Site-Specific Weather Information Without On-Site Sensors

Comparison of air temperature measured in a vineyard canopy and at a standard weather station

The complex environment within a crop canopy leads to a high variability of the air temperature within the canopy, and, therefore, air temperature measured at a weather station (WS) does not represent the internal energy within a crop. The objectives of this study were to quantify the difference between the air temperature measured at a standard WS and the air temperature within a six-year-old vineyard (cv. Chardonnay) and to determine the degree of uncertainty associated with the assumption that there is no difference between the two temperatures when air temperature is used as input in grapevine models. Thermistors and thermocouples were installed within the vine canopy at heights of 0.5 m and 1.2 m above the soil surface and immediately adjacent to the berry clusters. In the middle of the clusters sensors were installed to determine the temperature of the air surrounding the clusters facing east and west. The data were recorded within the canopy from December 2015 to June 2017 as well as at the standard WS that was installed close to the vineyard (410 m). Significant differences were found between the air temperatures measured at the WS and those within the vineyard during the summer when the average daily minimum air temperature within the canopy was 1.2°C less than at the WS and the average daily maximum air temperature in the canopy was 2.0°C higher than at the WS. The mean maximum air temperature measured in the clusters facing east was 1.5°C higher and west 4.0°C higher than the temperature measured at the WS. Therefore, models that assume that air temperature measured at a weather station is similar to air temperature measured in the vineyard canopy could have greater uncertainty than models that consider the temperature within the canopy.

Optimizing Cercospora Leaf Spot Control in Table Beet Using Action Thresholds and Disease Forecasting

Cercospora leaf spot (CLS), caused by the fungus Cercospora beticola, is the dominant foliar disease affecting table-beet production in New York. CLS epidemics occur annually and, if uncontrolled, will rapidly lead to defoliation. In broad-acre production, season-long maintenance of healthy leaves is important to facilitate harvest by top-pulling. Fungicides are the dominant means of CLS control and applications are initiated at an action threshold of 1 CLS lesion/leaf. Regular fungicide application occurs thereafter without regard for scheduling based on weather-based risk. The current action threshold was evaluated with selected fungicides in two replicated field trials. Copper oxychloride + copper hydroxide and propiconazole significantly improved CLS control if initiated prior to infection. Pydiflumetofen + difenoconazole significantly reduced area under the disease progress stairs compared with other fungicides tested and was most efficacious when applications began at 1 CLS lesion/leaf. Six replicated field trials also evaluated the utility of scheduling fungicides on weather-based risk rather than a calendar approach. Two risk thresholds (moderate and high) integrating the accumulation of daily infection values based on temperature and relative humidity from a forecaster for CLS in sugar beet were evaluated. Applications of pydiflumetofen + difenoconazole were reduced from three to two by using the forecaster at either risk threshold compared with calendar applications without affecting CLS control. For propiconazole, the moderate risk threshold provided CLS control equivalent to calendar applications and saved one spray per season. Thus, there was substantial scope to reduce spray frequency by scheduling based on weather-based risk rather than calendar applications. The optimal risk thresholds for pydiflumetofen + difenoconazole and propiconazole were high and moderate, respectively. In these trials, periods of high risk occurred less frequently than moderate risk, increasing the reapplication intervals and, hence, represented a less conservative approach to disease management.

Application of Numerical Weather Prediction Data to Estimate Infection Risk of Bacterial Grain Rot of Rice in Korea

This study was conducted to evaluate usefulness of numerical weather prediction data generated by the Unified Model (UM) for plant disease forecast. Using the UM06- and UM18-predicted weather data, which were released at 0600 and 1800 Universal Time Coordinated (UTC), respectively, by the Korea Meteorological Administration (KMA), disease forecast on bacterial grain rot (BGR) of rice was examined as compared with the model output based on the automated weather stations (AWS)-observed weather data. We analyzed performance of BGRcast based on the UM-predicted and the AWS-observed daily minimum temperature and average relative humidity in 2014 and 2015 from 29 locations representing major rice growing areas in Korea using regression analysis and two-way contingency table analysis. Temporal changes in weather conduciveness at two locations in 2014 were also analyzed with regard to daily weather conduciveness (C _i ) and the 20-day and 7-day moving averages of C _i for the inoculum build-up phase (C _inc ) prior to the panicle emergence of rice plants and the infection phase (C _inf ) during the heading stage of rice plants, respectively. Based on C _inc and C _inf , we were able to obtain the same disease warnings at all locations regardless of the sources of weather data. In conclusion, the numerical weather prediction data from KMA could be reliable to apply as input data for plant disease forecast models. Weather prediction data would facilitate applications of weather-driven disease models for better disease management. Crop growers would have better options for disease control including both protective and curative measures when weather prediction data are used for disease warning.

Validating Sclerotinia sclerotiorum Apothecial Models to Predict Sclerotinia Stem Rot in Soybean (Glycine max) Fields

In soybean, Sclerotinia sclerotiorum apothecia are the sources of primary inoculum (ascospores) critical for Sclerotinia stem rot (SSR) development. We recently developed logistic regression models to predict the presence of apothecia in irrigated and nonirrigated soybean fields. In 2017, small-plot trials were established to validate two weather-based models (one for irrigated fields and one for nonirrigated fields) to predict SSR development. Additionally, apothecial scouting and disease monitoring were conducted in 60 commercial fields in three states between 2016 and 2017 to evaluate model accuracy across the growing region. Site-specific air temperature, relative humidity, and wind speed data were obtained through the Integrated Pest Information Platform for Extension and Education (iPiPE) and Dark Sky weather networks. Across all locations, iPiPE-driven model predictions during the soybean flowering period (R1 to R4 growth stages) explained end-of-season disease observations with an accuracy of 81.8% using a probability action threshold of 35%. Dark Sky data, incorporating bias corrections for weather variables, explained end-of-season disease observations with 87.9% accuracy (in 2017 commercial locations in Wisconsin) using a 40% probability threshold. Overall, these validations indicate that these two weather-based apothecial models, using either weather data source, provide disease risk predictions that both reduce unnecessary chemical application and accurately advise applications at critical times.

Weather-Based Models for Assessing the Risk of Sclerotinia sclerotiorum Apothecial Presence in Soybean (Glycine max) Fields

Sclerotinia stem rot (SSR) epidemics in soybean, caused by Sclerotinia sclerotiorum, are currently responsible for annual yield reductions in the United States of up to 1 million metric tons. In-season disease management is largely dependent on chemical control but its efficiency and cost-effectiveness depends on both the chemistry used and the risk of apothecia formation, germination, and further dispersal of ascospores during susceptible soybean growth stages. Hence, accurate prediction of the S. sclerotiorum apothecial risk during the soybean flowering period could enable farmers to improve in-season SSR management. From 2014 to 2016, apothecial presence or absence was monitored in three irrigated (n = 1,505 plot-level observations) and six nonirrigated (n = 2,361 plot-level observations) field trials located in Iowa (n = 156), Michigan (n = 1,400), and Wisconsin (n = 2,310), for a total of 3,866 plot-level observations. Hourly air temperature, relative humidity, dew point, wind speed, leaf wetness, and rainfall were also monitored continuously, throughout the season, at each location using high-resolution gridded weather data. Logistic regression models were developed for irrigated and nonirrigated conditions using apothecial presence as a binary response variable. Agronomic variables (row width) and weather-related variables (defined as 30-day moving averages, prior to apothecial presence) were tested for their predictive ability. In irrigated soybean fields, apothecial presence was best explained by row width (r = -0.41, P < 0.0001), 30-day moving averages of daily maximum air temperature (r = 0.27, P < 0.0001), and daily maximum relative humidity (r = 0.16, P < 0.05). In nonirrigated fields, apothecial presence was best explained by using moving averages of daily maximum air temperature (r = -0.30, P < 0.0001) and wind speed (r = -0.27, P < 0.0001). These models correctly predicted (overall accuracy of 67 to 70%) apothecial presence during the soybean flowering period for four independent datasets (n = 1,102 plot-level observations or 30 daily mean observations).

Modelling the impacts of pests and diseases on agricultural systems

The improvement and application of pest and disease models to analyse and predict yield losses including those due to climate change is still a challenge for the scientific community. Applied modelling of crop diseases and pests has mostly targeted the development of support capabilities to schedule scouting or pesticide applications. There is a need for research to both broaden the scope and evaluate the capabilities of pest and disease models. Key research questions not only involve the assessment of the potential effects of climate change on known pathosystems, but also on new pathogens which could alter the (still incompletely documented) impacts of pests and diseases on agricultural systems. Yield loss data collected in various current environments may no longer represent a adequate reference to develop tactical, decision-oriented, models for plant diseases and pests and their impacts, because of the ongoing changes in climate patterns. Process-based agricultural simulation modelling, on the other hand, appears to represent a viable methodology to estimate the impacts of these potential effects. A new generation of tools based on state-of-the-art knowledge and technologies is needed to allow systems analysis including key processes and their dynamics over appropriate suitable range of environmental variables. This paper offers a brief overview of the current state of development in coupling pest and disease models to crop models, and discusses technical and scientific challenges. We propose a five-stage roadmap to improve the simulation of the impacts caused by plant diseases and pests; i) improve the quality and availability of data for model inputs; ii) improve the quality and availability of data for model evaluation; iii) improve the integration with crop models; iv) improve the processes for model evaluation; and v) develop a community of plant pest and disease modelers.

Reconsidering Leaf Wetness Duration Determination for Plant Disease Management

Relationships between leaf wetness and plant diseases have been studied for centuries. The progress and risk of many bacterial, fungal, and oomycete diseases on a variety of crops have been linked to the presence of free water on foliage and fruit under temperatures favorable to infection. Whereas the rate parameters for infection or epidemic models have frequently been linked with temperature during the wet periods, leaf wetness periods of specific time duration are necessary for the propagule germination of most phytopathogenic fungi and for their penetration of plant tissues. Using these types of relationships, disease-warning systems were developed and are now being used by grower communities for a variety of crops. As a component of Integrated Pest Management, disease-warning systems provide growers with information regarding the optimum timing for chemical or biological management practices based on weather variables most suitable for pathogen dispersal or host infection. Although these systems are robust enough to permit some errors in the estimates or measurements of leaf wetness duration, the need for highly accurate leaf wetness duration data remains a priority to achieve the most efficient disease management.

Sensitivity of Disease Management Decision Aids to Temperature Input Errors Associated with Sampling Interval and Out-of-Canopy Sensor Placement

Many plant disease epidemic models, and the disease management decision aids developed from them, are created based on temperature or other weather conditions measured in or above the crop canopy at intervals of 15 or 30 min. Disease management decision aids, however, commonly are implemented based on hourly weather measurements made from sensors sited at a standard placement of 1.5 m above the ground or are estimated from off-site weather measurements. We investigated temperature measurement errors introduced when sampling interval was increased from 15 to 60 min, and when actual in-canopy conditions were represented by temperature measurements collected by standard-placement sensors (1.5 m above the ground, outside the canopy) in each of three crops (grass seed, grape, and hops) and assessed the impact of these errors on outcomes of decision aids for grass stem rust as well as grape and hops powdery mildews. Decreasing time resolution from 15 to 60 min resulted in statistically significant underestimates of daily maximum temperatures and overestimates of daily minimum temperatures that averaged 0.2 to 0.4°C. Sensor location (in-canopy versus standard-placement) also had a statistically significant effect on measured temperature, and this effect was significantly less in grape or hops than in the grass seed crop. Effects of these temperature errors on performance of disease management decision aids were affected by magnitude of the errors as well as the type of decision aid. The grape and hops powdery mildew decision aids used rule-based indices, and the relatively small (±0.8°C) differences in temperature observed between in-canopy and standard placement sensors in these crops resulted in differences in rule outcomes when actual in-canopy temperatures were near a threshold for declaring that a rule had been met. However, there were only minor differences in the management decision (i.e., fungicide application interval). The decision aid for grass stem rust was a simulation model, for which temperature recording errors associated with location of the weather station resulted in incremental (not threshold) effects on the model of pathogen growth and plant infection probability. Simple algorithms were devised to correct the recorded temperatures or the computed infection probability to produce outcomes similar to those resulting from in-canopy temperature measurements. This study illustrates an example of evaluating (and, if necessary, correcting) temperature measurement errors from weather station sensors not located within the crop canopy, and provides an estimate of uncertainty in temperature measurements associated with location and sampling interval of weather station sensors.

Variability Among Forecast Models for the Apple Sooty Blotch/Flyspeck Disease Complex

Several disease forecast models have been developed to guide treatment of the sooty blotch and flyspeck (SBFS) disease complex of apple. Generally, these empirical models are based on the accumulation of hours of leaf wetness (leaf wetness duration [LWD]) from a biofix at or near the phenological growth stage petal fall, when apple flower petals senesce and drop. The models recommend timing of the initial fungicide application targeting SBFS. However, there are significant differences among SBFS forecast models in terms of biofix and the length of LWD thresholds. A comparison of models using a single input data set generated recommendations for the first SBFS fungicide application that differed by up to 5 weeks. In an attempt to improve consistency among models, potential sources for differences were examined. Leaf wetness (LW) is a particularly variable parameter among models, depending on whether on-site or remote weather data were used, the types of sensors and their placement for on-site monitors, and the models used to estimate LW remotely. When SBFS models are applied in the field, recommended treatment thresholds do not always match the method of data acquisition, leading to potential failures. Horticultural factors, such as tree size, canopy density, and cultivar, and orchard site factors such as the distance to potential inoculum sources can impact risk of SBFS and should also be considered in forecast models. The number of fungal species identified as contributors to the SBFS disease complex has expanded tremendously in recent years. A lack of understanding of key epidemiological factors for different fungi in the complex, and which fungi represent the most challenging management problems, are obvious issues in the development of improved SBFS models. If SBFS forecast models are to be adopted, researchers will need to address these issues.

Site-Specific Risk Factors for Ray Blight in Tasmanian Pyrethrum Fields

Ray blight of pyrethrum (Tanacetum cinerariifolium), caused by Phoma ligulicola var. inoxydablis, can cause defoliation and reductions of crop growth and pyrethrin yield. Logistic regression was used to model relationships among edaphic factors and interpolated weather variables associated with severe disease outbreaks (i.e., defoliation severity ≥40%). A model for September defoliation severity included a variable for the product of number of days with rain of at least 0.1 mm and a moving average of maximum temperatures in the last 14 days, which correctly classified (accuracy) the disease severity class for 64.8% of data sets. The percentage of data sets where disease severity was correctly classified as at least 40% defoliation severity (sensitivity) or below 40% defoliation severity (specificity) were 55.8 and 71%, respectively. A model for October defoliation severity included the number of days with at least 1 mm of rain in the past 14 days, stem height in September, and the product of the number of days with at least 10 mm of rain in the last 30 days and September defoliation severity. Accuracy, sensitivity, and specificity were 72.6, 73.6, and 71.4%, respectively. Youden's index identified predictive thresholds of 0.25 and 0.57 for the September and October models, respectively. When economic considerations of the costs of false positive and false negative decisions and disease prevalence were integrated into receiver operating characteristic (ROC) curves for the October model, the optimal predictive threshold to minimize average management costs was 0 for values of disease prevalence greater than 0.2 due to the high cost of false negative predictions. ROC curve analysis indicated that management of the disease should be routine when disease prevalence is greater than 0.2. The models developed in this research are the first steps toward identifying and weighting site and weather disease risk variables to develop a decision-support aid for the management of ray blight of pyrethrum.

Contamination of fresh and ensiled maize by multiple penicillium mycotoxins

Toxins produced by Penicillium species are reported in maize silage and have been associated with health problems in cattle. Our objectives were to evaluate the prevalence and dynamics of patulin (PAT), mycophenolic acid (MPA), cyclopiazonic acid (CPA), and roquefortine C (ROC) in fresh and ensiled maize. To achieve these objectives we developed a high-performance liquid chromatography method coupled with mass spectrometry to detect all four toxins simultaneously in silage. In addition we collected weather data, information on agronomic practices, and silage fermentation characteristics for each study site. Silage was collected at harvest and after ensiling in 2001 and 2002 from 30 Pennsylvania dairies. The average concentration of toxins (range in parentheses) was: PAT 0.08 microg/g (0.01 to 1.21), MPA 0.16 microg/g (0.02 to 1.30), CPA 0.12 microg/g (0.02 to 1.43), and ROC 0.38 microg/g (0.01 to 5.71). ROC was the most frequently detected toxin (60%), followed by MPA (42%), CPA (37%), and PAT (23%). Of 120 samples tested, 15% contained no detectible levels of toxin, 25% were contaminated with one toxin, 32% with two, 18% with three, and 10% with all four toxins. All four mycotoxins were found in freshly harvested material, contradicting the belief that Penicillium toxin formation occurs exclusively during storage. We observed that weather conditions during specific growth stages of the crop affected the final concentration of toxins in freshly harvested maize. In ensiled material, PAT levels were affected by concentrations of propionic and isobutyric acids. Based on our data, Penicillium mycotoxins can form while the crop is in the field and after ensiling, suggesting that preventative measures should begin prior to ensiling.

A Site-Specific, Weather-Based Disease Regression Model for Sclerotinia Blight of Peanut

In North Carolina, losses due to Sclerotinia blight of peanut, caused by the fungus Sclerotinia minor, are an estimated 1 to 4 million dollars annually. In general, peanut (Arachis hypogaea) is very susceptible to Sclerotinia blight, but some partially resistant virginia-type cultivars are available. Up to three fungicide applications per season are necessary to maintain a healthy crop in years highly favorable for disease development. Improved prediction of epidemic initiation and identification of periods when fungicides are not required would increase fungicide efficiency and reduce production costs on resistant and susceptible cultivars. A Sclerotinia blight disease model was developed using regression strategies in an effort to describe the relationships between modeled environmental variables and disease increase. Changes in incremental disease incidence (% of newly infected plants of the total plant population per plot) for the 2002-2005 growing seasons were statistically transformed and described using 5-day moving averages of modeled site-specific weather variables (localized, mathematical estimations of weather data derived at a remote location) obtained from SkyBit (ZedX, Inc.). Variables in the regression to describe the Sclerotinia blight disease index included: mean relative humidity (linear and quadratic), mean soil temperature (quadratic), maximum air temperature (linear and quadratic), maximum relative humidity (linear and quadratic), minimum air temperature (linear and quadratic), minimum relative humidity (linear and quadratic), and minimum soil temperature (linear and quadratic). The model explained approximately 50% of the variability in Sclerotinia blight index over 4 years of field research in eight environments. The relationships between weather variables and Sclerotinia blight index were independent of host partial resistance. Linear regression models were used to describe progress of Sclerotinia blight on cultivars and breeding lines with varying levels of partial resistance. Resistance affected the rate of disease progress, but not disease onset. The results of this study will be used to develop site- and cultivar-specific spray advisories for Sclerotinia blight.

Relationship of sphinganine analog mycotoxin contamination in maize silage to seasonal weather conditions and to agronomic and ensiling practices

ABSTRACT Sphinganine analog mycotoxins (SAMs) are reported in maize and maize based feeds. Our objectives were to detect and quantify fumonisins B(1) and B(2) and Alternaria toxins (AAL toxins) AAL-TA and AAL-TB and determine how agronomic practices, weather conditions, and ensiling affected the occurrence and levels in maize silage. Silage was collected at harvest and after ensiling in 2001 and 2002 from 30 to 40 dairies, representing four regions in Pennsylvania. SAMs were quantified using high pressure liquid chromatography (HPLC) with fluorescence detection and high pressure liquid chromatography-mass spectrometry HPLC-MS. The average concentrations and ranges were as follows: fumonisin B(1) 2.02 mug/g (0.20 to 10.10), fumonisin B(2) 0.98 mug/g (0.20 to 20.30), AAL-TA 0.17 mug/g (0.20 to 2.01), and AAL-TB 0.05 mug/g (0.03 to 0.90). Fumonisin B(1) was the most frequently detected toxin (92%) in all samples, followed by fumonisin B(2) (55%), AAL-TA (23%), and -TB (13%). Temperature during maize development was positively correlated with fumonisin occurrence and levels and negatively with AAL-TA, while moisture events were negatively correlated with fumonisins and positively with AAL-TA. Fumonisin levels were higher in silage harvested at later developmental stages (dough through physiological maturity). Ensiling did not affect toxin concentration nor did agronomic practices (tillage system, inoculant use, or silo type) or silage characteristics (dry matter, pH, or organic acid concentration). This is the first report of AAL-TB in silage and on factors that affect SAM frequency and levels in maize silage.

Leaf wetness duration measurement: comparison of cylindrical and flat plate sensors under different field conditions

In general, leaf wetness duration (LWD) is a key parameter influencing plant disease epidemiology, since it provides the free water required by pathogens to infect foliar tissue. LWD is used as an input in many disease warning systems, which help growers to decide the best time to spray their crops against diseases. Since there is no observation standard either for sensor or exposure, LWD measurement is often problematic. To assess the performance of electronic sensors, LWD measurements obtained with painted cylindrical and flat plate sensors were compared under different field conditions in Elora, Ontario, Canada, and in Piracicaba, São Paulo, Brazil. The sensors were tested in four different crop environments--mowed turfgrass, maize, soybean, and tomatoes--during the summer of 2003 and 2004 in Elora and during the winter of 2005 in Piracicaba. Flat plate sensors were deployed facing north and at 45 degrees to horizontal, and cylindrical sensors were deployed horizontally. At the turfgrass site, both sensors were installed 30 cm above the ground, while at the crop fields, the sensors were installed at the top and inside the canopy (except for maize, with a sensor only at the top). Considering the flat plate sensor as a reference (Sentelhas et al. Operational exposure of leaf wetness sensors. Agric For Meteorol 126:59-72, 2004a), the results in the more humid climate at Elora showed that the cylindrical sensor overestimated LWD by 1.1-4.2 h, depending on the crop and canopy position. The main cause of the overestimation was the accumulation of big water drops along the bottom of the cylindrical sensors, which required much more energy and, consequently, time to evaporate. The overall difference between sensors when evaporating wetness formed during the night was around 1.6 h. Cylindrical sensors also detected wetness earlier than did flat plates--around 0.6 h. Agreement between plate and cylinder sensors was much better in the drier climate at Piracicaba. These results allow us to caution that cylindrical sensors may overestimate wetness for operational LWD measurements in humid climates and that the effect of other protocols for angling or positioning this sensor should be investigated for different crops.

Plant pathogen forensics: capabilities, needs, and recommendations

A biological attack on U.S. crops, rangelands, or forests could reduce yield and quality, erode consumer confidence, affect economic health and the environment, and possibly impact human nutrition and international relations. Preparedness for a crop bioterror event requires a strong national security plan that includes steps for microbial forensics and criminal attribution. However, U.S. crop producers, consultants, and agricultural scientists have traditionally focused primarily on strategies for prevention and management of diseases introduced naturally or unintentionally rather than on responding appropriately to an intentional pathogen introduction. We assess currently available information, technologies, and resources that were developed originally to ensure plant health but also could be utilized for postintroduction plant pathogen forensics. Recommendations for prioritization of efforts and resource expenditures needed to enhance our plant pathogen forensics capabilities are presented.

Relationships Between Weather Conditions, Agronomic Practices, and Fermentation Characteristics with Deoxynivalenol Content in Fresh and Ensiled Maize

The deoxynivalenol (DON) content of maize silage was determined in samples collected at harvest and after ensiling in 2001 and 2002 from 30 to 40 Pennsylvania dairies. Information on cultural practices, hybrid maturity, planting, and harvest date was collected from each site. Site-specific weather data and a corn development model were used to estimate hybrid development at each site. Correlation analysis was used to assess the relationship between weather data, hybrid development, cultural practices and preharvest DON. Fermentation characteristics (moisture, pH, and so on) of ensiled samples were measured to study their relationship to postharvest DON contamination. No significant difference (P ≤ 0.05) was noted between the numbers of samples containing DON in 2001 and 2002, although concentration was higher in 2002 samples. A positive correlation was observed between DON concentration of harvest samples and daily average temperature, minimum temperature, and growing degree day during tasselling, silking, and milk stages. A negative correlation was observed between daily average precipitation at blister stage and DON concentration in harvest samples. Samples from no-till or minimum-till locations had higher DON concentrations than moldboard or mixed-till locations. Harvest samples had higher DON concentration than ensiled samples, suggesting that some physical, chemical, or microbiological changes, resulting from ensiling, may reduce DON in storage.

Spatial variability of leaf wetness duration in different crop canopies

The spatial variability of leaf wetness duration (LWD) was evaluated in four different height-structure crop canopies: apple, coffee, maize, and grape. LWD measurements were made using painted flat plate, printed-circuit wetness sensors deployed in different positions above and inside the crops, with inclination angles ranging from 30 to 45 degrees. For apple trees, the sensors were installed in 12 east-west positions: 4 at each of the top (3.3 m), middle (2.1 m), and bottom (1.1 m) levels. For young coffee plants (80 cm tall), four sensors were installed close to the leaves at heights of 20, 40, 60, and 80 cm. For the maize and grape crops, LWD sensors were installed in two positions, one just below the canopy top and another inside the canopy. Adjacent to each experiment, LWD was measured above nearby mowed turfgrass with the same kind of flat plate sensor, deployed at 30 cm and between 30 and 45 degrees. We found average LWD varied by canopy position for apple and maize (P<0.05). In these cases, LWD was longer at the top, particularly when dew was the source of wetness. For grapes, cultivated in a hedgerow system and for young coffee plants, average LWD did not differ between the top and inside the canopy. The comparison by geometric mean regression analysis between crop and turfgrass LWD measurements showed that sensors at 30 cm over turfgrass provided quite accurate estimates of LWD at the top of the crops, despite large differences in crop height and structure, but poorer estimates for wetness within leaf canopies.

A simple generic infection model for foliar fungal plant pathogens

ABSTRACT In this study, a simple generic infection model was developed for predicting infection periods by fungal foliar pathogens. The model is designed primarily for use in forecasting pathogens that do not have extensive epidemiological data. Most existing infection models require a background epidemiological data set, usually including laboratory estimates of infection at multiple temperature and wetness combinations. The model developed in this study can use inputs based on subjective estimates of the cardinal temperatures and the wetness duration requirement. These inputs are available for many pathogens or may be estimated from related pathogens. The model uses a temperature response function which is scaled to the minimum and optimum values of the surface wetness duration requirement. The minimum wetness duration requirement (W(min)) is the number of hours required to produce 20% disease incidence or 5% disease severity on inoculated plant parts at a given temperature. The model was validated with published data from 53 controlled laboratory studies, each with at least four combinations of temperature and wetness. Validation yielded an average correlation coefficient of 0.83 and a root mean square error of 4.9 h, but there was uncertainty about the value of the input parameters for some pathogens. The value of W(min) varied from 1 to 48 h and was relatively uniform for species in the genera Cercospora, Alternaria, and Puccinia but less so for species of Phytophthora, Venturia, and Colletotrichum. Operationally, infection models may use hourly or daily weather inputs. In the case of the former, information also is required to estimate the critical dry-period interruption value, defined as the duration of a dry period at relative humidities <95% that will result in a 50% reduction in disease compared with a continuous wetness period. Pathogens were classified into three groups based on their critical dry-period interruption value. The infection model is being used to create risk maps of exotic pests for the U.S. Department of Agriculture's Animal Plant Health and Inspection Service.

Adaptation of Fire Blight Forecasting to Optimize the Use of Biological Controls

We investigated adaptation of fire blight forecasting concepts to incorporate and optimize the use of biological agents for disease suppression. The effect of temperature on growth of the bacterial antagonists, Pseudomonas fluorescens A506 and Pantoea agglomerans C9-1S, and of the pathogen Erwinia amylovora153N, on pear and apple blossoms was evaluated in growth chamber and screenhouse experiments. New blossoms were inoculated with the strains and subsequent growth was measured over 96 h. Bacterial growth rates on blossoms were described as functions of temperature. A degree hour-based "bacterial growth index" (96-h moving total of degree hours >10°C) was created to assess conduciveness of orchard environments for antagonist colonization. A comparison of this index to a disease risk index indicated that biocon-trol treatments could be timed such that the antagonists could be expected to grow to an effective population size before the disease index shifted from "low" to "moderate" risk. For six pear- and apple-production areas of Oregon and Washington, regression of actual values of the bacterial growth and disease risk indices on index values derived from 4-day temperature forecasts resulted in coefficients of determination that averaged 0.75. The "bacterial growth index" and its estimation via temperature forecasts were incorporated into a decision matrix designed to guide optimal treatment timing.

Validation of Potato Early Blight Disease Forecast Models for Colorado Using Various Sources of Meteorological Data

Disease forecasts from regional or remotely sensed meteorological data free growers from infield weather data monitoring and may improve disease forecast implementation. This study was initiated to validate potato early blight forecast models in Colorado and to determine the influence of sources of meteorological data on forecast accuracy. Hourly temperatures were recorded by Campbell Scientific CR-10, Pessl Instruments μMetos Model MCR300, and Spectrum Technologies Model 450 WatchDog weather stations and data loggers within potato fields, field-specific temperature estimations generated by mPOWER3/EMERGE from off-site weather stations, and regional COAGMET CR-10 weather stations. Mean hourly temperature deviations between mPOWER3/EMERGE or in-field stations and COAGMET varied from 0.93°C greater to 1.11°C less than COAGMET observations. Initial appearance of early blight lesions was predicted using a 300 physiological day threshold in commercial fields in each year from 1998 to 2001 and in experimental plots in each year from 1997 to 2001 as determined by COAGMET meteorological observations. All sources of meteorological data generated early blight forecasts within 6 days of each other across all locations and years. COAGMET weather stations should free potato growers and integrated pest management personnel from collecting in-field microclimatic data and speed the implementation of disease forecasting.