EVALUATION OF THE DECISION SUPPORT SYSTEM FOR IRRIGATION SCHEDULING OF PROCESSING TOMATO ADOPTED IN THE EMILIA-ROMAGNA REGION

1999 ◽  
pp. 507-512 ◽  
Author(s):  
A. Battilani ◽  
A. Piva ◽  
M. Dadomo
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Irrigation Scheduling ◽  
Processing Tomato ◽  
Emilia Romagna Region

AQUAMAN: a web-based decision support system for irrigation scheduling in peanuts

2011 ◽  
Vol 31 (3) ◽  
pp. 271-283 ◽  
Author(s):  
Yashvir S. Chauhan ◽  
Graeme C. Wright ◽  
Dean Holzworth ◽  
Rao C. N. Rachaputi ◽  
José O. Payero
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Irrigation Scheduling ◽  
Web Based

Irrigator Pro: Progression of a Peanut Irrigation Scheduling Decision Support System

2020 ◽  
Vol 36 (5) ◽  
pp. 785-795
Author(s):  
Christopher L Butts ◽  
Ronald B Sorensen ◽  
Marshall C Lamb
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Soil Temperature ◽  
Water Potential ◽  
Soil Water ◽  
Support System ◽  
Soil Water Potential ◽  
Irrigation Scheduling ◽  
List Type ◽  
Web Based

HighlightsThe logic used in developing a decision support system for irrigating peanut based on max/min soil temperature is describedLogic to transform decision support system from peanut to irrigate corn and cotton with and without soil sensors.Progression of a decision support system from a desktop program to a web/mobile applicationAbstract. Irrigator Pro is a decision support tool for scheduling irrigation events in peanut. It was deployed in 1995 as a rule-based system using crop history, yield potential, soil type, in-season irrigation/rainfall and maximum/minimum soil temperature. As computing platforms have progressed from desktop personal computers to mobile web-based platforms, Irrigator Pro has been updated and is now deployed as a web-based program and an application for mobile devices. Irrigator Pro not only works for peanuts but has been modified to irrigate both corn and cotton. The irrigation decisions are now based on in-field soil water potential measurements in addition to the traditional checkbook with max/min soil temperatures. Users are individual growers, extension agents, and agronomic consultants. The objective of this manuscript is to document the initial development of Irrigator Pro as an expert system combining data and experiential knowledge and the progression from a checkbook-based decision support system to a hybrid system using observed weather data and soil moisture measurement. The background knowledge, equations, and thresholds for triggering irrigation recommendations are included. Keywords: Decision support system, Irrigation scheduling, Irrigator Pro, Mobile app, Peanut, Soil water potential.

scholarly journals A Model-Based Real-Time Decision Support System for Irrigation Scheduling to Improve Water Productivity

Agronomy ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 686 ◽  
Author(s):  
Chen ◽  
Qi ◽  
Gui ◽  
Gu ◽  
Ma ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Real Time ◽  
Support System ◽  
Deficit Irrigation ◽  
Water Productivity ◽  
Irrigation Scheduling ◽  
Cotton Field ◽  
Crop Water ◽  
Significant Difference

A precisely timed irrigation schedule to match crop water demand is vital to improving water use efficiency in arid farmland. In this study, a real-time irrigation-scheduling infrastructure, Decision Support System for Irrigation Scheduling (DSSIS), based on water stresses predicted by an agro-hydrological model, was constructed and evaluated. The DSSIS employed the Root Zone Water Quality Model (RZWQM2) to predict crop water stresses and soil water content, which were used to trigger irrigation and calculate irrigation amount, respectively, along with forecasted rainfall. The new DSSIS was evaluated through a cotton field experiment in Xinjiang, China in 2016 and 2017. Three irrigation scheduling methods (DSSIS-based (D), soil moisture sensor-based (S), and conventional experience-based (E)), factorially combined with two irrigation rates (full irrigation (FI), and deficit irrigation (DI, 75% of FI)) were compared. The DSSIS significantly increased water productivity (WP) by 26% and 65.7%, compared to sensor-based and experience-based irrigation scheduling methods (p < 0.05), respectively. No significant difference was observed in WP between full and deficit irrigation treatments. In addition, the DSSIS showed economic advantage over sensor- and experience-based methods. Our results suggested that DSSIS is a promising tool for irrigation scheduling.

scholarly journals A Decision Support System for Irrigation Management: Analysis and Implementation of Different Learning Techniques

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 548 ◽  
Author(s):  
Roque Torres-Sanchez ◽  
Honorio Navarro-Hellin ◽  
Antonio Guillamon-Frutos ◽  
Rubén San-Segundo ◽  
Maria Carmen Ruiz-Abellón ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Agricultural Sector ◽  
Irrigation System ◽  
Irrigation Management ◽  
Irrigation Scheduling ◽  
Support Vector ◽  
Model Errors ◽  
Learning Techniques

Automatic irrigation scheduling systems are highly demanded in the agricultural sector due to their ability to both save water and manage deficit irrigation strategies. Elaborating a functional and efficient automatic irrigation system is a very complex task due to the high number of factors that the technician considers when managing irrigation in an optimal way. Automatic learning systems propose an alternative to traditional irrigation management by means of the automatic elaboration of predictions based on the learning of an agronomist (DSS). The aim of this paper is the study of several learning techniques in order to determine the goodness and error relative to expert decision. Nine orchards were tested during 2018 using linear regression (LR), random forest regression (RFR), and support vector regression (SVR) methods as engines of the irrigation decision support system (IDSS) proposed. The results obtained by the learning methods in three of these orchards have been compared with the decisions made by the agronomist over an entire year. The prediction model errors determined the best fitting regression model. The results obtained lead to the conclusion that these methods are valid engines to develop automatic irrigation scheduling systems.

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