3 resultados para Using mobile phones for development

em eResearch Archive - Queensland Department of Agriculture


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Farmlets, each of 20 cows, were established to field test five milk production systems and provide a learning platform for farmers and researchers in a subtropical environment. The systems were developed through desktop modelling and industry consultation in response to the need for substantial increases in farm milk production following deregulation of the industry. Four of the systems were based on grazing and the continued use of existing farmland resource bases, whereas the fifth comprised a feedlot and associated forage base developed as a greenfield site. The field evaluation was conducted over 4 years under more adverse environmental conditions than anticipated with below average rainfall and restrictions on irrigation. For the grazed systems, mean annual milk yield per cow ranged from 6330 kg/year (1.9 cows/ha) for a herd based on rain-grown tropical pastures to 7617 kg/year (3.0 cows/ha) where animals were based on temperate and tropical irrigated forages. For the feedlot herd, production of 9460 kg/cow.year (4.3 cows/ha of forage base) was achieved. For all herds, the level of production achieved required annual inputs of concentrates of similar to 3 t DM/animal and purchased conserved fodder from 0.3 to 1.5 t DM/animal. This level of supplementary feeding made a major contribution to total farm nutrient inputs, contributing 50% or more of the nitrogen, phosphorus and potassium entering the farming system, and presents challenges to the management of manure and urine that results from the higher stocking rates enabled. Mean annual milk production for the five systems ranged from 88 to 105% of that predicted by the desktop modelling. This level of agreement for the grazed systems was achieved with minimal overall change in predicted feed inputs; however, the feedlot system required a substantial increase in inputs over those predicted. Reproductive performance for all systems was poorer than anticipated, particularly over the summer mating period. We conclude that the desktop model, developed as a rapid response to assist farmers modify their current farming systems, provided a reasonable prediction of inputs required and milk production. Further model development would need to consider more closely climate variability, the limitations summer temperatures place on reproductive success and the feed requirements of feedlot herds.

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Khaya senegalensis (African mahogany or dry-zone mahogany) is a high-value hardwood timber species with great potential for forest plantations in northern Australia. The species is distributed across the sub-Saharan belt from Senegal to Sudan and Uganda. Because of heavy exploitation and constraints on natural regeneration and sustainable planting, it is now classified as a vulnerable species. Here, we describe the development of microsatellite markers for K. senegalensis using next-generation sequencing to assess its intra-specific diversity across its natural range, which is a key for successful breeding programs and effective conservation management of the species. Next-generation sequencing yielded 93943 sequences with an average read length of 234bp. The assembled sequences contained 1030 simple sequence repeats, with primers designed for 522 microsatellite loci. Twenty-one microsatellite loci were tested with 11 showing reliable amplification and polymorphism in K. senegalensis. The 11 novel microsatellites, together with one previously published, were used to assess 73 accessions belonging to the Australian K. senegalensis domestication program, sampled from across the natural range of the species. STRUCTURE analysis shows two major clusters, one comprising mainly accessions from west Africa (Senegal to Benin) and the second based in the far eastern limits of the range in Sudan and Uganda. Higher levels of genetic diversity were found in material from western Africa. This suggests that new seed collections from this region may yield more diverse genotypes than those originating from Sudan and Uganda in eastern Africa.

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BACKGROUND: In order to rapidly and efficiently screen potential biofuel feedstock candidates for quintessential traits, robust high-throughput analytical techniques must be developed and honed. The traditional methods of measuring lignin syringyl/guaiacyl (S/G) ratio can be laborious, involve hazardous reagents, and/or be destructive. Vibrational spectroscopy can furnish high-throughput instrumentation without the limitations of the traditional techniques. Spectral data from mid-infrared, near-infrared, and Raman spectroscopies was combined with S/G ratios, obtained using pyrolysis molecular beam mass spectrometry, from 245 different eucalypt and Acacia trees across 17 species. Iterations of spectral processing allowed the assembly of robust predictive models using partial least squares (PLS). RESULTS: The PLS models were rigorously evaluated using three different randomly generated calibration and validation sets for each spectral processing approach. Root mean standard errors of prediction for validation sets were lowest for models comprised of Raman (0.13 to 0.16) and mid-infrared (0.13 to 0.15) spectral data, while near-infrared spectroscopy led to more erroneous predictions (0.18 to 0.21). Correlation coefficients (r) for the validation sets followed a similar pattern: Raman (0.89 to 0.91), mid-infrared (0.87 to 0.91), and near-infrared (0.79 to 0.82). These statistics signify that Raman and mid-infrared spectroscopy led to the most accurate predictions of S/G ratio in a diverse consortium of feedstocks. CONCLUSION: Eucalypts present an attractive option for biofuel and biochemical production. Given the assortment of over 900 different species of Eucalyptus and Corymbia, in addition to various species of Acacia, it is necessary to isolate those possessing ideal biofuel traits. This research has demonstrated the validity of vibrational spectroscopy to efficiently partition different potential biofuel feedstocks according to lignin S/G ratio, significantly reducing experiment and analysis time and expense while providing non-destructive, accurate, global, predictive models encompassing a diverse array of feedstocks.