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Hopkins, B
Hashim, Z.K
Kweon, G
Garcia, L
Kablan, L
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Authors
Lund, E
Maxton, C
Kweon, G
Garcia, L
Elhaddad, A
Kablan, L
Chabot, V
Mailloux, A
Bouchard, M
Fontaine, D
Bruulsema, T
Turner, I
Kerry, R
Jensen, R
Woolley, E
Hansen, N
Hopkins, B
Kulmany, I.M
Horváth, B
Kukorelli, G
Zsebő, S
Stencinger, D
Borbás, Z
Pecze, R
Bede, L
Varga, Z
Kósa, A
Pinke, G
Hashim, Z.K
Hegedűs, G
Abdinoor, J.A
Agampodi, G.S
Topics
Proximal Sensing in Precision Agriculture
Remote Sensing Applications in Precision Agriculture
Site-Specific Nutrient, Lime and Seed Management
Drainage Optimization and Variable Rate Irrigation
Proximal and Remote Sensing of Soils and Crops (including Phenotyping)
Type
Poster
Oral
Year
2012
2010
2018
2022
2024
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Filter results5 paper(s) found.

1. Using A Surface Energy Model (reset) To Determine The Spatial Variability Of ET Within And Between Agricultural Fields

Remote sensing algorithms are currently being used to estimate regional surface fluxes (e.g. evapotranspiration (ET)). Many of these surface energy balance models use information derived from satellite imagery such as aircraft, Landsat, AVHRR, ASTER, and MODIS to estimate ET. The remote sensing approach to estimating ET provides advantages over traditional methods. One of the most important advantages is that it can provide estimates of actual ET for each pixel in the image. Most conventional... L. Garcia, A. Elhaddad

2. The Ultimate Soil Survey in One Pass: Soil Texture, Organic Matter, pH, Elevation, Slope, and Curvature

The goal of accurately mapping soil variability preceded GPS-aided agriculture, and has been a challenging aspect of precision agriculture since its inception.  Many studies have found the range of spatial dependence is shorter than the distances used in most grid sampling.  Other studies have examined variability within government soil surveys and concluded that they have limited utility in many precision applications.  Proximal soil sensing has long been envisioned as a method... E. Lund, C. Maxton, G. Kweon

3. Variability in Corn Yield Response to Nitrogen Fertilizer in Quebec

Optimizing nitrogen (N) fertilization is important to improve corn yield and to reduce N losses to the environment. The economic optimum nitrogen rate  (EONR) is variable and depends on many factors, including weather conditions and crop management.  The main objective of this study was to examine how grain corn yield response to N varies with planting date, soil texture and spring weather across sites and years in Monteregie, which is the most important with 64% of total area and 69%... L. Kablan, V. Chabot, A. Mailloux, M. Bouchard, D. Fontaine, T. Bruulsema

4. Investigation of Automated Analysis of Snowmelt from Time-series Sentinel 2 Imagery to Inform Spatial Patterns of Spring Soil Moisture in the American Mountain West

Variable rate irrigation of crops is a promising approach for saving water whilst maintaining crop yields in the semi-arid American Mountain West – much of which is currently experiencing a mega drought. The first step in determining irrigation zones involves characterizing the patterns of spatial variation in soil moisture and determining if these are relatively stable temporally in relation to topographic features and soil texture. Characterizing variable rate irrigation zones is usually... I. Turner, R. Kerry, R. Jensen, E. Woolley, N. Hansen, B. Hopkins

5. 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, Hungary

As 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