A Combined Approach of Sensor Data Fusion and Multivariate Geostatistics for Delineation of Homogeneous Zones in an Agricultural Field

Abstract

To assess spatial variability at the very fine scale required by Precision Agriculture, different proximal and remote sensors have been used. They provide large amounts and different types of data which need to be combined. An integrated approach, using multivariate geostatistical data-fusion techniques and multi-source geophysical sensor data to determine simple summary scale-dependent indices, is described here. These indices can be used to delineate management zones to be submitted to differential management. Such a data fusion approach with geophysical sensors was applied in a soil of an agronomic field cropped with tomato. The synthetic regionalized factors determined, contributed to split the 3D edaphic environment into two main horizontal structures with different hydraulic properties and to disclose two main horizons in the 0–1.0-m depth with a discontinuity probably occurring between 0.40 m and 0.70 m. Comparing this partition with the soil properties measured with a shallow sampling, it was possible to verify the coherence in the topsoil between the dielectric properties and other properties more directly related to agronomic management. These results confirm the advantages of using proximal sensing as a preliminary step in the application of site-specific management. Combining disparate spatial data (data fusion) is not at all a naive problem and novel and powerful methods need to be developed.

Effect of Three Types of Exogenous Organic Carbon on Soil Organic Matter and Physical Properties of a Sandy Technosol

“…Spatial variability in soil properties within the same field is commonly observed and has various origins [45,46]. We sampled field soil on two successive dates to reproduce the microbial behavior that occurs when mixing fresh organic products with soils.…”

Use of Farmer Knowledge in the Delineation of Potential Management Zones in Precision Agriculture: A Case Study in Maize (Zea mays L.)

“…In terms of farmers as well as food and agricultural companies, progressively more and more users are starting to use some kind of PA-based system [3]. Many of them are focused on automation of agricultural tasks, remote sensing crop monitoring (e.g., [4]), and on segmentation of within-field variability to delineate potential management zones [5,6]. Nevertheless, although PA is little by little gaining weight in the day-to-day of farms, there is still a big gap between scientific research and real implementation and/or full adoption.…”

Climate Changes Challenges to the Management of Mediterranean Montado Ecosystem: Perspectives for Use of Precision Agriculture Technologies

“…According to Moral et al [44] modelling the relationships between primary soil variables and EC a is not an easy task due to the dependency of EC a on various soil properties, over different spatial scales. Castrignanò et al [43] reinforces that such EC a measurements are generally affected by more than one agronomic soil characteristic, consequently, obtaining accurate information about one property by using only one sensing technique is extremely difficult. Also, Corwin and Lesch [21] and Sudduth et al [22] alert to some inconsistency in the relationship between EC a and soil characteristics.…”

“…The core of PA is effectively managing spatial and temporal variability related to all aspects of agricultural production for the purpose of improving crop performance and environmental quality. Therefore, assessing soil and crop variability is the first critical step and a necessary condition in PA [43]. Quantifying spatial variation of forage biomass yield, vegetation quality, and soil properties can help improve pasture management practices such as grazing rotations, nutrient management, and yield prediction [18].…”