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Jayasuriya, H
Akin, S
Jiang, H
Apolinário, E
Jasper, J
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Authors
Gnyp, M.L
Panitzki, M
Reusch, S
Jasper, J
Bolten, A
Bareth, G
Jayasuriya, H
Al-Busaidi, A
Ahmed, M
Li, D
Jiang, H
Chen, S
Wang, C
Apolinário, E
Souza, W.J
Akin, S
Arnall, B
Derrick, J
Akin, S
Sharry, R
Arnall, B
Topics
Remote Sensing Applications in Precision Agriculture
Decision Support Systems
In-Season Nitrogen Management
Geospatial Data
Precision Agriculture and Global Food Security
On Farm Experimentation with Site-Specific Technologies
Type
Oral
Poster
Year
2016
2018
2024
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Filter results6 paper(s) found.

1. Comparison Between Tractor-based and UAV-based Spectrometer Measurements in Winter Wheat

In-season variable rate nitrogen fertilizer application needs a fast and efficient determination of nitrogen status in crops. Common sensor-based monitoring of nitrogen status mainly relies on tractor mounted active or passive sensors. Over the last few years, researchers tested different sensors and indicated the potential of in-season monitoring of nitrogen status by unmanned aerial vehicles (UAVs) in various crops. However, the UAV-platforms and the available sensors are not yet accepted to... M. Gnyp, M. Panitzki, S. Reusch, J. Jasper, A. Bolten, G. Bareth

2. Precision Irrigation Management Through Conjunctive Use of Treated Wastewater and Groundwater in Oman

Agriculture under arid environment is always become a challenge due to water scarcity and salinity problems.  With average rainfall of 100 mm, agriculture in Oman is limited due to the arid climate and limited arable lands. More than 50 percent of the arable lands are located in the 300 km northern coastal belt of Al-Batinah region. In addition, country is facing severe problem of sea water intrusion into the groundwater aquifers due to undisciplined excessive groundwater (GW) abstraction... H. Jayasuriya, A. Al-busaidi, M. Ahmed

3. Estimating Litchi Canopy Nitrogen Content Using Simulated Multispectral Remote Sensing Data

This study aims at evaluating the performance of seven highly spatial resolution remote sensing data in litchi canopy nitrogen content estimation. The litchi canopy reflectance were collected by ASD field spectrometer. Then the canopy spectral data were resampled based on the spectral response functions of each satellite sensors (Geo-eye, GF-WFV1, Rapid-eye, WV-2, Landsat 8, WV-3, and Sentinel-2). The spectral indices in literature were derived based on the simulated data. Meanwhile, the successive... D. Li, H. Jiang, S. Chen, C. Wang

4. Soybean Production Components As Indicators of Soil Variability As a Subsidy for Precision Agriculture

The soil variability in its physical, chemical and biological parameters can be analyzed using direct methods applicable to each variable studied. Plant responses, manifested in the establishment of the final population, biomass production and grain productivity can reflect the soil conditions, associating them with the variability observed in the area. Localized soil management and the use of machines with variable rate applications, including drones for applications in specific sites, depend... E. Apolinário, W.J. Souza

5. The Evaluation of Spatial Response to Potassium in Soybeans

In agriculture, the nutrients that are in the largest demand are nitrogen (N), phosphorus (P), and potassium (K), as product demand increases  so does demand for fertilizers. In the case of potassium, most soils can provide potassium in amounts that exceed crop demand; however the potassium within the soil is not always readily available to the crop, this leads to producers apply potassium to their crops even though soil tests suggests otherwise. One such crop where potassium is in demand... S. Akin, B. Arnall

6. Influence of Potassium Variability on Soybean Yield

Due to its role as a plant essential nutrient, Potassium (K) serves as a fundamental component for plant growth. Soybeans are heavily reliant upon this nutrient for root growth and the production of pods, so much so that after nitrogen, potassium is the second most in-demand nutrient. Much of the overall soybean crop grown in Oklahoma is not managed with the fertility of K directly in mind. However, as the potential and expectation for greater yield increases, so does interest from producers... J. Derrick, S. Akin, R. Sharry, B. Arnall