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.

Driving better vegetable irrigation through profitable practice change

The availability and quality of irrigation water has become an issue limiting productivity in many Australian vegetable regions. Production is also under competitive pressure from supply chain forces. Producers look to new technologies, including changing irrigation infrastructure, exploring new water sources, and more complex irrigation management, to survive these stresses. Often there is little objective information investigating which improvements could improve outcomes for vegetable producers, and external communities (e.g. meeting NRM targets). This has led to investment in inappropriate technologies, and costly repetition of errors, as business independently discover the worth of technologies by personal experience. In our project, we investigated technology improvements for vegetable irrigation. Through engagement with industry and other researchers, we identified technologies most applicable to growers, particularly those that addressed priority issues. We developed analytical tools for ‘what if’ scenario testing of technologies. We conducted nine detailed experiments in the Lockyer Valley and Riverina vegetable growing districts, as well as case studies on grower properties in southern Queensland. We investigated root zone monitoring tools (FullStop™ wetting front detectors and Soil Solution Extraction Tubes - SSET), drip system layout, fertigation equipment, and altering planting arrangements. Our project team developed and validated models for broccoli, sweet corn, green beans and lettuce, and spreadsheets for evaluating economic risks associated with new technologies. We presented project outcomes at over 100 extension events, including irrigation showcases, conferences, field days, farm walks and workshops. The FullStops™ were excellent for monitoring root zone conditions (EC, nitrate levels), and managing irrigation with poor quality water. They were easier to interpret than the SSET. The SSET were simpler to install, but required wet soil to be reliable. SSET were an option for monitoring deeper soil zones, unsuitable for FullStop™ installations. Because these root zone tools require expertise, and are labour intensive, we recommend they be used to address specific problems, or as a periodic auditing strategy, not for routine monitoring. In our research, we routinely found high residual N in horticultural soils, with subsequently little crop yield response to additional nitrogen fertiliser. With improved irrigation efficiency (and less leaching), it may be timely to re-examine nitrogen budgets and recommendations for vegetable crops. Where the drip irrigation tube was located close to the crop row (i.e. within 5-8 cm), management of irrigation was easier. It improved nitrogen uptake, water use efficiency, and reduced the risk of poor crop performance through moisture stress, particularly in the early crop establishment phases. Close proximity of the drip tube to the crop row gives the producer more options for managing salty water, and more flexibility in taking risks with forecast rain. In many vegetable crops, proximate drip systems may not be cost-effective. The next best alternative is to push crop rows closer to the drip tube (leading to an asymmetric row structure). The vegetable crop models are good at predicting crop phenology (development stages, time to harvest), input use (water, fertiliser), environmental impacts (nutrient, salt movement) and total yields. The two immediate applications for the models are understanding/predicting/manipulating harvest dates and nitrogen movements in vegetable cropping systems. From the economic tools, the major influences on accumulated profit are price and yield. In doing ‘what if’ analyses, it is very important to be as accurate as possible in ascertaining what the assumed yield and price ranges are. In most vegetable production systems, lowering the required inputs (e.g. irrigation requirement, fertiliser requirement) is unlikely to have a major influence on accumulated profit. However, if a resource is constraining (e.g. available irrigation water), it is usually most profitable to maximise return per unit of that resource.

Insect pests in tropical forestry

The layout of this second edition follows that of the first, though the content has been substantially rewritten to reflect 10 years of research and development, as well as the emergence of new pest species. Chapter 1 presents an overview, from a somewhat entomological perspective, of tropical forestry in its many guises. Chapters 2, 3 and 4 then discuss the 'pure' biology and ecology of tropical insects and their co-evolved relationships with the trees and forests in which they live. Chapter 5 is necessarily the largest chapter in the book, looking in detail at a selection of major pest species from all over the tropical world. Chapters 6, 7, 8 and 9 then discuss the theory and practice of insect pest management, starting at the fundamental planning stage, before any seeds hit the soil. Nursery management and stand management were considered in Chapters 7 and 8. Chapter 9 covers the topics of forest health surveillance, quarantine and forest invasive species, topics which again have significance at all stages of forestry but for convenience are presented after nursery and forest management. This, in fact, we attempt to do in the final chapter, Chapter 10, which combines most of the previous nine chapters in examples illustrating the concept of integrated pest management. ©CABI Publishing CABI Publishing