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| Filter results23 paper(s) found. |
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1. Networking Advances Emerging Agricultural TechnologiesInnovative Nebraska farmers and agribusinesses partnered with University of Nebraska-Lincoln (UNL) extension in 2001 to form the Nebraska Agricultural Technologies Association (NeATA). UNL Extension faculty and NeATA members have collaborated for nearly a decade to further agriculturists' understanding and adoption of emerging agricultural technologies via machinery/technology field days, hands-on GIS/GPS computer workshops, aerial imagery experiential learning,... D.L. Varner |
2. Minimizing On-farm Point Source Contamination Of Pesticides Using The “Biobed” MethodMicroorganisms play a vital role in maintaining the environmental quality through their metabolic activities. The highly versatile metabolic capabilities of fungi and bacteria can be used to reclaim polluted ecosystems. A variety of biological, chemical, and physical methods have been used for degradation and detoxification of pesticides. However, conventional clean- up methods are costly and not always effective. Agrochemical storage and handling practices have been targeted... F. Eivazi |
3. Precision Agriculture Education Program In NebraskaWith the cost of agricultural inputs and the instability of commodity prices increasing, demand is growing for training in the essential skills needed to successfully implement site-specific crop management. This set of skills is uniquely interdisciplinary in nature. Thus, it is essential for potential users of precision agriculture to understand the basics of geodetic and electronic control equipment, principles of geographic information systems, fundamentals... V.I. Adamchuk, R.B. Ferguson |
4. Comparison Of Spectral Indices Derived From Active Crop Canopy Sensors For Assessing Nitrogen And Water Status... L. Shiratsuchi, R.B. Ferguson, J.F. Shanahan, V.I. Adamchuk, G. Slater |
5. Vision Of Farm Of Tomorrow... K. Charvat, P. Gnip |
6. New Geospatial Technologies For Precision Farming... K. Charvat, J. Cepicky, P. Gnip |
7. Vlite Node – New Sensor Technology For Precision Farming... K. Charvat, J. Jezek, M. Musil, Z. Krivanek, P. Gnip |
8. Analysis Of Principles For Adaptive Knowledge Management On Pilot FarmsCollected data, which are used in this research, are coming from several different data sources and time periods (soil test, satellite images, airborne pictures, soil type’s maps, yield predictions maps and other agronomist data). According to above mentioned data was calculate also variable rate for application of Nitrogen, Potash, Phosphor and Calcium and applied time table during the 10 years period. Main goal of this... P. Gnip |
9. Modeling Soil Carbon Spatial Variation: Case Study In The Palouse RegionSoil organic carbon (Cs) levels in the soil profile reflect the transient state or equilibrium conditions determined by organic carbon inputs and outputs. In areas with strong topography, erosion, transport and deposition control de soil carbon balance and determine strong within-field differences in soil carbon. Carbon gains or losses are therefore difficult to predict for the average field. Total Cs ranged from 54 to 272 Mg C ha-1, with 42% (range 25 to 78%) of Cs in the top 0.3-m of the soil... A.R. Kemanian, D.R. Huggins, D.P. Uberuaga |
10. Integrated Crop Canopy Sensing System for Spatial Analysis of In-Season Crop PerformanceOver the past decade, the relationships between leaf color, chlorophyll content, nitrogen supply, biomass and grain yield of agronomic crops have been studied widely.... L. Shiratsuchi, C.C. Lutz, R.B. Ferguson, V.I. Adamchuk |
11. An Approach to Selection of Soil Water Content Monitoring Locations within FieldsIncreased input efficiency is one of the main challenges for a modern agricultural enterprise. One way to optimize production cycles is to rationalize crop residue utilization. In conditions where there is limited use of mineral fertilizers and without applying manure, plant residues may be used as an organic fertilizer as... V.I. Adamchuk, L. Pan, R.B. Ferguson |
12. Landscape Influences on Soil Nitrogen Supply and Water Holding Capacity for Irrigated Corn... T. Shaver, M. Schmer, S. Irmak, S. Van donk, B. Wienhold, V. Jin, A. Bereuter, D. Francis, D. Rudnick, N. Ward, L. Hendrickson, R. Ferguson, V.I. Adamchuk |
13. Development And Evaluation Of A Leaf Monitoring System For Continuous Measurement Of Plant Water Status In Almond And Walnut CropsAbstract: Leaf temperature measurements using handheld infrared thermometers have been used to predict plant water stress by calculating crop water stress index (CWSI). However, for CWSI calculations it is recommended to measure canopy temperature of trees under saturated, stressed and current conditions simultaneously, which is not very practical while using handheld units. An inexpensive, easy to use sensing system was developed to predict plant water status for tree crops by measuring... F. Rojo, J. Roach, R. Coates, S. Upadhyaya, M. Delwiche, C. Han, R. Dhillon |
14. In-Season Nitrogen Requirement For Maize Using Model And Sensor-Based Recommendation ApproachesNitrogen (N), an essential element, is often limiting to plant growth. There is great value in determining the optimum quantity and timing of N application to meet crop needs while minimizing losses. Low nitrogen use efficiency (NUE) has been attributed to several factors including poor synchrony between N fertilizer and crop demand, unaccounted for spatial variability resulting in varying crop N needs, and temporal variances in crop N needs. Applying a portion... L.J. Stevens, R.B. Ferguson, D.W. Franzen, N.R. Kitchen |
15. Modifying the University of Missouri Corn Canopy Sensor Algorithm Using Soil and Weather InformationCorn production across the U.S. Corn belt can be often limited by the loss of nitrogen (N) due to leaching, volatilization and denitrification. The use of canopy sensors for making in-season N fertilizer applications has been proven effective in matching plant N requirements with periods of rapid N uptake (V7-V11), reducing the amount of N lost to these processes. However, N recommendation algorithms used in conjunction with canopy sensor measurements have not proven accurate in making N recommendations... G. Bean, N.R. Kitchen, D.W. Franzen, R.J. Miles, C. Ransom, P. Scharf, J. Camberato, P. Carter, R.B. Ferguson, F. Fernandez, C. Laboski, E. Nafziger, J. Sawyer, J. Shanahan |
16. Field-scale Nitrogen Recommendation Tools for Improving a Canopy Reflectance Sensor AlgorithmNitrogen (N) rate recommendation tools are utilized to help producers maximize grain yield production. Many of these tools provide recommendations at field scales but often fail when corn N requirements are variable across the field. This may result in excess N being lost to the environment or producers receiving decreased economic returns on yield. Canopy reflectance sensors are capable of capturing within-field variability, although the sensor algorithm recommendations may not always be as accurate... C.J. Ransom, M. Bean, N. Kitchen, J. Camberato, P. Carter, R. Ferguson, F. Fernandez, D. Franzen, C. Laboski, E. Nafziger, J. Sawyer, J. Shanahan |
17. Active and Passive Crop Canopy Sensors As Tools for Nitrogen Management in CornThe objectives of this research were to (i) assess the correlation between active and passive crop canopy sensors’ vegetation indices at different corn growth stages and (ii) assess sidedress variable rate nitrogen (N) recommendation accuracy of active and passive sensors compared to the agronomic optimum N rate (AONR). The experiment was conducted near Central City, Nebraska on a Novina sandy loam planted to corn on 15 April 2015. The experiment was a randomized complete-block design with... L. Bastos, R. Ferguson |
18. Liquid Flow Control Requirements for Crop Canopy Sensor-Based N Management in Corn: A Project SENSE Case StudyWhile on-farm adoption of crop canopy sensors for directing in-season nitrogen (N) application has been slow, research focused on these systems has been significant for decades. Much emphasis has been placed on developing and testing algorithms based on sensor output to predict N needs, but little information has been published regarding liquid flow control requirements on equipment used in conjunction with these sensing systems. Addition of a sensor-based system to a standard spray rate controller... J. Luck, J. Parrish, L. Thompson, B. Krienke, K. Glewen, R.B. Ferguson |
19. Active and Passive Sensor Comparison for Variable Rate Nitrogen Determination and Accuracy in Irrigated CornThe objectives of this research were to (i) compare active and passive crop canopy sensors’ sidedress variable rate nitrogen (VRN) derived from different vegetation indices (VI) and (ii) assess VRN recommendation accuracy of active and passive sensors as compared to the agronomic optimum N rate (AONR) in irrigated corn. This study is comprised of six site-years (SY), conducted in 2015, 2016 and 2017 on different soil types (silt loam, loam and sandy loam) and with a range of preplant-applied... L. Bastos, R.B. Ferguson |
20. Fruit Fly Electronic Monitoring SystemInsects are a constant threat to agriculture, especially the cultivation of various types of fruits such as apples, pears, guava, etc. In this sense, it is worth mentioning the Anastrepha genus flies (known as fruit fly), responsible for billionaire losses in the fruit growing sector around the world, due to the severity of their attack on orchards. In Brazil, this type of pests has been controlled in most product areas by spraying insecticides, which due to the need for prior knowledge regarding... C.L. Bazzi, F.V. Silva, L. Gebler, E.G. Souza, K. Schenatto, R. Sobjak, R.S. Dos santos, A.M. Hachisuca, F. Franz |
21. Yield Mapping in Fruit FarmingDue to the importance of increasing the quantity and quality of world agricultural production, the use of technologies to assist in production processes is essential. Despite this, a timid adoption by precision agriculture (PA) technologies is verified by the Brazilian fruit producers, even though it is one of the segments that had been stood out in recent years in the country's economy. In the PA context, yield maps are rich sources of information, especially by species harvested through... C.L. Bazzi, M.R. Martins, L. Gebler, E.G. Souza, K. Schenatto, R. Sobjak, A. . Hachisuca, F. Franz |
22. Developing a Machine Learning and Proximal Sensing-based In-season Site-specific Nitrogen Management Strategy for Corn in the US MidwestEffective in-season site-specific nitrogen (N) management strategies are urgently needed to ensure both food security and sustainable agricultural development. Different active canopy sensor-based precision N management strategies have been developed and evaluated in different parts of the world. Recent studies evaluating several sensor-based N recommendation algorithms across the US Midwest indicated that these locally developed algorithms generally did not perform well when used broadly across... D. Li, Y. Miao, .G. Fernández, N.R. Kitchen, C. . Ransom, G.M. Bean, .E. Sawyer, J.J. Camberato, .R. Carter, R.B. Ferguson, D.W. Franzen, D.W. Franzen, D.W. Franzen, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J.F. Shanahan |
23. Portable Soil EC - Development of an Electronic Device for Determining Soil Electrical ConductivityDecision-making in agriculture demands continuous monitoring, a factor that propels the advancement of tools within Agriculture 4.0. In this context, understanding soil characteristics is essential. Electrical conductivity (EC) sensors play a pivotal role in this comprehension. Given this backdrop, the core motivation of this research was developing an accessible and effective electronic device to measure the apparent EC of the soil. It provides features like geolocation, recording of the date... C.L. Bazzi, L.A. Rauber, W.K. Oliveira, R. Sobjak, K. Schenatto, L. Gebler, L.M. Rabello |