All research projects below were funded with competitive federal grants and matched from non-federal sources. 

Economic Impact Study of the Irrigation Industry

Goal: To update and expand understanding of the irrigation equipment and services industry’s economic impact
Partners: Irrigation Association, Headwaters Corporation, IIC’s five partner universities (CSU, KSU, Fresno State, TX A&M, UNL)
Contact: John Farner johnfarner@irrigation.org

Abstract: Very little data exists on the economic impact of irrigation manufacturing, distribution, consulting, design, installation, and maintenance of irrigation technologies and products. A 2010 Economic Impact Study led by the Irrigation Association estimated the macro-economic impacts of the irrigation equipment and services industry for four major demand sectors: agricultural, residential, commercial, and golf courses. The 2010 study confirmed the size and importance of industry but contained relatively little analysis about important economic drivers and other value-added aspects related to irrigation. 10 years on, an update of this study will look at quantifying how much the irrigation sector adds to the U.S. and regional economies while providing additional insights related to predicting future demand for irrigation goods and services—information necessary for identifying and targeting research gaps and sound opportunities for investment. The team will also perform data analysis and collect anecdotal observations from industry professionals related to COVID-19’s rippling impact on the irrigation equipment and service industry in the U.S.

 


 

(2 Part Project)
I. Advancing Development of the Parallel 41 Flux Network for Real-Time Evapotranspiration Monitoring
II. Deployment and Maintenance of Flux Towers in Kansas to be Integrated to the Parallel 41 Flux Networks to Support Multi-State Real-Time Evapotranspiration Estimates

Goal: To expand a multi-state network of eddy covariance flux towers that provide real-time evapotranspiration (ET) estimates used by farmers and other stakeholders.
Partners: University of Nebraska-Lincoln, Kansas State University, LICOR, The Climate Corporation
Contact: Christopher Neale cneale@unl.edu, Eduardo Santos esantos@ksu.edu

Abstract for UNL-hosted project: The objective of this project is to further augment the Parallel 41 network of eddy covariance flux stations being implemented initially in the Central Plains of the US, by including five additional eddy covariance towers donated by our Industry Partners, The Climate Corporation. The network has been partially established with 2018 and 2019 funding from the IIC with 10 towers located in Iowa (3), Nebraska (5), Kansas (1), and Colorado(1). The 2020 funding will facilitate the installation and operation of five additional towers with at least one to be in Kansas. The support for operating this and other towers in Kansas is being covered through a separate proposal to the IIC. The eddy covariance flux towers will be networked together using the LICOR FluxSuite software app and SmartFlux hardware installed at each tower, that conducts real-time processing and all necessary corrections. These in-field real-time actual evapotranspiration measurements distributed across the central plains along with satellite-based spatial products will be used by water and agricultural crop managers in the participating states as well as farmers and irrigators through online and cell phone apps.

Abstract for KSU-hosted project: The objective of this project is to set up and maintain three eddy covariance (EC)flux towers in Kansas to be integrated to the Parallel 41 Flux Network in the US Great Plains. The purpose of this network is to provide real-time, quality controlled and processed crop and natural vegetation evapotranspiration (ET), an important parameter for irrigation water management and water balance studies in watersheds and groundwater recharge estimations. One of the flux towers will be set up over turfgrass at the K-State Rocky Ford Turfgrass Research Center in Manhattan, KS. The other two towers will be installed in wheat production systems in Central Kansas. On-site flux calculations will be performed using a new flux processing system (SmartFlux 2 System, LI-COR) at the turfgrass and one of the agricultural sites. To ensure the data quality provided to different stakeholders, the SmartFlux 2 System ET estimates will be compared with ET estimates following traditional flux calculation protocols. We will also evaluate the feasibility of using existing flux gap-filling protocols to provide real-time estimates of ET when atmospheric conditions are not suitable for EC measurements. The ultimate goal of this evaluation is to identify measurement and gap-filling protocols that allow near-instantaneous accurate ET estimates. This will minimize data gaps in the Parallel 41 Flux Network improving the ET dataset quality provided to stakeholders.

 


 

Optimizing Irrigation of Turfgrass Using Sensors, IOT, LoRa Technology and Artificial Intelligence

Goal: To conduct some of the first field studies specifically designed to improve irrigation management using internet-of-things (IoT) sensor technology coupled with machine learning (A.I.) and cloud computing.
Partners: Colorado State University, AeXonis, Toro
Contact: Jay Ham jay.ham@colostate.edu

Abstract: We believe irrigation management of turfgrass will be transformed by a revolution in A.I.-based autonomous control of irrigation systems within the urban landscape. However, field research on the best way to use IoT sensors, machine learning, and other breakthrough technologies to achieve this goal is sorely lacking. The objective of this project is to conduct some of the first field studies specifically designed to improve irrigation management using internet-of-things (IoT) sensor technology coupled with machine learning (A.I.) and cloud computing. The effort is bolstered by LoRa Wireless technology from AeXonis, an IoT mediation company, and our industry partner for this research. Teams in Colorado and Kansas will deploy low-cost soil sensors, developed at CSU, on matching 16-plot turfgrass research sites under different irrigation management regimes. These two sites are identical in design but feature
different weather, soils, and turf (cool season vs. warm season). Soil moisture data and plant-based measurements of water stress (spectral reflectance and IR canopy temperature) will be sent to the AeXonis cloud platform using their LoRa wireless gateways. Once on the cloud, data will be used by machine learning algorithms as well as traditional water balance models to train the A.I. for optimal irrigation management. The performance of the autonomous irrigation control strategies will be compared with traditional methods. The goal is to perform the fundamental science needed to develop A.I.-based irrigation control schemes that use sensors, weather forecasts data, and big data. Sensor technologies will also be deployed on commercial golf courses in both Fort Collins and Manhattan to get feedback from potential users. This project will deliver a practical framework for developing the next major innovation in A.I.- and sensor-based irrigation controllers.

 


 

Toward pivot automation with proximal sensing for Maize and Soybean in the Great Plains

Goals: 1) Evaluate the accuracy of canopy temperature measurements from pivot-mounted sensors by comparing to stationary sensors and sensors deployed on unmanned aircraft; 2) Develop a best management practice that could be automated for conventional irrigation and speed-control irrigation based on IRTs mounted on a center pivot and Watermark soil water sensors, and 3) Test and evaluate an existing patented system for SIS with pivot-mounted sensors (ISSCADA).
Partners: University of Nebraska-Lincoln, USDA-ARS Bushland, TX, Valmont Industries
Contact: Derek Heeren derek.heeren@unl.edu

Abstract: The next step forward for advancing irrigation management, especially in the sub-humid eastern portion of the Great Plains, is increasing the adoption rates of scientific irrigation scheduling (SIS). Adoption rates of SIS for center pivot irrigation in the Great Plains would likely be much higher if SIS was automated and reliable. Field experiments will be carried out on a standard size (60 ha) center-pivot-irrigated field at the UNL Eastern Nebraska Research and Extension Center (ENREC) near Mead, NE. Treatments will include: IRT and Watermark (full irrigation), IRT and Watermark (deficit irrigation), ISSCADA System (IRTs and Acclima soil water sensors), Spatial Evapotranspiration Model, Common Practice, and Rainfed (no irrigation). Although one of the treatments in the field trials(ISSCADA) will be specific to Valmont Industries, the other research findings will be in the pre-competitive space and could be incorporated by other manufacturers and service providers, moving the industry toward center pivot automation.

 


 

A Remote Sensing Approach to Identify Critical Areas in California Orchards for Improving Irrigation Water Management through Precision Agriculture Technology

Goal: To use satellite imagery to effectively evaluate crop water status for orchards located in 18 California counties.
Partners: California State University-Fresno, WiseConn USA, Irrometer
Contact: Dilruba Yeasmin dyeasmin@mail.fresnostate.edu

Abstract: In 2013, California growers from 18 counties responded to a survey on their perceived changes in irrigation water use due to adopting precision agriculture technology such as soil moisture sensors as part of a study conducted by CIT, and Fresno State, funded by PG&E. That study indicated significant opportunities to improve irrigation efficiency by adopting technology such as soil moisture sensors. In that context, this project proposes a remote sensing approach to evaluate crop water status in the orchards of the same counties in California. While UAV based imageries can provide real-time data on crop water status, long term and large scale monitoring are possible through free of cost satellite imageries, even some cases readily available vegetation indices datasets such as NDVI’s. Few examples are MODIS-NDVI through USGS Earth Explorer, Sentinel multispectral image, Landsat, AVHRR, etc. Different field measurements for available water in plants like stem water potential, leaf water content are time consuming and cover a small area. With the launch of Landsat1 in 1972, agricultural monitoring by satellite imageries started (Leslie, R. et al. 2017). Utilizing satellite imageries, this project will analyze crop water status in critical summer months from 2017 to 2019 for all major fruit and nut orchards (cropland data layer, USDA). Orchards that are frequently facing water stress issues (consistently shows low values for vegetation indices such as NDVI, EVI, etc.) will be identified. A survey will be conducted among growers of those areas to explore if they are adopting any precision agriculture technology. With geocoding and spatial analysis “no-precision-technology” orchards can be identified from the survey responses. Geo-spatial analysis using satellite imageries and survey output will generate final products as series of datasets, maps, and tables that are helpful to identify orchards where irrigation water management can be improved through the adoption of precision agriculture technology.

 


 

Connecting field scale performance to watershed health: the added power of sharing data/Calculating producer water use in real time

Goal: To work with irrigators in Western Nebraska using a new tool that tracks and translates power consumption to water consumption in order to better help irrigators and their groundwater district understand how much water is being pumped for crop production, and when.
Partners: Nebraska Water Balance Alliance, University of Nebraska-Lincoln, Growers Information Services Cooperative, Olsson Engineering, Twin Platt Natural Resource District.
Contact: Dayle McDermitt, dayle.mcdermitt@unl.edu

Abstract: Using two test areas in western Nebraska—one with stable well depths and outputs, the other with in-season variability in well depth and output—this project will evaluate and compare the accuracy of a new tool that uses energy use records to estimate consumptive water use. This analysis will help improve the ability to use energy use data to provide producers accurate, real-time information about their water use, giving the producer and the regulator a common lens to evaluate action and response at multiple scales.

 


 

On-site solar-based nitrogen production

Goal: To test if a novel, solar-based nitrogen fertilizer system coupled to an irrigation system can supply the nitrogen required to produce a small crop of processing tomatoes.
Partners: California State University-Fresno, Nitricity
Contact: Shawn Askhan sashkan@mail.fresnostate.edu

Abstract: For the past century, nitrogen fertilizer has been produced in centralized facilities hundreds to thousands of miles away from farmers who need the fertilizer. Immense market inefficiencies and safety hazards are involved in the distribution chain that leads to a farmer’s cost of fertilizer being 2-5 times higher than production gate costs. Environmentally, the industrial production of nitrogen fertilizer today relies on coal or natural gas and is responsible for over 1.6% of global CO2emissions. Even more CO2 emissions are expected in the distribution of nitrogen fertilizer from hundreds of centralized plants to billions of acres of farmland. Nitricity has developed a technology that produces nitrogen fertilizer on-farm using air, water, and renewable electricity and will test this technology with the Center for Irrigation Technology (CIT). The purpose of this collaboration is to test if Nitricity’s solar-based nitrogen fertilizer system, coupled to a CIT irrigation system, can supply the nitrogen requirement of a small crop of processing tomatoes. Crops grown using the Nitricity fertilizer treatment will be compared to crops grown using conventional fertilizer applied on the same schedule.