This paper reviews animal treading and the associated effects on soil physical properties and pasture productivity from treading-induced soil compaction and pugging. Response curve relationships between soil physical properties (e.g. macroporosity, air-filled porosity, bulk density) and pasture and crop yield are reviewed. Optimum soil macroporosity for maximum pasture and crop yield ranges from 6 to 17% v/v, but there is a paucity of yield response curves for pastoral systems, particularly critical or optimum values of soil physical properties. There is little information available on the effects of cattle treading on soil physical properties and consequently pasture yield in seasons when soil pugging and poaching is minimised. Such information is needed to provide practical and rigorously tested decision support tools for land managers during grazing seasons. Knowledge of yield response curves, and critical or optimum values of soil physical properties for field pasture-based grazing systems, is required for improved farm-system production and economic decision support.

No abstract

No abstract

No abstract

We investigated a relatively unexplored area of soil science: the fitting of parameterized models to particle-size distribution (a subject more thoroughly explored in sedimentology). Comparative fitting of different models requires the use of statistical indices enabling rational selection of an optimum model, i.e., a model that balances the improvement in fit often achieved by increasing the number of parameters,/>, against model simplicity retained by minimizing p. Five models were tested on cumulative mass-size data for 71 texturally diverse New Zealand soils: a one-parameter (p = 1) Jaky model borrowed from geotechnics; the standard lognormal model (p = 2); two modified lognormal models (each withp= 3); and the bimodal lognormal model (p = 3). The Jaky and modified lognormal models have not previously been introduced into the soil science literature. Three statistical comparators were used: the coefficient of determination, R 2 ; the F statistic; and the C f statistic of Mallows. The bimodal model and one modified lognormal model (denoted ORL) best fit the data. The bimodal model gave a marginally better fit, but incorporates a sub-clay mode (untestable with the present data), so we adopted the ORL model as the physically best benchmark for comparison of other models. The simple Jaky oneparameter model gave a good fit to data for many of the soils, better than the standard lognormal model for 23 soils. The model comparison methods described have potential utility in other areas of soil science. The C p statistic is advocated as the best statistic for model selection.A FREQUENT NEED in soil science is to fit parameterized models to data. Examples include the fitting of adjustable, analytic functions to data for the soil moisture characteristic, hydraulic conductivity function, or PSD. Often several candidate models exist, posing the problem of choice. In general, algorithms for fitting such models minimize an aggregated discrepancy between observed and model-estimated data. A lower bound to this discrepancy is set by experimental errors in the observed data. Often (though not always), increasing/; in a model will improve the fit; however, increasing/? may sacrifice simplicity and utility of the model, and may simply be an empirical expedient for conforming the model to fit the data. The first test for admitting an additional parameter is to check for its statistical significance. This can be done via a Student's Mest or Wald test (Gallant, 1987). Failure in this test means the additional parameter overparameterizes the model. Also, if the aggregate error produced by the model is less than random experimental error, the model is again overparameterized, though in a different sense. Selection of an optimum model from a group thus requires use of a sensitive discriminating statistic. Here, an optimum model is defined as one selected by balancing the minimization of some objective function (measuring aggregate discrepancy) against minimization of p.We explored the application of new parametric Mallows (1...