Abstract
A preliminary study was conducted to analyze the sustainability of barley production through: (i) investigating sensor-based nitrogen (N) application on barley performance, compared with conventional N management (CT); (ii) assessing the potential of the Normalized Difference Vegetation Index (NDVI) at different growth stages for within-season predictions of crop parameters; and (iii) evaluating sensor-based fertilization benefits in the form of greenhouse gasses mitigation. Barley was grown under CT, sensor-based management (RF) and with no N fertilization (Control). NDVI measurements and RF fertilization were performed using a GreenSeeker™ 505 hand-held optical sensor. Gas emissions were measured using a static chamber method with a portable gas analyzer. Results showed that barley yield was not statistically different under RF and CF, while they both differed significantly from Control. Highly significant positive correlations were observed between NDVI and production parameters at harvesting from the middle of stem elongation to the medium milk stage across treatments. Our findings suggest that RF is able to decrease CO2 emission in comparison with CF. The relationship between N fertilization and CH4 emission showed high variability. These preliminary results provide an indication of the benefits achieved using a simple proximal sensing methodology to support N fertilization.
Highlights
In recent years, one of the most investigated solutions for improving fertilization efficiency involves taking into account the spatial and temporal crop and soil variability within a field, in an approach called “precision farming management” [1]
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Summary
One of the most investigated solutions for improving fertilization efficiency involves taking into account the spatial and temporal crop and soil variability within a field, in an approach called “precision farming management” [1]. This approach is the result of the development and implementation of various technologies, such as the Global Positioning System (GPS), Geographic Information System (GIS), automatic control, variable rate technologies, proximal and remote sensing, computer-driven control devices and telecommunications [2]. The first approach is more popular among farmers, the real-time method is currently receiving attention due to recent developments in proximal sensing technologies. Randelovicet al. [5] used a machine learning model and vegetation indices extracted by a Unmanned
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