A5036813192- Soil Geostatistics and Mapping
- Soil Carbon and Nitrogen Dynamics
- Soil and Land Suitability Analysis
- Plant and animal studies
- Forest ecology and management
- Insect Utilization and Effects
- Plant Genetic and Mutation Studies
- Soil and Unsaturated Flow
- Smart Agriculture and AI
- Plant-Microbe Interactions and Immunity
- Phytase and its Applications
- Remote Sensing and LiDAR Applications
- Image Processing and 3D Reconstruction
- Hydrology and Watershed Management Studies
- Polar Research and Ecology
- Remote Sensing in Agriculture
- Plant tissue culture and regeneration
- Lepidoptera: Biology and Taxonomy
Stellenbosch University
2019-2024
ISRIC - World Soil Information
2024
Wageningen University & Research
2021-2024
In digital soil mapping (DSM), maps are usually produced in a univariate manner, that is, each map is independently and therefore, when multiple properties mapped the underlying dependence structure between these ignored. This may lead to inconsistent predictions simulations. For example, organic carbon (SOC) total nitrogen (TN) show unrealistic carbon–nitrogen (C:N) ratios. last decade production of with machine learning models has become increasingly popular as able capture complex...
This paper presents a two-stage maximum likelihood framework to deal with measurement errors in digital soil mapping (DSM) when using machine learning (ML) model. The is implemented random forest and projection pursuit regression illustrate two different areas of learning, i.e. ensemble trees feature-learning. In our proposed framework, error variance (MEV) incorporated as weight the log-likelihood function so that measurements larger MEV receive less ML model calibrated. We evaluate...
Abstract In digital soil mapping, modelling thickness poses a challenge due to the prevalent issue of right‐censored data. This means that true exceeds depth sampling, and neglecting account for censored nature data can lead poor model performance underestimation thickness. Survival analysis is well‐established domain statistical deal with The random survival forest notable example survival‐related machine learning approach used address property in mapping. Previous studies employed this...
Abstract Soil microbes are essential for soil nutrient cycling. However, frequent tillage and the use of synthetic agrochemicals can reduce microbial diversity enzyme activity. In this study, effects four treatments (mouldboard plough, shallow tine‐tillage, no‐tillage, rotation) two rates (standard reduced, with biostimulants) on activity were investigated between 2018 2020 in a Mediterranean climate zone South Africa. It was hypothesized that reduction frequency quantity agrochemical...