Integrating Real Time Weather Data With Dynamic Crop Development Models
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Integrating Real-time Weather Data with Dynamic Crop Development Models
Crop development models are commonly used in research. However, their use as crop management tools for growers is rare. Decision support systems (DSS), which combine crop models with expert systems, are being developed to provide management assistance to growers. Researchers at Oregon State University are in the process of developing a DSS. Research was conducted to develop a computer program to provide current and generated weather data for use by the DSS. The objectives of this research were to obtain a weather station, develop a set of quality control procedures to check data from the station, obtain a weather generator program, and create a weather data manager program to implement the above objectives. A weather station was obtained and was placed near two existing weather stations for ten months. Data from the weather station was compared with the other two stations for values of monthly average maximum temperature, minimum temperature, and daily total solar radiation and monthly total precipitation. The weather station performed well. Only measurements of total daily solar radiation were consistently different from the other stations. Based on a comparison of the weather station with an Eppley pyranometer, a factor was calculated to correct the solar radiation readings. The quality control procedures used on the weather data were adapted from automated procedures given in the literature. When tested, the procedures performed as desired. When used on actual data from the weather station, values that failed the procedures were apparently legitimate values. Options were added to the data manager program that allow the user to quickly decide what to do with failed values. For a weather data generator, WGEN was chosen from the generators presented in the literature. An input parameter file was created for the Corvallis, Oregon area and thirty years of data were generated. Monthly means from this data were compared with thirty-year historical monthly means for Corvallis. Precipitation data from WGEN compared well with the historical data. The generated data for maximum and minimum temperature and daily total solar radiation had great differences from the historical data. It is believed that the input parameters for the Corvallis area suggested by the authors of WGEN are not appropriate. The weather data manager program was written in the C programming language, and occupies approximately 98 kilobytes of disk space, not including the eleven files created directly and indirectly by the program. The main functions of the program are: 1) retrieving data from the weather station and performing quality control procedures on the data (allowing the user to decide what to do with values that failed QC); 2) viewing and editing of files by the user; 3) weather data generation (creating a file of only generated data or appending generated data to the file of current data from the weather station to create a file containing a full year of weather data); and 4) miscellaneous functions (monitoring the weather station, setting the calendar in the station's datalogger, and changing information used by the data manager program). It is hoped that this program will be a significant contribution towards the development of a decision support system.
Artificial Intelligence in Microbial Research
This book explores the convergence of microbiology and artificial intelligence (AI) and delves into the intricate world of microbial systems enhanced by cutting-edge AI technologies. The book begins by establishing a foundation in the fundamentals of microbial ecosystems and AI principles. It elucidates the integration of AI in microbial genomics, demonstrating how advanced algorithms analyze genomic data and contribute to genetic engineering. Bioinformatics and computational microbiology are explored, showcasing AI's role in predictive modeling and computational tools. The intersection of AI and microbial applications extends to drug discovery, precision agriculture, and pathogen detection. Readers gain insights into AI-driven drug development, the optimization of agricultural practices using microbial biostimulants, and early warning systems for crop diseases. The book highlights AI's role in microbial biotechnology, elucidating its impact on bioprocessing, fermentation, and other biotechnological applications. Climate-smart agriculture and microbial adaptations to environmental challenges are discussed, emphasizing sustainable practices. This book caters to a diverse audience including teachers, researchers, microbiologist, computer bioinformaticians, plant and environmental scientists. The book serves as additional reading material for undergraduate and graduate students of computer science, biomedical, agriculture, human science, forestry, ecology, soil science, and environmental sciences and policy makers to be a useful to read.
Developing smart agri-food supply chains
Author: Professor Louise Manning
language: en
Publisher: Burleigh Dodds Science Publishing
Release Date: 2021-12-07
Highlights current issues that challenge the safety of agri-food supply chains (e.g. food adulteration, malicious contamination) Assesses the recent developments implemented to improve safety and quality at all levels of the agri-food supply chain, including the use of smart agri-food systems Emphasis on the need for improved tracking and traceability systems of food products to prevent and manage potential threats to safety