Concurrent temporal stability of the apparent electrical conductivity and soil water content

Abstract Knowledge of spatio-temporal soil-water content (SWC) variability in agricultural fields is useful for improving crop management. Spatial patterns of SWC can be characterized using temporal stability analysis of difficult-to-obtain data from high spatial density and temporal frequency. Soil apparent electrical conductivity (ECa) measurements with high spatial density have been widely used to infer the spatial variability of SWC. The objective of this work is to test the hypothesis that temporal stability of ECa can be demonstrated and that relationships between temporal stability characteristics of SWC and ECa can be established. Apparent electrical conductivity and topsoil gravimetric SWC ( θ ) were periodically measured in an olive orchard in southwest Spain on 6 and 18 occasions, respectively. A temporal stability analysis of ECa elucidated three zones where ECa was close to, consistently substantially smaller than, and substantially larger than the spatial average ECa throughout the study period. Representative locations for θ were found with a chance of 75% within the representative zone for ECa. Yet, the driest locations, with consistently smaller θ than the field average ( 〈 θ 〉 ), could be successfully identified (89%) within the zone with consistently smaller ECa than average. The θ  −  〈 θ 〉 relations showed generally a linear behaviour, although a better fit was obtained at the highest θ using either exponential or power law equations at half of the locations. The former provided the best fit within the zone with ECa consistently smaller than average, while the latter performed best in the zone with ECa consistently larger than average. The linear equation provided the best fit within the representative ECa zone. This study demonstrates that temporal stability characteristics of ECa and SWC are linked and that ECa surveys can be used to delimit zones with representative locations for SWC measurement or estimation. Such information is of importance for a range of agricultural applications, i.e . irrigation, crop protection, fertilizer management, and soil and water conservation.