Matt Aitkenhead

TL;DR: Key challenges in modeling soil processes are identified, including the systematic incorporation of heterogeneity and uncertainty, the integration of data and models, and strategies for effective integration of knowledge on physical, chemical, and biological soil processes.

TL;DR: In this article, the authors describe the state of the art of soil spectroscopy as well as its potential to facilitate soil monitoring, and highlight that the widespread use of spectroscopes to monitor the status of the soil should be encouraged by the creation of a standard for the collection of laboratory soil spectra, to promote the sharing of spectral libraries, and to scan existing soil archives.

TL;DR: Two methods were applied to recognise carrot seedlings from those of ryegrass and Fat Hen using digital imaging, showing that a neural network-based methodology exists which allows the system to learn and discriminate between species to an accuracy exceeding 75% without predefined plant descriptions being necessary.

TL;DR: An evolutionary method is presented which allows decision tree flexibility through the use of co-evolving competition between the decision tree and the training data set, which gives results comparable with or superior to other classification methods.

TL;DR: In this paper, the authors present an evaluation of the uncertainty expected in national-scale simulations of Scotland, using data from the National Soil Inventory of Scotland (NSIS), which provides measurements of C change for the range of soils, climates and land-use types found across Scotland.