Annual forecasting of the Australian macadamia crop - integrating tree census data with statistical climate-adjustment models

To facilitate marketing and export, the Australian macadamia industry requires accurate crop forecasts. Each year, two levels of crop predictions are produced for this industry. The first is an overall longer-term forecast based on tree census data of growers in the Australian Macadamia Society (AMS). This data set currently accounts for around 70% of total production, and is supplemented by our best estimates of non-AMS orchards. Given these total tree numbers, average yields per tree are needed to complete the long-term forecasts. Yields from regional variety trials were initially used, but were found to be consistently higher than the average yields that growers were obtaining. Hence, a statistical model was developed using growers' historical yields, also taken from the AMS database. This model accounted for the effects of tree age, variety, year, region and tree spacing, and explained 65% of the total variation in the yield per tree data. The second level of crop prediction is an annual climate adjustment of these overall long-term estimates, taking into account the expected effects on production of the previous year's climate. This adjustment is based on relative historical yields, measured as the percentage deviance between expected and actual production. The dominant climatic variables are observed temperature, evaporation, solar radiation and modelled water stress. Initially, a number of alternate statistical models showed good agreement within the historical data, with jack-knife cross-validation R2 values of 96% or better. However, forecasts varied quite widely between these alternate models. Exploratory multivariate analyses and nearest-neighbour methods were used to investigate these differences. For 2001-2003, the overall forecasts were in the right direction (when compared with the long-term expected values), but were over-estimates. In 2004 the forecast was well under the observed production, and in 2005 the revised models produced a forecast within 5.1% of the actual production. Over the first five years of forecasting, the absolute deviance for the climate-adjustment models averaged 10.1%, just outside the targeted objective of 10%.

Development of a New Zealand database of plant virus and virus-like organisms.

The recent 8th Australasian plant virology workshop in Rotorua, New Zealand, discussed the development of a New Zealand database of plant virus and virus-like organisms. Key points of discussion included: (i) the purpose of such a database; (ii) who would benefit from the information in a database; (iii) the scope of a database and its associated collections; (iv) database information and format; and (v) potential funding of such a database. From the workshop and further research, we conclude that the preservation and verification of specimens within the collections and the development of a New Zealand database of plant virus and virus-like organisms is essential. Such a collection will help to fulfil statutory requirements in New Zealand and assist in fulfilling international obligations under the International Plant Protection Convention. Sustaining such a database will assist New Zealand virologists and statutory bodies to undertake scientifically sound research. Establishing reliable records and an interactive database will help to ensure accurate and timely diagnoses of diseases caused by plant viruses and virus-like organisms. Detection of new incursions and their diagnosis will be further enhanced by the use of such reference collections and their associated database. Connecting and associating this information to similar overseas databases would assist international collaborations and allow access to the latest taxonomic and diagnostic resources. Associated scientists working in the areas of plant breeding, export phytosanitary assurance and in the area of the conservation estate would also benefit from access to verified specimens of plant viruses and virus-like organisms. We conclude that funding of a New Zealand database of virus and virus-like organisms and its associated collections should be based partly on Crown funds, as it is a nationally significant biological resource.

Radish leaves and radish roots: are we eating the right part for cancer prevention?

Radishes are most commonly consumed as a root vegetable, although radish leaves are occasionally used in salads and cooking. While both the radish root and shoot contain glucosinolates with anti-cancer potential, the glucosinolate profile of the root and the shoot are very different. Whereas the root contains mainly glucodehydroerucin (2.8 mol/gFW) (also known as glucoraphasatin), the main glucosinolate components of the shoot are glucoraphanin (2.8 mol/gFW) and glucoraphenin (2.1 mol/gFW). Upon hydrolysis, the latter glucosinolates produce sulforaphane and sulforaphene respectively, both potent inducers of mammalian phase 2 enzymes. Previously, radishes have been dismissed as having minimal anti-cancer potential based on studies with radish roots. However, depending on the cultivar, radish shoots can have up to 45 times the capacity of roots to induce phase 2 enzymes. In fact, shoots of a number of radish cultivars (eg. 'Black Spanish') have similar or greater anti-cancer potential than broccoli florets, a vegetable that has received considerable interest in this area.

CRC27012 A National Diagnostic Database for Emergency Plant Pests

Development of a national diagnostic database for Emergency Plant Pests which will be web-accessible.

A Bayesian hurdle model for analysis of an insect resistance monitoring database

Motivated by the analysis of the Australian Grain Insect Resistance Database (AGIRD), we develop a Bayesian hurdle modelling approach to assess trends in strong resistance of stored grain insects to phosphine over time. The binary response variable from AGIRD indicating presence or absence of strong resistance is characterized by a majority of absence observations and the hurdle model is a two step approach that is useful when analyzing such a binary response dataset. The proposed hurdle model utilizes Bayesian classification trees to firstly identify covariates and covariate levels pertaining to possible presence or absence of strong resistance. Secondly, generalized additive models (GAMs) with spike and slab priors for variable selection are fitted to the subset of the dataset identified from the Bayesian classification tree indicating possibility of presence of strong resistance. From the GAM we assess trends, biosecurity issues and site specific variables influencing the presence of strong resistance using a variable selection approach. The proposed Bayesian hurdle model is compared to its frequentist counterpart, and also to a naive Bayesian approach which fits a GAM to the entire dataset. The Bayesian hurdle model has the benefit of providing a set of good trees for use in the first step and appears to provide enough flexibility to represent the influence of variables on strong resistance compared to the frequentist model, but also captures the subtle changes in the trend that are missed by the frequentist and naive Bayesian models. © 2014 Springer Science+Business Media New York.

SorGSD: a sorghum genome SNP database

Sorghum (Sorghum bicolor) is one of the most important cereal crops globally and a potential energy plant for biofuel production. In order to explore genetic gain for a range of important quantitative traits, such as drought and heat tolerance, grain yield, stem sugar accumulation, and biomass production, via the use of molecular breeding and genomic selection strategies, knowledge of the available genetic variation and the underlying sequence polymorphisms, is required.