scholarly journals Evaluation of decision support system for agrotechnology transfer SUBSTOR potato model (v4.5) under tropical conditions

2016 ◽  
Vol 34 (1) ◽  
pp. 1 ◽  
Author(s):  
Moleen Monita Nand ◽  
Viliamu Iese ◽  
Upendra Singh ◽  
Morgan Wairiu ◽  
Anjeela Jokhan ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Dry Weight ◽  
Growing Season ◽  
Daily Basis ◽  
Weather Data ◽  
Experimental Conditions ◽  
Plant Component ◽  
Tropical Conditions

Decision Support System for Agrotechnology Transfer (DSSAT) SUBSTOR Potato model (v4.5) was calibrated using Desiree variety. DSSAT SUBSTOR Potato model simulates on a daily basis the development and growth of potatoes using inputs such as climate, soil and crop management. The experiment was conducted in Banisogosogo, Fiji Islands, during the potato growing season of 2012. Fresh and dry weights of belowground plant component (tubers) were taken during progressive harvests. The DSSAT SUBSTOR Potato model was calibrated using experimental field data, soil and weather data of the growing season. The manual calibration steps involved recalculation of soil water content and the adjustments of genetic co-efficient to suit the temperature and daylength regime similar to the experimental conditions. Tuber dry weight was used as the main parameter to evaluate the model. The R2 values of the observed and simulated model outputs before calibration for replicate plot 1, replicate plot 2 and replicate plot 3 were 0.52, 0.49 and 0.61 respectively. After calibration, the R2 values for tuber dry yield for replicate plot 1, replicate plot 2 and replicate plot 3 were 0.88, 0.66 and 0.92 respectively indicating a strong positive relationship between the simulated and the observed yield.

The Warning Decision Support System–Integrated Information

2007 ◽  
Vol 22 (3) ◽  
pp. 596-612 ◽  
Author(s):  
Valliappa Lakshmanan ◽  
Travis Smith ◽  
Gregory Stumpf ◽  
Kurt Hondl
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Severe Weather ◽  
Weather Data ◽  
Spatial Coverage ◽  
Analysis System ◽  
Automated Algorithms ◽  
The Individual ◽  
Integrated Information

Abstract The Warning Decision Support System–Integrated Information (WDSS-II) is the second generation of a system of tools for the analysis, diagnosis, and visualization of remotely sensed weather data. WDSS-II provides a number of automated algorithms that operate on data from multiple radars to provide information with a greater temporal resolution and better spatial coverage than their currently operational counterparts. The individual automated algorithms that have been developed using the WDSS-II infrastructure together yield a forecasting and analysis system providing real-time products useful in severe weather nowcasting. The purposes of the individual algorithms and their relationships to each other are described, as is the method of dissemination of the created products.

scholarly journals Disease warning models for brown rot fungi of fruit crops

2013 ◽  
Vol 19 (1-2) ◽  
Author(s):  
I. J. Holb
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Brown Rot ◽  
Growing Season ◽  
Fruit Crops ◽  
Monilinia Fructigena ◽  
Brown Rot Fungi ◽  
Decision Support Models ◽  
Warning Model

In this review, disease warning models for brown rot fungi, including Monilinia fructigena, M. laxa and M. fructicola, were summarized. Few studies have been made to relate epidemiology and disease warning in brown rot infection caused by M. fructicola and M. laxa in order to predict infections or develop decision support models for fungicide applications during the growing season. More recently a disease warning model and a decision support system were also performed for M. fructigena for organic apple orchards. This review gives an overview on some details of the above disease warning models and decision support system.

Operational Margin From Weather and Motion Database for Heavy Transport Vessels

2011 ◽  
Author(s):  
Jan B. de Jonge ◽  
Onno A. J. Peters
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Ship Motion ◽  
Environmental Data ◽  
Weather Data ◽  
Background Information ◽  
Motion Data ◽  
Design Values ◽  
Wave Heights

While shipping large and heavy cargo like jack-up rigs or semi-submersibles, the Motion Monitoring and Captain Decision Support system is a valuable tool to ensure a safe and economical voyage. Using the dynamic characteristics of the vessel, in combination with 5-day weather forecasts and design limits like maximum accelerations at the cargo location, roll motion and/or leg bending moments, more and better information is available to the Master to choose safe route, heading and speed. This way the best knowledge of what to expect is contributing to the safety of cargo, vessel and crew. The Octopus onboard system gathers a large amount of information about ship position, speed, heading, nowcast weather data and corresponding ship motion data. Reference is made to the paper of Peters [2] for background information of the Octopus Motion Monitoring and Decision Support system and an overview of methods used by the motion measurement system. In May 2008 the first Dockwise vessel started to gather weather and ship motion data. It is estimated that each vessel gathers around 50.000 nautical miles of data in a year, which is all collected in a database. The paper presents how this information is used for general research to environmental data, ship motion data and comparison to design values. Scatter diagrams from nowcast weather data can be produced. After collecting a certain amount of measurements, so called Dockwise scatter diagrams could be used as input for future voyage calculations. With this engineering approach Masters decisions for weather routing and bad weather avoidance is taken into account. This could lead for example to reduced design wave for a passage around the Cape of Good Hope. Now casted weather data and ship motions data is compared to design values from the cargo securing manual. Statistics like maximum difference, average difference give extensive data and insight in the operational margin of Dockwise transports. The calculation of the operational margin is independent of the standard safety margin valid for each transport. The conclusion is that the recorded nowcast significant wave height for the analyzed voyages never exceeded 5.0 [m]. With larger design wave heights the minimum operational margin increases to more than 40%, while the lowest operational margin occurs at design wave heights around 4.5 [m]. The database built by gathering all relevant information from the system and from crew observations, increases insight in the operational margins, which contributes to increased knowledge and safety.

scholarly journals Evaluation of the BlightPro Decision Support System for Management of Potato Late Blight Using Computer Simulation and Field Validation

2015 ◽  
Vol 105 (12) ◽  
pp. 1545-1554 ◽  
Author(s):  
Ian M. Small ◽  
Laura Joseph ◽  
William E. Fry
Keyword(s):  
Computer Simulation ◽  
Decision Support ◽  
Decision Support System ◽  
Late Blight ◽  
Support System ◽  
Field Experiments ◽  
Crop Protection ◽  
Disease Suppression ◽  
Weather Data ◽  
Interactive System

The objective of this study was to evaluate the utility of the BlightPro decision support system (DSS) for late blight management using computer simulation and field tests. Three fungicide schedules were evaluated: (i) calendar-based (weekly) applications, (ii) applications according to the DSS, or (iii) no fungicide. Simulation experiments utilized 14 years of weather data from 59 locations in potato-producing states. In situations with unfavorable weather for late blight, the DSS recommended fewer fungicide applications with no loss of disease suppression; and, in situations of very favorable weather for late blight, the DSS recommended more fungicide applications but with improved disease suppression. Field evaluation was conducted in 2010, 2011, 2012, and 2013. All experiments involved at least two cultivars with different levels of resistance. DSS-guided and weekly scheduled fungicide treatments were successful at protecting against late blight in all field experiments. As expected, DSS-guided schedules were influenced by prevailing weather (observed and forecast) and host resistance and resulted in schedules that maintained or improved disease suppression and average fungicide use efficiency relative to calendar-based applications. The DSS provides an interactive system that helps users maximize the efficiency of their crop protection strategy by enabling well-informed decisions.

A Meteorological Data Service System and its Application

2011 ◽  
Vol 304 ◽  
pp. 310-315 ◽  
Author(s):  
Xin Wen Yu ◽  
Yan Chen Yang ◽  
Xu Zhang
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Service System ◽  
Meteorological Data ◽  
Data Retrieval ◽  
Weather Data ◽  
Data Service ◽  
Agricultural Practice ◽  
Data Source

Meteorological conditions play an important role in agricultural practice and agricultural DSS usually takes weather data as a critical data source. A meteorological data service system was designed and implemented to provide better performance for Chinese users. Based on the service system, a web application providing online weather data retrieval and downloading was also developed. The service system was practically used in a decision support system for eucalypt management, and proved to be very feasible as an online weather data source for agricultural decision support system. Base on this service system, it is expected that agricultural researchers and decision support systems can easily obtain weather data and further improve their agricultural decision making process.

scholarly journals Evaluating the Predictive Accuracy of the Weather-Rice-Nutrient Integrated Decision Support System (WeRise) to Improve Rainfed Rice Productivity in Southeast Asia

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 346
Author(s):  
Keiichi Hayashi ◽  
Lizzida Llorca ◽  
Iris Bugayong ◽  
Nurwulan Agustiani ◽  
Ailon Oliver Capistrano
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Southeast Asia ◽  
Support System ◽  
General Circulation ◽  
Predictive Accuracy ◽  
Circulation Model ◽  
Weather Data ◽  
Rice Productivity ◽  
Rainfed Rice

The weather-rice-nutrient integrated decision support system (WeRise) is an information and communications technology (ICT)-based tool developed to improve rainfed rice productivity. It integrates localized seasonal climate prediction based on the statistical downscaling of the Scale Interaction Experiment-Frontier Research Center for Global Change (SINTEX-F) ocean-atmosphere coupled general circulation model and real-time weather data with a crop growth model (ORYZA), to provide advisories on the optimum sowing timing using suitable varieties. Field validations were conducted to determine the applicability of WeRise and SINTEX-F in North Sumatra and West Nusa Tenggara, Indonesia, and Iloilo, Nueva Ecija and Tarlac, Philippines. Results showed that downscaled SINTEX-F outputs were applicable in these target provinces. Hindcast analysis using these outputs also showed a good model performance against locally observed historical weather data for both countries. Moreover, the on-farm experiments showed that higher grain yields were obtained using WeRise advisories on optimum sowing timing compared to the farmers’ sowing timings. Improved fertilizer recovery rates were also observed when WeRise advisories were followed. The results imply that WeRise can improve rainfed rice productivity in Southeast Asia. Further validation is recommended to determine its applicability in more countries of Southeast Asia.

Decision Support System Development Based On Android Using Analytical Hierarchy Process (AHP) Method For Determining The Middle Small Business Development Priority In Sorong City

2019 ◽  
Vol 5 (2) ◽  
pp. 25-39
Author(s):  
Luluk Suryani ◽  
Raditya Faisal Waliulu ◽  
Ery Murniyasih
Keyword(s):  
Decision Support ◽  
Small Business ◽  
Decision Support System ◽  
Support System ◽  
Analytical Hierarchy Process ◽  
System Development ◽  
Business Development ◽  
Ahp Method ◽  
Small Business Development ◽  
Hierarchy Process

Usaha Kecil Menengah (UKM) adalah salah satu penggerak perekonomian suatu daerah, termasuk Kota Sorong. UKM di Kota Sorong belum berkembang secara optimal. Ada beberapa penyebab diantaranya adalah mengenai finansial, lokasi, bahan baku dan lain-lain. Untuk menyelesaikan permasalah tersebut peneliti terdorong untuk melakukan pengembangan Aplikasi yang dapat membantu menentukan prioritas UKM yang sesuai dengan kondisi pelaku usaha. Pada penelitian ini akan digunakan metode Analitycal Hierarchy Process (AHP), untuk pengambilan keputusannya. Metode AHP dipilih karena mampu menyeleksi dan menentukan alternatif terbaik dari sejumlah alternatif yang tersedia. Dalam hal ini alternatif yang dimaksudkan yaitu UKM terbaik yang dapat dipilih oleh pelaku usaha sesuai dengan kriteria yang telah ditentukan. Penelitian dilakukan dengan mencari nilai bobot untuk setiap atribut, kemudian dilakukan proses perankingan yang akan menentukan alternatif yang optimal, yaitu UKM. Aplikasi Sistem Pendukung Keputusan yang dikembangkan berbasis Android, dimana pengguna akan mudah menggunakannya sewaktu-waktu jika terjadi perubahan bobot pada kriteria atau intensitas.  Hasil akhir menunjukkan bahwa metode AHP berhasil diterapkan pada Aplikasi Penentuan Prioritas Pengembangan UKM.

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