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Algorithm for Variable‐Rate Nitrogen Application in Sugarcane Based on Active Crop Canopy Sensor
Author(s) -
Amaral Lucas R.,
Molin José P.,
Schepers James S.
Publication year - 2015
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj14.0494
Subject(s) - canopy , saccharum , biomass (ecology) , agronomy , crop , plant canopy , yield (engineering) , environmental science , crop yield , mathematics , range (aeronautics) , row crop , agricultural engineering , biology , engineering , agriculture , botany , ecology , materials science , metallurgy , aerospace engineering
Nitrogen fertilization is challenging for sugarcane ( Saccharum spp.) producers due to its complex interaction with the crop and soil. Thus, the main goal of this study was to develop a feasible approach to guide variable‐rate N application in sugarcane based on canopy sensor readings. This study was conducted for 5 yr. Several plot and strip N‐rate experiments were conducted under a wide range of crop conditions in Brazil and evaluated with the Crop Circle active canopy sensor (Holland Scientific Inc.). Because of variability in crop density and growth development within sugarcane fields, the use of an N‐rich reference area to estimate the crop response to N application was compromised. Biomass was the main crop parameter influencing canopy sensor readings, allowing yield estimation because biomass typically results in stalk yield. Thus, canopy sensor readings can efficiently predict relative sugarcane yield when working with data that are normalized to the mean for the field. Hence, an algorithm that takes into account this relationship was established. The concept of this algorithm is to apply higher N fertilization rates where the sugarcane yield potential is higher. Such an approach was determined to be useful to guide N application in sugarcane fields. Nevertheless, field validation is needed to confirm this N management strategy. Besides, more information about sugarcane biomass variability within fields may be required to increase algorithm efficiency.