Infusing the use of seasonal climate forecasting into crop management practice in North East Australia using discussion support software

Seasonal climate forecasting offers potential for improving management of crop production risks in the cropping systems of NE Australia. But how is this capability best connected to management practice? Over the past decade, we have pursued participative systems approaches involving simulation-aided discussion with advisers and decision-makers. This has led to the development of discussion support software as a key vehicle for facilitating infusion of forecasting capability into practice. In this paper, we set out the basis of our approach, its implementation and preliminary evaluation. We outline the development of the discussion support software Whopper Cropper, which was designed for, and in close consultation with, public and private advisers. Whopper Cropper consists of a database of simulation output and a graphical user interface to generate analyses of risks associated with crop management options. The charts produced provide conversation pieces for advisers to use with their farmer clients in relation to the significant decisions they face. An example application, detail of the software development process and an initial survey of user needs are presented. We suggest that discussion support software is about moving beyond traditional notions of supply-driven decision support systems. Discussion support software is largely demand-driven and can compliment participatory action research programs by providing cost-effective general delivery of simulation-aided discussions about relevant management actions. The critical role of farm management advisers and dialogue among key players is highlighted. We argue that the discussion support concept, as exemplified by the software tool Whopper Cropper and the group processes surrounding it, provides an effective means to infuse innovations, like seasonal climate forecasting, into farming practice. Crown Copyright (C) 2002 Published by Elsevier Science Ltd. All rights reserved.

Do ICMS ao IVA: a quest??o das transa????es interestaduais no Brasil dos anos 90

O trabalho examina as estat??sticas produzidas pelos Estados acerca das transa????es, submetidas ou n??o ?? incid??ncia do ICMS, realizadas entre eles no ano de 1999. Comparam se os resultados com dados anteriores, relativos ao ano de 1985. O objetivo ?? compreender o significado dessas transa????es para a arrecada????o do imposto, especialmente no momento em que se avizinha, no ??mbito da reforma tribut??ria, a mudan??a no princ??pio de tributa????o, do ???misto???, preponderante atualmente, para o ???de destino???. As estat??sticas produzidas a partir de 1997, em raz??o do processo de discuss??o da reforma tribut??ria, s??o examinadas sobretudo com vistas ?? mudan??a no regime de distribui????o da receita entre as unidades federadas. O trabalho indica, sumariamente, as caracter??sticas relacionadas com as transa????es interestaduais, previstas no debate, para o principal imposto brasileiro ??? o ICMS.

A categoria dos auditores fiscais da Receita Federal do Brasil : a (des) construção da identidade profissional

Este artigo busca revelar o processo de desenvolvimento da categoria dos auditores fiscais da Receita Federal do Brasil (AFRFBs), a partir das mudanças no contexto social e profissional, e a forma como essas mutações concorreram para a construção de uma identidade profissional própria desses servidores. O que se pretende é entender como o contexto político-econômico vem alterando as percepções que esses profissionais têm de si próprios e como as reformas transformaram o modo de exercerem suas funções. A pesquisa, que se dá a partir de uma proposta de transação "quanti-quali", permitiu explorar, na trilha teórica de Dubar, aspectos relevantes do contínuo processo de construção das identidades profissionais ou de perfis identitários desses servidores. Os conhecimentos sobre o trabalho e as formas de identificação profissional na categoria dos auditores fiscais da Receita Federal do Brasil podem contribuir para a reflexão sobre relações de trabalho e processos de gestão pública no Brasil.

Survival Analysis Employed in Predicting Corporate Failure: A Forecasting Model Proposal

In face of the current economic and financial environment, predicting corporate bankruptcy is arguably a phenomenon of increasing interest to investors, creditors, borrowing firms, and governments alike. Within the strand of literature focused on bankruptcy forecasting we can find diverse types of research employing a wide variety of techniques, but only a few researchers have used survival analysis for the examination of this issue. We propose a model for the prediction of corporate bankruptcy based on survival analysis, a technique which stands on its own merits. In this research, the hazard rate is the probability of ‘‘bankruptcy’’ as of time t, conditional upon having survived until time t. Many hazard models are applied in a context where the running of time naturally affects the hazard rate. The model employed in this paper uses the time of survival or the hazard risk as dependent variable, considering the unsuccessful companies as censured observations.

Financial strategies for minimizing corporate income taxes under Brazil's new global tax system

In 1996, Brazil adopted a worldwide income tax system for corporations. This system represents a fundamental change in how the Brazílian government treats multinational transactions and the tax minimizing strategies relevant to businesses. In this article, we describe the conceptual basis for worldwide tax systems and the problem of double taxation that they create. Responses to double taxation by both the governments and the priva te sector are considered. Namely, the imperfect mechanisms developed by Brazil and other countries for mitigating double taxation are analyzed. We ultimately focus on the strategies that companies utilize in order not only to avoid double texetion, but also to take advantage of tax havens.

Book-tax differences e gerenciamento de resultados no mercado de ações do Brasil

Este estudo verifica a relação entre book-tax differences (BTD) e gerenciamento de resultado em companhias listadas na BM&FBovespa no período de 2005 a 2009. Metodologicamente, foram empregadas duas abordagens: (i) distribuição de frequências e (ii) accruals discricionários - Modelo Jones Modificado. Os achados indicam uma relação diretamente proporcional entre a BTD e os accruals discricionários. Foram identificadas evidências de que as entidades preponderantemente gerenciam seus resultados na mesma direção do sinal observado da BTD, além de buscarem apresentar o montante de BTD em nível e em variação em torno do ponto zero e desta forma evitar sinalizar baixa qualidade do lucro. Adicionalmente, foram encontradas evidências de que o tamanho da firma e a adoção do regime tributário de transição estão relacionados de forma inversamente proporcional ao nível dos accruals discricionários.

New source-material for the socio-economic history of the Hindus of Goa

The Mhamai brothers were the suppliers of daily commodities / stationery to the viceroys / governors of Goa. Since late 18th century their agency house worked in partnership with several other trading houses all over the west coast of India. They also served as brokers for the French East India company in Goa during the critical period of anglo-french wars. The Mhamais were also revenue farmers, particularly customs and tobacco tax farming. I had the privilege of taking their family archives to the Xavier Centre of Historical Research in 1979 and making the history of the family known worldwide.

An artificial neural network approach for short-term wind power forecasting in Portugal

This paper presents an artificial neural network approach for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. The accuracy of the wind power forecasting attained with the proposed approach is evaluated against persistence and ARIMA approaches, reporting the numerical results from a real-world case study.

Volatility forecasting with range models: An evaluation of new alternatives to the CARR model

The aim of this paper is to analyze the forecasting ability of the CARR model proposed by Chou (2005) using the S&P 500. We extend the data sample, allowing for the analysis of different stock market circumstances and propose the use of various range estimators in order to analyze their forecasting performance. Our results show that there are two range-based models that outperform the forecasting ability of the GARCH model. The Parkinson model is better for upward trends and volatilities which are higher and lower than the mean while the CARR model is better for downward trends and mean volatilities.

Short-term electricity prices forecasting in a competitive market by a hybrid PSO-ANFIS approach

In this paper, a novel hybrid approach is proposed for electricity prices forecasting in a competitive market, considering a time horizon of 1 week. The proposed approach is based on the combination of particle swarm optimization and adaptive-network based fuzzy inference system. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications, to demonstrate its effectiveness regarding forecasting accuracy and computation time. Finally, conclusions are duly drawn. (C) 2012 Elsevier Ltd. All rights reserved.

Application of adaptive neuro-fuzzy inference for wind power short-term forecasting

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. In this paper, an adaptive neuro-fuzzy inference approach is proposed for short-term wind power forecasting. Results from a real-world case study are presented. A thorough comparison is carried out, taking into account the results obtained with other approaches. Numerical results are presented and conclusions are duly drawn. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Short-term wind forecasting for energy resources scheduling

This paper proposes a wind power forecasting methodology based on two methods: direct wind power forecasting and wind speed forecasting in the first phase followed by wind power forecasting using turbines characteristics and the aforementioned wind speed forecast. The proposed forecasting methodology aims to support the operation in the scope of the intraday resources scheduling model, namely with a time horizon of 5 minutes. This intraday model supports distribution network operators in the short-term scheduling problem, in the smart grid context. A case study using a real database of 12 months recorded from a Portuguese wind power farm was used. The results show that the straightforward methodology can be applied in the intraday model with high wind speed and wind power accuracy. The wind power forecast direct method shows better performance than wind power forecast using turbine characteristics and wind speed forecast obtained in first phase.

Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Electricity Prices Forecasting

A novel hybrid approach, combining wavelet transform, particle swarm optimization, and adaptive-network-based fuzzy inference system, is proposed in this paper for short-term electricity prices forecasting in a competitive market. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Finally, conclusions are duly drawn.

ANN-Based LMP forecasting in a distribution network with large penetration of DG

In recent years, power systems have experienced many changes in their paradigm. The introduction of new players in the management of distributed generation leads to the decentralization of control and decision-making, so that each player is able to play in the market environment. In the new context, it will be very relevant that aggregator players allow midsize, small and micro players to act in a competitive environment. In order to achieve their objectives, virtual power players and single players are required to optimize their energy resource management process. To achieve this, it is essential to have financial resources capable of providing access to appropriate decision support tools. As small players have difficulties in having access to such tools, it is necessary that these players can benefit from alternative methodologies to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), and intended to support smaller players. In this case the present methodology uses a training set that is created using energy resource scheduling solutions obtained using a mixed-integer linear programming (MIP) approach as the reference optimization methodology. The trained network is used to obtain locational marginal prices in a distribution network. The main goal of the paper is to verify the accuracy of the ANN based approach. Moreover, the use of a single ANN is compared with the use of two or more ANN to forecast the locational marginal price.