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Raeth, P.G
Johnson, R.M
N.L., R
Roux, S
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Authors
Shanwad, U
H, V
N.L., R
Kanannnavar, P.S
Swamy, S
Patil, M.B
Viator, B.J
Johnson, R.M
Pasquel, D
Roux, S
Tisseyre, B
Taylor, J.A
Raeth, P.G
Topics
Precision Nutrient Management
Precision Nutrient Management
Geospatial Data
Wireless Sensor Networks and Farm Connectivity
Type
Poster
Oral
Year
2012
2014
2022
2024
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1. Precision Nutrient Management in Cotton- A Case Study from India

Cotton is being one of the important commercial crops in India, farmers have adopted cultivating hybrid cotton to achieve higher yield. In this context, cotton is becoming input intensive crop... U. Shanwad, V. H, R. N.l., P.S. Kanannnavar, S. Swamy, M.B. Patil

2. Variable-Rate Application Of Nitrogen And Potassium Fertilizers In Louisiana Sugarcane Production Systems.

If sugar and cane yields are to be optimized and profitability improved, it is critical that a sugarcane crop receive the proper levels of plant nutrients.  Under-fertilization can result in reduced cane yields, while over-fertilization can reduce sugar recovery.  In addition, improper fertilization may increase crop susceptibility to environmental stresses and disease and insect pests. Nitrogen (N) continues to be one of the most important and costly... B.J. Viator, R.M. Johnson

3. Comparison of Different Aspatial and Spatial Indicators to Assess Performance of Spatialized Crop Models at Different Within-field Scales

Most current crop models are point-based models, i.e. they simulate agronomic variables on a spatial footprint on which they were initially designed (e.g. plant, field, region scale). To assess their performances, many indicators based on the comparison of estimated vs observed data, can be used such as root mean square error (RMSE) or Willmott index of agreement (D-index) among others. However, shifting model use from a strategic objective to tactical in-season management is becoming a significant... D. Pasquel, S. Roux, B. Tisseyre, J.A. Taylor

4. LoRa Flood-messaging Sensor-data Transport

The practice of precision agriculture assumes the ability to place and monitor sensors. Remote monitoring is often employed as a means of alleviating tedious manual data gathering and recording. For remote monitoring to work, there has to be some automated means of reading sensor values and transmitting them to a basestation, someplace where the data is recorded and analyzed. If the data are recorded and analyzed at the point of sensing, some means is still required to send the results to wherever... P.G. Raeth