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Brockgreitens, J
Pronk, A
Wang, W
Felderhoff, T
Asgedom, H
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Authors
Brockgreitens, J
Bui, M
Abbas, A
Mulla, D
Zhang, J
Wang, W
Fu, Z
Cao, Q
Tian, Y
Zhu, Y
Cao, W
liu, X
van Evert, F
Van Oort, P
Maestrini, B
Pronk, A
Boersma, S
Kopanja, M
Mimić, G
Bari, M.A
Bakshi, A
Witt, T
Caragea, D
Jagadish, K
Felderhoff, T
Pramanik, S
Choton, J
Asgedom, H
Hehar, G
Willness, C
Anderson, W
Duddu, H
Mooleki, P
Schoenau, J
Khakbazan, M
Lemke, R
Derdall, E
Shang, J
Liu, K
Sulik, J
Karppinen, E
Mbakwe, I
Topics
Engineering Technologies and Advances
In-Season Nitrogen Management
Big Data, Data Mining and Deep Learning
Site-Specific Nutrient, Lime and Seed Management
Type
Poster
Oral
Year
2016
2024
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Filter results5 paper(s) found.

1. Field Sampling and Electrochemical Detection of Nitrate in Agricultural Soils

Nitrate is an essential plant nutrient and is added to farm fields to increase crop yields. While the addition of nitrate is important for production, over-fertilization with nitrate can lead to leaching and contamination of water bodies. Increased nitrate loading in water sources then leads to eutrophication and hypoxia in downstream regions. Many efforts are being made to accurately control nitrate fertilizer additions to fields. Here, we present a soil sampling device that directly samples... J. Brockgreitens, M. Bui, A. Abbas, D. Mulla

2. Potential Benefits of Variable Rate Nitrogen Topdressing Strategy Coupled with Zoning Technique: a Case Study in a Town-scale Rice Production System

Integrating remote sensing (RS)-based variable rate nitrogen (N) recommendation (VRNR) algorithms and management zones (MZs) may improve the accuracy and efficiency of site-specific N management. However, its potential benefits for application in commercial rice production systems can hardly be assessed, since it requires to intervene in common agricultural practices and causes certain economic and environmental consequences. Through a machine learning approach, this study aims to comprehensively... J. Zhang, W. Wang, Z. Fu, Q. Cao, Y. Tian, Y. Zhu, W. Cao, X. Liu

3. A Digital Twin for Arable Crops and for Grass

There is an opportunity to use process-based cropping systems models (CSMs) to support tactical farm management decisions, by monitoring the status of the farm, by predicting what will happen in the next few weeks, and by exploring scenarios. In practice, the responses of a CSM will deviate more and more from reality as time progresses because the model is an abstraction of the real system and only approximates the responses of the real system. This limitation may be overcome by using the CSM... F. Van evert, P. Van oort, B. Maestrini, A. Pronk, S. Boersma, M. Kopanja, G. Mimić

4. Deep Learning to Estimate Sorghum Yield with Uncrewed Aerial System Imagery

In the face of growing demand for food, feed, and fuel, plant breeders are challenged to accelerate yield potential through quick and efficient cultivar development. Plant breeders often conduct large-scale trials in multiple locations and years to address these goals. Sorghum breeding, integral to these efforts, requires early, accurate, and scalable harvestable yield predictions, traditionally possible only after harvest, which is time-consuming and laborious. This research harnesses high-throughput... M.A. Bari, A. Bakshi, T. Witt, D. Caragea, K. Jagadish, T. Felderhoff

5. Response of Canola and Wheat to Application of Enhanced Efficiency Nitrogen Fertilizers on Contrasting Management Zones

Investment on nitrogen (N) fertilizers is a major cost of growers, and variable rate (VR) application of N fertilizers could help optimize its usage. In the growing season of 2023, field experiments were conducted at four sites (i.e., Watrous – Saskatchewan SK and two fields in the vicinity of Strathmore, Alberta AB, Canada). The main objectives were to (i) determine performance of Enhanced Efficiency N Fertilizers - EENF (i.e., Coated urea, urea with double inhibitors - DI, urea mixed with... H. Asgedom, G. Hehar, C. Willness, W. Anderson, H. Duddu, P. Mooleki, J. Schoenau, M. Khakbazan, R. Lemke, E. derdall, J. Shang, K. Liu, J. Sulik, E. Karppinen, I. Mbakwe