Brief Summary of Minutes of Annual Meeting

March 23, 2011 (Field Tour) A field tour to the Nordex USA Inc. plant in Jonesboro, AR. was organized as per the following schedule: 12:00 p.m. Leave Crowne Plaza Hotel 02.45 p.m.  4.30 p.m.Tour Nordex USA Inc. plant at Jonesboro, AR 07.15 p.m.  Return Crowne Plaza Hotel March 24, 2011 Meeting started at 7.45 a.m. with registration and continental breakfast. At 8.15 a.m., Dharmendra Saraswat, the committee chair, called the meeting to order. Dharmendra invited Dr. Rick Cartwright, Associate Director (Agriculture and Natural Resources),University of Arkansas Division of Agriculture Cooperative Extension Service to welcome the group. Dr. Cartwright welcomed everyone to the State of Arkansas, provided a brief overview of challenges facing agriculture, and exhorted the group to provide appropriate solutions. Invited Speakers A total of eight invited speakers, four in the forenoon and four in the afternoon made presentations. A title of the presentations along with the speakers is given below. 1. Title: Make the Most of Spatial Technologies- An Ag Retailers Perspective Clint Jayroe, Jimmy Sanders Inc. 2. Title: Incentives and Barriers to Site Specific Farming- A Farmers Perspective Steve Stevens, Chair, Arkansas Cotton Support Committee 3. Title: Specialty Crop Development for Small Farms Dr. Reza Ehsani  University of Florida, Citrus Research and Extension Center 4. Title: MRBI: Challenges and Opportunities Nancy Young, NRCS Arkansas State Resource Conservationist 5. Title: Optic Mapper Eric Lund, Veris Technologies 6. Title: Soil Sampling Probe Jim and Jeffrey Burton, AgRobotics Autoprobe 7. Title: Precision Agriculture Status and Needs in Arkansas Dr. Sreekala Bajwa, University of Arkansas 8. Title: BioChar Dr. David Clay- South Dakota State University (Presented by Dr. Van Kelley). Dharmendra thanked Dr. Kelley for becoming Administrative Advisor to the group. Jim and Jeffrey Burton conducted demonstration of Soil Sampling Probe and answered questions. Tim Stombaugh provided an update on standard activities and also briefed the group on discussions that took place on data format standardization during Ag Connect meeting in Atlanta. The meeting was adjourned around 5.00 p.m. March 25 Daniel Schmoldt joined via video conferencing and made a presentation about funding opportunities available for the group. He answered questions by the group. Dharmendra initiated a discussion on the needs and new approaches to be pursued by the group. It was decided that a database of participants will be made available at the new website being maintained by Tom Mueller. Jose Hernandez from Minnesota agreed to provide support to Tom for the website. The group took a note of dwindling participation from the Industry and discussed ways to encourage their participation. A sub committee was formed consisting of incoming chair, Pedro Andrade and Timothy Stombaugh to come up with a plan in this regard. It was suggested that besides equipment manufacturers, data management companies should also be extended an invitation to become a part of the group. Van Kelley provided an update about the new proposal and requested everyone to provide a brief report on accomplishments, publications, and impact statement to Pedro at the earliest. Raj Khosla provided an updated on ICPA and ISPA (International Society of Precision Agriculture) activities. He informed the group about the successful organization of Indo-US workshop on precision agriculture. He went through features of ISPA, which is now a legal entity in the state of Illinois. ISPA has members from 38 countries and has 26 country representatives. He informed that the group should make use of the ISPA website (http://www.internationalsocietyofprecisionagriculture.org/) for new announcements such as job related postings. In addition, the ISPA site will serve as a platform to receive donations and post pictures of precision ag activities as well as announcements of new venues. Raj informed on the upcoming the European and Asian Precision Ag. Meetings (in Japan and Prague) in July 2011. He discussed possible dates for organization of ICPA at Indianapolis, IN during 2012. Raj also informed about a new book being put together by David Clay and Fran Pierce with Springer on the state of precision farming, and collected information about potential topics and contributors, along with contact information for passing on to David and Fran. Raj Khosla reminded the audience of the ASA symposium in precision ag. in 2012. Pedro informed that 2012 meeting will be organized in Phoenix , AZ during the month of March. The dates of the meeting will be decided after soliciting inputs from the group. A discussion followed in regards to topics/general concepts for the 2012 meeting (such as Advanced Technologies) and industry/academics participation in future NCERA-180 annual meetings. Pedro Andrade volunteered to construct a questionnaire and distribute among industry people (mostly farm equipment companies, but make sure to extend it to new small data management companies). The objective of the questionnaire will be to determine their interests and what would they like to obtain from us. To increase participation one idea that circulated was to meet during the InfoAg meeting in Springfield IL on July 12-14 and use this time to talk to key players in precision agriculture from industry, academics, and regulatory agencies about our work in NCERA-180. We discussed about possible reasons for diminishing participation among academics; limited funding is one reason, and refocus on a multi-disciplinary approach should help to re-capture the interest in our project among academics. Tom Muller volunteered to help Pedro in the organization of next years meeting. The general theme will turn around whats useful to the producers and the interests for industry to fill in. Lei Tian, University of Illinois, Urbana Champagne (UIUC) was unanimously elected to be chair for 2013. Lie Tian provided an update on continuous imaging approach being used at UIUC. Timothy Stombaugh provided updates on precision ag sessions in ASABE International meeting. Terry Griffin provided an update on USDA-ARMS (farm level) survey and CropLife survey. He informed that Purdue University led CropLife survey could not be conducted in 2010 due to a variety of reasons. John Nowatzki joined via video conferencing from North Dakota and requested the group to contribute actively for eXtension initiative on precision agriculture. Moreover a discussion followed on trends in federal funding (i.e. less formula funding) and the need to increase multi-state multi-disciplinary participation. In this discussion it was suggested that NCERA-180 members should communicate with their respective Stakeholders to pressure Congress for more appropriation funds that will benefit research and extension in agriculture. Tom Muller informed that the new NCERA-180 proposal was submitted in Nov 2010. Approval is pending, but the comments received were favorable. Pedro Andrade will take on the responsibility of writing the 2010 NCERA report and file it within 60 days. The state representatives will send information on proposals, accomplishments, impact statements and publications to Pedro to be compiled in the report. Towards the end, Dharmendra thanked everybody associated with the organization of this meeting including his team at the University of Arkansas Division of Agriculture Cooperative Extension Service. The group appreciated Dharmendra for his efforts in successfully organizing the meeting in Arkansas. The meeting ended at 12.30 pm.

Accomplishments in Research by State: Arizona 1. Precision canopy and water management of specialty crops through sensor-based decision making. This project uses proximal sensors mounted on a mobile platform to provide the information desired by stakeholders. These include information on canopy architecture and light interception using Pohtosynthetically Active Radiation (PAR) sensors, plant-soil water status using a sensor suite consisting of a thermal IR gun, ambient temperature, humidity and wind speed sensors. Moreover, this project aims to develop a data visualization software and a decision support system to assist with management decisions. 2. Assessment of hail damage in cotton using active-light spectral sensors. This project is about using sensor technology to make a quick assessment of the amount of canopy and rate of recovery after a simulated hail event. This project will give information on sensor-based spectral indices that can represent the extent of damage of cotton plants and generate savings to the crop insurance industry. 3. Field distribution of soil and plant variables affecting wheat grain protein content and yield: A field-scale study to improve farm management. The purpose of this study is to relate certain soil and plant characteristics measured before harvest to durum protein content and yield on a field-scale. We will study the relationships among 1) soil nitrogen, texture, and electrical conductivity, 2) plant nitrogen content and growth, 3) grain yield, and 4) grain protein content. 4. Characterization of spatial variation in wheat yield and protein using soil and plant sensors. An improved scheme of field-level research will be carried out with particular attention to capturing the dynamics of soil/plant Nitrogen. This will be achieved with soil/plant sampling for laboratory analysis of N status at tillering, jointing, booting, and flowering of durum wheat; along with spectral measurements of the crop using hand-held instruments. 5. Improving Arizona tree crop weed management. This project will evaluate newly registered pre-emergence herbicides to determine how many post-emergence herbicide sprays can be eliminated annually and to develop herbicide programs that minimize the risk of developing herbicide resistant weeds by measuring the light reflectance characteristics of the orchard floor and obtain the technical data needed to develop a more robust automatic spot spraying system. 6. Soil compaction reduction of date yields. Date palms have a shallow root system that differs from most tree crops. This projects aims at characterizing the dynamics of soil strength and root growth through the growing season, and establish the nature of the relationship between compaction levels and yield components, especially date quality. 7.Characterizing plant height, canopy temperature and reflectance for high-thruput phenotyping. We are doing extensive testing of proximal sensing techniques with high-clearance ground systems for field-based high-thruput phenotyping. We are collaborating in this project with scientists of the USDA-ALARC. 8. Cotton yield monitoring in commercial fields. For the 2010 cotton harvest season we used a commercially available yield monitoring system installed in a 6-row John Deere cotton picker. The system required interfacing GPS and micro-wave sensors to a controller located in the cab. We collected geo-referenced yield data in several fields in Buckeye and Paloma AZ. Arkansas Detection of charcoal rot in soybean with remote sensing Charcoal rot is soybean disease that causes significant economic losses. It often strikes the plant when the plant is already stressed due to lack of water and nutrients, or during reproduction. Hence it is difficult to detect. Some of the cultivars of soybean have shown some resistance to this disease. If the onset of disease can be detected with remote sensing in multiple cultivars with different degree of resistance, that would save a lot of time and effort on the part of the grower. The part of this multistate project that I focus on is to identify whether remote sensing is an effective tool to monitor the disease, especially early during the infestation. We (I in collaboration with John Rupe) have conducted two years of micro-plot experiment with 4 different cultivars (with different degree of resistance), two levels of disease treatment, and two levels of water stress. The plants were monitored for canopy reflectance, stomatal conductance, canopy temperature, disease rating, etc. Unfortunately, the excessively wet 2008 season was not favorable for active disease proliferation. We are still analyzing the data from 2009. California Precision Canopy and Water Management of Specialty Crops through Sensor-Based Decision Making There is a need to develop and deploy farm-based, reconfigurable, sensors and/or sensor suites that can be retrofitted to a mobile platform and associated decision support tools to assist growers in making better management decisions to improve crop quality and increase production efficiency and farm profitability, while reducing their environmental footprint. To address these issues we have developed a multi-state, cross disciplinary project with specific objectives: (i) Measure canopy architecture and PAR absorption; (ii) Detect soil and plant water status; (iii) Develop a Universal Navigation Computer (UNC); (iv) Develop a visualization and decision support system; (v) Develop a variable rate water application system; (vi) Conduct economic analysis; (vii) Evaluate social implications. The development and refinement of a PAR measurement system (obj#1), sensor suite for plant water status (obj#2), autonomous vehicle (obj#3), and wireless network for irrigation management (obj#5) will occur during year 1 of the study. These devices/systems will be field tested during year 2 of the study. Extensive data collection will also occur during the second year. The decision support system (DSS) will be developed during the first year (obj#4). DSS will be implemented during the second year. During year 1 growers will be identified for socio-economic analysis (Obj #6 and 7). Some preliminary data collection will occur. During the second year majority of the data would be collected and analyzed. Data collection and analysis will continue into year 3 of the project. Outreach activities will start with the second year of the project. Webinars detailing the progress of the project will be developed during year 2. Main emphasis during year 3 will be presentations and demonstrations during field days, and development of instructional videos that detail most promising technologies developed for grower audience. Four states in the western region  Arizona (pecans), California (almonds, grapes, and walnuts), Oregon (hazelnuts), and Washington (apples and grapes)  will participate in this project. The expected outputs of this project are  (i) field verified sensors mounted on mobile platforms for measuring canopy architecture and plant water status, (ii) a decision support system to implement canopy management and irrigation management based on sensor data, (iii) development of a technology to apply water to individual or block of trees based on soil and plant water status, and (vi) determination of socio-economic implications of the developed technologies. Canopy management and irrigation management of orchard and vineyard crops were identified as the most critical needs of the special crops industry. Four states in the western region  Arizona (pecans), California (almonds, grapes, and walnuts), Oregon (hazelnuts), and Washington (tree-fruits and grapes)  are collaborating to address these major issues. The expected outcomes of this project are  (i) field verified sensors mounted on mobile platforms for measuring canopy architecture and plant water status, (ii) a decision support system to implement canopy management and irrigation management based on sensor data, (iii) development of a technology to apply water to individual or block of trees based on soil and plant water status. Better canopy management is expected to results in increased light absorption and yield, while improved irrigation management is expected to help conserve precious water resource in the dry western region. Colorado Rates of dissipation of Atrazine and Metolachlor across different soil management zones The objectives of a recently completed study were to compare the rates of dissipation of atrazine and metolachlor across different soil management zones from three dryland no-tillage fields under laboratory incubation conditions and determine if rapid dissipation of atrazine and/or metolachlor occurred in dryland soils. Herbicide dissipation was evaluated at time points between 0 and 35 days after soil treatment using a toluene extraction procedure with GC/MS analysis. Our findings indicate that differential rates of atrazine and metolachlor dissipation occur across soil zones on two of three fields that we evaluated. In addition, accelerated atrazine dissipation occurred in soil from all fields of this study with half-lives ranging from 1.8 to 3.2 days in the laboratory. The rapid atrazine dissipation rates found were likely attributed to the history of atrazine use on all fields investigated in this study. Metolachlor dissipation was not considered accelerated and exhibited half-lives varying from 9.0 to 10.7 days in the laboratory. While, accelerated dissipation of atrazine as exhibited on all fields of our study has been reported previously. Our study, however, is among the first to report such high rates of atrazine dissipation in soil from semi-arid dryland cropping environments (1.8 to 3.2 days, as compared to previously reported half-lives for atrazine ranging from 45  180 days previously reported in literature). Further work under field conditions needs to be performed to confirm the results of this research and to determine the agronomic significance (i.e., effects on weed control) of the different rates of herbicide dissipation. Florida 1. Autonomous vehicles offer additional labor-saving benefits for farms of the future. In 2010 we completed the design and fabrication of a variable rate orchard sprayer controller for use with John Deere autonomous tractors currently being tested at Southern Gardens grove. The difference between the conventional controller and the sprayer controller for driverless autonomous tractor use is the additional sensors that are required to detect faults, monitor spray operations, and interface with the autonomous tractor computer. The controller system therefore consists of sensors for monitoring flows, system pressure, tank level, filter clogging, PTO, fan, agitator and tire pressure and actuators for motorized on-off valves. During 2010 the ability to independently regulate chemical flow in the 20 spray nozzles of the precision sprayer was added by redesigning and building a more sophisticated circuit board with 13 embedded microcontrollers. Performance tests (spray coverage) were conducted using Surround (kaolin) in commercial citrus trees of varying size and canopy densities. 2. A number of sensing techniques were investigated for stress detection in citrus. The different sensing techniques that were investigated are summarized as follows: MID-INFRARED SPECTROSCOPY: Mid-infrared spectroscopy was applied for detection of Huanglongbing (HLB) disease and nutrient-deficiency in citrus leaves. The basis behind the detection of disease and its differentiation from healthy and nutrient-deficient leaves was the identification of starch accumulation that specifically occurs in diseased leaves. This method showed potential in the detection of disease even in asymptomatic stages, during which the accumulation of starch begins. This method of HLB detection showed potential to identify the disease in asymptomatic stages based on starch accumulation. When the citrus tree is infected with disease, the starch begins to accumulate even before the symptoms appear. The detection of HLB in asymptomatic stages is critical as it would reduce the spread of disease through identification and removal of infected trees in early stages. The method showed a classification accuracy of 90% and higher VISIBLE-NEAR INFRARED SPECTROMETRY: This is a non-destructive, field-based method of stress detection Visible-near infrared spectrometry was applied for detection of HLB in asymptomatic and symptomatic stages, and nutrient-deficiency in leaves. As this region of electromagnetic spectra is sensitive to the light changes, a large set of data (in hundreds) was collected to account for the variability in field condition. Preliminary analysis indicated that this method is suitable for disease detection in symptomatic stages. Further investigations on the sensor applicability for disease detection during asymptomatic stages and nutrient-deficiencies in leaves are ongoing. Through experimentation, it was established that the leaves with HLB symptoms can be clearly differentiated from that of healthy leaves using this method. This method is further being investigated to reduce the cost of the sensor system and further improve the efficiency and accuracy of the system FLUORESCENCE SPECTROSCOPY: A portable field-based active fluorescence sensor was used for disease and nutrient-deficiency detection in leaves. Field and laboratory fluorescence data have been collected from Hamlin and Valencia varieties and are currently being processed to validate the suitability of the method. In addition to these techniques, other sensors or sensing techniques that were investigated are: multi-band sensors for citrus canopy anomaly detection (active four-band sensor and passive five band sensor with auto-calibration abilities), hyperspectral imaging, fluorescence imaging for detection of citrus diseases (citrus variegated chlorosis, HLB, canker, etc), visible-near infrared-based sensing for Laurel Wilt detection in Avocado, and application of high-resolution aerial imaging for stress detection in citrus, nursery, sugarcane and apples. 3. A continuous citrus canopy shake and catch harvester has been modified by exchanging a regular de-stemmer with an extended de-stemmer for more efficient elimination of debris (also known as trash materials such as leaves, twigs and branches) from harvested citrus fruits. An extended de-stemmer with a set of ten 36 inch long rollers was used at a citrus grove for conducting experiments. A modified catch frame of the harvester using a longer de-stemmer was tested to remove petioles from harvested fruit, and its efficiency was 80%. The trash removal efficiency of the catch frame de-stemmer was 99.86% of the total trash. During 2010 harvesting season, we have tested a longer de-stemmer for a continuous canopy shake and catch harvester and found that the longer de-stemmer was more effective in removing trash materials during harvesting. This longer de-stemmer can be implemented on all mechanical harvesters for more efficient debris removal. 4. An algorithm for automatically estimating mass of debris in a citrus canopy shake and catch harvester using machine vision was investigated. An experimental test bench was set up to train and to validate the image processing algorithm. It included steps of image rectification, overlapped area removal, morphological operations and removal of undesired debris on the ground using a novel Parse and Add algorithm. A representative set of 180 images were processed with the algorithm and the debris objects were identified. The results from the machine vision algorithm for estimating debris mass showed that the coefficient of determination (R-squared) between the pixel area and debris mass for calibration was 0.946 and R-squared between actual and estimated mass for validation set from the test bench was 0.815 with an RMSE of 1.88 kg. For the field experiment, the R-squared between the actual and estimated debris mass for individual images was 0.78, and the RMSE was 0.02 kg. The error between total actual and estimated mass for the field experiment was 25.3%. The debris mass estimates were also correlated with GPS data to create a geo-referenced map of the debris gathered. The developed debris mass estimation system could play a crucial role in solving the problem of safe and economical disposal of diseased leaves and twigs. For the green citrus fruit detection algorithm, 75.3% of the actual fruits were successfully identified using the proposed algorithm for the validation set. 5. For a rapid detection of the citrus greening disease, an aerial hyperspectral image in 457-921 nm with 128 bands was acquired from an HLB infected citrus grove in December 2009 in Florida. A multispectral image with 4 bands (red, green, blue, and NIR) in 480-830 nm with 40 nm bandwidth was also acquired. Polymerase chain reaction (PCR) test based ground truthing had been carried out where the infected tree canopy coordinates were recorded. An image derived spectral library was built and categories of Healthy and HLB infected pixels were created based on the PCR results and locations of the infected trees. Ground measurements were obtained for Healthy and HLB infected citrus trees with their degrees of infection. HLB infected areas were identified using image-derived spectral library, the mixture tuned matched filtering (MTMF), the spectral angle mapping (SAM), and linear spectral unmixing (LSU). A new set of aerial images of HLB infected groves has been acquired on December 3, 2010 at two different sites using three camera systems (color, multispectral and hyperspectral cameras) with the cooperation of the USDA ARS group in Texas. The first grove was located near Clewiston in Hendry County and the second was at the CREC (North 40 and CREC) in Lake Alfred in Polk County. Reflectance of healthy and infected canopies were measured and their coordinates were also recorded using an RTK GPS receiver. For aerial hyperspectral HLB detection, it was observed that accuracy of MTMF method was greater than the other methods. The accuracy of SAM using multispectral images was comparable to the results of the MTMF and also gave higher accuracy when compared to SAM analysis on hyperspectral images. A fairly high detection accuracy of 80% was achieved using MTMF on hyperspectral image. SAM with multispectral images also gave a very high detection rate of 87%. A better estimate of accuracy can be achieved with more PCR results and a more comprehensive ground survey. This would help in the quantization of false positives in the result. 6. A machine vision algorithm was developed to detect and count immature green citrus fruits in natural canopies using color images. Color, circular Gabor texture analysis and eigenfruit approach were used for green citrus detection. A shifting sub-window was classified three times by eigenfruit approach using intensity component, eigenfruit approach using saturation component, and circular Gabor texture. Minnesota Fusion of hyper-spectral and thermal images for evaluating Nitrogen and water status in potato fields for variable rate application. Field experiments are being conducted (2010-2011) at the University of Minnesota Sand Plain Research Farm near Becker, MN. In 2010 experiments were conducted on two potato varieties (Russet Burbank & Alpine Russet), with two irrigation rates (conventional irrigation and water stressed), and with five different N treatments. Each treatment was replicated four times in a randomized split-split plot design. Petiole and leaflet samples and chlorophyll meter readings were taken five times throughout the growing season. Chlorophyll readings were taken with a Minolta SPAD-502 chlorophyll meter. Leaf area index (LAI) was measured with a LAI-2000 plant canopy analyzer on five dates throughout the growing season. Ground measurements for reflectance were taken on six dates with an MSR16R Cropscan on the same day or within two days of the SPAD readings. Soil matric tension was measured with granular matrix soil moisture sensors (Watermark Model 200) in and below the root zone. Data loggers were located in four locations and each recorded matric potential (kPa) in one of four plots. Leaf canopy temperature was measured with infrared radiometers (Apogee Model SI-111). For measurement of soil water NO3 concentration, lysimeters were installed 120 cm vertically below the third hill of each Russet Burbank plot. Soil water samples were collected weekly or following any significant rain event in which drainage was suspected to occur. Samples will also be collected after ground thaw in the spring of 2011. Finally, aerial hyperspectral and thermal remotely sensed images were acquired with an AISA Eagle VNIR hyperspectral imaging sensor and a FLIR Systems ThermaCam SC640, respectively, by the Center for Advanced Land Management Information Technologies (CALMIT) at the University of Nebraska-Lincoln, USA. Analyses are being carried out for the 2010 season and the 2011 experiment will begin in May 2011. Accomplishments in Extension Extension bulletins: Things to know about applying precision agriculture technologies in Arizona. Andrade-Sanchez, P., and Heun, J. T. 2010. Things to know about applying precision agriculture technologies in Arizona. Cooperative Extension Service, University of Arizona, Bulletin AZ1535 In Press. Understanding technical terms and acronyms used in precision agriculture. Peer-reviewed. Andrade-Sanchez, P., and Heun, J. T. 2010. Understanding technical terms and acronyms used in precision agriculture. Cooperative Extension Service, University of Arizona, Bulletin AZ1534 In Press. Integrating variable rate technologies for soil-applied herbicides in Arizona vegetable production. Peer-reviewed. Nolte, K. D., Siemens M. C., and Andrade-Sanchez, P. 2010. Integrating variable rate technologies for soil-applied herbicides in Arizona vegetable production. Cooperative Extension Service, University of Arizona, Bulletin AZ1538 In Press. Workshops and training: "Using precision technologies to increase efficiency in cotton production" Cotton Early Season Meetings. Maricopa Agricultural Center, 2/23/2010; Marana AZ 3/2/2010, 2010. Demonstration on cotton close cultivation with RTK autoguidance. Field and machinery provided by Grower Tom Clark, Marana, AZ. 6/13/2010. Talk with the title: "Potential use of soil and plant sensors to improve in-season management" Cotton Mid Season Meetings. Safford Agricultural Center, 7/15/2010; Parker, AZ, 8/13/2010. Demonstration on variable rate injection of fertilizer in cotton production. MAC Annual Field Day. Maricopa Agricultural Center, 10/19/2010. "Potential use of soil and plant sensors to improve in-season management" Cotton Mid Season Meetings. Safford Agricultural Center, 7/15/2010; Parker, AZ, 8/13/2010. Saraswat, D., J.L. Peterson, J. Salle, M. Smolen, and R. Faucette. 2010. Introduction to GIS/GPS Tools for Agriculture, Natural Resources, 4-H, and Watershed Management. Tulsa, April 12, 13 attendees. Saraswat, D. 2010. Introduction to GPS. Marianna, AR, March 17, 7 attendees. Radcliffe, D., D. Saraswat, J. L. Peterson, R. Faucette, S.S. Panda, and T. Sweeney. 2010. Introduction to GIS/GPS Tools for Agriculture, Natural Resources, 4-H, and Watershed Management. Hilton Head, February 25, 32 attendees. Saraswat, D. 2010. Introductory ArcGIS. Little Rock, AR, February 17, 8 attendees. Saraswat, D. 2010. Google Earth applications for crop consultants. In Arkansas Crop Management Conference, Little Rock, January 21. 31 attendees. Saraswat, D. K.Prasanna, and J.L. Peterson. 2010. Google Earth Workshop. 65 pp. Saraswat, D. 2010. Introductory ArcGIS 9.3.1. 87 pp. Robbins, J., D. Saraswat, R. Ehsani, J. Maja, S. Doane, J. Owen, and J. Kupillas. 2010. Multi-rotor systems prove beneficial for inventory data collection. Nursery Management, November 18 issue. Available at http://www.nurserymanagementonline.com/nmpro-1110-nursery-picture-this-tools.aspx Accessed January 10, 2011. Accomplishments in Grant Proposals: A multistate, cross disciplinary project, Precision Canopy and Water Management of Specialty Crops through Sensor-Based Decision Making was written and submitted to USDA, NIFA for funding under SCRI grant funding opportunity. This is a multi-state project lead by Dr. Shrinivasa Upadhyaya (UCDavis). It includes NCERA-180 members from California, Arizona, and Washington. It also included participants from Oregon State, and Industries such as Trimble Navigation Ltd. and VERIS Technologies Inc. The above proposal was funded and is currently active. It started on Sepetmebr 1, 2010 and will end on August 31, 2013 (USDA-SCRI- 2010-01213) A USDA Specialty Crop Research Initiative (SCRI) Research Planning Proposal titled, Improving the profitability of blueberry production with a comprehensive precision agriculture program was funded in 2009. On February 9-10, 2010, a research proposal planning meeting has been held in Orlando, Florida and a full research proposal development has been discussed. A total of 17 people from industries, grower organizations, and universities from multiple states and Canada have participated. A full proposal was developed and submitted to the USDA SCRI funding program in January 2011. ND Corn Council, relate active optical sensors (Greenseeker and Holland Crop Circle) with corn N status, funded April 2010-March 2012. Franzen, PI.

Sun, H., D. C. Slaughter, M. P. Ruiz, C. Gliver, S. K. Upadhyaya, and R. F. Smith. 2010. RTK GPS mapping of transplanted row crops. Computer and Electronics in Agriculture. 71:32-37. Upadhyaya, S. K., D. K. Giles, S. Haneklaus, and E. Schnug. 2010. Advanced engineering systems for specialty crops: A review of Precision Agriculture for water, chemical, nutrient application, and yield monitoring. Author and editor. vTi Special Issue 340. Jahn, B. R., and S. K. Upadhyaya. 2010. Determination of soil nitrate and organic matter content using portable, filter-based mid-infrared spectroscopy. Chapter 12. In Proximal Soil sensing. Edited by R. A. Viscarra Rosel et al. Progress in Soil Science 1. Udompetaikul,V, S. K. Upadhyaya, B. Lampinen, and D. Slaughter. 2010. Development of a sensor suite to determine plant water potential. ASABE paper 1009450. ASABE, St. Joseph, MI 49085. Also presented at the 10th International Conference of Precision Agriculture in Denver, CO. ICPA 10-391. Bajwa, S.G., A. R. Mishra, and R. J. Norman. 2010. Canopy reflectance response to plant nitrogen accumulation in rice. Precision Agriculture 11(5): 488-506. Kulkarni, S. S., and S.G. Bajwa. 2010. Investigation on effects of soil compaction in cotton. Transactions of the ASABE 53(3): 667-674. Bajwa, S.G., A. R. Mishra, and R. Norman. 2010. Plant nitrogen accumulation dynamics in rice (Oryza sativa L.) in response to nitrogen management. Communications in Soil Science and Plant Analysis 41(4): 454-471. Bajwa, S.G., and J. A. Apple. 2010. Non-linear modeling of quality of cooked ground beef patties with visible-NIR spectroscopy. In: Food Engineering, B. C.Siegler (Ed). Nova Science Publishers, Inc., Hauppauge, New York. ISBN 978-1-61728-913-2. Bajwa, S.G. and S. S. Kulkarni. 2010. Hyperspectral Data Mining. In:.Hyperspectral Remote Sensing of Vegetation, P.S. Thenkabail, J. G. Lyon and A.Huete (Eds). ISBN 978-1-4398453-7-0, CRC Press. Saraswat, D. 2010. Geospatial Technologies: Unlimited Possibilities for Outreach Education. Delivered before faculty, staff and graduate students of the Department of Food, Agricultural and Biological Engineering, The Ohio State University, Columbus, OH, October 15. Saraswat, D., L. Espinoza, S. Kulkarni, and Terry Griffin. 2010. Comparative Performance Evaluation of Open and Closed Loop System for Spinner Disc Control on Dry Fertilizer Spreader. In ASABE 2010 Annual International Meeting, Pittsburg, PA, June 20-23. Maja, J.M., R. Ehsani, D. Saraswat, and J. Robbins. 2010. A Tree Counting and Caliper Measurement System for Open Field Nursery Using Image Processing. In ASABE 2010 Annual International Meeting, Pittsburg, PA, June 20-23. Kulkarni, S.S, L.T. Barber, E. Barnes, T.W. Griffin, and D. Saraswat. 2010. Cotton Canopy Response to Nitrogen Treatments in Arkansas: Observations Using Crop Circle (ACS-470). In ASABE 2010 Annual International Meeting, Pittsburg, PA, June 20-23. Traywick, D. and D. Saraswat. 2010. Precision Agriculture Sensors used in Arkansas. In 2010 Teen Leader Conference. 4-H Center, Ferndale, AR, June 9. Saraswat, D. and T. Griffin. 2010. Delivering Solutions for Agriculture and Natural Resources through Geospatial Technologies. In 2010 Arkansas GIS User Forum Spring Meeting, Little Rock, AR, April 8. Espinoza, L., D. Saraswat, and M. Ismanov. 2010. Assessment of selected electrical conductivity sensors. In Beltwide Cotton Conference. New Orleans, LA, January 4-7. Saraswat, D. 2010. Remote sensing for tree counting. Presented before Oregon Nursery Association (ONA) members and nursery industry representatives, Portland, OR, September, 9. L. Espinoza, D. Saraswat, D. Traywick, M. Ismanov, and P. Ballantyne. 2010 Managing Spatial Variability in Crop Production in Arkansas. In Cotton Research Field Day, Marianna, AR, August 19. K. Prasanna and D.Saraswat. 2010 Geospatial Technology Website: Where Data Meets Your Need. In Rice Field Day, Stuttgart, AR, August 11. Saraswat, D., L. Espinoza, D. Traywick, M. Ismanov, and P. Ballantyne. 2010 Managing Spatial Variability in Crop Production in Arkansas. In Optical Sensing Nitrogen Use Efficiency Conference, Stillwater, OK, August 2-4. Saraswat, D., M. Daniels, and D. Traywick. 2010. Agritourism and Geospatial Technologies: An A-mazing Combination. In 7th Natural Resource Extension Professionals Conference, Fairbanks, AK , June 27-30. Khosla, R., Westfall, D., Reich, R., Mahal, J.S., and Gangloff, W.J. 2010. Spatial Variation and Site-specific Management Zones. In. Geo-statistical Applications in Precision Agriculture. (ed) M. Oliver. Springer Publishers, Netherlands. Pg 195-219. [Book Chapter] Krueger, E., Khosla, R., Kurtener, D., and Ermakov, R. 2010. Application of tool for fuzzy multi attributive comparison of different N management strategies. In. Applications of Soft Computing in Agricultural Researches (ed.) V.P. Yakushev, H. A. Torbert, D.A. Kurtener. Agrophysical Research Institute, St. Petersburg, Russia. [Book Chapter] Krueger, E., Kurtener, D., and Khosla, R. 2010. Application of Adaptive Neuro-Fuzzy Inference System for study of relation between Normalized Difference Vegetation Index, yield and soil color-based management zones in irrigated maize. In. Applications of Soft Computing in Agricultural Researches. (ed.) V.P. Yakushev, H. A. Torbert, D.A. Kurtener. Agrophysical Research Institute, St. Petersburg, Russia. [Book Chapter] Shaver, T., Khosla, R., Westfall, D.G. 2010. Evaluation of two ground-based active remote sensors for N variability determination in maize under greenhouse conditions. J. of Soil Sci. Soc. of Amer. 74:2101-2108. Schwartz, H., Gent, D.H., Fichtner, S.M., Khosla, R., Mahaffey, L.A., Camper, M.A., and Cranshaw, W.S. 2010. Spatial and Temporal Distribution of Iris yellow spot virus and Thrips in Colorado Onion Fields. J. of Plant Health Progress. Bausch, W., and Khosla, R. 2010. QuickBird satellite versus ground-based multispectral data for estimating nitrogen status of irrigated maize. J. of Preci Agri. 10: (6) 1-17. Khosla, R. 2010. Precision Agriculture and Resource Management for Livelihood Security in Small Agricultural Systems. In the Proceedings of the Indian Society of Agronomy Meetings in Bangalore, India. Dec 2010. Khosla, R. 2010. Precision Agriculture: Challenges and Opportunities in a Flat World. In the Proceedings of the 19th World Congress of Soil Science, Brisbane, Australia, August 2010. Moshia, M.E., Khosla, R., Davis, J., and Westfall, D.G. 2010. Precision Manure Management: It matters where you put your manure. In the CD-Rom Proceedings of the 10th International Conference on Precision Agriculture, Denver, CO. Stromberger, M.E., Khosla, R., and Shaner, D. 2010. Spatio-Temporal Analysis of Atrazine Degradation And Associate Attributes In Eastern Colorado Soils. In the CD-Rom Proceedings of the 10th International Conference on Precision Agriculture, Denver, CO. July 2010. Shaner, D., Khosla, R., and Stromberger, M.E. 2010. Spatial and Temporal Changes in Atrazine Degradation Rates in Soil. In the CD-Rom Proceedings of the 10th International Conference on Precision Agriculture, Denver, CO. July 2010. Shaver, T., Khosla, R., Westfall, D.G. 2010. Development Of A Nitrogen Requirement Algorithm Using Ground-based Active Remote Sensors. In Irrigated Maize. In the CD-Rom Proceedings of the 10th International Conference on Precision Agriculture, Denver, CO. July 2010. Krueger, E.D., Kurtener, D.A., Yakishev, V.P., and Ermakov, R.N., and Khosla, R. 2010. Evaluation of Different N Management Strategies Using A Tool For Fuzzy Multi Attributive Comparison Of Alternatives. In Irrigated Maize. In the CD-Rom Proceedings of the 10th International Conference on Precision Agriculture, Denver, CO. July 2010. Miao, Y., Qiang, C., Cui, Z., Li, F., Dao, T.H., Khosla, R., and Chen, X. 2010. Quantifying Spatial Variability of Indigenous Nitrogen Supply for Precision Nitrogen Management in North China Plain. In the CD-Rom Proceedings of the 10th International Conference on Precision Agriculture, Denver, CO. Khosla, R. 2010. Precision Agriculture: Opportunities and Challenges in a Flat World. In the Proceedings of the Great Plains Soil Fertility Conference. Denver, CO. Khosla, R., Shaver, T., Westfall, D.G. 2010. Nitrogen and Water Management Across Site Specific Management Zones Using Active remote Sensing. In the CD-ROM Proceedings of the Fluid Fertilizer Foundation Conference, February, Scottsdale, AZ. Moshia, M.E., Khosla, R., Westfall, D.G., Davis, J., and Reich, R. 2010. Precision Manure Management Strategies Site Specific Management Zones for Enhancing Corn Grain Yield. In Abstracts of the Annual meetings of the American Society of Agronomy, Long Beach, CA. Nov., 1-5th. Shaner, D., Khosla, R., and Stromberger, M. 2010. How rapidly does enhanced atrazine degradation develop? In the Abstracts of the Annual meetings of the Weed Science Society of America, Denver, CO. Khosla, R., and Moshia, M.E. 2010. Precision Manure Management: It matters where you put your manure. In the Abstracts of the annual meetings of the Western Society of Soil Science, Las Vegas, NV, June 2010. Khosla, R., Shaner, D., Stromberger, M., Bosley, B., and Helm, A. 2010. Spatial Distribution of Enhanced Atrazine Degradation Across North Eastern Colorado: A Survey. In the Abstracts of the Annual meetings of the Western Weed Science Society of America, Big Island, HI, March, 2010. Bansal, R., W. S. Lee, R. Shankar, and R. Ehsani. 2010. Automated trash estimation in a citrus canopy shake and catch harvester using machine vision. ASABE Paper No. FL10-123. St. Joseph, Mich.: ASABE. Ehsani, R., and S. Sankaran. 2010. Sensors and sensing technologies for disease detection. Citrus Industry, June, 2010, pp. 14-17. 5. Ehsani, R., S. Sankaran, and C. Dima. 2010. Growers expectations of new technologies for applications in precision horticulture (AE467). Agricultural and Biological Engineering Department, Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida. Jones, C. D., J. B. Jones, and W. S. Lee. 2010. Diagnosis of bacterial spot of tomato using spectral signatures. Computers and Electronics in Agriculture 74(2): 329-335. Kumar, A., W. S. Lee, R. Ehsani, L. G. Albrigo, C. Yang, and R. L. Mangan. 2010. Citrus greening disease detection using airborne multispectral and hyperspectral imaging. 10th International Conference on Precision Agriculture. July 18-21, 2010, Hyatt Regency Tech Center, Denver CO Lee, W. S., V. Alchanatis, C. Yang, M. Hirafuji, D. Moshou, and C. Li. 2010. Sensing technologies for precision specialty crop production. Computers and Electronics in Agriculture 74(1): 2-33. Mishra, A., R. Ehsani, G. Albrigo and S. Sankaran. 2010. Application of hyperspectral imaging for the detection of citrus greening, Paper No. 1009998, ASABE 2010 Annual International Meeting, Pittsburg, PA, June 20-23, 2010. Patil, R., W. S. Lee, R. Ehsani, and F. Roka. 2010. Elimination of debris using de-stemmers on a continuous citrus canopy shake and catch harvester. ASABE Paper No. 1008384. St. Joseph, Mich.: ASABE. Sankaran, S., Ehsani, R., and E. Etxeberria. 2010. Mid-infrared spectroscopy for detection of Huanglongbing (greening) in citrus leaves, Talanta, 83, 574-581. Sankaran, S., Mishra, A., Ehsani, R., and C. Davis. 2010. A review of advanced techniques for detecting plant diseases, Comput. Electron. Agric., 72 (1), 1-13. 3. Sankaran, S., R. Ehsani, and C. Dima. 2010. Development of ground-based sensor system for automated agricultural vehicle to detect diseases in citrus plantations, Paper No. 304, 10th International Conference Precision Agriculture, July 18-21, 2010, Denver, CO. Sankaran, S., and R. Ehsani. 2010. Optical methods for Huanglongbing (HLB) detection in citrus orchards, Florida Society Horticultural Society (FSHS) 2010 Annual Meeting, Paper No. C-25, June 6-8, Plantation Inn, Crystal River, FL. Sankaran, S., and R. Ehsani. 2010. Detection of Huanglongbing (greening) in citrus plantations using mid-infrared spectroscopy, Paper No. 1009199, American Society of Agricultural and Biological Engineers (ASABE) 2010 Annual International Meeting, Pittsburg, PA, June 20-23, 2010. Sankaran, S., R. Ehsani, and C. Dima. 2010. Development of ground-based sensor system for automated agricultural vehicle to detect diseases in citrus plantations, Paper No. 304, 10th International Conference Precision Agriculture, July 18-21, 2010, Denver, CO. Sankaran, S., and R. Ehsani. 2010. Detection of Huanglongbing (greening) in citrus plantations using mid-infrared spectroscopy, Paper No. 1009199 (Poster), American Society of Agricultural and Biological Engineers (ASABE) 2010 Annual International Meeting, Pittsburg, PA, June 20-23, 2010. Sankaran, S., and R. Ehsani. 2010. Optical methods for Huanglongbing (HLB) detection in citrus orchards, FSHS 2010 Annual Meeting, Paper No. C-25, June 6-8, Plantation Inn, Crystal River, FL. Griffin, T.W.; Dobbins, C.L.; Florax, R.J.G.M.; Lowenberg-DeBoer, J.M.; Vyn, T.J. 2010. Spatial Analysis of Precision Agriculture Data: Role for Extension. J. National Assoc. Agric. Agents. Available at: http://www.nacaa.com/journal/index.php?jid=40 Lowenberg-DeBoer, J. and B. Erickson, 2010. Precision Agriculture in Africa. Georgetown Journal of International Affairs, 11(2) (2010), p. 107-116. Murrell, T.S., and T. J. Vyn. 2010. Precision management of root zone potassium for corn: Considerations for the future. Better Crops Vol. 94 (No. 4), p. 24-25. http://www.ipni.net/ppiweb/bcrops.nsf/$webindex/7C1B2FB0205F44F3852577EB00574F8F/$file/BC+4+2010+pg+24.pdf Erickson, Bruce J. Economics and Adoption of Precision Farming Technologies, Purdue Women in Agriculture Conference, February 2011, Jeffersonville, Indiana. Erickson, Bruce J., Terry Griffin, and David Waits. Technology & Equipment: The Logistics of Maximizing Yield, panel discussion at GROWMARK Grower Conference, January 2011, Peoria, Illinois. Erickson, Bruce J. Investments in Precision Agriculture and Other Farm Management Topics, Ohio State Agronomic Crops In-Service Training, January 2011, Columbus, Ohio. Erickson, Bruce J. Adoption of Precision Agriculture, Contemporary Trends, Central Ohio Agronomy Day December 2010, Newark ,Ohio. Erickson, Bruce J. Agronomic Input Trends, Informa Economics Fall Outlook Conference, November 2010, Memphis, Tennessee. Erickson, Bruce J. Economics and Adoption of Precision Farming Technology, Brazilian farmers group visit to Purdue University, August 2010. Erickson, Bruce J. Economics and Adoption of Precision Farming Technology, Argentina CREA Maria Teresa visit to Purdue University, August 2010. Erickson, Bruce J. Practical Applications of Precision Agriculture for Todays Grower, Heartland Technology Solutions Media Field Day, June 2010, Harlan, IA Erickson, Bruce J. Using Production Technology to Increase Food Security While Mitigating Climate Change, 2010 Rotary World Affairs Conference, March 2010. Erickson, Bruce J. Precision Agriculture Collaboration: Agronomic, Engineering and Economics, NCERA-180 Site-Specific Management Industry/Academic Annual Meeting, Lexington, KY, January 2010. Franzen, D., D. Long, A. Sims, J. Lamb, F. Casey et al., 2010. Evaluation of methods to determine residual soil nitrate zones across the northern Great Plains of the USA. Published on-line, November 2010 in Journal of Precision Agriculture Franzen, D.W. 2011. Collecting and analyzing soil spatial information using kriging and inverse distance. p. 61-80 In GIS Applications in Agriculture. Vol. 2 Nutrient Management for Energy Efficiency. D.E. Clay and J.F. Shanahan, eds. CRC Press, Boca Raton, FL Franzen, D.W., R. Ashley, G. Endres, J. Lukach, J. Staricka, and K. McKay. Revising nitrogen recommendations for wheat in response to the need for support of variable-rate nitrogen application. International Precision Ag Conference, Denver, CO, July, 2010.