Inferential, non-parametric statistics to assess quality of probabilistic forecast systems

Many statistical forecast systems are available to interested users. In order to be useful for decision-making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and their statistical manifestation have been firmly established, the forecasts must also provide some quantitative evidence of `quality’. However, the quality of statistical climate forecast systems (forecast quality) is an ill-defined and frequently misunderstood property. Often, providers and users of such forecast systems are unclear about what ‘quality’ entails and how to measure it, leading to confusion and misinformation. Here we present a generic framework to quantify aspects of forecast quality using an inferential approach to calculate nominal significance levels (p-values) that can be obtained either by directly applying non-parametric statistical tests such as Kruskal-Wallis (KW) or Kolmogorov-Smirnov (KS) or by using Monte-Carlo methods (in the case of forecast skill scores). Once converted to p-values, these forecast quality measures provide a means to objectively evaluate and compare temporal and spatial patterns of forecast quality across datasets and forecast systems. Our analysis demonstrates the importance of providing p-values rather than adopting some arbitrarily chosen significance levels such as p < 0.05 or p < 0.01, which is still common practice. This is illustrated by applying non-parametric tests (such as KW and KS) and skill scoring methods (LEPS and RPSS) to the 5-phase Southern Oscillation Index classification system using historical rainfall data from Australia, The Republic of South Africa and India. The selection of quality measures is solely based on their common use and does not constitute endorsement. We found that non-parametric statistical tests can be adequate proxies for skill measures such as LEPS or RPSS. The framework can be implemented anywhere, regardless of dataset, forecast system or quality measure. Eventually such inferential evidence should be complimented by descriptive statistical methods in order to fully assist in operational risk management.

Laboratory information management software for genotyping workflows: Applications in high throughput crop genotyping

Background: With the advances in DNA sequencer-based technologies, it has become possible to automate several steps of the genotyping process leading to increased throughput. To efficiently handle the large amounts of genotypic data generated and help with quality control, there is a strong need for a software system that can help with the tracking of samples and capture and management of data at different steps of the process. Such systems, while serving to manage the workflow precisely, also encourage good laboratory practice by standardizing protocols, recording and annotating data from every step of the workflow Results: A laboratory information management system (LIMS) has been designed and implemented at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) that meets the requirements of a moderately high throughput molecular genotyping facility. The application is designed as modules and is simple to learn and use. The application leads the user through each step of the process from starting an experiment to the storing of output data from the genotype detection step with auto-binning of alleles; thus ensuring that every DNA sample is handled in an identical manner and all the necessary data are captured. The application keeps track of DNA samples and generated data. Data entry into the system is through the use of forms for file uploads. The LIMS provides functions to trace back to the electrophoresis gel files or sample source for any genotypic data and for repeating experiments. The LIMS is being presently used for the capture of high throughput SSR (simple-sequence repeat) genotyping data from the legume (chickpea, groundnut and pigeonpea) and cereal (sorghum and millets) crops of importance in the semi-arid tropics. Conclusions: A laboratory information management system is available that has been found useful in the management of microsatellite genotype data in a moderately high throughput genotyping laboratory. The application with source code is freely available for academic users and can be downloaded from http://www.icrisat.org/bt-software-d-lims.htm

EMS in the pastoral industries of western Queensland: From customisation to implementation

This paper outlines the customisation of Environmental Management Systems (EMS) for the pastoral industry of western Queensland, the recruitment and training of pastoral producers, and their development and implementation of EMS. EMS was simplified to a 7-step process and producers were recruited to trial this customised EMS. Producers from 40 properties received EMS training, either as groups or individually. Of these, 37 commenced Pastoral EMS development through a facilitated approach that allowed them to learn about EMS while developing an EMS for their property. EMS implementation has been more effective with producers who were trained in groups. At this stage, however, most producers do not see value in EMS as there are currently no strong drivers to warrant continued development and implementation. Key findings resulting from this work were that personal contact and assistance is vital to encourage producers to trial EMS, and that a staged approach to EMS implementation, commencing with a self-assessment, is recommended. EMS training is most successful in a group situation; however, an alternative method of delivery should be provided for those producers who, either by choice or isolation, have to work alone. A support network is also necessary to encourage and maintain progress with EMS development and implementation, particularly where no strong drivers exist.

Growth of koster’s curse (Clidemia hirta) from seedlings to reproductive maturity and following mechanical damage

Koster´s curse is a highly invasive, perennial shrub with potential to become a major weed in many parts of Queensland and elsewhere in Australia. Presently, there is one infestation discovered in Australia and the species is a Class 1 weed. It grows to 5 m and can produce over 500 berries annually which are dispersed by birds and water. This study quantified growth and the effects of damage on survival and time to reproduction under both field and shade house conditions in the Wet Tropics of north Queensland. Plants recovered to their original size and were capable of setting seed in as few as 86 days and 194 days after being cut back to 10 cm and 0 cm respectively.