PARTNERSHIP: MAKING THE MOST OF A LIMITED WATER SUPPLY BY IMPROVING A SITE-SPECIFIC IRRIGATION MANAGEMENT DECISION SUPPORT SYSTEM

• Sponsoring Institution: National Institute of Food and Agriculture
• Project Status: ACTIVE
• Funding Source: AFRI COMPETITIVE GRANT
• Reporting Frequency: Annual
• Accession No.: 1031011
• Grant No.: 2023-67022-40558
• Cumulative Award Amt.: $745,200.00
• Proposal No.: 2022-11353
• Project Start Date: Jul 1, 2023
• Project End Date: Jun 30, 2026
• Grant Year: 2023
• Program Code: [A1551]- Engineering for for Precision Water and Crop Management

Recipient Organization
UNIVERSITY OF NEVADA
(N/A)
RENO,NV 89557

Performing Department
(N/A)

Non Technical Summary
Increasing pressure over water resources in the Western U.S. is forcing many farmers in this region to irrigate crops without meeting their full evapotranspiration demands. This practice, known as Deficit Irrigation (DI), can lead to significant yield losses unless crop water stress levels are maintained within acceptable levels. The long-term goal of this research project is to help farmers in the Western U.S. to ameliorate the economic impact of producing crops with a limited water supply by improving a site-specific irrigation management Decision Support System (DSS) that can be used to achieve an effective application of DI levels established for each irrigation management zone in a field. This DSS will provide a much-needed alternative to available commercial software developed by irrigation companies to generate site-specific irrigation prescription maps. The intrinsic competitive nature of software developed by such companies influences their development as 'black-boxes', which in turn prevents the rigorous analysis of their underlying methods and restricts the evaluation of new site-specific irrigation management strategies using commercial software.Improvements to the DSS to be developed by this project consist of making it compatible with any type or brand of center pivot or linear move irrigation system with variable rate irrigation capabilities and incorporating into its software an interface to a crop water use and yield model. The resulting DSS will be a computer program that the community of agricultural researchers, extensionists and consultants can use as an open platform upon which novel site-specific irrigation management strategies based on sound scientific principles can be implemented and evaluated. The DSS will be evaluated with alfalfa, corn, and cotton experiments that will be conducted in Reno, NV, Bushland, TX, and Davis, CA, respectively. We'll release to the public the computer code of the DSS so that it can be improved or modified by other agricultural researchers, extensionists and consultants. Training workshops will be conducted to teach the operation of the DSS and how to modify its source code. Results from this project will be communicated to stakeholders during field days organized by PIs at each collaborating location (Reno, NV, Bushland, TX, and Davis, CA).

Animal Health Component

40%

Research Effort Categories

Basic

10%

Applied

40%

Developmental

50%

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
111	0210	2020	100%

Knowledge Area
111 - Conservation and Efficient Use of Water;

Subject Of Investigation
0210 - Water resources;

Field Of Science
2020 - Engineering;

Keywords

irrigation

deficit irrigation

decision support system

crop water productivity

Goals / Objectives
The long-term goal of this research project is to help farmers in the Western U.S. ameliorate the economic impact of producing crops with a limited water supply by developing an open platform that can be used to facilitate the evaluation, implementation, and adoption of innovative strategies for the site-specific irrigation management of self-propelled sprinkler irrigation systems with Variable Rate Irrigation (VRI) capabilities. This open platform will be built upon an Irrigation Scheduling Supervisory Control and Data Acquisition System (ISSCADAS) that integrates a wireless network of weather, plant, and soil water sensors to support the site-specific irrigation management of self-propelled sprinkler irrigation systems. The ISSCADAS is a DSS for VRI systems that relies on its ARSPivot software to automate the collection and processing of data from weather, plant, and soil water sensing systems to generate site-specific prescription maps for center pivot irrigation systems equipped for VRI. The ISSCADAS and the ARSPivot software have been successfully used to conduct experiments with multiple crops and in different regions in the U.S. using VRI center pivots. However, the ARSPivot software only supports VRI center pivots from a single manufacturer (Valley Inc., Valley NE) that are operated with an outdated control panel. Due to this limitation, the ISSCADAS cannot be used on VRI linear move irrigation systems nor on VRI center pivot systems from other manufacturers.An updated and open-source version of the ARSPivot software that can be used on center pivots and linear move irrigation systems from multiple manufacturers can accelerate the adoption of VRI technologies by providing a much-needed open platform that facilitates the generation, testing, and implementation of site-specific irrigation management strategies. The incorporation of a crop water use and yield model into the new ARSPivot software can also promote the development of alternative site-specific irrigation scheduling methods that combine sensing feedback and yield prediction to improve crop water productivity.The specific objectives of this research project are thus to:Improve the ISSCADAS and develop a new and open-source version of the ARSPivot software that can be used to generate site-specific irrigation prescription maps for VRI center pivot and linear move irrigation systems, and to generate site-specific maps of estimated water use and yield for fields irrigated with these systems.Evaluate the improved ISSCADAS and ARSPivot with experiments that will be conducted in three locations in the Western U.S. to determine if Deficit Irrigation (DI) scheduling methods incorporated in ARSPivot can improve the Crop Water Productivity (CWP) of alfalfa, corn and cotton irrigated with linear move and center pivot irrigation systems.Communicate the results from this project to growers and stakeholders and conduct training workshops about the operation of the new ARSPivot and the modification of its open-source code.

Project Methods
Objective 1. Improve the Irrigation Scheduling Supervisory Control And Data Acquisition System (ISSCADAS) and develop a new and open-source version of the ARSPivot software.Task 1: Write a new module in ARSPivot so that it can obtain the position of a self-propelled sprinkler irrigation system using an independent Global Position System (GPS) receiver. This task will consist of providing the ISSCADAS with an independent GPS with the purpose of obtaining time and position records of center pivot and linear move irrigation systems, as they traverse a field.Task 2: Update the source code of ARSPivot so that it can support linear move irrigation systems in addition to center pivot irrigation systems. ARSPivot is a software package that consists of two stand-alone programs interacting through a client-server architecture. The updates to the client program will consist of i) removing modules containing proprietary code required to establish a communication with the Pro2 control panel, ii) developing new modules to receive and process data collected by the independent GPS units and the ultrasonic plant height sensors, and iii) develop new modules to obtain weather data. The updates to the server program will involve, among others, i) the development of a new data capture interface for linear move irrigation systems, ii) revamping of the modules required to draw a scaled representation of a field, so that the area of a field can not only be represented by a circular (center pivot) but also by a rectangular (linear move) shape, iii) development of a new data capture interface so that users can enter agronomic inputs required by the crop water use and yield model, iv) writing code that interfaces with a crop water use and yield model to generate automatic site-specific estimations of yield in a field, and v) a new module that saves site-specific irrigation prescription maps using a shapefile (.shp).Task 3: An off-the-shelf economical ultrasonic distance sensor capable of measuring distances between 3 cm to 3 m will be interfaced with an electronic board containing a microcontroller and RF radio module. Prototypes will be deployed onto a center pivot system over winter wheat, and sensor measurements will again be tested for accuracy and refined based on air temperature and relative humidity measurements. Data from the wireless ultrasonic sensor will be used to estimate mean plant height per management zone (MZ) and used as input to the crop model modules. Four wireless ultrasonic sensors will be provided to each collaborating location.Task 4: Incorporate in ARSPivot an interface to a crop water use and yield model that can be used to generate site-specific maps of estimated water used and yield at any time during the growing season. This task will consist of enhancing the FARMS (Food, Agriculture and Resources Management System) web app and linking it to ARSPivot. FARMS is a geospatial web application that runs the DSSAT-CSM crop model without requiring users to input weather, climate, or soil data, and displays outputs intuitively. Currently FARMs is only capable of simulating corn and alfalfa. To enhance its capability to simulate cotton, the code for the web app will need to be updated. The update will include activating the CSM-CROPGRO-Cotton module in DSSAT to work in FARMs, calibrating and testing the model, and creating database skimmers for automating input data generation and scenario creation. To support the linkage between ARSPivot and FARMs, the VRI management zones created by ARSPivot will be passed as user field boundaries shapefiles to FARMs that will trigger generation of weather and soil input data for running DSSAT models in FARMs.Objective 2. Evaluate the improved ISSCADAS and ARSPivot with experiments that will be conducted in three locations in the Western U.S. to determine if DI scheduling methods incorporated in ARSPivot can improve the Crop Water Productivity (CWP) of alfalfa, corn and cotton irrigated with linear move and center pivot irrigation systems.Task 1: An alfalfa field experiment will be conducted at the UNR Valley Road Field Lab, located in Reno, NV, with the purpose of evaluating DI scheduling methods implemented in the ISSCADAS against conventional DI management practices. A linear move sprinkler irrigation system equipped with a Variable Rate Irrigation (VRI) package and a Low Elevation Spray Application (LESA) system will be used to irrigate an alfalfa variety marketed as drought tolerant (Nexgrow 6373R). Experimental plots will be established in this field with dimensions of 30 ft (9.1 m) long by 20 ft (6.1 m) wide each. The plots will be assigned one of the following irrigation treatments: i) Full Irrigation (FI), defined as full replenishment of soil water depletion in the top 1.5 m of soil to Field Capacity (FC); ii) Mild conventional DI (80% of FI); iii) Moderate conventional DI (60% of FI); iv) Mild iCWSI-based DI (80% irrigation level); v) Moderate iCWSI-based DI (60% irrigation level); vi) Mild hybrid (iCWSI & SWD)-based DI (80% irrigation level); and vii) Moderate hybrid (iCWSI & SWD)-based DI (60% irrigation level).Task 2: A corn VRI experiment will be conducted at the UC Davis Campbell Track research farm located near Davis CA using a linear move irrigation system. The plots at Davis will be assigned one of the following six treatments: i) FI, defined as 100% replenishment of ETa from the California Irrigation Management Information System (CIMIS) multiplied by UC ANR recommended crop coefficient; ii) mild ET-based DI (80% of FI); iii) moderate ET-based DI (50% of FI); iv) mild ISSCADA-iCWSI-based DI (80% of FI); v) moderate ISSCADA-iCWSI-based DI (50% of FI); and vi) mild ISSCADA-hybrid-based DI (80% of FI). The experimental design will be a RCBD with each treatment replicated four times for a total of 24 experimental plots.Task 3: A cotton field experiment will be conducted at the CPRL lab in Bushland, TX, using a center pivot irrigation system to compare DI scheduling methods implemented in the ISSCADAS (iCWSI or hybrid) with manual DI scheduling using weekly Neutron Probe (NP) readings. Experimental plots will be 30 ftwide × 75 ftlong. NP access tubes 1.5 indiameter X 8 ftlong will be installed in the center of each treatment plot. The plots will be assigned one of the following treatments: i) FI, defined as 100% replenishment of soil water depletion to field capacity in the top 1.5 m; ii) mild manual DI (75% of FI); iii) moderate manual DI (50% of FI); iv) mild ISSCADA-iCWSI-based DI (75% of FI); v) moderate ISSCADA-iCWSI-based DI (50% of FI); vi) mild ISSCADA-hybrid-based DI (75% of FI); vii) moderate ISSCADA-hybrid-based DI (50% of FI); and viii) rainfed, defined as no irrigation after establishing the plant stand.Objective 3. Communicate the results from this project to growers and stakeholders and conduct training workshops about the operation of the new ARSPivot and the modification of its open-source code.Task 1: A user manual will be written for the new version of ARSPivot describing how to use the software. An advanced user manual will be also written describing the purpose, input and output data of the different modules and functions in the new version of ARSPivot, as well as the contents of the different plain text and database files used by the software to store the data of an irrigation system.Task 2: Two free online training workshops will be organized during Year 3 of the project. One workshop will be oriented towards all stakeholders and will focus on teaching the general operation of ARSPivot. The other workshop will be oriented towards stakeholders interested in modifying ARSPivot and will focus on teaching how to make changes to its source code.Task 3: The results obtained from this project will be shared with stakeholders during field days organized by PIs at each collaborating location to demonstrate the improved ISSCADAS and ARSPivot software.

Progress 07/01/23 to 06/30/24

Outputs
Target Audience:The main target audience that we reached during this reporting period were agricultural researchers, graduate and undergraduate students, and farmers and ranchers in Nevada and other Western states. Changes/Problems:It was not possible for PI Andrade-Rodriguez to hire a postdoctoral researcher due to a 5% reduction in the budget awarded to this project, and a mandatory 15% increase in the salary of postdoctoral researchers hired by the University of Nevada, Reno (UNR). The main focus of the postdoctoral researcher was going to be the development of the new version of ARSPivot (V2). PI Andrade-Rodriguez hired Mahipal Reddy Ramireddy, a Ph.D. student with a strong background in computer programming and agricultural engineering. Mahipal has been working on the development of ARSPivot V2 and a GPS receiver. PI Andrade-Rodriguez hired a second graduate student with a M.Sc. in Computer Engineering with the purpose of helping Mahipal to have a beta version of ARSPivot V2 ready for the 2024 growing season, when field experiments in Reno, NV (alfalfa), Bushland, TX (cotton), and Davis, CA (corn) where scheduled to start. Unfortunately, the second graduate student left the project after working on it for only five months and thus it was not possible to have the beta version of ARSPivot V2 ready before the start of the 2024 growing season. PI Andrade-Rodriguez, Co-PI Susan O'Shaughnessy, and Co-PI Isaya Kisekka (who will be leading the field experiments in Reno, Bushland, and Davis, respectively) agreed to delay the start of the experiments until the 2025 growing season to give enough time to the UNR team to have a beta version of ARSPivot V2 ready for the experiments. The progress in the development of ARSPivot V2 is now advanced enough that we are confident that a beta version of the software will be ready before the start of the 2025 growing season. What opportunities for training and professional development has the project provided? PI Andrade-Rodriguez mentored Mahipal Reddy Ramireddy, a Ph.D. student enrolled in the Environmental Sciences and Health Interdisciplinary Graduate Program of the University of Nevada, Reno (UNR), Nusrat Disha, a Master's student enrolled in the Master and Business Administration program of UNR, and Robert Keyser, an undergraduate student worker of UNR. Co-PI Susan O'Shaughnessy mentored Connor Etheredge and Cole Lomax, an undergraduate, and a M.Sc. student enrolled in the College of Agriculture and Engineering programs at West Texas A&M University. Co-PI Isaya Kisekka mentored Matthew Maciosek, an undergraduate student worker enrolled in the Dept. of Biological and Agricultural Engineering at the University of California, Davis. PI Andrade-Rodriguez, and Ph.D. student Mahipal Reddy Ramireddy attended the 2024 Annual International Meeting of the American Society of Agricultural and Biological Engineers (ASABE) in Anaheim, CA, July 28-31, 2024. PI Andrade-Rodriguez attended the 2024 Project Director's Meeting of the USDA-NIFA A1551 Engineering for Precision Crop and Water Management Program. How have the results been disseminated to communities of interest? Results obtained during the first year of this project were communicated to the academic community of agricultural researchers, graduate and undergraduate students through one peer-reviewed paper, one proceedings paper, two presentations, and one poster. Results obtained during the first year of this project were communicated to the academic community, as well as farmers and ranchers in Nevada and other Western states during a field Day in Reno, NV. Results obtained during the first year of this project were communicated to the academic community, as well as farmers and ranchers in Nevada and other Western states through a poster presentation at the 2023 Western Alfalfa & Forage Symposium in Sparks, NV. What do you plan to do during the next reporting period to accomplish the goals? We will continue the development of ARSPivot V2, with particular emphasis on developing a Graphical User Interface (GUI) that facilitates the evaluation of site-specific irrigation prescription maps that will be generated by the software. We will start testing ARSPivot V2 and evaluating the site-specific irrigation scheduling methods implemented in the software with three field experiments that will be conducted in Reno, NV (alfalfa), Bushland, TX (cotton), and Davis, CA (corn). We will improve our prototype of a GPS receiver and its accuracy by implementing a Kalman filter that will allow ARSPivot V2 to receive an accurate position of linear move and center pivot irrigation systems. We will improve our prototype of a plant height sensor by continuing the outdoor measurements of various crops using the handheld prototype. These measurements will allow us to assess its accuracy and develop calibration equations for different crops. We will also work on transitioning the prototype from a handheld sensor to a sensor that can be mounted on a linear move or center pivot irrigation system.

Impacts
What was accomplished under these goals? We started the development of ARSPivot V2, a new and open-source version of the ARSPivot decision support software. We started the development of ARSPivot V2 using a microservices architecture, where the code is divided into multiple and smaller parts. This architecture is better fitted for open-source software projects that are developed and maintained by a community of developers than the monolithic architecture used for the development of a previous version of ARSPivot (V1). We migrated all the functions in ARSPivot V1 that are part of a large codebase (i.e., monolithic architecture) to microservices that we generated by grouping functions dependent on the same database and domain specific related functions. We started the technical documentation of the development of ARSPivot V2. This documentation includes details regarding the transition of the monolithic architecture of ARSPivot V1 into the microservices architecture that we are incorporating into ARSPivot V2. We are also documenting the code that we are writing for each microservice in ARSPivot V2. These documentation efforts will facilitate the future development of ARSPivot V2 as an open-source project maintained by a community of developers. We developed a prototype of a Global Positioning System (GPS) receiver. ARSPivot V2 will use this GPS receiver to determine the position of a linear move irrigation system or a center pivot irrigation system without having to obtain this information from the control panel operating the irrigation system. We developed a prototype of a handheld plant height sensor. The prototype was evaluated in alfalfa, wheat and corn field experiments conducted at Bushland, TX. We established an alfalfa experiment in Reno, NV, where, starting on the second year of this project, we will commence the testing ARSPivot V2 and the evaluation of site-specific irrigation scheduling methods implemented in ARSPivot V2 (which are based on plant feedback and on the combined used of plant and soil water feedback) against best irrigation management practices in Northern Nevada. We also tilled a research field at Bushland, TX, where a cotton experiment with the same purposes will be conducted on the second year of this project. We shared preliminary results obtained during the first year of this experiment with growers and stakeholders during a field day that took place on May 31, 2024 at the Valley Road Field Laboratory in Reno, NV.

Publications

• Type: Journal Articles Status: Published Year Published: 2024 Citation: Cholula, U., Andrade, M., and Solomon, J. (2024) Leaf Area Index Estimation of Fully and Deficit Irrigated Alfalfa through Canopy Cover and Canopy Height. AgriEngineering, 6, 2101-2114. https://doi.org/10.3390/agriengineering6030123
• Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Ramireddy, M., Andrade, M., OShaughnessy, S., Kisekka, I., and Evett, S. (2024) Monolith to Microservices: Refactoring the Architecture and Documentation of ARSPivot. In Proc. 2024 ASABE Annual International Meeting, Anaheim, CA.
• Type: Conference Papers and Presentations Status: Other Year Published: 2024 Citation: Kisekka, I. 2024. Advances in Management and Technology at the Nexus of Groundwater and Sustainable Agricultural Systems. Invited Speaker Environmental Science Seminar, UC Riverside May 29th, 2024.
• Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: " Cholula, U., Andrade, M., and Solomon, J. (2023). Impacts of deficit irrigation in alfalfa production and quality in Northern Nevada. In 2023 Western Alfalfa & Forage Symposium, Sparks, NV. California Alfalfa and Forage Association.