Showing papers by "Karin Müller published in 2020"

Time series of remote sensing and water deficit to predict the occurrence of soil water repellency in New Zealand pastures

Abstract: Soil water repellency (SWR) is a natural phenomenon occurring in soils throughout the world, which impacts upon ecosystem services at multiple temporal and spatial scales (nano to ecosystem scale). In pastures, the development of SWR is primarily determined by the cycling of hydrophobic materials at the soil surface, and is controlled by climate, soil and water management, and soil properties. The complex interactions between these factors make it an intricate system to understand and model. Detailed spatiotemporal characterization of the surface moisture and biomass in pastoral ecosystems would allow for a better understanding of this phenomenon. Normalized Difference Vegetation Index (NDVI) and Synthetic Aperture Radar (SAR) backscatter are good predictors for surface biomass and soil moisture, respectively. Machine learning on remote sensing time series (TS) data shows promise to predict the occurrence of SWR in pastures. This study evaluates the ability of remote sensing TS data to predict the occurrence of SWR in New Zealand pastures, using three machine learning algorithms. Soil water repellency data were collected from 58 pastoral sites. Machine learning models were trained and cross-validated on a monthly aggregated remote sensing and water deficit TS data to predict SWR level. Prediction output from artificial neural networks (ANN), random forest (RF), and support vector machine (SVM) were compared using root mean squared error (RMSE). When using NDVI TS data from 58 site as predictors of SWR, SVM and RF (RMSE = 0.82 and 0.87, respectively) outperformed ANN (RMSE = 1.23). Random forest was used to map SWR magnitude over Hawke’s Bay region in the North Island of New Zealand, and the overall accuracy was equal to 86%. This study is the first investigation implicating remote sensing TS data to predict the occurrence of SWR at the regional scale. Mapping the potential SWR will aid in identifying critical zones of SWR, to attenuate its effect on pastures through adapted management.

Effect of deferred grazing during late spring and summer on pasture productivity in Waikato and Bay of Plenty hill country

Abstract: Deferred grazing is a commonly used tool to manage feed surpluses. The effect of deferred grazing on pasture nutritive value and productivity was quantified in a split-paddock trial on three hill country farms in Waikato and Bay of Plenty from October 2018 until May 2020. Livestock were excluded from the deferred pasture between mid-October 2018 and March 2019. Thereafter, both treatments were rotationally grazed in common with cattle or sheep depending on the farm. Total annual dry matter production was 15% greater in the deferred than grazed treatment for the 12 months after deferring (8.9 vs 7.7 t DM/ha, P<0.05). Metabolisable energy (ME) values at the end of the deferred period were lower in the deferred than grazed treatment (P<0.01) but similar in both treatments thereafter. The content of legumes other than white clover (Trifolium repens) was higher in deferred than grazed pastures in spring 2019 on one of the farms (treatment × farm interaction P<0.05). Ground cover of perennial ryegrass was greater and the area of bare ground smaller, in the deferred than grazed treatment on three of five occasions from after the beginning of the deferred period until up to 8 months after deferring (P<0.05). There was no difference between treatments in decomposition and stabilisation of organic matter (P>0.05). The topsoil water content was higher in the deferred than grazed treatment for 12 months after deferring. In comparison to regular grazing between October and March, deferred pastures provided drought feed in autumn 2019. Pasture productivity was increased after the deferred period without negative impacts on ME.