Integrating soil water monitoring technology and weather based crop modelling to provide improved decision support for sugarcane irrigation management

2014 ◽  
Vol 105 ◽  
pp. 44-53 ◽  
Author(s):  
A.L. Paraskevopoulos ◽  
A. Singels
Keyword(s):  
Decision Support ◽  
Soil Water ◽  
Irrigation Management ◽  
Water Monitoring ◽  
Monitoring Technology ◽  
Crop Modelling

scholarly journals ARSPivot, A Sensor-Based Decision Support Software for Variable-Rate Irrigation Center Pivot Systems: Part B. Application

2020 ◽  
Vol 63 (5) ◽  
pp. 1535-1547
Author(s):  
Manuel A. Andrade ◽  
Susan A. O’Shaughnessy ◽  
Steven R. Evett
Keyword(s):  
Information System ◽  
Decision Support ◽  
Geographic Information System ◽  
Soil Water ◽  
Irrigation Management ◽  
Plant Stress ◽  
Geographic Information ◽  
Variable Rate ◽  
Center Pivot ◽  
Water Sensing

HighlightsThe ARSPivot software facilitates variable-rate irrigation management of a center pivot irrigation system.The software embodies a system capable of generating site-specific prescription maps based on weather, plant, and soil water information.ARSPivot’s graphical user interface (GUI) incorporates easy-to-use geographic information system (GIS) tools that help its users to make irrigation management decisions.Abstract. The ARSPivot software was developed for the seamless operation of a complex network consisting of a variable-rate irrigation (VRI) center pivot system and an Irrigation Scheduling Supervisory Control and Data Acquisition (ISSCADA) system that interfaces with weather, plant, and soil water sensing systems. ARSPivot’s graphical user interface (GUI) incorporates a built-in geographic information system (GIS) that maps a center pivot system and facilitates the analysis of data relevant to its operation. The GIS was developed following a minimalistic approach with the objective of making its geospatial data analysis tools accessible to a wide range of users (farmers, irrigation consultants, and researchers). The post-harvest analyses of two experiments carried out in the Texas High Plains during the summers of 2016 and 2017 using a three-span VRI center pivot are presented to illustrate the advantages of using ARSPivot as a decision support tool and how its GIS tools help its users make better informed decisions regarding irrigation management. In these experiments, the north-northwest (NNW) portion of a field planted with corn (Zea mays L.) was irrigated using VRI zone control, and the south-southeast (SSE) portion was irrigated using VRI speed control. Experimental plots in the NNW portion were assigned one of three irrigation levels (80%, 50%, or 30% replenishment of soil water depletion to field capacity in the top 1.5 m), and their irrigation was scheduled using either a plant stress-based algorithm implemented in the ARSPivot software or manual weekly neutron probe (NP) readings. Plots in the SSE portion were assigned a single irrigation level of 80%, and their irrigation was scheduled using either the plant stress method or a two-step hybrid approach in which soil water sensing was combined with the plant stress method to determine irrigation depths. Soil water sensing data for the ISSCADA system were provided by NP readings during the 2016 season and by sets of time-domain reflectometers (TDRs) installed at depths of 15, 30, and 45 cm during the 2017 season. No significant differences were found during either season in terms of mean dry grain yield and crop water productivity (CWP) obtained from plots irrigated at the 80% level in both sides of the field, regardless of the irrigation scheduling method or the type of VRI application method used for irrigation. No significant differences were found during either season between mean dry grain yield and CWP of plots in the NNW portion irrigated using the plant stress-based method and NP readings at the 80% irrigation level. The lack of significant differences documented the potential of the ARSPivot system as a plant and soil water sensing-based decision support software for site-specific irrigation management of corn using a VRI center pivot system. Keywords: Center pivot irrigation, Decision support system, Geographic information system, Precision agriculture, Software.

Seasonal precipitation pattern analysis for decision support of agricultural irrigation management in Louisiana, USA

2021 ◽  
Vol 254 ◽  
pp. 106970
Author(s):  
Clement D. Sohoulande Djebou ◽  
Stacia Conger ◽  
Ariel A. Szogi ◽  
Kenneth C. Stone ◽  
Jerry H. Martin
Keyword(s):  
Decision Support ◽  
Pattern Analysis ◽  
Irrigation Management ◽  
Precipitation Pattern ◽  
Agricultural Irrigation ◽  
Seasonal Precipitation

scholarly journals Effects of water deficit in two phenological stages on production of japanese cucumber cultived in greenhouse

2011 ◽  
Vol 31 (4) ◽  
pp. 676-686 ◽  
Author(s):  
Eduardo C. Oliveira ◽  
Jacinto de A. Carvalho ◽  
Wellington G. da Silva ◽  
Fátima C. Rezende ◽  
Willian F. de Almeida
Keyword(s):  
Soil Water ◽  
Water Deficit ◽  
Irrigation Management ◽  
Soil Water Potential ◽  
Phenological Stages ◽  
Cucumis Sativus L ◽  
Randomized Design ◽  
Minas Gerais State ◽  
Engineering Department ◽  
Four Levels

The experiment was performed in the experimental area of the Engineering Department Federal University of Lavras, Minas Gerais State, Brazil. It aimed at identifying the adequate irrigation management of the greenhouse-cultivated Japanese cucumber (Cucumis sativus L.). complete randomized design, with four levels of soil water potential (15; 30; 60 e 120 kPa) at two phenological phases (vegetative and reproductive), and 5 replications. Overall, the results showed decrease of yield according to increase of soil water potentials. During the reproductive stage, Japanese cucumber plants were more sensitive to water deficit, resulting in further decrease in yield compared to applied water deficit during the vegetative stage of the culture.

scholarly journals Soil water crop modeling for decision support in millet-based systems in the Sahel: a challenge

2014 ◽  
Vol 9 (22) ◽  
pp. 1700-1713 ◽  
Author(s):  
P. B. I. Akponikp ◽  
B. Grard ◽  
C. L Bielders
Keyword(s):  
Decision Support ◽  
Soil Water ◽  
Crop Modeling

scholarly journals Analysis of Water Stress Prediction Quality as Influenced by the Number and Placement of Temporal Soil-Water Monitoring Sites

2014 ◽  
Vol 06 (11) ◽  
pp. 961-971
Author(s):  
Luan Pan ◽  
Viacheslav I. Adamchuk ◽  
Richard B. Ferguson ◽  
Pierre R. L. Dutilleul ◽  
Shiv O. Prasher
Keyword(s):  
Water Stress ◽  
Soil Water ◽  
Water Monitoring ◽  
Prediction Quality ◽  
Stress Prediction
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