Disease forecasting

Weather and Climate forecasting is the application of science and technology to predict the state of the atmosphere for a given location. This provide occurrence or change in severity of plant diseases. At the field scale, these systems are used by growers to make economic decisions about disease treatments for control. Often the systems ask the grower a series of questions about the susceptibility of the host crop, and incorporate current and forecast weather conditions to make a recommendation. Forecasting system provide information about plants disease which happen due to Weather and Climate changes and it happen in such a fashion that disease can occur and cause economic losses.

Early Blight, Forecast model, Iran, Meteorological data, Potato Early blight is a very common disease of potato in Iran. It causes leaf spots and tuber blight on potato. The disease can occur over a wide range of climatic conditions and can be very destructive if left uncontrolled, often resulting in complete defoliation of plants. In contrast to the name, it rarely develops early, but usually appears on mature foliage. This study was performed to forecast potato early blight in Kermanshah province, west of Iran. Hourly meteorological data were recorded by an iMETOS automatic weather station installed in the field and were compared with regional station. The primary symptom occurrence of early blight lesions was predicted by Phenological day (PD) and degree day (DD) models during 2013 and 2014 growing seasons. By application of both models using two data sets, the time of symptoms appearances was predicted between 1 to 3 days in advance. Results showed that early symptoms of disease can be observed after 300P-day and 625DD in the vision. The results suggested that in case of unavailability of on-site meteorological parameters, the data obtained from a close weather station (with less than 10 kilometers distance) can be used for disease epidemic forecast. Further studies in other climates are suggested for more scrutiny.

Idojaras, 2007

In this paper, the LAPS surface scheme and the BAHUS biometeorological model are shortly described. LAPS has been applied for within-crown microclimate simulations in an apple orchard at experimental site Rimski Sancevi in the northern part of Serbia. The simulated values of leaf wetness duration, air temperature, and relative humidity within the tree crown are compared with the data measured in the orchard during the 2003 apple growing season. On the basis of biological and meteorological inputs coming from the outputs of either the automatic or the climatological weather station, or LAPS, BAHUS was applied in order to give the messages on occurrence of apple scab and fire blight diseases. BAHUS outputs obtained for the three meteorological input data sets are compared with time and intensity of infections observed in the apple orchard.

A full-fledged algorithm for performing statistical forecasting and estimating the agricultural yield for a variety of Rabi and Kharif crops has been developed. A model for forecasting of crop yield based on historical data and pertinent external climatic information was developed. The technique included development of suitable weather indices which were used as regressors in the model, determining their suitable weights for the true determination and minimizing the error term. Apart from the crop produce, pests and diseases, major factors limiting the production, are also influenced by weather conditions. Therefore, an ordinal logistic model was developed for forewarning of important pests/diseases in rice, mustard, pigeon pea, sugarcane, groundnut, mango, sugarcane, cauliflower, sorghum, banana, citrus, soyabean and cotton at various locations. The forewarnings through these models can prove to very useful in taking timely control measures. Finally, to facilitate a graphical user interface for the rural community, a windows based application was developed for the same.

Computers and Electronics in Agriculture, 2007

Determinacy analysis, logistic regression, discriminant analysis and neural network models were compared for their accuracy in 5-day (120 h) forecasts of daily potato late blight risk according to a modified-Wallin disease severity model. For 12 locations in Michigan, variables derived from extended forecast data (MEX) from the National Weather Service model output statistics (MOS) were compared with those similarly derived from Unedited Local Climatological Data (ULCD) for the growing seasons [2001][2002][2003][2004]. The most effective model for late blight risk prediction based on comparison with risk estimated with ULCD was a resilient propagation (Rprop) neural network model with 49 variables and 10 hidden nodes. The neural network model had significantly higher overall accuracy than the other models, and was particularly successful at predicting risk values in June, July, and August when knowledge of potato late blight risk is most critical to growers making management decisions with regard to fungicide sprays and irrigation scheduling. The neural network model was also significantly more accurate than the regional average of days with no late blight risk (0.72%). For each of the four models, monthly accuracy at any single station was negatively correlated with the percentage of days per month classified as risk days for potato late blight (P = 0.01). Although no validation with disease data was conducted, such models are still useful in the context of advising growers of forecast conditions that may be favorable for late blight according to model values, such as Wallin style disease severity values, with which they are familiar.

Scientia Agricola, 2008

Disease-warning systems are decision support tools designed to help growers determine when to apply control measures to suppress crop diseases. Weather data are nearly ubiquitous inputs to warning systems. This contribution reviews ways in which weather data are gathered for use as inputs to disease-warning systems, and the associated logistical challenges. Grower-operated weather monitoring is contrasted with obtaining data from networks of weather stations, and the advantages and disadvantages of measuring vs. estimating weather data are discussed. Special emphasis is given to leaf wetness duration (LWD), not only because LWD data are inputs to many disease-warning systems but also because accurate data are uniquely challenging to obtain. It is concluded that there is no single "best" method to acquire weather data for use in disease-warning systems; instead, local, regional, and national circumstances are likely to influence which strategy is most successful.

HortScience, 2005

Weather-based disease advisories have allowed vegetable producers to optimize their fungicide applications. These models typically use only past weather data to identify times of potential disease outbreak. The Oklahoma Mesonet has developed a new Spinach White Rust Advisory that improves grower disease decision support by combining forecast, current, and past weather data in calculating infection periods. The decision-support component issues initial spray advisories, based on infection hour accumulation from the first true-leaf stage or from a previous fungicide application date for subsequent sprays. The advancement in this model in relation to traditional weather-based disease advisories are: incorporation of an 84-hour forecast, hourly model recalculation, cultural practice customization, user site selection from any of 110+ statewide sites, and immediate access to detailed historical data. The model is available on the Oklahoma Mesonet AgWeather website (http://agweather.meson...