A generic design environment for the rural industry knowledge acquisition

This paper describes a new knowledge acquisition method using a generic design environment where context-sensitive knowledge is used to build specific DSS for rural business. Although standard knowledge acquisition methods have been applied in rural business applications, uptake remains low and familiar weaknesses such as obsolescence and brittleness apply. We describe a decision support system (DSS) building environment where contextual factors relevant to the end users are directly taken into consideration. This "end user enabled design environment" (EUEDE) engages both domain experts in creating an expert knowledge base and business operators/end users (such as farmers) in using this knowledge for building their specific DSS. We document the knowledge organisation for the problem domain, namely a dairy industry application. This development involved a case-study research approach used to explore dairy operational knowledge. In this system end users can tailor their decision-making requirements using their own judgement to build specific DSSs. In a specific end user's farming context, each specific DSS provides expert suggestions to assist farmers in improving their farming practice. The paper also shows the environment's generic capability.

Effect of the puroindoline locus and environment on Chinese fresh noodle texture

Grain produced from doubled-haploid (DH) wheat lines, developed from a hard- and a soft-grained wheat cultivar, were bulked according to Pinb (puroindoline b) genotypes for an assessment of Chinese fresh noodle texture by a trained taste panel. Each DH line was designated as 'soft' or 'hard' grained, based on a PCR amplification of the wildtype, soft allele, or the mutant, hard allele. Theoretically, the soft and hard grain bulks represented respective Pinb alleles and an independent assortment of unlinked alleles from the parents, Sunco and Chuanyu 12. Grains from the parents and DH lines were grown at 2 locations in Queensland, Australia, and one in Sichuan, China. The grains were milled and processed for a taste panel evaluation in Chengdu, Sichuan. Results suggest the Pinb alleles had a significant effect on noodle softness and explained 30% of the variation; the 'soft' Pinb allele conferred a softer noodle texture. Location had a significant effect on noodle smoothness; wheat grain grown at Biloela, Queensland, produced a smoother noodle texture than grain grown in Sichuan. The effect of location confirms the importance of environment as a variable for this quality character. This investigation exemplifies the utility of Pinb markers for specifically altering Chinese Fresh Noodle texture.

The accuracy of varietal selection using factor analytic models for multi-environment plant breeding trials

Modeling of cultivar x trial effects for multienvironment trials (METs) within a mixed model framework is now common practice in many plant breeding programs. The factor analytic (FA) model is a parsimonious form used to approximate the fully unstructured form of the genetic variance-covariance matrix in the model for MET data. In this study, we demonstrate that the FA model is generally the model of best fit across a range of data sets taken from early generation trials in a breeding program. In addition, we demonstrate the superiority of the FA model in achieving the most common aim of METs, namely the selection of superior genotypes. Selection is achieved using best linear unbiased predictions (BLUPs) of cultivar effects at each environment, considered either individually or as a weighted average across environments. In practice, empirical BLUPs (E-BLUPs) of cultivar effects must be used instead of BLUPs since variance parameters in the model must be estimated rather than assumed known. While the optimal properties of minimum mean squared error of prediction (MSEP) and maximum correlation between true and predicted effects possessed by BLUPs do not hold for E-BLUPs, a simulation study shows that E-BLUPs perform well in terms of MSEP.

Strawberry breeding in a subtropical environment

Strawberry breeding aims to provide cultivars that maximise consumer satisfaction and producer profitability in a changing environment. In this paper some concepts of profitability, consumer satisfaction and sustainability are explored for a subtropical climate using Queensland Australia, and Florida USA, as examples. The typical production environment is annual autumn planting of bare rooted runners into polythene covered raised beds at about 40000 plants/ha. Harvesting is late autumn to early spring, with fruit arriving at the major markets up to 2000km away from the production area within 1-4 days of harvest. The basic premise in the breed-big work is that consumers must enjoy the experience of eating strawberries, and that perceived flavour, sweetness, and juiciness are the major contributors to this experience. Using market chain information, we developed a basic value model comprised of costs, returns, and sustainability of market. To this basic outline are applied operational descriptors, such as 'speed of harvest', and associated plant characteristics, such as 'fruit display'. The expression of each plant characteristic is ascribed a value or level and together numerically describe the phenotype. This description is mathematically manipulated to provide a 'value index' for the cultivar. Nine cultivars including 'Strawberry Festival', 'Kabarla', 'DPI Rubygem' and 'Sweet Charlie' are described, and environmental issues that may impact on the subtropical strawberry breeding objectives are discussed. Product differentiation and the use of exotic germplasm as a new source of genes for flavour and resistance to disease and environmental stress will likely be the cornerstones of future progress in subtropical strawberry breeding. This approach should satisfy both consumers and producers.