Cost-benefit analysis of alternative forms of hedgerow intercropping in the Philippine uplands

Considerable resources have been expended promoting hedgerow intercropping with shrub legumes to farmers in the Philippine uplands. Despite the resources committed to research and extension, persistent adoption by farmers has been limited to low cost versions of the technology including natural vegetation and grass strips. In this paper, cost-benefit analysis is used to compare the economic returns from traditional open-field maize farming with returns from intercropping maize between leguminous shrub hedgerows, natural vegetation strips and grass strips. An erosion/productivity model, Soil Changes Under Agroforestry, was used to predict the effect of erosion on maize yields. Key informant surveys with experienced maize farmers were used to derive production budgets for the alternative farming methods. The economic incentives revealed by the cost-benefit analysis help to explain the adoption of maize farming methods in the Philippine uplands. Open-field farming without hedgerows has been by far the most popular method of maize production, often with two or more fields cropped in rotation. There is little persistent adoption of hedgerow intercropping with shrub legumes because sustained maize yields are not realised rapidly enough to compensate farmers for establishment and maintenance costs. Natural vegetation and grass strips are more attractive to farmers because of lower establishment costs, and provide intermediate steps to adoption. Rural finance, commodity pricing and agrarian reform policies influence the incentives for maize farmers in the Philippine uplands to adopt and maintain hedgerow intercropping.

Visualization of learning in multilayer perceptron networks using principal component analysis

This paper is concerned with the use of scientific visualization methods for the analysis of feedforward neural networks (NNs). Inevitably, the kinds of data associated with the design and implementation of neural networks are of very high dimensionality, presenting a major challenge for visualization. A method is described using the well-known statistical technique of principal component analysis (PCA). This is found to be an effective and useful method of visualizing the learning trajectories of many learning algorithms such as back-propagation and can also be used to provide insight into the learning process and the nature of the error surface.

Analysis of dynamic process models for structural insight and model reduction .1. Structural identification measures

An important consideration in the development of mathematical models for dynamic simulation, is the identification of the appropriate mathematical structure. By building models with an efficient structure which is devoid of redundancy, it is possible to create simple, accurate and functional models. This leads not only to efficient simulation, but to a deeper understanding of the important dynamic relationships within the process. In this paper, a method is proposed for systematic model development for startup and shutdown simulation which is based on the identification of the essential process structure. The key tool in this analysis is the method of nonlinear perturbations for structural identification and model reduction. Starting from a detailed mathematical process description both singular and regular structural perturbations are detected. These techniques are then used to give insight into the system structure and where appropriate to eliminate superfluous model equations or reduce them to other forms. This process retains the ability to interpret the reduced order model in terms of the physico-chemical phenomena. Using this model reduction technique it is possible to attribute observable dynamics to particular unit operations within the process. This relationship then highlights the unit operations which must be accurately modelled in order to develop a robust plant model. The technique generates detailed insight into the dynamic structure of the models providing a basis for system re-design and dynamic analysis. The technique is illustrated on the modelling for an evaporator startup. Copyright (C) 1996 Elsevier Science Ltd

Assessment of body composition by bioelectrical impedance analysis without the need for measurement of height

Conventional whole-body single frequency bioelectrical impedance analysis (BIA) of body composition typically uses height as a surrogate measure of conductor length. A new method of BIA analysis for the prediction of body cell mass (BCM) and extracellular water (ECW, as % body weight) not using height has been introduced-the Soft Tissue Analyser (STA(TM), Akern Sri, Florence, Italy)-making it ideal for use in subjects where measurement of height is difficult or impossible. The performance of the new analytical method in predicting BCM and ECW in 139 normal control subjects was assessed by comparison with reference data obtained from a four-component (4-C) model of body composition and with predictions obtained from conventional BIA analysis. Both predicted BCM and ECW were strongly (r = 0.82, SEE = 6.3 kg and 0.89, SEE = 1.3 kg respectively) correlated with the corresponding 4-C model measurements although differing significantly from the lines of identity (P < 0.0001). Fat-free mass, calculated from STA estimates of BCM and ECW, was better predicted (r = 0.91, SEE = 5.6 kg). The significant differences in STA-group mean values for BCM and ECW and wide limits of agreement compared with the reference data indicate that the method cannot be used with confidence for prediction of these body compartments despite the obvious advantage of not requiring an accurate measurement of height. (C) 2001 Harcourt Publishers Ltd.

Analysis of an array of monopoles with the use of a radial waveguide approach

A radial guide field matching method (RGFMM) is used to analyze a circular array antenna consisting of one active monopole surrounded by a concentric array of passive monopoles terminated in arbarary loads. An equivalent admittance matrix for this antenna system is determined to study the input admittance of the active monopole when the peripheral elements are terminated in open or short circuits. RGFMM results are compared with free-space method of moments (FS-MoM) results for a small switched-beam array a seven monopoles. Good agreement is noted. (C) 2002 Wiley Periodicals, Inc.

Application of spatial analysis techniques to adjust for fertility trends and identify interplot competition in early stage sugarcane selection trials

Most sugarcane breeding programs in Australia use large unreplicated trials to evaluate clones in the early stages of selection. Commercial varieties that are replicated provide a method of local control of soil fertility. Although such methods may be useful in detecting broad trends in the field, variation often occurs on a much smaller scale. Methods such as spatial analysis adjust a plot for variability by using information from immediate neighbours. These techniques are routinely used to analyse cereal data in Australia and have resulted in increased accuracy and precision in the estimates of variety effects. In this paper, spatial analyses in which the variability is decomposed into local, natural, and extraneous components are applied to early selection trials in sugarcane. Interplot competition in cane yield and trend in sugar content were substantial in many of the trials and there were often large differences in the selections between the spatial and current method used by the Bureau of Sugar Experiment Stations. A joint modelling approach for tonnes sugar per hectare in response to fertility trends and interplot competition is recommended.

A new method for analysing the equilibrium and time-dependent behaviour of Markovian models

Many large-scale stochastic systems, such as telecommunications networks, can be modelled using a continuous-time Markov chain. However, it is frequently the case that a satisfactory analysis of their time-dependent, or even equilibrium, behaviour is impossible. In this paper, we propose a new method of analyzing Markovian models, whereby the existing transition structure is replaced by a more amenable one. Using rates of transition given by the equilibrium expected rates of the corresponding transitions of the original chain, we are able to approximate its behaviour. We present two formulations of the idea of expected rates. The first provides a method for analysing time-dependent behaviour, while the second provides a highly accurate means of analysing equilibrium behaviour. We shall illustrate our approach with reference to a variety of models, giving particular attention to queueing and loss networks. (C) 2003 Elsevier Ltd. All rights reserved.