Progress 07/01/18 to 06/30/23
Outputs
Target Audience:The remote sensing-based precision nitrogen management technology we developed for corn greatly benefited corn growers in Minnesota and Indiana. Over 50 on-farm trials were conducted by working closely with farmers and crop consultants, which enabled corn growers and consultants to test this technology on their own farms. Most farmers increased their economic returns, and recognized the importance of adjusting their nitrogen rates according to weather and soil-landscape conditions. Some farmers wanted to adopt this technology. Farmers and consultants also realized the potential of variable rate sulfur management, which was very new information for them. The farmers who benefited from this research are in central, southeast and western Minnesota, and in Indiana. Farmers and crop consultants were impacted. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?We trained 5 graduate students, 2 research scientists, 3 postdocs, 12 undergraduate students directly through the research activities. How have the results been disseminated to communities of interest?We incorporated our research results into the courses Dr. Miao teaches (Soil 3416: Plant Nutrients in the Environment and Soil 4111: Introduction to Precision Agriculture). We also organized and participated in field days and project meetings to share the results with farmers and crop consultants, industry people, researchers and graduate students. Publications: Wang, X., Y. Miao, R. Dong, K. Kusnierek. 2023. Minimizing active canopy sensor differences in nitrogen status diagnosis and in-season nitrogen recommendation for maize with multi-source data fusion and machine learning. Precis. Agric 24: 2549-2565. Dong, R. Y.X. Miao, P. Berry, X.B. Wang, F. Yuan, K. Kusnierek, C. Baker, M. Sterling. 2023 In-season prediction of maize stem lodging risk using an active canopy sensor Eur. J. Agron., 151 (2023), Article 126956, 10.1016/j.eja.2023.126956 Wang T, Jin H, Sieverding H et al (2023) Understanding farmer views of precision agriculture profitability in the U.S. Midwest. Ecol Econ 213:107950. https://doi.org/10.1016/j.ecolecon.2023.107950 Shao, H.; Miao, Y.; Fernández, F.G.; Kitchen, N.R.; Ransom, C.J.; Camberato, J.J.; Carter, P.R.; Ferguson, R.B.; Franzen, D.W.; Laboski, C.A.M.; et al. Evaluating Critical Nitrogen Dilution Curves for Assessing Maize Nitrogen Status across the US Midwest. Agronomy 2023, 13, 1948. https://doi.org/10.3390/agronomy13071948 Liang, J.; Ren, W.; Liu, X.; Zha, H.; Wu, X.; He, C.; Sun, J.; Zhu, M.; Mi, G.; Chen, F.; et al. Improving Nitrogen Status Diagnosis and Recommendation of Maize Using UAV Remote Sensing Data. Agronomy 2023, 13, 1994. https://doi.org/10.3390/agronomy13081994 Bohman BJ, Culshaw-Maurer MJ, Ben Abdallah F, Giletto C, Bélanger G, Fernández GF, Miao Y, Mulla DJ, Rosen CJ (2023) Quantifying critical N dilution curves across G×E×M effects for potato using a partially-pooled Bayesian hierarchical method. Eur J Agron 144:126744. https://doi.org/10.1016/j.eja.2023.126744 Li D, Miao Y, Ransom CJ, Bean GM, Kitchen NR, Fernández FG, Sawyer JE, Camberato JJ, Carter PR, Ferguson RB, Franzen DW, Laboski CAM, Nafziger ED, Shanahan JF. Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning. 2022. Remote Sensing 14(2):394. https://doi.org/10.3390/rs14020394 Li, F.; Miao, Y.; Chen, X.; Sun, Z.; Stueve, K.; Yuan, F. In-Season Prediction of Corn Grain Yield through PlanetScope and Sentinel-2 Images. Agronomy 2022, 12, 3176. https://doi.org/10.3390/agronomy12123176 Li, Y, Y Miao, J Zhang, D Cammarano, S Li, X Liu, Y Tian, Y Zhu, W Cao, Q Cao. 2022.Improving nitrogen status estimation of winter wheat using random forest by integrating multi-source data across different agro-ecological zones.Frontiers in Plant Science 13:890892. Li, X, S T Ata-UI-Karim, Y Li, F Yuan, Y Miao, K Yoichiro, T Cheng, L Tang, X Tian, X Liu, Y Tian, Y Zhu, W Cao, Q Cao. 2022. Advances in the estimations and applications of critical nitrogen dilution curve and nitrogen nutrition index of major cereal crops. A review. Computers and Electronics in Agriculture 197:106998. Lu, J., E. Dai, Y. Miao, K. Kusnierek. 2022. Improving active canopy sensor-based in-season rice nitrogen status diagnosis and recommendation using multi-source data fusion with machine learning. J. Clean. Prod. 380:134926. Lu J, Wang H, Miao Y, Zhao L, Zhao G, Cao Q, Kusnierek K. Developing an Active Canopy Sensor-Based Integrated Precision Rice Management System for Improving Grain Yield and Quality, Nitrogen Use Efficiency, and Lodging Resistance. 2022. Remote Sensing 14(10):2440. https://doi.org/10.3390/rs14102440 Yuan, Y., Miao, Y., Yuan, F. et al. Delineating soil nutrient management zones based on optimal sampling interval in medium- and small-scale intensive farming systems. Precision Agric 23, 538-558 (2022). https://doi.org/10.1007/s11119-021-09848-1 Dong, R, Y Miao, X Wang, F Yuan, K Kusnierek. 2022. Combining leaf fluorescence and active canopy reflectance sensing technologies to diagnose maize nitrogen status across growth stages. Precision Agriculture 23:939-960. Dong R, Miao Y, Wang X et al (2021a) Improving maize nitrogen nutrition index prediction using leaf fluorescence sensor combined with environmental and management variables. Field Crop Res 269:108180. https://doi.org/10.1016/j.fcr.2021.108180 Franzen, D.W., Miao, Y., Kitchen, N.R., Schepers, J.S., Scharf, P.C. (2021). Sensing for Health, Vigour and Disease Detection in Row and Grain Crops. In: Kerry, R., Escolà, A. (eds) Sensing Approaches for Precision Agriculture. Progress in Precision Agriculture. Springer, Cham. https://doi.org/10.1007/978-3-030-78431-7_6 Dong R, Miao Y, Wang X, Yuan F, Kusnierek K. Canopy Fluorescence Sensing for In-Season Maize Nitrogen Status Diagnosis. Remote Sensing. 2021; 13(24):5141. https://doi.org/10.3390/rs13245141 Wang, X., Y. Miao, R. Dong, H. Zha, T. Xia, Z. Chen et al. 2021. Machine learning-based in-season nitrogen status diagnosis and side-dress nitrogen recommendation for corn. Eur. J. Agron. 123:126193. Wang X, Miao Y, Batchelor WD et al (2021b) Evaluating model-based strategies for in-season nitrogen management of maize using weather data fusion. Agric For Meteorol 308-309:108564. https://doi.org/10.1016/j.agrformet.2021.108564 Wang X, Miao Y, Dong R et al (2020) Economic optimal nitrogen rate variability of maize in response to soil and weather conditions: implications for site-specific nitrogen management. Agronomy 10(9):1237. https://doi.org/10.3390/agronomy10091237 Chen Z, Miao Y, Lu J et al (2019) In-season diagnosis of winter wheat nitrogen status in smallholder farmer fields across a village using unmanned aerial vehicle-based remote sensing. Agronomy 9(10):619. https://doi.org/10.3390/agronomy9100619 Miao, Y., D.J. Mulla, P.C. Robert. 2018. An integrated approach to site-specific management zone delineation. Front. Agric. Sci. Eng. 5: 432-441. Wakahara, S, Y Miao, K Mizuta, J Zhang, D Li, S Gupta, C. Rosen. 2022. Evaluating the Potential of Improving In-season Nitrogen Status Diagnosis of Potato Using Leaf Fluorescence Sensors and Machine Learning. In Proceedings of the 15th International Conference on Precision Agriculture (online). Minneapolis, MN: International Society of Precision Agriculture. Lacerda, LN, Y Miao, K Mizuta, K Stueve. 2022. Identifying Key Factors Influencing Yield Spatial Pattern and Temporal Stability for Management Zone Delineation. In Proceedings of the 15th International Conference on Precision Agriculture (online). Minneapolis, MN: International Society of Precision Agriculture. Wang, T., H. Jin, H. L Sieverding, X. Rao, Y. Miao, S. Kumar, D. Redfearn, A. Nafchi. 2022. Understanding farmer perceptions of precision agriculture profitability in the US Midwest. Selected Paper prepared for presentation at the 2022 Agricultural & Applied Economics Association Annual Meeting, Anaheim, CA; July 31-August 2, 2022. Sela, S, N Graff, K Mizuta, Y Miao. 2022. Spatially explicit prediction of soil nutrients and characteristics in corn fields using soil electrical conductivity data and terrain attributes. In Proceedings of the 15th International Conference on Precision Agriculture (online). Minneapolis, MN: International Society of Precision Agriculture. What do you plan to do during the next reporting period to accomplish the goals?
Nothing Reported
Impacts
What was accomplished under these goals?
It is a great challenge to optimize nutrient management to increase crop yield, nutrient use efficiency and profitability while protecting the environment under climate change. This is important for farmers because this will directly influence their farming business. This is also important for the public, because improper nutrient management will pollute the environment, including drinking water. This is also important for the government for regulations and making policies. We developed a strategy to divide a farmer's field into a few relatively homogeneous management zones using relative elevation, soil organic matter, slope, electrical conductivity, spatial yield trend map, and yield temporal stability map, so each zone can be managed differently with different rate of fertilizers, seeds or irrigation. We have been improving this strategy with more easily available data, like historical yield, bare soil remote sensing image, and landscape factors like relative elevation, slope and other terrain variables. It has low cost and is easier to be adopted by farmers. A remote sensing and calibration strip-based precision nitrogen management technology has been developed. This technology uses site-, year- and hybrid-specific calibration information to guide in-season site-specific side-dress application for corn. High spatial and temporal resolution PlanetScope satellite remote sensing images are used in this strategy, making it more practical due to its global coverage. This can be implemented in most of the farmers' fields directly without the need of previous data accumulation, and farmers don't need to purchase any new sensors. It has been evaluated in farmers' fields across Minnesota and Indiana to determine the agronomic, economic and environmental benefits. The results indicated that this technology can achieve similar crop yield with less nitrogen fertilizers, and increase economic returns and reduce nitrogen losses to the environment in wet or normal years. Machine learning-based multi-source data fusion has been used to improve in-season corn nitrogen status diagnosis, yield prediction and in-season nitrogen recommendations across diverse conditions. Three proximal sensor-based precision nitrogen management strategies for potato have been developed and are being evaluated in experiments conducted in Becker, MN in comparison with farmer practice. We can significantly increase nitrogen use efficiency and economic returns with less fertilizers and reduced nitrate leaching losses. Integrated precision nitrogen and water management using Crop Circle Phenom sensor and UAV-based integrated multispectral and thermal sensing technologies are being developed and being evaluated in field experiments. On-farm variable rate sulfur trials have been conducted to determine within-field variability in optimal sulfur rate and identify key influencing variables. Precision sulfur management strategies are being developed. Graduate students, postdocs, research scientists, faculty members, farmers, crop consultants, agricultural industries offering precision agriculture services, and government staff were involved. The improved nitrogen management in corn production can improve nitrogen use efficiency and reduce nitrogen losses to the environment, which will protect surface and groundwater quality and mitigate climate change. This will benefit the broader public both immediately and over long-term.
Publications
|
Progress 10/01/21 to 09/30/22
Outputs
Target Audience:I reached 36 undergraduate students through my classroom teaching of Soil 3416 Plant Nutrients in the Environment in the Spring semester (30)and Soil 4111 Introduction to Precision Agriculture in the fall semester (6), and 3 graduate students inSoil 4111 Introduction to Precision Agriculture and 4 undergraduate students and 3 graduate students from my research programs. Through on-farm and small plot research, I reached scientific communities, corn growers, potato growers, crop consultants, Minnesota Department of Agriculture staff, USDA ARS scientist, USDA NRCS staff and scientists, corn and potato comodity groups, agricultural and remote sensing companies (Sentek Systems, Granular, Soil Optix, PDMI, VegaMix, Sentera,AgMatix, etc.), undergraduate students, graduate students, researchers,and visiting scholars, and helped them to understand and/or evaluate the precision N management technologies we are developing. We also presented and shared our research at two field days, ASA-CSSA-SSSA annual meeting, Internaitonal Conference on Precision Agriculture, NUE Workshop, Training courses, reaching farmers, research scientists, industry people, graudate students and extension agents. We organized a post confernece tour in June after International Confernece of Precision Agricuture to visit UMN and we introduced our research activities, reaching about 40 internaitonal scientists and researchers. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?We organized a Virtual On-farm N Trial Summary Meeting in Jan., 2022 using Zoom and we invited cooperative farmers, crop consultants, researchers, industry people, government representatives, graduate students and postdocsto join this meeting to share research progress and explain the details of the new precision N management technology we developed to train them for on-farm adoption. Two Ph.D students and one M.S. student are been being trained, as well as two postdocs. This project provide opportunities ofon-farm trial design and implement, proximal, UAV and satellite remote sensing for soil and crop properties, management zone delienation, machine learning, plant and soil sampling and processing, suction cup lysimeter installation and data collection, data analysis, progression writing, proposal writing, communication with farmers and crop consultants, presentation at local, national and international meetings and conferences, etc. How have the results been disseminated to communities of interest?The results have been disseminated to communicties of interests through 8 peer reviewed publications in scientific journals (Remote Sensing, Field Crops Research, Precision Agriculture, Computers and Electronics in Agriculture and Frontiers in Plant Science), 6 presentations at ASA-CSSA-SSSA Annula Meeting, 4 presentations at International Conference on Precision Agriculture, 1 presentation at NUE Workshop,1 presentation at an International Workshop on Precision Agriculture, and 1 presentation at Internaitonal Confernece on Agro-geoinformatics,2 presentations at comodity group meetings, 2 presentations at two field day events. A video was also created to introduce the precision nitrogen management strategy we developed. We also shared news related to this project usign Twitter andLinkedin. What do you plan to do during the next reporting period to accomplish the goals?We will present a paper oncombining remote sensing, crop growth modelng andmachine learning to improve corn nitrogen management. at European Conference on Precision Agriculture, and publish the results asa peer-reviewed journal paper.We will publish a paper on the remote sensing and calibration strip-based precision nitrogen management technology as a peer-reviewer journal paper. We will present the results on integrated precision nitrogen and irrigation management, variable rate S trials, on-farm evaluation of remote sensing and calibration strip-based precision nitrogen management, management zone-based precision nitrogen management, and proxial sensor-based precision nitrogen management strategies for potato.We willorganize an On-farm Trial Summary Meeting to share the on-farm trial results with farmers and crop consultants in Jan., 2023. We are planning to collabroate with a company to develop a decision support tool to faciliate the adoption of the developed technology. We will present the results in ASACSSA-SSSA Annual Meetings.
Impacts
What was accomplished under these goals?
Proper nutrient management is crucially important for food security and sustianable development. We have made significant progress in developingpractical and innovative remote sensing and calbration strip-based precision nitrogen management strategies that can produce similar yield with less nitrogen impacts as compared with farmers' current practices, and increase econimic returns with reduced environemntal impactrs. They can be easily adopted by farmers. They are being systematically evaluate the agronomic, economic and environmental benefits of this technology by conducting on-farm trials across Minnesota and Indiana. Objective 1) Management Zone Delineation: In addition to developing a management zone delineation strategy using relative elevation, soil organic matter, slope, electrical conditivtiy, spatial yield trend map, and yield temporal stability map, studies have been conducted to identify field-specific variables to be used for management zone delienation using more easily available data, like historical yield, bare soil remote sensing image, and landscape factors like relative elevation, slope and other terrain varaibles. It has low cost and is easier to be adoted by farmers. Objective 2) Evaluation of the potential of management zone-specific management: On-farm nitrogen trials have been conducted, and the potential of management zone for varaible rate nitrogen application is being evaluated. Corn yield, N rates, soil and plant data have been collected and more results will be reported in the next report. Objective 3) Proximal and remote sensing-based precision nutrient management: A remote sensing and calibration strip-based precision nitrogen managemnt technology has been developed. This technology uses site-, year- and hybrid-specific calibration informaiton to guide in-season site-specific side-dress applicaiton for corn. High spatial and temporal resolution PlanetScope satellite remote sensing images are used in this strategy, making it more practical due to its global coverage. This can be implemented in most of the farmers' fields directly without the need of previous data accumulation, and farmers don't need to purchase any new sensors. It is being evlauated in farmers' fields across Minnesota and Indiana to determine the agronomic, economic and environemntal benefits. Preliminary results indicated that this technology can achieve similar crop yield with less nitrogen fertilzers, and increase economic returns and reduce nitrogen losses to the environment in wet or normal years. However, in dry years, sidedress nitrogen application can be delayed, resulting in potential yield losses. Machine learning-based multi-source data fusion has been used to improve in-season cornnitrogen status diagnosis, yield prediction and in-season nitrogen recommendations across diverse conditions. Three proximal sensor-based precision nitrogen management strategies for potato have been developed and are being evaluated in experiments conducted in Becker, MN in comparson with farmer practice. Objective 4) Integrated precision crop management syetms: Studies are being conducted to develop integrated procision nitrogen and water management using Crop Circle Phenom sensor and UAV-based integrated multispectral and thermal sensing technologies. The developed integrated precision nitorgen and irirgation management technology is being evaluated in a field experiment in Becker in 2022 and the results will be reported in the next report. On-farm variable rate sulfurtrials are being conducted to determine within-field variability in optimal sulfur rate and identify key influencing variables. Data are being collected and the results will be included in the next report.
Publications
- Type:
Book Chapters
Status:
Published
Year Published:
2021
Citation:
Franzen, D.W., Miao, Y., Kitchen, N.R., Schepers, J.S., Scharf, P.C. (2021). Sensing for Health, Vigour and Disease Detection in Row and Grain Crops. In: Kerry, R., Escol�, A. (eds) Sensing Approaches for Precision Agriculture. Progress in Precision Agriculture. Springer, Cham. https://doi.org/10.1007/978-3-030-78431-7_6
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Dong R, Miao Y, Wang X, Yuan F, Kusnierek K. Canopy Fluorescence Sensing for In-Season Maize Nitrogen Status Diagnosis. Remote Sensing. 2021; 13(24):5141. https://doi.org/10.3390/rs13245141
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Sharma, V, AE Flores, Y Miao, FG Fernandez. 2021.Influence of Irrigation and Nitrogen Management on Corn Yield, Water and Nitrogen Use Efficiency and Nitrate Leaching in Minnesota. ASA, CSSA, SSSA International Annual Meeting
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Improving Decision Making in Precision Agriculture Using Artificial Intelligence: Case Studies for Precision Nitrogen Management. ASA, CSSA, SSSA International Annual Meeting.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Mizuta, K, Y Miao, LN Lacerda, C Rosen, SK Gupta, S Kang, Y Huang. 2021. Improving Potato Yield Mapping Using High Spatial and Temporal Resolution Satellite Remote Sensing and Machine Learning. ASA, CSSA, SSSA International Annual Meeting.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Lacerda,LN, Y Miao, V Sharma, APE Flores, K Mizuta. 2021. Evaluating the Potential of Simultaneous Diagnosis of Nitrogen and Water Status in Corn Using the Crop Circle Phenom Sensor System. ASA, CSSA, SSSA International Annual Meeting.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Fabrizzi, K. P., Fernandez, F. G., Kaiser, D. E., Miao, Y., Pagliari, P. H., Pease, L. A., Sims, A. L., Strock, J., Vetsch, J. A., Rosen, C., & Wilson, M. (2021) Small-Scale Variability in Economic Optimum Nitrogen Rate for Corn in Minnesota [Abstract]. ASA, CSSA, SSSA International Annual Meeting, Salt Lake City, UT. https://scisoc.confex.com/scisoc/2021am/meetingapp.cgi/Paper/134966
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Yuan, Y., Miao, Y., Yuan, F. et al. Delineating soil nutrient management zones based on optimal sampling interval in medium- and small-scale intensive farming systems. Precision Agric 23, 538558 (2022). https://doi.org/10.1007/s11119-021-09848-1
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Wang, T., H. Jin, H. L Sieverding, X. Rao, Y. Miao, S. Kumar, D. Redfearn, A. Nafchi. 2022. Understanding farmer perceptions of precision agriculture profitability in the US Midwest. Selected Paper prepared for presentation at the 2022 Agricultural & Applied Economics Association Annual Meeting, Anaheim, CA; July 31-August 2, 2022.
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Dong, R, Y Miao, X Wang, F Yuan, K Kusnierek. 2022. Combining leaf fluorescence and active canopy reflectance sensing technologies to diagnose maize nitrogen status across growth stages. Precision Agriculture 23:939-960.
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Li D, Miao Y, Ransom CJ, Bean GM, Kitchen NR, Fern�ndez FG, Sawyer JE, Camberato JJ, Carter PR, Ferguson RB, Franzen DW, Laboski CAM, Nafziger ED, Shanahan JF. Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning. 2022. Remote Sensing 14(2):394. https://doi.org/10.3390/rs14020394
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Li, Y, Y Miao, J Zhang, D Cammarano, S Li, X Liu, Y Tian, Y Zhu, W Cao, Q Cao. 2022.Improving nitrogen status estimation of winter wheat using random forest by integrating multi-source data across different agro-ecological zones. Frontiers in Plant Science 13:890892.
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Lu J, Wang H, Miao Y, Zhao L, Zhao G, Cao Q, Kusnierek K. Developing an Active Canopy Sensor-Based Integrated Precision Rice Management System for Improving Grain Yield and Quality, Nitrogen Use Efficiency, and Lodging Resistance. 2022. Remote Sensing 14(10):2440. https://doi.org/10.3390/rs14102440
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Li, X, S T Ata-UI-Karim, Y Li, F Yuan, Y Miao, K Yoichiro, T Cheng, L Tang, X Tian, X Liu, Y Tian, Y Zhu, W Cao, Q Cao. 2022. Advances in the estimations and applications of critical nitrogen dilution curve and nitrogen nutrition index of major cereal crops. A review. Computers and Electronics in Agriculture 197:106998.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Lacerda, LN, Y Miao, K Mizuta, K Stueve. 2022. Identifying Key Factors Influencing Yield Spatial Pattern and Temporal Stability for Management Zone Delineation. In Proceedings of the 15th International Conference on Precision Agriculture (online). Minneapolis, MN: International Society of Precision Agriculture.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Wakahara, S, Y Miao, K Mizuta, J Zhang, D Li, S Gupta, C. Rosen. 2022. Evaluating the Potential of Improving In-season Nitrogen Status Diagnosis of Potato Using Leaf Fluorescence Sensors and Machine Learning. In Proceedings of the 15th International Conference on Precision Agriculture (online). Minneapolis, MN: International Society of Precision Agriculture.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Sela, S, N Graff, K Mizuta, Y Miao. 2022. Spatially explicit prediction of soil nutrients and characteristics in corn fields using soil electrical conductivity data and terrain attributes. In Proceedings of the 15th International Conference on Precision Agriculture (online). Minneapolis, MN: International Society of Precision Agriculture.
|
Progress 10/01/20 to 09/30/21
Outputs
Target Audience:I reached 44 undergraduate studentsthrough my classroom teaching of Soil 3416 Plant Nutrients in the Environment in the Spring semester (40) and Soil 4111 Introduction to Precision Agriculture in the fall semester (4), and 6 graduate students in LAAS 5416 Precision Agriculture and Nutrient Management and Soil 4111. Through on-farm and small plot research, I reached scientific communities, corn growers, potato growers, crop consultants, Minnesota Department of Agriculture staff, USDA ARS scientist, USDA NRCS staff and scientists, corn and potato comodity groups, agricultural and remote sensing companies (Sentek Systems, Granular, Soil Optix, PDMI, VegaMix, Sentera, Helium Aero, AgMatix, Ceres Imaging, and Deveron, etc.),undergraduate students, graduate students, researchers,and visiting scholars, and helped them to understand and/or evaluate the precision N management technologies we are developing. I also presented and shared our research at two field days, reaching farmers, industry people, graudate students and researchers. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?We organized a VirtualOn-farm N Trial SummaryMeeting in Dec., 2020 using Zoom and we invited cooperative farmers, and crop consultants to join this meeting to share research progress and explain the details of the new precision N management technology we developed to train them for on-farm adoption. A M.S. graduate student was trained in this project and he has graduated in Dec., 2020. Two Ph.D student has been recruited and are being trained. Two postdocs are being trained in this project involving on-farm trials, proximal, UAV and satellite remote sensing for soil and crop properties, management zone delienation, machine learning, plant and soil sampling and processing, suction cup lysimeter installation and data collection, data analysis, progression writing, proposal writing, communication, etc. How have the results been disseminated to communities of interest?The results have been disseminated to communicaties of interests through 7 peer reviewed publications in scientific journals (European Journal of Agronomy, Remote Sensing, Field Crops Research, Agricultural and Foresty Meterology), 4 presentations at European Conference on Precision Agriculture and 4 publicaitons in the conferene proceedings, 1 presentation at 7th Annual Nitrogen: Minnesota's Grand Challenge and Compelling Opportunity Conference organized by University of Minnesota Extension and the Minnesota Agricultural Water Resource Center, 1 presentation at 47th Annual Hermiston Farm Fair organized by Oregon State University, 2 presentations at comodity group meetings, and 2 presentations at 2 field day events. We also created two blog posts to introduce the research results. What do you plan to do during the next reporting period to accomplish the goals?We will presenta paper on using machine learning models to identify field-specific key variables for management zone delienation and present it at International Conferene on Precision Agriculture (ICPA 2022), and analyze on-farm trial data to evaluate the potential of management zone-based precision nitrogen management for corn. We will prepare a paper on the remote sensing and calibration strip based precision nitrogen management technology and submit it to Precision Agriculture journal. We will analyze the on-farm precision N management trial results and present it at the ICPA 2022. We will also do crop modeling analysis to evaluate the potential of management zone-based precision crop management practies. We will organize an On-farm Trial Summary Meeting to share the on-farm trial results with farmers and crop consultants. We will try to develop a decision support tool to faciliate the adoption of the developed technology. We will present the results in ASA-CSSA-SSSA Annual Meetings. We will do more analysis to evaluate the potential of integrated precision N and water management.
Impacts
What was accomplished under these goals?
We have made significant progress in developing a practical andinnovative remote sensing and calbration strip-based precision nitrogen management technology that can be easily adopted by farmers. We have attracted more funding to systematically evaluate the agronomic, economic and environmental benefits of this technology by conducting on-farm trials across Minnesota and Indiana. Objective 1) Management Zone Delineation: In addition to developing a management zone delineation strategy using relative elevation, soil organic matter, slope, electrical conditivtiy, spatial yield trend map, and yield temporal stability map, studies have been conducted to identify field-specific variables to be used for management zone delienation using more easily available data, like yield, remote sensing image, and landscape factors. Objective 2) Evaluation of the potential of management zone-specific management:On-farm nitrogen trials have been conducted, and the potential of management zone for varaible rate nitrogen application is being evaluated. Objective 3) Proximal and remote sensing-based precision nutrient management: A remote sensing and calibration strip-based precision nitrogen managemnt technology has been developed. This technology uses site-, year- and hybrid-specific calibration informaiton to guide in-season site-specific side-dress applicaiton for corn.High spatial and temporal resolution PlanetScope satellite remote sensing images are used in this strategy, making it more practical due to its global coverage. This can be implemented in most of the farmers' fields directly without the need of previous data accumulation, and farmers don't need to purchase any new sensors.It is being evlauated in farmers' fieldsacross Minnesota and Indiana to determine the agronomic, economic and environemntal benefits. An innovative integrated active canopy sensor system, Crop Circle Phenom, has been evaluated for in-season diagnosis of corn nitrogen status under different tillage and drainage condutions using machine learning. This sensor system is being evaluated for simultaneous identification of crop nitrogen and water stresses. Objective 4) Integrated precision crop management syetms:Studies are being conducted to develop integrated procisionnitrogen and water management using Crop Circle Phenom sensor and UAV-basedintegrated multispectral and thermal sensing technologies.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Wang, X., Miao, Y., Dong, R., Zha, H., Xia, T., Chen, Z., . . . Li, M. 2021. Machine learning-based in-season nitrogen status diagnosis and side-dress nitrogen recommendation for corn. European Journal of Agronomy, 123(2), 126193.
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Cummings, C., Miao, Y., Paiao, G. D., Kang, S., & Fernandez, F. G. 2021. Corn Nitrogen Status Diagnosis with an Innovative Multi-Parameter Crop Circle Phenom Sensing System. Remote Sensing, 13(3), 401.
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Wang, X., Miao, Y., Batchelor, W. D., Dong, R., & Kusnierek, K. 2021. Evaluating model-based strategies for in-season nitrogen management of maize using weather data fusion. Agricultural and Forestry Meteorology, 308-309, 108564.
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Dong, R., Miao, Y., Wang, X., Chen, Z., & Yuan, F. 2021. Improving maize nitrogen nutrition index prediction using leaf fluorescence sensor combined with environmental and management variables. Field Crops Research, 269(15), 108180.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Cummings, C., Miao, Y., Kang, S., & Stueve, K. 2021. Developing a remote sensing and calibration strip-based in-season nitrogen management strategy for corn. Precision agriculture21 805--826. Wageningen Academic Publishers.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Wang, X., Miao, Y., Dong, R., Chen, Z., & Kusnierek, K. 2021. Improving in-season nitrogen status diagnosis using a three-band active canopy sensor and ancillary data with machine learning. Precision agriculture21 451-457. Wageningen Academic Publishers.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Zha, H., Lu, J., Li, Y., Miao, Y., Kusnierek, K., & Batchelor, W. 2021. In-season calibration of the CERES-Rice model using proximal active canopy sensing data for yield prediction. Precision agriculture21 927-932. Wageningen Academic Publishers.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Dong, R., Miao, Y., Wang, X., & Berry, P. 2021. In-season prediction of maize lodging characteristics using an active crop sensor. Precision agriculture21 299-305. Wageningen Academic Publishers.
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Li, D., Miao, Y., Gupta, S., Rosen, C., Yuan, F., Wang, C., Wang, L., Huang, Y. 2021. Improving potato yield prediction by combining cultivar information and UAV remote sensing data using machine learning. Remote Sensing, 13(16), 3322.
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Berry, P. M. (Corresponding Author), Baker, C. J., Hatley, D., Dong, R., Wang, X., Blackburn, G. A., . . . Whyatt, J. D. 2021. Development and application of a model for calculating the risk of stem and root lodging in maize. Field Crops Research, 262, 108037.
- Type:
Theses/Dissertations
Status:
Published
Year Published:
2020
Citation:
Cummings, Cadan. 2020. In-season Corn Nitrogen Status Diagnosis and Precision Management with Proximal and Remote Sensing. M.S. Thesis, University of Minnesota, St. Paul, MN, USA.
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Cammarano, D., Zha, H., Wilson, L., Li, Y., Batchelor, W. D., & Miao, Y. 2020. A Remote Sensing-Based Approach to Management Zone Delineation in Small Scale Farming Systems. Agronomy, 10(11), 1767.
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Progress 10/01/19 to 09/30/20
Outputs
Target Audience:I reached 53 undergraduate students and 2 graduate students through my classroom teaching of Soil 3416 Plant Nutrients in the Environment and LAAS 5416 Precision Agriculture and Nutrient Management. Through on-farm research, I reached scientific communities, corn growers, potato growers, crop consultants, Minnesota Department of Agriculture staff, comodity groups, undergraduate students, graduate students,researchers, visiting scholars and industry people, and helped them to understand and/orevaluate the precision N management technologies we are developing. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?We organized an On-farm Precision Agriculture Research Meeting in Jan., 2020 and we invited cooperative farmers, crop consultant and some students to join this meeting to share research progress and explain the details of the new precision N management technology we developed to train them for on-farm adoption. An undergraduate intern was trained in this project in the summer of 2020 for soil and plant data collection, leaf and active canopy sensor data collectin and analysis. A M.S. graduate student was trained in this project, and a Ph.D student has been recruited and is being trained. A research scientist (Shujiang Kang) was trained in this project for 1.5 years to improve his capability for research and development in precision agriculture and he has beenhired by BASF for research and development in digital agriculture. How have the results been disseminated to communities of interest?The results have been disseminated to communicaties of interests through 4 peer reviewed publications in scientific journals (Agronomy, Remote Sensing), presentationsat stakeholder workshops (Advanced Crop Advisor Worskhop and On-Farm Precision Agriculture Research Summary Meeting), and ASA-CSSA-SSSA annual meeting (2019) (3 presentations). What do you plan to do during the next reporting period to accomplish the goals?We will work more on management zone delineation, new sensor evaluation, new precision N management technologies development and evaluation and better share the results through scientific journal paper publication, blog posts and Twitter sharing. We will also organize a On-farm Precision Agriculture Experiment Netowork to better facilitate result dissemination, technology training and farmer and crop advisor adoption of our developed technologies.
Impacts
What was accomplished under these goals?
1) A practical site-specific management zone (MZ) delienation strategy has been developed using relative elevation, soil organic matter, slope, electrical conductivity, spatial yield trend map, and yield temporal stability map. More studies are being conducted to improvethis strategy; 2) Studies are being performed to evaluate the potential of MZ-specific varaible rate N management of corn; 3) Developed a leaf flouresence sensor-based corn N concentration prediction method by combining sensor data and days after sowing; Developed a remote sensing and calibration strip-based precision N management strategy for corn and was evaluated using on-farm experiments. Strategies are being developedto use leaf sensor, active canopy sensor, UAV, aerial and satellite remote sensing for in-season corn and potato N status diagnosis; 4) Efforts are being made to develop strategies to optimize corn N and water management.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Zha, H., Miao, Y., Wang, T., Li, Y., Zhang, J., Sun, W., Feng, Z., Kusnierek, K. (2020). Improving unmanned aerial vehicle remote sensing-based rice nitrogen nutrition index prediction with machine learning. Remote Sensing, 12(2), 215.
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Lu, J., Miao, Y., Shi, W., Li, J., Hu, X., Chen, Z.,Wang, X., Kusnierek, K. (2020). Developing a Proximal Active Canopy Sensor-based Precision Nitrogen Management Strategy for High-Yielding Rice. Remote Sensing, 12(9), 1440.
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Wang, X., Miao, Y., Dong, R., Chen, Z., Kusnierek, K., Mi, G., & Mulla, D. J. (2020). Economic Optimal Nitrogen Rate Variability of Maize in Response to Soil and Weather Conditions: Implications for Site-Specific Nitrogen Management. Agronomy, 10(9), 1237.
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Dong, R., Miao, Y., Wang, X., Chen, Z., Yuan, F., Zhang, W., & Li, H. (2020). Estimating Plant Nitrogen Concentration of Maize using a Leaf Fluorescence Sensor across Growth Stages. Remote Sensing, 12(7), 1139.
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Progress 10/01/18 to 09/30/19
Outputs
Target Audience:Farmers working on corn management through on-farm precision nitrogen management research. Undergraduate students through formal classroom teaching of Soil 3416 Plant Nutrients in the Environment. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?
Nothing Reported
How have the results been disseminated to communities of interest?
Nothing Reported
What do you plan to do during the next reporting period to accomplish the goals?We will analyze the data we collected in the previous reporting period and share the results at local, national and international conferences.
Impacts
What was accomplished under these goals?
We are in the first year of data collection and have not made enough progress to have an impact.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Wang, X., Miao, Y., Dong, R.*, Chen, Z., Guan, Y.*, Yue, X.*, Fang, Z.*, & Mulla, D. J. (2019). Developing Active Canopy Sensor-Based Precision Nitrogen Management Strategies for Maize in Northeast China. Sustainability, 11(3), 706.
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Huang, S., Miao, Y., Yuan, F., Cao, Q., Ye, H., Lenz-Wiedemann, V. I.S., & Bareth, G. (2019). In-season diagnosis of rice nitrogen status using proximal fluorescence canopy sensor at different growth stages. Remote Sensing, 11(6), 1847.
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Chen, Z., Miao, Y., Ju, J., Zhou, L., Li, Y., Zhang, H., Lou, W., Zhang, Z., Kusnierek, K., Liu, Chang. 2019. In-season diagnosis of winter wheat nitrogen status in smallholder farmer fields across a village using unmanned aerial vehicle-based remote sensing. Agronomy, 9(10), 619.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2019
Citation:
Wang, X.*, Miao, Y., Dong, R.*, Guan, Y.*, & Mulla, D. J. (2019). Evaluating the Potential Benefits of Field-Specific Nitrogen Management of Spring Maize in Northeast China. Precision Agriculture'19,12th European Conference on Precision Agriculture. July 8-11, 2019, Montpelier, France. (pp. 877-882). Wageningen: Wageningen Academic Publishers.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2019
Citation:
Cummings, C., Miao, Y., Fern�ndez, F. G., & Paiao, G. D. Evaluating Crop Circle Phenom Active Canopy Sensor for Corn Nitrogen Status Diagnosis in Minnesota. Madison, WI: ASA-CSSA-SSSA. [Non-Refereed] Annual ASA-CSSA-SSSA Meeting, Nov. 10-13, 2019, San Antonio, TX.
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Progress 07/01/18 to 09/30/18
Outputs
Target Audience:
Nothing Reported
Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?
Nothing Reported
How have the results been disseminated to communities of interest?
Nothing Reported
What do you plan to do during the next reporting period to accomplish the goals?Some preliminary results will be reported in next reporting period.
Impacts
What was accomplished under these goals?
On-farm corn and soybean experiments were conducted to determine with-field variability in optimum soybean planting density andcorn N status and evaluate different crop sensing approaches for in-season corn N status diagnosis. Data are being analyzed.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2018
Citation:
Miao, Y., Mulla, D. J., Robert, P. C. (2018). An integrated approach to site-specific management zone delineation. Frontiers of Agricultural Sciences and Engineering, 5(4), 432-441.
- Type:
Journal Articles
Status:
Published
Year Published:
2018
Citation:
Zhang, J., Miao, Y.*, Batchelor, W. D., Lu, J., Wang, H., & Kang, S. (2018). Improving high-latitude rice nitrogen management with the CERES-RICE crop model. Agronomy, 8(11), 263.
- Type:
Journal Articles
Status:
Published
Year Published:
2018
Citation:
Cao, Q., Miao, Y.*, Shen, J., Yuan, F., Cheng, S., & Cui, Z. (2018). Evaluating Two Crop Circle Active Canopy Sensors for In-Season Diagnosis of Winter Wheat Nitrogen Status. Agronomy, 8(10), 201.
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