The influence of planting density on the production of 'Goldfinger' (Musa spp., AAAB) in the subtropics

'Goldfinger', a tetraploid banana produced from the Fundación Hondureña de Investigación Agrícola (FHIA) breeding program, was released to the Australian industry in 1995. It was promoted as an apple-flavoured dessert banana with resistance to Fusarium wilt race 1 and subtropical race 4, as well as resistance to black and yellow Sigatoka (Mycosphaerella fijiensis and M. musicola, respectively). This study was initiated to provide agronomic information to the banana industry, which was under threat from Fusarium wilt, on a new cultivar which could replace 'Williams' (AAA, Cavendish subgroup) or 'Lady Finger' (AAB, Pome subgroup) in those areas affected by Fusarium wilt. Also few studies had reported on the production characteristics of the new tetraploid hybrids, especially from subtropical areas, and therefore two field sites, one a steep-land farm and the other a level, more productive site, were selected for planting density and spatial arrangement treatments. The optimum density in terms of commercial production, taking into account bunch weight, finger size, length of the production cycle, plant height and ease of management, was 1680 plants/ha on the steep-land site where plants were planted in single rows with 2.5 m × 2.5 m spacings. However on the level site a double-row triangular layout with inter-row distances of 4.5 m to allow vehicular access (1724 plants/ha) gave the best results. With this arrangement plants were in an alternate, triangular arrangement along a row and a spacing of 1.5 m between plants at the points of each triangle and between each block of triangles.

A comparison of plotless density estimators using Monte Carlo simulation on totally enumerated field data sets

Background: Plotless density estimators are those that are based on distance measures rather than counts per unit area (quadrats or plots) to estimate the density of some usually stationary event, e.g. burrow openings, damage to plant stems, etc. These estimators typically use distance measures between events and from random points to events to derive an estimate of density. The error and bias of these estimators for the various spatial patterns found in nature have been examined using simulated populations only. In this study we investigated eight plotless density estimators to determine which were robust across a wide range of data sets from fully mapped field sites. They covered a wide range of situations including animal damage to rice and corn, nest locations, active rodent burrows and distribution of plants. Monte Carlo simulations were applied to sample the data sets, and in all cases the error of the estimate (measured as relative root mean square error) was reduced with increasing sample size. The method of calculation and ease of use in the field were also used to judge the usefulness of the estimator. Estimators were evaluated in their original published forms, although the variable area transect (VAT) and ordered distance methods have been the subjects of optimization studies. Results: An estimator that was a compound of three basic distance estimators was found to be robust across all spatial patterns for sample sizes of 25 or greater. The same field methodology can be used either with the basic distance formula or the formula used with the Kendall-Moran estimator in which case a reduction in error may be gained for sample sizes less than 25, however, there is no improvement for larger sample sizes. The variable area transect (VAT) method performed moderately well, is easy to use in the field, and its calculations easy to undertake. Conclusion: Plotless density estimators can provide an estimate of density in situations where it would not be practical to layout a plot or quadrat and can in many cases reduce the workload in the field.

Use of optical density as a measure of Claviceps africana conidial suspension concentration

Sorghum ergot, caused by Claviceps africana, has remained a major disease problem in Australia since it was first recorded in 1996, and is the focus of a range of biological and integrated management research. Artificial inoculation using conidial suspensions is an important tool in this research. Ergot infection is greatly influenced by environmental factors, so it is important to reduce controllable sources of variation such as inoculum concentration. The use of optical density was tested as a method of quantifying conidial suspensions of C. africana, as an alternative to haemocytometer counts. This method was found to be accurate and time efficient, with possible applications in other disease systems.