Proceedings of the COST SUSVAR/ECO-PB Workshop on organic plant breeding strategies and the use of molecular markers

In many countries,national projects are in progress to investigate the sustainable low-input approach.In the present COST network,these projects are coordinated by means of exchange of materials,establishing common methods for assessment and statistical analyses and by combining national experimental results.The common framework is cereal production in low-input sustainable systems with emphasis on crop diversity.The network is organised into six Working Groups,five focusing on specific research areas and one focusing on the practical application of the research results for variety testing:1)plant genetics and plant breeding,2)biostatistics,3)plant nutrition and soil microbiology,4)weed biology and plant competition,5)plant pathology and plant disease resistance biology and 6)variety testing and certification.It is essential that scientists from many disciplines work together to investigate the complex interactions between the crop and its environment,in order to be able to exploit the natural regulatory mechanisms of different agricultural systems for stabilising and increasing yield and quality.The results of this cooperation will contribute to commercial plant breeding as well as official variety testing,when participants from these areas disperse the knowledge achieved through the EU COST Action

Animal breeding in organic farming

After a general introduction into the available breeding techniques for animal breeding and an overview of the organic principles, points for discussion are identified and scenario's for organically accepted breeding methods are discussed

Animal breeding in organic farming:Discussion paper

It is uncertain whether animals which have been bred for conventional production are capable of optimum performance in organic conditions. In conventional agriculture there is a movement towards maximum control of production conditions in order to optimise animals' yield in intensive production systems. By contrast, organic agriculture is based on natural processes and closed cycles, and takes into account the underlying connections between production factors. Following organic ideology, production capacity should be curtailed by acting in accordance with guiding principles such as naturalness, animal welfare, efficient use of fossil fuels in the farm cycle, and agri-biodiversity (IFOAM, 1994). Organic production should be tied to the land, with farms preferably being self-sufficient mixed farms with closed cycles. An additional point of concern are the reproduction techniques used in conventional breeding. Artificial insemination (AI) and embryo transfer (ET) are commonplace in conventional animal breeding. But these techniques are 'artificial' and they deprive animals of natural mating behaviour and negatively affect the animals' welfare and integrity. By bringing in animals from conventional agriculture, organic farmers are indirectly making use of these techniques. These and other concerns have led to the project 'Organic breeding: a long way to go', which aims to lay down clear visions and an action plan for an organic breeding system

A Cereal Chemist's Quick Guide to Genetics, Plant Breeding and BioIT

This book is intended as a guide for cereal chemists in quality testing laboratories and grain product development companies, to help them in their understanding of fundamental genetics, functional genomics and other concepts of relevance during their interactions with crop breeding programs. Consequently the emphasis is on quick definitions of terms and concepts, assuming that the expertise of the reader is predominantly in another field.Established and supported under the Australian Government’s Cooperative Research Centre Progra

Environmental impacts of organic farming in Europe

Organic farming has become an important element of European agri-environmental policy due to increasing concern about the impact of agriculture on the environment. This book describes in detail the environmental and resource use impacts of organic farming relative to conventional farming systems, based on a set of environmental indicators for the agricultural sector on a European level. The policy relevance of the results is also discussed in detail

Music as complex emergent behaviour : an approach to interactive music systems

Access to the full-text thesis is no longer available at the author's request, due to 3rd party copyright restrictions. Access removed on 28.11.2016 by CS (TIS).Metadata merged with duplicate record (http://hdl.handle.net/10026.1/770) on 20.12.2016 by CS (TIS).This is a digitised version of a thesis that was deposited in the University Library. If you are the author please contact PEARL Admin ([email protected]) to discuss options.This thesis suggests a new model of human-machine interaction in the domain of non-idiomatic musical improvisation. Musical results are viewed as emergent phenomena issuing from complex internal systems behaviour in relation to input from a single human performer. We investigate the prospect of rewarding interaction whereby a system modifies itself in coherent though non-trivial ways as a result of exposure to a human interactor. In addition, we explore whether such interactions can be sustained over extended time spans. These objectives translate into four criteria for evaluation; maximisation of human influence, blending of human and machine influence in the creation of machine responses, the maintenance of independent machine motivations in order to support machine autonomy and finally, a combination of global emergent behaviour and variable behaviour in the long run. Our implementation is heavily inspired by ideas and engineering approaches from the discipline of Artificial Life. However, we also address a collection of representative existing systems from the field of interactive composing, some of which are implemented using techniques of conventional Artificial Intelligence. All systems serve as a contextual background and comparative framework helping the assessment of the work reported here. This thesis advocates a networked model incorporating functionality for listening, playing and the synthesis of machine motivations. The latter incorporate dynamic relationships instructing the machine to either integrate with a musical context suggested by the human performer or, in contrast, perform as an individual musical character irrespective of context. Techniques of evolutionary computing are used to optimise system components over time. Evolution proceeds based on an implicit fitness measure; the melodic distance between consecutive musical statements made by human and machine in relation to the currently prevailing machine motivation. A substantial number of systematic experiments reveal complex emergent behaviour inside and between the various systems modules. Music scores document how global systems behaviour is rendered into actual musical output. The concluding chapter offers evidence of how the research criteria were accomplished and proposes recommendations for future research

Adaptive management of Ramsar wetlands

Abstract The Macquarie Marshes are one of Australia’s iconic wetlands, recognised for their international importance, providing habitat for some of the continent’s more important waterbird breeding sites as well as complex and extensive flood-dependent vegetation communities. Part of the area is recognised as a wetland of international importance, under the Ramsar Convention. River regulation has affected their resilience, which may increase with climate change. Counteracting these impacts, the increased amount of environmental flow provided to the wetland through the buy-back and increased wildlife allocation have redressed some of the impacts of river regulation. This project assists in the development of an adaptive management framework for this Ramsar-listed wetland. It brings together current management and available science to provide an informed hierarchy of objectives that incorporates climate change adaptation and assists transparent management. The project adopts a generic approach allowing the framework to be transferred to other wetlands, including Ramsar-listed wetlands, supplied by rivers ranging from highly regulated to free flowing. The integration of management with science allows key indicators to be monitored that will inform management and promote increasingly informed decisions. The project involved a multi-disciplinary team of scientists and managers working on one of the more difficult challenges for Australia, exacerbated by increasing impacts of climate change on flows and inundation patterns

Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm

Reservoirs and dams are vital human-built infrastructures that play essential roles in flood control, hydroelectric power generation, water supply, navigation, and other functions. The realization of those functions requires efficient reservoir operation, and the effective controls on the outflow from a reservoir or dam. Over the last decade, artificial intelligence (AI) techniques have become increasingly popular in the field of streamflow forecasts, reservoir operation planning and scheduling approaches. In this study, three AI models, namely, the backpropagation (BP) neural network, support vector regression (SVR) technique, and long short-term memory (LSTM) model, are employed to simulate reservoir operation at monthly, daily, and hourly time scales, using approximately 30 years of historical reservoir operation records. This study aims to summarize the influence of the parameter settings on model performance and to explore the applicability of the LSTM model to reservoir operation simulation. The results show the following: (1) for the BP neural network and LSTM model, the effects of the number of maximum iterations on model performance should be prioritized; for the SVR model, the simulation performance is directly related to the selection of the kernel function, and sigmoid and RBF kernel functions should be prioritized; (2) the BP neural network and SVR are suitable for the model to learn the operation rules of a reservoir from a small amount of data; and (3) the LSTM model is able to effectively reduce the time consumption and memory storage required by other AI models, and demonstrate good capability in simulating low-flow conditions and the outflow curve for the peak operation period

Modelling mechanisms of change in crop populations

Computer -based simulation models of changes occurring within crop populations when subjected to agents of phenotypic change, have been developed for use on commonly available personal computer equipment. As an underlying developmental principle, the models have been designed as general -case, mechanistic, stochastic models, in contrast to the predominantly empirically- derived, system -specific, deterministic (predictive) models currently available. A modelling methodology has evolved, to develop portable simulation models, written in high - level, general purpose code, allowing for use, modification and continued development by biologists with little requirement for computer programming expertise.The initial subject of these modelling activities was the simulation of the effects of selection and other agents of genetic change in crop populations, resulting in the computer model, PSELECT. Output from PSELECT, specifically phenotypic and genotypic response to phenotypic truncation selection, conformed to expectation, as defined by results from established analogue modelling work. Validation of the model by comparison of output with the results from an experimental -scale plant breeding exercise was less conclusive, and, owing to the fact that the genetic basis of the phenotypic characters used in the selection programme was insufficiently defined, the validation exercise provided only broad qualitative agreement with the model output. By virtue of the predominantly subjective nature of plant breeding programmes, the development of PSELECT resulted in a model of theoretical interest, but with little current practical application.Modelling techniques from the development of the PSELECT model were applied to the simulation of plant disease epidemics, where the modelled system is well characterised, and simulation modelling is an area of active research. The model SATSUMA, simulating the spatial and temporal development of diseases within crop populations, was developed. The model generates output which conforms to current epidemiological theory, and is compatible with contemporary methods of temporal and spatial analysis of crop disease epidemics. Temporal disease progress in the simulations was accurately described by variations of a generalised logistic model. Analysis of the spatial pattern of simulated epidemics by frequency distribution fitting or distance class methods was found to give good qualitative agreement with observed biological systems.The mechanistic nature of SATSUMA and its deliberate design as a general case model make it especially suitable for the investigation of component processes in a generalised plant disease epidemic, and valuable as an educational tool. Subject to validation against observational data, such models can be utilised as predictive tools by the incorporation of information (concerning crop species, pathogen etc.) specifically relevant to the modelled system. In addition to its educational use, SATSUMA has been used as research tool for the examination of the effect of spatial pattern of disease and disease incidence on the efficiency of sampling protocols and in parameterising a general theoretical model for describing the spatio -temporal development of plant diseases