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Fajardo, M
Ferguson, R.B
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Authors
Bean, G.M
Kitchen, N.R
Camberato, J.J
Ferguson, R.B
Fernandez, F.G
Franzen, D.W
Laboski, C.A
Nafziger, E.D
Sawyer, J.E
Scharf, P.C
Ransom, C.J
Kitchen, N.R
Camberato, J.J
Carter, P.R
Ferguson, R.B
Fernandez, F.G
Franzen, D.W
Laboski, C.A
Nafziger, E.D
Shanahan, J
Sawyer, J.E
Filippi, P
Jones, E.J
Fajardo, M
Whelan, B.M
Bishop, T.F
Whelan, B
Fajardo, M
Topics
In-Season Nitrogen Management
Big Data, Data Mining and Deep Learning
Decision Support Systems
Type
Oral
Year
2018
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Filter results4 paper(s) found.

1. Corn Nitrogen Fertilizer Recommendation Models Based on Soil Hydrologic Groups Aid in Predicting Economically Optimal Nitrogen Rates

Nitrogen (N) fertilizer recommendations that match corn (Zea mays L.) N needs maximize grower profits and minimize water quality consequences. However, spatial and temporal variability makes determining future N requirements difficult. Studies have shown no single soil or weather measurement is consistently increases accuracy, especially when applied over a regional scale, in predicting economically optimal N rate (EONR). Basing site N response on soil hydrological group could help account for... G.M. Bean, N.R. Kitchen, J.J. Camberato, R.B. Ferguson, F.G. Fernandez, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J.E. Sawyer, P.C. Scharf

2. Improving Corn Nitrogen Rate Recommendations Through Tool Fusion

 Improving corn (Zea maysL,) nitrogen (N) fertilizer rate recommendation tools can improve farmer’s profits and help mitigate N pollution. One way to improve N recommendation methods is to not rely on a single tool, but to employ two or more tools. Thiscould be thoughtof as “tool fusion”.The objective of this analysis was to improve N management by combining N recommendation tools used for guiding rates for an in-seasonN application. This evaluation was... C.J. Ransom, N.R. Kitchen, J.J. Camberato, P.R. Carter, R.B. Ferguson, F.G. Fernandez, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J. Shanahan, J.E. Sawyer

3. Forecasting Crop Yield Using Multi-Layered, Whole-Farm Data Sets and Machine Learning

The ultimate goal of Precision Agriculture is to improve decision making in the business of farming. Many broadacre farmers now have a number of years of crop yield data for their fields which are often augmented with additional spatial data, such as apparent soil electrical conductivity (ECa), soil gamma radiometrics, terrain attributes and soil sample information. In addition there are now freely available public datasets, such as rainfall, digital soil maps and archives of satellite remote... P. Filippi, E.J. Jones, M. Fajardo, B.M. Whelan, T.F. Bishop

4. Optimising Nitrogen Use in Cereal Crops Using Site-Specific Management Classes and Crop Reflectance Sensors

The relative cost of Nitrogen (N) fertilisers in a cropping input budget, the 33% Nitrogen use efficiency (NUE) seen in global cereal grain production and the potential environmental costs of over-application are leading to changes in the application rates and timing of N fertiliser. Precision agriculture (PA) provides tools for producers to achieve greater synchrony between N supply and crop N demand. To help achieve these goals this research has explored the use of management classes derived... B. Whelan, M. Fajardo