Management practices influence the competitive potential of weed communities and their value to biodiversity in South African vineyards.

Weeds have negative impacts on crop production but also play a role in sustaining biodiversity in agricultural landscapes. This trade‐off raises the question of whether it is possible to promote weed communities with low competitive potential but high value to biodiversity. Here, we explored how weed communities respond to different vineyard management practices in South Africa's Western Cape, aiming to identify whether any specific practices are associated with more beneficial weed communities. Eight weed community characteristics representative of abundance, diversity and functional composition were used as indicators of competitive potential and biodiversity value. We explored how these responded to farm management strategy (organic, low input or conventional) and weed management practices (herbicides, tillage, mowing or combinations of these) using ordination and mixed models. Mown sites were associated with weed communities of high biodiversity value, with higher weed cover in both winter and summer, higher diversity and more native weeds. Mowing also promoted shorter weeds than either tillage or herbicides, considered to be less competitive with grapevines. However, high summer weed cover may be problematic where competition for water is critical, in which case tillage offers a method to limit summer weed cover that did not adversely affect diversity or native weeds. In contrast, herbicide‐treated sites had characteristics indicative of a lower biodiversity value and higher potential for competitiveness with few native weeds, lower diversity and relatively tall, small‐seeded weeds. Mowing in winter combined with tillage in spring may thus optimise the biodiversity benefits and production costs of Western Cape vineyard weeds

Isoelastic Agents and Wealth Updates in Machine Learning Markets

Recently, prediction markets have shown considerable promise for developing flexible mechanisms for machine learning. In this paper, agents with isoelastic utilities are considered. It is shown that the costs associated with homogeneous markets of agents with isoelastic utilities produce equilibrium prices corresponding to alpha-mixtures, with a particular form of mixing component relating to each agent's wealth. We also demonstrate that wealth accumulation for logarithmic and other isoelastic agents (through payoffs on prediction of training targets) can implement both Bayesian model updates and mixture weight updates by imposing different market payoff structures. An iterative algorithm is given for market equilibrium computation. We demonstrate that inhomogeneous markets of agents with isoelastic utilities outperform state of the art aggregate classifiers such as random forests, as well as single classifiers (neural networks, decision trees) on a number of machine learning benchmarks, and show that isoelastic combination methods are generally better than their logarithmic counterparts.Comment: Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012

The supervised hierarchical Dirichlet process

We propose the supervised hierarchical Dirichlet process (sHDP), a nonparametric generative model for the joint distribution of a group of observations and a response variable directly associated with that whole group. We compare the sHDP with another leading method for regression on grouped data, the supervised latent Dirichlet allocation (sLDA) model. We evaluate our method on two real-world classification problems and two real-world regression problems. Bayesian nonparametric regression models based on the Dirichlet process, such as the Dirichlet process-generalised linear models (DP-GLM) have previously been explored; these models allow flexibility in modelling nonlinear relationships. However, until now, Hierarchical Dirichlet Process (HDP) mixtures have not seen significant use in supervised problems with grouped data since a straightforward application of the HDP on the grouped data results in learnt clusters that are not predictive of the responses. The sHDP solves this problem by allowing for clusters to be learnt jointly from the group structure and from the label assigned to each group.Comment: 14 page