Adapting a Cloud-Based Irrigation Scheduler for Sugar Beets in the High Plains (original) (raw)
Related papers
2016
The goal of the work reported here was to compare three different irrigation scheduling strategies in two different tillage systems (conservation and conventional). The three irrigation scheduling strategies were the University of Georgia Checkbook Method for Cotton, the SmartIrrigation Cotton App and the University of Georgia Smart Sensor Array (UGA SSA). The study was conducted during the 2015 growing season at the University of Georgia’s Stripling Irrigation Research Park near Camilla. The growing season was unusually wet with more than 23in of rain. Rainfed plots had the highest yields and highest water use efficiency and in this rainy year, irrigation suppressed yield. The major conclusion of the study was that we still have much to learn about the timing of irrigation during wet years.
Cell phone app to optimize irrigation cycles for US crops
Meteorological Applications
Evaporation losses can be reduced by adapting a scheduled irrigation time and having a precise irrigation cycle, but owing to high costs, water optimization tools have thus far been available only to large firms. The study presents all the required steps to develop a smart phone app that can predict the optimal irrigation times for a future 15 days on a sample of 80 crops at any location in the United States. The app uses a soil water balance approach together with data provided by weather sites via application programming interfaces (API). This low-cost, portable, water irrigation planner only requires the entry of both crop and sprinkler type in order to enhance greatly small farms' and private households' ability to lower their consumption of water and the associated costs.
Azsched V2.0: Climate-Based Irrigation Scheduling in Arizona
Timely information on crop water needs is essential for any effective irrigation scheduling strategy. Use of historical or average weather data may suffice in the short term, but often causes significant errors in crop water use estimates when used over long periods of time. AZSCHED (AriZona Irrigation SCHEDuling) program utilizes real-time weather data from the AZMET (AriZona METeorological) database to estimate reference crop evapotranspiration (ET o ). These data are then combined with crop coefficient data (K c ) to estimate daily crop water use for 28 different crops grown in Arizona and the Southwest. AZSCHED V1.0 is already available on the Internet and has been downloaded to over 300 users. This new version allows for the use of tree crops and incorporates many new features that can be used with drip and micro sprinkler systems. This paper discusses some of the new features and how the new V2.0 system operates.
Decision Support for Optimised Irrigation Scheduling
Acta Horticulturae, 2009
The system, developed under FLOW-AID (an FP6 project) 1 , is a water management system that can generally be used at farm level in situations where the water availability and quality is limited. This market-ready precision irrigation management system is focused on new hardware and software. The hardware platform delivers a maintenance-free low cost dielectric tensiometer and several low-end irrigation or fertigation controllers for serving different situations. The software includes a complete, web based, Decision Support System that consists of an expert planner for farm zoning (MOPECO) and a universal irrigation scheduler, based on crop-water stress models (UNIPI) and water and nutrient uptake calculations. The system, designed also to service greenhouse fertigation and hydroponics, is scalable from one to many zones and consists of 1) a data gathering tool that uploads agronomic data, from monitored crops around the world that have internet connectivity, to a central web DB, and 2) a web based DSS that processes intelligently (Crop Response Models, Nutrient Uptake Models, Water Uptake Models) the data of the crop and downloads to the fertigation controller a command file containing water scheduling and nutrient supply guidelines.
Development and testing of an irrigation scheduling model
Agricultural Water Management, 2000
An irrigation scheduling model (ISM) consisting of a database management system (DBMS), model base and graphical user interface (GUI) was developed for performing irrigation scheduling under various management options for both single and multiple ®elds. The ISM is based on a daily water balance approach and uses climatological, crop and soil data as input. Depending on the availability of climatological data, the model offers a choice of one or more methods of estimating reference evapotranspiration (ETo). The GUI is based on mouse-driven approach with pop-up windows, pull-down menus and button controls. The model was tested against ®eld data and the CROPWAT model. The model-predicted soil moisture contents were compared with the ®eldmeasured data for both single and multiple ®eld cases. The two models, ISM and CROPWAT, gave similar values of soil moisture but showed some variation after the second irrigation. For both the single-and multiple-®eld cases, simulated bean yield was slightly higher than measured yield. Also, except for the Turc method, all ETo estimation methods resulted in higher yield as compared to measured yield. The ISM is a¯exible and user-friendly irrigation-scheduling tool, which can be used for ef®cient use of irrigation water. #
The Journal of South Carolina Water Resources
In recent years, there has been an increased interest in South Carolina regarding the amount of water used by different consumers, especially agricultural producers. This interest has sparked conversations among different stakeholders, including the media, policy makers, producers, scientists, and the general public, regarding the current state and future of water resources in the state. Central to these discussions, from the agricultural sector perspective, is the question of how much water producers really need to grow crops. The objective of this study was, therefore, to develop an online tool to use local South Carolina historic weather data to estimate daily and seasonal crop evapotranspiration and irrigation requirements for different crops. The overall goal was for the new tool to assist farmers and other stakeholders to better plan irrigation water allocations and management. Therefore, an interactive online tool called ETcCalc was created to address this objective. ETcCalc,...
Thermal Crop Water Stress Index Base Line Temperatures for Sugarbeet in Arid Western U.S
Keywords: sugarbeet canopy temperature irrigation scheduling water stress A B S T R A C T Sugarbeet is a deep-rooted crop in unrestricted soil profiles that can readily utilize stored soil water to reduce seasonal irrigation requirements. Soil water below 0.6 m is not commonly considered for irrigation scheduling due to the labor and expense of soil water monitoring at deeper depths and uncertainty in effective rooting depth and soil water holding capacity. Thermal-based crop water stress index (CWSI) irrigation scheduling for su-garbeet has the potential to overcome soil water monitoring limitations and facilitate utilization of stored soil water. In this study, canopy temperature of irrigated sugarbeet under full irrigation (FIT) and 25%FIT in 2014, 2015, 2017 and 2018 in southcentral Idaho and FIT and 60%FIT in 2018 in northwestern Wyoming USA was monitored from full cover through harvest along with meteorological conditions and soil water content. A neural network (NN) was used to predict well-watered canopy temperature based on 15-min average values for solar radiation, air temperature, relative humidity, and wind speed collected-1 to +2.5 hours of solar noon (13:00-16:00 MDT). A linear regression driven physical model for estimating the difference between a non-transpiring canopy and air temperature resulted in a value of 13.7°C for the meteorological conditions of the study. A daily CWSI value calculated as the average 15-min CWSI calculated between 13:00 and 16:00 MDT was well correlated with irrigation amounts and timing. The daily CWSI value provided a more responsive indication of crop water stress than soil water monitoring in deficit irrigation treatments. The methodology used to calculate a daily CWSI could be used in irrigation scheduling to utilize soil water storage without knowledge of soil depth, crop rooting depth, or deep (> 0.6 m) soil water monitoring.
DATA-DRIVEN MODELS FOR CANOPY TEMPERATURE- BASED IRRIGATION SCHEDULING
Collection Research HIGHLIGHTS Artificial neural network modeling was used to predict crop water stress index lower reference canopy temperature. Root mean square error of predicted lower reference temperatures was <1.1°C for sugarbeet and Pinot noir wine grape. Energy balance model was used to dynamically predict crop water stress index upper reference canopy temperature. Crop water stress index for sugarbeet was well correlated with irrigation and soil water status. Crop water stress index was well correlated with midday leaf water potential of wine grape. ABSTRACT. Normalized crop canopy temperature, termed crop water stress index (CWSI), was proposed over 40 years ago as an irrigation management tool but has experienced limited adoption in production agriculture. Development of generalized crop-specific upper and lower reference temperatures is critical for implementation of CWSI-based irrigation scheduling. The objective of this study was to develop and evaluate data-driven models for predicting the reference canopy temperatures needed to compute CWSI for sugarbeet and wine grape. Reference canopy temperatures for sugarbeet and wine grape were predicted using machine learning and regression models developed from measured canopy temperatures of sugarbeet, grown in Idaho and Wyoming, and wine grape, grown in Idaho and Oregon, over five years under full and severe deficit irrigation. Lower reference temperatures (T LL) were estimated using neural network models with Nash-Sutcliffe model efficiencies exceeding 0.88 and root mean square error less than 1.1°C. The relationship between T LL minus ambient air temperature and vapor pressure deficit was represented with a linear model that maximized the regression coefficient rather than minimized the sum of squared error. The linear models were used to estimate upper reference temperatures that were nearly double the values reported in previous studies. A daily CWSI, calculated as the average of 15 min CWSI values between 13:00 and 16:00 MDT for sugarbeet and between 13:00 and 15:00 local time for wine grape, were well correlated with irrigation events and amounts. There was a significant (p < 0.001) linear relationship between the daily CWSI and midday leaf water potential of Malbec and Syrah wine grapes, with an R 2 of 0.53. The data-driven models developed in this study to estimate reference temperatures enable automated calculation of the CWSI for effective assessment of crop water stress. However, measurements taken under conditions of wet canopy or low solar radiation should be disregarded as they can result in irrational values of the CWSI.
Irrigation in hot, humid areas is particularly challenging because irrigation must be applied in a timely manner to prevent yield loss due to crop water stress, yet avoid flooding should a rain event follow irrigation. Moreover, it is difficult to detect the onset of crop water stress under environmental conditions that limit evaporative cooling. The goal of this project is to develop reliable, easy to use irrigation scheduling tools that integrate crop monitoring and accurate weather predictions to improve the timing and application of irrigation in humid, high rainfall environments for better water management. The irrigation decision support system is based on calculations of crop water use from weather data collected from weather stations throughout Mississippi using crop coefficients developed from weighing lysimeters and other sources. A water balance approach is used to indicate when supplemental irrigation is needed based on available water and crop water use. This is integrated with other publicly available, spatially registered farm and soil databases to develop specific irrigation scheduling recommendations. A web-based interface is being developed to deliver the irrigation decision support system to producers through an easy to use and readily accessible format. Training materials will be developed and presented to producers through on-site training and other standard Extension mechanisms.