Browsing by Author "Devantier B"

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    Biochar increases soil enzyme activities in two contrasting pastoral soils under different grazing management
    (CSIRO Publishing, 2022-12) Garbuz S; Mackay A; Camps-Arbestain M; Devantier B; Minor M; Solaiman Z
    Context: Soil enzyme activities are key regulators of carbon and nutrient cycling in grazed pastures. Aims: We investigated the effect of biochar addition on the activity of seven enzymes involved in the carbon, nitrogen and phosphorus cycles in a Sil-Andic Andosol and a Dystric Cambisol under permanent pastures. Methods: The study consisted of a one-year field-based mesocosm experiment involving four pastures under different nutrient and livestock practices: with and without effluent under dairy cow grazing on the Andosol, and with either nil or high phosphorus fertiliser input under sheep grazing on the Cambisol. Soil treatments were: (1) willow biochar added at 1% w/w; (2) lime added at the liming equivalence of biochar (positive control); (3) no amendments (negative control). Key results: Compared with the Cambisol, the Andosol had higher dehydrogenase, urease, alkaline and acid phosphatase and, especially, nitrate-reductase activities, aligning with its higher pH and fertility. In both soils, biochar addition increased the activity of all enzymes, except for acid phosphatase and peroxidase; lime addition increased peroxidase and nitrate-reductase activity. Conclusions: The increased enzyme activity was strongly positively correlated with soil biological activity following biochar addition. Biochar caused a 40-45% increase in cellulase activity, attributed to increased root biomass following biochar addition. The response in acid and alkaline phosphatase activity can be attributed to the impact of biochar and lime addition on soil pH. Implications: The results provide more insights in realising the potential benefits of biochar to the provision of ecosystem services for grazed pastures.
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    Effects of spatial data resolution on the modelling and mapping of soil organic carbon content in hill country grassland landscapes
    (John Wiley and Sons Ltd on behalf of British Society of Soil Science, 2024-01-19) Tran DX; Dominati E; Lowry J; Mackay A; Vibart R; Pearson D; Devantier B; Noakes E
    Limited use has been made of spatially explicit modelling of soil organic carbon (SOC) in highly complex farmed landscapes to advance current mapping efforts. This study aimed to address this gap in knowledge by evaluating the spatial prediction of SOC content in the 0–75 mm soil depth in hill country landscapes in New Zealand (NZ) using point-based training data, along with topographic covariates and Sentinel 2 spectral band ratios using an automated set of machine learning (AutoML) tools in ArcGIS. Subsequently, it also focused on quantifying the effects of spatial data resolution (i.e., 1, 8, 15, and 25 m) in terms of predicted map accuracy. Farmlets with contrasting phosphorus fertilizer and sheep grazing histories located at the Ballantrae Hill Country Research Station, NZ were selected to conduct the research. Six candidate algorithms incorporated in the AutoML tools (i.e., XGBoost, LightGBM, linear regression, decision trees, extra trees, random forest) and ensemble model were utilized to model the spatial pattern of SOC content. The results show that the ensemble model that combine predictions of various algorithms applied for 1 m data resolution enables the highest performance and accuracy (i.e., R2 =.76, RMSE = 0.66%). Among the predictive variables used in the model, slope, wetness, and topographic position indices were found to be the most important topographical features that explain SOC patterns in the study area. Inclusion of spectral indices derived from remote sensing, including surface soil moisture and clay minerals ratio, made further improvement to the SOC content prediction. The study reveals that a decrease in the resolution of the geospatial data does not substantively affect the mean SOC content estimation of a farm-scale modelling. However, using coarser resolution data reduces the ability of the model to predict changes in the spatial pattern of SOC content across a hill country grassland landscape.