Brief Summary of Minutes of Annual Meeting

Objective 1.  Coordinate the documentation of crop coefficients used in irrigation scheduling.

Colorado: Hourly and daily crop evapotranspiration (ET_C) 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_c data will be used in conjunction with ASCE Standardized reference ET to develop crop coefficients (K_c) of corn and grass hay appropriate for the semi-arid conditions of Southeast Colorado.  A published sugar beet crop coefficient (K_cr) 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_cr (Andales et al., 2020).

Florida: 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.

Louisiana: This year, progress was made on using soil moisture sensor data to estimate crop evapotranspiration (ET_C) from three replicated plots of cotton grown in 2015 and 2016.  Reference evapotranspiration (ET_O) 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_C and ET_O 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.

Minnesota:  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.

Nebraska: 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.

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.

Oklahoma: 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.

USDA-ARS Texas:  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^-1, 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.

Utah: 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.

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.

California: 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 (p <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).

Colorado: 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).

Florida: Several extension presentations were given to board audiences, including extension agents, growers, and homeowners. Dates of events and topics of presentations were:

• 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.

• May 21, 2020: Irrigation Scheduling Techniques. In-Service Training on the Role of Smart Technologies in Tackling Water Quantity and Quality Issues.

• October 26, 2020: Soil-water-plant relationships and smart irrigation systems. Water Ambassador Course organized by Broward County Extension. 

Louisiana: 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. 

Minnesota:  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%.

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 (http://ima.respec.com/), 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.

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.

In collaboration with the Minnesota Department of Agriculture, we are working on establishing a network of weather stations use for agricultural irrigation in MN.

Oklahoma: Oklahoma continued efforts toward promoting the use of sensor-based technologies to improve irrigation scheduling:

1. 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.
2. 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.

USDA-ARS Texas: 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).

Utah: 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.

Objective 3.  Coordinate the development of quality control (QC) procedures for weather data used for irrigation scheduling.

Colorado: Short-term weather forecasts (up to 7 days) during the 2020 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.

Florida: 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.

Louisiana: 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.

Oklahoma: 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.

USDA-ARS Texas:  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).