Abstract
ABSTRACT: Optimization of N management is one of the great challenges to be overcome in grain production, as it is directly related to productivity and can also cause environmental damage. Precision agriculture aims to solve this problem by applying nitrogen fertilizer at varying rates. Reflectance sensors are instruments capable of estimating N needs in various crops, including grain crops. However, it is not clear how these sensors perform under varying solar radiation and cloud cover, due to a lack of research on their temporal variability. Thus, this study examined the temporal variability of the NDVI (normalized difference vegetation index), as measured by an active reflectance sensor, in both soybean and wheat crops. The NDVI data were collected using a GreenSeeker sensor every 15 minutes over 12 or 14 consecutive hours. Incident solar radiation was recorded using an Instrutherm MES-100 pyranometer. In all experiments in soybean and wheat, NDVI was negatively influenced by irradiation, showing higher values at the beginning and end of the day. Changes in cloud cover also affected NDVI values during the experiments.
Highlights
Optimizing N management remains one of the greatest challenges in grain production
The model defined by exploratory data analysis for wheat (Equation 5) indicates that normalized difference vegetation index (NDVI) as a dependent variable is influenced by the stage of development of the crop, solar irradiation, instantaneous transparency index (ITI) and ITI*ITI
The NDVI coefficients of variation determined in soybean stages R1 and R2 were 2.8% and 1.8%, respectively, which can be considered low (Table 1)
Summary
Optimizing N management remains one of the greatest challenges in grain production. Because productivity is directly related to N availability (BARRACLOUGH et al, 2010), N is often applied in excess (DELLINGER et al, 2008, SCHMIDT et al, 2011), causing financial losses and environmental damage due to leaching and groundwater contamination (BURKART & JAMES, 1999; HONG et al, 2007). Among the technologies used in precision agriculture is the application of fertilizer at varying rates, in which the appropriate amount of fertilizer is identified and applied at each point of the cultivated area (THRIKAWALA et al, 1999; MULLA, 2013; SAPKOTA et al, 2014) This approach can increase productivity and reduce its environmental impact (SCHARF et al, 2011; LI et al, 2014). There are studies indicating that even active sensors can be influenced by the time of day and the weather conditions, creating distortions in the collected data (SCHARF et al, 2010; KIPP et al, 2014; OLIVEIRA & SCHARF, 2014; ELSAYED et al, 2015) To confront this challenge, this work investigated the changes in the measurements made by an active reflectance sensor in soybean and wheat crops during different times of day, with consequent variation in solar radiation and cloud cover
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