Abstract
Soil fertility attributes have different scales and forms of spatial and temporal variations in agricultural fields. Adequate spatiotemporal characterization of these attributes is fundamental to the successful development of strategies for variable rate application of fertilizers, enabling the classic benefits of precision agriculture (PA). Studies on Brazilian soil have shown that at least 1 sample ha-1 is required for the reliable mapping of key fertility attributes. However, this sampling density is difficult owing to the operational challenges of sample collection and the cost of laboratory analyses. Given this limitation, soil sensors have emerged as a practical and complementary technique for obtaining information on soil attributes, at high spatial density, without the production of chemical residues and at a reduced cost. Scientists worldwide have devoted their attention to the development and application of sensor systems for this purpose. The concept of proximal soil sensing (PSS) was established in 2011 and involves the application of soil sensors directly on the field. PSS techniques involve different disciplines, such as instrumentation, data science, geostatistics, and predictive modeling. The integration of these different disciplines has allowed successful sensor application for the spatial diagnosis of soil fertility attributes. The present work aimed to present a bibliographic review of the concepts involved and main techniques used in soil sensing to predict fertility attributes. We sought to present a broad view of the challenges, advances, and perspectives of sensor application in Brazilian tropical soils in the context of PA.
Highlights
Linked to the technological advances of the last decades and considered by some authors as one of the top 10 revolutions in agriculture in the last 50 years (Crookston, 2006; Mulla, 2013), precision agriculture (PA) advocates the adequate treatment of spatial and temporal variability of crops (Molin et al, 2015)
The innovative appeal is related to recent advances in nano and microengineering, which have enabled the construction of equipment with reduced weight and size, greater robustness, and at affordable prices, making them more compatible with in situ works (Dhawale et al, 2015)
For a proper diagnosis of a specific soil attribute using grid sampling, researchers recommend that the minimum spacing between samples should be equal to or less than half of the spatial dependence range (Molin et al, 2015), i.e., a sampling density that is greater than 1 sample ha-1
Summary
Linked to the technological advances of the last decades and considered by some authors as one of the top 10 revolutions in agriculture in the last 50 years (Crookston, 2006; Mulla, 2013), precision agriculture (PA) advocates the adequate treatment of spatial and temporal variability of crops (Molin et al, 2015). The innovative appeal is related to recent advances in nano and microengineering, which have enabled the construction of equipment with reduced weight and size, greater robustness, and at affordable prices, making them more compatible with in situ works (Dhawale et al, 2015) Another challenge is to take advantage of the current knowledge regarding the use of each sensor and adapt it to use these sensors directly in the field as a tool for the management of fertility of tropical soils. New technologies to characterize spatial variability in soil, e.g. gamma-rays (Castrignanò et al, 2012), visible and near-infrared spectroscopy (vis-NIR) (Mouazen & Kuang, 2016), and X-ray fluorescence (XRF) (Nawar et al, 2019) have been gaining attention from scientists These new technologies, as well as the development of statistical techniques, multivariate geostatistics, and artificial intelligence, have greatly increased the ability to collect, analyze, and predict spatial information related to soils (Brevik et al, 2016). Sensor systems for mapping soil fertility attributes: challenges, advances, and perspectives in Brazilian tropical soils
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