Proceedings
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| Filter results10 paper(s) found. |
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1. Estimating Soil Quality Indicators with Diffuse Reflectance SpectroscopyKnowledge of within-field spatial variability in soil quality indicators is important to assess the impact of site-specific management on the soil. Standard methods for measuring these properties require considerable time and expense, so sensor-based approaches would be... R.J. Kremer, N.R. Kitchen, K.A. Sudduth, D.B. Myers |
2. 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 |
3. First Results Of Development Of A Smart Farm In The NetherlandsGNSS technology has been introduced on about 20 % of the Dutch arable farms in The Netherlands today. Use of sensor technology is also slowly but gradually being adopted by farmers, providing them large amounts of digital data on soil, crop and climate conditions. Typical data are spatial variation in soil organic matter, crop biomass, crop yield, and presence of pests and diseases. We still have to make major steps to use all this data in a way that agriculture becomes more sustainable. We... T. Feher, C. Kocks, C. Kempenaar, K. Westerdijk |
4. Precision Agriculture As Bricolage: Understanding The Site Specific FarmerThere is an immediate paradox apparent in precision farming because it applies all of it ‘s precision and recognition of variability to the land, yet operates under the assumption of idealism and normative notions when it comes to considering the farmer. Precision Agriculture (PA) systems have often considered the farmer as an optimiser of profit, or maximiser of efficiency, and therefore replaceable with mathematical constructs, so that although at the centre of decision... I.J. Yule, B.A. Wood |
5. Surplus Science and a Non-linear Model for the Development of Precision Agriculture TechnologyThe advent of ‘big data technologies’ such as hyperspectral imaging means that Precision Agriculture (PA) developers now have access to superabundant and highly heterogeneous data. The authors explore the limitations of the classic science model in this situation and propose a new non-linear process that is not based on the premise of controlled data scarcity. The study followed a science team tasked with developing highly advanced hyperspectral techniques for a ‘low... M.Z. Cushnahan, I.J. Yule, B.A. Wood, R. Wilson |
6. Seeding and Planting Plots for Crop Performance Evaluation Using Gps-rtk Auto SteeringCrop performance evaluation plots are seeded both on and off the University of Nebraska West Central Research and Extension Center. Plots off the Center must match the producer’s rows for pesticide application, cultivation, ditching, irrigation, fertilization and any other operations performed in the fields. With row crops the producer blank-plants the plot area before we can follow up with planting the plots. This means that we have to wait for the producer to plant in the field. Blank-planting... R.N. Klein, J.A. Golus, A.S. Cox |
7. Comparison of NDVI Values at Different Phenological Stages of Winter Wheat (Triticum Aestivum L.)The main objective of this study is to monitor, detect and quantify the presence of live green vegetation with the MicaSense RedEdge-MX Dual Camera System (MS) mounted on a DJI Matrice 210 V2 and GreenSeeker HCS 250 (GS) in winter wheat (Triticum aestivum L.) by using Normalized Difference Vegetation Index (NDVI). Surveys were conducted in the North-Western part of Hungary, in Mosonmagyaróvár on six different dates. A small-scale field trial in winter wheat was constructed as a randomized... S. Zsebő, G. Kukorelli, V. Vona, L. Bede, D. Stencinger, A. Kovacs, G. Milics, I.M. Kulmany, B. Horváth, G. Hegedűs, J.A. Abdinoor |
8. Delineation of Site-Specific Management Zones using Sensor-based Data for Precision N managementNitrogen is a critical nutrient influencing crop yield, but the common practice of uniform application of nitrogen fertilizer across a field often results in spatially variable nitrogen availability for the crop, leading to over-application in some areas and under-application in others. This imbalance can cause economic losses and significant environmental issues. Precision nitrogen application involves application of N fertilizers based on soil conditions and crop requirements. One approach for... R. Joshi, R. Khosla, D. Mandal, R. Unruh, W.A. Admasu |
9. Delineating Dynamic Variable Rate Irrigation Management ZonesAgriculture irrigation strategies have traditionally been made without accounting for the natural small-scale variability in the field, leading to uniform applications that often over-irrigate parts of the field that do not need as much water. The future success of irrigated agriculture depends on advancements in the capability to account for and leverage the natural variability in croplands for optimum irrigation management both in space and time. Variable Rate Irrigation (VRI) management offers... R. Unruh, W.A. Yilma, D. Mandal, R. Joshi, R. Khosla |
10. Evaluation of the Effect of Different Herbicide Treatments by Using UAV in Maise (Zea mays L.) Cultivation – First Experiences in a Long-term Experiment at Széchenyi István University, HungaryAs part of the Green Deal, the European Union has set a goal to reduce the use of chemical pesticides by 50 percent until 2030. To achieve this goal, in addition to reducing the amount of pesticide used, attention must also be paid to monitoring the temporal and spatial effects of pesticides on weeds during the cultivation of various crops. Hence, Syngenta Ltd., collaborating with researchers, aimed to monitor the effect of five different types of herbicides by UAV in two tillage treatments (CN... I.M. Kulmany, B. Horváth, G. Kukorelli, S. Zsebő, D. Stencinger, Z. Borbás, R. Pecze, L. Bede, Z. Varga, A. Kósa, G. Pinke, Z.K. Hashim, G. Hegedűs, J.A. Abdinoor, G.S. Agampodi |