 2599 2661 4 O O 2 67 444 1008 1657 2204 2415 2603 2661 5 0 O. 2 69 458 1030 1681 2212 2423 2610 2661 6 0 0 2 74 468 1054 1695 2220 2431 2618 2662 7 0 O 2 81 474 1074 1706 2227 2437 2629 2662 8 0 O 2 90 480 1094 1719 2237 2446 2635 2662 9 O 0 2 99 491 1108 1741 2242 2456 2639 2662 10 O O 2 108 507 1124 1763 2253 2462 2640 2662 11 O O 2 120 524 1134 1787 2267 2464 2640 2662 12 O O 2 130 536 1143 1805 2271 2469 2640 2662 13 O O 2 136 552 1156 1829 2273 2487 2640 2663 14 O O 2 146 568 1167 1845 2278 2503 2640 2665 15 O 0 2 152 583 1187 1864 2286 2515 2640 2665 16 O 0 3 157 593 1211 1884 2296 2516 2642 2665 17 O O 8 166 609 1238 1906 2304 2517 2647 2665 18 O O 17 178 628 1262 1920 2320 2518 2653 2665 19 0 0 20 194 649 1288 1930 2336 2519 2656 2665 20 O 1 20 216 675 1310 1944 2341 2522 2659 2665 21 O 2 20 240 699 1330 1962 2349 2531 2659 2665 22 0 2 22 263 725 1354 1980 2353 2537 2659 2665 23 0 2 24 285 751 1377 2000 2357 2550 2659 2665 24 O 2 25 302 773 1401 2024 2361 2566 2659 2665 25 0 2 26 322 795 1417 2048 2364 2574 2659 2665 26 O 2 28 342 815 1431 2068 2368 2575 2659 2665 27 O 2 29 360 837 1455 2083 2373 2578 2659 2665 28 0 2 29 373 861 1476 2099 2380 2584 2659 2665 29 O 2 38 386 888 1498 2119 2387 2585 2659 2665 30 0 2 47 403 912 1524 2137 2396 2585 2661 2665 31 O 2 47 411 912 1552 2149 2396 2586 2661 2665 APPENDIX H DIET USED FOR REARING WHITE CUTNORMS 117 118 Table H1. Diet used for rearing white cutworms. Diet obtained from Drs. Dupre and McLeod, Agriculture Canada. Ingredients Quantity A. Soaked white beans 854 gm Distilled H20 1000 ml Formaldehyde 8 ml 8. Ascorbic Acid 13 gm Brewers Yeast 128 gm Methyl-P-hydroxy benzoate 8 gm Sorbic Acid 4 gm Wheat Germ 200 gm Mositol 4 gm C. Distilled H20 2000 ml Agar 7 100 gm 1. Blend A in a blender until smooth. 2. B is mixed dry then added to A and blend again until smooth (81). 3. C is brought to 188°-l90°F to insure that agar is dissolved. 4. Let C cool to about 70°F then mix with 81 in a large container. 5. Pour into containers and refrigerate--do not freeze. APPENDIX I WHITE CUTWORM DEVELOPMENTAL TIMES 119 120 Table Il. Developmental times for white cutworms at different temperature. Larval development obtained from Dupre and McLeod unpublished. Photoperiod = l6 hours light, 8 hours dark. Rearing Temperature °F Stage 40° 50° 60° 70° 80° Egg * * 20 9 6 59°A 68° 77°A 77° 86° 1 18.3 6.3 4.4 4.6 3 2 l4.3 5 5 3.7 4 l 3. 3 l4.6 5.9 4.7 4 5 3. 4 l9.8 7.3 5 9 5 8 4. 5 24.8 9.0 8.5 7 6 6 21.2 8.6 l2.l 9.4 8. 7 8 D 45.4 49.7 44.9 34.0 30. TOTAL 204.75 92.3 84.2 7l.l 60. *No eggs hatched after five months. Photoperiod = 0 hours light, 24 hours dark. A59° Photoperiod = 0 hours light, 24 hours dark. 77° Photoperiod = l2 hours light, l2 hours dark. APPENDIX J INSTARS AND NEIGHTED MEAN INSTAR 0F FIELD COLLECTED LARVAE 121 122 Table J1. Total number of instars collected by date. Date Instar WMI 2 3 4 5 6 7 5/ 6/74 1 68 24 6.42 10/ 5/74 10 2 5.18 11/ 1/74 35 23 6.58 5/ 3/75 6 2 46.41 5/ 4/75 3 l 6.41 5/ 5/75 6 7O 3 6.01 5/ 6/75 2 21 15 6.55 5/ 7/75 1 21 2 6.13 5/ 8/75 1 6 1 6.13 5/ 9/75 1 12 2 6.19 5/10/75 7 3 6.48 5/11/75 9 2 6.32 5/12/75 1 1 6 1 5.99 5/13/75 5 4 6.63 5/14/75 18 11 6.56 5/15/75 2 6.00 5/16/75 4 4 6.68 5/17/75 6 3 6.52 5/18/75 3 6 6.81 5/19/75 9 83 6.95 5/20/75 3 3 6.68 5/21/75 5 7.00 5/22/75 3 4 6.74 5/23/75 1 4 6.89 5/24/75 3 7.00 5/25/75 1 7.00 5/26/75 4 7.00 5/27/75 1 4 6.89 5/28/75 4 7.00 5/29/75 3 3 6.68 5/30/75 1 7.00 6/ 1/75 4 7.00 6/ 2/75 8 7.00 6/ 3/75 5 7.00 6/ 6/75 1 7.00 6/ 7/75 3 7.00 6/ 8/75 2 7.00 6/ 9/75 1 7.00 6/10/75 2 7.00 6/11/75 1 2 6.64 6/21/75 4 6.00 6/22/75 3 6.00 6/23/75 2 6.00 6/24/75 2 3 6.76 Table J1, continued. 123 Date Instar WMI 2 3 4 5 6 7 7/ 1/75 1 7.00 7/ 2/75 1 1 6.68 7/ 4/75 1 7.00 8/26/75 1 2 17 1 4-10 9/ 7/75 6 47 4-92 9/ 8/75 10 73 1 4.92 9/ 9/75 14 116 1 4.93 9/10/75 4 24 1 4.94 9/12/75 28 2 5.07 11/15/75 4 1 5.75 APPENDIX K SMALL OPEN-BAITED PLOT RESULTS 124 125 Table Kl. Results of the small open-baited plots for white cutworm detection. Plot-Row LARVAE COLLECTED PER DATE Relationship 9/6 9/7 9/8 9/9 9/10 Between two** Plot 1 * 0 2 2 * l 0 3 * 3 3 4 * 0 2 5 * l O 6 * 8 2 0 7 * 9 ll 5 8 * l6 9 2 9 * 8 8 l l0 * 22 6 2 Perpendicular Plot ll * 4 2 l 0 l2 * 2 l 2 0 l3 * 0 l 0 2 l4 * 4 3 3 4 l5 * l 0 0 0 l6 * 0 l 4 2 l7 * 3 5 l 2 l8 * l 0 l 0 l9 * 4 0 5 0 20 * 3 0 3 2 2l * 0 0 l 0 Parallel Plot 22 * l l 23 * l 0 24 * l 0 25 * 0 l 26 * l l * day plot initialized * plot 1 to 5 were low density plot 6 - l0 were high density APPENDIX L PERCENT OF FOOD UNITS THAT ARE SPEARS 126 l27 Fm.m mm.m mm.mp ¢¢.mp mm.w~ mm.m_ Pw.~m mo.mm om\m mm mm.@ mm.m mo.mp Pm.m_ mm.w~ ~N.w_ ¢N.NN w¢.wm ¢m\o mm mm.PP vo.mp mw.wp Po.om mm.mm mm.mm on.om mm.o¢ mm\o cm m¢.op em.PF mm.¢p mm.wp m¢.om mv.om wo.wm em.om om\o mm Pm. m—.m mm.op mp.m_ mm.mp mm.mp NP.om om.om wp\o mm um.m mp.PF mm.vp Nm.¢~ mm.wp wo.wp mm.~m n~._m o—\o Fm om.w wo.o Fm._P ¢N.vp w~.¢~ mm.mp mm.mp ¢N.¢N ¢_\o om em.m_ m~.qp mm.mp wo.mm ¢P.nm ¢P.nm mm.mm mm.mm ~F\m mF No.0? om.m~ ow.op mm.om F¢.¢N P¢.¢N wo.mm Fo.um op\m mp mu.n «m.m mm.¢_ Fm.op NF.PN P¢.©N p¢.om mo.mm w\o up mm.P_ mm.m_ ~m.om n©.om N¢.Pm N¢.Pm mm.~¢ mo.mm m\o 9P mm.m mm.m P©.NF Nw.mp mm.mp c¢.nm mm.¢m wo.mm N\© mF mm.m Kn.w am.w mm.o_ mo.mp Pm.v— w_.mp mm.mm OM\m ¢_ cm.m cw.m mw.mp ow.N— om.op PP.wF em.¢N om.mm mm\m mp mm.m mm.m mm.m mm.m mm.m ov.- mm.¢~ mm.pm mm\m N— mm.mp mm.m— em.m_ no.m~ mo.PN mo.pm mo.¢m w¢.mm ¢N\m —_ ¢m.m~ cm.m— em.m_ cm.m~ om.op mm.m~ m~.mm —m.wm mm\m op mm.m mm.m mm.w mm.m mm.m mo.pp Pn.©P Fm.o_ _~\m m mq.m_ wv.mp w<.~P mq.mp m¢.m~ m_.¢F m¢.PN nn.mm om\m w mo.o— mo.op om.op mm.o_ mm.op mo.op Pw.¢P mm.~m mp\m n mo.¢— mo.¢~ mm.¢_ mo.ep mm.¢_ mm.¢~ om.np om.pm w_\m m oo.om oo.om oo.om oo.om oo.om oo.om oo.om No._m n_\m m mm._m m©.~m oo.~m m©.PN o©.—N mo.Fm mo.PN mo.Pm ¢~\m v mm.P¢ mm.P¢ mm.p¢ mm.~¢ mm.F¢ mm.pv mm._¢ mm.P¢ mp\m m mm._m ow.Fm ow._m mm.Fm mm.Pm mm.Fm mm._m ow.Fm Np\m N oo.oop oo.oo_ oo.oop 00.00? oo.oo— oo.oo_ oo.oop oo.oo— ~F\m _ Pm mp op NP op m m m mama umw>LmI a_nmpmpma mpoam mxmo .mmmp cw cpoww FwwoLmEEoo m to; auPPanpmpmn puss mo mgpmcmp mcmxgm> 26cc: mgmmum mew pug» wave: uoom to ucmogma ._4 mpnmh 128 o~.~_ ~¢.mp mn.mp «a.mm No.mm mm.mm m¢.mm w~.Pm ~p\o op mm.op ¢N.P_ mm.m~ mm.NN mw.nm mw.nm Fo.mm pv.mm m\m mp mm.m mN.oP N¢.m— mm.mp mm.m~ mm.mm mo.mm wo.mm o\o «F oo.o ¢~.w o¢.op m¢.¢F Fm.mp m~.om mm.nm mm.ov ¢\m mp vm.m m¢.op mn.¢P mN.n_ mo.mm o~.~m om.mm mo.mm N\c NF mo.“ mm.n o¢.~F eo.mp “a.mp mo.m_ op.mm mm.mm om\m PF mm.m cm.m mm.~F mm.p_ mo.m~ mm.m~ No.NN N@.NN mm\m op om.m om.m ¢¢.m om.n ¢P.PF ¢~.__ o¢.mp em.np N~\m m om.m cm.m ¢P.m mo.m om.mp om.mp Fm.mp m~.~m om\m w no.mm mo.m~ Po.mm ¢P.mm mm.mm mo.mm ~.om mp.mm mp\m m mo.mp mo.m_ ow.mp mo.m_ mm.m_ «a.mp mm.mm nw.m~ op\m w mm.m~ mm.m~ mm.¢_ mm.mp mm.mp mm.op on.mp mm.mm ¢_\m m mm.¢¢ mm.¢¢ mm.¢¢ mm.¢¢ mm.ee mm.v¢ mw.n¢ oo.¢m Np\m ¢ mm.no mm.~o mw.mm mm.um mm.nm mw.no mm.no mw.no .m\m m up.mm mp.mm mF.mm mp.mm mp.mm mp.mm op.mm m~.mm N\m m oo.oo_ oo.oop oc.oo_ oo.oo~ oo.oop oo.oo_ oo.oo~ oo.oo~ m\m P hm mp «P N_ op m n m mums pmm>gmz mpnaua_ma mppzm mama .mump cw 526% mauom mg» Low »u__wnmpmpma buss we mcpmcmp mcwxgm> ewes: mgmmqm one was“ mare: coom mo ucmogma .NA mFQmH 129 Fo.m mp.P mF.F 05. mm. me. em. we. mm. mm. mm. my._ N©.~ mo.m N¢.N .o mm mo.m mm.m m¢.m NN.N Rn.P mm._ No._ Rm.P mm. mo._ mm.m mm.m om.e wo.o mm.“ .o m mm._m mm.- ¢N.om mm.mp om.mm mo.mp om.m_ mm.P_ mm._— em.mm mm.mp um.m_ «a.me Fm.mm mm.om fizz. zocaeH dz m.mm F.NN m._m m.m~ F.m~ n.m_ m.w_ m.mp a.mp o.¢m a.mp m.o_ m.~m o.wm P._m ooF mmx¢¢ oz a.mm o.~m m.m~ o.o~ a.mm F.N_ P.w_ m.o_ w.op «.mm N.op N.m_ o.m¢ m.mn m.no oop FFN u: a.mm m.o~ P.m~ m.om m.mm N.m_ a.mp m.o_ ¢.NF m.om m.n— m.- ¢.~¢ “.mo m.oo cop mom on m.o~ m.e~ o.w~ «.mp n.0m P.5F a.mp o.~_ «.mp m.pm m.op m.NN _.~¢ o.~n ~.oo cop mu 0: a.mm m.om “.mm m.mp m.om ~.e~ m.op m.oF m.~p N.mm m.mp F.0P ~.o¢ a.mo N.¢e cop no u: a.mm m.- o.om m.- p.mm m.m_ ~.m_ P.__ ~.N~ a.mm m.mp a.mp m.¢¢ m.nm N.mm cop copmcwgmmz x26: o.mN F._m F.mm N.op N.om o.mp n.¢~ m.e_ ~.op m.¢m P.¢P P.wF a.me a.mo N.mo cop P :m: 3 2 3 2 NF 2 S m w n m m e m N F 235:5 h m m > m < : .mxmc cop tom mpamHmFma mupzm .mmm_ .Ecme mzuom on» an :zocm mammcmqmm mo mmwumwcm> may: egg to; mgmmam men umgu muwcz coco we “smegma .mb m_amh APPENDIX M DEGREE-DAY ACCUMULATION FOR THE THERMOGRAPH OPERATED AT THE FARM OF MR. LYLE SHELDON. SHELBY, MICHIGAN 130 Table M1. 1974 at -6 inches. 131 Degree-Day Accumulations for Sheldon. °D>50°F DAY FEB MAR APR MAY JUN JUL AUG SEP OCT Nov DEC 1 0 0 0 0 0 0 0 0 0 34 41 2 0 0 0 0 0 0 0 0 0 38 41 3 0 0 0 0 0 o 0 0 o 38 41 4 o o 0 0 o 0 0 o 0 38 41 5 0 0 0 0 0 0 0 0 0 41 41 6 o o o o o 0 0 o o 41 41 7 0 0 0 0 0 0 0 O 0 41 41 8 o o o o o o 0 o o 41 41 9 0 0 0 0 0 0 0 0 0 41 41 10 0 0 0 o o 0 0 0 2 41 41 11 0 0 o 0 0 0 0 0 4 41 41 12 o o o 0 0 o 0 o 10 41 41 13 0 0 0 0 0 0 0 o 12 41 41 14 o o 0 0 0 o o o 12 41 41 15 0 0 0 0 0 0 o 0 12 41 41 16 o 0 0 o 0 0 0 0 12 41 41 17 0 o 0 0 0 o o o 12 41 41 18 o 0 0 0 0 0 0 o 12 41 41 19 0 0 0 0 o 0 0 o 12 41 41 20 o 0 o 0 o 0 0 0 12 41 41 21 o 0 0 0 0 0 0 o 12 41 41 22 0 0 0 0 0 0 0 0 12 41 41 23 0 0 0 0 0 0 o o 12 41 41 24 0 0 O 0 0 0 0 0 12 41 41 25 0 o 0 0 0 0 0 0 12 41 41 26 0 0 0 0 0 0 0 0 12 41 41 27 0 0 0 0 0 0 0 0 12 41 41 28 0 0 0 0 0 0 0 0 14 41 41 29 0 o 0 0 0 o 0 0 16 41 41 30 0 0 O 0 0 0 0 0 22 41 41 31 o o o o o o o o 28 41 41 Table M1. 1975 at —6 inches. Continued. 132 °D>50°F DAY FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1 0 0 O 40 562 1234 2133 2913 3215 3396 0 2 0 0 O 47 576 1268 2165 2937 3225 3398 0 3 0 0 O 51 590 1304 2193 2955 3235 3402 O 4 O 0 0 53 600 1338 2229 2973 3245 3404 0 5 0 O 0 54 616 1372 2261 2987 3255 3407 0 6 0 0 O 60 628 1404 2291 3003 3264 3413 0 7 O 0 O 71 638 1434 2319 3015 3271 3419 0 8 0 O O 83 651 1462 2346 3015 3280 3425 0 9 O 0 0 91 670 1487 2372 3027 3290 3429 0 10 O 0 O 100 694 1512 2398 3041 3298 3431 O 11 O 0 O 114 713 1530 2425 3058 3302 3431 O 12 0 O 0 130 727 1548 2451 3068 3305 3431 0 13 0 O O 150 745 1568 2478 3076 3317 3431 0 14 0 0 0 169 767 1586 2502 3088 3331 3431 0 15 0 0 1 188 785 1608 2529 3098 3340 3431 O 16 0 0 4 207 799 1634 2557 3110 3346 3431 0 17 0 O 10 226 815 1663 2586 3122 3348 3431 0 18 O 0 14 245 835 1689 2610 3136 3350 3431 0 19 O 0 14 269 859 1718 2634 3152 3351 3431 0 20 O O 15 291 887 1746 2654 3162 3353 3431 O 21 0 0 17 313 917 1776 2673 3169 3359 3431 O 22 0 0 21 337 949 1808 2693 3178 3363 3431 0 23 0 O 22 362 981 1840 2711 3184 3373 3431 0 24 0 0 22 389 1007 1870 2735 3193 3385 3431 0 25 0 0 23 416 1037 1898 2757 3197 3389 3431 0 26 0 O 25 444 1066 1922 2783 3205 3390 3431 O 27 0 0 25 469 1096 1954 2810 3205 3392 3431 O 28 O O 25 487 1128 1988 2834 3205 3393 3431 0 29 0 0 28 511 1163 2022 2856 3205 3393 3431 0 30 0 0 34 529 1199 2058 2876 3205 3393 3431 0 31 0 0 34 547 1199 2095 2893 3205 3393 3431 0 Table M1. Continued. 1974 at -1 inch. 133 °D>50°F DAY FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1 0 0 0 0 0 0 0 0 0 62 68 2 O 0 0 0 0 0 0 0 0 67 68 3 0 0 0 0 0 0 0 0 0 67 68 4 0 0 0 O 0 0 0 0 0 67 68 5 0 0 0 0 0 0 0 0 O 67 68 6 0 0 0 0 0 0 0 0 0 68 68 7 O 0 0 0 0 0 0 0 2 68 68 8 0 0 0 0 0 0 0 0 2 68 68 9 0 0 0 0 0 0 0 0 3 68 68 10 O 0 0 0 0 0 0 0 8 68 68 11 0 0 0 0 0 0 0 0 11 68 68 12 O O 0 O 0 0 0 0 20 68 68 13 0 0 0 0 0 0 0 0 22 68 68 14 0 0 0 0 0 0 0 0 23 68 68 15 0 0 0 0 0 0 0 O 25 68 68 16 o 0 0 0 0 o o ' 0 27 68 68 17 0 0 0 0 0 0 0 0 27 68 68 18 0 0 0 0 0 0 0 0 27 68 68 19 0 0 0 0 0 0 0 0 27 68 68 20 0 0 0 0 0 0 0 0 27 68 68 21 o 0 o 0 0 o 0 0 27 68 68 22 0 0 0 0 0 O 0 0 27 68 68 23 0 0 0 0 0 0 0 0 30 68 68 24 0 0 0 0 0 0 0 0 30 68 68 25 0 0 0 0 0 0 0 0 31 68 68 26 0 0 0 0 0 0 0 0 32 68 68 27 0 0 0 0 0 0 0 0 33 68 68 28 0 0 0 0 0 0 0 0 39 68 68 29 0 0 o 0 0 o 0 0 41 68 68 30 O 0 0 0 O 0 0 0 47 68 68 31 0 0 0 0 0 0 0 0 55 68 68 134 Table M1. Continued. 1975 at -1 inch. °D>50°F DAY FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1 0 0 0 81 655 1378 2289 3093 3435 3649 0 2 0 O 0 93 676 1408 2320 3119 3445 3651 0 3 0 0 O 99 690 1442 2352 3137 3455 3655 0 4 o 0 o 102 700 1477 2384 3151 3465 3658 0 5 0 0 0 105 717 1512 2420 3160 3475 3668 0 6 o 0 0 112 731 1544 2450 3176 3486 3676 o 7 O 0 o 126 743 1571 2478 3188 3495 3680 o 8 0 0 0 137 757 1599 2504 3202 3506 3688 0 9 O 0 o 143 779 1623 2531 3214 3517 3692 0 10 0 0 0 155 807 1647 2559 3229 3524 3694 0 11 0 0 0 171 825 1665 2588 3241 3528 3694 O 12 0 0 0 190 839 1682 2614 3253 3533 3694 O 13 0 o 1 214 861 1701 2643 3257 3551 3694 0 14 o 0 1 234 885 1718 2669 3270 3568 3694 o 15 0 0 5 254 899 1742 2699 3280 3577 3694 0 16 o o 11 274 914 1770 2729 3292 3583 3694 0 17 0 0 17 294 932 1802 2759 3305 3586 3694 0 18 0 0 25 314 954 1828 2783 3326 3590 3694 0 19 0 0 25 342 982 1858 2807 3336 3591 3694 0 20 0 0 30 360 1014 1888 2827 3344 3594 3694 0 21 0 0 35 382 1046 1922 2845 3352 3601 3694 0 22 0 0 43 410 1082 1956 2865 3363 3606 3694 0 23 0 0 45 438 1116 1989 2882 3369 3618 3694 0 24 0 O 45 468 1143 2021 2907 3380 3632 3694 0 25 0 0 50 498 1175 2050 2927 3384 3637 3694 0 26 o 0 55 530 1205 2076 2955 3394 3643 3694 0 27 0 0 55 557 1237 2108 2983 3405 3646 3694 0 28 0 0 55 581 1271 2142 3008 3417 3647 3694 O 29 0 0 63 607 1307 2178 3032 3425 3648 3694 0 30 0 0 72' 619 1342 2214 3052 3425 3648 3694 0 31 0 0 72 637 1342 2251 3070 3425 3648 3694 0 135 Table Ml. Continued. 1974 at +10 inches. °D>50°F DAY FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1 0 0 0 O 0 O 0 0 0 140 184 2 0 0 0 0 0 0 0 0 0 153 184 3 0 O 0 0 0 O O 0 O 153 184 4 0 0 0 0 0 0 0 0 0 155 184 5 0 0 0 0 0 0 0 0 0 155 184 6 0 O 0 O 0 O O 0 0 161 184 7 0 0 0 0 0 0 0 0 3 164 184 8 0 0 0 0 0 0 0 0 8 173 184 9 0 0 0 0 0 0 0 0 10 177 184 10 0 0 0 0 0 0 0 0 20 177 184 11 0 0 0 0 0 0 0 0 28 177 184 12 0 0 0 O O 0 0 0 41 177 184 13 0 0 0 0 0 0 0 0 43 177 184 14 O O 0 0 0 0 O 0 46 177 184 15 0 O 0 0 0 0 0 0 53 177 184 16 0 0 0 0 0 0 0 O 57 177 184 17 0 0 0 ; 0 0 0 0 0 59 177 184 18 O 0 0 0 0 0 0 0 59 177 184 19 0 0 0 0 0 0 0 0 59 180 184 20 0 0 0 0 0 0 0 0 60 180 184 21 0 0 0 0 0 0 0 0 61 180 184 22 0 0 0 0 0 0 0 0 66 180 184 23 0 0 0 0 0 0 0 0 80 182 184 24 O 0 0 0 0 0 0 0 82 182 184 25 0 0 0 0 0 0 0 0 85 182 184 26 0 0 0 0 0 0 0 0 95 182 184 27 0 0 0 0 0 0 0 0 101 182 184 28 0 0 0 0 0 0 0 0 107 182 184 29 0 0 0 0 0 0 0 0 113 184 184 30 0 0 0 O 0 0 0 0 118 184 184 31 0 0 0 0 0 0 0 0 130 184 184 Table M1. Continued. 1975 at +10 inches. 136 °D>50°F DAY FEB APR MAY JUN JUL AUG OCT NOV 1 2 2 6 108 642 1269 2025 3125 3443 0 2 2 2 6 121 654 1297 2049 3137 3445 0 3 2 2 6 130 662 1329 2079 3149 3451 0 4 2 2 6 131 672 1357 2107 3161 3455 O 5 2 2 6 137 686 1384 2137 3173 3465 0 6 2 2 6 145 698 1411 2159 3182 3479 0 7 2 2 7 158 703 1435 2177 3190 3489 0 8 2 2 8 172 711 1459 2195 3207 3503 0 9 2 2 8 175 731 1477 2224 3219 3510 O 10 2 2 9 184 756 1495 2253 3231 3514 0 11 2 2 10 197 770 1505 2277 3243 3519 0 12 2 2 11 215 783 1514 2301 3251 3519 0 13 2 2 12 231 805 1526 2325 3271 3519 0 14 2 2 14 250 825 1540 2349 3289 3519 0 15 2 2 19 269 839 1564 2376 3302 3520 0 16 2 2 23 288 859 1590 2402 3313 3520 0 17 2 2 28 307 877 1620 2429 3321 3520 0 18 2 2 43 326 901 1644 2448 3327 3520 0 19 2 2 44 360 932 1674 2467 3328 3520 0 20 2 2 48 384 963 1699 2489 3335 3520 O 21 2 2 49 414 991 1726 2510 3347 3520 0 22 2 2 52 443 1023 1752 2528 3359 3520 0 23 2 2 54 469 1052 1780 2550 3376 3520 0 24 2 3 54 496 1080 1810 2580 3393 3520 O 25 2 ' 3 60 522 1108 1827 2598 3401 3520 0 26 2 5 70 546 1138 1844 2628 3406 3520 0 27 2 5 70 563 1166 1878 2652 3412 3520 0 28 2 5 70 582 1195 1906 2675 3419 3520 0 29 2 5 85 609 1229 1933 2697 3424 3520 0 30 2 5 99 619 1243 1963 2716 3425 3520 0 31 2 6 99 633 1243 1993 2735 3428 3520 0 "11141 1111144 14111 1'55