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Paz-Kagan, T
Wilson, G.L
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Authors
Wilson, G.L
Mulla, D.J
Galzki, J
Laacouri, A
Vetsch, J
Rozenstein, O
Cohen, Y
Alchanatis , V
Behrendt, K
Bonfil, D.J
Eshel, G
Harari, A
Harris, W.E
Klapp, I
Laor, Y
Linker, R
Paz-Kagan, T
Peets, S
Rutter, M.S
Salzer, Y
Lowenberg-DeBoer, J
Topics
In-Season Nitrogen Management
Drivers and Barriers to Adoption of Precision Ag Technologies or Digital Agriculture
Type
Oral
Year
2018
2024
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1. Predicted Nitrate-N Loads for Fall, Spring, and VRN Fertilizer Application in Southern Minnesota

Nitrate-N from agricultural fields is a source of pollution to fresh and marine waters via subsurface tile drainage.  Sensor-based technologies that allow for in-season monitoring of crop nitrogen requirements may represent a way to reduce nitrate-N loadings to surface waters by allowing for fertilizer application on a more precise spatial and temporal resolution.  However, little research has been done to determine its effectiveness in reducing nitrate-N losses.  In this study,... G.L. Wilson, D.J. Mulla, J. Galzki, A. Laacouri, J. Vetsch

2. Data-driven Agriculture and Sustainable Farming: Friends or Foes?

Sustainability in our food and fiber agriculture systems is inherently knowledge intensive.  It is more likely to be achieved by using all the knowledge, technology, and resources available, including data-driven agricultural technology and precision agriculture methods, than by relying entirely on human powers of observation, analysis, and memory following practical experience.  Data collected by sensors and digested by artificial intelligence (AI) can help farmers learn about synergies... O. Rozenstein, Y. Cohen, V. Alchanatis , K. Behrendt, D.J. Bonfil, G. Eshel, A. Harari, W.E. Harris, I. Klapp, Y. Laor, R. Linker, T. Paz-kagan, S. Peets, M.S. Rutter, Y. Salzer, J. Lowenberg-deboer