Integrating soil moisture measurements into pasture growth forecasting in New Zealand’s hill country

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Forecasting pasture growth in hill country landscapes requires information about soil water retention characteristics, which will help to quantify both water uptake, and its percolation below the root zone. Despite the importance of soil moisture data in pasture productivity predictions, current models use low-resolution estimates of water input into their soil water balance equations and plant growth simulations. As a result, they frequently fail to capture the spatial and temporal variability of soil moisture in hill country soils. Wireless Sensor Networks (WSN) are promising in-situ measurement systems for monitoring soil moisture dynamics with high temporal resolution in agricultural soils. This paper presents the deployment of a soil moisture sensing network, utilising WSN technology and multi-sensor probes, to monitor soil water changes over a hill country farm in the northern Wairarapa region of the North Island. Processed capacitance-based raw data was converted to volumetric water content by means of a factory calibration function to assess sensor accuracy and to calculate soil water storage within the pasture root zone. The derived volumetric soil moisture data was examined in terms of its dependence on the variability and influences of hill country landscape characteristics such as aspect. The integration of spatially distributed sensors and multi-depth soil moisture measurements from various hillslope positions showed that slope and aspect exerted a significant impact on soil moisture values. Furthermore, considerable differences were identified in soil water profile responses to significant rainfall events and subsequent soil water redistribution. Initial indications are that high-resolution time series of accurate multi-depth soil moisture measurements collected by a WSN are valuable for investigating root zone water movement. Sensor evaluation and data analysis suggest that these devices and their associated datasets are able to contribute to an improved understanding of drying and wetting cycles and soil moisture variability. Potentially, this will create an opportunity to generate improved pasture growth predictions in pastoral hill country environments.