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.

Model of the Decision Support System under Condition of Non-Determination for Manufacturing Process Support

2014 ◽  
Vol 611 ◽  
pp. 416-423
Author(s):  
Cyril Klimeš ◽  
Radim Farana
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Risk Analysis ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Support System ◽  
Manufacturing Process ◽  
Uncertain Information ◽  
Process Support ◽  
Decision Support Models

Decision support systems mean interactive computer systems, which assist to decision making subjects to utilize both data and models to solve non-structured issues. These systems were established mainly on the basis of a risk analysis, utilizing the experience/skills, conclusion making and intuition, enabling very fast and flexible analysis with a good response, enabling the application of manager intuition and judgment this way. However, such decisions are often based on uncertain information. This fact requires the establishment of other decision support models.

Delineation of the three brown rot fungi of fruit crops (Monilinia spp.) on the basis of quantitative characteristics

1998 ◽  
Vol 76 (12) ◽  
pp. 2042-2050 ◽  
Author(s):  
GCM van Leeuwen ◽  
H A van Kesteren
Keyword(s):  
Growth Rate ◽  
Germ Tube ◽  
Brown Rot ◽  
Fruit Crops ◽  
Monilinia Fructigena ◽  
Light Regimes ◽  
Brown Rot Fungi ◽  
Quantitative Characteristics ◽  
Germ Tubes ◽  
Tube Distance

The three Monilinia spp., known as the brown rot fungi of fruit crops, are usually distinguished from each other on the basis of (qualitative) colony characteristics. We linked these qualitative features to unambiguously defined, quantitative colony and germ tube characteristics. A wide collection of isolates of Monilinia fructicola (Winter) Honey, Monilinia laxa (Aderhold & Ruhland) Honey, and Monilinia fructigena (Aderhold & Ruhland) Honey was used to determine growth rate and sporulation intensity on potato dextrose agar (PDA) at 22°C under two light regimes (darkness, 12 h light : 12 h dark). The following germ tube characteristics were determined on water agar after incubation for 18 h at 22°C in darkness: length of the (leading) germ tube, distance to the first branch, and the number of germ tubes per conidium. Increase in colony diameter from day 3 to day 5 and sporulation intensity measured after 14 days was the highest in M. fructicola, whilst M. laxa and M. fructigena showed considerable overlap in these features. The length of the germ tube after 18 h incubation was shortest in M. laxa, ranging from 161 to 466 µm. In M. fructicola and M. fructigena these ranges were 465-851 and 307-806 µm, respectively. The occurrence of more than one germ tube per conidium was most prominent in M. fructigena. Discriminant analysis on the basis of different combinations of the quantitative characteristics measured, showed that the combination of growth rate on PDA and length of the germ tube was sufficient to delineate the three brown rot fungi. One of 11 M. fructicola isolates was misclassified, the same held for M. fructigena (one misclassification of nine isolates). No misclassifications occurred in M. laxa.Key words: brown rot fungi, growth characteristics, Monilinia spp., taxonomy.

scholarly journals The brown rot fungi of fruit crops (Monilinia spp.): III. Important features of disease management (Review paper)

2004 ◽  
Vol 10 (4) ◽  
Author(s):  
I. J. Holb
Keyword(s):  
Disease Management ◽  
Chemical Control ◽  
Management Practices ◽  
Brown Rot ◽  
Control Measures ◽  
Fruit Crops ◽  
Monilinia Fructigena ◽  
Brown Rot Fungi ◽  
Monilia Polystroma ◽  
Rot Disease

In the third part of this review, important features of disease management are summarised for brown rot fungi of fruit crops (Monilinia fructigena, Monilinia laxa, Monilinia fructicola and Monilia polystroma). Several methods of brown rot disease management practices were collected and interpreted in five main chapters. In these chapters, details are given about the legislative control measures, the cultural, physical, biological and chemical control methods. Chemical control is divided into two parts: pre-harvest and post-harvest chemical control. In addition, host resistance and fungicide resistance statuses are also included in this part of the review. Finally, future aspects of brown rot disease control are discussed.

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.

scholarly journals Decision Support System Of Fruit Cultivation Using Technique For Other Reference Method By Similarity To Ideal Solution (TOPSIS)

2018 ◽  
Vol 215 ◽  
pp. 01006
Author(s):  
Eva Yulianti ◽  
Rahmat Rizki Nanda
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Crop Yields ◽  
Reference Method ◽  
Cultivated Plants ◽  
Natural State ◽  
Ideal Solution ◽  
Fruit Crops ◽  
Fruit Cultivation

Fruit cultivation is very promising for farmers. But there are problems faced by farmers, namely the lack of understanding of farmers with natural conditions that can affect crop yields. So with the selection of plants that are not appropriate, it does not get satisfactory results. Various types of fruit crops can be cultivated by farmers, such as oranges, guava, rambutan, watermelon, avocado, and so on. But it should be noted, that in the cultivation is expected to be cultivated plants in accordance with natural circumstances. This Designing created application to help determine the cultivation of fruit crops. In determining the cultivation of fruit crops in accordance with the criteria using the Technique Method for Others Reference by Similarity to Ideal Solution (TOPSIS) as a tool to facilitate decision-making process with several criteria of comparison, namely: temperature, sunlight, rainfall and humidity, height, harvest, selling power, susceptibility to disease, and care levels. This application is made based on the web by using PHP as a programming language and MySQL as the data storage. The use of decision support system application of the cultivation of these fruit crops can provide an output or alternative in accordance with the natural state of the area. And helps in making decisions to determine which fruits are suitable for cultivating for users based on selected criteria.

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