A SMARTIRRIGATION MOBILE APP FOR CORN, COTTON, PEANUT, AND SOYBEAN

• Sponsoring Institution: National Institute of Food and Agriculture
• Project Status: ACTIVE
• Funding Source: AFRI COMPETITIVE GRANT
• Reporting Frequency: Annual
• Accession No.: 1023273
• Grant No.: 2020-67021-31962
• Cumulative Award Amt.: $493,512.00
• Proposal No.: 2019-06453
• Project Start Date: Jul 1, 2020
• Project End Date: Jun 30, 2026
• Grant Year: 2025
• Program Code: [A1521]- Agricultural Engineering

Recipient Organization
UNIVERSITY OF GEORGIA
200 D.W. BROOKS DR
ATHENS,GA 30602-5016

Performing Department
(N/A)

Non Technical Summary
For most crops,water is critical for producing high yields. Even short periods without adequate soil moisture can permanently decrease yield potential. Yet most growers do not use science-based methods to manage soil moisture. Our project's vision is to deliver a truly disruptive mobile application (App), which offers intelligent management of irrigation scheduling for the most extensively irrigated agronomic crops in the United States - corn, cotton, peanut and soybean. The App will be cost-free, engaging, dynamic, and provide actionable information to users. It will be customizable to individual management zones within fields and our expectations are that it will result in irrigation water use efficiency gains of up to 40% when compared to standard grower methods. The App will leverage irrigation scheduling apps that our team has already developed. The App will use meteorological data from multiple sources, satellite images, and the USDA NRCS SSURGO to drive models that will estimate daily plant available soil water and soil nitrogen within the root zone. The App will send notifications to the user when irrigation is needed. Our project consists of three phases - App development, field-testing, and on-farm evaluation to promote adoption and use of the App. We will deliver an easy-to-use irrigation mobile app that integrates site-specific weather, sensor, and model-based data for decision-making.

Animal Health Component

50%

Research Effort Categories

Basic

(N/A)

Applied

50%

Developmental

50%

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
102	1599	1020	40%
402	1599	2020	60%

Knowledge Area
102 - Soil, Plant, Water, Nutrient Relationships; 402 - Engineering Systems and Equipment;

Subject Of Investigation
1599 - Grain crops, general/other;

Field Of Science
2020 - Engineering; 1020 - Physiology;

Keywords

et

nitrogen use efficiency

on-farm evaluation

soil water balance

water use efficiency

Goals / Objectives
Our proposal's deliverable is the new SmartIrrigation CropFit App which will offer intelligent management of irrigation for the most extensively irrigated agronomic crops in the United States - corn, cotton, peanut and soybean. The CropFit App will leverage and integrate the existing SmartIrrigation Apps for corn, cotton, and soybean, will add peanut, and will include several new features. It will be cost-free, engaging, dynamic, and provide actionable information to users. The CropFit App will be customizable to individual management zones within fields and our expectations are that it will result in IWUE gains of up to 40% when compared to standard grower scheduling methods. If the CropFit App is widely adopted, this project may result in large gains in the sustainability of our agricultural production systems. Although the CropFit App will be designed to have a national footprint, available funding limits our field-testing to the Southeast.Convert the Irrigator Pro decision support tool into an ET-based soil water balance model.Integrate the existing Corn, Cotton, and Soybean Apps and the modified Irrigator Pro into the new CropFit App.Evaluate and incorporate features that will customize the CropFit App for individual fields.Evaluate the CropFit App with replicated plot studies in Alabama and Georgia.Conduct stakeholder engagement, social science research, and project evaluation activities.Promote adoption of the CropFit App by conducting on-farm evaluations with growers and Extension agents in Alabama and Georgia.

Project Methods
Objective 1 - Convert the Irrigator Pro decision support tool into an ET-based soil water balance modelThe most current version of Irrigator Pro developed by Co-PI Butts uses matric potential measured at 0.20, 0.40, and 0.60 m with soil moisture sensor probes to estimate the plant available soil water in the soil profile. We will modify the model so that it uses the ET-based algorithms utilized by the Cotton, Corn, and Soybean SmartIrrigation Apps to estimate plant available soil water. This modification will allow us to incorporate the many strengths of Irrigator Pro into the CropFit App. We will use data collected by the NPRL over the past three decades to develop a GDD-based crop coefficient curve. The ET-based Irrigator Pro model will be field-tested at NPRL.Objective 2 - Integrate the existing Corn, Cotton, and Soybean Apps and the modified Irrigator Pro into the new CropFit AppWe will integrate the existing corn, cotton, soybean and modified Irrigator Pro models into a single new SmartIrrigation CropFit App. One of the advantages of consolidating the Apps is that it will allow our programmer to streamline and reduce the effort required to maintain and update the Apps.One of the frustrations of current users of the SmartIrrigation Apps is that they must install and maintain apps for individual crops. Even though all the Apps are driven by notifications, users must still interact with individual Apps when entering irrigation events, checking crop phenology, or editing field information.Objective 3 - Evaluate and incorporate features that will customize the CropFit App for individual fields.The existing SmartIrrigation Apps use the location of a field only to retrieve meteorological data. Soil type is selected by the user from a generic list of seven soil types. The Apps were purposely designed this way to minimize set-up time and to reduce complexity. We will add a number of features to the CropFit App that will allow users to customize the App for their field. These features include extracting soils information from SSURGO, benchmarking the crop coefficient (Kc) curves to actual field conditions using satellite-based NDVI and expand the sources of meteorological data.Objective 4 - Evaluate the CropFit App with replicated plot studies in Alabama and Georgia.During Years 3 and 4 of the project, the CropFit App will be coded and ready for beta testing. We will conduct replicated plot studies at UGA's SIRP, Auburn University's E.V. Smith Research Center, and the NPRL's Hooks-Hanner Environmental Resource Center,to evaluate the performance of the CropFit App in comparison to other scheduling tools. The field experiments will be incorporated into other ongoing field research that takes place continuously by the project team members at these research centers and will leverage significant resources from the other ongoing projects.Objective 5 - Stakeholder engagement, social science research, and project evaluation.Although our suite of SmartIrrigation Apps has a relatively large number of downloads and registered fields, our observations from speaking with growers is that few are consistently using the Apps for season-long irrigation scheduling. Instead, growers and Extension agents have reported a preference for the single soil moisture probe in a 100 ha field over the use of our existing suite of SmartIrrigation Apps. Our goal is to increase the use of tools like the Apps since they have been proven to be as effective as soil moisture sensor probes, cost considerbly less,and require considerably less effort to use. Social science research will focus un helping us understand: 1) why our stakeholders are making these decisions, 2) what improvements can be made to increase adoption of the Apps, and 3) what types of App training resonates with users so that we can ensure future use of these important tools.Research and outreach will follow a detailed stakeholder engagement strategy and target two stakeholder groups: Growers and Extension agents.Stakeholder engagement will be two fold, encompassing 1) Fact finding and 2) Collaborative learning. First during Year 2 of the project,we will select a group of 12 growers and 12 Extension agents in Alabama and the same number in Georgia for one-on-one, semi-structured, open-ended interviews. The primary purpose of the interviews will be to assess stakeholder needs and identify general concerns and questions about using tools like apps for scheduling decisions. The information from these interviews will be used to develop collaborative learning opportunities through training workshops for Extension agents and growers that will take place in Year 3 and to design the on-farm evaluation of the CropFit App described in Objective 6 that will take place during Year 4.Training workshops will have a collaborative learning aspect, as they will provide stakeholders and project team members an opportunity to directly engage and discuss the design and usability of SmartIrrigation Apps. The project's social scientist will participate in the workshops and use before and after surveys to gauge learning outcomes. During the on-farm evaluation, the project's social scientistwill meet with and interview the participating growers and Extension agents prior to, during, and following the growing season to assess expectations, satisfaction, and problems encountered. Research design is iterative and will be conducted in a way that best meets the needs of the stakeholder group. This information along with observations from the project team during the training workshops will be used to make design changes and improvements to the CropFit App prior to its public release.We will develop project evaluation methods and interview all project team members annually to assess the quality of teamwork, progress towards meeting project deliverables, and overall satisfaction of the individual team members. The project's social scientistwill present findings to team members at regularly scheduled team meetings and in writing to be included in project progress reports.Objective 6 - Promote adoption of the CropFit App by conducting on-farm evaluations.To promote the adoption of the CropFit App, in Georgia, we will paircounty agents with growers in Alabama and Georgia. After training, the agents will workwith their partner growers to schedule irrigation in the fields using the CropFit App.County agents and growers will be distributed across the regions of the states where most row crops are grown. Each grower will participate with two fields. Each field will contain a different crop.In Georgia, we will pair six county agents with two growers each (12 growers total). Using the same model, in Alabama, we will pair three regional Extension agents with two growers each (six growers total).We will train participating Extension agents and growers in Year 3. We will conduct training workshops for Extension agents at SIRP and E.V. Smith where we will be conducting the replicated plot studies so that they can personally observe the crops' response to the CropFit App. We will also conduct field days at SIRP and E.V. Smith to which we will invite the selected growers so that they too will be exposed to the CropFit App prior to the on-farm evaluations.Final improvements to the CropFit App will be made following the Year 5 growing season and in response to data from the replicated plot studies, the on-farm evaluations, and results from the stakeholder engagement interviews. The CropFit App will be released publicly in the Apple and Google Play stores prior to the end of the project.

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

Outputs
Target Audience:The primary target audience during this reporting period were researchers and graduate students working on the project as well as county agents and growers who are the ultimate users of SmartIrrigation CropFit. Changes/Problems:A 1-year no-cost extension was requested and granted to complete project deliverables that were delayed by research restrictions during Covid (2020-2021). What opportunities for training and professional development has the project provided?Project graduate students Emily Bedwell (Ph.D.) and Vinicius Trevisan (M.S.) presented their work at international conferences. Mr. Trevisan is new to the project and had limited opportunities. Ms. Bedwell is a 4th year student and had several additional opportunities forprofessional development during the reporting period. They are listed below. Received the Outstanding Graduate Student Award from the International Society of Precision Agriculture. 1st place in the Evapotranspiration Modeling Community Oral Ph.D. competition at the ASA, CSSA, SSSA International Annual Meeting. ASA-CSSA-SSSA, November 10 - 13, 2024, San Antonio, TX. Invited presentation to the Southeastern Fruit and Vegetable Growers Conference. Invited presentation to the EMAP Precision Agriculture Workshop at the Federal University of Lavras, Brazil. Selected to participate in the2024 International Soil Moisture School in Budapest, Hungary and was one of only three graduate students in the United States awarded a Fulbright scholarship that covered her travel. How have the results been disseminated to communities of interest?Results been disseminated to communities of interest with presentations at scientific and industry conferences, and field days. Results have been presented to growers and county agents at county meetings by the University of Georgia Ag Water Team. What do you plan to do during the next reporting period to accomplish the goals?Continue to improve the crop models in SI CropFit and add new functionalities to the tool.

Impacts
What was accomplished under these goals? 1. A new version of Irrigator Pro was released that allows use of soil moisture sensors that report in terms of volumetric water content. These are also known as capacitence sensors. Irrigator Pro now has the ability to use temperature, matric potential, and volumetric water content data to schedule irrigation. These data are all accessed from data portals using APIs. 2. The ET-based peanut model was incorported into SI CropFit instead of Irrigator Pro. During 2024 it was beta-tested by select growers, county agents, and by researchers. It performed as well as sensor-based irrigation. Improvements are being made to some features of the peanut model prior to the 2025 growing season. 3. An ET-based sweet corn model was developed, incorporated into SI CropFit, and tested with plot studies. Improvements are being made and it will be tested again in 2025 before being released to the public in 2026. 4.All four crop models in SI CropFit estimate daily crop water use (ETc) using the universally accepted FAO-56 method. The models operate using a growing degree day (GDD) -based crop coefficient (Kc) curve developed for local conditions. ETc is calculated by multiplying theoretical evapotranspiration (ETo) with a daily Kc value extracted from the Kc curve (ETc = ETo × Kc). Using the correct Kc is critical to accurately estimating ETc. The Kc curves used in SI CropFit aregeneric for a well-managed crops and donot consider the actual growth stage of the crop in individual fields. Past research conducted by colleagues in Arizona (Hunsaker et al., 2003), South Carolina (Stone et al., 2016), and Georgia (Bedwell et al., 2025) have found empirical relationships between vegetation indices (VIs) such as NDVI and Kc for cotton, field corn, and sweet corn, respectively. These relationships allow NDVI to be used to estimate Kc for individual fields.One of our goals for improving the performance of SI CropFit, is to use the Kc-NDVI relationship to customize the Kc curve for individual fields. During 2024, the methodology was developed for the SI CropFit cotton model using satellite-based reflectance data to calculate NDVI. Using data from 30 grower fields, a linear regression relationship was developed between NDVI and Kc that can be used to correct Kc for individual fields. The relationship will be field-tested in 2025 and if robust, incorporated into the SI CropFit cotton model. The methodology will then be applied to the other crops.

Publications

• Type: Peer Reviewed Journal Articles Status: Published Year Published: 2024 Citation: Gallios, I., Butts, C. L., Sorensen, R. B., Porter, W., & Vellidis, G. (2024). Incorporating volumetric water content (capacitance) sensors as an automated data entry solution for Irrigator Pro. J. ASABE, 67(6), 1561-1574. https://doi.org/10.13031/ja.16052
• Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Bedwell, E., Nunes Lacerda, L., McAvoy, T., Ortiz, B., Snider, J., & Vellidis, G. (2024). Using Remote Sensing to Benchmark Crop Coefficient Curves of Crops Grown in the Southeastern United States. In In ASA, CSSA, SSSA International Annual Meeting (pp. 1). https://scisoc.confex.com/scisoc/2024am/meetingapp.cgi/Paper/156779: ASA, CSSA, SSSA
• Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Bedwell, E., Nunes Lacerda, L., McAvoy, T., Ortiz, B., Snider, J., Yu, Z., & Vellidis, G. (2024). Using Remote Sensing to Benchmark Crop Coefficient Curves of Sweet Corn Grown in the Southeastern United States. In Proceedings of the 16th International Conference of Precision Agriculture (pp. 1-8). https://www.ispag.org/proceedings/?action=abstract&id=10612: International Society of Precision Agriculture.
• Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Soncini Trevisan, V., Mendes Bastos, L., Nunes Lacerda, L., & Vellidis, G. (2024). Smart Solutions for Agricultural Water Management Integrating Remote Sensing and Machine Learning. In In ASA, CSSA, SSSA International Annual Meeting. https://scisoc.confex.com/scisoc/2024am/meetingapp.cgi/Paper/157355: ASA, CSSA, SSSA

Progress 07/01/22 to 06/30/23

Outputs
Target Audience:The primary target audience during this reporting period were researchers and graduate students working on the project. However, presentations were made at field days in Georgia and Florida describing the upcoming release of the CropFit App. ? Changes/Problems:Nothing to report. What opportunities for training and professional development has the project provided?Ms. Emily Bedwell, a Ph.D. student in the University of Georgia's Crop and Soil Sciences Department attendedthe 2022 ASA, CSSA, SSSA International Annual Meeting in Baltimore, MD,the International Conference on Precision Agriculture that took place in Minneapolis, MN, and the Envisioning 2050 in the Southeast: AI-Driven Innovations in Agriculture Conference at Auburn, AL. She presented posters on her research at all three conferences. Mr. Giannis Gallios, a M.S. student in the University of Georgia's Crop and Soil Sciences Department presented his research on developing the peanut model of the CropFit App at the 2022 ASA, CSSA, SSSA International Annual Meeting in Baltimore, MD. He also presentedposters on his research at the he Envisioning 2050 in the Southeast: AI-Driven Innovations in Agriculture Conference at Auburn, AL, and theInternational Conference on Precision Agriculture that took place in Minneapolis, MN. Ms. Shelby Sangster, graduated from the University of Georgia with her M.S. in Crop and Soil Sciences. How have the results been disseminated to communities of interest?The CropFit App was promoted at county Extension meetings, field days, and workshops in Alabama, Georgia, and Florida. The CropFit App was also disseminated to colleagues at international and national conferences with oral and poster presentations. What do you plan to do during the next reporting period to accomplish the goals?Continue making progress on objectives 1-4 and 6. Begin activities in objective 5.

Impacts
What was accomplished under these goals? 1. Mr. Giannis Gallios, a Master's student in the University of Georgia's Crop and Soil Sciences Departmentcontinued working on this component. The peanut model was incorporated into the CropFit App but was accessible only to project team members. The model was evaluated in a replicated plot trial at two locations in Georgia. Peanut plots were instrumented with matric potential and volumetric water content soil moisture sensors. 2. The peanut irrigation scheduling model was integrated into the CropFit App but was made available only to project team members duirng its evaluation phase. 3. Work began on evaluating vegetation indices as a benchmarking tool for the crop coefficient curves used in the CropFit irrigation scheduling models for corn, cotton, and soybean.APIs (Application Programming Interfaces) were developed to extract data from the METER company's compact, all-in-one, Atmos 41 weather station.? 4. The CropFit App was evaluated for corn, cotton, and peanutat the University of Geogia's Stripling Irrigation Research Park.Ms. Shelby Sangster, a Master's student in theUniversity of Georgia's Crop and Soil Sciences Department continued evaluating the cotton model in the CropFit App by comparingit to other advanced irrigation scheduling methods. Ms. Emily Bedwell, a Ph.D.student in theUniversity of Georgia's Crop and Soil Sciences Department evaluated the soybean model in the CropFit App by comparingit to other advanced irrigation scheduling methods. Colleagues at Auburn University and Mississippi State University evaluated the CropFit app for corn and cotton (Auburn) and soybean (Mississippi State). Performance was very good with no statistically significant difference in yield and irrigation water use when compared to scheduling with soil moisture sensors. Yields increased from 5 - 15% and water used decreased by 15-40% whencompared to traditional calendar-based irrigation scheduling. 5. No activity for this objective. 6. On-farm evaulations of the CropFit App for irrigation scheduling in corn and cotton were conducted by Dr. Brenda Ortiz in southern and central Alabama. Results were compared to scheduling with soil moisture sensors and performance was similar.The CropFit App was promoted at county Extension meetings, field days, and workshops in Alabama, Georgia, and Florida.

Publications

• Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Vellidis, G., Hall, D., Mallard, J., & Porter, W. (2022). Water Optimization Approaches in the Eastern United States. In 2022 ASA, CSSA, SSSA International Annual Meeting. Baltimore, Maryland: ASA, CSSA, SSSA. Retrieved from https://www.acsmeetings.org/, International, Invited
• Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Gallios, I., & Butts, C. (2022). Developing an ET-Based Version of Irrigator Pro for Peanut Irrigation Scheduling in the Southeast. In 2022 ASA, CSSA, SSSA International Annual Meeting. Baltimore, Maryland: ASA, CSSA, SSSA International Annual Meeting. Retrieved from https://www.acsmeetings.org/, International
• Type: Theses/Dissertations Status: Published Year Published: 2022 Citation: Sangster, S. (2022). Assessing the Feasibility of Fertigation on Cotton [University of Georgia]. https://esploro.libs.uga.edu/esploro/outputs/graduate/Assessing-the-Feasibility-of-Fertigation-on/9949515823502959#file-0

Progress 07/01/21 to 06/30/22

Outputs
Target Audience:The primary target audience during this reporting period were researchers and graduate students working on the project. However, presentations were made at field days in Georgia and Florida describing the upcoming release of the CropFit App. Changes/Problems:Dr. Vasileios Liakos, an Assistant Research Scientist and one of the project co-PIs, left the University of Georgia for an Assistant Professor of Precision Agriculture position at the University of Thessaly in Greece. His responsibilites were shifted to newly recruited Ph.D. student Ms. Emily Bedwell. What opportunities for training and professional development has the project provided?Ms. Emily Bedwell, a Ph.D. student in the University of Georgia's Crop and Soil Sciences Department was recruited to work on incorporating NDVI groundtruhting into the SmartIrrigtion CropFit App. Mr. Giannis Gallios, a M.S. student in the University of Georgia's Crop and Soil Sciences Department presented his research on developing the peanut model of the CropFit App at the 2021 ASA, CSSA, SSSA International Annual Meeting inSalt Lake City, UT. Ms.Shelby Sangster, a M.S. student in the University of Georgia's Crop and Soil Sciences Department presented her research on evaluating the SmartIrrigation Cotton App which will be incorporated into theCropFit App at the2021 ASA, CSSA, SSSA International Annual Meeting inSalt Lake City, UT. How have the results been disseminated to communities of interest?The standalone Corn, Cotton, and Soybean Apps were presented to growers and other stakeholders during field days in Georgia and Florida. The upcoming SmartIrrigation CropFit App was described and promoted. What do you plan to do during the next reporting period to accomplish the goals?Continue making progress on objectives 1-4. Begin activities in objective 5.

Impacts
What was accomplished under these goals? 1. Mr. Giannis Gallios, a Master's student in the University of Georgia's Crop and Soil Sciences Department continuedworking on this component. Spreadsheet versions of the scheduling tool were developed and evaluated in a replicated plot trial at two locations in Geogia. Peanut plots were instrumented with matric potential and volumetric water content soilmoisture sensors. 2. The Corn App was released as a standalone tool. The Corn, Cotton, and Soybean Apps were integrated into the CropFit App which was released in beta versionfor evaluation by the project team.Ms. Shelby Sangster, aMaster's student in the University of Georgia's Crop and Soil Sciences Department continuedevaluating the SmartIrrigation Cotton App by comparing it to other advanced irrigation scheduling methods.? 3. Software was developed to delineate fields and access soils from the NRCS SSURGO Web Soil Survey. An additional commercial rain gage providers were added to those available for users which increased the total to 5. The Mississippi mesonet was added as an option formeteorological data. 4. The beta version of the CropFit App was evaluated for corn, cotton, and peanut during the 2021 and 2022 growingseasons at the University of Geogia's Stripling Irrigation Research Park. 5. The standalone Corn, Cotton, and Soybean Apps were presented to growers and other stakeholders during field days in Georgia and Florida. 6. Nothing to report.

Publications

• Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Vellidis, G., Butts, C., Gallios, I., & Ortiz, B. (2021). Cropfit - an Integrated Smartirrigation Mobile App for Corn, Cotton, Peanut, and Soybean. In 2021 ASA, CSSA, SSSA International Annual Meeting. Salt Lake City, UT: ASA, CSSA, SSSA. Retrieved from https://scisoc.confex.com/scisoc/2021am/prelim.cgi/Paper/135167, International
• Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Gallios, I., Butts, C., Perry, C., & Vellidis, G. (2021). Making Irrigator Pro and Easier to Use Irrigation Scheduling Tool. In 2021 ASA, CSSA, SSSA International Annual Meeting. Salt Lake City, UT: ASA, CSSA, SSSA. Retrieved from https://scisoc.confex.com/scisoc/2021am/prelim.cgi/Paper/135255, International
• Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Sangster, S., Gruver, M., Lacerda, L., Perry, C., Washington, B., & Vellidis, G. (2021). Evaluation of Irrigation and Fertilization Strategies to Improve Irrigation and Nitrogen Water Use Efficiencies in Cotton. In 2021 ASA, CSSA, SSSA International Annual Meeting. Salt Lake City, UT: ASA, CSSA, SSSA. Retrieved from https://scisoc.confex.com/scisoc/2021am/prelim.cgi/Paper/136430, International

Progress 07/01/20 to 06/30/21

Outputs
Target Audience:The target audience during this reporting period were researchers and graduate students working on the project. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Mr.Giannis Gallios, aMaster's student in the University of Georgia's Crop and Soil Sciences Department was recruited to begin development of the ET version of Irrigator Pro. How have the results been disseminated to communities of interest?Progress on this project was reported to theInformation and Communication Technologies (ICT) in Precision Food Systems Working Group on 25 May 2021. Progress was also reported during an Irrigation Technologies webinar organized by Dr. Brenda Ortiz at Auburn University. It is viewable at this link:https://www.facebook.com/AlabamaPrecisionAgOnline/videos/324568012737033/ . The webinar's target audience is row-crop growers in Alabama and Georgia. What do you plan to do during the next reporting period to accomplish the goals?Continue making progress on objectives 1-4. Begin activities in objective 5.

Impacts
What was accomplished under these goals? Accomplishments are listed by Objective number. 1. Mr.Giannis Gallios, aMaster's student in the University of Georgia's Crop and Soil Sciences Department was recruited to begin working on this component.Peanut plots were instrumented with matric potential and volumetric water content soil moisture sensors to begin development of the ET version of Irrigator Pro. 2. The programming framework for the integrative CropFit App was developed. 3. Software was developed to delineate fields and access soils from the NRCS SSURGO Web Soil Survey. Two additional commercial rain gage providers were added to those available for users. The Arizona mesonet was added as an option for meteorological data. 4. The Corn, Cotton, and Soybean Apps that will be integrated into the CropFit App were evaluated during the 2020 growing season at the University of Geogia's Stripling Irrigation Research Park. 5. Nothing to report. 6. Nothing to report.

Publications