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Development of a web-based disease forecasting system for strawberries
Computers and Electronics in Agriculture 75 (2011) 169–175
Contents lists available at ScienceDirect
Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag
Original paper
Development of a web-based disease forecasting system for strawberries W. Pavan a,∗ , C.W. Fraisse a , N.A. Peres b a b
Agricultural and Biological Engineering, University of Florida, Gainesville, FL, USA Gulf Coast Research and Education Center, University of Florida, Wimauma, FL, USA
a r t i c l e
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Article history: Received 16 June 2010 Received in revised form 9 October 2010 Accepted 28 October 2010 Keywords: Climate Simulation modeling Decision support system Web-based interface Google Maps
a b s t r a c t Florida produces about 16 million flats of strawberries every year, 15% of berries produced in the U.S. and virtually all the berries grown in the winter. Fungicides are applied on a weekly schedule to control Anthracnose and Botrytis fruit rot from December through March. Different predictive models for these diseases were evaluated and systems developed to time fungicide applications that reduced the number of sprays by about 50%. The models utilized leaf wetness and temperature during the wet period to predict disease outbreaks. The most effective models were embedded in a web-based tool developed for use by growers to schedule their fungicide applications. This internet-based forecasting system to predict these diseases, the Strawberry Advisory System (SAS), was implemented on the AgroClimate website using weather data from the Florida Agricultural Weather Network. Growers can select the location closest to their plantings and SAS will provide a prediction of disease incidence and recommendations for fungicide applications. Users can also be provided warnings of the need to spray via email or text messages. In preliminary trials, SAS has been successful in eliminating many unnecessary fungicide applications and has proven user friendly. © 2010 Elsevier B.V. All rights reserved.
1. Introduction The prediction of plant diseases has emerged as a wellestablished component of epidemiology that is rapidly being incorporated into disease management. The mathematics of the disease progress has matured to a point of becoming a powerful and respected component in the management and prediction of epidemics. However, many models for prediction of plant diseases are theoretical and have not proven useful for disease management. The assumption is that a disease prediction model should make projections of the main events in the development of diseases, which most models do not (Seem, 2001). This would be especially valuable for disease management if models would eliminate unnecessary pesticide applications and reduce production costs. Computational modeling has enabled the development of tools that use weather events to predict epidemics. Traditionally, plant disease models have used leaf wetness duration (LWD) combined with temperature, to predict infection and colonization, and then identify the risks of an epidemic. These types of models have been used with observed climate records to track the favorable periods, indicating tactics or strategies of control (Jabrzemski and Sutherland, 2006).
∗ Corresponding author at: 271 Frazier Rogers Hall, P.O. Box 110570, Gainesville, FL 32611-0570, USA. E-mail addresses: wpavan@ufl.edu (W. Pavan), cfraisse@ufl.edu (C.W. Fraisse), nperes@ufl.edu (N.A. Peres). 0168-1699/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.compag.2010.10.013
In some cases, inoculum levels can be incorporated into disease models (Magarey et al., 2002; Peres et al., 2002), but with many diseases, inoculum is in excess and not useful in disease prediction (Bhatia et al., 2003; Biggs and Turechek, 2010). Currently, the revolutionary advances in information technology and the emergence of the Internet, as well as the global connectivity and integration with modern programming languages, have produced new concepts and ways to be explored further in the production and transfer of knowledge. Revolutions in web-based technologies are bringing major changes in the development and use of decision-support systems by producers and specialists in the management of plant diseases (Fernandes et al., 2007a,b). The main objectives of this research were to develop and implement a web-based forecasting system to predict Anthracnose and Botrytis fruit rot epidemics on strawberries and help strawberry producers in Florida avoid unnecessary applications of pesticide and reduce production costs. Disease models used in the system were developed and evaluated in previous studies for both diseases (MacKenzie and Peres, in preparation-a,b) and the forecasting and grower alert systems were implemented in the AgroClimate system (Fraisse et al., 2006). 1.1. Strawberry production and disease models in Florida Strawberries are one of the most valuable crops in Florida. The state produces about 16 million flats of strawberries every year,
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which represents 15% of nation’s berries and virtually all the berries grown during the winter. In 2009, about 8800 acres were devoted to strawberries with an estimated return to the grower approaching to $250 million (USDA, 2009). Anthracnose fruit rot (AFR), caused by Colletotrichum acutatum (Smith, 1998), and Botrytis fruit rot (BFR), caused by Botrytis cinerea (Sutton, 1998), are the most important diseases for production of annual strawberries in Central Florida and worldwide. Anthracnose is a serious disease that affects the fruit in addition to flowers and petioles. It is favored by warm temperatures (>18 ◦ C) and wet weather. Losses due to the anthracnose can exceed 50% when conditions favor disease development, even in well-managed fields (Turechek et al., 2006). Its control is very difficult when conditions are favorable. Although the disease occurs primarily in the warmer production regions, losses due to anthracnose fruit rot have increased across North America (Turechek et al., 2006). Botrytis fruit rot is an important pre-harvest and postharvest disease of strawberry, infecting the floral parts, including stamens and petals. The conidia are wind- and splash-dispersed, requiring free moisture (>4 h of leaf wetness) and cool temperatures (15–22 ◦ C) to infect and sporulate. The disease can be controlled by a combination of cultural practices and chemical methods, but there is no completely resistant strawberry cultivar (Legard et al., 2005). The high value of the strawberry crop often compels growers to protect their fruit by applying fungicides preventively, mainly for control of Anthracnose and Botrytis fruit rots. In Florida, fungicides are applied weekly from December through March (Legard et al., 2005). Different types of models, from simple to complex, have been developed to help understand the development of disease epidemics and assist growers in making decisions in various cropping systems (Maanen and Xu, 2003). As for other crops, models have been developed to predict strawberry diseases (Xu et al., 2000; Broome et al., 1995; Bulger et al., 1987). These models successfully predict disease incidence, but have never been used to forecast the need for fungicide applications. 1.2. Florida climate Most of Florida lies within the extreme southern portion of the Northern Hemisphere humid subtropical climate zone noted for its long hot and humid summers and mild and wet winters. Mean average temperatures during January, Florida’s coldest month, range from about 10 ◦ C in the north to about 20 ◦ C in the south. Precipitation in the south is highly concentrated in the warmer months when convectional rain falls (Winsberg, 2003). Fall in Florida is normally drier than summer because convectional rainstorms are not so frequent without the intense heating of the ground that occurs during summer. Weather during the winter is affected by the Azores-Bermuda High Pressure system that precludes high precipitation. As ocean waters around the state warm during the spring, the high-pressure system over Florida weakens and the summer rains begin. ˜ Climate in Florida is also affected by the El Nino-Southern Oscillation (ENSO) phenomenon. ENSO is the strongest driver of inter-annual climate variability around the world including the southeastern United States (Fraisse et al., 2006). ENSO is a natural, coupled atmospheric-oceanic cycle that occurs in the tropical Pacific Ocean approximately every 2–7 years. When sea surface temperature (SST) in the eastern equatorial Pacific Ocean is higher ˜ than the long-term average the phenomenon is called El Nino. When the SST is lower than normal, the phenomenon is called ˜ When the SST is normal, the event is called “neutral”. La Nina. ˜ is expected to bring 30–40% more rainfall In Florida, El Nino and cooler temperatures than normal during winter and spring, ˜ is expected to bring a warmer and drier than whereas La Nina
normal winter and spring seasons. The high rainfall associated ˜ in Central Florida, where most of the strawberwith El Nino ries are produced may result in a much higher incidence of fruit rots. 1.3. AgroClimate AgroClimate is a web-based climate information and decision support system (http://www.agroclimate.org) that was developed to provide extension agents, producers, and natural resource managers with tools to aid their decision-making aimed at reducing risks associated with climate variability. It was designed and implemented by the Southeast Climate Consortium (SECChttp://seclimate.org) in partnership with the Florida Cooperative State Extension Service, being updated and maintained periodically to ensure the relevance of the information and decision support tools contained in the system. Information available in AgroClimate includes climate forecasts combined with risk management tools and information for selected crops, forestry, pasture, and livestock. The system was developed to allow easy expansion of the topic areas, number of commodities, and risk management tools available for users. This modularity is a very important aspect of the overall design, making it possible to manage the system and attach new features. The contents are added and maintained by the administrators, who can easily maintain the system without knowledge of web-programming languages. Administration of the site and its contents is decentralized, facilitating the delegation of responsibility for maintenance and updates of the different sections by individual groups within the SECC (Fraisse et al., 2006). The system was developed to be hosted in Linux/Unix platforms but can easily be transferred to others. The dynamic tools were developed using the PHP web programming language, Javascript language, HTML, Cascading Style Sheets (CSS) and MySQL database. AgroClimate provides a large amount of information for the decision-makers and stakeholders in general. The information provided is divided into groups with the main ones being Tools and Forecasts and Outlooks. The dynamic tools were designed to work at the county or station levels and are organized in five groups: (1) climate risk; (2) crop development; (3) crop diseases; (4) crop yield; (5) drought. Dynamic tools always default to the current ENSO phase for the evaluation of climate-associated risks. However, the user can also evaluate the results for alternate ENSO phases and management scenarios. Seasonal forecasts and monthly climate outlooks include brief discussions of anticipated climate patterns in the Southeast and how they mesh with current conditions, anticipated impacts of climate patterns on agricultural commodities, El ˜ ˜ discussion, as well as discussions about seasonal cliNino/La Nina mate issues such as hurricanes, wildfires, drought, and extreme temperatures. Links to national and international forecasts are also provided. 2. System development As with the approach used for the development of AgroClimate, an important aspect of the design methodology used for developing this disease forecasting system was a strong interaction with extension agents and producers. Thus, the information provided in the system was relevant for user needs and that the language and formats used were easy and appropriate. While a number of activities did not necessarily require interactions with end users, such as the development of regional climate and agronomic databases, the design of layouts and functionalities were based on an interaction with end users for testing and evaluation.
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2.1. Disease models Different systems and thresholds of the model developed by Wilson et al. (1990) were evaluated in replicated field trials during 3 consecutive strawberry seasons for timing fungicide applications. The model utilizes wetness duration and temperature to predict infection by Colletotrichum acutatum in controlled conditions. To make the model applicable for controlling disease in the field, daily wetness intervals and average temperature during wetness periods were calculated from weather data collected over the previous five seasons. Thus, a baseline infection threshold below which no fungicide applications was established and a second threshold that allowed infections which could be eliminated by curative fungicide applications, was also established (Peres and MacKenzie, 2009a; Peres et al., 2009; MacKenzie and Peres, in preparation-a). For Botrytis fruit rot (BFR), three models, one model developed by Xu et al. (2000) for strawberry, one developed by Bulger et al. (1987) for strawberry, and one by Broome et al. (1995) for grape were evaluated. Different infection thresholds for the three models were evaluated. Both models enabled to control the disease with significantly fewer sprays than the recommended calendar program (Peres and MacKenzie, 2009a; Peres et al., 2009; MacKenzie and Peres, in preparation-b). 2.2. Meteorological data The first step for implementing the tool was the development of a weather database to store data derived from different sources and formats. Weather observations were compiled using recent and current weather conditions data collected by the Florida Automated Weather Network (FAWN). Short-term weather forecasts were obtained from the National Weather Service-National Digital Forecast Database (NWS-NDFD). The FAWN database and website have provided a vehicle to collect and disseminate real time weather data to a wide variety of users since 1998. A number of weather variables including temperature, precipitation, relative humidity, wind speed, and solar radiation are monitored at FAWN stations. These data are archived in real time and is available at http://fawn.ifas.ufl.edu. The FAWN network is composed of 35 automated weather stations, located throughout Florida. For the purposes of the system, leaf wetness duration and temperature during the wetness period were used to predict disease incidence at 4 locations: Balm, Dover, Lake Alfred, and Arcadia located in the southwest region of the state. These locations cover over 95% of the strawberry production area in Florida, which is concentrated in Hillsborough County. The NWS-NDFD short-term weather forecast is provided in a grid format and includes sensible weather elements (e.g., cloud cover, maximum temperature, relative humidity). NDFD contains a seamless mosaic of digital forecasts from NWS field offices working in collaboration with the National Centers for Environmental Prediction (NCEP). The database is available for members of the public to use in creating text, graphic, grids and image products of their own. For the purpose of the system, predicted relative humidity above 95% was used to estimate leaf wetness duration. 2.3. Information technologies Well-established design patterns and rules were used to develop the system allowing the establishment of standards for eventual AgroClimate expansions. The establishment of standards using well-known and studied techniques helps the developer to understand and model the behavior of applications (Pavan and Julho, 2007). Different types of design patterns have a specific purpose, naming, identifying and abstracting the key of a structure. Description
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of already tested solutions to certain design problems creates a solid-based solution, which act as building blocks for complex software systems. The quality of software is closely related to the use of these patterns. Examples of existing design patterns are Business Delegate, Composite Entity, Composite View, and Data Access Object. The MVC (Model-View-Controller) is the design pattern best known and used. It is responsible for separating the data access, business logic data presentation, and user interaction (Pavan and Julho, 2007). The MVC design pattern was selected for the development of this application, allowing the development of the system using the following programming layers: • Model: Disease Models (Botrytis and Anthracnose) and database; • View: Interface with the user (Web-based); • Controller: Disease models management and handling view requests; The disease models were implemented using the R statistical analysis software system (http://www.r-project.org), which is a language and environment for statistical computing and graphics generation, similar to the S language and environment, developed at Bell Laboratories by John Chambers and colleagues (Ribeiro Junior, 2001; R Development Core Team, 2006). R is an integrated environment of software for data manipulation, calculation and generation of graphics, including effective data processing and storage facilities. It can be easily expanded adding new functionalities by defining new functions. As in many other languages, most of the libraries in the R language were developed in their own language and are available in package form, making them very powerful and dynamic (R Development Core Team, 2006). The languages C++ and Fortran were used to implement specific code functions for certain tasks that required high processing power. These functions were integrated with R and called during the execution of the models. With the provision of a fast and easy way to access the information in mind, the design of the view layer (client side) was based on the Google Maps API Interface, a free web mapping technology, developed by Google and currently used in thousands of systems around the world (Chow, 2008). JavaScript (user interaction) and Ajax (asynchronous connection with the server-model layer) were used in this layer of the system. Results from the model layer are static information, basically formed by text, XML, HTML, and graphics (plots). The layout of the web-page is based in CSS (Cascading Style Sheets). The MySql v.5.0.54, a popular free and open source relational database management system, was used to store data and model outputs. 3. Results and discussion The prototype of the web-based strawberry disease forecasting system, named Strawberry Advisory System (SAS), was implemented, refined, and made available for commercial strawberry growers in Florida since the 2009/2010 cropping season. Its structure is shown in Fig. 1 and initial results are discussed below. The system was divided into three stages: (i) obtain weather data and short-term weather forecasts, (ii) execution of simulation models, and (iii) availability of information processed through the web system. 3.1. Backstage As the tool was designed to be part of AgroClimate, the definition of the database followed the same pattern, structure and technology, being supported by the MySQL relational database management system, which has the ability to efficiently
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Fig. 1. The system infrastructure.
store, search, and retrieve data in large databases (Fraisse et al., 2006). Observed weather data and short-term weather forecast data are automatically taken by PHP scripts (Hypertext Preprocessor), called by “cronjobs” and executed in hourly and daily basis, respectively, storing them in the database to be used by disease simulation models. The disease simulation models, developed in R language, are automatically executed every hour on the same server that hosts the web advisory system. The results of simulation are stored in the same database to be presented to users via the web tool. The generated images (plots) are stored in specific folders on the server and are included into the tool on the fly. SMS messages and e-mails are sent to registered users whenever the simulated infection index crosses the moderate and high-risk thresholds. Messages are sent just once a day to avoid multiple messages when the index fluctuates around threshold levels. The cost of communication via SMS is reasonably low and the technology is easily implemented giving the system the ability to communicate with users at any time. SMS technology has been used successfully in other disease alert systems such as Sisalert in southern Brazil (Pavan et al., 2006). E-mail and Short Message Service technology (SMS) were tested during the 2009–2010 strawberry season to evaluate the ease of rapid communication with producers and extension agents. 3.2. Web system Information provided by the Strawberry Advisory System includes disease infection risk levels and disease simulation, recent weather and short-term forecast data, and news and bulletins about the commodity. The main page is based on an interactive geographic information system (GIS). A GIS integrates hardware, software, and data for capturing, managing, analyzing, and displaying all forms of geographically referenced information. Users of the system can interact with satellite images and maps supplied by the Google Maps API via zooming and movement tools. Each point on the map is associated with specific information and can be viewed both in text or image. Beside points, polygons are drawn and over-
lap on the map, providing information such as county boundaries in the area of interest and allowing additional possibilities to the user. The system was designed to allow easy addition of new diseases and locations (stations) with modularity as the most important aspect of the overall design. The contents are added and maintained by an administrator through a private web-based tool, with no knowledge of web programming languages required. The Strawberry Advisory System, when loaded, shows a page divided in five different sections (Fig. 2). Section A (Fig. 2A) displays information about the AgroClimate portal, the current phase of the ENSO cycle, language options and county mapping tools. On section B (Fig. 2B), the user can easily find and select available weather stations. When a specific weather station is selected, the map in the section D (Fig. 2D) is moved to the correspondent location, showing the information related to that station. With the option “Display County Boundary”, the user can view the boundaries of the county where the selected station is located. Links to related publications and a link to a list of recommended fungicides are presented on section C (Fig. 2C). Information showing in this area can be updated and published by the researchers working on the project or by web administrators. Important links, contact information and the gate to the administration page are found on the section E (Fig. 2E). The main component of the system is found in section D, the Google Map (Fig. 2D). Most functions available in the system are presented in this area. As the main idea was simplicity, the map starts showing the weather stations available for a particular location or region, and allows the interaction with the system. Basically, the system is composed by the following components: 1. Map of the strawberry producing regions showing weather stations with current disease risk level flag. 2. Spray recommendations for Botrytis and Anthracnose based on a simple questionnaire about previous applications and crop development stage. 3. Model outputs in graphic and table formats indicate the disease risk levels (High risk, Moderate risk, or No risk) based on weather
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Fig. 2. The main web page for the strawberry tool.
data observed at the station and forecast for the next 3 days (National Weather Service Pinpoint Forecast). 4. Display of weather data observed during the most recent 72-h period.
In the spray recommendation, fungicide suggestions are provided along with a fungicide list providing the chemical groups and a reminder to avoid repeated applications of products within the same group to decrease the risk of developing resistance.
3.2.1. Map Decision-makers usually look for simple tools that can provide them with information in a clear and objective way. Based on this idea, the system starts by showing the available stations using different colors to quickly present basic and important information: presence or absence of conditions favorable for the development of the diseases. The system presents risk levels using three different colors: green (no risk), yellow (moderate risk) and red (high risk). Users of the system can check station names and confirm risk levels for each disease by passing the mouse over the stations. If more specific information is desired, users can select a weather station with a simple click and obtain additional information in a balloon. If a recommendation is required to make a decision, the user can click on the button “+”, available on the top of balloon, or on the link “Click here to check the Recommendation”, that expand the balloon presenting a simple questionnaire that guides the decisionmaking process to provide a recommendation.
3.2.3. Disease model outputs Botrytis and Anthracnose risk levels are presented in separated tabs and displayed in plots and a tabular format (Fig. 4). Plots are shown at the top of the page and illustrate the simulated infection indices for the last 30 days. The dashed line indicates the forecast infection index levels for the next 3 days. In the case of the forecast, the system assumes that leaf wetness will be present when the relative humidity (RH) is greater than or equal to 95%. Fig. 4 shows the results of the application of this technique after the dashed line (“# Forecast #” area). As in the map marker, risk thresholds are indicated in three colors: green (no risk), yellow (moderate risk) and red (high risk). A disease alert is considered when the line crosses the moderate or high risk threshold. When the user clicks on the plot, a new and expanded window is opened allowing a better visualization of the results. Disease simulation outputs are shown below the plotting area in a tabular format. Each line indicates the date, number of uninterrupted hours of leaf wetness duration, temperature in Fahrenheit and Celsius, infection index (0–1), and the description of the disease level (No risk, Moderate risk, and High risk). Dates are listed in reverse chronological order with the forecast at the top followed by today and recent dates and older results at the bottom of the page.
3.2.2. Spray recommendations If the conditions are favorable for disease development (moderate or high risk levels), a simple set of questions is shown to the user (Fig. 3). In this case, the recommendation is based on a set of rules for each disease. Once the responses are entered, the system applies the rules and displays three alternative recommendations:
1. No spray! 2. Spray Systemic Fungicide! 3. Spray Contact Fungicide!
3.2.4. Weather data Recent weather data is presented in the “Weather tab” to provide users with the ability to verify weather conditions associated with simulated risk levels. This information may be useful to decision-makers and researchers to assist them in the interpretation and understanding of the weather conditions associated with
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Fig. 3. Spray recommendation.
Fig. 4. Model outputs in graphic and table formats.
the diseases. This component displays weather variables such as temperature, relative humidity, wind speed, rain, and leaf wetness duration for the last 72 h.
following the recommendations of the system without affecting disease control and yield. The benefits of using the system will be especially emphasized during those seasons where conditions are ˜ events which result not favorable for disease, such as during La Nina in drier conditions in the region.
3.3. System evaluation The system was evaluated by 3 commercial strawberry farms during 2009/2010 season. Weather conditions were not favorable for the development of diseases during the season due to the extreme low temperatures for the region in January and February. In general, the commercial strawberry producers were able to reduce the number of fungicide applications by about half by
4. Conclusions and future research The implementation of this web-based decision support system to monitor and predict the risk of Anthracnose and Botrytis fruit rot on strawberries enabled growers to easily access the information necessary for them to decide about the need for a fungicide appli-
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cation. Growers using the system can apply fungicides only when conditions are favorable for disease development, thus reducing the number of applications and production costs, without compromising disease control or yields. Future research plans include the analysis of potential disease pressure based on seasonal climate forecasting. We plan to develop and implement a seasonal disease pressure forecast for the cropping season and a probabilistic forecast of the expected number of fungicide applications that may be required to successfully control Anthracnose and Botrytis fruit rot in the main strawberry production areas of the state of Florida. We also plan to expand the system to other strawberry producing regions of the country. Acknowledgements The authors acknowledge the USDA-Risk Management Agency (USDA-RMA) for funding this project. References Bhatia, A., Roberts, P.D., Timmer, L.W., 2003. Evaluation of the alter-rater model for timing of fungicide applications for control of alternaria brown spot of citrus. Plant Disease 87 (9), 1089–1093, http://apsjournals.apsnet. org/doi/abs/10.1094/PDIS.2003.87.9.1089. Biggs, A.R., Turechek, W.W., 2010. Fire blight of apples and pears: epidemiological concepts comprising the maryblyt forecasting program. Plant Health Progress, http://www.plantmanagementnetwork.org/sub/php/research/2010/fire. Broome, J.C., English, J.T., Marois, J.J., Latorre, B.A., Aviles, J.C., 1995. Development of an infection model for botrytis bunch rot of grapes based on wetness duration and temperature. Phytopathology 85, 97–102. Bulger, M.A., Ellis, M.A., Madden, L.V., 1987. Influence of temperature and wetness duration on infection of strawberry flowers by botrytis cinerea and disease incidence of fruit originating from infectedflowers. Phytopathology 77, 1225–1230. Chow, T.E., 2008. The potential of maps apis for internet gis applications. Transactions in GIS 12 (2), 179–191. Fernandes, J., Ponte, E.D., Pavan, W., Cunha, G., 2007a. Climate Prediction and Agriculture. Springer, Berlin Heidelberg, Ch. Web-based System to True-Forecast Disease Epidemics—Case Study for Fusarium Head Blight of Wheat, pp. 265–271. Fernandes, J., Ponte, E., Pavan, W., Cunha, G., 2007b. Wheat Production in Stressed Environments. Springer, Netherlands, Ch. Web-based System to True-Forecast Disease Epidemics, pp. 259–264. Fraisse, C., Breuer, N., Zierden, D., Bellow, J., Paz, J., Cabrera, V., y Garcia, A.G., Ingram, K., Hatch, U., Hoogenboom, G., Jones, J., O’Brien, J., 2006. Agclimate: a climate forecast information system for agricultural risk management in the southeastern usa. Computers and Electronics in Agriculture 53 (1), 13–27. Jabrzemski, R., Sutherland, A., 2006. An innovative approach to weather-based decision-support for agricultural models. In: 22nd International Conference on Interactive Information Processing Systems for Meteorology, Oceanography, and Hydrology. American Meteorological Society, Washington, DC, USA.
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Contents lists available at ScienceDirect
Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag
Original paper
Development of a web-based disease forecasting system for strawberries W. Pavan a,∗ , C.W. Fraisse a , N.A. Peres b a b
Agricultural and Biological Engineering, University of Florida, Gainesville, FL, USA Gulf Coast Research and Education Center, University of Florida, Wimauma, FL, USA
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Article history: Received 16 June 2010 Received in revised form 9 October 2010 Accepted 28 October 2010 Keywords: Climate Simulation modeling Decision support system Web-based interface Google Maps
a b s t r a c t Florida produces about 16 million flats of strawberries every year, 15% of berries produced in the U.S. and virtually all the berries grown in the winter. Fungicides are applied on a weekly schedule to control Anthracnose and Botrytis fruit rot from December through March. Different predictive models for these diseases were evaluated and systems developed to time fungicide applications that reduced the number of sprays by about 50%. The models utilized leaf wetness and temperature during the wet period to predict disease outbreaks. The most effective models were embedded in a web-based tool developed for use by growers to schedule their fungicide applications. This internet-based forecasting system to predict these diseases, the Strawberry Advisory System (SAS), was implemented on the AgroClimate website using weather data from the Florida Agricultural Weather Network. Growers can select the location closest to their plantings and SAS will provide a prediction of disease incidence and recommendations for fungicide applications. Users can also be provided warnings of the need to spray via email or text messages. In preliminary trials, SAS has been successful in eliminating many unnecessary fungicide applications and has proven user friendly. © 2010 Elsevier B.V. All rights reserved.
1. Introduction The prediction of plant diseases has emerged as a wellestablished component of epidemiology that is rapidly being incorporated into disease management. The mathematics of the disease progress has matured to a point of becoming a powerful and respected component in the management and prediction of epidemics. However, many models for prediction of plant diseases are theoretical and have not proven useful for disease management. The assumption is that a disease prediction model should make projections of the main events in the development of diseases, which most models do not (Seem, 2001). This would be especially valuable for disease management if models would eliminate unnecessary pesticide applications and reduce production costs. Computational modeling has enabled the development of tools that use weather events to predict epidemics. Traditionally, plant disease models have used leaf wetness duration (LWD) combined with temperature, to predict infection and colonization, and then identify the risks of an epidemic. These types of models have been used with observed climate records to track the favorable periods, indicating tactics or strategies of control (Jabrzemski and Sutherland, 2006).
∗ Corresponding author at: 271 Frazier Rogers Hall, P.O. Box 110570, Gainesville, FL 32611-0570, USA. E-mail addresses: wpavan@ufl.edu (W. Pavan), cfraisse@ufl.edu (C.W. Fraisse), nperes@ufl.edu (N.A. Peres). 0168-1699/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.compag.2010.10.013
In some cases, inoculum levels can be incorporated into disease models (Magarey et al., 2002; Peres et al., 2002), but with many diseases, inoculum is in excess and not useful in disease prediction (Bhatia et al., 2003; Biggs and Turechek, 2010). Currently, the revolutionary advances in information technology and the emergence of the Internet, as well as the global connectivity and integration with modern programming languages, have produced new concepts and ways to be explored further in the production and transfer of knowledge. Revolutions in web-based technologies are bringing major changes in the development and use of decision-support systems by producers and specialists in the management of plant diseases (Fernandes et al., 2007a,b). The main objectives of this research were to develop and implement a web-based forecasting system to predict Anthracnose and Botrytis fruit rot epidemics on strawberries and help strawberry producers in Florida avoid unnecessary applications of pesticide and reduce production costs. Disease models used in the system were developed and evaluated in previous studies for both diseases (MacKenzie and Peres, in preparation-a,b) and the forecasting and grower alert systems were implemented in the AgroClimate system (Fraisse et al., 2006). 1.1. Strawberry production and disease models in Florida Strawberries are one of the most valuable crops in Florida. The state produces about 16 million flats of strawberries every year,
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which represents 15% of nation’s berries and virtually all the berries grown during the winter. In 2009, about 8800 acres were devoted to strawberries with an estimated return to the grower approaching to $250 million (USDA, 2009). Anthracnose fruit rot (AFR), caused by Colletotrichum acutatum (Smith, 1998), and Botrytis fruit rot (BFR), caused by Botrytis cinerea (Sutton, 1998), are the most important diseases for production of annual strawberries in Central Florida and worldwide. Anthracnose is a serious disease that affects the fruit in addition to flowers and petioles. It is favored by warm temperatures (>18 ◦ C) and wet weather. Losses due to the anthracnose can exceed 50% when conditions favor disease development, even in well-managed fields (Turechek et al., 2006). Its control is very difficult when conditions are favorable. Although the disease occurs primarily in the warmer production regions, losses due to anthracnose fruit rot have increased across North America (Turechek et al., 2006). Botrytis fruit rot is an important pre-harvest and postharvest disease of strawberry, infecting the floral parts, including stamens and petals. The conidia are wind- and splash-dispersed, requiring free moisture (>4 h of leaf wetness) and cool temperatures (15–22 ◦ C) to infect and sporulate. The disease can be controlled by a combination of cultural practices and chemical methods, but there is no completely resistant strawberry cultivar (Legard et al., 2005). The high value of the strawberry crop often compels growers to protect their fruit by applying fungicides preventively, mainly for control of Anthracnose and Botrytis fruit rots. In Florida, fungicides are applied weekly from December through March (Legard et al., 2005). Different types of models, from simple to complex, have been developed to help understand the development of disease epidemics and assist growers in making decisions in various cropping systems (Maanen and Xu, 2003). As for other crops, models have been developed to predict strawberry diseases (Xu et al., 2000; Broome et al., 1995; Bulger et al., 1987). These models successfully predict disease incidence, but have never been used to forecast the need for fungicide applications. 1.2. Florida climate Most of Florida lies within the extreme southern portion of the Northern Hemisphere humid subtropical climate zone noted for its long hot and humid summers and mild and wet winters. Mean average temperatures during January, Florida’s coldest month, range from about 10 ◦ C in the north to about 20 ◦ C in the south. Precipitation in the south is highly concentrated in the warmer months when convectional rain falls (Winsberg, 2003). Fall in Florida is normally drier than summer because convectional rainstorms are not so frequent without the intense heating of the ground that occurs during summer. Weather during the winter is affected by the Azores-Bermuda High Pressure system that precludes high precipitation. As ocean waters around the state warm during the spring, the high-pressure system over Florida weakens and the summer rains begin. ˜ Climate in Florida is also affected by the El Nino-Southern Oscillation (ENSO) phenomenon. ENSO is the strongest driver of inter-annual climate variability around the world including the southeastern United States (Fraisse et al., 2006). ENSO is a natural, coupled atmospheric-oceanic cycle that occurs in the tropical Pacific Ocean approximately every 2–7 years. When sea surface temperature (SST) in the eastern equatorial Pacific Ocean is higher ˜ than the long-term average the phenomenon is called El Nino. When the SST is lower than normal, the phenomenon is called ˜ When the SST is normal, the event is called “neutral”. La Nina. ˜ is expected to bring 30–40% more rainfall In Florida, El Nino and cooler temperatures than normal during winter and spring, ˜ is expected to bring a warmer and drier than whereas La Nina
normal winter and spring seasons. The high rainfall associated ˜ in Central Florida, where most of the strawberwith El Nino ries are produced may result in a much higher incidence of fruit rots. 1.3. AgroClimate AgroClimate is a web-based climate information and decision support system (http://www.agroclimate.org) that was developed to provide extension agents, producers, and natural resource managers with tools to aid their decision-making aimed at reducing risks associated with climate variability. It was designed and implemented by the Southeast Climate Consortium (SECChttp://seclimate.org) in partnership with the Florida Cooperative State Extension Service, being updated and maintained periodically to ensure the relevance of the information and decision support tools contained in the system. Information available in AgroClimate includes climate forecasts combined with risk management tools and information for selected crops, forestry, pasture, and livestock. The system was developed to allow easy expansion of the topic areas, number of commodities, and risk management tools available for users. This modularity is a very important aspect of the overall design, making it possible to manage the system and attach new features. The contents are added and maintained by the administrators, who can easily maintain the system without knowledge of web-programming languages. Administration of the site and its contents is decentralized, facilitating the delegation of responsibility for maintenance and updates of the different sections by individual groups within the SECC (Fraisse et al., 2006). The system was developed to be hosted in Linux/Unix platforms but can easily be transferred to others. The dynamic tools were developed using the PHP web programming language, Javascript language, HTML, Cascading Style Sheets (CSS) and MySQL database. AgroClimate provides a large amount of information for the decision-makers and stakeholders in general. The information provided is divided into groups with the main ones being Tools and Forecasts and Outlooks. The dynamic tools were designed to work at the county or station levels and are organized in five groups: (1) climate risk; (2) crop development; (3) crop diseases; (4) crop yield; (5) drought. Dynamic tools always default to the current ENSO phase for the evaluation of climate-associated risks. However, the user can also evaluate the results for alternate ENSO phases and management scenarios. Seasonal forecasts and monthly climate outlooks include brief discussions of anticipated climate patterns in the Southeast and how they mesh with current conditions, anticipated impacts of climate patterns on agricultural commodities, El ˜ ˜ discussion, as well as discussions about seasonal cliNino/La Nina mate issues such as hurricanes, wildfires, drought, and extreme temperatures. Links to national and international forecasts are also provided. 2. System development As with the approach used for the development of AgroClimate, an important aspect of the design methodology used for developing this disease forecasting system was a strong interaction with extension agents and producers. Thus, the information provided in the system was relevant for user needs and that the language and formats used were easy and appropriate. While a number of activities did not necessarily require interactions with end users, such as the development of regional climate and agronomic databases, the design of layouts and functionalities were based on an interaction with end users for testing and evaluation.
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2.1. Disease models Different systems and thresholds of the model developed by Wilson et al. (1990) were evaluated in replicated field trials during 3 consecutive strawberry seasons for timing fungicide applications. The model utilizes wetness duration and temperature to predict infection by Colletotrichum acutatum in controlled conditions. To make the model applicable for controlling disease in the field, daily wetness intervals and average temperature during wetness periods were calculated from weather data collected over the previous five seasons. Thus, a baseline infection threshold below which no fungicide applications was established and a second threshold that allowed infections which could be eliminated by curative fungicide applications, was also established (Peres and MacKenzie, 2009a; Peres et al., 2009; MacKenzie and Peres, in preparation-a). For Botrytis fruit rot (BFR), three models, one model developed by Xu et al. (2000) for strawberry, one developed by Bulger et al. (1987) for strawberry, and one by Broome et al. (1995) for grape were evaluated. Different infection thresholds for the three models were evaluated. Both models enabled to control the disease with significantly fewer sprays than the recommended calendar program (Peres and MacKenzie, 2009a; Peres et al., 2009; MacKenzie and Peres, in preparation-b). 2.2. Meteorological data The first step for implementing the tool was the development of a weather database to store data derived from different sources and formats. Weather observations were compiled using recent and current weather conditions data collected by the Florida Automated Weather Network (FAWN). Short-term weather forecasts were obtained from the National Weather Service-National Digital Forecast Database (NWS-NDFD). The FAWN database and website have provided a vehicle to collect and disseminate real time weather data to a wide variety of users since 1998. A number of weather variables including temperature, precipitation, relative humidity, wind speed, and solar radiation are monitored at FAWN stations. These data are archived in real time and is available at http://fawn.ifas.ufl.edu. The FAWN network is composed of 35 automated weather stations, located throughout Florida. For the purposes of the system, leaf wetness duration and temperature during the wetness period were used to predict disease incidence at 4 locations: Balm, Dover, Lake Alfred, and Arcadia located in the southwest region of the state. These locations cover over 95% of the strawberry production area in Florida, which is concentrated in Hillsborough County. The NWS-NDFD short-term weather forecast is provided in a grid format and includes sensible weather elements (e.g., cloud cover, maximum temperature, relative humidity). NDFD contains a seamless mosaic of digital forecasts from NWS field offices working in collaboration with the National Centers for Environmental Prediction (NCEP). The database is available for members of the public to use in creating text, graphic, grids and image products of their own. For the purpose of the system, predicted relative humidity above 95% was used to estimate leaf wetness duration. 2.3. Information technologies Well-established design patterns and rules were used to develop the system allowing the establishment of standards for eventual AgroClimate expansions. The establishment of standards using well-known and studied techniques helps the developer to understand and model the behavior of applications (Pavan and Julho, 2007). Different types of design patterns have a specific purpose, naming, identifying and abstracting the key of a structure. Description
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of already tested solutions to certain design problems creates a solid-based solution, which act as building blocks for complex software systems. The quality of software is closely related to the use of these patterns. Examples of existing design patterns are Business Delegate, Composite Entity, Composite View, and Data Access Object. The MVC (Model-View-Controller) is the design pattern best known and used. It is responsible for separating the data access, business logic data presentation, and user interaction (Pavan and Julho, 2007). The MVC design pattern was selected for the development of this application, allowing the development of the system using the following programming layers: • Model: Disease Models (Botrytis and Anthracnose) and database; • View: Interface with the user (Web-based); • Controller: Disease models management and handling view requests; The disease models were implemented using the R statistical analysis software system (http://www.r-project.org), which is a language and environment for statistical computing and graphics generation, similar to the S language and environment, developed at Bell Laboratories by John Chambers and colleagues (Ribeiro Junior, 2001; R Development Core Team, 2006). R is an integrated environment of software for data manipulation, calculation and generation of graphics, including effective data processing and storage facilities. It can be easily expanded adding new functionalities by defining new functions. As in many other languages, most of the libraries in the R language were developed in their own language and are available in package form, making them very powerful and dynamic (R Development Core Team, 2006). The languages C++ and Fortran were used to implement specific code functions for certain tasks that required high processing power. These functions were integrated with R and called during the execution of the models. With the provision of a fast and easy way to access the information in mind, the design of the view layer (client side) was based on the Google Maps API Interface, a free web mapping technology, developed by Google and currently used in thousands of systems around the world (Chow, 2008). JavaScript (user interaction) and Ajax (asynchronous connection with the server-model layer) were used in this layer of the system. Results from the model layer are static information, basically formed by text, XML, HTML, and graphics (plots). The layout of the web-page is based in CSS (Cascading Style Sheets). The MySql v.5.0.54, a popular free and open source relational database management system, was used to store data and model outputs. 3. Results and discussion The prototype of the web-based strawberry disease forecasting system, named Strawberry Advisory System (SAS), was implemented, refined, and made available for commercial strawberry growers in Florida since the 2009/2010 cropping season. Its structure is shown in Fig. 1 and initial results are discussed below. The system was divided into three stages: (i) obtain weather data and short-term weather forecasts, (ii) execution of simulation models, and (iii) availability of information processed through the web system. 3.1. Backstage As the tool was designed to be part of AgroClimate, the definition of the database followed the same pattern, structure and technology, being supported by the MySQL relational database management system, which has the ability to efficiently
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Fig. 1. The system infrastructure.
store, search, and retrieve data in large databases (Fraisse et al., 2006). Observed weather data and short-term weather forecast data are automatically taken by PHP scripts (Hypertext Preprocessor), called by “cronjobs” and executed in hourly and daily basis, respectively, storing them in the database to be used by disease simulation models. The disease simulation models, developed in R language, are automatically executed every hour on the same server that hosts the web advisory system. The results of simulation are stored in the same database to be presented to users via the web tool. The generated images (plots) are stored in specific folders on the server and are included into the tool on the fly. SMS messages and e-mails are sent to registered users whenever the simulated infection index crosses the moderate and high-risk thresholds. Messages are sent just once a day to avoid multiple messages when the index fluctuates around threshold levels. The cost of communication via SMS is reasonably low and the technology is easily implemented giving the system the ability to communicate with users at any time. SMS technology has been used successfully in other disease alert systems such as Sisalert in southern Brazil (Pavan et al., 2006). E-mail and Short Message Service technology (SMS) were tested during the 2009–2010 strawberry season to evaluate the ease of rapid communication with producers and extension agents. 3.2. Web system Information provided by the Strawberry Advisory System includes disease infection risk levels and disease simulation, recent weather and short-term forecast data, and news and bulletins about the commodity. The main page is based on an interactive geographic information system (GIS). A GIS integrates hardware, software, and data for capturing, managing, analyzing, and displaying all forms of geographically referenced information. Users of the system can interact with satellite images and maps supplied by the Google Maps API via zooming and movement tools. Each point on the map is associated with specific information and can be viewed both in text or image. Beside points, polygons are drawn and over-
lap on the map, providing information such as county boundaries in the area of interest and allowing additional possibilities to the user. The system was designed to allow easy addition of new diseases and locations (stations) with modularity as the most important aspect of the overall design. The contents are added and maintained by an administrator through a private web-based tool, with no knowledge of web programming languages required. The Strawberry Advisory System, when loaded, shows a page divided in five different sections (Fig. 2). Section A (Fig. 2A) displays information about the AgroClimate portal, the current phase of the ENSO cycle, language options and county mapping tools. On section B (Fig. 2B), the user can easily find and select available weather stations. When a specific weather station is selected, the map in the section D (Fig. 2D) is moved to the correspondent location, showing the information related to that station. With the option “Display County Boundary”, the user can view the boundaries of the county where the selected station is located. Links to related publications and a link to a list of recommended fungicides are presented on section C (Fig. 2C). Information showing in this area can be updated and published by the researchers working on the project or by web administrators. Important links, contact information and the gate to the administration page are found on the section E (Fig. 2E). The main component of the system is found in section D, the Google Map (Fig. 2D). Most functions available in the system are presented in this area. As the main idea was simplicity, the map starts showing the weather stations available for a particular location or region, and allows the interaction with the system. Basically, the system is composed by the following components: 1. Map of the strawberry producing regions showing weather stations with current disease risk level flag. 2. Spray recommendations for Botrytis and Anthracnose based on a simple questionnaire about previous applications and crop development stage. 3. Model outputs in graphic and table formats indicate the disease risk levels (High risk, Moderate risk, or No risk) based on weather
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Fig. 2. The main web page for the strawberry tool.
data observed at the station and forecast for the next 3 days (National Weather Service Pinpoint Forecast). 4. Display of weather data observed during the most recent 72-h period.
In the spray recommendation, fungicide suggestions are provided along with a fungicide list providing the chemical groups and a reminder to avoid repeated applications of products within the same group to decrease the risk of developing resistance.
3.2.1. Map Decision-makers usually look for simple tools that can provide them with information in a clear and objective way. Based on this idea, the system starts by showing the available stations using different colors to quickly present basic and important information: presence or absence of conditions favorable for the development of the diseases. The system presents risk levels using three different colors: green (no risk), yellow (moderate risk) and red (high risk). Users of the system can check station names and confirm risk levels for each disease by passing the mouse over the stations. If more specific information is desired, users can select a weather station with a simple click and obtain additional information in a balloon. If a recommendation is required to make a decision, the user can click on the button “+”, available on the top of balloon, or on the link “Click here to check the Recommendation”, that expand the balloon presenting a simple questionnaire that guides the decisionmaking process to provide a recommendation.
3.2.3. Disease model outputs Botrytis and Anthracnose risk levels are presented in separated tabs and displayed in plots and a tabular format (Fig. 4). Plots are shown at the top of the page and illustrate the simulated infection indices for the last 30 days. The dashed line indicates the forecast infection index levels for the next 3 days. In the case of the forecast, the system assumes that leaf wetness will be present when the relative humidity (RH) is greater than or equal to 95%. Fig. 4 shows the results of the application of this technique after the dashed line (“# Forecast #” area). As in the map marker, risk thresholds are indicated in three colors: green (no risk), yellow (moderate risk) and red (high risk). A disease alert is considered when the line crosses the moderate or high risk threshold. When the user clicks on the plot, a new and expanded window is opened allowing a better visualization of the results. Disease simulation outputs are shown below the plotting area in a tabular format. Each line indicates the date, number of uninterrupted hours of leaf wetness duration, temperature in Fahrenheit and Celsius, infection index (0–1), and the description of the disease level (No risk, Moderate risk, and High risk). Dates are listed in reverse chronological order with the forecast at the top followed by today and recent dates and older results at the bottom of the page.
3.2.2. Spray recommendations If the conditions are favorable for disease development (moderate or high risk levels), a simple set of questions is shown to the user (Fig. 3). In this case, the recommendation is based on a set of rules for each disease. Once the responses are entered, the system applies the rules and displays three alternative recommendations:
1. No spray! 2. Spray Systemic Fungicide! 3. Spray Contact Fungicide!
3.2.4. Weather data Recent weather data is presented in the “Weather tab” to provide users with the ability to verify weather conditions associated with simulated risk levels. This information may be useful to decision-makers and researchers to assist them in the interpretation and understanding of the weather conditions associated with
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Fig. 3. Spray recommendation.
Fig. 4. Model outputs in graphic and table formats.
the diseases. This component displays weather variables such as temperature, relative humidity, wind speed, rain, and leaf wetness duration for the last 72 h.
following the recommendations of the system without affecting disease control and yield. The benefits of using the system will be especially emphasized during those seasons where conditions are ˜ events which result not favorable for disease, such as during La Nina in drier conditions in the region.
3.3. System evaluation The system was evaluated by 3 commercial strawberry farms during 2009/2010 season. Weather conditions were not favorable for the development of diseases during the season due to the extreme low temperatures for the region in January and February. In general, the commercial strawberry producers were able to reduce the number of fungicide applications by about half by
4. Conclusions and future research The implementation of this web-based decision support system to monitor and predict the risk of Anthracnose and Botrytis fruit rot on strawberries enabled growers to easily access the information necessary for them to decide about the need for a fungicide appli-
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cation. Growers using the system can apply fungicides only when conditions are favorable for disease development, thus reducing the number of applications and production costs, without compromising disease control or yields. Future research plans include the analysis of potential disease pressure based on seasonal climate forecasting. We plan to develop and implement a seasonal disease pressure forecast for the cropping season and a probabilistic forecast of the expected number of fungicide applications that may be required to successfully control Anthracnose and Botrytis fruit rot in the main strawberry production areas of the state of Florida. We also plan to expand the system to other strawberry producing regions of the country. Acknowledgements The authors acknowledge the USDA-Risk Management Agency (USDA-RMA) for funding this project. References Bhatia, A., Roberts, P.D., Timmer, L.W., 2003. Evaluation of the alter-rater model for timing of fungicide applications for control of alternaria brown spot of citrus. Plant Disease 87 (9), 1089–1093, http://apsjournals.apsnet. org/doi/abs/10.1094/PDIS.2003.87.9.1089. Biggs, A.R., Turechek, W.W., 2010. Fire blight of apples and pears: epidemiological concepts comprising the maryblyt forecasting program. Plant Health Progress, http://www.plantmanagementnetwork.org/sub/php/research/2010/fire. Broome, J.C., English, J.T., Marois, J.J., Latorre, B.A., Aviles, J.C., 1995. Development of an infection model for botrytis bunch rot of grapes based on wetness duration and temperature. Phytopathology 85, 97–102. Bulger, M.A., Ellis, M.A., Madden, L.V., 1987. Influence of temperature and wetness duration on infection of strawberry flowers by botrytis cinerea and disease incidence of fruit originating from infectedflowers. Phytopathology 77, 1225–1230. Chow, T.E., 2008. The potential of maps apis for internet gis applications. Transactions in GIS 12 (2), 179–191. Fernandes, J., Ponte, E.D., Pavan, W., Cunha, G., 2007a. Climate Prediction and Agriculture. Springer, Berlin Heidelberg, Ch. Web-based System to True-Forecast Disease Epidemics—Case Study for Fusarium Head Blight of Wheat, pp. 265–271. Fernandes, J., Ponte, E., Pavan, W., Cunha, G., 2007b. Wheat Production in Stressed Environments. Springer, Netherlands, Ch. Web-based System to True-Forecast Disease Epidemics, pp. 259–264. Fraisse, C., Breuer, N., Zierden, D., Bellow, J., Paz, J., Cabrera, V., y Garcia, A.G., Ingram, K., Hatch, U., Hoogenboom, G., Jones, J., O’Brien, J., 2006. Agclimate: a climate forecast information system for agricultural risk management in the southeastern usa. Computers and Electronics in Agriculture 53 (1), 13–27. Jabrzemski, R., Sutherland, A., 2006. An innovative approach to weather-based decision-support for agricultural models. In: 22nd International Conference on Interactive Information Processing Systems for Meteorology, Oceanography, and Hydrology. American Meteorological Society, Washington, DC, USA.
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