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Funded Project |
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Funding Program:
Regional IPM Competitive Grants - Northeastern |
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Project Title:
Reduced Antibiotic Use on Apples with Revised MARYBLYT Forecasting |
Project Directors (PDs):
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Lead State: NY Lead Organization: Cornell University |
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Cooperating State(s):
West Virginia |
| Extension Funding: $57,466 |
| Research Funding: $127,711 |
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Start Date: Jul-01-2001 End Date: Jun-30-2004 |
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Pests Involved: fire blight |
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Site/Commodity: apples, pears |
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Area of Emphasis: forecasting, modeling |
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Summary:
Fire blight is one of the most destructive and difficult-to-control diseases of apple. Over the last decade, major changes in horticultural practices have increased the chances for infection and level of damage likely to occur. A common approach to disease management in the Northeast is to time streptomycin antibiotic sprays during the blossoming period using the disease forecaster MARYBLYT. However, MARYBLYT does not account for varietal susceptibility, orchard age, or inoculum pressure (factors that may dramatically reduce (or increase) the risk of infection). As a result, disease management sometimes fails and, because outbreaks are so erratic, often results in unnecessary treatments when conditions do not support infection. While many growers are willing to forego the cost of possibly ineffective treatments as insurance against the potential losses of fire blight, unnecessary applications are costly and can lead to the appearance of resistant strains in the pathogen population. This project focuses on revising MARYBLYT, a computer forecaster for fire blight on apple and pear to account for varietal susceptibility, orchard age, or inoculum pressure.
PROBLEM, BACKGROUND, AND JUSTIFICATION Fire blight, caused by the bacterium Erwinia amylovora, is one of the most destructive and difficult-to-control diseases of apple and pears (1,36,41). Throughout the nearly 200 year history of fire blight the disease has been an elusive malady in that sometimes severe epidemics develop in young orchards with no history of the disease and sometimes few symptoms appear in established orchards with a recent history of severe blight. Epidemics develop quickly, destroying blossoms, vegetative shoots, major limbs and, sometimes, whole trees. The 2000 growing season was perhaps one of the worst years for fire blight ever recorded in the Midwest and Northeast United States (5,17,29). In fact, that the US House of Representative's Agriculture Appropriations Conference Committee has appropriated $38 million in the fiscal 2001 agriculture spending bill (HR 4461) for losses incurred as a partial result of fire blight. It is years like these that emphasize the need to improve our understanding of fire blight so that we better develop our tools for disease management, especially in the face of a depressed and rapidly changing global market where a single unprofitable year can force a grower out of business (44). Over the last decade, consumer and market demands have forced major changes in horticultural practices. These changes have not only increased chances for infection but the level of damage/infection likely to occur (41). For example, high density orchards of 250-500 trees/acre are replacing older orchards with 80 to 120 trees/acre. Clonal apple rootstocks with uniform susceptibility to infection (e.g., M.9, M.26) are almost exclusively used in newly-planted orchards. It is now known that systemic invasion of rootstocks resulting from blossom infection is common and, in a large proportion of instances, this invasion can cause girdling that can kill a tree within one season (26,41). The choice of rootstocks and management practices encourage early bearing (second to third leaf). Perhaps most important, is the widespread increase in new high market value apple cultivars like Gala, Fuji, Honeycrisp, Jonagold, Braeburn and others that are highly susceptible to fire blight (41). Most contemporary control programs emphasize thorough orchard sanitation and dormant pruning to remove or reduce sources of inoculum, early season applications of copper materials, limited use of nitrogen to avoid an excess of succulent growth, insect control, and the frequent use of protective bactericide's through the highly susceptible flowering period every year to prevent primary infections (4). The most widely used approach in the U.S. is to apply a series of streptomycin antibiotic sprays at frequent intervals during the blossoming period (4,20); 2-4 routine treatments per year in most locations, but some use 8 or more when shoot blight is also targeted. This approach, while generally adequate, seldom affords complete control, sometimes fails and, because outbreaks are so erratic, often results in unnecessary treatments when the conditions do not support infection. While many growers are willing to forego the cost of possibly ineffective treatments as 'insurance' against the potential losses of fire blight, unnecessary applications are costly and the excessive use of antibiotics can lead to the appearance of resistant strains of the pathogen (18,19,20,41). Furthermore, the use of antibiotics as a means of pest control is problematic and is scrutinized by both the public (15) and scientific community (20). Until recently, our ability to predict the onset of fire blight epidemics accurately and reliably has been the most limiting factor in improving the overall management of the disease. MARYBLYTTM (16,35) is a computer program for forecasting fire blight in apples and pears that predicts the four distinct types of infection events (i.e., blossom, shoot, canker, and trauma blight) incited by E. amylovora as well as the appearance of symptoms that follow (33,34). It was first introduced in 1989, released for commercial distribution in 1992 (16,35), and is now used by fruit growers and in various research, teaching and extension programs in 30 US states and over 20 countries worldwide. The program's popularity and widespread use are attributed to: 1) The destructive nature of fire blight and the high costs of control; 2) Its ability to predict specific infection events far enough in advance that protective treatments can be made and eradication measures can be timed for maximum effectiveness; 3) Data inputs are relatively simple and easy to acquire; 4) The program claims insensitivity to geographical climate differences, operates independently from calendar dates, and can be used with either U.S. or metric units; and 5) Predictions are obtained within minutes and are accompanied by a variety of visual and audio prompts are given with respect to risks and treatment warnings. Blossom blight is the most threatening and destructive phase of the disease; providing the inoculum for the shoot, root, and trauma blight phases (1,4,33). As a result, management practices focus on controlling this phase. In the blossom blight submodel of MARYBLYT four risk factors are monitored to identify possible infection events. The risk factors and the associated minimum conditions necessary for blossom infection are as follows: 1) flowers open with stigmas and petals intact; 2) accumulation of at least 110 cumulative degree hours (CDH) greater than 18.3 C from the start of bloom; 3) a wetting event of at least 0.25 mm of rain or heavy dew or a rain of 2.5 mm or more the previous day; and 4) an average daily temperature of 15.6 C (33,41). MARYBLYT characterizes risk as either low, moderate, high, or infection depending on whether one, two, three, or all four of the risk factors have exceeded their minimum values. Observation has shown that when all 4 of these parameters were met, early symptoms of blossom infection could be predicted and observed with the accumulation of 57 degree days >12.8 C after an identified infection event. It was also found that the first symptoms of shoot blight occurred approximately 57 degree days >12.8oC after the appearance of either blossom blight symptoms, except in years when the appearance of the winged adults of the white apple leafhopper was delayed (33). Despite its appeal, MARYBLYT is not a perfect forecaster. The model tends to predict infections when none occur, especially in less susceptible varieties and in areas with no history of fire blight; resulting in the needless applications of antibiotics (13,32,42). The excessive use of antibiotics promotes the development of antibiotic resistant strains of the pathogen and, potentially, in untargeted populations of bacteria (15,20). As output, the model only generates qualitative assessments of infection potential without regard to which risk factors have exceeded their minimums or, for those factors that have exceeded their minimums, to what degree have the minimums been exceeded. As Steiner observed: "When environmental conditions meet these criteria only marginally, the incidence of blossom blight infections is usually low with severity varying due to individual site differences (variation in bloom, elevation, local dews, blight history, grower management practices, etc.). By contrast, severe epidemics affecting large areas are most likely to occur when all or several of the criteria are well above the minimum activity thresholds" (33). This is particularly problematic when a grower attempts to factor in the influence of varietal resistance, orchard age, or inoculum pressure. These factors are known to play a role in fire blight susceptibility but are not taken into account by MARYBLYT (1,41). A newer fire blight forecaster, named Courgarblight, was developed by Tim Smith at Washington State University (32). The model was developed in response to the poor performance of other fire blight risk assessment models when used in the Pacific Northwest; MARYBLYT tended to over-predict infections. The inaccuracies of MARYBLYT were partially attributed to how MARYBLYT factors in 'average temperature' and its lack of consideration of inoculum pressure. Using three years of weather data, Breth et al. (4) compared the performance of MARYBLYT and Cougarblight under New York conditions and found that they performed similarly. Cougarblight shares some of the same shortcomings of MARYBLYT, for example varietal susceptibility is not explicitly defined in the model. However, Cougarblight is not (yet?) a stand-alone program like MARYBLYT; predictions are obtained using a 'lookup' chart. In truth, either of these two models could be targeted for improvement based on the limitations outlined below, but we chose MARYBLYT because of its wide usage and acceptability in the Northeast and it is ready availability as a stand-alone program. Justification Despite its wide-scale use, major limitations to MARYBLYT are: (1) objective aspects are limited to qualitative predictions (i.e., + an infection event); quantitative assessments depend on experience and subjective considerations (i.e, degree to which minimum thresholds are exceeded, rate and timing of epiphytic inoculum potential increase; level of previous fire blight and thoroughness of blight management program); (2) MARYBLYT assumes that each risk factor contributes equally to the risk of developing blossom blight; (3) the risk of infection does not increase relative to the degree in which individual risk factors exceed their minimums; (4) MARYBLYT assumes that risk the factors are uncorrelated i.e., the cumulative effect of the factors on blossom blight are considered neither synergistic nor antagonistic; (5) MARYBLYT lacks varietal specificity; i.e., all varieties are considered equally susceptible; (6) the model is based on the assumption of abundant inoculum; and (7) growers and extension agents often find the current DOS-based version of MARYBLYT out-of-date and difficult to use. Because of these limitations, growers sometimes make the decision to spray when it is not needed (e.g., because varietal resistance compensated for exceeding the MARYBLYT high risk advisory) resulting in the inefficient and/or excessive use of antibiotics and potentially fostering resistance development. Or growers failed to spray when needed resulting in economic losses to the grower (e.g., because the grower could not gauge the risk of infection, for example, when two of the MARYBLYT criteria greatly exceeded their minimums). Other successful forecasters, such as those developed for powdery mildew of grapes caused by Uncinula necator (8), sclerotinia stem rot in oil seed rape caused by Sclerotinia sclerotiorum (39), alternaria leaf blight of carrots caused by Alternaria dauci (7), late blight of potato caused by Phytophthora infestans (14), downy mildew of hops caused by Pseudoperonospora humuli (30), and apple scab caused by Venturia inaequalis (23) are based on the accumulation of 'risk points' (typically as a function of weather and important crop factors). When the summation of risk points exceed some target level (i.e., a threshold) then a management action is taken. The advantage of such an approach is that disease pressure is directly related to the accumulation of risk points. This incorporates flexibility in the model because action thresholds (based on the accumulation of risk points) can vary in accordance to a grower's comfort level for assuming risk, market fluctuations, varietal differences (if varietal susceptibility is not explicitly defined in the forecaster), etc. Inherent to the success of these forecasters is the choice of appropriate risk factors. The risk factors chosen for the MARYBLYT blossom blight submodel came about as the result of extensive laboratory investigations and empirical field evidence (3,24,28,37,38,43,45,46). Thus, the set of risk factors is not in question, but rather the relationship among factors and their defined minimums relative to disease development, especially on different varieties. As apple production changes, so does the risk of fire blight. Some practices increase the risk, some may decrease it. MARYBLYT must be flexible in order accommodate these changes if we intend to use this model in the future. By evidence of its current widespread usage, MARYBLYT has the potential to greatly impact how we manage this disease. Failed predictions or numerous over-predictions will cause growers to abandon MARYBLYT. However, if we address the weaknesses now, MARYBLYT's use in the industry will expand, allowing efficient and minimal use of antibiotics for fire blight management. Objectives: 1) Revise MARYBLYT to calculate a system of `risk points' as function of the MARYBLYT risk factors, then using the revisions, evaluate various management-action thresholds for fire blight disease management based on the accumulation of `risk points'; 2) Modify MARYBLYT to account for varietal susceptibility, orchard age, and inoculum potential; and 3) Develop a user-friendly, Windows-based version of MARYBLYT. USDA CRIS data |
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