WERA1022: Meteorological and Climate Data to Support ET-Based Irrigation Scheduling, Water Conservation, and Water Resources Management (from WDC18)
(Multistate Research Coordinating Committee and Information Exchange Group)
Status: Inactive/Terminating
Date of Annual Report: 10/31/2018
Report Information
Annual Meeting Dates: 09/25/2018
- 09/27/2018
Period the Report Covers: 10/01/2017 - 09/30/2018
Period the Report Covers: 10/01/2017 - 09/30/2018
Participants
Jama Hamel, US Bureau of Reclamation; Stacia Conger, Louisiana State University; Gene Stevens, Missouri State University; James Adkin, University of Delaware; Wei Ren, University of Kentucky; Vasudha Sharma, University of Minnesota; Vivek Sharma, University of Wyoming; Keven Brinson, University of Delaware; Ed Martin, Admin, Advisor.Brief Summary of Minutes
Accomplishments
<p><strong>Western Education/Extension and Research Activity (WERA) 1022 </strong></p><br /> <p>Meteorological and Climate Data to Support ET-Based Irrigation Scheduling, Water Conservation, and Water Resources Management</p><br /> <p><strong><sup>Date of Annual Report: October 1, 2017 – September 30, 2018</sup></strong></p><br /> <p><strong><sup>Accomplishments</sup></strong></p><br /> <p><strong>Objective 1. </strong>Coordinate the documentation of crop coefficients used in irrigation scheduling.</p><br /> <p><strong>California</strong>: Two irrigation infrastructure (a total of 108 fully automated research plots) were built to initiate two turfgrass irrigation research trials in Southern and Central California at the University of California Riverside Agricultural Experiment Station and University of California Division of Agriculture and Natural Resources Kearney Research and Extension Center, respectively. Two grass species were studied including tall fescue and bermudagrass. Tall fescue is a cool season grass that is well adapted to sunny conditions. Bermudagrass is a warm season grass (requiring about 25 percent less water than cool season grasses) and is currently a popular lawn species in California. Multiple irrigation treatments ranging from full to deficit irrigation scenarios and two watering days were imposed in order to develop water conservation strategies to keep the plants alive with minimum water.</p><br /> <p><strong>Colorado:</strong> Hourly and daily crop evapotranspiration (ETC) rates of maize and grass hay were collected from two precision weighing lysimeters at the CSU Arkansas Valley Research Center in Southeast Colorado during 2018. The ETc data will be used in conjunction with ASCE Standardized reference ET to develop crop coefficients of maize and grass hay. A seasonal crop coefficient curve for grain sorghum was also developed from lysimeter data collected in 2017.</p><br /> <p><strong>Florida</strong>: This year work continued on the evaluation and use of smart irrigation controllers to schedule landscape irrigation. In addition, soil moisture sensors are being used for real-time irrigation scheduling information of commodity crops such as maize and peanut.</p><br /> <p><strong>Louisiana:</strong> In effort to begin estimating crop coefficients for Louisiana grain crops, soil moisture sensors that measure volumetric water content were installed in corn, cotton, and soybean research plots in three locations across northern Louisiana. Five sensors were installed at 6-10 inch intervals up to a maximum of 36 inches during the 2015 and 2016 crop seasons.</p><br /> <p>Soil types played a large part in the accuracy of the soil moisture estimations. Only the cotton plots on sandy clay loam soil provided good enough data to proceed with analysis. In this location only, but for both 2015 and 2016 seasons, crop coefficients were calculated by estimating the crop evapotranspiration (ET<sub>C</sub>) from the change in soil moisture within the active root zone on a daily basis. Crop coefficients (K<sub>C</sub>) dropped periodically during reproduction, but these drops in K<sub>C</sub> were clearly correlated to water stress visible in the soil moisture data. Thus, a stress coefficient (K<sub>S</sub>) was introduced, resulting in K<sub>C</sub>*K<sub>S</sub> and not K<sub>C</sub> only.</p><br /> <p><strong>Minnesota</strong>: Currently, in Minnesota, there is a lack of information on the crop coefficients (Kc) that are specific to the soils, climate and management practices of Minnesota for almost all crops that are grown in the state. We are making efforts to start a new research project that evaluates the interaction effects of different irrigation strategies and different N rates on grain yield, nitrate-N leaching, crop evapotranspiration, and N and water use efficiency in continuous maize and maize-potatoes-edible beans rotation to develop the best management system aimed at maximum maize production and minimum nitrate leaching. The other objective of this research is to develop crop coefficients for maize, potatoes and edible beans for various N-rates and irrigation strategies under typical crop management practices and climatic conditions for irrigation scheduling in central sands region of Minnesota.</p><br /> <p><strong>Montana: </strong>Alfalfa irrigation (100ET, 50ET, and rainfed) on various alfalfa fall dormancy (2, 3, and 6). The very low rainfall events in July and August led to statistical significance among the three water treatments. A low lignin type dormancy 6.0 had the lowest yield. The highest yield is also another fall dormancy 6.0. No interaction between irrigation and fall dormancy. Irrigation strategies for various hard red spring in 2018 of wheat traits such as tillering genes and protein genes had a different impact compared with the year 2017 drought year. It seems the cooler months of June and early occurrence of rainfall was advantageous for yield this year. The 100ET, 75ET, 50ET, and even early irrigation terminations had comparable yields. Only the rainfed check was significantly different. No interaction between irrigation and genetics. Highest yield response was from the tillering alleles. A crop coefficient for canola was estimated through an Eddy covariance approach. Still, it needs a second-year validation. It seems to be that crop ET is high early in the vegetative stage. Although canola reaches 100 ground coverage early in the season (large leaves and 6-inches planting density), an in-depth analysis has to be done to make sure early morning dew, rainfall, and evaporation don’t interfere with the actual water escape through the stomata.Eddy Covariance data was collected for chickpea this year. The analysis is yet to be done.</p><br /> <p><strong>Nebraska: </strong>Since water is a crucial input for agricultural productivity, effective planning, managing, allocating and forecasting of water resources through research and science-based Extension/outreach programs is critical to maintain the sustainability of agricultural production. To encounter some of the water availability vs. agricultural production issues, an unprecedented effort had been taken in 2004-2005 and the Nebraska Agricultural Water Management Network [NAWMN; <a href="http://water.unl.edu/cropswater/nawmn">http://water.unl.edu/cropswater/nawmn</a> (Irmak, 2006; Irmak et al. 2010; 2012)] was formed from an interdisciplinary team of partners, including UNL Extension, Natural Resources Districts (NRD), USDA-Natural Resources Conservation Service (NRCS), farmers, crop consultants, and other agricultural professionals. The main goal of the Network is to enable the transfer of high quality research-based information to farmers and their advisors through an unparalleled series of demonstration projects (>800) established in farmers’ fields throughout Nebraska and implement newer tools and technologies to address and enhance crop water use efficiency, water conservation, and reduce energy consumption for irrigation and reduce nitrogen leaching to ground and surface water resources. The Network was also established to be proactive in terms of water conservation and management even in areas that may not have water availability issues currently. The Network has been having substantial positive environmental impacts with over 1,400 farmer cooperators, representing about 2.5 million acres of irrigated cropland and with an average of 2 inches of reduction in water withdrawal for irrigation per growing season since 2005. In 2005, there were only 15 farmer cooperators in the Network, representing only 1,428 acres of irrigated land. The NAWMN teaches and demonstrates farmers how to utilize soil moisture monitoring and crop water use estimates from either ET<sub>gages</sub> or weather station climate data in their practices to enhance irrigation water management and crop production efficiency. The use of climate information [precipitation, temperature, reference (potential) evapotranspiration, crop coefficients, and actual crop evapotranspiration] has also been taught in the NAWMN programs. T<em>he NAWMN is one of the largest and most impactful research-based programs in Nebraska that accomplished substantial adoption of technology and information transfer in agriculture through strong and dedicated partnership of university faculty, private industry, state and federal agencies, producers, irrigation districts and crop consultants and changed the behavior of producers in terms of how they managed water resources. The Network is an excellent example of one of the very few large scale programs that accomplished varitable integration of science, research and Extension/outreach/education to make a difference in the real world. </em></p><br /> <p><strong>Texas USDA-ARS: </strong>Published crop coefficients have traditionally not been classified as to the irrigation application method used in determining them. With the increased use of subsurface drip irrigation (SDI) for field and specialty crops, it has become clear that crop coefficients determined using sprinkler/spray irrigation systems are not suitable for irrigation scheduling of SDI systems. In 2013, the four large weighing lysimeters at Bushland, TX, were modified so that two of the lysimeters and their surrounding 4.4-ha fields would be irrigated using SDI, while the other two would continue to be irrigated using a ten-span lateral move system equipped for mid elevation spray application (MESA). In 2014, the SDI system on the two lysimeters so equipped was modified to clearly distinguish between irrigation and ET (Evett et al., 2018a). Four years of grain corn and grain sorghum production have shown consistently smaller ET for SDI compared with MESA, ranging from 138 to 151 mm (5.4 to 5.9 inch) per corn season (Evett et al., 2018b) and from 50 to 112 mm per short-season sorghum season. Future work will focus on determining crop coefficients using the dual coefficient approach and ASCE 2005 Standardized Penman-Monteith reference ET for a tall crop (alfalfa).</p><br /> <p><strong>US Bureau of Reclamation - AgriMet:</strong></p><br /> <ul><br /> <li>Reclamation documents all crop coefficient information on its website including sources and methods used to derive the coefficients. https://www.usbr.gov/pn/agrimet/cropcurves/crop_curves.html</li><br /> <li>Reclamation works with other entities and agencies to coordinate sharing of crop coefficient information.</li><br /> <li>Reclamation representatives attend committee meetings and continuing education to continue coordination.</li><br /> </ul><br /> <p><strong>Utah:</strong> We continued work on developing crop coefficients for pasture for both fully irrigated and under deficit irrigation scenarios. We completed a study, Verification of Water Conservation from Deficit Irrigation Pilot Projects in the Upper Colorado River Basin. Walton Family Foundation and S. D. Bechtel Jr Foundation (Niel Allen and Alfonso Torres-Rua). The study used information from Landsat 7 and 8 satellites with the METRICTM algorithm for estimation of evapotranspiration, DAYMET’s spatial meteorological information to estimate rainfall in the absence of weather stations, and agricultural weather stations for ground or local information for calibration and improvement of remotely sensed and spatial products. From 2014-2016, monthly crop coefficients (for grass reference ASCE Penman-Montieth) were calculated for 42 high elevation pastures in Colorado River Basin of Colorado and Wyoming are presented in Table 1. The higher Colorado crop coefficients may result from lower elevation and/or management for higher yields (more irrigation, fertilizer, etc.). The variation is also due to different irrigation water supplies available or utilized at specific fields.</p><br /> <p>Table 1. Monthly crop coefficients for pastures at a high elevation in Colorado and Wyoming.</p><br /> <p>Colorado (6,350 to 6,400 feet above msl)</p><br /> <p> Apr May Jun Jul Aug Sep Oct</p><br /> <p> Average 0.46 0.78 0.90 0.78 0.78 0.82 0.78</p><br /> <p> 25 percentile 0.38 0.67 0.85 0.65 0.67 0.72 0.69</p><br /> <p>75 percentile 0.56 0.89 1.02 0.92 0.89 0.92 0.88</p><br /> <p>Wyoming (Elevation range 6,800 to 7,500 feet above msl)</p><br /> <p> </p><br /> <p> Apr May Jun Jul Aug Sep Oct</p><br /> <p> Average 0.34 0.62 0.79 0.76 0.71 0.60 0.48</p><br /> <p> 25 percentile 0.27 0.51 0.69 0.66 0.59 0.49 0.38</p><br /> <p>75 percentile 0.40 0.71 0.88 0.87 0.82 0.72 0.58</p><br /> <p> </p><br /> <p><strong>Washington: </strong>We are maintaining a database of Kc Values from several sources. These include:</p><br /> <ul><br /> <li>An old compilation of Kc values used in Washington state irrigation scheduling tool, WISE.</li><br /> <li>Crop coefficients modified to the ASCE Standardized equation from the AgriMet set of crop coefficients</li><br /> <li>UC Davis crop coefficients modified to alfalfa ET.</li><br /> </ul><br /> <p>It is apparent that the most critical value for crop coefficients is the peak ET value. It is apparent that we have a problem because we don’t have different curves for different crop varieties or harvest dates/and or growth strategies. We need to work through these because these crop coefficients are used extensively to estimate water rights, which are extremely valuable and contentious in the Western US. We are also working on using unmanned aerial vehicles (UAVs) to estimate crop water use and detect water stressed areas within a particular field.</p><br /> <p><strong>Wyoming:</strong> Numerous research and extension activities have been conducted at University of Wyoming during the report period of October 2017 to September 2018. A main focus has been on promoting the adoption of instrumentation to improve irrigation scheduling. As part of this effort, PI has installed one Bowen Ratio Energy Balance System at Powell Research and Extension Center for continuous monitoring of crop and development of localized crop coefficients. Systems was installed in June 2017 in sugarbeet and in 2018, data was collected for dry beans. For both years, resulting Kc values for sugarbeet and dry beans was associated to crop phenological duration and growing degree days to boost the adoption of Kc values for efficient irrigation scheduling. PI completed the two-year study on understanding impact of deficit irrigation on dry beans production, water use efficiency and development of crop coefficients under semi-arid region of inter-mountainous west. Five irrigation treatments were evaluated in this experiment mainly FIT, 125%FIT, 75%FIT, 50%FIT and 25%FIT. Kc values developed from this experiment for FIT was in agreement with the BREBS measured data. This work supported a graduate student. In addition, efforts are underway at Powell Research and Extension Center (PREC) to understand the impact of different irrigation and nitrogen applications and its interaction on sugarbeet yield, percent sucrose concentration, crop evapotranspiration and Kc. In this experiment three irrigation treatment and five different nitrogen rates were tested. Preliminary results indicated that there is an optimal N level for each irrigation regime, and, in general, lower N application rates are required to produce acceptable tonnage and maximum sucrose content. Sugarbeet ETa vary from maximum of 685 mm (26.9 inches) for FIT and minimum of 457 mm (17.9 inches) for 60% FIT.</p><br /> <p><strong> </strong></p><br /> <p><strong>Objective 2. </strong>Coordinate efforts to promote adoption of improved irrigation scheduling technology, including computer models based on crop coefficients and ET<sub>ref</sub>, remote sensing and instrumentation that will help producers more efficiently apply irrigation water.</p><br /> <p><strong>California: </strong>Moving toward autonomous efficient landscape irrigation management in urban settings is a long term goal of the urban irrigation projects conducted by Haghverdi Water Management Group at UC Riverside (ucrwater.com). Irrigation scheduling was automatically performed by Weathermatic Smartline SL 4800 smart ET-based irrigation controllers connected to a SLW5 wireless weather sensor and a SLFSI-T10 1” flow sensor. The irrigation controller was programmed to impose the irrigation treatments at the beginning of the experimental period. Throughout the experiment, however, all the irrigation applications were automatically calculated and applied by the irrigation controller. The controller estimated daily reference evapotranspiration (ETref) using Hargreaves model which subsequently was used to determine the water deficit each day at midnight. The next irrigation then was applied considering the accumulated water deficit. The water deficit was set back to zero when watering was finished. The performance of the smart controller was closely monitored and evaluated against ETref data obtained from two California Irrigation Management Information System (CIMIS) weather stations adjacent to the experimental sites. A preliminary assessment of the results revealed promising performance for the weather-based controllers during summer months when crop water requirement is highest.</p><br /> <p><strong>Colorado:</strong> Two irrigation scheduling presentations were given at: (1) the Rocky Mountain Agribusiness Association’s 66th Annual Convention & Trade Show on 1/11/2018 in Denver, CO (63 participants; producers, crop consultants, conservationists); and (2) the Irrigation Association Faculty Academy on 6/25/2018 in Fort Collins, CO (17 participants – Junior College and University faculty from around the U.S.). Presentations were given on the parameterization of the (bulk) surface resistance of the Penman-Monteith evapotranspiration equation (1965) for corn to enable to estimation of corn water use in the so-called one step approach. The presentation was delivered at the ASCE EWRI meeting in Sacramento, CA (citation below). Similarly, a presentation was given at the SPIE meeting in Orlando, FL (citation below) on the use of unmanned aerial systems (UASs or drones) to acquire multispectral imagery, produce surface reflectance, vegetation indices, percent cover and updated crop coefficients for corn under full and limited irrigation to improve irrigation scheduling. A similar presentation was delivered in Colby, KS.</p><br /> <p><strong>Louisiana: </strong>Currently, Louisiana does not offer irrigation scheduling software to agricultural producers. To address this, a soil water balance was developed in an excel spreadsheet and validated using the soil moisture sensor data collected from the previously described field plots. The spreadsheet matched the soil moisture data with good accuracy except in rainfall situations where infiltration did not occur in reality (likely high intensity causing significant runoff), but the soil water storage was available and filled the profile to field capacity in the model. Thus, work must be done to include infiltration considerations to the calculations. The spreadsheet was designed to require minimal information so that the producer doesn’t have to spend much time setting up each field. In addition to the infiltration calculations, the current task is to develop a user manual as a numbered extension publication to release with the spreadsheet for instructional purposes. We may also create video instructions in the future as well.</p><br /> <p><strong>Minnesota: </strong>Currently, in Minnesota, most of the irrigators are using the checkbook method for irrigation scheduling. This method is based on the simplified estimate of water inputs, water stored in the soil profile based on its water holding capacity and water out, based on crop water use. The major drawback of this method is that the crop water use (ET) tables that are used in the checkbook method were developed around three decades ago. With the change in hybrids and management practice, ET values need to be updated. Also, if the checkbook method is not supported by weekly soil moisture measurement, it may result in over-irrigation by as much as 50%. We are planning to develop an Irrigation management program through which we will educate farmers and community on different methods of irrigation scheduling, how to utilize these methods in their practices to enhance crop water use efficiency and reduce irrigation-induced environmental pollution and encourage the adoption of best irrigation management practices through workshops, training and demonstrational field days. The other major part of this program is to promote and expand the Irrigation Management Assistant tool (http://ima.respec.com/) throughout the state. In collaboration with the Minnesota Department of Agriculture (MDA) and RESPEC, we managed to secure funding for educational activities from Clean Water Council to pursue this project. This program will further focus on establishing an ETgage network in the irrigated regions of the state where irrigator can use ETgage data for reference ET in case weather station is not available. In addition, this program will also focus on expanding the agriculture weather network in the state.</p><br /> <p><strong>Missouri: </strong>The Extension Crop Water Use soil water balance app used by Missouri farmers for scheduling irrigation in fields does not currently distinguish between soybean maturity groups. In the program, a crop coefficient adjustment factor for soybean, developed in 1995 by Dr. Wayne Decker, is multiplied by daily estimated short crop evapotranspiration calculated using the Penman-Monteith equation and local weather data. The adjustment factor divides the season into vegetative, flowering, pod and seed development, and physiological maturity growth stages. A field test was conducted in 2018 at Portageville, Missouri to study weekly changes in light interception from ten soybean cultivars ranging in maturity group from 4.0 to 5.5 with two irrigation regimes. Weekly light interception measurements included percent canopy closure, leaf area index, and normalized difference vegetative index. The crop coefficient adjust factor in the irrigation app will be compared to measured light interception for soybean cultivars at each growth stage. Cloudy weather during crop reproduction periods has been shown to reduce yields. A new function called the solar radiation stress index is being added to the Crop Water Use app for 2019. Dr. Michele Reba, USDA-ARS at Jonesboro, Arkansas shared with us her method of calculating the stress index using daily Rso, clear sky radiation. To be classified as a stress day, the ratio must be below 0.5 for three consecutive days. Using historical Missouri weather data, daily solar radiation stress indexes were calculated for 2016 and 2017 and compared it to yields for those years.</p><br /> <p><strong>Montana:</strong> <strong> </strong>Irrigation strategies for various hard red spring in 2018 of wheat traits such as tillering genes and protein genes had a different impact compared with the year 2017 drought year. It seems the cooler months of June and early occurrence of rainfall was advantageous for yield this year. The 100ET, 75ET, 50ET, and even early irrigation terminations had comparable yields. Only the rainfed check was significantly different. No interaction between irrigation and genetics. Highest yield response was from the tillering alleles.</p><br /> <p><strong>Ohio:</strong> Since 2016, supported by the NIFA McIntyre-Stennis Grant, a project, “Ecosystem Sustainment of the Appalachian Forest Regions under Natural Gas Exploration in Ohio” has been in progress. Hydraulic fracturing impacts both water quantity and quality. According to the Department of Energy commissioned study, drilling and fracking needs 2-4 million gallons water per well annually. About 0.1% to 0.8% of water available in a water basin is used for the oil and gas exploration activities. The long term goal of the study is to design a sustainable pathway for natural gas exploration in Ohio which optimizes energy production, forest ecosystem services, and agriculture production. In 2015, Dr. Kandiah developed an index to estimate the cumulative impact of water withdrawals for drilling and fracking at the county-scale on hydrological and socioeconomic factors. Together with the modified index, W-DoubleQ-Index, Soil and Water Assessment Tool (SWAT) (and its web equivalent HAWQS) will be used in this study to estimate the changes in run off, nutrient levels, and evapotranspiration will be analyzed through water balance studies in the forested area of eastern Ohio between 2005-2015. The HAWQS Model on Tuscarawas Watershed also looked into the change in the ET in the study period. In this pre- hydraulic fracturing oil and gas exploration period (2001-2005), Hydrology, Sediment, Nitrogen Cycle, Phosphorus Cycle, Plant Growth and Landscape Nutrient Losses were estimated for the Tuscarawas Watershed (HUC 05040001) with the preliminary data. Average Runoff for the period was 508.480 mm. The Organic Nitrogen increased by 2.1% in the soil. Average Upland Sediment Yield in the watershed was found 0.090 Mg/ha. Table 1 shows the Water Balance Ratios in the TW for the period 2003-2005. Figure 3 shows the HAWQS Hydrological Balance in the TW for the same period. Figure 4 shows the HAWQS Sediment Yield.</p><br /> <p>Table 1: Water Balance Ratio in the Tuscarawas Watershed in 2003-2005</p><br /> <p>Streamflow/Precip 0.594</p><br /> <p>Baseflow/Total Flow 0.3</p><br /> <p>Surface Runoff/Total Flow 0.7</p><br /> <p>Perc/Precip 0.029</p><br /> <p>Deep Recharge/Precip 0.001</p><br /> <p>ET/Precipitation 0.381</p><br /> <p> </p><br /> <p><strong>Oklahoma:</strong> As part of a multistate project (Oklahoma, Texas, and Kansas) funded by USDA-NRCS Conservation Innovation Grant, seven demonstration sites were selected across western Oklahoma. These sites were under variable soils, crops, and irrigation systems and were instrumented in collaboration with local producers. The performance of different types of commercially-available sensors in developing improved irrigation scheduling was investigated. Information on science-based irrigation scheduling was disseminated through:</p><br /> <ol><br /> <li>Six presentations at four local and national conferences of scientific communities.</li><br /> <li>Thirteen presentations at field days and meetings with growers and crop consultants in Oklahoma, Texas, Kansas, and Nebraska. The total number of extension contact hours(face-to-face interaction with clientele) was 342 hours during the reporting period.</li><br /> </ol><br /> <p><strong>Texas </strong><strong>USDA-ARS</strong><strong>: </strong>Since 2002, ARS has been developing a center pivot variable rate irrigation (VRI) decision support system based on proximal sensing of plant and soil water stress indices. This Irrigation Scheduling Supervisory Control And Data Acquisition (ISSCADA) system, patented in 2014, was licensed by Valmont Industries in 2018. The ARS team at Bushland, Texas, coordinated ISSCADA field trials with ARS and university partners in Alberta, Missouri, Mississippi, South Carolina and Texas in 2018, continuing multi-state field trials that began in 2016. The ISSCADA client-server software system was improved with the addition of soil water sensing (Andrade et al., 2018). The team also developed methods for calibrating and testing commercial infrared thermometers used in irrigation scheduling (Colaizzi et al., 2018), and patented a novel computer vision qualified infrared temperature sensor that automatically improves the quality of canopy temperature data used in irrigation scheduling (O’Shaughnessy et al., 2018). The team continued cooperation with Acclima, Inc. and partners in Beltsville, MD, through two CRADAs, one to develop advanced soil water sensors based on time domain reflectometry and the second to develop a wireless node and gateway system to acquire data from sensors using the SDI-12 data transmission protocol, transmit the data using the LoRa radio protocol from node to gateway, and transmit data from gateway to the Cloud using cellular network data transmission. The team also contributed to understanding of how soil water sensor field testing can be done to assure accurate and informative test results (Schwartz et al., 2018). ARS also investigated the Cosmic Ray Neutron Probe (CRNP) for use in scheduling irrigations by comparing CRNP data with data from spatially distributed networks of field-calibrated soil water sensors installed at depths from 0.05 to 1.00 m, with data from eight neutron probe access tubes and with ET and soil water data from a large weighing lysimeter at Bushland. Partners at the ARS location in El Reno, OK, performed a similar experiment without the lysimeter and neutron probe data. This experiment is coordinated with the International Atomic Energy Agency (IAEA), and the report to IAEA concluded that the CRNP had almost no value for irrigation management due to its insensitivity to soil water content changes at depths greater than 0.20 m (Evett et al., 2018c). ARS at Bushland, TX, supported ARS at Beltsville, MD, in development of a wireless node and gateway system for datalogging of soil water sensor data and transmission of the data using the LoRa radio transmission scheme from nodes in the field to a gateway at the edge of the field. The inexpensive (~$150 US), solar powered node collected data from up to eight sensors using the SDI-12 wired data protocol and transmitted the data over distances exceeding 300 m to the gateway (~$150 US) where the data were stored prior to upload to a smart phone, tablet or other device equipped with Bluetooth. Both model TDR-315L and model CS655 sensors were used successfully with this system in four states in the southeast US. In 2018, a CRADA with Acclima, Inc. was instituted by ARS Beltsville with ARS Bushland support to develop a commercial version of this system that will upload the soil water sensor data to the Cloud via a cellular network.</p><br /> <p><strong>US Bureau of Reclamation - AgriMet:</strong></p><br /> <ul><br /> <li>The Metric ET Remote Sensing model utilizes AgriMet data as “ground truths” for calibration of the model. Data collection methods and computations are done in conjunction with Dr. Rick Allen, University of Idaho, Kimberly (developer of Metric) to optimize data accuracy.</li><br /> <li>The AgriMet program is currently providing technical assistance and data for the WWCRA climate change project affiliated with numerous regions and government entities.</li><br /> <li>Numerous special data requests were filled, providing high quality agricultural weather and crop water use data to a variety of users.</li><br /> <li>Access to all historical weather and crop water use information is available on Reclamation’s AgriMet Home Page on the Internet.</li><br /> <li>AgriMet weather data is utilized daily by the Oregon State University Integrated Plant Protection Center for degree day and pest management modeling.</li><br /> <li>AgriMet soil temperature data is used by the USDA World Agricultural Outlook Board for assessing agricultural conditions in the Pacific Northwest.</li><br /> <li>AgriMet crop consumptive water use information is used by Oregon State University’s Online Irrigation Scheduling Program. This program assists irrigators in efficient irrigation scheduling practices.</li><br /> <li>AgriMet is being used as the source of ET information for residential lawn “Smart Controllers” in several locations in the Northwest though Irrisoft’s “Weather Reach” program.</li><br /> <li>A network of six weather stations was installed for the Warm Springs Tribe to look at micro climates and frost pockets for future projects.</li><br /> <li>A station was installed at the Entiat National Fish Hatchery to compliment Reclamation’s irrigation well installation throughout the watershed.</li><br /> <li>Two stations were installed at the Boise and Twin Falls fairgrounds with a live data feed and display inside the Ag Pavilion during the fair.</li><br /> <li>The AgriMet program coordinator made several presentations in 2018 to highlight the importance of agricultural weather data collection and ET modeling in the West. </li><br /> <li>Reclamation continues to cooperate with the NOAA Air Resources Laboratory to transfer information from eleven existing stations to Reclamation’s computer system for use in crop water modeling.</li><br /> <li>Reclamation ingests data from over 60 weather stations managed by Utah Climate Center, Nevada Desert Research Institute and Colorado Climate Center.</li><br /> <li>Enhancements continue to be made to Reclamation’s AgriMet Home Page on the World Wide Web to improve access to weather and crop water use information.</li><br /> <li>Near-real time weather data from AgriMet stations continue to be incorporated into several other networks to improve the delivery of timely weather data to a variety of users: the Mesowest Network (sponsored by the University of Utah), the National Weather Service in Missoula, MT (Current surface observations in the Pacific Northwest), USDA World Agricultural Outlook Board, Oregon State University’s Integrated Plant Protection Center, Oregon State University’s Irrigation Water Management Online Program, AWIS Weather Services, Inc., and Irrisoft’s Weather Reach irrigation water management system.</li><br /> <li>Reclamation is participating in the Committee for Integrated Observing Systems (CIOS), an initiative by the Federal Government to integrate numerous weather networks.</li><br /> <li>Crop water use charts were generated for each station each day of the growing season, April through mid-October -- a total of almost 10,000 crop water use charts. These chartsare specifically tailored to 50 crops grown in the Pacific Northwest region.</li><br /> <li>Crop water use charts, annual evapotranspiration summaries, and daily weather summaries were made available to thousands of users through a home page on the World Wide Web. Much of this information is then re-disseminated by agricultural consultants, irrigation districts, and local newspapers.</li><br /> <li>Crop water use charts were generated for each station each day of the growing season, April through mid-October -- a total of almost 10,000 crop water use charts. These charts are specifically tailored to 50 crops grown in the Pacific Northwest region.</li><br /> <li>Crop water use charts, annual evapotranspiration summaries, and daily weather summaries were made available to thousands of users through a home page on the World Wide Web. Much of this information is then re-disseminated by agricultural consultants, irrigation districts, and local newspapers.</li><br /> <li>Numerous special data requests were filled, providing high quality agricultural weather and crop water use data to a variety of users.</li><br /> <li>Access to all historical weather and crop water use information is available on Reclamation’s AgriMet Home Page on the Internet.</li><br /> </ul><br /> <p><strong>Utah:</strong> Procedures were developed for spatial and temporal analysis of precipitation and effective rainfall using gauge observations, satellite, and gridded climate data for agricultural water management in the Upper Colorado River Basin (Mahyar Aboutalebi, Alfonso F. Torres-Rua, and Niel Allen). In this study, we evaluate gridded precipitation information at farm scale. Precipitation products from TRMM-3B42, PRISM, Daymet, and gauge observations were evaluated on two case studies located in Colorado and Wyoming during the 2014 to 2016 irrigation seasons. The study considered resolution at farm level, bias occurrence at different time scales (daily to monthly), effect of coverage area of the gauge stations, and effect of the dominant wind direction on the coverage area. Daymet data works the best for the farm level, but adjustments need to be made based on wind direction. The method works well to prepare spatial monthly precipitation map that can be used with METRIC to develop spatial depletion using METRIC to calculate ET.</p><br /> <p><strong>Washington:</strong> We continue to support and refine the user-friendly irrigation scheduler that we have named Irrigation Scheduler Mobile. We added Utah data to it this year. It works on mobile phones as well as any web browser (http://weather.wsu.edu/ism). The code is open source (written in PHP and MySQL). The code is available for download at http://irrigation.wsu.edu/Content/ism.zip. There is also a user’s manual at http://weather.wsu.edu/ism/ISMManual.pdf. The Android and iPhone apps have been released and can be found by searching for “Irrigation Scheduler Mobile” on iTunes or in the Google Play Store.</p><br /> <p><strong>Wyoming:</strong> Research and extension efforts are underway to develop the Wyoming Agricultural Water Management Program (Wyo-AWMP) to effectively manage the agricultural water resources in Wyoming. In 2017 PI in collaboration with University of Wyoming Water Resources Data Systems and Wyoming Climate Office started Wyoming Agricultural Climate Network (WACNet) that has 26 agricultural weather station (5 weather station are under PI and 19 are under Wyoming climate office). All the data is current disseminated through University of Wyoming Water Resources Data Systems web-based portal to agricultural producers, irrigators, water resources managers, and other state and federal agencies. (http://www.wrds.uwyo.edu/WACNet/WACNet.html). PI is currently working with the University of Wyoming Water Resources Data Systems personnel to add real time reference ET product that will be used for irrigation scheduling purposes. In addition, major emphasis in Wyoming Agricultural Water Management Program (Wyo-AWMP) is given to the adoption of soil moisture sensors for irrigation scheduling, which were missing. PI developed a soil moisture lab at Powell research and Extension Center to evaluate different soil moisture sensors. Fifteen different soil moisture sensors were installed in 2017 and 2018 crop growing season to understand the variation in soil moisture content. In addition, PI and his extension team established a partnership with producers and other stakeholder to work together to increase the adoption of soil moisture sensors for irrigation scheduling. Ten producers in 2017 and 15 producers in 2018 were participated in this program. In 2016 and 2018, five field days and six extension presentation were delivered and eight extension articles and bulletins were published.</p><br /> <p> </p><br /> <p><strong>Objective 3.</strong> Coordinate the development of quality control (QC) procedures for weather data used for irrigation scheduling.</p><br /> <p><strong>Colorado:</strong> Short-term weather forecasts (up to 7 days) during the latter half of the 2018 growing season have been downloaded via the aWhere (http://www.awhere.com/) Weather Info API. The weather forecasts will be compared to selected Colorado Agricultural Meteorological Network (CoAgMet) station data in a historical (hindsight) analysis that will assess the accuracy and usability of the information in forecasting irrigation requirements.</p><br /> <p><strong>Louisiana: </strong>The new LSU AgCenter Agriclimatic Information System (LAIS) weather stations were purchased prior to 2012, but not all weather stations have been installed. It has also been reported that the program linking the weather station data to the website is ancient and they are unable to make changes to it. Instead, they are in the process of writing a new program for the new weather stations only. This allowed information technology to keep updating the old weather station data, but those stations are not maintained regularly and likely need calibration. They stopped maintaining the old stations due to the anticipation of the new stations. Once the new stations are fully operational and reporting on the LSU AgCenter website, then we will work to develop modern quality control procedures.</p><br /> <p><strong>Texas </strong><strong>USDA-ARS</strong><strong>: </strong>Quality weather data are essential not only for irrigation scheduling based on crop coefficients and reference ET, but are also essential for development and testing of ET models embedded in crop growth and water use simulation models that are now begin widely tested by the multi-state and international AgMIP team. These simulation models represent the next step in delivering crop growth and yield estimates along with ET values for irrigation scheduling so that economic factors can be included in more sophisticated irrigation management schemes. The Bushland large weighing lysimeter ET data sets are widely used for model development and testing, but are not fully useful without accompanying standard weather data that are produced with the same degree of quality assurance and control as the lysimeter data. The USDA-ARS team at Bushland, Texas, developed quality assurance (QA) and QC procedures for research weather data compiling data from a grassed weather station, four large weighing lysimeters and a U.S. Weather Service station at Bushland. Application of these procedures not only produced quality 15-minute, 365-day weather data, but documented the necessity of redundant instrumentation and weather stations for data verification and gap filling (Evett et al., submitted).</p><br /> <p><strong>US Bureau of Reclamation - AgriMet:</strong></p><br /> <ul><br /> <li>Remedial maintenance visits were made as needed to weather stations in order to maintain operational status and data quality standards.</li><br /> <li>Calibration and maintenance of sensors was performed at AgriMet stations during the spring in preparation for the growing season.</li><br /> <li>Graphical quality control procedures using Excel and Visual Basic continue to improve daily data quality control procedures.</li><br /> <li>Reclamation and WSU received funding from Reclamation’s Science and Technology Research Program for converting WSU’s Irrigation Scheduler to a smart phone application and enhancing features.</li><br /> <li>CR1000 data loggers and Raven XT cellular modems were implemented for each site. The cell modems provide two way communication and allow for fewer data gaps and better quality data in comparison to less reliable and clean GOES satellite transmissions.</li><br /> <li>October-December 2017: 22 sites were visited for maintenance. Two weather stations near Warm Springs, OR were damaged in wildfires, requiring extensive repair. The KFLO station was temporarily moved to allow the field to be laser leveled, it will return to the original location in the spring. Soil temp sensors were installed at all stations sponsored by Anheuser-Busch. The web platform upgrade to Linux was completed the end of December with transfer of Hydromet data. Prior to the upgrade, the two data sets were on different machines requiring support of two systems. Hosting on a single, more modern server will now allow for increased productivity and tools for data analysis.</li><br /> <li>January-April 2018: 16 sites were visited for maintenance. The temporary move for the KFLO site became a permanent location, and the site was visited to make the installation permanent. Jama attended an irrigation and center pivot training for center pivot operation and irrigation management in Gooding, Idaho. Jama also met with the Upper Snake Field Office and NRCS to discuss a possible joint irrigation efficiency project. The daily data feed from the Linux back to the legacy system was discontinued and both AgriMet and Hydromet are now soley on the Linux system.</li><br /> <li>April-June 2018: 15 sites were calibrated and 12 visited for maintenance. Jama assisted Colorado Climate Center installing 3 new stations funded by the Upper Colorado Region housekeeping and documentation on the AgriMet program in preparation for Karl’s departure from Reclamation.</li><br /> <li>July-September 2018: 78 sites were calibrated and 10 visited for maintenance. Jama presented to the WERA 102 and WERA 1022 committees. Karl accepted a job with the USACE and relocated to Davis, CA, his position has not yet been filled. Jama has been working to replace funding after termination of this contract. This is the final report to be submitted to BPA, the AgriMet program would like to thank BPA for a successful 35 year partnership.</li><br /> </ul><br /> <p><strong>Utah:</strong> Quality control procedures are developed and implemented by the Utah Climate Center.</p><br /> <p><strong>Washington:</strong> We have not made much progress on this, but have plans to kick off a plan to revise our weather network QA/QC procedures this next year so will have results to report next year.</p>Publications
<p>Aboutalebi, M., Torres-Rua, A.F. and Allen, N., 2018, May. Multispectral remote sensing for yield estimation using high-resolution imagery from an unmanned aerial vehicle. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III (Vol. 10664, p. 106640K). International Society for Optics and Photonics.</p><br /> <p>Aegerter, C., J. Wang, B. Oglesby, S. Irmak, B. Wardlow, H. Yang and Cui Ge. 2017. Mesoscale modeling of the meteorological impacts of irrigation during the 2012 central plains drought. J. Applied Meteorology and Climatology 56: 1259-1283. doi: 10.1175/JAMC-D-16-0292.1.</p><br /> <p>Allen, L.N. and Torres-Rua, A.F., April 2018. (Report) Verification of Water Conservation from Deficit Irrigation Pilot Projects in the Upper Colorado River Basin - Findings and Recommendations. Walton Family Foundation and S.D. Bechtel, Jr. Foundation, Utah Water Research Laboratory, Utah Agriculture Experiment Station.</p><br /> <p>Andales, A.A., Simmons, L.H., Bartolo, M.E. 2018. Determination of consumptive water use of grain sorghum in the Arkansas Valley of Colorado (2017 Season). Completion report to the Colorado Water Conservation Board and Colorado Water Institute. 14 pp.</p><br /> <p>Andales, A.A., Straw, D., Simmons, L.H., Marek, T.H., Bartolo, M.E., and Ley, T.W. 2018. Design and Construction of a Precision Weighing Lysimeter in Southeast Colorado. Transactions of the ASABE 61(2):509-521. DOI:10.13031/trans.12282</p><br /> <p>Andrade, M.A., S.A. O’Shaughnessy, S.R. Evett and P.D. Colaizzi. 2018. “ARSmartPivot – A decision support system for variable rate center pivot irrigation systems”, presented at the Sino-US Water Saving Technologies Meeting, Zhenjiang, China, Aug. 17, 2018.</p><br /> <p>BAE-1292. Understanding Motor and Gear Drive Nameplate Information for Irrigation Pump Evaluations. May 2018.</p><br /> <p>BAE-1538. Measuring Depth to Groundwater in Irrigation Wells. Nov 2017.</p><br /> <p>BAE-1541. Tracking Drought Using Soil Moisture Information. Aug 2018.</p><br /> <p>Bean, E., R. Huffaker, K. Migliaccio. 2018. Estimating field capacity from volumetric soil water content time series using automated processing algorithms. Vadose Zone J. Accepted. DOI: 10.2136/vzj2018.04.0073</p><br /> <p>Boyer, M. J., M. D. Dukes., I. Duerr, and N. Bliznyuk. 2018. Water conservation benefits of long-term residential irrigation restrictions in southwest Florida. Journal American Water Works Association, <span style="text-decoration: underline;"><a href="https://doi.org/10.5942/jawwa.2018.110.0019">https://doi.org/10.5942/jawwa.2018.110.0019</a></span> (published online).</p><br /> <p>Brar, D., W.L. Kranz, T. Lo, S. Irmak and D.L. Martin. 2017. Conservation of energy using variable frequency drive for center pivot irrigation: Standard systems. Transactions of the ASABE 60(1): 95-106. doi:10.13031/trans.11683.</p><br /> <p>Cardenas, B. and M. D. Dukes. 2018. Dry out periods of two rain sensors and three soil textures; comparisons, water savings potential and manufacturing recommendations. <em>Vadose Zone Journal</em>. (accepted)</p><br /> <p>Chávez, J.L,, and López-Urrea, R. 2018. Modeling corn surface resistance to estimate actual water use. In Proceedings of the 2018 ASCE EWRI World Environmental and Water Resources Congress 2018, Minneapolis, Minnesota, June 2-7, 2018, pp 62-73. Site: <a href="https://ascelibrary.org/doi/book/10.1061/9780784481400">https://ascelibrary.org/doi/book/10.1061/9780784481400</a></p><br /> <p>Chávez, J.L,, Zhang, H., Capurro, M.C., Masih, A., Altenhofen, J. 2018. “Evaluation of multispectral unmanned aerial systems for irrigation management,” In Proceedings of the 2018 SPIE Conference Volume 10664: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III, 106640Q (15 May 2018); doi: 10.1117/12.2305076; https://doi.org/10.1117/12.2305076, Orlando, FL, 17 April, 2018.</p><br /> <p>Chávez, J.L. and H. Zhang. 2018. Determining crop soil water deficit with an UAS. In Proceedings of the 30th Annual Central Plains Irrigation Conference (CPIC), Colby, KS, February 20-21, 2018, CPIA, 760 N. Thompson, Colby, KS. Website: <a href="http://www.k-state.edu/irrigate/oow/cpic18.html">http://www.k-state.edu/irrigate/oow/cpic18.html</a></p><br /> <p>Colaizzi, P.D., S.A. O'Shaughnessy, and S.R. Evett. 2018. Calibration and tests of commercial wireless infrared thermometers. Appl. Engr. Agric. 34(4): 647-658. ISSN 0883-8542 <a href="https://doi.org/10.13031/aea.12577">https://doi.org/10.13031/aea.12577</a>.</p><br /> <p>Conger, S. L. D. 2018. Are You Experiencing Water Stress? Louisiana Crops Newsletter, May 2018.</p><br /> <p>da Silva, A. L. B. R., L. Zotare, M. D. Dukes, S. Agehara, S. Asseng, and E. van Santen. 2018. Irrigation <span style="text-decoration: underline;">lli</span> method and application timing effect on potato nitrogen fertilizer uptake efficiency. <em>Nutrient Cycling in Agroecosystems</em>. (online 17 Aug 2018).</p><br /> <p>Davis, S. L. 2018. Irrigation Management Plans for the Upcoming Irrigation Season. Louisiana Crops Newsletter, February 2018.</p><br /> <p>Davis, S. L. 2018. Irrigation scheduling becomes most important in transitional rainfall environments. Louisiana Irrigation Association Newsletter, Spring 2018 Ed.</p><br /> <p>Djaman K., K. Koudahe, C.O. Akinbile, S. Irmak. 2017. Evaluation of eleven reference evapotranspiration models in semiarid conditions. Journal of Water Resource and Protection 9:1469-1490. <a href="https://doi.org/10.4236/jwarp.2017.912094">https://doi.org/10.4236/jwarp.2017.912094</a>.</p><br /> <p>Djaman, K., A.B. Balde, K.B. Muller, D. Rudnick and S. Irmak. 2017. Long-term trend analysis in climate variables and agricultural adaptation strategies to climate change in the Senegal River Basin. Int. J. Climatology (Royal Meteorological Society). 37(6):2873-2888. doi:10.1002/joc.4885.</p><br /> <p>Djaman, K., D. Rudnick, D. Mutiibwa, L. Diop, M. Sall, I. Kabenge, A. Bodian, H. Tabari and S. Irmak. 2017. Evaluation of the Valiantzas’ simplified forms of the FAO-56 Penman-Monteith reference evapotranspiration model under humid climate. J. Irrigation and Drainage Engineering. 143(8):06017005-1. doi:10.1061/(ASCE)IR.1943-4774.0001191.</p><br /> <p>Djaman, K., K. Kouhade, S. Allen, M. O”Neill, and S. Irmak. 2017. Validation of Valiantzas’ reference evapotranspiration equation under different climatic conditions. J. Irigation and Drainage Systems Engineering 6(3): 1-7. doi:10.4172/2168-9768.1000196.</p><br /> <p>Djaman, K., Koudahe, K., Sall, M., Kabenge, I., Rudnick, D., and Irmak S. 2017. Performance of twelve mass transfer based reference evapotranspiration models under humid climate. Journal of Water Resource and Protection 9:1347-1363. <a href="https://doi.org/10.4236/jwarp.2017.912086">https://doi.org/10.4236/jwarp.2017.912086</a>.</p><br /> <p>Djaman, K., M. O'Neill, C. Owen, D. Smeal, K. Koudahe, M. West, S. Allen, K. Lombard, and S. Irmak. 2018. Crop evapotranspiration, irrigation water requirement, and water productivity of maize from meteorological data under semiarid climate. Water 10(405):2-17. doi:10.3390/w10040405.</p><br /> <p>Duerr, I., H. R. Merrill, C. Wang, R. Bai, M. Boyer, M. D. Dukes, and N. Bliznyuk. 2018. Forecasting urban water demand with statistical and machine learning methods using large space-time data. <em>Environmental Modelling and Software</em>, 102(2018): 29-38, <span style="text-decoration: underline;"><a href="https://doi.org/10.1016/j.envsoft.2018.01.002">https://doi.org/10.1016/j.envsoft.2018.01.002</a></span></p><br /> <p>Dukes, M. D. Importance of irrigation efficiency in landscapes. Watersmart Innovations Conference. Oct. 3-5, Las Vegas, NV, <span style="text-decoration: underline;"><a href="https://ceregportal.com/wsi/documents/sessions/2018/T-1852.pdf">https://ceregportal.com/wsi/documents/sessions/2018/T-1852.pdf</a></span></p><br /> <p>Dukes, M. D., M. Zamora, D. L. Rowland. 2018. Effect of irrigation scheduling technique and fertility level on corn yield and nitrogen movement. In World Environmental and Water Resources Congress 2018. Reston, VA: American Society of Civil Engineers.</p><br /> <p>Evett, S.R., G.W. Marek, K.S. Copeland and P.D. Colaizzi. Quality management for research weather data - Bushland, Texas. Submitted to Agrosystems, Geosciences & Environment, Sep 7, 2018.</p><br /> <p>Evett, S.R., G.W. Marek, P.D. Colaizzi, B.B. Ruthardt and K.S. Copeland. 2018a. A subsurface drip irrigation system for weighing lysimetry. Appl. Engineer. Agric. 34(1):213-221. <a href="https://dx.doi.org/10.13031/aea.12597">https://dx.doi.org/10.13031/aea.12597</a>.</p><br /> <p>Evett, S.R., P.D. Colaizzi, G.W. Marek, J.E. Moorhead, D.K. Brauer, K.S. Copeland and B.B. Ruthardt. 2018b. “Evaporative Losses from Sprinkler and Subsurface Drip Irrigation in a Region of Large Evaporative Demand”, presented at the 2018 Annual International Meeting of ASABE, July 29- Aug 1, 2018, Detroit, MI.</p><br /> <p>Evett, S.R., R.C. Schwartz and H.S. Schomberg. 2018c. Electromagnetic and nuclear soil water sensing methods: Comparisons and newer technologies. In Report of the Second Research Coordination Meeting of the Coordinated Research Project, “Nuclear Techniques for a Better Understanding of the Impact of Climate Change on Soil Erosion in Upland Agro-ecosystems (D1.50.17)”, held in Rabat, Morocco, 16 to 20 April 2018.</p><br /> <p>Haghverdi, A., & Laosheng, W. U. (2018). Accounting for salinity leaching in the application of recycled water for landscape irrigation. Southern California Salinity Coalition.</p><br /> <p>Heitholt, J., V. Sharma, and A. Pierson. 2017. Yield in 36 Dry Bean Genotypes and its Correlations with Agronomic Traits. University of Wyoming, Agricultural Experiment Station Field Day Bulletin, pp 46-47 <a href="http://www.uwyo.edu/uwexpstn/publications/field-days-bulletin/2017-field-days-bulletin.pdf">http://www.uwyo.edu/uwexpstn/publications/field-days-bulletin/2017-field-days-bulletin.pdf</a>.</p><br /> <p>Her, Y.G., K.J. Boote, K.W. Migliaccio, C. Fraisse, D. Letson, O. Mbuya, A. Anandhi, H. Chi, L. Ngatia, S. Asseng. 2017. Climate change impacts and adaptation in Florida’s Agriculture. Florida Climate. Florida Climate Institute, Gainesville, FL. <a href="http://www.uwyo.edu/uwexpstn/publications/field-days-bulletin/2017-field-days-bulletin.pdf">http://www.uwyo.edu/uwexpstn/publications/field-days-bulletin/2017-field-days-bulletin.pdf</a></p><br /> <p>Jones, A.S., Andales A.A., Chávez, J.L., McGovern, C., Smith, G.E.B. 2017. Weather data integration into irrigation scheduling tools. In: Colorado Water (Mokry, M. ed.), vol. 34(6) (November/December 2017): 24-28, Colorado Water Institute, Colorado State University, Fort Collins, CO. (<a href="http://issuu.com/coloradowater/docs/cw_34_6?e=1964603/55042047">http://issuu.com/coloradowater/docs/cw_34_6?e=1964603/55042047</a>)</p><br /> <p>Kukal, M., and S. Irmak. 2017. Spatial and temporal changes in maize and soybean grain yields, precipitation use efficiency and crop water use efficiency in the USA Great Plains. Transactions of the ASABE 60(4):1189-1208. doi 10.13031/trans.12072.</p><br /> <p>Kukal, M., S. Irmak, and A. Kilic. 2017. Long-term spatial and temporal maize and soybean evapotranspiration derived from ground and satellite-based NDVI datasets over the USA Great Plains. J. Irrigation and Drainage Engineering 143(9):04017031. doi:10.1061/(ASCE)IR.1943-4774.0001212.</p><br /> <p>L. Niel Allen and Jennifer MacAdam. October 2018 7th Edition of Forages, Volume II, The Science of Grassland Agriculture, Chapter 27 - Irrigation and Water Management, in final editing.</p><br /> <p>Liu, G., Y.C. Li, K.W. Migliaccio, Y. Ouyang, A. K. Alva. 2017. Identification of factors most important for ammonia emission from fertilized soils for potato production using principal component analysis. Journal of Sustainable Watershed Science and Management 1(1)21-30.</p><br /> <p>Mbabazi, D. G, K.W. Migliaccio, J.H. Crane, C. Fraisse, L. Zotarelli, K.T. Morgan, N. Kiggundu. 2017. An irrigation schedule testing model for optimization of the Smartirrigation avocado app. Agricultural Water Management 179:390-400. <a href="http://dx.doi.org/10.1016/j.agwat.2016.09.006">http://dx.doi.org/10.1016/j.agwat.2016.09.006</a></p><br /> <p>Mello, S.C., Y.C. Li, K.W. Migliaccio, E.P. Linares, J. Colee, J. Angelotti-Mendonca. 2017. Effects of polymer coated urea and irrigation rates on lantana growth and nitrogen leaching. Soil Sci. Soc. Am. J. 81:546–555. doi:10.2136/sssaj2016.09.0307</p><br /> <p>Mohamed, A.Z., R.T. Peters, X. Zhu, and A. Sarwar. 2018. Adjusting Irrigation Uniformity Coefficients for Unimportant Variability on a Small Scale. Accepted for publication in Agricultural Water Management. </p><br /> <p>Morera, M. C., P. F. Monaghan, and M. D. Dukes. 2018. Evolving response to smart irrigation controllers in high water-use central Florida homes. <em>Journal American Water Works Association</em> (accepted).</p><br /> <p>Nguyen, D.C.H., Ascough II, J.C., Maier, H.R., Dandy, G.C. and Andales, A. A. 2017. Optimization of Irrigation Scheduling Using Ant Colony Algorithms and an Advanced Cropping System Model. Environmental Modelling & Software 97:32-45. DOI:10.1016/j.envsoft.2017.07.002</p><br /> <p>O'Shaughnessy, S.A., J.J. Casanova, S.R. Evett and P.D. Colaizzi. 2018. Computer vision qualified infrared temperature sensor. United States Patent No. 9,866,768 B1. Issued January 9, 2018.</p><br /> <p>Pratt, T., Allen, L.N., Rosenberg, D.E., Keller, A.A. and Kopp, K., 2018. Urban agriculture and small farm water use: Case studies and trends from Cache Valley, Utah. Agricultural Water Management, 213, pp.24-35.</p><br /> <p>Rai, A., J. Heitholt, and V. Sharma. 2018. Dry Bean Growth Dynamics in Response to Deficit Irrigation under Surface- and Sprinkler-Irrigation Systems. University of Wyoming, Agricultural Experiment Station Field Day Bulletin. <a href="http://www.uwyo.edu/uwexpstn/publications/field-days-bulletin/2018-field-days-bulletin.pdf">http://www.uwyo.edu/uwexpstn/publications/field-days-bulletin/2018-field-days-bulletin.pdf</a>.</p><br /> <p>Ramanitharan Kandiah, Gilbert Robinson, Krishna Kumar Nedunuri, Subramania Sritharan. Hydrology- Environment- Cost Index for Hydraulic Fracturing: A Comparative study in Ohio, California and Texas. 2019 World Environmental & Water Resources Congress. Pittsburgh, PA. May 19-23, 2019. (Oral, Abstract submitted)</p><br /> <p>Ramanitharan Kandiah, Xiaofang Wei, Krishna Kumar Nedunuri, and Subramania Sritharan. Decadal Changes in the Water Quantity and Quality in Tuscarawas Watershed, Ohio. 2018 World Environmental & Water Resources Congress. Minneapolis, MN. June 3–7, 2018 (Poster)</p><br /> <p>Ringenberg, D., W. Kranz, S. Irmak, and B. Dvorak. 2018. Extending Extension’s Outreach: Efficacy of using student interns to obtain implementation of irrigation improvements. Journal of Extension 56(4):1-6.</p><br /> <p>Ruckert, Alice, L. Niel Allen, and Ricardo A. Ramirez. Combinations of plant water-stress and neonicotinoids can lead to secondary outbreaks of Banks grass mite (Oligonychus pratensis Banks). PloS one 13, no. 2 (2018): e0191536.</p><br /> <p>Schwartz, R.C., S.R. Evett and R.J. Lascano. 2018. Letter to the Editor: Comments on "J. Singh et al., Performance assessment of factory and field calibrations for electromagnetic sensors in a loam soil" [Agric. Water Manage. 196 (2018) 87-98]. Agric. Water Manage. 203(2018):236-239. <a href="https://doi.org/10.1016/j.agwat.2018.02.029">https://doi.org/10.1016/j.agwat.2018.02.029</a>.</p><br /> <p>Sharma, V. 2018. Development of Sugarbeet Crop Coefficients. University of Wyoming, Agricultural Experiment Station Field Day Bulletin. <a href="http://www.uwyo.edu/uwexpstn/publications/field-days-bulletin/2018-field-days-bulletin.pdf">http://www.uwyo.edu/uwexpstn/publications/field-days-bulletin/2018-field-days-bulletin.pdf</a></p><br /> <p>Sharma, V. 2018. Irrigation Management – Basics of Soil Water. University of Wyoming Extension.http://www.wyoextension.org/publications/Search_Details.php?pubid=1987&pub=B-1330</p><br /> <p>Sharma, V. 2018. Methods and Techniques of Soil Moisture Monitoring. University of Wyoming Extension (Accepted, In press).</p><br /> <p>Sharma, V. 2018. Quantification of Growing-Season Crop Evapotranspiration for Sugarbeet in Wyoming. University of Wyoming, Agricultural Experiment Station Field Day Bulletin. <a href="http://www.uwyo.edu/uwexpstn/publications/field-days-bulletin/2018-field-days-bulletin.pdf">http://www.uwyo.edu/uwexpstn/publications/field-days-bulletin/2018-field-days-bulletin.pdf</a></p><br /> <p>Sharma, V. 2018. Soil Moisture Sensors Boost Irrigation Effectiveness. University of Wyoming Extension. Barnyard & Backyards. Summer 2018</p><br /> <p>Sharma, V. 2018. Strategies to save water when irrigating. Navigating Drought in Wyoming. Page 22. http://www.wyoextension.org/agpubs/pubs/B-1325_Drought_web.pdf.</p><br /> <p>Sharma, V., A. Pierson, and C. Reynolds. 2017. Effect of Variable Irrigation and Nitrogen Application on Sugarbeet Root and Sugar Yield. University of Wyoming, Agricultural Experiment Station Field Day Bulletin, pp 66-67.</p><br /> <p>Sharma, V., A. Pierson, and J. Heitholt. 2017. Dynamics of Soil Moisture and Crop Canopy Architecture Traits for Dry Beans in Wyoming. University of Wyoming, Agricultural Experiment Station Field Day Bulletin, pp 52-53.</p><br /> <p>Sharma, V., A. Rai*, and J. Heitholt. 2018. Dry Bean Yield Response to Deficit Irrigation under Surface and Sprinkler Irrigation Systems. University of Wyoming, Agricultural Experiment Station Field Day Bulletin. <a href="http://www.uwyo.edu/uwexpstn/publications/field-days-bulletin/2018-field-days-bulletin.pdf">http://www.uwyo.edu/uwexpstn/publications/field-days-bulletin/2018-field-days-bulletin.pdf</a></p><br /> <p>Sharma, V., and J. Heitholt. 2018. Screening Dry Bean Genotypes for Drought Tolerance in Wyoming. University of Wyoming, Agricultural Experiment Station Field Day Bulletin. <a href="http://www.uwyo.edu/uwexpstn/publications/field-days-bulletin/2018-field-days-bulletin.pdf">http://www.uwyo.edu/uwexpstn/publications/field-days-bulletin/2018-field-days-bulletin.pdf</a></p><br /> <p>Sharma, V., C. Nicholson, T. Bergantino, J. Cowley, B. Hess, and J. Tanaka. 2018. Wyoming Agricultural Climate Network (WACNet). University of Wyoming, Agricultural Experiment Station Field Day Bulletin. <a href="http://www.uwyo.edu/uwexpstn/publications/field-days-bulletin/2018-field-days-bulletin.pdf">http://www.uwyo.edu/uwexpstn/publications/field-days-bulletin/2018-field-days-bulletin.pdf</a></p><br /> <p>Sharma, V., J. Heitholt, and J. Vardiman. 2017. Dry bean water management and yield response under surface and sprinkler irrigation. University of Wyoming, Agricultural Experiment Station Field Day Bulletin, pp 32. <a href="http://www.uwyo.edu/uwexpstn/publications/field-days-bulletin/2017-field-days-bulletin.pdf">http://www.uwyo.edu/uwexpstn/publications/field-days-bulletin/2017-field-days-bulletin.pdf</a></p><br /> <p>Stevens, G., M. Rhine and J. Heiser. 2018. Rice production with furrow irrigation in the Mississippi River Delta Region of the USA. P. 69-80. <em>In</em> F. Shah, Z. Hayat Khan and A. Iqbal (eds.), Rice Crop. DOI: 10.5772/Intechopen.74820. Available from: <a href="https://www.intechopen.com/books/rice-crop-current-developments/rice-production-with-furrow-irrigation-in-the-mississippi-river-delta-region-of-the-usa">https://www.intechopen.com/books/rice-crop-current-developments/rice-production-with-furrow-irrigation-in-the-mississippi-river-delta-region-of-the-usa</a></p><br /> <p>Stevens, G., M. Rhine, and J. Nelson. 2017. Soil water balances for irrigation cotton using a phone app. Beltwide Cotton Conf., Dallas, TX. Jan. 5.</p><br /> <p>Torrion, J.A. and R.N. Stougaard. 2017. Impacts and limits of irrigation water management on spring wheat yield and quality. Crop Sci. J 57:1-13.</p><br /> <p>Trenton Barnes. 2017. Impact Index for Measuring Fracking Impact on Water Resources. Louis Stokes Midwest Center of Excellence Conference- 2017. Sheraton Indianapolis Hotel, Keystone Crossing, IN. October 6, 2017 (Poster)</p><br /> <p>Vincent, C., B. Schaffer, D.L. Rowland, K.W. Migliaccio, J.H. Crane, and Y.C. Li. 2017. Sunn hemp intercrop and mulch increases papaya growth and reduces wind speed and virus damage. Scientia Horticulturae 218:304-315.</p><br /> <p>Zambrano-Vaca, C., L. Zotarelli, K.W. Migliaccio, R Beeson, K. Morgan, J. Chaparro, and M. Olmstead. 2018. Irrigation practices for peaches in Florida. HS1316. Horticultural Sciences, Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida., 6 pgs.</p><br /> <p>Zamora, M. and Dukes, M. D. etc. 2018. A partial nitrogen (N) budget: Inputs and potential outputs from corn fields in Florida sandy soils. ASABE International Meeting, July 29-Aug 1, Detroit, MI.</p><br /> <p>Zamora, M. M. Dukes, D. Rowland. 2018. Effect of irrigation scheduling technique and fertility level on corn yield and nitrogen management. 14<sup>th</sup> International Conference on Precision Agriculture. June 24-27, Montreal, Canada.</p><br /> <p>Zamora, M., M. Dukes, S. Rath. 2018. A partial nitrogen (N) budget: Inputs and potential outputs from corn fields in Florida sandy soils. In Proceedings of the American Society of Agricultural and Biological Engineers Annual International Meeting. Jul 29 – Aug 1, Detroit, MI, Paper No. 1801676. ASABE, St. Joseph, MI.</p><br /> <p>Zhang, M. P, H. Singh P, K.W. Migliaccio, I. Kisekka. 2017. Evaluating water table response to rainfall events in a shallow aquifer and canal system. Hydrological Processes 31:3907-3919. 10.1002/hyp.11306</p><br /> <p>Zhang, M., Y.G. Her, K.W. Migliaccio. 2017. Florida Rainfall Data Source and Types. AE517. Agricultural and Biological Engineering, Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida. http://edis.ifas.ufl.edu/ae517, 5 pgs.</p><br /> <p>Zhang, M.P, C. De LeonG, K.W. Migliaccio. 2018. Evaluation and comparison of interpolated gauge rainfall data and gridded rainfall data in Florida. Hydrological Sciences Journal doi: 10.1080/02626667.2018.1444767</p><br /> <p>Zurweller, B.A., D.L. Rowland, B.L. Tillman, P. Payton, K.W. Migliaccio, D. Wright, J. Erickson. 2018. Assessing above- and below-ground traits of disparate peanut genotypes for determining adaptability to soil hydrologic conditions. Field Crops Research 219: 98-105.</p>Impact Statements
- Utah: Irrigation factsheets downloads from internet approximately 5,500 from October 2017 through September 2018.
Date of Annual Report: 12/18/2019
Report Information
Annual Meeting Dates: 12/06/2019
- 12/06/2019
Period the Report Covers: 10/01/2018 - 09/30/2019
Period the Report Covers: 10/01/2018 - 09/30/2019
Participants
David Yates – Climate Scientist, NCARMike Tansey – US Bureau of Reclamation
Neil Allen – Faculty, Utah State University
Allan Andales – Faculty, Colorado State University
James Han – FieldNET Advisor Specialist, Lindsay Corporation
Chris Henry – Faculty, Arkansas State University
Xinhua Jia – Faculty, North Dakota State University
Jonathan Aguilar – Faculty, Kansas State University
Charles Hillyer – Director, Center for Irrigation Technology, Fresno State University
Daran Rudnick – Faculty, University of Nebraska
Stacia Conger – Faculty, Louisiana State University Agricultural Center
Brief Summary of Minutes
Accomplishments
<p><strong>WERA 1022 – Accomplishments</strong></p><br /> <p><strong>October 1, 2018 – September 30, 2019</strong></p><br /> <p> <br /> <strong>Objective 1. Coordinate the documentation of crop coefficients used in irrigation scheduling.</strong></p><br /> <p><strong>Colorado: </strong>Hourly and daily crop evapotranspiration (ETC) rates of dry beans and grass hay were collected from two precision weighing lysimeters at the CSU Arkansas Valley Research Center in Southeast Colorado during 2019. The ETc data will be used in conjunction with ASCE Standardized reference ET to develop crop coefficients (Kc) of dry bean and grass hay. The seasonal soil water balance and daily actual ETc for grass hay were also calculated from lysimeter data collected in 2018. The 2018 grass hay ETc data will be combined with the 2019 data to develop a grass hay Kc curve appropriate for the semi-arid conditions of Southeast Colorado.</p><br /> <p><strong>Minnesota:</strong> This year in Minnesota, we started a new research project that evaluates the interaction effects of different irrigation strategies and different nitrogen (N) rates on grain yield, nitrate-N leaching, crop evapotranspiration, and N and water use efficiency in continuous maize. The goal of this study is to develop the best management system aimed at maximum maize production and minimum nitrate leaching. The other objective of this research is to develop crop coefficients (Kc) for maize, under various irrigation and nitrogen management practices in the central sands region of Minnesota. For now, ASCE manual 70 crop coefficients adjusted for Minnesota climate are being used for irrigation water management in the state. This year was exceptionally wet in Minnesota, resulted in limited irrigation requirements.</p><br /> <p><strong>Utah:</strong> We conducted a study comparing the ET of drip-irrigated onions to surface irrigated onions. ET was measured with a soil water budget and surface temperatures. Three arrays of 10 Acclima TDR-15H were used in each of the drip and surface irrigated fields along with surface temperature infrared radiometers. The soil moisture sensors were located under both the furrow and bed. The amount of water applied with the drip system was less than 25 percent of surface irrigation. The primary difference between the drip and surface irrigation was the soil water evaporation was much greater for surface irrigation. We are still analyzing the data.</p><br /> <p><strong>Washington:</strong> This year in Washington state we continued to tabulate and revise their tables of crop coefficients. We created a database of these that we converted over to SQL and used these to create some online tools for estimating historical crop water use and create Woodruff charts and irrigation system design capacity estimator. We plan to make the entire database of crop coefficients available online and provide a way to get feedback from the public to get revised and updated season dates and or accept data from users as to whether they have doubts about the existing crop coefficients. In Washington we have a very large variety of crops and we don’t have good crop coefficients for most of them and these are estimated from other crops. Our growers are also fairly educated and sophisticated. Thus we are open to growers who might have more data that they would be willing to share that would better inform the estimates that we are forced to make for what these crop coefficients are.</p><br /> <p> </p><br /> <p><strong>Objective 2. Coordinate efforts to promote adoption of improved irrigation scheduling technology, including computer models based on crop coefficients and ETref, remote sensing and instrumentation that will help producers more efficiently apply irrigation water.</strong></p><br /> <p><strong>Arkansas: </strong>This year Arkansas hosted an Irrigation Yield Contest to promote the adoption of Irrigation Water Management Practices. The three practices that are being adopted are surge irrigation, computerized hole selection for lay-flat pipe and soil moisture monitoring. Fifty-nine irrigators have participated in this program which includes a large cash prize for winners in three the commodity categories of corn soybeans and rice. The average rice yield attained by contestants in 2018 was 210.7 bpa with a water use efficiency of 5.17 bu/ac-in using 29.2 inches of irrigation. The winner achieved a yield of 229 bpa with a water use efficiency of 7.8 bu/ac-in using only 15.9 inches of irrigation. For reference, the average yield in the Rice Research and verification program was 186 bpa and the average depth of irrigation applied was 24.6 ac-in/ac. This winner achieved a very good rice crop on 58% less water than the expected irrigation needs for rice. This program is providing key data on water use, yields, and water use efficiency. The participants are demonstrating extremely high yields, low water use, and high water use efficacies. Most importantly the contest demonstrates the full effect and results that can be achieved when irrigators apply highly managed crop production and irrigation management practices. The program is demonstrating how a comprehensive approach to IWM can achieve sustainability. </p><br /> <p>To support this program, Irrigation Schools have been developed. 74 participants attended surge schools and 81 attended the soil moisture schools for a total of 472 contact hours. Schools are limited to 20 people per school and these are designed as intense learning environments, with an average of 2-5 contact hours. Soil moisture sensor schools resulted in substantial learning, 95% reported substantial learning on how to assemble and install soil moisture sensors. Using the mobile app to interpret sensors resulted in 85% of respondents reporting substantial learning about this key skill. Participants were using sensors on 12% of their acres before the workshop and indicated that they could be used on over 60% of their acres. </p><br /> <p>Mobile apps to help with rice irrigation design and soil moisture monitoring have been developed and are used by Arkansas irrigators. </p><br /> <p><strong>California:</strong> Two adjacent irrigation trials (a total of 144 landscape irrigation plots) were established in early 2019 at the University of California, Riverside Agricultural Experiment Station in Riverside, California to develop crop coefficient and irrigation management information for twelve groundcover species. The selected species are <strong>(a):</strong> Ice Plant (<em>Ruschia lineolata nana</em>), (<strong>b):</strong> Creeping Australian Saltbush (<em>Rhagodia spinescens</em>); (<strong>c):</strong> Rosemary (<em>Rosmarinus officinalis ‘Roman Beauty’</em>), (<strong>d):</strong> Gold Emu Bush (<em>Eremphila glabra ‘Mingenew Gold’</em>); (<strong>e</strong>)<strong>:</strong> Coyote Bush (<em>Baccharis x ‘Starn’ Thompson</em>); (<strong>f): </strong>Saltillo Evening Primrose (<em>Oenothera stubbei</em>); (<strong>g</strong>) Buckwheat (<em>Eriogonum fasciculatum ‘Warriner Lytle’</em>); (<strong>h</strong>) Sea Heath (<em>Frankenia thymifolia</em>); (<strong>i</strong>) Lantana (<em>Lantana montevidensis</em>); (<strong>j</strong>) Jasmine (<em>Trachelospermum jasminoides</em>); (<strong>k</strong>) Honeysuckle (<em>Lonicera japonica</em>); and (<strong>l</strong>) Ice plant (<em>Delosperma cooperi ‘John Profitt’</em>).</p><br /> <p>Each 10 feet × 10 feet plot was equipped with four quarter-circle (pop-up heads) sprinklers, all four connected to a solenoid valve allowing independent irrigation control for each plot. To eliminate plot edge effect and avoid interference between adjacent plots, adequate borders (~ 4 feet) were considered. Two Weathermatic smart evapotranspiration-based irrigation controllers were installed and wired to all the solenoid valves. In addition, two Badger flowmeters, and two Weathermatic weather sensors were installed and attached to the smart controllers. A total of 288 Acclima true TDR-315L soil moisture sensors were installed at the center of 36 plots. The sensors were installed at 8 depths up to 5 feet deep to monitor soil water status within and below the active root zone of the plants.</p><br /> <p>Recently, we started imposing a varying degree of irrigation treatments. We introduced four irrigation treatments in early September. Four irrigation treatments were set to schedule autonomously to fulfill 100% of reference evapotranspiration for all species. The irrigation level was reduced to 80% in late September across the treatments. However, there is a difference in the frequency of irrigation applications. One treatment allows the smart controller to apply irrigation 7 days per week while the remaining treatments restrict the irrigation applications to 5, 4, and 3 days per week. We will collect digital images from plots (for visual assessment) as well as NDVI data using handheld and drone-mounted sensors. These data will be analyzed along with the soil moisture data to determine the patterns of water uptake and the response of different species to the irrigation treatments.</p><br /> <p><strong>Colorado: </strong>Four ET-based irrigation scheduling presentations were given to a broad audience. The presentation events and audiences were:</p><br /> <p>2019 August 28, Measurements of dry bean evapotranspiration in the Arkansas Basin using a precision weighing lysimeter, Dry bean Field Day, Lucerne, CO. (13 attendees – bean processors, farmers, Extension agents)</p><br /> <p>2019 February 26-27. Irrigation scheduling using a water balance model and soil moisture sensors. 31st Annual Central Plains Irrigation Conference, Kearney, NE, (Invited; 40 producers, crop advisors, and researchers attended across 2 sessions)</p><br /> <p>2019 February 7, Variable Rate Irrigation and its Feasibility in the San Luis Valley (Modeling approach using the Water Irrigation Scheduler for Efficient Application), Southern Rocky Mountain Ag Conference, Monte Vista, CO. (Invited; rated 4.53 out of 5.0 by audience; ~ 75 attendees – farmers, crop consultants, agricultural scientists)</p><br /> <p>2019 January 31, Irrigation Timing (ET and Water Balance Modeling Approach), MillerCoors, Golden, CO. (Invited; 8 barley agronomists from Western U.S. and 3 managers)</p><br /> <p><strong>Florida:</strong> Ferrarezi Citrus Horticulture Lab</p><br /> <p>Study 1: Plant density, fertilizer type and irrigation systems for sweet orange production at the Indian River District</p><br /> <p>Sweet oranges (<em>Citrus sinensis</em>) are impacted by huanglongbing (HLB), a disease associated with Candidatus <em>Liberibacter asiaticus</em>. The disease is threatening the citrus industry, with devastating effects on fruit production. Higher plant density can increase fruit yield per area under high HLB pressure, maximizing income and extending grove survival until a definite cure is found. This study evaluated the effect of tree planting density, fertilizer type and irrigation systems on fruit yield and quality. ‘Valencia’ orange on ‘Kuharske’ citrange (<em>C. sinensis</em> × <em>Poncirus trifoliata</em>) trees were planted in Sept/2013 (2,995 trees in 1.61 ha). We tested three treatments: standard tree spacing (3.8×7 m, 357 trees/ha) + dry granular fertilizer + microsprinkler irrigation (one emitter per tree; microsprinkler 50 green nozzle, 16.7 GPH at 20 psi) (Bowsmith, Exeter, CA), high density staggered ([2.7×1.5×0.9 m]×6.1 m, 953 trees/ha) + fertigation + microsprinkler irrigation (one emitter per two trees), and high density staggered + fertigation + drip irrigation (two lines per row; Emitterline 0.58 GPH at 10 psi, 12-inch spacing) (Jain Irrigation), in a complete randomized block design with eight replications.</p><br /> <p>Study 2: Effect of propagation methods on citrus rootstock water uptake</p><br /> <p>Huanglongbing or greening disease increased the need for new plantings and resetting in the field. To meet the high tree demand, citrus nurseries need high-quality, fast-growing rootstocks. Vegetative propagation is an alternative to the traditional seedling production due to the increased turnaround in the nursery. However, it may induce changes in the root system architecture and the development of adventitious roots instead of the taproot, altering root morphology and potentially the water uptake performance. The objective of this study was to compare the plant water uptake of citrus rootstocks propagated using different methods. We tested four citrus rootstocks {‘Swingle’ [<em>Citrus paradisi</em> × <em>Poncirus trifoliata</em>], ‘US-942’ [‘Sunki’ (<em>Citrus reticulata</em>) × ‘Flying Dragon’ (<em>Poncirus trifoliata</em>)], ‘US-897’ [‘Cleopatra’ (<em>Citrus reticulata</em>) × ‘Flying Dragon’ (<em>Poncirus trifoliata</em>)], and ‘US-802’ [‘Siamese’ (<em>Citrus grandis</em>) × ‘Gotha Road’ (<em>Poncirus trifoliata</em>)]} and three propagation methods (seed propagation, stem cuttings and tissue culture).</p><br /> <p>Study 3: High-density grapefruit production in open hydroponics system</p><br /> <p>Precise irrigation and fertigation management provide a less-limiting environment to roots while minimizing over-irrigation and leaching of nutrients. This concept can improve tree growth in the presence of HLB and help optimize water and nutrient use. Higher tree density can increase fruit yield per area under high HLB pressure. We conducted a study to evaluate the efficiency of open hydroponics on ‘Ray Ruby’ grapefruit production under different irrigation systems and tree density. We tested a combination of rootstocks (Sour orange and US897), tree spacing [standard and high density staggered (HDS)], fertilization (dry granular and fertigation), and irrigation systems (drip and microjet).</p><br /> <p><strong>Louisiana: </strong> This year, work continued on developing the STAMP irrigation scheduling tool for Louisiana agronomic producers. This scheduling tool is a spreadsheet using the soil water balance or checkbook method. The spreadsheet was prepopulated with agronomic and soil data so that minimal information was required for field use. The calculations were updated to handle surface runoff and surface storage using the curve number method. The model was tested for its ability to predict an appropriate irrigation schedule against a two-year field plot study. Future work on this tool includes developing a users manual and updating the infiltration calculations to better estimate effective depths of irrigation and rainfall.</p><br /> <p>During the testing of the STAMP irrigation scheduling tool, it was observed that there are few reliable resources for estimating reference evapotranspiration (ET<sub>O</sub>) and rainfall within the state. These two pieces of information are critical to calculating a predictive irrigation schedule. Thus, a study was initiated to evaluate the use of atmometers for measuring ET<sub>O</sub> in the field. Three atmometers were installed at the LSU AgCenter Red River Research Station in Bossier City, LA as replications. These devices were installed within 50 m of a research-grade weather station to estimate ET<sub>O</sub> from both data sources for comparison. This study was replicated at the LSU AgCenter Dean Lee Research Station in Alexandria, LA. Rainfall variability and data quality is also being evaluated. We have collaborated with Dr. Clement Sohoulande from USDA ARS in Florence, SC to run all available precipitation data through his model to characterize our rainfall patterns and determine spatial divergence in those patterns. All of this work is ongoing.</p><br /> <p><strong>Minnesota:</strong> Since the last report, efforts to promote efficient irrigation management practices throughout the state are continued through educational and training opportunities. Various field days and workshops were organized attended by farmers, county agents, and soil water conservation districts (SWCD).</p><br /> <p>An on-farm irrigation water management program was started this year where ETgage and soil moisture-based irrigation scheduling was compared with control fields without irrigation management. The project was carried in close collaboration with farmers and SWCDs. The goal of this program is to establish an ETgage network in the irrigated regions of the state where irrigators can use ETgage data for reference ET in case weather stations are not available. Right now we started with three demonstration fields but the goal is to expand the network.</p><br /> <p>Currently, in Minnesota, most of the irrigators are either using hand-feel or the checkbook method for irrigation scheduling. The checkbook method is based on the simplified estimate of water inputs, water stored in the soil profile based on its water holding capacity and water out, based on crop water use. The major drawback of this method is that the crop water use (ET) tables that are used in the checkbook method were developed around three decades ago. With the change in hybrids and management practice, ET values need to be updated. Also, if the checkbook method is not supported by weekly soil moisture measurement, it may result in over-irrigation by as much as 50%. The other goal of the irrigation management program is to promote advanced methods of irrigation scheduling. Through various educational events, farmers and agency personals were familiarized with different methods of irrigation scheduling including soil moisture sensors, Irrigation Management Assistant tool (<a href="http://ima.respec.com/">http://ima.respec.com/</a>), ET based irrigation scheduling and how to utilize these methods in their practices to enhance crop water use efficiency and reduce irrigation-induced environmental pollution and encourage the adoption of best irrigation management practices through workshops, training and demonstration field days.</p><br /> <p>In collaboration with the Minnesota Department of Agriculture, a new weather station was installed in Dakota County, MN which is one of the highest irrigated counties of the state. This weather station will provide daily ET values for efficient irrigation water management.</p><br /> <p><strong>Nebraska:</strong> The Nebraska Agricultural Water Management Network (NAWMN) functions continued in 2018 and 2019. The Network provides climate data, soil moisture data and how they can be used for irrigation management in agricultural production fields under different irrigation methods (surface, subsurface drip and center-pivot irrigation). The NAWMN [<a href="http://water.unl.edu/cropswater/nawmn">http://water.unl.edu/cropswater/nawmn</a> (Irmak, 2006; Irmak et al. 2010; 2012)] was formed from an interdisciplinary team of partners, including UNL Extension, Natural Resources Districts (NRD), USDA-Natural Resources Conservation Service (NRCS), farmers, crop consultants, and other agricultural professionals o encounter some of the water availability vs. agricultural production issues through an unprecedented effort since 2004-2005 and. The main goal of the Network is to enable the transfer of high-quality research-based information to farmers and their advisors through an unparalleled series of demonstration projects (>850) established in farmers’ fields throughout Nebraska and implement newer tools and technologies to address and enhance crop water use efficiency, water conservation, and reduce energy consumption for irrigation and reduce nitrogen leaching to ground and surface water resources.</p><br /> <p><em>The fundamental objective of the NAWMN is to integrate science, research and education/outreach principles to provide citizens the best information available to help them to make better-informed decisions in their irrigation management practices.</em></p><br /> <p>The Network has been having significant impacts on both water and energy conservation and reduction in nitrogen leaching to ground and surface water resources due to farmers adopting/implementing technologies, information and strategies learned in NAWMN in their irrigation management practices. The network has grown to be the largest coordinated agricultural water management program in the United States. The Network presented an excellent example as to how and what a dedicated and committed team can accomplish through working in harmony and selflessly towards a common goal of protecting and sustaining prestigious natural resources.</p><br /> <p><strong>SPECIFIC GOALS AND OBJECTIVES:</strong></p><br /> <ul><br /> <li>Transfer high-quality research-based information and data on soil water status, crop water use, and crop growth stage measurements to farmers and their advisors through a series of demonstration projects established in their fields. Pictures of some examples of the field activities to provide extensive one-on-one training to citizens in the production fields are provided in the Appendix. </li><br /> <li>Foster adoption of newer irrigation/water management technologies to help farmers to increase water use efficiency, save water, reduce energy consumption, and protect environmental services.</li><br /> <li>Enhance communication and enable idea and information exchange between growers, crop consultants, academics, NRDs, NRCS, DNR, irrigation districts, etc.</li><br /> <li>Educate youth on soil and water resources and advanced/next-generation technologies. </li><br /> <li>Enhance the scientific literacy of current and next-generation producers and agricultural professionals in agricultural and related topics (the Network Team was actually initiating programs on scientific literacy back in 2005 when this concept was not on the radar screen at UNL).</li><br /> <li>Develop next-generation water, soil, and crop management tools to continue technology implementation in agriculture and be recognized as one of the best in agricultural research and education in the nation/world.</li><br /> <li>Quantify short- and long-term measurable impacts of the Network.</li><br /> </ul><br /> <p><em>The NAWMN is one of the largest and most impactful research-based programs in Nebraska that accomplished substantial adoption of technology and information transfer in agriculture through strong and dedicated partnership of university faculty, private industry, state and federal agencies, producers, irrigation districts and crop consultants and changed the behavior of producers in terms of how they managed water resources. The Network is an excellent example of one of the very few large-scale programs that accomplished varitable integration of science, research and Extension/outreach/education to make a difference in the real world. Currently, over 1,500 active farmer partners/collaborators participate in the NAWMN program.</em><em><br /></em></p><br /> <p><strong>Oklahoma:</strong> Efforts were continued at Oklahoma State University toward promoting the use of sensor-based technologies to improve irrigation scheduling:</p><br /> <ol><br /> <li>As part of a multistate project (Oklahoma, Texas, and Kansas) funded by USDA-NRCS Conservation Innovation Grant, six demonstration sites were established across western Oklahoma, with a total irrigated area of about 750 acres. These sites were under variable soils, crops, and irrigation systems and were instrumented in collaboration with local producers. The performance of different types of commercially-available sensors in developing improved irrigation scheduling was investigated.</li><br /> </ol><br /> <ol><br /> <li>An irrigation scheduling technology demonstration event was organized on Sep. 11, 2019 at the Oklahoma Panhandle Research and Extension Center.</li><br /> </ol><br /> <p><strong>Utah:</strong> In 2019 we worked with 12 growers in central Utah to determine yield impacts from replacing and updating pivot sprinklers/nozzles and of decreasing as section nozzle flowrates by 10 percent. As part of research we used Washington State’s Irrigation Scheduling application (Kc based) to schedule a portion of each field to schedule irrigations, another portion of each field was scheduled using soil moisture sensors, and the balance of each field was irrigated based on irrigators preference. While the yield differences were not great, the research showed that irrigation amount could be decreased without significantly impacting yield.</p><br /> <p><strong>Washington:</strong> In Washington we continue to develop and revise our irrigation scheduler online tool and mobile app. We are currently revising it using the grower’s irrigation design capacity (irrigation system’s capable application rate to the field as a whole in in/day or gpm/acre, etc.) such that the grower simply has to enter system on and system off times. This works well for center pivots and most solid-set systems where the grower simply has to make decisions as to whether they can shut the system down for a few days and when they need to leave it running.</p><br /> <p> </p><br /> <p><strong>Objective 3. Coordinate the development of quality control (QC) procedures for weather data used for irrigation scheduling.</strong></p><br /> <p><strong>Colorado: </strong>Short-term weather forecasts (up to 7 days) during the 2019 growing season have been downloaded via the aWhere (http://www.awhere.com/) Weather Info API. The weather forecasts will be compared to selected Colorado Agricultural Meteorological Network (CoAgMet) station data in a historical (hindsight) analysis that will assess the accuracy and usability of the information in forecasting irrigation requirements. Two peer-reviewed papers on the use of predictive weather in irrigation scheduling were also published in the Journal of the American Water Resources Association.</p><br /> <p><strong>Washington</strong>: I have been essentially frozen out of this work in our state and it is handled by our AgWeatherNet director and his staff.</p><br /> <p> </p>Publications
Impact Statements
- Utah: Over 30 presentations on irrigation management and scheduling have been made to over 1,500 farmers and irrigation equipment dealers and distributors.
Date of Annual Report: 11/20/2020
Report Information
Annual Meeting Dates: 09/28/2020
- 09/29/2020
Period the Report Covers: 10/01/2019 - 09/30/2020
Period the Report Covers: 10/01/2019 - 09/30/2020
Participants
Saleh TaghvaeianTroy Peters
Stacia Conger
Niel Allen
Allan Andales
Amir Haghverdi
Steve Evett
Chris Henry
Biswanath Dari
David Yates
Edward Martin
Jama Hamel
Jonathan Aguilar
Paul Colaizzi
Vasudha Sharma
Vivek Sharma
Suat Irmak
Gary Marek
Haimanote Bayabil
Brief Summary of Minutes
Accomplishments
<p><strong>Objective 1. Coordinate the documentation of crop coefficients used in irrigation scheduling.</strong></p><br /> <p><strong>Colorado: </strong>Hourly and daily crop evapotranspiration (ET<sub>C</sub>) rates of sprinkler-irrigated corn and furrow-irrigated grass hay were collected from two precision weighing lysimeters at the CSU Arkansas Valley Research Center in Southeast Colorado during 2020. The ET<sub>c</sub> data will be used in conjunction with ASCE Standardized reference ET to develop crop coefficients (K<sub>c</sub>) of corn and grass hay appropriate for the semi-arid conditions of Southeast Colorado. A published sugar beet crop coefficient (K<sub>cr</sub>) curve based on alfalfa reference ET and derived from weighing lysimeter data in Kimberly, Idaho, was adjusted for conditions in northeast Colorado. Two years (2013 – 2014) of leaf area index (LAI) and growing degree day data from four center pivot irrigated sugar beet fields in northeast Colorado were used to make adjustments to the timing of mid-season (peak) K<sub>cr</sub> (Andales et al., 2020).</p><br /> <p><strong>Florida: </strong>We conducted a study to investigate the effects of irrigation level on evapotranspiration, growth, and yield of three sweet corn cultivars (1170, 8021, and Battalion) commonly grown in south Florida. The experiment was conducted under a drip system using 3.79-liter containers. Three irrigation treatments were applied. Daily evapotranspiration (ET) rates were determined using a digital scale. Leaf chlorophyll index was measured twice a week with a SPAD meter. The stomatal conductance of water vapor was also measured with a leaf porometer. Above-ground biomass and leaf area were measured from harvested plants in each treatment, three times during the experiment. Based on the results of this experiment, a cultivar that is sensitive to water stress will be used for a USDA/NIFA field-based project under a linear move system with variable rate irrigation.</p><br /> <p><strong>Louisiana:</strong> This year, progress was made on using soil moisture sensor data to estimate crop evapotranspiration (ET<sub>C</sub>) from three replicated plots of cotton grown in 2015 and 2016. Reference evapotranspiration (ET<sub>O</sub>) was calculated using the ASCE standardized method with weather data collected from a research-grade weather station operated as part of the Louisiana Agriclimatic Information System (LAIS). Crop coefficients were calculated as the ratio of ET<sub>C</sub> and ET<sub>O</sub> on a daily basis when irrigation and rainfall did not occur. This work is currently included in a journal article that will be submitted for peer-reviewed publication later this year.</p><br /> <p><strong>Minnesota:</strong> This year in Minnesota, we evaluated the interaction effects of different irrigation strategies and different N rates on grain yield, nitrate-N leaching, crop evapotranspiration, and N and water use efficiency in continuous maize to develop the best management system aimed at maximum maize production and minimum nitrate leaching. One of the objectives of this research is to develop crop coefficients (Kc) for maize under various irrigation and nitrogen management practices in the central sands region of Minnesota. For now, ASCE manual 70 crop coefficients adjusted for Minnesota climate are being used for irrigation water management in the state.</p><br /> <p><strong>Nebraska:</strong> The Nebraska Agricultural Water Management Network (NAWMN) functions continued in 2019 and 2020. The Network provides climate data, soil moisture data, crop growth stage information, and how they can be used for irrigation management in agricultural production fields. The Network was formed from an interdisciplinary team of partners, including UNL Extension, Natural Resources Districts (NRD), USDA-Natural Resources Conservation Service (NRCS), farmers, crop consultants, and other agricultural professionals to encounter some of the water availability vs. agricultural production issues through an unprecedented effort since 2004-2005 (Irmak, 2006; Irmak et al. 2010; 2012)]. The main goal of the Network is to enable the transfer of high-quality research-based information to farmers and their advisors through an unparalleled series of demonstration projects (>850) established in farmers’ fields throughout Nebraska and implement newer tools and technologies to address and enhance crop water use efficiency, water conservation, and reduce energy consumption for irrigation and reduce nitrogen leaching to ground and surface water resources. The fundamental objective of the NAWMN is to integrate science, research, and education/outreach principles to provide citizens the best information available to help them to make better-informed decisions in their irrigation management practices.</p><br /> <p>The Network has been having significant impacts on both water and energy conservation and reduction in nitrogen leaching to ground and surface water resources due to farmers adopting/implementing technologies, information, and strategies learned in NAWMN in their irrigation management practices. The network has grown to be the largest coordinated agricultural water management program in the United States. The Network presented an excellent example as to how and what a dedicated and committed team can accomplish through working in harmony and selflessly towards a common goal of protecting and sustaining prestigious natural resources.</p><br /> <p><strong>Oklahoma:</strong> The Oklahoma PI has been involved in the Crop Coefficient Task Committee of the American Society of Civil Engineers (serving as the chair), where a team of scientists and engineers are working on developing refereed publications and manuals on recommended documentations for measuring, estimating, and reporting crop coefficients. A review article is planned to be developed and published during the next reporting period.</p><br /> <p><strong>USDA-ARS Texas:</strong> Published crop coefficients have traditionally not been classified as to the irrigation application method used in determining them. With the increased use of subsurface drip irrigation (SDI) for field and specialty crops, it has become clear that crop coefficients determined using sprinkler/spray irrigation systems are not suitable for irrigation scheduling with SDI systems. In 2020, we reported differences in crop coefficients for SDI and mid-elevation spray application (MESA) irrigation application methods (Evett et al., 2020a). We concluded that basal crop coefficients for SDI should be 10% to 15% smaller than those developed for sprinkler irrigation throughout the cropping season. The published results were for the 2013 and 2016 cropping years, and results from the 2018 season supported that conclusion. Reductions in irrigation application with SDI can translate into large savings in pumping costs (Evett et al., 2019. See impact statement). In 2020, we also reported crop coefficient values for legacy corn hybrids compared with those derived from a modern DT corn hybrid (Marek et al., 2020). Midseason daily Kc values were similar for all hybrids. The average season length was ~25 days shorter for the modern DT hybrid, characterized by a shortened initial growth period followed by a more rapid increase of Kc during the development period. However, plots of Kc over thermal time illustrated that the differences in season length were likely attributable to later planting dates associated with the DT corn hybrids. Average seasonal water use was 730 and 811 mm for the legacy and modern DT hybrids, respectively (three years each), and corresponding average yields were 1.2 and 1.4 kg ha<sup>-1</sup>, respectively. Results suggest that published Kc and Kcb values developed with legacy corn hybrids remain largely applicable to modern DT corn hybrids when used with accurate estimates of effective canopy-based growth stages and climate-specific Kc functions.</p><br /> <p><strong>Utah:</strong> Irrigation depletion studies were conducted on drip and surface irrigated onions, silage corn, alfalfa, and triticale. Data collected include soil moisture readings every half-hour from 18 locations with 7 to 10 soil moisture sensors at each location. Each field has recording flow meters for inflow and outflow recorded about every 15 minutes. Crop coefficients will be calculated from the data. One objective of the research is to determine the annual water use of alfalfa compared to double cropping silage corn and fall-planted triticale.</p><br /> <p> </p><br /> <p><strong>Objective 2. Coordinate efforts to promote adoption of improved irrigation scheduling technology, including computer models based on crop coefficients and ETref, remote sensing, and instrumentation that will help producers more efficiently apply irrigation water.</strong></p><br /> <p><strong>California:</strong> Two adjacent irrigation trials (a total of 144 landscape irrigation plots) were established by Haghverdi lab in early 2019 at the University of California, Riverside Agricultural Experiment Station in Riverside, California. The objective is to develop crop coefficient and irrigation management information for twelve groundcover species representing a wide range of plant types, including woody, herbaceous, and succulent, with different growth habits and water requirements. In May 2020, four irrigation treatments (i.e., 80%, 60%, 40%, and 20% of ETo) were initiated in a randomized complete block design replicated three times. The preliminary results showed that the effect of irrigation rates on NDVI, canopy temperature, LAI, and stomatal conductance are statistically significant (<em>p</em> <0.05). A new study was initiated in 2020 to evaluate the performance of different temperature-based ET equations to estimate reference ET (ETo) against the ETo calculated by CIMIS stations across California. Smart ET-based irrigation controllers often use Hargreaves temperature-based model (Hargreaves and Samani 1985) to estimate ET. Our field research trials showed that the Hargreaves equation estimates ETo with acceptable accuracy in inland southern California during high ET summer months. The news study analyzes the performance of multiple models (Hargreaves developed by Hargreaves and Samani (1985), Blaney–Criddle (Blaney and Criddle 1950), Kharrufa (Kharrufa 1985), Linacre (Linacre 1977), and Hamon (Hamon 1961) based on long-term weather data. The objective is to determine the annual and monthly error statistics for each equation and determine the performance of the models for each climate division of California based on the aridity index. A total of 101 CIMIS stations were selected that were active in the year 2020. All the sites included at least 10 years of data, ensuring that a wide range of weather conditions and drought events were considered. A global map of the aridity index (CGIAR-CSI Global-Aridity Database) was used. The aridity classes were mapped following the classification recommended by the United Nations Environment Programme (UNEP). The US Climate Divisional dataset was obtained from the National Climatic Data Center- National Oceanic and Atmospheric Administration (NCDC-NOAA).</p><br /> <p><strong>Colorado: </strong>Weather-based irrigation scheduling presentations were given as follows: 1) 2020 September 15, Weather-based irrigation scheduling, Colorado Water Congress, Webinar via Zoom (Invited); 2) 2020 February 17, Weather forecasting and evapotranspiration (joint presentation with Dr. Chad Godsey), Colorado Master Irrigator Program, Wray, CO. (Invited; 23 producers attended).</p><br /> <p><strong>Florida:</strong> Several extension presentations were given to board audiences, including extension agents, growers, and homeowners. Dates of events and topics of presentations were:</p><br /> <ul><br /> <li>June 26, 2020: Smart Technologies for Efficient Use of Water Resources. Florida-Friendly Landscaping South In-Service Training organized by Fort Lauderdale Research and Education Center.</li><br /> </ul><br /> <ul><br /> <li>May 21, 2020: Irrigation Scheduling Techniques. In-Service Training on the Role of Smart Technologies in Tackling Water Quantity and Quality Issues.</li><br /> </ul><br /> <ul><br /> <li>October 26, 2020: Soil-water-plant relationships and smart irrigation systems. Water Ambassador Course organized by Broward County Extension. </li><br /> </ul><br /> <p><strong>Louisiana: </strong>This year, a new on-farm demonstration was initiated to evaluate irrigation scheduling requirements for furrow-irrigated sugarcane in central Louisiana. One field was split into four replications of two treatments: 1) irrigation based on sensor, and 2) non-irrigated. Two Sentek soil moisture sensors were installed within one replication of the irrigated treatment at approximately one-third and two-thirds of the furrow length from the irrigation pipe. One sensor was placed within the drill line while the other sensor was installed closer to the edge of the bed. Once the 2020 crop season completes, the yield will be collected and analyzed to determine treatment differences. These data will be used to continue calibration and validation of the Smart Technologies for Agricultural Management and Production (STAMP) Irrigation Scheduling Tool that was discussed in previous years. </p><br /> <p><strong>Minnesota:</strong> Since the last report, efforts to promote efficient irrigation management practices throughout the state are continued through educational and training opportunities. Various field days and workshops were organized attended by farmers, county agents, and soil water conservation districts (SWCD). Currently, in Minnesota, most of the irrigators are either using hand-feel or the checkbook method for irrigation scheduling. The checkbook method is based on the simplified estimate of water inputs, water stored in the soil profile based on its water holding capacity, and water out, based on crop water use. The major drawback of this method is that the crop water use (ET) tables that are used in the checkbook method were developed around three decades ago. With the change in hybrids and management practice, ET values need to be updated. Also, if the checkbook method is not supported by weekly soil moisture measurement, it may result in over-irrigation by as much as 50%.</p><br /> <p>The goal of the Minnesota irrigation management program is to promote advanced methods of irrigation scheduling. Through various educational events, farmers and agency personals were introduced to different methods of irrigation scheduling, including soil moisture sensors, Irrigation Management Assistant (IMA) tool (<a href="http://ima.respec.com/">http://ima.respec.com/</a>), ET based irrigation scheduling, and how to utilize these methods in their practices to enhance crop water use efficiency and reduce irrigation-induced environmental pollution and encourage the adoption of best irrigation management practices.</p><br /> <p>In collaboration with other University of Minnesota researchers, we are expanding the geographic coverage of the IMA tool to the entire state of Minnesota; expanding and improving the input data and crop models of the IMA tool, so it is more useful for farmers, covering a wider array of irrigation approaches, including recycled drainage water and increasing tool adoption by engaging farmers, SWCD staff, and crop consultants through extension and outreach. This project aims to reduce groundwater use to levels that are sustainable over the long run and improve water quality in Minnesota. An accurate, easy to use, accessible, and economically viable online irrigation scheduling tool for growers will help us achieve the ultimate goal of groundwater protection.</p><br /> <p>In collaboration with the Minnesota Department of Agriculture, we are working on establishing a network of weather stations use for agricultural irrigation in MN.</p><br /> <p><strong>Oklahoma:</strong> Oklahoma continued efforts toward promoting the use of sensor-based technologies to improve irrigation scheduling:</p><br /> <ol><br /> <li>As part of a multistate project (Oklahoma, Mississippi, Utah, and California) funded by USDA-NRCS Conservation Innovation Grant, a research site was established in southwest Oklahoma to investigate the challenges and opportunities of using two main types of sensor technologies, namely soil water content and canopy temperature, in developing improved irrigation scheduling for furrow-irrigated cotton. A virtual field tour was held at the location of the research site to educate cotton growers on sensor-based irrigation scheduling.</li><br /> <li>A new commercial irrigation scheduling product developed for irrigated cotton in Australia was installed at over 40 collaborating farms across western Oklahoma to assess the performance of this product in achieving irrigation water conservation. This product relies on three technologies of remotely sensed crop coefficients, in-situ soil water content, and in-situ canopy temperature to provide information on cotton water stress and irrigation requirement. The research (item 1) and demonstration (item 2) projects were designed to complement each other.</li><br /> </ol><br /> <p><strong>USDA-ARS Texas:</strong> Since 2002, ARS has been developing a center pivot variable rate irrigation (VRI) decision support system based on proximal sensing of plant and soil water stress indices. This Irrigation Scheduling Supervisory Control And Data Acquisition (ISSCADA) system, patented in 2014, was licensed by Valmont Industries in 2018. The ARS team at Bushland, Texas, coordinated ISSCADA field trials with ARS and university partners in Alberta, Missouri, Mississippi, South Carolina, and Texas in 2019 and 2020, continuing multi-state field trials that began in 2016. We documented the latest version of the ISSCADA client-server software system, named ARSPivot, which was improved with the addition of several new features (Andrade et al., 2020a,b,c). ARS Bushland reported on the calibration and testing of wireless infrared thermometers used in ISSCADA (Colaizzi et al., 2018; 2019) that were developed and commercialized through a CRADA with Dynamax, Inc. ARS Bushland also reported on the use of the ISSCADA system to manage center pivot irrigation of grain sorghum using plant and soil water sensing feedback (O’Shaughnessy et al., 2020a), and potatoes (O’Shaughnessy et al., 2020b). We also cooperated with partners to report on the use of the ISSCADA system to manage corn irrigation in South Caroline (Stone et al., 2020), cotton in Missouri (Vories et al., 2020), and soybean in Mississippi (Sui et al., 2020). The team also worked to improve the use of crop coefficient-based evapotranspiration algorithms in the DSSAT modeling system (Thorp et al., 2020a,b). The team continued cooperation with Acclima, Inc., and partners in Beltsville, MD, through two CRADAs, one to develop advanced soil water sensors based on time-domain reflectometry and the second to develop a wireless node and gateway system to acquire data from sensors using the SDI-12 data transmission protocol, transmit the data using the LoRa radio protocol from node to gateway and transmit data from gateway to the Cloud using cellular network data transmission. The soil water sensors and the node and gateway system are now commercially available and used in several states, including by other WERA-1022 members, and internationally. The team also contributed to an understanding of how soil water sensing systems can be used for irrigation and salinity management (Schwartz et al., 2020a). ARS at Bushland, TX, supported ARS at Beltsville, MD, in the development of the wireless node and gateway system for datalogging of soil water sensor data and wireless transmission of the data to the Cloud. Bushland ARS coordinated the installation of systems in Jordan, Uzbekistan, and Texas for field tests and use in water management, including with a weighing lysimeter system in Jordan. We worked with Acclima on the design of new node and gateway hardware and with ARS Beltsville on improvements to and testing of firmware to improve system performance (Evett et al., 2020b).</p><br /> <p><strong>Utah:</strong> Continued working with 12 growers in central Utah to determine yield impacts from replacing and updating pivot sprinklers/nozzles and of decreasing as section nozzle flowrates by 10 percent. As part of the research, we used Washington State’s Irrigation Scheduling application (Kc based) to schedule a portion of each field to schedule irrigations, another portion of each field was scheduled using soil moisture sensors, and the balance of each field was irrigated based on irrigators preference. While the yield differences were small and inconsistent, the research showed that irrigation amount could be decreased without significantly impacting yield. Possible salinity impacts will be evaluated after a few years.</p><br /> <p> </p><br /> <p><strong>Objective 3. Coordinate the development of quality control (QC) procedures for weather data used for irrigation scheduling.</strong></p><br /> <p><strong>Colorado: </strong>Short-term weather forecasts (up to 7 days) during the 2020 growing season have been downloaded via the aWhere (<a href="about:blank">http://www.awhere.com/</a>) Weather Info API. The weather forecasts will be compared to selected Colorado Agricultural Meteorological Network (CoAgMet) station data in a historical (hindsight) analysis that will assess the accuracy and usability of the information in forecasting irrigation requirements.</p><br /> <p><strong>Florida: </strong>A study was started to investigate the reliability of the DarkSky (©Apple inc.) weather forecast data as inputs for irrigation scheduling and water resource management decision making. DarkSky provides down to minute by minute weather forecast information for most locations throughout the world. However, the accuracy of the DarkSky weather forecast information is yet to be fully verified. Daily weather forecast based on eight-month observations at 124 weather stations distributed across Florida and Georgia. For the 124 weather stations, current weather conditions and a seven days day-by-day forecast were obtained daily using the Dark Sky’s API for selected weather parameters, including precipitation, minimum and maximum temperatures, wind speed, dew point, and relative humidity. Currently, we are finalizing the study, and a paper will be submitted for publication.</p><br /> <p><strong>Louisiana:</strong> This year, the LAIS system of eight research-grade weather stations began reporting summary weather data at the minute, hourly, and daily timesteps to the LSU AgCenter website. However, the data is available in a limited capacity without enough station information for use in research applications (i.e., sensor type, sensor height, units listed for each parameter, maintenance logs, etc.). These deficiencies will continue to be addressed over time. Also, this year, the LAIS stations were tested by Hurricane Laura that made landfall in Cameron and traveled north through Louisiana. Maximum wind speeds collected at 3-second intervals for each station were compared to contour lines calculated by the National Institute of Standards and Technology from one-minute peak gusts collected from all publically available stations within the area of the hurricane. Though validation could not be conducted since the LAIS data was used in creating the contour lines, the contour lines approximated the wind speeds that define the category of 4 at landfall and 2 near Alexandria, LA.</p><br /> <p><strong>Oklahoma:</strong> Oklahoma has one of the most comprehensive and well-maintained weather station networks, with 121 stations scattered uniformly across the state. However, most of these sites lack the standard well-watered short or tall grass vegetation at adequate fetch required for accurate estimation of the reference ET. A research project was initiated to determine the magnitude and extent of this issue, to assess its impact on estimated reference ET values, and to develop correction factors to modify reference ET values.</p><br /> <p><strong>USDA-ARS Texas: </strong>Quality weather data are essential not only for irrigation scheduling based on crop coefficients and reference ET (Marek et al., 2020) but are also essential for the development and testing of ET models embedded in crop growth and water use simulation models that are now being widely tested by the multi-state and international AgMIP teams. These simulation models represent the next step in delivering crop growth and yield estimates along with ET values for irrigation scheduling so that economic factors can be included in more sophisticated irrigation management schemes (e.g., Barnes et al., 2020; Dhungel et al., 2019a; 2019b; 2020; Rho et al. 2020; Schwartz et al., 2020b). The Bushland large weighing lysimeter ET data sets are widely used for model development and testing but are not fully useful without accompanying standard weather data that are produced with the same degree of quality assurance and control as the lysimeter data. The USDA-ARS team at Bushland, Texas, previously developed quality assurance (QA) and QC procedures for weighing lysimeter data from the four large weighing lysimeters at Bushland (Marek et al., 2014), as well as for research weather data compiled from a grassed research weather station, four large weighing lysimeters and a U.S. Weather Service station at Bushland (Evett et al., 2018). In FY2020, we applied these procedures to produce quality 15-minute, 365-day weather, and lysimeter ET data that were shared with the AgMIP maize modeling team (2013 and 2016 Bushland corn datasets) and with the AgMIP winter wheat modeling team (1989-1990, 1991-1992, and 1992-1993 winter wheat datasets). Work is underway to apply these procedures to 30+ years of Bushland weighing lysimeter and weather data for sharing on the USDA National Agriculture Library Ag Data Commons, similar to that already posted there (Evett et al., 2018b).</p>Publications
Impact Statements
- UT: Presented ET-based irrigation scheduling, water conservation, and water resources management presentations in 15 workshops. Held a multi-day virtual crop and irrigation field day attended by over 300 participants.
Date of Annual Report: 12/21/2021
Report Information
Annual Meeting Dates: 09/24/2021
- 09/24/2021
Period the Report Covers: 10/01/2020 - 09/30/2021
Period the Report Covers: 10/01/2020 - 09/30/2021
Participants
Saleh TaghvaeianTroy Peters
Stacia Conger
Niel Allen
Allan Andales
Amir Haghverdi
Steve Evett
Chris Henry
Biswanath Dari
David Yates
Edward Martin
Jama Hamel
Jonathan Aguilar
Vasudha Sharma
Vivek Sharma
Haimanote Bayabil
Brief Summary of Minutes
The committee met via Zoom and gave their reports.
WERA 1022 - Meteorological and Climate Data to Support ET-Based Irrigation Scheduling, Water Conservation, and Water Resources Management
Times are Pacific Daylight Time, so Adjust according to your time zone.
Thursday
8:00 AM: Welcome and Business Meeting
8:30 AM: Revision of the Proposal
9:30 AM: Break
10:00 AM: State, Agency, and Visitor Reports
12:00 PM: Lunch
1:00 PM: State, Agency, and Visitor Reports
1:30 PM: Break
2:00 PM: State, Agency, and Visitor Reports
3:30 PM: Adjourn
Accomplishments
<p><strong>WERA 1022 – Accomplishments</strong></p><br /> <p><strong>October 1, 2020 – September 30, 2021</strong></p><br /> <p><strong><span style="text-decoration: underline;">Objective 1.</span></strong><strong> Coordinate the documentation of crop coefficients used in irrigation scheduling.</strong></p><br /> <p><strong>Colorado: </strong> Hourly and daily crop evapotranspiration (ET<sub>C</sub>) rates of sprinkler-irrigated dry beans and furrow-irrigated grass hay were collected from two precision weighing lysimeters at the CSU Arkansas Valley Research Center (AVRC) in Southeast Colorado during 2021. The ET<sub>c</sub> data will be used in conjunction with ASCE Standardized reference ET to develop crop coefficients (K<sub>c</sub>) of dry beans and grass hay appropriate for the semi-arid conditions of Southeast Colorado.</p><br /> <p><strong>Florida: </strong> <strong><em><span style="text-decoration: underline;">Bayabil Water Resources Lab</span></em></strong><strong><span style="text-decoration: underline;">. </span>A</strong> variable rate irrigation field experiment funded by USDA/NIFA was conducted under a linear move system. The goal of the study is to develop a crop moisture stress-based irrigation scheduling method. The first-year study (November 2020 – February 2021) was completed and several data on soil-water-plant parameters were collected. The experiment involved four irrigation treatments with four replications. Collected data will be used to estimate crop evapotranspiration/crop coefficients at plot and field levels</p><br /> <p><strong>Kansas: </strong>As part of the Rattlesnake Creek Watershed Project, Kansas is using KanSched in at least 25 irrigated fields to track soil moisture content using ET data in the region. With the field data and frequent site visitations to check actual soil moisture, there is documentation on the performance of crop coefficients and KanSched. For the reporting period, a couple of issues were identified and were immediately corrected.</p><br /> <p><strong>Louisiana:</strong> This objective was not pursued in the last year.</p><br /> <p><strong>Minnesota:</strong> This year in Minnesota, we evaluated the interaction effects of different irrigation strategies and different N rates on grain yield, nitrate-N leaching, crop evapotranspiration, and N and water use efficiency in continuous maize to develop the best management system aimed at maximum maize production and minimum nitrate leaching. One of the objectives of this research is to develop crop coefficients (Kc) for maize, under various irrigation and nitrogen management practices in central sands region of Minnesota. For now, ASCE manual 70 crop coefficients adjusted for Minnesota climate are being used for irrigation water management in the state. This an on-going three year research project and in 2021 we have completed the second year of the study.</p><br /> <p><strong>Oklahoma:</strong> The Oklahoma PI has been involved in the Crop Coefficient Task Committee of the American Society of Civil Engineers (serving as the chair), where a team of scientists, engineers, and industry representatives work on developing refereed publications and manuals on recommended documentations for measuring, estimating, and reporting crop coefficients.</p><br /> <p><strong>USDA-ARS Texas</strong><strong>: </strong>Short-season soybean [Glycine max (L.) Merr.] is often planted as a catch crop after cotton failure in the Texas High Plains. However, crop coefficients developed for full-season soybean are not directly applicable to irrigation scheduling of short-season varieties. Marek et al. (2021) reported crop coefficients for short-season soybean grown after cotton failure at Bushland, Texas. They found that maximum daily Kc values were not different from those published elsewhere but that season length was 24 to 29 days shorter. Due to the compressed season, crop water use was smaller than most values found in earlier studies of full-season soybean at Bushland. Crop coefficient trapezoidal functions were reported for both short-crop (grass) and tall-crop (alfalfa) reference ET and for both subsurface drip irrigation (SDI) and low-elevation spray irrigation (LESA). Mid-season crop coefficient values were larger for LESA irrigation than for SDI. Although preliminary, it appears that SDI Kc values were ~6.5% smaller than those for LESA. Water use was smaller and crop water productivity was larger for SDI compared to LESA irrigation.</p><br /> <p><strong>Utah:</strong> Crop coefficients for onions were developed for drip and surface irrigated onions grown in northern Utah calculated from the data based on data from 2019 and 2020. The Kc initial stage Kc is 0.2, mid stage is 1.2 for both drip and surface, but the transition for initial stage to mid-stage is about one month earlier for surface irrigation. Irrigation depletion studies are continuing for silage corn, alfalfa, and triticale. Data collected include soil moisture readings, climate data, including an eddy covariance tower. Each field has recording flow meters for inflow and outflow recorded about every 15 minutes. Crop coefficients will be developed for alfalfa, silage corn, and forage triticale for southwest Utah from the data. One objective of the research is to determine the annual water use of alfalfa compared to double cropping silage corn and fall-planted triticale.</p><br /> <p> </p><br /> <p><strong><span style="text-decoration: underline;">Objective 2.</span> Coordinate efforts to promote adoption of improved irrigation scheduling technology, including computer models based on crop coefficients and ETref, remote sensing and instrumentation that will help producers more efficiently apply irrigation water.</strong></p><br /> <p><strong>California:</strong> <em><span style="text-decoration: underline;">California: I. Groundcover irrigation management in inland southern California: </span></em>Two adjacent irrigation trials were conducted by Haghverdi lab in early 2021 at the University of California, Riverside Agricultural Experiment Station in Riverside, California. The objective is to develop crop coefficient and irrigation management information for ten groundcover species representing a wide range of plant types, including woody, herbaceous, and succulent, with different growth habits and water requirements. In May 2021, four irrigation treatments (i.e., 80%, 60%, 40%, and 20% of ETo) were imposed in a randomized complete block design replicated three times. We continuously monitored the effect of irrigation on the growth and health of the plant species by measuring the NDVI values (Normalized Difference Vegetation Index, a measure of plant greenness and health) and canopy temperature (to determine the water stress in plants) for all the ten species. NDVI was measured using the handheld sensor (GreenSeeker, Trimble Inc., CA), and canopy temperature was measured using the handheld infrared temperature sensor.</p><br /> <p>The effect of irrigation, species, and their interaction significantly (p<0.001) affected the quality and growth of the groundcover species as measured by the NDVI index. The effect of irrigation, groundcover species and their interaction was also found significant (p<0.001) on the canopy temperature of the groundcover. For all four irrigation treatments, the species <em>Rhagodia spinescens</em> (Creeping Australian saltbush) showed acceptable visual growth and did not show significant signs of water stress during the experimental period. <em>Eriogonum fasciculatum</em> ‘Warriner Lytle’ (Buckwheat) also was not negatively affected by irrigation treatments; however, as the trial progressed, the NDVI values decreased from around 0.7 (highest) to 0.4 (lowest). <em>Baccharis</em> x ‘Starn’ Thompson (Coyote bush) also maintained its acceptable quality for all irrigation treatments. The lowest irrigation level of 20% ET<em>ref</em> caused a significant decreased in the growth and quality of the remaining groundcovers species including <em>Lantana montevidensis</em> (Lantana), <em>Trachelospermum jasminoides</em> (Jasmine), <em>Rosmarinun officinalis</em> ‘Roman beauty’ (Rosemary), <em>Eremphila glabra</em> ‘Mingenew Gold’ (Gold Emu Bush), and <em>Oenothera stubbei</em> (Saltillo Evening Primrose). At 60- and 80-% ET<em>ref</em>, all groundcovers showed similar growth and development.</p><br /> <ol><br /> <li><em><span style="text-decoration: underline;"> Turfgrass irrigation management in Central California: </span></em>Research-based information regarding the accuracy and reliability of smart irrigation controllers for autonomous landscape irrigation water conservation is limited in central California. A two-year irrigation research trial (2018–2019) was conducted in Parlier, California, to study the response of hybrid bermudagrass and tall fescue to varying irrigation scenarios (irrigation levels and irrigation frequency) autonomously applied using a Weathermatic ET-based smart controller. The response of turfgrass species to the irrigation treatments was visually assessed and rated. In addition, turfgrass water response functions (TWRFs) were developed to estimate the impact of irrigation scenarios on the turfgrass species based on long-term mean reference evapotranspiration (ET<sub>o</sub>) data. The Weathermatic controller overestimated ET<sub>o</sub> between 5and 7% in 2018 and between 5 and 8% in 2019 compared with California Irrigation Management Information System values. The controller closely followed programmed watering-days restrictions across treatments in 2018 and 2019 and adjusted the watering-days based on ET<sub>o</sub> demand when no restriction was applied. The low half distribution uniformity and precipitation rate of the irrigation system were 0.78 and 28 mm h<sup>−1</sup>, respectively. The catch-cans method substantially underestimated the precipitation rate of the irrigation system and caused over-irrigation by the smart controller. No water-saving and turfgrass quality improvement was observed owing to restricting irrigation frequency (watering days). For the hybrid bermudagrass, the visual rating (VR) for 101% ET<sub>o</sub> treatment stayed above the minimum acceptable value of six during the trial. For tall fescue, the 108% ET<sub>o</sub> level with 3 d wk<sup>−1</sup> frequency kept the VR values in the acceptable range in 2018 except for a short period in mid-trial. The TWRF provided a good fit to experimental data with <em>r</em> values of 0.79 and 0.75 for tall fescue and hybrid bermudagrass, respectively. The estimated VR values by TWRF suggested 70–80% ET<sub>o</sub> as the minimum irrigation application to maintain the acceptable hybrid bermudagrass quality in central California during the high water demand months (i.e., May to August) based on long-term mean ET<sub>o</sub> data. The TWRF estimations suggest that 100% ET<sub>o</sub> would be sufficient to maintain the tall fescue quality for only 55 days. This might be an overestimation impacted by the relatively small tall fescue VR data in 2019 owing to minimal fertilizer applications and should be further investigated in the future.</li><br /> </ol><br /> <p>This two-year field irrigation project (2018–2019) also focused on the application of optical and thermal remote sensing for turfgrass irrigation management in central California. We monitored the response of hybrid bermudagrass and tall fescue to varying irrigation treatments, including irrigation levels (percentages of reference evapotranspiration, ET<sub>o</sub>) and irrigation frequency. The ground-based remote sensing data included NDVI and canopy temperature, which was subsequently used to calculate the crop water stress index (CWSI). The measurements were done within two hours of solar noon under cloud-free conditions. The NDVI and canopy temperature data were collected 21 times in 2018 and 10 times in 2019. For the tall fescue, a strong relationship was observed between NDVI and visual rating (VR) values in both 2018 (<em>r</em> = 0.92) and 2019 (<em>r</em> = 0.83). For the hybrid bermudagrass, there was no correlation in 2018 and a moderate correlation (<em>r </em>= 0.72) in 2019. There was a moderate correlation of 0.64 and 0.88 in 2018 and 2019 between tall fescue canopy minus air temperature difference (<em>dt</em>) and vapor pressure deficit (VPD) for the lower CWSI baseline. The correlation between hybrid bermudagrass <em>dt</em> and VPD for the lower baseline was 0.69 in 2018 and 0.64 in 2019. Irrigation levels significantly impacted tall fescue canopy temperature but showed no significant effect on hybrid bermudagrass canopy temperature. For the same irrigation levels, increasing irrigation frequency slightly but consistently decreased canopy temperature without compromising the turfgrass quality. The empirical CWSI values violated the minimum expected value (of 0) 38% of the time. Our results suggest NDVI thresholds of 0.6–0.65 for tall fescue and 0.5 for hybrid bermudagrass to maintain acceptable quality in the central California region. Further investigation is needed to verify the thresholds obtained in this study, particularly for hybrid bermudagrass, as the recommendation is only based on 2019 data. No CWSI threshold was determined to maintain turf quality in the acceptable range because of the high variability of CWSI values over time and their low correlation with VR values.</p><br /> <p><strong>Colorado: </strong> A prototype deep learning-based (artificial intelligence) model was developed to combine high-resolution, low-frequency satellite data (Landsat) with low-resolution, high-frequency satellite data (MODIS), along with a process-based ET model (SSEBop) to generate high-resolution, high-frequency actual ET estimates (daily, 10 m resolution) for the Continental US. The deep learning model is currently being trained with Ameriflux and SSEBop ET data. The project is funded by the USDA-National Institute of Food and Agriculture (NIFA) – Food and Agriculture Cyberinformatics and Tools (FACT) program. The project is titled “A Scalable Infrastructure for High-precision Evapotranspiration Estimations and Effective Farm-level Decision Making” (2020 – 2023) and is led by Dr. Sangmi Pallickara (CSU Computer Science Department).</p><br /> <p><strong>Florida:</strong> Ferrarezi Citrus Horticulture Lab</p><br /> <p>Study 1: Use of Thermal Imaging to Assess Water Status in Citrus Plants in Greenhouses</p><br /> <p>The direct examination of plant canopy temperature can assist in optimizing citrus irrigation management in greenhouses. This study aimed to develop a method to measure canopy temperature using thermal imaging in one-year-old citrus plants in a greenhouse to identify plants with water stress and verify its potential to be used as a tool to assess citrus water status. The experiment was conducted for 48 days (27 November 2019 to 13 January 2020). We evaluated the influence of five levels of irrigation on two citrus species (‘Red Ruby’ grapefruit (Citrus paradisi) and ‘Valencia’ sweet orange (Citrus sinensis (L.) Osbeck)). Images were taken using a portable thermal camera and analyzed using open-source software. We determined canopy temperature, leaf photosynthesis and transpiration, and plant biomass. The results indicated a positive relationship between the amount of water applied and the temperature response of plants exposed to different water levels. Grapefruit and sweet orange plants that received less water and were submitted to water restrictions showed higher canopy temperatures than the air (up to 6 °C). The thermal images easily identified water-stressed plants. Our proof-of-concept study allowed quickly obtaining the canopy temperature using readily available equipment and can be used as a tool to assess citrus water status in one-year-old citrus plants in greenhouses and perhaps in commercial operations with mature trees in the field after specific experimentation. This technique, coupled with an automated system, can be used for irrigation scheduling. Thus, setting up a limit temperature is necessary to start the irrigation system and set the irrigation time based on the soil water content. To use this process on a large scale, it is necessary to apply an automation routine to process the thermal images in real time and remove the weeds from the background to determine the canopy temperature.</p><br /> <p>Study 2: Pre-sprouted Sugarcane Plantlets Produced in Ebb-and-flow Subirrigation Automated by Soil Moisture Sensors</p><br /> <p>There is a growing demand for innovation in the sugarcane production chain to increase crop productivity. The recent use of sugarcane plantlets to improve planting efficiency in the field is a viable option, and nurseries are seeking for enhanced irrigation systems to accelerate the production of high-quality plantlets and optimize water and fertilizer inputs. This research aimed to determine the adequate substrate volumetric water content (VWC) to produce pre-sprouted sugarcane plantlets using ebb-and-flow subirrigation automated by soil moisture sensors and the effects on plant growth in greenhouse on acclimation stages #1 and #2. We tested three thresholds to trigger subirrigation automatically when sensor readings dropped below the setpoints: VWC 45% (v/v), VWC 35%, and VWC 25%. Plantlets were cultivated in 54-cell flat trays with 130-cm3 cone-shaped containers filled with commercial substrate in ebb-and-flow benches controlled by capacitive soil moisture sensors connected to a datalogger. The results indicated the automated subirrigation system worked properly, successfully irrigating the plantlets based on the target VWC. VWC 35% was the most efficient treatment, reducing fertilizer solution consumption in 44.78% (368.47 L and 2.41 mm d−1) in comparison with VWC 45% (667.28 L and 4.37 mm d−1) and increasing water use efficiency in 43.18% (7.62 g L−1) when compared to VWC 45% (4.33 g L−1), which produced less biomass with the same amount of water. VWC 45% treatment also resulted in the highest number of irrigation events, with the greatest electrical conductivity value in the substrate at the end of the experiment. All plant growth variables such as stem diameter, plant height, first ligule height, and shoot and root dry weight responded positively to the increase in VWC (P < 0.05). VWC 45% resulted in the highest biomass except root dry weight where VWC 35% was superior. VWC 35% was statistically the same to VWC 45% in all biometric responses (P < 0.05) and showed the best efficiency rate in fertilizer solution consumption and water use. Based on our results, we recommended VWC 35% as the setpoint for water management to produce pre-sprouted plantlets using automated ebb-and-flow subirrigation.</p><br /> <p>Study 3: Automated ebb-and-flow subirrigation conserves water and enhances citrus liner growth compared to capillary mat and overhead irrigation methods</p><br /> <p>Most citrus nurseries in Florida, USA use overhead irrigation, but subirrigation methods, including ebb-and-flow and capillary mats, have been shown to conserve water and accelerate plant growth relative to overhead irrigation for other nursery species and may be a viable alternative to overhead irrigation in citrus liner production. The objectives of this study were to (1) automate an ebb-and-flow system for citrus liner production using capacitance sensors, and (2) evaluate how subirrigation and overhead irrigation methods affect water use, plant growth parameters, and substrate chemical properties. A study was conducted from 22 May to 23 September 2018 in which liners of six commercially important rootstock cultivars in cone-shaped containers were subjected to one of the following irrigation methods: ebb-and-flow triggered at substrate volumetric water contents (θ) of 0.24, 0.36, or 0.48 m3 m−3, capillary mats, and overhead irrigation. Capacitance sensors successfully monitored irrigation throughout the study. Ebb-and-flow benches used substantially less water (~411 L) than either capillary mats (13,098 L) or overhead irrigation (3193 L). By the end of the study, rootstock cultivars propagated using subirrigation methods were approximately 22% taller with 7% more total biomass than plants subjected to overhead irrigation. Additionally, plant growth at the 0.24 m3 m−3 threshold used to trigger ebb-and-flow was as great or greater than growth at 0.36 and 0.48 m3 m−3 thresholds. During the final five weeks of the study, substrate electrical conductivity was higher using subirrigation methods (0.84–1.3 ds m−1) than under overhead irrigation (0.55–0.8 ds m−1), but there were no symptoms of salt stress observed in plants at any time. Results from this study show that ebb-and-flow is a viable alternative to overhead irrigation and is superior to capillary mats for water conservation. In automated ebb-and-flow systems in Florida, we recommend using the 0.24 m3 m−3 threshold to produce the citrus rootstock cultivars used in this study with peat: perlite substrate.</p><br /> <p>Study 4: Sweet Orange Orchard Architecture Design, Fertilizer, and Irrigation Management Strategies under Huanglongbing-endemic Conditions in the Indian River Citrus District</p><br /> <p>The prevalence of Huanglongbing (HLB) in Florida has forced growers to search for new management strategies to optimize fruit yield in young orchards and enable earlier economic returns given the likelihood of HLB-induced yield reductions during later years. There has been considerable interest in modifying orchard architecture design and fertilizer and irrigation management practices as strategies for increasing profitability. Our objectives were to evaluate how different combinations of horticultural practices including tree density, fertilization methods, and irrigation systems affect growth, foliar nutrient content, fruit yield, and fruit quality of young ‘Valencia’ sweet orange [Citrus sinensis (L.) Osbeck] trees during the early years of production under HLB-endemic conditions. The study was conducted in Fort Pierce, FL, from 2014 to 2020 on a 1- to 7-year-old orchard and evaluated the following treatments: standard tree density (358 trees/ha) and controlled-release fertilizer with microsprinkler irrigation (STD_dry_MS), high tree density (955 trees/ha) with fertigation and microsprinkler irrigation (HDS_fert_MS), and high tree density with fertigation and double-line drip irrigation (HDS_fert_DD). Annual foliar nutrient concentrations were usually within or higher than the recommended ranges throughout the study, with a tendency for decreases in several nutrients over time regardless of treatment, suggesting all fertilization strategies adequately met the tree nutrient demand. During fruit-bearing years, canopy volume, on a per-tree basis, was higher under STD_dry_MS (6.2–7.2 m3) than HDS_fert_MS (4.3–5.3 m3) or HDS_fert_DD (4.9–5.9 m3); however, high tree density resulted in greater canopy volume on an area basis, which explained the 86% to 300% increase in fruit yield per ha that resulted in moving from standard to high tree density. Although fruit yields per ha were generally greatest under HDS_fert_MS and HDS_fert_DD, they were lower than the 10-year Florida state average (26.5 Mg·ha−1) for standard tree density orchards, possibly due to the HLB incidence and the rootstock chosen. Although tree growth parameters and foliar nutrient concentrations varied in response to treatments, management practices that included high tree density and fertigation irrespective of irrigation systems produced the highest fruit yields and highest yield of solids. Soluble solids content (SSC) and titratable acidity (TA) were lower, and the SSC-to-TA ratio was highest under STD_dry_MS in 2016–17, with no treatment effects on quality parameters detected in other years. Both drip and microsprinkler fertigation methods sufficiently met tree nutrient demand at high tree density, but additional research is needed to determine optimal fertilization rates and better rootstock cultivars in young high-density sweet orange orchards under HLB-endemic conditions in the Indian River Citrus District.</p><br /> <p>Bayabil - During the period covered by this report, workshops and in-service trainings were were given to board audiences including extension agents, growers, and homeowners. Dates of events and topics of presentations were:</p><br /> <ul><br /> <li>May 21, 2021: Irrigation Scheduling Techniques. In-Service Training lead by Bayabil’s lab. </li><br /> <li>Feb 02, 2021: Optimizing irrigation rates for beans and sweetcorn to improve plant health, yield and quality. Virtual Field Day. </li><br /> <li>September 10, 2021. Best Management Practices for Protection of Water Resources by The Green Industries. Workshop for irrigation/landscaping contractors</li><br /> </ul><br /> <p><strong>Kansas: </strong>Several projects are being conducted that partially addresses this objective.</p><br /> <p>In coordination with the Nature Conservancy, the Rattlesnake Creek Watershed Project promotes increased adoption of MDI, soil moisture sensors, and the KanSched irrigation scheduling mobile app, to test improvements in irrigation efficiency and irrigation water management, maintaining crop water productivity while minimizing groundwater withdrawals. In addition, this project also aims to develop water budgets and irrigation scheduling tools, facilitate a peer-to-peer mentoring network for enhanced communication, and identify successful techniques and strategies that could be adapted to other communities trying to minimize groundwater withdrawals and sustain local aquifers. The project partnered with NRCS soil and range conservationists to develop strategies that fulfill the needs of local agricultural producers and aid in policy and procedure development.</p><br /> <p>Leveraging on this project, we have developed <a href="https://www.youtube.com/channel/UCtgMYlNkJ_ri6tsvWDl48OQ/featured">video tutorials</a> for using KanSched, conduct field days and have multiple conversations with the farmers to help them better water managers. KanSched has undergone some software and programming updates. Most of the updates are behind the scene just trying to catch-up with technological and software development advances. One of these relatively minor but very significant change programming update is the consolidation of our management tools under the new and shortened website, milab.ksu.edu (formerly bae.ksu.edu/mobileirrigationlab).</p><br /> <p>In cooperation with the Global Food Systems, the project Quantifying ET, water stress, and economic benefits for sustainable cotton production in Kansas has a main goal of developing strategies for economically-sound cotton production for Kansas. It also aims to determine water use and irrigation strategies, based on crop evapotranspiration (ET) rates, water stress, and growing conditions in southwestern Kansas. We are leveraging on this project to generate the Kc for cotton on this region.</p><br /> <p><strong>Louisiana: </strong>After completing last year’s on-farm demonstration successfully, the American Sugarcane League agreed to fund the continuation of a small on-farm research study designed to develop irrigation scheduling recommendations for sugar cane grown in central Louisiana. This field was split into four replications of two treatments: 1) irrigation based on sensor, and 2) non-irrigated. In addition to collecting yield response from irrigation, sensor placement was also considered important to the study. Thus, two Sentek Drill-and-Drop soil moisture sensors were installed in one replication of each treatment (totaling four sensor installations) with placement occurring in the top third of the field at the row center and offset from the row center. Uncharacteristic weather conditions including unprecedented rainfall amounts outside of hurricane season resulted in very little irrigation needs in 2021; only one irrigation event occurred at the end of August. Sensor placement indicated that the edge of the row and furrow areas experience compaction that affects available water holding capacity and was independent of irrigation status. Harvest is expected to occur in November to officially assess treatment results.</p><br /> <p><strong>Minnesota:</strong> This year Minnesota has continued the efforts to promote efficient irrigation management practices throughout the state through research, educational and training opportunities.</p><br /> <p>In terms of research, we have used remote sensing as well as proximal sensing platforms such as UAV and crop circle phenom to develop in-season sensor based non-destructive irrigation recommendations for corn at different nitrogen levels. We have used crop circle phenom which is an integrated active canopy sensor that measures infrared temperature, relative humidity, atmospheric pressure, ambient air temperature, incident PAR, various vegetation indices etc. We are trying to correlate these parameters to in-season ground truth measured data to develop algorithms for irrigation management. In a similar way, we are using UAV that has RGB, red edge, NIR and FLIR thermal camera. By measuring these parameters and relating them to in-season soil moisture, we will develop UAV based in season irrigation mangement techniques.</p><br /> <p>In addition, in collaboration with other University of Minnesota researchers, we are expanding the geographic coverage of the irrigation management assistant (IMA) tool which is an ET based online irrigation scheduling tool, to the entire state of Minnesota; expanding and improving the input data and crop models of the IMA tool so it is more useful for farmers, covering a wider array of irrigation approaches, including recycled drainage water and increasing tool adoption by engaging farmers, SWCD staff, and crop consultants through extension and outreach. This project aims to reduce the groundwater use to levels that are sustainable over the long run and improve water quality in Minnesota.</p><br /> <p>In terms of extension and outreach, various field days and workshops were organized attended by farmers, county agents, and soil water conservation districts (SWCD). Through various educational events, farmers and agency personals were introduced to different methods of irrigation scheduling including soil moisture sensors, Irrigation Management Assistant (IMA) tool (<a href="http://ima.respec.com/">http://ima.respec.com/</a>), ET based irrigation scheduling and how to utilize these methods in their practices to enhance crop water use efficiency and reduce irrigation-induced environmental pollution and encourage the adoption of best irrigation management practices.</p><br /> <p><strong>Oklahoma:</strong> Oklahoma continued efforts toward promoting the use of sensor-based technologies to improve irrigation scheduling:</p><br /> <ol><br /> <li>The use of soil moisture probes in estimating and updating crop ET and crop coefficients was evaluated by conducting a field experiment in west-central Oklahoma, where soil moisture probes, rain gauges, and canopy temperature sensors were installed at irrigated cotton plots (two varieties). Besides using sensor readings to estimating ET and crop coefficients, the effectiveness of sensors to be used for sensor-based irrigation scheduling is evaluated.</li><br /> <li>A new commercial irrigation scheduling product developed for irrigated cotton in Australia was installed at over 22 collaborating farms in southwestern Oklahoma to assess the performance of this product in achieving irrigation water conservation. This product relies on three technologies of remotely sensed crop coefficients, in-situ soil water content, and in-situ canopy temperature to provide information on cotton water stress and irrigation requirement.</li><br /> </ol><br /> <p><strong>USDA-ARS Texas:</strong> Since 2002, ARS has been developing a center pivot variable rate irrigation (VRI) decision support system based on proximal sensing of plant and soil water stress indices. This Irrigation Scheduling Supervisory Control And Data Acquisition (ISSCADA) system, patented in 2014, was licensed by Valmont Industries in 2018. The ARS team at Bushland, Texas, coordinated ISSCADA field trials with ARS and university partners in Alberta, Missouri, Mississippi, South Carolina and Texas in 2019-2021, continuing multi-state field trials that began in 2016. Several journal articles reporting on the ISSCADA system were reported in last year’s report to WERA 1022 and will not be reiterated here. However, comparable articles are expected in the proceedings of the Decennial Irrigation Symposium, which was postponed from 2020 to 2021.</p><br /> <p>Researchers from Texas A&M AgriLife, TAMU, and ARS Bushland recently developed a center pivot automation and control system (CPACS) that integrates innovative hardware, software, and logic technologies. The system combines GPS guided location and speed control, real-time soil water monitoring, and precipitation forecasting with distributed crop models to generate and perform targeted irrigation prescriptions. Field studies have demonstrated the effectiveness of CPACS’s water-saving technologies and can be used to optimize equipment currently available on the market. The system has U.S. and international patents pending, and the developers are ready to work with industry licensing partners. The CPAC team was recently honored with the Texas Water Development Board’s Blue Legacy Award in the agriculture category and awarded a Texas A&M AgriLife Research Director’s Award.</p><br /> <p>In 2021, Evett et al. (2021) reported on the ability of a solar-powered, wireless node and gateway system to withstand the long-term subfreezing and subzero (F) temperatures, snow and cloudiness brought on by the February polar vortex outbreak that covered the central US, also known as Winter Storm Uri. The node and gateway system, developed by ARS in cooperation with Acclima, Inc., interfaces with SDI-12 sensor systems such as those for soil water, canopy temperature, and weather, which makes it useful for irrigation and environmental management. It is being integrated into the ISSCADA system. A separate paper reported development of the node and gateway system and improvements in its capabilities (Thompson et al., 2021).</p><br /> <p>Kutikoff et al. (2021) reported on differences in water vapor density and turbulent fluxes from three generations of fast-response infrared gas analyzers that were deployed over an irrigated maize field at Bushland, Texas. There were differences in mean and variance values, although the former did not influence flux magnitudes. Although the three different analyzers produced different results, most differences in fluxes could be corrected using the energy balance ratio to estimate systematic bias.</p><br /> <p>Marek et al. (2021) reported on maize grown under full and deficit irrigation on a Pullman silty clay loam near Bushland, TX in 2018 to compare seasonal water use of two sprinkler irrigation management approaches. The USDA-ARS CPRL weighing lysimeter fields were generally irrigated twice weekly using irrigation depths ranging from 19 to 32 mm. The Texas A&M AgriLife Research Emeny field was irrigated only once per week using greater application depths ranging from 35 to 42 mm. Yield and crop water productivity (CWP) values for the 100 and 75% lysimeter field irrigation treatments were greater than corresponding values for the Emeny field. Results suggested evaporative losses associated with the more frequent, smaller irrigations on the lysimeter fields did not contribute to appreciably smaller CWP values. One reason may be that losses were likely mitigated by the rapid development of the corn canopy. Another reason may be that evaporative loss from the Pullman soil continues for several days after irrigation on the soil surface and continues longer after larger irrigations (Tolk et al., 2015), reducing the difference in losses between once-a-week and twice-a-week irrigation scheduling. These findings suggest that corn yield on the Pullman soil is principally dependent upon seasonal water inputs. and that, outside of incomplete canopy conditions, losses from frequent, smaller irrigation are comparable to those from larger, once-a-week irrigations.</p><br /> <p>USDA ARS scientists gave 45-minute presentations followed by Q&A in the <em>ICARDA/FAO Webinar Series on the</em> “Measurement of Evapotranspiration: Basic principles<em>,</em> and measurement methods.” One presentation was titled, “Comparison of ET estimates from a surface layer scintillometer and a large weighing lysimeter”, while the other was titled, “ET by Soil Water Balance: Weighing lysimetry and soil water sensing approaches.” The webinar series ran from 2021-09-15 through 2021-11-24. Gary Marek presented results from an evaluation of a SLS scintillometer to estimate hourly and daily ET (Moorhead et al, 2017) as part of the 2021 ICARDA/FAO webinar series on measuring ET. A Surface Layer Scintillometer (SLS) was evaluated for accuracy in determining ETsls, as well as sensible and latent heat fluxes, by comparison with values obtained from a large weighing lysimeter field at ARS bushland. The SLS was positioned over irrigated grain sorghum (Sorghum bicolor (L.) Moench) from July 29 – August 17 in 2015 and over grain corn (Zea mays L.) from June 23 – October 2 in 2016. Results showed poor correlation for sensible heat flux, but much better correlation with ET, with r<sup>2</sup> values of 0.83 and 0.87 for hourly and daily ETsls, respectively. The accuracy of the SLS was comparable to other ET sensing instruments with an RMSE of 0.13 mm h<sup>-1</sup> (31%) for hourly ETsls. However, summing hourly data to daily values reduced the ETsls error to 14% (0.75 mm d<sup>-1</sup>). The reduction in error was likely due to over- and underestimations in hourly values canceling out upon summation. With error rates as small as 14%, the SLS exhibits potential for use in water management activities such as developing crop coefficients or irrigation scheduling when daily data can be used. However, for shorter time steps, error rates are larger and comparable to error rates from other instruments such as eddy covariance systems. Additional research is needed to identify the cause(s) of discrepancies in the hourly data which may lead to improvements in accuracy of the method. Evaluating data from scenarios having larger sensible heat values, such as dryland conditions, may provide more information regarding discrepancies in sensible heat values.</p><br /> <p><strong>Utah:</strong> Grower meetings and field days were held with growers to share information on yield impacts from replacing and updating pivot sprinklers/nozzles and of decreasing as section nozzle flowrates by 10 percent. As part of the research, we used Washington State’s Irrigation Scheduling application (Kc based) to schedule a portion of each field to schedule irrigations, another portion of each field was scheduled using soil moisture sensors, and the balance of each field was irrigated based on irrigators preference. While the yield differences were small and inconsistent, the research showed that irrigation amount could be decreased without significantly impacting yield. Possible salinity impacts will be evaluated after a few years.</p><br /> <p> </p><br /> <p><strong><span style="text-decoration: underline;">Objective 3.</span></strong><strong> Coordinate the development of quality control (QC) procedures for weather data used for irrigation scheduling.</strong></p><br /> <p><strong>Colorado: </strong> Short-term weather forecasts (up to 7 days) during the 2021 growing season have been downloaded via the aWhere (<a href="about:blank">http://www.awhere.com/</a>) Weather Info API. The 2021 weather forecasts will be added to archived 2020 forecast data and compared to selected Colorado Agricultural Meteorological Network (CoAgMet) station data in a historical (hindsight) analysis that will assess the accuracy and usability of the information in forecasting irrigation requirements.</p><br /> <p><strong>Florida: </strong> Bayabil - A study was conducted to investigate the reliability of the DarkSky (©Apple inc.) weather forecast data based on eight-months observations at 124 weather stations distributed across Florida and Georgia. A manuscript is submitted to the Journal of American Water Resources Association (JAWRA) and is currently under review.</p><br /> <p><strong>Kansas:</strong> This year Kansas did not engage in Objective 3.</p><br /> <p><strong>Louisiana:</strong> The LSU AgCenter provides access to weather data through the Louisiana Agriclimatic Information System (LAIS). In 2019, LAIS was transitioned onto the LSU AgCenter’s new website using a similar format to what was used prior to the update. As a result, high quality weather data is now available (beginning May 2019) for each of eight weather stations located across the state. However, work continues in providing reference evapotranspiration estimations for use in irrigation scheduling. Also, the data has some limited usefulness until station information is specified (i.e. sensor type, sensor height, units listed for each parameter, maintenance logs, etc.). LSU AgCenter IT was directed toward comparable resources for review such as University of Florida’s FAWN, California’s CIMIS, and Bureau of Reclamation’s AgriMet.</p><br /> <p><strong>Minnesota:</strong> This year Minnesota did not engage in Objective 3.</p><br /> <p><strong>Oklahoma:</strong> Our efforts on investigating the quality of reference ET estimates as impacted by non-reference condition continued in the reporting period. A large number of Oklahoma Mesonet stations in the western half of the state, where close to 90% of irrigated land is located, suffer from non-reference conditions and as such the reference ET values are overestimated. At some sites, the percentage of time when the maximum relative humidity is less than 80% (an indicator of station aridity) reached 36%.</p><br /> <p><strong>USDA-ARS Texas: </strong>The Bushland large weighing lysimeter ET and corresponding weather data sets are widely used for model development. The USDA-ARS team at Bushland, Texas, previously developed quality assurance (QA) and QC procedures for weighing lysimeter data from the four large weighing lysimeters at Bushland, as well as for research weather data compiled from a grassed research weather station, four large weighing lysimeters and a U.S. Weather Service station at Bushland. In FY2021, we applied these procedures to produce four years of quality 15-minute, 365-day weather and lysimeter ET data that were shared with alfalfa ET modelers. Work is underway to apply these procedures to 30+ years of Bushland weighing lysimeter and weather data for sharing on the USDA National Agriculture Library Ag Data Commons, similar to that already posted there.</p><br /> <p><strong>Utah:</strong> No new development on Object 3. The ongoing quality control procedures are developed and implemented by the Utah Climate Center.</p>Publications
<p><strong>California</strong></p><br /> <p>Haghverdi, A., Singh, A., Sapkota, A., Ghodsi, S., Reiter, M. (2021). Developing Irrigation Water Conservation Strategies For Hybrid Bermudagrass Using An Evapotranspiration-Based Smart Irrigation Controller In Inland Southern California. Agricultural Water Management.</p><br /> <p>Haghverdi, A., Reiter, M., Singh, A., Sapkota, A. (2021). Hybrid Bermudagrass and Tall fescue Turfgrass Irrigation in Central California: II. Assessment of NDVI, CWSI and Canopy Temperature Dynamics. Agronomy. Agronomy 2021, 11, 1733. <a href="https://doi.org/10.3390/agronomy11091733">https://doi.org/10.3390/agronomy11091733</a>.</p><br /> <p>Haghverdi, A.; Reiter, M.; Sapkota, A.; Singh, A. (2021). Hybrid Bermudagrass and Tall Fescue Turfgrass Irrigation in Central California: I. Assessment of Visual Quality, Soil Moisture and Performance of an ET-based Smart Controller. Agronomy, 11, 1666. <a href="https://doi.org/10.3390/agronomy%2011081666">https://doi.org/10.3390/agronomy 11081666</a>.</p><br /> <p>Singh, A., Haghverdi, A., Nemati, M., Hartin, J. (2020). Efficient Urban Water Management: II. Weather-based Smart Irrigation Controllers. UCANR Publications.</p><br /> <p><strong>Florida</strong></p><br /> <p><span style="text-decoration: underline;">Kadyampakeni, D.</span>; Morgan, K.; Zekri, M.; Ferrarezi, R. S.; Schumann, A. W.; Obreza, T.A. 2021. Irrigation management of citrus trees (2021-2022 Citrus Production Guide). UF/IFAS Extension, Agronomy Department, #CPG12. <em>EDIS Publication</em>, URL: org/10.32473/edis-cg093-2021</p><br /> <p><span style="text-decoration: underline;">Vieira, G. H. S.</span>; Ferrarezi, R. S. Use of thermal imaging to assess water status in citrus plants in greenhouses. <em>Horticulturae. </em>7(8): 249. DOI: <a href="https://doi.org/10.3390/horticulturae7080249">10.3390/horticulturae7080249</a></p><br /> <p><span style="text-decoration: underline;">Da Silva, T. P. C. T.</span> (g); Soares, F. T.; Matsura, E. E.; Xavier, M. A.; Ohashi, A. Y. P.; Macan, N. P. F.; Ferrarezi, R. S. Pre-Sprouted sugarcane plantlets produced in ebb-and-flow subirrigation automated by soil moisture sensors. <em>Sugar Tech. </em>25(5): 974-985. DOI: <a href="https://doi.org/10.1007/s12355-020-00913-z">10.1007/s12355-020-00913-z</a></p><br /> <p><span style="text-decoration: underline;">Jani, A. D.</span>; Meadows, T. D.; Eckman, M. A.; Ferrarezi, R. S. Automated ebb-and-flow subirrigation conserves water and enhances citrus liner growth compared to capillary mat and overhead irrigation methods. <em>Agricultural Water Management </em>246(106711): 1-12. DOI: <a href="https://doi.org/10.1016/j.agwat.2020.106711">10.1016/j.agwat.2020.106711</a></p><br /> <p><span style="text-decoration: underline;">Ferrarezi, R. S.</span>; Jani, A. D.; James, H. T.; Gil, C.; Ritenour, M. A.; Wright, A. L. 2020. Sweet orange orchard architecture design, fertilizer and irrigation management strategies under Huanglongbing-endemic conditions in the Indian River Citrus District. <em>HortScience</em>. 55(12): 2028-2036. DOI: <a href="https://doi.org/10.21273/HORTSCI15390-20">21273/HORTSCI15390-20</a></p><br /> <p>Bayabil, H.K., L. Vasquez, L. Lomeli, and P. Martin. 2021. Lessons from a Landscape Irrigation Rebate Program in Miami Dade County. Journal of Extension. Journal of Extension. 59(2) 12. 10.34068/joe.59.02.13</p><br /> <p>Bayabil, H.K., J.H. Crane, K.W. Migliaccio, Y. Li, F.H. Ballen, and S. Guzman. Programación de Riego Basado en el Método de Evapotranspiración Para Papaya (<em>Carica papaya</em>) en Florida. <em>University of Florida IFAS Extension Publication #AE547.</em> 2020/6: <a href="https://edis.ifas.ufl.edu/ae547">https://edis.ifas.ufl.edu/ae547</a></p><br /> <p>Bayabil, H.K., K.W. Migliaccio, M.D. Dukes, L. Vasquez, and y C. Balerdi. Consejos Basicos para Diseñar Sistemas Eficientes de Riego. <em>University of Florida IFAS Extension. Publication #AE549. </em> <a href="https://edis.ifas.ufl.edu/ae539">https://edis.ifas.ufl.edu/ae539</a></p><br /> <p>Getachew, F., K. Bayabil, G. Hoogenboom, F.T. Teshome, and E. Zewdu. 2021. Irrigation and shifting planting date as climate change adaptation strategies for sorghum. Agricultural Water Management. 255:106988. <a href="https://doi.org/10.1016/j.agwat.2021.106988">https://doi.org/10.1016/j.agwat.2021.106988</a>.</p><br /> <p><strong>Kansas:</strong></p><br /> <p>Oker, T.E., Sheshukov, A.Y., Aguilar, J., Rogers, D.H. and Kisekka, I., 2021. Evaluating Soil Water Redistribution under Mobile Drip Irrigation, Low-Elevation Spray Application, and Low-Energy Precision Application Using HYDRUS. Journal of Irrigation and Drainage Engineering, 147(6), p.04021016.</p><br /> <p>Koudahe, K., Sheshukov, A.Y., Aguilar, J. and Djaman, K., 2021. Irrigation-Water Management and Productivity of Cotton: A Review. Sustainability 2021, 131, 70.</p><br /> <p>Aguilar, J., Currie, R.S., Tomsicek, D., Haag, L. and Duncan, S., 2021. Testing Irrigated Cotton Production. Kansas Agricultural Experiment Station Research Reports, 7(7), p.6.</p><br /> <p>Aguilar, J., A. Sheshukov, C. Redmond, J. Thompson. 2021. Accessing ET for Kansas Irrigation Scheduling. K-State Research & Extension. MF2850. 2 pgs.</p><br /> <p><strong>Louisiana </strong></p><br /> <p>Sohoulande Djebou, C. D., S. L. D. Conger, A. A. Szogi, K C. Stone, and J. H. Martin. (2021). Seasonal precipitation pattern analysis for decision support of agricultural irrigation management in Louisiana, USA. Agric. Water Manage., 254(2021) 106970. https://doi.org/10.1016/j.agwat.2021.106970</p><br /> <p>Conger, S. L. D., R. L. Frazier, B. Garner, D. Burns, D. R. Lee, and K. Miller. (2020). On-farm furrow irrigation technology demonstrations in Louisiana. J. NACAA, 13(2). Available at: https://www.nacaa.com/journal/index.php?jid=1149. Accessed on 19 Jan 2021.</p><br /> <p><strong>Minnesota</strong></p><br /> <p>Sharma, V. (2021). <em>Crop water use and irrigation timing</em>. University of Minnesota Extension. <a href="https://blog-crop-news.extension.umn.edu/2021/07/crop-water-use-and-irrigation-timing.html">https://blog-crop-news.extension.umn.edu/2021/07/crop-water-use-and-irrigation-timing.html</a> [Non-Refereed]</p><br /> <p>Sharma, V., & Fernandez, F. (2021). <em>How to calculate a nitrogen credit from irrigation water</em>. University of Minnesota Extension. <a href="https://blog-crop-news.extension.umn.edu/2021/07/how-to-calculate-nitrogen-credit-from.html">https://blog-crop-news.extension.umn.edu/2021/07/how-to-calculate-nitrogen-credit-from.html</a> [Non-Refereed]</p><br /> <p>Sharma*, V., Naeve*, S., & Coulter*, J. (2021). <em>Early season drought effects on corn and soybean</em>. University of Minnesota Extension. <a href="https://blog-crop-news.extension.umn.edu/2021/06/early-season-drought-effects-on-corn.html">https://blog-crop-news.extension.umn.edu/2021/06/early-season-drought-effects-on-corn.html</a> [Non-Refereed]</p><br /> <p>Sharma, V., & Becker*, T. (2021). <em>It’s time to start thinking about your irrigation water management</em>. University of Minnesota Extension. <a href="https://blog-crop-news.extension.umn.edu/2021/05/its-time-to-start-thinking-about-your.html">https://blog-crop-news.extension.umn.edu/2021/05/its-time-to-start-thinking-about-your.html</a> [Non-Refereed]</p><br /> <p>Sharma, V. (2020). <em>Checklist for winterizing your irrigation system</em>. University of Minnesota Extension. <a href="https://blog-crop-news.extension.umn.edu/2020/10/checklist-for-winterizing-your.html">https://blog-crop-news.extension.umn.edu/2020/10/checklist-for-winterizing-your.html</a> [Non-Refereed]</p><br /> <p>Sharma, V., Naeve, S. N., & Coulter, J. (2021). Early season crop water use and drought stress. University of Minnesota Extension. <a href="https://strategicfarming.transistor.fm/episodes/early-season-crop-water-use-and-drought-stress">https://strategicfarming.transistor.fm/episodes/early-season-crop-water-use-and-drought-stress</a> [Non-Refereed]</p><br /> <p>Sharma*, V., Fernandez*, F., Becker*, T., & Nelson*, A. (2021). Irrigation and nutrient management: What to know. University of Minnesota Extension. <a href="https://blog-crop-news.extension.umn.edu/2021/06/irrigation-and-nutrient-management-what.html">https://blog-crop-news.extension.umn.edu/2021/06/irrigation-and-nutrient-management-what.html</a> [Non-Refereed] Author</p><br /> <p><strong>Oklahoma</strong></p><br /> <p>Datta, S., Mehata, M., Taghvaeian, S., Moriasi, D., & Starks, P. J. (2021). Quantifying Water Fluxes of Irrigated Fields in an Agricultural Watershed in Oklahoma. Journal of Irrigation and Drainage Engineering, 147(7), 04021026.</p><br /> <p>Khand, K., Bhattarai, N., Taghvaeian, S., Wagle, P., Gowda, P. H., & Alderman, P. D. (2021). Modeling Evapotranspiration of Winter Wheat Using Contextual and Pixel-Based Surface Energy Balance Models. Transactions of the ASABE, 64(2), 507-519.</p><br /> <p>Taghvaeian, S., Andales, A. A., Allen, L. N., Kisekka, I., O’Shaughnessy, S. A., Porter, D. O., ... & Aguilar, J. (2020). Irrigation scheduling for agriculture in the United States: The progress made and the path forward. Transactions of the ASABE, 63(5), 1603-1618.</p><br /> <p><strong>USDA-ARS Texas</strong></p><br /> <p>Evett, S.R., A.I. Thompson, H.H. Schomberg, and J. Anderson. 2021. Solar node and gateway wireless system functions in record breaking polar vortex outbreak of February 2021. Accepted July 2021 by Agrosystems, Geosciences and Environment. <a href="https://doi.org/10.1002/agg2.20193.%202021">https://doi.org/10.1002/agg2.20193. 2021</a>.</p><br /> <p>Kutikoff, S., Z. Lin, S.R. Evett, P. Gowda, D. Brauer, J. Moorhead, G. Marek, P. Colaizzi, R. Aiken, L. Xu, and C. Owensby. 2021. Water vapor density and turbulent fluxes from three generations of infrared gas analyzers. Atmos. Meas. Tech., 14, 1253–1266, 2021. <a href="https://doi.org/10.5194/amt-14-1253-2021">https://doi.org/10.5194/amt-14-1253-2021</a></p><br /> <p>Marek, G. W., Marek, T. H., Evett, S. R., Chen, Y., Heflin, K. R., Moorhead, J. E., & Brauer, D. K. 2021. Irrigation management effects on crop water productivity for maize production in the Texas High Plains. Water Conserv Sci Eng, 6(1), 37-43. <a href="https://doi.org/10.1007/s41101-020-00100-x">https://doi.org/10.1007/s41101-020-00100-x</a></p><br /> <p>Marek, G.W., S.R. Evett, P.D. Colaizzi, and D.K. Brauer. 2021. Preliminary crop coefficients for late planted short-seasonsoybean: Texas High Plains. Agrosyst Geosci Environ. 2021;4:e20177. <a href="https://doi.org/10.1002/agg2.20177">https://doi.org/10.1002/agg2.20177</a>.</p><br /> <p>Thompson, A.I., H.H. Schomberg, S.R. Evett, D.K. Fisher, S.B. Mirsky, and S.C. Reberg-Horton. 2021. Gateway-node wireless data collection system for environmental sensing. Accepted by Agrosystems, Geosciences and Environment, 2021.</p><br /> <p>Moorehead, J.E., G.W. Marek, P.D. Colaizzi, P.H. Gowda, S.R. Evett, D.K. Brauer, T.H. Marek and D.O. Porter. 2017. Evaluation of sensible heat flux and evapotranspiration estimates using a surface layer scintillometer and a large weighing lysimeter. Sensors 2017, 17, 2350. <a href="https://doi.org/10.3390/s17102350">https://doi.org/10.3390/s17102350</a></p><br /> <p>Tolk, J.A., S.R. Evett and R.C. Schwartz. 2015. Field-measured, hourly soil water evaporation stages in relation to reference evapotranspiration rate and soil to air temperature ratio. Vadose Zone J. <a href="http://doi.org/10.2136/vzj2014.07.0079">http://doi.org/10.2136/vzj2014.07.0079</a>.</p><br /> <p><a href="https://agrilifetoday.tamu.edu/2020/01/09/amarillo-water-management-team-earns-agrilife-research-honors/">https://agrilifetoday.tamu.edu/2020/01/09/amarillo-water-management-team-earns-agrilife-research-honors/</a></p><br /> <p><a href="https://agrilifetoday.tamu.edu/2021/03/25/water-management-research-earns-blue-legacy-award/">https://agrilifetoday.tamu.edu/2021/03/25/water-management-research-earns-blue-legacy-award/</a></p><br /> <p><strong>Utah</strong></p><br /> <p>Effects of Short Season Irrigation on Pasture Yield and Predicting Yield with Sentinel-2 Satellite, MS Thesis by Ihsan Bugra Bugdayci, Utah State University, December 2020</p><br /> <p>Fruit Tree Responses to Water Stress: Automated Physiological Measurements and Rootstock Responses, Ph.D. Dissertation by William D. Wheeler, Utah State University, December 2020</p><br /> <p>Development and Application of a Decision Framework to Support Improved River Basin Water Management, Ph.D. dissertation by Leah Meeks, Utah State University, August 2021</p><br /> <p>TRENDS IN US CROP YIELDS & WATER USE by Britta L. Schumacher, MS thesis, Utah State University, June 2021.</p><br /> <p>Innovative Water Management Using Advanced Irrigation Systems and Biochar by Jonathan A. Holt, MS thesis, Utah State University, May 2021.</p><br /> <p>Irrigation Water Use – Drip v. Surface Irrigation of Onions Report Utah Agricultural Water Optimization by L. Niel Allen, Alfonso Torres-Rua, Anastasia Thayer Hassett, Ryan Larsen, Matt Yost</p><br /> <p>Utah State University, July 2021.</p><br /> <p><strong>References </strong></p><br /> <p><strong>USDA-ARS Texas</strong></p><br /> <p>Moorehead, J.E., G.W. Marek, P.D. Colaizzi, P.H. Gowda, S.R. Evett, D.K. Brauer, T.H. Marek and D.O. Porter. 2017. Evaluation of sensible heat flux and evapotranspiration estimates using a surface layer scintillometer and a large weighing lysimeter. Sensors 2017, 17, 2350. <a href="https://doi.org/10.3390/s17102350">https://doi.org/10.3390/s17102350</a></p><br /> <p>Tolk, J.A., S.R. Evett and R.C. Schwartz. 2015. Field-measured, hourly soil water evaporation stages in relation to reference evapotranspiration rate and soil to air temperature ratio. Vadose Zone J. <a href="http://doi.org/10.2136/vzj2014.07.0079">http://doi.org/10.2136/vzj2014.07.0079</a>.</p><br /> <p><a href="https://agrilifetoday.tamu.edu/2020/01/09/amarillo-water-management-team-earns-agrilife-research-honors/">https://agrilifetoday.tamu.edu/2020/01/09/amarillo-water-management-team-earns-agrilife-research-honors/</a></p><br /> <p><a href="https://agrilifetoday.tamu.edu/2021/03/25/water-management-research-earns-blue-legacy-award/">https://agrilifetoday.tamu.edu/2021/03/25/water-management-research-earns-blue-legacy-award/</a></p><br /> <p> </p>Impact Statements
- USDA-ARS TX: The wireless, solar-powered node and gateway system is being used for environmental and hydrologic monitoring in Jordan, Lebanon, the Palestinian Authority West Bank, the US, and Uzbekistan. In the US, the Precision Sustainable Agriculture network (https://precisionsustainableag.org/) uses the node & gateway system and sensors developed by ARS on ~100 farms with 120+ scientists in 25 states.