The economic viability and potential of a novel poultry agroforestry system

Investigating agroforestry systems that incorporate poultry is warranted in Northern Europe as they may offer benefits including: improved welfare and use of range; reduced feed costs; price premia on products; reduced payback periods for forests; and, greater returns on investment. Free-range egg production accounts for 27% of the United Kingdom egg market and demand for outdoor broilers is increasing. No research has been conducted recently on the economic viability of agroforestry systems with poultry. An economic model was constructed to: assess economic viability of a broiler agroforestry system; and, investigate the sensitivity of economic performance to key factors and interactions, and identify those which warrant attention in research and management. The system modelled is a commercial trial established in Southern England in 2002 where deciduous trees were planted and broilers reared in six- or nine-week periods. The model uses Monte Carlo simulation and financial performance analyses run for a 120-year period. An Internal Rate of Return (IRR) of 15.5% is predicted for the six-week system which remains viable under a 'worst case' scenario (IRR of 12.6%). Factors which affect financial performance most (decreasing in magnitude) are prices achieved for broilers, costs of brooding houses, chicks, arks, feed and timber prices. The main anticipated effects of biological interactions on financial performance (increased ranging on feed conversion and excess nutrient supply on tree health) were not supported by analysis. Further research is particularly warranted on the welfare benefits offered by the tree component and its relation to price premia.

Influence of freezable/non-freezable water and sucrose on the viability of Theobroma cacao somatic embryos following desiccation and freezing

Encapsulated cocoa (Theobroma cacao L.) somatic embryos subjected to 0.08-1.25 M sucrose treatments were analyzed for embryo soluble sugar content, non-freezable water content, moisture level after desiccation and viability after desiccation and freezing. Results indicated that the higher the sucrose concentration in the treatment medium, the greater was the extent of sucrose accumulation in the embryos. Sucrose treatment greatly assisted embryo post-desiccation recovery since only 40% of the control embryos survived desiccation, whereas a survival rate of 60-95% was recorded for embryos exposed to 0.5-1.25 M sucrose. The non-freezable water content of the embryos was estimated at between 0.26 and 0.61 g H2O g(-1)dw depending on the sucrose treatment, and no obvious relationship could be found between the endogenous sucrose level and the amount of non-freezable water in the embryos. Cocoa somatic embryos could withstand the loss of a fraction of their non-freezable water without losing viability following desiccation. Nevertheless, the complete removal of potentially freezable water was not sufficient for most embryos to survive freezing.

Viability of Salmonella enterica subsp. enterica during the preparation and cold storage of Egyptian soft cheeses and ice-cream

Changes occurring in the viability of Salmonella enterica subsp. enterica during the preparation and cold storage of Domiati cheese, Kariesh cheese and ice-cream were examined. A significant decrease in numbers was observed after whey drainage during the manufacture of Domiati cheese, but Salmonella remained viable for 13 weeks in cheeses prepared from milks with between 60 and 100 g/L NaCl; the viability declined in Domiati cheese made from highly salted milk during the later stages of storage. The method of coagulation used in the preparation of Kariesh cheese affected the survival time of the pathogen, and it varied from 2 to 3 weeks in cheeses made with a slow-acid coagulation method to 4-5 weeks for an acid-rennet coagulation method. This difference was attributed to the higher salt-in-moisture levels and lower pH values of Kariesh cheese prepared by the slow-acid coagulation method. A slight decrease in the numbers of Salmonella resulted from ageing ice-cream mix for 24 h at 0degreesC, but a greater reduction was evident after one day of frozen storage at -20degreesC. The pathogen survived further frozen storage for four months without any substantial change in numbers.

Antioxidant and tyrosinase inhibitory activity of mango seed kernel by product

The antioxidant and tyrosinase inhibitory properties of extracts of mango seed kernel (Mangifera indica L.), which is normally discarded when the fruit is processed, were studied. Extracts contained phenolic components by a high antioxidant activity, which was assessed in homogeneous solution by the 2,2-diphenyt-1-picrylhydrazyl radical and 2,2'-azinobis (3-ethylbenzothialozinesulfonic acid) radical cation-scavenging assays and in an emulsion with the ferric thiocyanate test. The extracts also possessed tyrosinase inhibitory activity. Drying conditions and extraction solvent were varied, and optimum conditions for preparation of mango seed kernel extract were found to be sun-drying with ethanol extraction at room temperature. Refluxing in acidified ethanol gave an increase in yield and the obtained extract had the highest content of total phenolics, and also was the most effective antioxidant with the highest radical-scavenging, metal-chelating and tyrosinase inhibitory activity. The extracts did not cause acute irritation of rabbit skins. Our study for the first time reveals the high total phenol content, radical-scavenging, metal-chelating and tyrosinase inhibitory activities of the extract from mango seed kernel. This extract may be suitable for use in food, cosmetic, nutraceutical and pharmaceutical applications. (C) 2009 Elsevier Ltd. All rights reserved.

A sparse kernel density estimation algorithm using forward constrained regression

Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward constrained regression manner. The leave-one-out (LOO) test score is used for kernel selection. The jackknife parameter estimator subject to positivity constraint check is used for the parameter estimation of a single parameter at each forward step. As such the proposed approach is simple to implement and the associated computational cost is very low. An illustrative example is employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with comparable accuracy to that of the classical Parzen window estimate.

Sparse kernel modelling: a unified approach

A unified approach is proposed for sparse kernel data modelling that includes regression and classification as well as probability density function estimation. The orthogonal-least-squares forward selection method based on the leave-one-out test criteria is presented within this unified data-modelling framework to construct sparse kernel models that generalise well. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic sparse kernel data modelling approach.

Sparse kernel density estimator using orthogonal regression based on D-optimality experimental design

A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates.

Sparse kernel regression modeling using combined locally regularized orthogonal least squares and D-optimality experimental design

The note proposes an efficient nonlinear identification algorithm by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a weighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Kernel density construction using orthogonal forward regression

An automatic algorithm is derived for constructing kernel density estimates based on a regression approach that directly optimizes generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. Local regularization is incorporated into the density construction process to further enforce sparsity. Examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample Parzen window density estimate.

Sparse kernel density construction using orthogonal forward regression with leave-one-out test score and local regularization

This paper presents an efficient construction algorithm for obtaining sparse kernel density estimates based on a regression approach that directly optimizes model generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. A local regularization method is incorporated naturally into the density construction process to further enforce sparsity. An additional advantage of the proposed algorithm is that it is fully automatic and the user is not required to specify any criterion to terminate the density construction procedure. This is in contrast to an existing state-of-art kernel density estimation method using the support vector machine (SVM), where the user is required to specify some critical algorithm parameter. Several examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample optimized Parzen window density estimate. Our experimental results also demonstrate that the proposed algorithm compares favorably with the SVM method, in terms of both test accuracy and sparsity, for constructing kernel density estimates.

An orthogonal forward regression technique for sparse kernel density estimation

Using the classical Parzen window (PW) estimate as the desired response, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density (SKD) estimates. The proposed algorithm incrementally minimises a leave-one-out test score to select a sparse kernel model, and a local regularisation method is incorporated into the density construction process to further enforce sparsity. The kernel weights of the selected sparse model are finally updated using the multiplicative nonnegative quadratic programming algorithm, which ensures the nonnegative and unity constraints for the kernel weights and has the desired ability to reduce the model size further. Except for the kernel width, the proposed method has no other parameters that need tuning, and the user is not required to specify any additional criterion to terminate the density construction procedure. Several examples demonstrate the ability of this simple regression-based approach to effectively construct a SKID estimate with comparable accuracy to that of the full-sample optimised PW density estimate. (c) 2007 Elsevier B.V. All rights reserved.

Identification of nonlinear systems using generalized kernel models

Nonlinear system identification is considered using a generalized kernel regression model. Unlike the standard kernel model, which employs a fixed common variance for all the kernel regressors, each kernel regressor in the generalized kernel model has an individually tuned diagonal covariance matrix that is determined by maximizing the correlation between the training data and the regressor using a repeated guided random search based on boosting optimization. An efficient construction algorithm based on orthogonal forward regression with leave-one-out (LOO) test statistic and local regularization (LR) is then used to select a parsimonious generalized kernel regression model from the resulting full regression matrix. The proposed modeling algorithm is fully automatic and the user is not required to specify any criterion to terminate the construction procedure. Experimental results involving two real data sets demonstrate the effectiveness of the proposed nonlinear system identification approach.

Kernel classifier construction using orthogonal forward selection and boosting with Fisher ratio class separability measure

A greedy technique is proposed to construct parsimonious kernel classifiers using the orthogonal forward selection method and boosting based on Fisher ratio for class separability measure. Unlike most kernel classification methods, which restrict kernel means to the training input data and use a fixed common variance for all the kernel terms, the proposed technique can tune both the mean vector and diagonal covariance matrix of individual kernel by incrementally maximizing Fisher ratio for class separability measure. An efficient weighted optimization method is developed based on boosting to append kernels one by one in an orthogonal forward selection procedure. Experimental results obtained using this construction technique demonstrate that it offers a viable alternative to the existing state-of-the-art kernel modeling methods for constructing sparse Gaussian radial basis function network classifiers. that generalize well.

Fast kernel classifier construction using orthogonal forward selection to minimise leave-one-out misclassification rate

We propose a simple yet computationally efficient construction algorithm for two-class kernel classifiers. In order to optimise classifier's generalisation capability, an orthogonal forward selection procedure is used to select kernels one by one by minimising the leave-one-out (LOO) misclassification rate directly. It is shown that the computation of the LOO misclassification rate is very efficient owing to orthogonalisation. Examples are used to demonstrate that the proposed algorithm is a viable alternative to construct sparse two-class kernel classifiers in terms of performance and computational efficiency.