Model-Based Forecasting of Agricultural Crop Disease Risk at the Regional Scale, Integrating Airborne Inoculum, Environmental, and Satellite-Based Monitoring Data

Crop diseases have the potential to cause devastating epidemics that threaten the world's food supply and vary widely in their dispersal pattern, prevalence, and severity. It remains unclear what the impact disease will have on sustainable crop yields in the future. Agricultural stakeholders are increasingly under pressure to adapt their decision-making to make more informed and efficient use of irrigation water, fertilizers, and pesticides. They also face increasing uncertainty in how best to respond to competing health, environment, and (sustainable) development impacts and risks. Disease dynamics involves a complex interaction between a host, a pathogen, and their environment, representing one of the largest risks facing the long-term sustainability of agriculture. New airborne inoculum, weather, and satellite-based technology provide new opportunities for combining disease monitoring data and predictive models—but this requires a robust analytical framework. Integrated model-based forecasting frameworks have the potential to improve the timeliness, effectiveness, and foresight for controlling crop diseases, while minimizing economic costs and environmental impacts, and yield losses. The feasibility of this approach is investigated involving model and data selection. It is tested against available disease data collected for wheat stripe (yellow) rust (Puccinia striiformis f.sp. tritici) (Pst) fungal disease within southern Alberta, Canada. Two candidate, stochastic models are evaluated; a simpler, site-specific model, and a more complex, spatially-explicit transmission model. The ability of these models to reproduce an observed infection pattern is tested using two climate datasets with different spatial resolution—a reanalysis dataset (~55 km) and weather station network township-aggregated data (~10 km). The complex spatially-explicit model using weather station network data had the highest forecast accuracy. A multi-scale airborne surveillance design that provides data would further improve disease risk forecast accuracy under heterogeneous modeling assumptions. In the future, a model-based forecasting approach, if supported with an airborne surveillance monitoring plan, could be made operational to provide agricultural stakeholders with reliable, cost-effective, and near-real-time information for protecting and sustaining crop production against multiple disease threats

A Prototype for predicting real estate investment performance in Kenya

Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Computer-Based Information Systems (MSIS) at Strathmore UniversityPredicting investment performance is central to attracting investors in any property or business venture. Investors are keen to predict the future in order to protect their investments and choose assets with the best returns. All asset returns are predictable to some extent, with returns on real estate relatively easier to forecast due to the nature of assets. Forecasting is thus an important component in property investment decision-making. Currently, majority of investors in Kenyan real estate sector, rely on speculation and sales comparison to make investment decisions. Multiple regression models have been applied successfully in forecasting real estate investments in other markets. They incorporate socio economic variables, housing and proximity characteristics to estimate the value of real estate assets. The researcher applied a multiple regression model for predicting house prices by setting house price as the dependent variable (Y) while holding the Gross Domestic Product, income of households, cost of land and housing units developed as the predictor variables (X).This predicted house prices (Y) on the basis of the X variables and determined the influence of the variables on the price. Agile development methodology was applied in the development a web application that integrated the forecasting model, an analytical backend helps to present the forecasts to investors in terms of figures, charts, and graphs that are easy to interpret and compare. Various tests were also performed on the prototype including integration and system tests. User acceptance testing was also carried out where majority of the respondents found the interface easy to use, and indicated that the application met its stated objectives as outlined in the usability questionnaire

Development of a Web-Based Prediction System for Wheat Stripe Rust

International audienceA web-based prediction system for wheat stripe rust was developed based on B/S (Browser/Server) mode in this study. Some existing prediction models of wheat stripe rust were collected, analyzed and then stored in SQL Server 2005 database according to certain rules. All these models could be called through this web-based system and used to predict wheat stripe rust. Meanwhile, Using multiple regression analysis principle, prediction regression model could be built based on the input historical data of wheat stripe rust through the network programming via this system, and significance tests of prediction factors could be conducted to obtain optimal prediction model and the built model could be stored into the model database for further prediction of this disease. Using WebGIS technologies, the prediction results of wheat stripe rust could be displayed in different colors in the web map according to the prediction values of disease prevalence. The web-based prediction system for wheat stripe rust developed in this study provided a convenient and fast way for the prediction of wheat stripe rust