Storage of Macadamia Nut-In-Shell

The effect of moisture content and storage temperature on the high quality storage life on macadamia nut-in-shell (NIS), and the subsequent influence of NIS storage on the shelf-life of roasted kernel, is being investigated. Macadamia integrifolia 'Keauhou" (HAES 246) NIS is being stored at 5°, 25°C and 40°C with a moisture content of 15.0, 12.5, 10.0, 7.5 and 3.5% for a maximum of 12 months. Preliminary results showed that unacceptable levels of visual mould developed on NIS with 15.0 and 12.5% moisture at 25°C following relatively short periods of storage. Discolouration and the production of an off-flavour in the raw kernel resulted after 1 month's storage of NIS with a moisture content of 10.0% at 40°C. Roasting times were reduced with increased storage duration of NIS with a moisture content of 15.0, 12.5 and 10.0% at 25°C, 15.0 and 12.5% at 5°C and 3.5% at 40°C. The percentage of roasted kernel rejects increased with increased storage duration of NIS with a moisture content of 15.0 and 12.5% at 25°C.

Alternative fungicides for controlling husk spot caused by Pseudocercospora macadamiae in macadamia

Husk spot, caused by Pseudocercospora macadamiae is a major fungal disease of macadamia in Australia. Chemicals to control the disease are limited and frequent failure to control the disease is a major concern to growers. The overall goal of this research was to improve the chemical control strategy of P. macadamiae through the provision of fungicides with different modes of action to carbendazim, which is the current industry standard. Husk spot incidence, premature fruit abscission, kernel quality and yield were evaluated following application of different fungicide products in replicated field experiments at three different sites. Results showed significant differences in disease incidence and premature fruit abscission between fungicide treatments, field sites and years. Generally, disease incidence and premature fruit abscission on trees treated with fungicide were significantly (P < 0.05) lower than the untreated control. Pyraclostrobin conferred significantly better protection than trifloxystrobin, reducing disease severity by 70% compared with a 50% reduction by trifloxystrobin. The pyraclostrobin treatment had a similar efficacy to the current industry standard (70% reduction cf. 73% reduction by tank-mixed carbendazim and copper). Higher amounts of immature kernels occurred in the untreated control, followed by difenoconazole and trifloxystrobin. Diseased fruit accounted for 78% of premature fruit abscission, which indicates that husk spot enhances fruit abscission in macadamia. Our results suggest that pyraclostrobin provided similar efficacy to the industry standard and could, therefore, play a key role in the management of husk spot.

Maintaining high moisture content of macadamia nuts-in-shell during storage induces brown centres in raw kernels

BACKGROUND Kernel brown centres in macadamia are a defect causing internal discolouration of kernels. This study investigates the effect on the incidence of brown centres in raw kernel after maintaining high moisture content in macadamia nuts-in-shell stored at temperatures of 30°C, 35°C, 40°C and 45°C. RESULTS Brown centres of raw kernel increased with nuts-in-shell storage time and temperature when high moisture content was maintained by sealing in polyethylene bags. Almost all kernels developed the defect when kept at high moisture content for 5 days at 45°C, and 44% developed brown centres after only 2 days of storage at high moisture content at 45°C. This contrasted with only 0.76% when stored for 2 days at 45°C but allowed to dry in open-mesh bags. At storage temperatures below 45°C, there were fewer brown centres, but there were still significant differences between those stored at high moisture content and those allowed to dry (P < 0.05). CONCLUSION Maintenance of high moisture content during macadamia nuts-in-shell storage increases the incidence of brown centres in raw kernels and the defect increases with time and temperature. On-farm nuts-in-shell drying and storage practices should rapidly remove moisture to reduce losses. Ideally, nuts-in-shell should not be stored at high moisture content on-farm at temperatures over 30°C. © 2013 Society of Chemical Industry

Higher yield and economic benefits are achieved in the macadamia crop by irrigation and intercropping with coffee

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The foraging behaviour of the Black rat (Rattus rattus) within Australian macadamia orchards

The black rat (Rattus rattus) has been shown to be the primary species responsible for causing significant crop losses within the Australian macadamia industry. This species success within macadamia orchards is directly related to the flexibility expressed in its foraging behaviour. In this paper a conceptual foraging model is presented which proposes that the utilisation of resources by rodents within various components of the system is related not only to their relative abundance, but also to predator avoidance behaviour. Nut removal from high predation risk habitats during periods of low resource abundance in low risk compartments of the system is considered an essential behaviour that allows high rodent densities to be maintained throughout the year.

Predicting loss and fragmentation of habitat of the vulnerable subtropical rainforest tree Macadamia integrifolia with models developed from compiled ecological data

Habitat models are widely used in ecology, however there are relatively few studies of rare species, primarily because of a paucity of survey records and lack of robust means of assessing accuracy of modelled spatial predictions. We investigated the potential of compiled ecological data in developing habitat models for Macadamia integrifolia, a vulnerable mid-stratum tree endemic to lowland subtropical rainforests of southeast Queensland, Australia. We compared performance of two binomial models—Classification and Regression Trees (CART) and Generalised Additive Models (GAM)—with Maximum Entropy (MAXENT) models developed from (i) presence records and available absence data and (ii) developed using presence records and background data. The GAM model was the best performer across the range of evaluation measures employed, however all models were assessed as potentially useful for informing in situ conservation of M. integrifolia, A significant loss in the amount of M. integrifolia habitat has occurred (p < 0.05), with only 37% of former habitat (pre-clearing) remaining in 2003. Remnant patches are significantly smaller, have larger edge-to-area ratios and are more isolated from each other compared to pre-clearing configurations (p < 0.05). Whilst the network of suitable habitat patches is still largely intact, there are numerous smaller patches that are more isolated in the contemporary landscape compared with their connectedness before clearing. These results suggest that in situ conservation of M. integrifolia may be best achieved through a landscape approach that considers the relative contribution of small remnant habitat fragments to the species as a whole, as facilitating connectivity among the entire network of habitat patches.

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is employed to find nonlinear relationships of the extracted color and texture features. The maximum likelihood approach is used to estimate the intrinsic dimensionality, which is then used as a criterion for automatic selection of optimal feature set from the fused feature. The proposed method is evaluated using SVM as the benchmark classifier and is applied to object-based vegetation species classification using high spatial resolution aerial imagery. Experimental results demonstrate that great improvement can be achieved by using proposed feature fusion method.

Learning the kernel matrix with semidefinite programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive semidefinite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space - classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -using the labeled part of the data one can learn an embedding also for the unlabeled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.

A unifying view of multiple kernel learning

Recent research on multiple kernel learning has lead to a number of approaches for combining kernels in regularized risk minimization. The proposed approaches include different formulations of objectives and varying regularization strategies. In this paper we present a unifying optimization criterion for multiple kernel learning and show how existing formulations are subsumed as special cases. We also derive the criterion’s dual representation, which is suitable for general smooth optimization algorithms. Finally, we evaluate multiple kernel learning in this framework analytically using a Rademacher complexity bound on the generalization error and empirically in a set of experiments.

A unifying view of multiple kernel learning

Recent research on multiple kernel learning has lead to a number of approaches for combining kernels in regularized risk minimization. The proposed approaches include different formulations of objectives and varying regularization strategies. In this paper we present a unifying general optimization criterion for multiple kernel learning and show how existing formulations are subsumed as special cases. We also derive the criterion's dual representation, which is suitable for general smooth optimization algorithms. Finally, we evaluate multiple kernel learning in this framework analytically using a Rademacher complexity bound on the generalization error and empirically in a set of experiments.