Publications by authors named "G B M Heuvelink"
Maize () is an important staple crop for food security in Sub-Saharan Africa. However, there is need to increase production to feed a growing population. In Ghana, this is mainly done by increasing acreage with adverse environmental consequences, rather than yield increment per unit area.
View Article and Find Full Text PDFSoil organic carbon (SOC) plays a vital role in global carbon cycling and sequestration, underpinning the need for a comprehensive understanding of its distribution and controls. This study explores the importance of various covariates on SOC spatial distribution at both local (up to 1.25 km) and continental (USA) scales using a deep learning approach.
View Article and Find Full Text PDFArticle Synopsis
- Monitoring soil organic carbon (SOC) is essential for understanding soil dynamics and supporting climate change research, with machine learning (ML) and process-oriented (PO) models offering different strengths.
- While ML excels in spatial predictions, it struggles with temporal changes, whereas PO models leverage mechanistic insights to track SOC over time.
- A new hybrid model combining PO and ML approaches was developed for predicting topsoil SOC in eastern China, showing improved accuracy compared to using ML alone and providing valuable insights for soil management and policy in a changing climate.*
Article Synopsis
- - The study emphasizes the importance of quickly detecting new infections for effective outbreak management and highlights how human mobility affects infection risks and spread, using spatial sampling to guide testing efforts in specific areas.
- - Researchers combined mobility data with different spatial sampling methods to optimize testing strategies for emerging infections, testing their effectiveness through analysis of real and simulated outbreak scenarios.
- - Results show that using case flow and transmission intensity data can significantly reduce the number of tests needed while maintaining accuracy, making this approach a cost-effective way to enhance community-level infection detection.
River discharges are often predicted based on a calibrated rainfall-runoff model. The major sources of uncertainty, namely input, parameter and model structural uncertainty must all be taken into account to obtain realistic estimates of the accuracy of discharge predictions. Over the past years, Bayesian calibration has emerged as a suitable method for quantifying uncertainty in model parameters and model structure, where the latter is usually modelled by an additive or multiplicative stochastic term.
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