Gross margins in Australian mohair enterprises and relationships with farm inputs, productivity and mohair quality

In the absence of financial information on Australian mohair enterprises we aimed to determine the gross margins (per dry sheep equivalent, DSE) and their relationships with farm inputs, productivity and mohair quality in Australian mohair enterprises. Using established Victorian Farm and Sheep Monitor Project protocols we collected data for the financial years 200405, 200506 and 200607 from farmers in south-eastern Australia and made comparisons with data from wool enterprises of similar farm area. Over 3 years the financial returns from mohair exceeded that from wool in terms of $/DSE ($23.0 v. 11.3) and $/ha ($132 v. $116). This result was achieved despite the mohair enterprises grazing their goats far less intensively compared with the grazing intensity of sheep (5.9 v. 10.311.1 DSE/ha) and by using far less phosphate fertiliser than used in the wool enterprises (2.2 v. 4.66.1 kg P/ha). These differences were counterbalanced by higher prices for mohair compared with fine wool ($13.15/kg v. $8.35/kg clean fibre). Gross margin for the mohair enterprise did not increase as stocking rate increased. Income from mohair sales declined as the proportion of does in the flock increased. Increasing the proportion of does in the flock was associated with a decline in the average price of mohair ($16/kg greasy at 42% does to $8/kg greasy at 83% does in the flock). This decline was closely associated with the increasing proportion of the total amount of mohair coarser than 34.0 μm (either fine hair or hair) plus stained mohair. The variation in profitability between farms indicates significant scope for many mohair enterprises to increase profit. A focus on producing finer quality mohair will increase mohair profitability.

Shear-enhanced solution precipitation : a simple process to produce short polymeric nanofibers

The growing interest in polymeric nanofibers has been increasing the push for the development of simple and efficient nanofiber-preparation techniques. We herein describe how a conventional solution process is readapted to suit the needs for fast and efficient production of short polymeric nanofibers. Poly(ethylene-co-acrylic acid) (PEAA), a semi-crystalline polymer, was used as model. When a PEAA solution was injected into an alcoholic non-solvent while simultaneously applying high shear to the non-solvent system, PEAA nanofibers were obtained with average diameter as thin as 113 nm and length as short as 4.5 _m. The fiber diameter and length were also adjustable by varying the operating parameters. This one-step technique advances the currently available nanofabrication tools by adjusting a widely accepted concept to the nano-scale. It may constitute a viable method for large-scale production of short polymeric nanofibers.

Uncertainty quantification for wind farm power generation

Accurate forecasting of wind farm power generation is essential for successful operation and management of wind farms and to minimize risks associated with their integration into energy systems. However, due to the inherent wind intermittency, wind power forecasts are highly prone to error and often far from being perfect. The purpose of this paper is to develop statistical methods for quantifying uncertainties associated with wind power generation forecasts. Prediction intervals (PIs) with a prescribed confidence level are constructed using the delta and bootstrap methods for neural network forecasts. The moving block bootstrap method is applied to preserve the correlation structure in wind power observations. The effectiveness and efficiency of these two methods for uncertainty quantification is examined using two month datasets taken from a wind farm in Australia. It is demonstrated that while all constructed PIs are theoretically valid, bootstrap PIs are more informative than delta PIs, and are therefore more useful for decision-making.

Variation in the whiteness and brightness of white Australian cashmere associated with farm of origin and fibre attributes

While white cashmere is preferred by processors, its whiteness and brightness is affected by country of origin, amino acid composition, nutrition and cashmere production of goats. This work aimed to quantify the factors which affect the whiteness and brightness of 36 batches of processed Australian white cashmere sourced from nine different farms. The cashmere was tested for tristimulus values brightness (Y) and whiteness, as measured by yellowness (Y-Z). Linear models, relating Y and Y-Z were fitted to farm of origin and other objective measurements. Mean attributes (range) were: mean fibre diameter, 16.9 µm (13.9–20.4 μm); fibre curvature, 45°/mm (31–59°/mm); clean washing yield, 91.3% (79.5–97.3%); Y, 78.7 (74.7–82.2); Y-Z, 11.9 (10.3–13.6). Farm alone accounted for 72% of the variation in Y and 65% of the variation in Y-Z (P < 0.001). Once farm had been taken into account only fibre curvature (P = 0.003) was significant in predicting Y and only clean washing yield (P = 0.047) affected Y-Z. Neither the proportion of the fleece present as guard hair (clean cashmere yield) nor cashmere staple length was a significant determinant of Y or Y-Z. For each 10°/mm increase in fibre curvature Y increased 1.3 units. For each 10% increase in clean washing yield Y-Z declined 0.9 units. Variations in Y and Y-Z among farms were probably related to differences in geographic and climatic conditions and were significantly correlated to cashmere production. The effect of clean washing yield was probably related to a reduction in suint content.

Sustainable farm families : future directions

The health and wellbeing of all Australians is pivotal for economic and social success of the nation. Current data reveals that the health status of people living in rural and remote populations is poorer than their metropolitan counterparts. However there is a lack of understanding of the specific health statistics of rural farming populations.

The Sustainable Farm Families (SFF) Future Directions program aims to fill this gap by providing ongoing evidence-based information and support to Australia’s agricultural industries, to gain insight into the health, wellbeing and safety of Australia’s rural farming populations.

Wind farm power uncertainty quantification using a mean-variance estimation method

This paper proposes an innovative optimized parametric method for construction of prediction intervals (PIs) for uncertainty quantification. The mean-variance estimation (MVE) method employs two separate neural network (NN) models to estimate the mean and variance of targets. A new training method is developed in this study that adjusts parameters of NN models through minimization of a PI-based cost functions. A simulated annealing method is applied for minimization of the nonlinear non-differentiable cost function. The performance of the proposed method for PI construction is examined using monthly data sets taken from a wind farm in Australia. PIs for the wind farm power generation are constructed with five confidence levels between 50% and 90%. Demonstrated results indicate that valid PIs constructed using the optimized MVE method have a quality much better than the traditional MVE-based PIs.

Variation in the whiteness and brightness of mohair associated with farm, season, and mohair attributes

This work aimed to quantify factors affecting the reflectance attributes of Australian white mohair sourced from five different farms and to evaluate the effect of season and year on mohair grown by goats of known genetic origin in a replicated study. For the season study the mohair was harvested every three months for two years. All goats and their fleeces were weighed. Mid-side samples were tested for fibre diameter attributes, clean washing yield (CWY), staple length (SL) and for tristimulus values X, Y, Z and Y-Z. For the farm study (n = 196), linear models, relating Y, Z and Y-Z were fitted to farm of origin and other objective measurements. For the season and year study (n = 176), data were analysed by ANOVA and then by linear analysis. The variation accounted for by farm alone was: X, 22%; Y, 24%; Z, 12%; Y-Z, 30% (P < 0.001). Once farm had been taken into account, the regression models for X, Y and Z had similar significant terms: mean fibre diameter (MFD), CWY, SL and fibre diameter CV; and correlation coefficients (057–0.65). For Y-Z, in addition to farm only MFD was significant (P = 1.8 × 10−9). While X, Y, Z and Y-Z were significantly associated with clean fleece weight (CFwt), CFwt was not significant in any final model. Season affected mohair Y (P = 2.5 × 10−24), Z (P = 2.3 × 10−20) and Y-Z (P = 6.8 × 10−22). Autumn grown mohair had higher Y and Z, and summer grown mohair had lower Z than mohair grown in other seasons. This resulted in summer grown mohair having the highest Y-Z and winter grown mohair having the lowest Y-Z than mohair grown in other seasons. The differences between years in Y, Z and Y-Z were significant but not large. When Y, Z and Y-Z were modeled with season and other mohair attributes, MFD, CWY, CFwt, incidence of medullated fibre (Med) and sire were also significant terms. This model accounted for 62.1% of the variance. Over the range of Med (0.3–4.2%), Y-Z increased by 11 T units. Increasing CFwt 0.5 kg was associated with a decline in Y-Z of 7.5 T units. The variation in Y, Z and Y-Z associated with sire effects were respectively 2.66, 3.77, and 1.04 T units. In the farm and the season studies increasing MFD was associated with lower Y and Z and higher Y-Z. The extent of the differences in tristimulus values between seasons and years, were unlikely to be of commercial importance. The extent of the differences between farms, and to variations in MFD and Med were large enough to be of commercial importance. Clean mohair colour was artefactually biased by MFD.

Addressing aquaculture-fisheries interactions through the implementation of the ecosystem approach to aquaculture (EAA

This review addresses how the ecosystem approach to aquaculture (EAA) can optimize aquaculture-fisheries interactions considering different spatial scales from farm, aquaculture zone and watershed through to the global market. Aquaculture and fisheries are closely related subsectors with frequent interactions, largely due to the sharing of common ecosystems and natural resources. Interactions are also born from the flow of biomass from fisheries to aquaculture through fish-based feeds (e.g. fishmeal, fish oil and trashfish), through the collection of wild seed and brookstock, and genetic resources and biomass transfer from aquaculture to fisheries through culture-based fisheries (CBF) and escapees. Negative effects include modification of habitats affecting fisheries resources and activities (e.g. mangrove clearing for shrimp ponds, seabed disturbances through anchoring of aquaculture cages or pens, damage to seagrasses, alteration to reproductive habitats, biodiversity loss). Eutrophication of waterbodies due to excess nutrient release leading to anoxia and fish mortality can also impact negatively on biodiversity and wild fish stocks. Release of diseases and chemicals also imposes some threats on fisheries. Yet there could be beneficial impacts; for example, aquaculture is increasingly contributing to capture fisheries through CBF and could contribute to restore overfished stocks. Aquaculture can offer alternative livelihoods to fisherfolk, providing increased opportunity to them and also to their families, and especially to women. Aquaculture-increased production and marketing can also enhance and indirectly improve processing and market access to similar fishery products. The ecosystem approach to aquaculture (EAA) is a strategy for the management of the sector that emphasizes intersectoral complementarities by taking into account the interactions between all the activities within ecologically meaningful boundaries and acknowledging the multiple services provided by ecosystems. The main objective of this review is to understand the status of aquaculture-fisheries interactions associated with the biological, technological, social, economic, environmental, policy, legal and other aspects of aquaculture development and to analyze how these interactions are or could be addressed with an EAA. Therefore, the review involves aspects of scoping, identification of issues, prioritizing, devising management tools and plans for minimizing negative effects and optimizing positive ones within the context of social-ecological resilience, at different relevant geographical scales. Many of the management measures suggested in this review must involve not only EAA but also an ecosystem approach to fisheries (EAF), especially to deal with issues such as fishery of wild seed and the management of fisheries to produce fishmeal/oil for pelleted feeds or for direct feeding with wet fish. The implementation of EAA and EAF should help to overcome the sectoral and intergovernmental fragmentation of resource management efforts and assist in the development of institutional mechanisms and private-sector arrangements for effective coordination among various sectors active in ecosystems in which aquaculture and fisheries operate and between the various levels of government. Ecosystem-based management involves a transition from traditional sectoral planning and decision-making to the application of a more holistic approach to integrated natural resource management in an adaptive manner.