Progress 09/01/24 to 08/31/25
Outputs
Target Audience:In the second year of the Drought Resilient Alfalfa Project (D-RAP), our work reached a wide range of audiences--each connected to the goals of improving forage production under drought conditions and strengthening water stewardship. Scientific and Academic Community We shared results with scientists across disciplines, including agronomy, forage production, soil and water sciences, and computational modeling. Our team presented posters and talks at national and regional meetings such as the ASA-CSSA-SSSA International Annual Meeting (San Antonio, November 2024), the Agricultural & Applied Economics Association (AAEA) Annual Meeting, and the Southern Pasture and Forage Crop Improvement Conference (SPFCIC, April 2025). At SPFCIC, 65 researchers, graduate students, and faculty engaged with our experimental site demonstrations and drought resilience findings. We also presented to USDA-ARS scientists in Bushland, TX, which led to a new formal collaboration on irrigation management in alfalfa. These connections helped validate our approaches, opened doors for joint experiments, and ensured that our findings contribute to the broader scientific conversation. Students and Workforce Development Graduate students, postdoctoral researchers, and advanced undergraduates were another central audience. At Kansas State University, they took part in irrigation trials, field data collection, and sensor-based measurements, gaining direct experience in technologies like UAS operations and computational modeling. At Texas A&M, graduate students and interns supported field trials and shared project outputs at conferences. We also hosted middle and high school groups to showcase how drones and sensing technologies can be applied in agriculture, introducing younger audiences to career pathways in science and agronomy. These experiences built technical expertise, broadened career awareness, and supported workforce development for the next generation of agricultural professionals. Producers and On-Farm Stakeholders Alfalfa producers are at the heart of this project, as they are the ultimate users of drought-resilient practices. At Kansas State's North Agronomy Farm, growers observed irrigation trials and discussed strategies for managing water stress. In Texas, we launched on-farm experiments in May, TX, with active involvement from producers, county extension agents, and specialists. Bob Whitney (Extension Organic Program Specialist) and Michael Berry (Comanche County Extension Agent) helped connect research with real-world management, supported field activities, and distributed findings through a widely read blog that reached over 600 subscribers. Producer-driven trials ensured that research outputs remained relevant and that grower perspectives shaped our work. Extension, Educators, and the General Public Extension agents and educators played an important role in broadening our outreach. Through classroom visits, farm demonstrations, and digital platforms, we introduced students, teachers, and community members to D-RAP objectives and technologies. Online platforms such as a project LinkedIn page and the Texas extension blog allowed us to reach agricultural professionals and community members beyond traditional academic channels. These efforts built awareness and created an informed audience supportive of sustainable, water-smart practices. Collaborators, Industry, and Funding Partners Finally, we engaged with collaborators, agencies, and private companies whose involvement helps sustain and scale the project. For example, we partnered with GoanaAG, an irrigation services company, to test and demonstrate their tools with producers. Formal collaboration with USDA-ARS was established to extend experiments to Bushland, TX. We also submitted competitive proposals that align D-RAP outcomes with USDA priorities, ensuring continued investment in water-efficient forage systems. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?This project has provided substantial opportunities for training and professional development for graduate students, undergraduate students, and early-career researchers involved in the Drought Resilient Alfalfa Project (D-RAP). Training has emphasized both technical competencies in experimental research and broader professional development for career advancement. Graduate Student and Postdoctoral Training Graduate students received hands-on training in proximal and remote sensing technologies, including the use of GreenSeeker (NDVI), SPAD chlorophyll meters, and hyperspectral sensors for measuring spectral reflectance of alfalfa under drought stress. Students were trained in field experimental design and implementation, including the installation of soil moisture and canopy sensors, operation of rainout shelters, and ETc-based irrigation scheduling. Data science skills were developed through training in machine learning algorithms (e.g., mRMR feature selection) and statistical modeling using R (linear models, Tukey's HSD post hoc analysis, and estimated marginal means). Graduate students gained experience preparing and submitting competitive research grants (e.g., NCR-SARE Graduate Student Grant - awarded; Grant A. Harris Fellowship - submitted), which strengthened their ability to design research proposals and navigate funding mechanisms. Undergraduate Training and Mentorship Undergraduate students assisting with field and greenhouse operations gained experience in data collection, biomass sampling, irrigation management, and sensor calibration, providing experiential learning opportunities that link coursework to applied agricultural research. Mentorship provided by graduate students and faculty offered professional development in scientific communication, teamwork, and project management. Professional Development through Dissemination Graduate students and scholars were actively involved in preparing and presenting poster presentations at major scientific conferences, including the ASA-CSSA-SSSA International Annual Meeting, the Research and the State Symposium at Kansas State University, and the AI and the Future Symposium. These opportunities enhanced skills in science communication, networking, and engagement with interdisciplinary audiences. Abstract submissions and acceptances at national conferences (e.g., AFGC Annual Conference 2025, Tri-Societies Meeting 2025) further advanced professional visibility and presentation experience for students. Capacity Building and Career Development Project involvement has fostered professional skills in team science, with students working in collaboration with faculty from agronomy, irrigation science, and data science. Students and scholars were encouraged to develop their professional profiles through digital outreach, including contributions to the project's LinkedIn page. Training and mentoring also emphasized preparation for academic careers, with students gaining experience in manuscript writing, data interpretation, and integration of agronomic research with socioeconomic considerations. How have the results been disseminated to communities of interest?During the reporting period, project results were disseminated through a combination of scientific conferences, institutional symposia, workshops, producer engagement activities, extension outlets, digital platforms, and direct collaboration with industry and federal partners. At the scientific level, results were presented at national and international conferences, including the ASA-CSSA-SSSA International Annual Meeting (San Antonio, TX, Nov. 2024), the Agricultural & Applied Economics Association (AAEA) Annual Meeting (New Orleans, 2024), and the Southern Pasture and Forage Crop Improvement Conference (SPFCIC, Corpus Christi, TX, Apr. 2025), where more than 65 researchers, students, and faculty engaged with D-RAP findings. Additional dissemination occurred through the Kansas State University "Research and the State" Symposium (Oct. 2024), the AI and the Future Symposium (Oct. 2024), and invited seminars delivered nationally and internationally, including sessions at the Texas A&M AgriLife Research and Extension Center, Charles Sturt University (Australia), the Federal University of Viçosa (Brazil), and Mexico's INIFAP. These venues reached agronomists, forage and soil scientists, data scientists, and economists working at the intersection of water management, drought resilience, and digital agriculture. At the student training level, results were incorporated into laboratory instruction, mentoring, and data analysis exercises for graduate and undergraduate students at Kansas State University, and into hands-on UAV and field phenotyping workshops at Texas A&M (e.g., the UAV-based Phenotyping Workshop, March 18, 2025). These efforts directly contributed to workforce development by equipping students with technical expertise in sensing technologies, irrigation management, and computational approaches. Outreach also extended to K-12 students through school visits that showcased UAS applications in agriculture, broadening awareness of precision water management among future generations. For producer and stakeholder audiences, dissemination occurred through on-farm trials, field days, and extension programming. At Kansas State's North Agronomy Farm, growers observed irrigation experiments and discussed drought stress management practices. In Texas, new on-farm experiments were launched in May, TX, involving county extension agents and producers, with Extension Specialist Bob Whitney and Agent Michael Berry facilitating knowledge exchange and sharing results through a blog that reached more than 600 subscribers across Texas. Additional dissemination took place at regional producer events such as the Tri-County Hay Show (Nov. 2024), the Beef and Forage Field Day (Beeville, TX, Apr. 2025, N=60), and financial/forage management workshops delivered through county programs and webinars, reaching audiences of 20-65 participants. Beyond direct academic and producer engagement, results were also communicated to collaborators, funding agencies, and industry partners. Competitive proposals and manuscript submissions ensured visibility among reviewers and agencies aligned with USDA and national water priorities. Formal collaboration was established with USDA-ARS Bushland to expand irrigation research in alfalfa, and a partnership with GoanaAG integrated commercial irrigation management technology into field demonstrations. Digital outreach supplemented these efforts through the launch of the project-specific LinkedIn account "Drought Resilient Alfalfa (D-RAP)," which provided updates, preliminary findings, and highlights to agricultural professionals, producers, students, and the general public. What do you plan to do during the next reporting period to accomplish the goals?Goal 1: Spatiotemporal quantification and management of water budgets in alfalfa production systems Continue field experiments under controlled rainout shelter and drip irrigation systems at the North Agronomy Farm, expanding the dataset across additional cutting cycles to capture seasonal variability in alfalfa yield, forage quality, and water-use efficiency. Refine integration of proximal sensing (NDVI, chlorophyll meters, hyperspectral reflectance) with canopy and soil moisture sensors to strengthen predictive models of crop water use. Incorporate satellite-derived datasets (e.g., Landsat, Sentinel-2, OpenET) to complement field-scale data, enabling multi-scale quantification of evapotranspiration and spatial variability in alfalfa water budgets. Advance machine learning model development by integrating greenhouse, field, and climate datasets to predict yield response under differential irrigation strategies. Special emphasis will be placed on variable selection and cross-validation across spatial and temporal datasets. Goal 2: Socioeconomic evaluation of precision water management strategies Deploy structured producer surveys and interviews to collect data on management practices, input costs, and perceptions of wireless sensor networks and precision irrigation tools. Initiate preliminary economic analyses of irrigation strategies tested under the D-RAP framework, comparing cost-effectiveness of full versus deficit irrigation regimes. Collaborate with NCR-SARE Graduate Student Grant partners to collect on-farm level data for evaluating adoption potential and economic trade-offs associated with drought-resilient alfalfa management. Expand interdisciplinary collaboration with agricultural economists and rural sociologists to ensure that socioeconomic data are fully integrated into the research framework. Goal 3: Knowledge transfer, extension, and dissemination Present updated project results at national and regional meetings, including the American Forage & Grassland Council Annual Conference (2025) and the Tri-Societies Annual Meeting (2025). Develop producer-focused extension materials such as factsheets, web-based updates, and short videos summarizing best practices for irrigation scheduling and drought management in alfalfa. Expand engagement through the D-RAP LinkedIn platform, increasing visibility of results and training opportunities for producers, students, and stakeholders. Organize at least one targeted outreach event or field demonstration at the North Agronomy Farm to share interim results with local producers and advisory groups. Strengthen graduate and undergraduate student training through workshops, field-based learning, and mentoring in scientific communication, grant development, and interdisciplinary collaboration.
Impacts
What was accomplished under these goals?
Goal 1: Spatiotemporal quantification and management of water budget in alfalfa production systems Established field experiments at Kansas State University's North Agronomy Farm using a rainout shelter and drip irrigation system to impose differential water treatments in alfalfa plots. Treatments were designed around crop evapotranspiration (ETc)-based irrigation scheduling, with canopy and soil moisture sensors deployed for real-time data acquisition. Completed greenhouse trials evaluating alfalfa biomass, growth traits, forage quality, and hyperspectral reflectance under limited irrigation. These datasets serve as a controlled baseline for understanding drought stress responses. Conducted preliminary analysis of treatment effects using linear modeling and post-hoc comparisons (Tukey's HSD, estimated marginal means) to evaluate irrigation impacts on dry matter yield and leaf area index. Results confirmed measurable differences across ETc levels after the onset of irrigation in July 2025, with 100% ETc treatments showing significantly higher yields. Advanced machine learning approaches, including mRMR-based feature selection, to identify key climatic and biophysical drivers of alfalfa yield variability. This analysis supported manuscript development on long-term climate impacts and predictive modeling of alfalfa production in Kansas. Initiated integration of sensor-derived indices (e.g., NDVI, chlorophyll content) with climate datasets to build a spatiotemporal framework for yield prediction and water budget quantification. Two additional studies initiated this year by the team included: Evaluating Alfalfa Genotypes for heat and water stress in South Texas: We are conducting small plot experiments in Corpus Christi and Beeville, Texas to evaluate alfalfa genotypes for drought and heat stress. Four genotypes are grown under three irrigation treatments in Corpus Christi and under two irrigation treatments in Beeville, Texas. RGB, Multi-spectral, and Lidar data are being collected at bi-weekly intervals in both locations. Ground based measurements of alfalfa biomass and height are also collected at different growth stages. Harvested biomass is overdried for dry matter content measurements and used to collect quality information. Genotype that performs consistently better in both locations with respect to water use, dry matter content, and forage quality will be identified. On-farm experiment in May, Texas: We established an on-farm irrigation management experiment on a 50 acres producers' field in May, Texas in Summer 2025. The experiment is conducted under a central pivot sprinkle irrigation system and has four different irrigation management treatments: 1. Farmer's schedule, 2. 50% of farmer's amount, 3. Irrigation managed by GoannaAG system, 4. Irrigation managed by method based on satellite imagery, 5. Irrigation managed by Crop Coefficient and Reference Evapotranspiration. Biomass samples, UAS sensors, and satellite imagery data are collected in this experimental site. Goal 2: Socioeconomic evaluation of precision water management strategies Developed the initial framework for evaluating producer adoption of wireless sensor networks and machine learning-based irrigation tools in alfalfa production. This included assembling datasets on irrigation costs, input management, and yield response under water-limited conditions. Engaged with producers informally during field operations, incorporating their perspectives on irrigation scheduling practices and economic tradeoffs in drought management. These interactions are being used to refine survey and data collection instruments. Initiated student-led competitive grant proposals that incorporated economic evaluation components. The NCR-SARE Graduate Student Grant (2025) was successfully awarded to investigate site-specific irrigation management using spectral responses and crop water stress indices. The Grant A. Harris Fellowship proposal was also submitted, though not funded. These grant activities directly supported capacity building and laid the groundwork for economic and adoption-related outputs tied to D-RAP. Goal 3: Knowledge transfer, extension, and dissemination Disseminated research outputs through poster presentations at high-profile conferences and symposia: ASA-CSSA-SSSA International Annual Meeting (San Antonio, TX, Nov. 2024). Research and the State Symposium, Kansas State University (Oct. 2024). AI and the Future Symposium, Kansas State University (Oct. 2024). These venues reached disciplinary scientists, interdisciplinary researchers, and graduate student peers. Submitted abstracts for upcoming conferences, including the American Forage & Grassland Council (AFGC) Annual Conference (2025) and the Tri-Societies Meeting (2025), ensuring continued dissemination. Created and launched a project-specific LinkedIn account ("Drought Resilient Alfalfa - D-RAP") to engage the broader agricultural stakeholder community and the public with updates, preliminary results, and outreach materials. Provided mentoring and professional development for graduate and undergraduate students, equipping them with training in proximal sensing, hyperspectral analysis, irrigation scheduling, and data science techniques. This training represents a critical educational output that supports the next generation of agricultural researchers and practitioners.
Publications
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2024
Citation:
Dey, S., Nazrul, F., Kim, J., Xu, X., Min, D., & Jha, G. (2024). Multidecadal climate impact on alfalfa production through machine learning in the Ogallala aquifer. Poster presented at ASA-CSSA-SSSA International Annual Meeting, San Antonio, TX (Nov. 1013, 2024)
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Dey, S., Nazrul, F., Kim, J., Xu, X., Jha, G., & Min, D. (2024). Unveiling climate impact on alfalfa production through machine learning in the Ogallala aquifer over the past decades. Poster presented at Research and the State, Kansas State University, Manhattan, KS (Oct. 31, 2024)
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Nazrul, F., Kim, J., Dey, S., Palla, S. P., Sihi, D., Whitaker, B., Min, D., & Jha, G. (2024). Machine learning algorithm in detecting long-term effect of climatic factors for alfalfa production in Kansas. Poster presented at AI and the Future Symposium: Trust AI?, Kansas State University, Manhattan, KS (Oct. 1517, 2024)
- Type:
Other
Status:
Under Review
Year Published:
2025
Citation:
Nazrul, F., Kim, J.Y., Dey, S., Min, D. & Jha, G. (2025). Data-Driven Feature Selection and Machine Learning Framework to Model Alfalfa Yield Response to Long-Term Climate Variability in the Kansas High Plains, USA. Scientific Reports (submitted).
- Type:
Other
Status:
Awaiting Publication
Year Published:
2025
Citation:
Kim, J. Y., Sey, S., Jha, G., & Min, D. (2025). Limited irrigation effects on biomass, growth characteristics, nutritive value, and hyperspectral wavelength in alfalfa. Legume Research. Manuscript submitted for publication and under review.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2025
Citation:
Kim, J. Y., Sey, S., Jha, G., & Min, D. (2025, January). Evaluation of biomass and growth characteristics of alfalfa under limited irrigation. Abstract accepted for presentation at the American Forage & Grassland Council (AFGC) Annual Conference, Kissimmee, FL, United States.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Malone, J. L. F., Bhandari, M., Goldsmith, L., Fernandez, O., Scott, J. L., Calil, Y., Min, D., Jha, G., Aguilar, J., Dey, S., & Kim, J. (2024, November 13). Application of UAV technology to improve irrigation efficiency in alfalfa. In ASA, CSSA, SSSA International Annual Meeting. ASA-CSSA-SSSA.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2025
Citation:
Bhandari, M., Foster, J., Tiwari, A., Saad, H., Ghansah, B., Zhao, L., & Landivar, J. (2025, April 22). Digital agriculture technologies in forage management. In Southern Pasture and Forage Crop Improvement Conference (SPFCIC). Corpus Christi, TX.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Oliveira, A. M. B., Calil, Y. C. D., Ribera, L., Landivar, J. (2024). New Insights about the Scientific Literature of Digital Twins in Agriculture: a Bibliometric Study. Agricultural & Applied Economics Association (AAEA) Annual Meeting. New Orleans, LA.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Calil, Y. C. D. Cattle Market Outlook and Decision Tools for Cow/Calf Producers Concerning Forages. TRI County Hay Show - Hay Show Results. Refugio, TX. November 01, 2024.
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Progress 09/01/23 to 08/31/24
Outputs
Target Audience:During this reporting period, our efforts were totargetaudiences that are pivotal to deveop and expand our D-RAP team; and also towards initiate outreach in tghe community targeted towards resilientforage production under limited water resources, specifically in the context of precision agriculture. The target audiences included - The primary audience reached through our outreach efforts consisted of alfalfa producers in Kansas and South Texas, who are directly impacted by drought conditions and the depletion of the Ogallala Aquifer. These producers are seeking innovative solutions to optimize water use for maintaining or improving forage yield and quality. Our research is targeted towards this group as it addresses the pressing need for effective water management strategies, particularly through the application of UAV-based thermal imaging and modeling. A significant portion of our efforts was directed towards students, both at the undergraduate and graduate levels. These students were engaged through formal educational programs, internships, and hands-on training opportunities related to digital agriculture and machine learning. For example, at Texas A&M University, two undergraduate female students were trained in UAV data collection and processing, which are critical skills for future agricultural professionals. Additionally, students at Kansas State University were involved in collecting and analyzing data from field experiments, providing them with experiential learning opportunities that bridge classroom theory with real-world agricultural challenges. Sourajit Dey, Ph.D. student on this project was recognized for his presentation at International Conference on Precision Agriculture-2024, where he received first place in student competition. Another important audience included agricultural researchers and academics who are focused on improving crop resilience and sustainability through innovative research. Our project reached this group through collaborations, conference presentations, and publications. For example, manuscripts were prepared and presented on topics such as detecting climate factors affecting alfalfa production using machine learning, contributing to the scientific discourse on sustainable agriculture. Community stakeholders, including representatives from industry, government agencies, and producer organizations, were engaged through our outreach activities. This group is important for as our research is aligned with the needs of the agricultural industry and that the results are effectively implemented on the ground. Efforts such as the South Texas Beef and Forage Field Day and the Digital Agriculture Symposium provided platforms to share insights and gather feedback from these stakeholders. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?Undergraduate students at Texas A&M University Kingsville received hands-on training in UAV data collection and processing, learning to operate drones and analyze multispectral imagery for alfalfa research. Graduate students and visiting scholar at Kansas State University were trained in usingsensors like GreenSeeker, multispectral, thermaland hyperspectral spectroradiometers, gaining experience in measuring NDVI, chlorophyll content, NDVI, thermal maps and hyperspectral data. Students also participated in writing grant proposals and submitted to regional funding ageny, enhancing their skills in research funding applications. Additionally, they presented their work at the 16th International Conference on Precision Agriculture, where they developed public speaking and networking skills. Participation in field days and workshops, such as the South Texas Beef and Forage Field Day, allowed students and faculty members to engage with local producers and demonstrate the practical applications of digital agriculture technologies, further enriching their educational experiences. How have the results been disseminated to communities of interest?We focused on alfalfa producers, extension professionals, researchers, and students in the Southern Great Plains. We participated in field days, like the South Texas Beef and Forage Field Day, to show how UAVs and sensors can monitor alfalfa growth and water stress. This helped producers learn about and adopt precision agriculture technologies. We also presented our research at the 16th International Conference on Precision Agriculture, where our work on machine learning in alfalfa production received top recognition. Additionally, we published an article in "Crops & Soils" magazine about precision irrigation. We began developing web-based tools to help producers make better decisions about water use in real-time. Students were involved in the project, gaining valuable experience and helping to share our findings through research and presentations. These efforts have helped spread our research to a wide audience and encouraged collaboration and innovation in agriculture. What do you plan to do during the next reporting period to accomplish the goals?1. We will continue collecting detailed data from our field experiments at the North Agronomy Farm in Manhattan, Kansas, and the Southwest Research-Extension Center in Garden City, Kansas, as well as from our sites in South Texas. The data will include measurements of alfalfa biomass, plant height, canopy cover, soil moisture, and evapotranspiration under various irrigation treatments. In Texas, we will specifically focus on the rainfed and limited irrigation plots to assess how these practices impact alfalfa growth under local climatic conditions. The collected data will be analyzed using machine learning algorithms to identify the most critical factors influencing water use efficiency in each region. 2.We will initiate the development of web-based decision support tools that provide real-time irrigation recommendations. These tools will integrate data from our Texas and Kansas sites, offering region-specific advice to help alfalfa producers optimize water use. 3.We will establish advisory groups for both Texas and Kansas, including industry representatives, government agencies, and alfalfa producers. These groups will review our findings and provide feedback to ensure that our research addresses the practical needs of producers in both regions. We will also explore opportunities to expand these advisory groups to include more local stakeholders from each state. 4.We plan to submit manuscripts detailing our research findings for two publications in peer-reviewed journals, focusing on the results from both Kansas and Texas sites.In Kansas, we will involve undergraduate and graduate students from Kansas State University in ongoing research activities, providing them with hands-on experience in field data collection, sensor technology, and data analysis. In Texas, we will continue to engage students from Texas A&M University Kingsville in similar activities, with a focus on data processing and analysis related to local conditions. 5. Wewill refine our research methodologies based on the insights gained from our ongoing experiments. This includes exploring new technologies, such as advanced sensors and data integration platforms, to further enhance water management practices in alfalfa production.
Impacts
What was accomplished under these goals?
Accomplishments under Goal 1 - Spatiotemporal Quantification and Management of Water Budget Development of UAV-Based Data Collection Pipeline -Theproject team developed a UAV-based data processing pipeline to standardize the collection and analysis of RGB and multispectral imagery, along with ground-based measurements. This will aim to advance research for developing models that predict water stress and quantify the water budget in alfalfa production systems. Field Experimentation and Data Collection -At the North Agronomy Farm, Kansas State University, plots were established under different irrigation treatments, including 100% ETc, 60% ETc, limited irrigation, and rainfed conditions. Data collected included canopy height, dry weight, and canopy cover, which are useful for understanding the water use efficiency of alfalfa under various water regimes. Machine Learning Algorithm Development-The team utilized machine learning algorithms to analyze the data collected from the UAVs and ground sensors. This allowed for the identification of key variables driving water use efficiency and drought resilience in alfalfa. A manuscript focusing on the detection of climate factors affecting alfalfa production through machine learning was prepared, contributing to the understanding of spatiotemporal water management in the region. Accomplishments under Goal 2 - Social and Economic Data Collection Economic Analysis of Precision Water Management-The project included the collection of social and economic data related to the use of wireless sensor networks and machine learning techniques in alfalfa production. This analysis is for understanding the cost-benefits of implementing D-RAP systems in real-world farming scenarios. Student Training and Grant Writing-The project provided opportunities for students to engage in research that blends economic analysis with precision agriculture. A graduate student grant proposal was written (by Sourajit Dey) focusing on precision water management, although it was not funded, the process provided valuable insights into the economic dimensions of implementing advanced technologies in agriculture. Accomplishments under Goal 3 - Social and Economic Data Collection Field Days and Workshops-The project team actively participated in South Texas Beef and Forage Field Day, where digital agriculture applications and alfalfa production systems were presented to the community. Conference Presentations-Research findings were disseminated at the International Society of Precision Agriculture-2024, where a poster presentation by Sourajit Dey on machine learning algorithms in alfalfa production was awarded first place. Student Involvement in Knowledge Dissemination-Training was provided to undergraduate students at Texas A&M University Kingsville in UAV data collection and processing.
Publications
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Nazrul, F., Kim, J., Dey, S., Palla, S. P., Sihi, D., Whitaker, B., Min, D., and Jha, G. (2024). Machine learning algorithm in detecting long-term effect of climatic factors for alfalfa production in Kansas. Poster presented at International Society of Precision Agriculture, Kansas State University, Manhattan, KS. (July 21-24, 2024). (First Place: Big Data, Data Mining, and Deep Learning section in the 16th International Conference on Precision Agriculture (ICPA))
- Type:
Journal Articles
Status:
Published
Year Published:
2024
Citation:
Dey, S., Jha, G., & Min, D. (2024). Precision Irrigation Technologies for Water?Wise and Climate Resilient Alfalfa Production. Crops & Soils. doi.org/10.1002/crso.20383
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Calil, Y. C. D., Oliveira, A. M. B., Ribera, L., Landivar, J. (2024). Digital Twin Models: Financial Strategies for Farm Management. Agricultural & Applied Economics Association (AAEA) Annual Meeting. New Orleans, LA.
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