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Bishop, T.F
Bisognin, M.B
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Authors
Amado, T.J
Santi, A.L
Corassa, G.M
Bisognin, M.B
Gaviraghi, R
Pires, J.L
Filippi, P
Jones, E.J
Fajardo, M
Whelan, B.M
Bishop, T.F
Topics
Spatial Variability in Crop, Soil and Natural Resources
Big Data, Data Mining and Deep Learning
Type
Poster
Oral
Year
2016
2018
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1. Response of Soybean Cultivars According to Management Zones in Southern Brazil

The positioning of soybean cultivars on fields according your environmental response is new strategy to obtain high soybean yields. The aim of this study was to investigate the agronomic response of six soybean cultivars according management zones in Southern Brazil. The study was conducted in 2013/2014 and in two fields located in Boa Vista das Missões, Rio Grande do Sul, Brazil. The experimental design was a randomized complete block in a factorial arrangement (3x6), with three management... T.J. Amado, A.L. Santi, G.M. Corassa, M.B. Bisognin, R. Gaviraghi, J.L. Pires

2. 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