University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Theses, Dissertations, and Student Research in Agronomy and Horticulture Department Agronomy and Horticulture 11-18-2011 INTEGRATION OF PLANT-BASED CANOPY SENSORS FOR SITE-SPECIFIC NITROGEN MANAGEMENT Luciano S. Shiratsuchi University of Nebraska-Lincoln, [email protected] Shiratsuchi, Luciano S., "INTEGRATION OF PLANT-BASED CANOPY SENSORS FOR SITE-SPECIFIC NITROGEN MANAGEMENT" (2011).Theses, Dissertations, and Student Research in Agronomy and Horticulture.Paper 36. http://digitalcommons.unl.edu/agronhortdiss/36 This Article is brought to you for free and open access by the Agronomy and Horticulture Department at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Theses, Dissertations, and Student Research in Agronomy and Horticulture by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. For more information, please [email protected]. INTEGRATION OF PLANT-BASED CANOPY SENSORS FOR SITE-SPECIFIC NITROGEN MANAGEMENT by Luciano Shozo Shiratsuchi A DISSERTATION Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Doctor of Philosophy Major: Agronomy Under the Supervision of Professors Richard Ferguson and John Shanahan Lincoln, Nebraska November, 2011 INTEGRATION OF PLANT-BASED CANOPY SENSORS FOR SITE-SPECIFIC NITROGEN MANAGEMENT Luciano S. Shiratsuchi, Ph.D. University of Nebraska, 2011 Advisors: Richard B. Ferguson and John F. Shanahan The soil’s nitrogen (N) supply can vary drastically in the field, spatially as well as temporally making any soil prediction difficult even with very detailed mapping. Consequently, a plant-based approach wherein the measured canopy can indicate the N needs in a reactive and spatially-variable way can be a better approach than mapping, because integrate the soil N supply and translate the crop need on-the-go. The first experiment evaluated the performance of various spectral indices for sensing N status of corn, where spectral variability might be confounded by water-induced variations in crop reflectance. We found that water and previous crops effects on vegetation indices (VI) must be considered, and also that some VIs are less susceptible to water with good ability for N differentiation. In the second experiment, the objective was to develop an approach that relies on local soil conditions as well as on active canopy sensor measurements for real-time adjustment of N application rate. We found that local variations in plant N availability must be considered to determine the optimal N rate on-the-go, and that the localized reference incorporated the spatial variability of the N-rich plot. Next, we determined the correlation between active canopy sensors assessments of N availability and ultrasonic sensor measurements of canopy height at several growth stages for corn. We found strong correlations between both sensors and that they had similar abilities to distinguish N-mediated differences in canopy development. The integrated use of both sensors improved the N estimation compared to the isolated use of either sensor. Based on these strong correlations, we developed an N recommendation algorithm based on ultrasonic plant height measurements to be used for on-the-go variable rate N application. Lastly, we evaluated the crop water status using infrared thermometry integrated with optical and ultrasonic sensors, we concluded that the integration of sensors was beneficial to detect water-stressed zones in the field, affecting yield and possibly promising to delineate zones for N and water management. iv ACKNOWLEDGEMENTS First of all, I would like to give special thanks for my advisers Drs. Richard Ferguson and John Shanahan and also Dr. Viacheslav Adamchuk for all initial and continuous support that they provided for me since the beginning of my first steps in experiencing a new life style here in Lincoln. They were much more than academic supervisors and they are still providing attention and care for us since the day that we met. I would also like to thank Dr. Donald Rundquist for not only being a member of my Scientific Committee, but also helping me to organize my remote sensing concepts and being an example of gracious conduct and effective teacher. I would like to thank also Dennis Francis, Mike Schlemmer and Jim Schepers who made my stay in USA to be one of the best times of life as researcher, and also for countless hours of great moments of thinking. I believe that all their recommendations and feelings about agricultural research conduct are impossible to describe with words. Dennis, Richard and their families showed unconditional love for our family since we met, so many thanks to them would not be enough. Special thanks go to all ARS – USDA and South Central Agricultural Laboratory (SCAL) crew that made possible the execution of what I had envisioned as the perfect on-farm research and I had thought that this was almost impossible. They showed that team work can really be effective when affinity between members exist. I was fortunate to meet Glen Slater whose coordination work at SCAL provided me all the resources and logistics to work at the experimental station independently. Without his help and others v (Dave, Perry, Irv, Terry, Cassy, Ryan and Nick) none of my field work would have been possible. I am grateful to the staff of the ARS and UNL team: Aaron Bereuter, Jeff Shanle, Myron Coleman, Paul Koerner, Luke Pesek, Jamie Pesek, Chris Bauer, Dan Walters, Anatoly Gitelson, Dave Marx, Brian Leavitt, Ahmad, Tri Setiyono, Jessica Torrion for all their help in all aspects and also for tremendous contributions to my professional career. Many Thanks to Kyle Holland that always provided several types of optical sensors making feasible and smooth the steps of this research program. Special thanks to Collin Lutz and Scot that helped me tremendously to put together the “bike system” (software and hardware), that was the major sensor platform for most experiments. Thanks to the farmers: Evan Brandes, Brandon Hunnicut, Ed Rathje and others that were always available and helpful allowing us to interfere in their regular operation. They are the major reason that justifies the conduction of this type of on-farm research. I wish to grateful acknowledge the Brazilian Agricultural Research Corporation (EMBRAPA) for all the resources, that made possible for my professional dream to come true. Thanks to Edemar Corazza and Special thanks go to Dr. Edson Sano who supported me with advices and tips on how to make my stay in the USA the best possible. I also would like to thank the graduate students: Darrin Roberts, Akwasi Abunyewa, Brian Krienke, Atefeh Hosseini, Nabaraj Banjara, Chris Proctor and Nick Ward for their company and laughs during my hard times. Special thanks to Krienke family (Brian and Abbey) who showed sincerely their friendship and the Vossler family (Greg, Cindy, Ethan and Colton) who made me feel at home with their hospitality in my great team roping experiences in Nebraska. vi Lastly, the most important thanks goes to my entire family though physically separated by great distance were always present, especially thanks to my wife Simone and my son Kevin who encouraged me with their love and faith, during our great stay here in Lincoln. Without them I could do nothing. Finally, eu gostaria de agradecer de joelhos à Deus e ao nosso Senhor Jesus Cristo por nos guiar e proteger durante toda nossa jornada terrestre. So, I say to you: Ask and it will be given to you; seek and you will find; knock and the door will be opened to you (Luke11:9). vii TABLE OF CONTENTS General introduction ............................................................................................................1 Research objectives ............................................................................................................12 References ..........................................................................................................................14 Chapter 1. Comparison of spectral vegetation indices derived from active crop canopy sensors for corn grown under different crop rotations and irrigation levels ......................20 Abstract ......................................................................................................................... 20 Introduction ................................................................................................................... 21 Material and methods .................................................................................................... 23 Results and discussion ................................................................................................... 27 Summary and conclusions ............................................................................................. 36 References ..................................................................................................................... 38 Chapter 2. Local reference: an approach for site-specific nitrogen fertilization using active canopy sensors .........................................................................................................55 Abstract ......................................................................................................................... 55 Introduction ................................................................................................................... 56 Material and methods .................................................................................................... 58 Results and discussion ................................................................................................... 67 Summary and conclusions ............................................................................................. 78 References ..................................................................................................................... 80 Chapter 3. Integration of ultrasonic and optical reflectance sensors to estimate the in- season nitrogen availability for corn ................................................................................112 Abstract ....................................................................................................................... 112 Introduction ................................................................................................................. 113 Material and methods .................................................................................................. 115 Results and discussion ................................................................................................. 117 Summary and conclusions ........................................................................................... 124 References ................................................................................................................... 125 viii Chapter 4. Nitrogen recommendation algorithm for corn based on plant height measured by ultrasonic distance sensors ..........................................................................................142 Abstract ....................................................................................................................... 142 Introduction ................................................................................................................. 143 Material and methods .................................................................................................. 145 Results and discussion ................................................................................................. 148 Summary and conclusions ........................................................................................... 153 References ................................................................................................................... 154 Chapter 5. Evaluation of crop water status using canopy sensor integration ..................165 Abstract ....................................................................................................................... 165 Introduction ................................................................................................................. 167 Material and methods .................................................................................................. 169 Results and discussion ................................................................................................. 176 Summary and conclusions ........................................................................................... 184 References ................................................................................................................... 186 General summary and future suggestions ........................................................................215 Appendix ..........................................................................................................................217 1 GENERAL INTRODUCTION Nitrogen Use Efficiency (NUE) Given its transformations and mobility in the soil profile, nitrogen (N) is the most dynamic nutrient in agricultural systems. Normally, it is the most limiting nutrient for the achievement of high yield. This complexity makes for uncertainty in its recommendation, based on traditional soil analysis. This dynamic characteristic of N in the soil suggests a need for research and development of new management practices or devices to predict when, where, and how much N is required (Schepers and Raun, 2008). Current practices for applying N have resulted in low nitrogen use efficiency (NUE) mainly because uniform applications disregard spatial and temporal variability in the soil and crop and also the use of N rates above the crop needs (Raun and Johnson, 1999). Increasing the problem, the risk of N loss (through denitrification, volatilization, surface runoff, leaching, etc.) can be considerably higher if the N fertilizer application timing and procedure are inadequate. One of the major causes for low NUE is the poor synchrony of soil N supply and crop demand (Shanahan et al., 2008). Normally, N application takes place before the time of N uptake from crops, for practical and operational reasons. When the method and timing of N application is not ideal, related high losses can occur. In most of the American corn crop production, the N fertilizers have been applied at preplant in early spring or in the late fall. Normally, the high N uptake for crops is two