Assessing the Utility of Primed Acclimation for Improving Water Savings in Cotton using a Sensor-Based Irrigation Scheduling System

Crop Science ◽  
2017 ◽  
Vol 57 (4) ◽  
pp. 2117-2129 ◽  
Author(s):  
Calvin D. Meeks ◽  
John L. Snider ◽  
Wesley M. Porter ◽  
George Vellidis ◽  
Gary Hawkins ◽  
...  
Keyword(s):  
Irrigation Scheduling ◽  
Water Savings ◽  
Scheduling System

AgriSens: IoT-Based Dynamic Irrigation Scheduling System for Water Management of Irrigated Crops

2020 ◽  
pp. 1-1
Author(s):  
Sanku Kumar Roy ◽  
Sudip Misra ◽  
Narendra Singh Raghuwanshi ◽  
Sajal K. Das
Keyword(s):  
Water Management ◽  
Irrigation Scheduling ◽  
Scheduling System ◽  
Irrigated Crops

Automatic Irrigation Scheduling System (AISSUM): Principles and Applications

1995 ◽  
Vol 121 (1) ◽  
pp. 43-56 ◽  
Author(s):  
Bhawan Singh ◽  
Jean Boivin ◽  
Glenn Kirkpatrick ◽  
Barry Hum
Keyword(s):  
Irrigation Scheduling ◽  
Scheduling System

scholarly journals Effects of Real-time Location-specific Drip Irrigation Scheduling on Water Use, Plant Growth, Nutrient Accumulation, and Yield of Florida Fresh-market Tomato

HortScience ◽  
2018 ◽  
Vol 53 (9) ◽  
pp. 1372-1378 ◽  
Author(s):  
Ibukun T. Ayankojo ◽  
Kelly T. Morgan ◽  
Monica Ozores-Hampton ◽  
Kati W. Migliaccio
Keyword(s):  
Real Time ◽  
Open Field ◽  
Sandy Soil ◽  
Irrigation Water ◽  
Biomass Accumulation ◽  
The United States ◽  
Irrigation Scheduling ◽  
Fresh Market ◽  
Water Savings ◽  
Tomato Production

Florida is the largest fresh-market tomato (Solanum lycopersicum L.)–producing state in the United States. Although vegetable production requires frequent water supply throughout the crop production cycle to produce maximum yield and ensure high-quality produce, overirrigation can reduce crop yield and increase negative environmental consequences. This study was conducted to evaluate and compare irrigation schedules by a real-time and location-specific evapotranspiration (ET)-based SmartIrrigation Vegetable App (SI) with a historic ET-based schedule (HI). A field study was conducted on drip-irrigated, fresh-market tomato during the Fall of 2015 and Spring of 2016 on a Florida sandy soil. The two scheduling methods (SI and HI) were evaluated for irrigation water application, plant biomass accumulation, nutrient uptake and partitioning, and yield in open-field tomato production. Treatments included 100% HI (T1); 66% SI (T2); 100% SI (T3); and 150% SI (T4). Treatments were arranged in a randomized complete block design with four replicates per treatment during the two production seasons. In both seasons, depth of irrigation water applied increased in the order of T2 < T3 < T1 < T4. Total water savings was greater for T3 schedule compared with T1 schedule at 22% and 16% for fall and spring seasons, respectively. No differences were observed among treatments for tomato biomass accumulation at all sampling periods during both seasons. However, T3 resulted in significantly greater total marketable yield compared with other treatments in both seasons. The impact of irrigation application rate was greater in fruit and leaf nitrogen accumulation compared with that of stem and root biomass. Based on the plant performance and water savings, this study concludes that under a sandy soil condition, a real-time location-specific irrigation scheduler improves irrigation scheduling accuracy in relation to actual crop water requirement in open-field tomato production.

scholarly journals Testing an Automated Irrigation System Based on Leaching Fraction Testing and Weather in a Container Nursery

2019 ◽  
Vol 29 (2) ◽  
pp. 114-121 ◽  
Author(s):  
Jeff B. Million ◽  
Thomas H. Yeager
Keyword(s):  
Irrigation System ◽  
Optimal Growth ◽  
Irrigation Scheduling ◽  
Plant Production ◽  
Automated System ◽  
Water Savings ◽  
Wide Range ◽  
Automated Technology ◽  
Leaching Fraction ◽  
Leachate Water

Irrigation scheduling in container nurseries is challenging due to the wide range of plant production conditions that must be accounted for at any given time. An irrigation scheduling system should also consider weather affecting evapotranspiration to apply proper amounts of water that will ensure optimal growth with minimal runoff (container drainage). We developed an automated system that relies on routine leaching fraction (leachate/water applied) testing and real-time weather recorded on-site to make adjustments to irrigation. A web-based program (CIRRIG) manages irrigation zone inputs [weather and leaching fraction (LF) test results] and outputs irrigation run times that can be implemented automatically with programmable logic controllers. In this study conducted at a nursery in central Florida, we compared the automated technology (CIRRIG) with the nursery’s traditional irrigation practice (TIP) of manually adjusting irrigation based on substrate moisture status of core samples taken twice weekly. Compared with TIP, CIRRIG reduced water use in six of seven unreplicated trials with water savings being greater for microirrigated crops grown in large containers than for sprinkler-irrigated crops in small containers. Reduced pumping cost associated with water savings by CIRRIG was estimated to be $3250 per year, which was insignificant compared with the labor savings of $35,000 to $40,000 anticipated by the nursery using CIRRIG in lieu of TIP. At the end of the project, the necessary hardware was installed to expand CIRRIG nursery-wide and control 156 zones of irrigation.

A Sensor and Actuator System for Evapotranspiration Based Irrigation Scheduling System

2021 ◽  
Author(s):  
Andi Hendra Putra Ganesha ◽  
Kevin Kinguantoro ◽  
Martinus Davin Herell ◽  
Wirenda Sekar Ayu ◽  
Gilang Mardian Kartiwa ◽  
...  
Keyword(s):  
Irrigation Scheduling ◽  
Scheduling System ◽  
Actuator System

Online professional irrigation scheduling system for greenhouse crops

2017 ◽  
pp. 221-228 ◽  
Author(s):  
N. Katsoulas ◽  
T. Bartzanas ◽  
C. Kittas
Keyword(s):  
Irrigation Scheduling ◽  
Scheduling System ◽  
Greenhouse Crops

scholarly journals Dynamic Neural Network Modelling of Soil Moisture Content for Predictive Irrigation Scheduling

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3408 ◽  
Author(s):  
Olutobi Adeyemi ◽  
Ivan Grove ◽  
Sven Peets ◽  
Yuvraj Domun ◽  
Tomas Norton
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Irrigation Scheduling ◽  
Neural Network Models ◽  
Dynamic Neural Network ◽  
Rule Based ◽  
Scheduling System ◽  
Rule Based System

Sustainable freshwater management is underpinned by technologies which improve the efficiency of agricultural irrigation systems. Irrigation scheduling has the potential to incorporate real-time feedback from soil moisture and climatic sensors. However, for robust closed-loop decision support, models of the soil moisture dynamics are essential in order to predict crop water needs while adapting to external perturbation and disturbances. This paper presents a Dynamic Neural Network approach for modelling of the temporal soil moisture fluxes. The models are trained to generate a one-day-ahead prediction of the volumetric soil moisture content based on past soil moisture, precipitation, and climatic measurements. Using field data from three sites, a R 2 value above 0.94 was obtained during model evaluation in all sites. The models were also able to generate robust soil moisture predictions for independent sites which were not used in training the models. The application of the Dynamic Neural Network models in a predictive irrigation scheduling system was demonstrated using AQUACROP simulations of the potato-growing season. The predictive irrigation scheduling system was evaluated against a rule-based system that applies irrigation based on predefined thresholds. Results indicate that the predictive system achieves a water saving ranging between 20 and 46% while realizing a yield and water use efficiency similar to that of the rule-based system.

Potential water savings associated with agricultural water efficiency improvements: a case study of California, USA

Water Policy ◽  
2011 ◽  
Vol 14 (2) ◽  
pp. 194-213 ◽  
Author(s):  
Juliet Christian-Smith ◽  
Heather Cooley ◽  
Peter H. Gleick
Keyword(s):  
Water Conservation ◽  
Irrigation Scheduling ◽  
Growth Stages ◽  
Agricultural Water ◽  
Regulated Deficit Irrigation ◽  
Water Savings ◽  
Local Climate ◽  
Scarce Water ◽  
Dry Water ◽  
California Agriculture

This study analyzes the potential for water savings from irrigation efficiency improvements in California, USA. We model water savings associated with three efficiency scenarios in wet, average and dry water years. The ‘efficient irrigation technology’ scenario shifts a fraction of the crops from flood irrigation to sprinkler and drip systems; the ‘improved irrigation scheduling’ scenario uses local climate and soil information to more precisely meet crop water needs; and the ‘regulated deficit irrigation’ applies less water to crops during drought-tolerant growth stages to save water and improve crop quality or yield. The three scenarios evaluated here each conservatively show the potential for significant water savings. Their combined potential applied water savings are between 5.6 × 109 m3 (4.5 million acre-feet (MAF)) in a wet year and 7.4 × 109 m3 (6.0 MAF) in a dry year. In total, these scenarios could reduce water applied to California agriculture by 17% or reduce water consumed by California agriculture by 13%. The results also indicate that water conservation and efficiency improvements are particularly effective in dry years, when agricultural water demand is greater and conflicts over scarce water resources are more severe. These approaches can reduce vulnerability to increasingly uncertain water supplies.

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