The Use of Crop Yield Autocorrelation Data as a Sustainable Approach to Adjust Agronomic Inputs

Agricultural fields have natural within-field soil variations that can be extensive, are usually contiguous, and are not always traceable. As a result, in many cases, site-specific attention is required to adjust inputs and optimize crop performance. Researchers, such as agronomists, agricultural en...

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Bibliographic Details
Published in:Sustainability 2021-02, Vol.13 (4), p.2362
Main Authors: Koutsos, Thomas M., Menexes, Georgios C., Mamolos, Andreas P.
Format: Article
Language:English
Subjects:
Agricultural engineering
Agricultural equipment
Agricultural land
Agricultural production
Agriculture
Agronomy
Autocorrelation
Case studies
Corn
Crop yield
Crops
Data processing
Decision making
Farmers
Management decisions
Rain
Spatial analysis
Statistical analysis
Sustainability
Sustainable development
Sustainable yield
Tractors
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Summary:Agricultural fields have natural within-field soil variations that can be extensive, are usually contiguous, and are not always traceable. As a result, in many cases, site-specific attention is required to adjust inputs and optimize crop performance. Researchers, such as agronomists, agricultural engineers, or economists and other scientists, have shown increased interest in performing yield monitor data analysis to improve farmers’ decision-making concerning the better management of the agronomic inputs in the fields, while following a much more sustainable approach. In this case, spatial analysis of crop yield data with the form of spatial autocorrelation analysis can be used as a practical sustainable approach to locate statistically significant low-production areas. The resulted insights can be used as prescription maps on the tractors to reduce overall inputs and farming costs. This aim of this work is to present the benefits of conducting spatial analysis of yield crop data as a sustainable approach. Current work proves that the implementation of this process is costless, easy to perform and provides a better understanding of the current agronomic needs for better decision-making within a short time, adopting a sustainable approach.
ISSN:2071-1050
2071-1050
DOI:10.3390/su13042362