10 resultados para Machine costs

em eResearch Archive - Queensland Department of Agriculture


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Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising methodology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.

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Replicable experimental studies using a novel experimental facility and a machine-based odour quantification technique were conducted to demonstrate the relationship between odour emission rates and pond loading rates. The odour quantification technique consisted of an electronic nose, AromaScan A32S, and an artificial neural network. Odour concentrations determined by olfactometry were used along with the AromaScan responses to train the artificial neural network. The trained network was able to predict the odour emission rates for the test data with a correlation coefficient of 0.98. Time averaged odour emission rates predicted by the machine-based odour quantification technique, were strongly correlated with volatile solids loading rate, demonstrating the increased magnitude of emissions from a heavily loaded effluent pond. However, it was not possible to obtain the same relationship between volatile solids loading rates and odour emission rates from the individual data. It is concluded that taking a limited number of odour samples over a short period is unlikely to provide a representative rate of odour emissions from an effluent pond. A continuous odour monitoring instrument will be required for that more demanding task.

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The paper revisits estimates of cost/benefit for eradication in Australia provided in 2001 which were based largely on information about a US ecosystem. The study had two major components; spread modelling using a cellular automation model provided by Joe Scanlan and an impact analysis undertaken by the remaining authors. The revised figures provided in this study increased the damage estimate from $2.8 billion to $45 billion and the benefit-cost ratio of eradication efforts improved from 25:1 to 390:1.

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Increase water use efficiency and productivity, and reduce energy and water usage and costs, of dairy and fodder enterprises, to reduce costs of milk production.

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This work was designed to provide the Australian structural radiata pine processing industry with some indications for improving stress grading methods and/or technologies to give an increase in structural grade yields, and significantly reduce processing costs without compromising product quality. To achieve this, advanced statistical techniques were used in conjunction with state-of-the-art property measurement systems applied to the same sample of sawn timber. Acoustic vibration analyses were conducted on green and dry boards. Raw data from existing in-line systems was captured on the same boards. The Metriguard HCLT stress rating system was used as the "reference" machine grading because of its current common use in the industry. A WoodEye optical scanning system and an X-ray LHG scanner were also able to provide relevant information on knots. The data set was analyzed using classical and advanced statistical tools to provide correlations between data sets, and to develop efficient strength and stiffness prediction equations. Reductions in non-structural dry volumes can be achieved..

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Executive summary. In this report we analyse implementation costs and benefits for agricultural management practices, grouped into farming systems. In order to do so, we compare plot scale gross margins for the dominant agricultural production systems (sugarcane, grazing and banana cultivation) in the NRM regions Wet Tropics, Burdekin Dry Tropics and Mackay Whitsundays. Furthermore, where available, we present investment requirements for changing to improved farming systems. It must be noted that transaction costs are not captured within this project. For sugarcane, this economic analysis shows that there are expected benefits to sugarcane growers in the different regions through transitions to C and B class farming systems. Further transition to A-class farming systems can come at a cost, depending on the capital investment required and the length of the investment period. Obviously, the costs and benefits will vary for each individual grower and will depend on their starting point and individual property scenario therefore each circumstance needs to be carefully considered before making a change in management practice. In grazing, overall, reducing stocking rates comes at a cost (reduced benefits). However, when operating at low utilisation rates in wetter country, lowering stocking rates can potentially come at a benefit. With win-win potential, extension is preferred to assist farmer in changing management practices to improve their land condition. When reducing stocking rates comes at a cost, incentives may be applicable to support change among farmers. For banana cultivation, the results indicate that the transition to C and B class management practices is a worthwhile proposition from an economic perspective. For a change from B to A class farming systems however, it is not worthwhile from a financial perspective. This is largely due to the large capital investment associated with the change in irrigation system and negative impact in whole of farm gross margin. Overall, benefits will vary for each individual grower depending on their starting point and their individual property scenario. The results presented in this report are one possible set of figures to show the changes in profitability of a grower operating in different management classes. The results in this report are not prescriptive of every landholder. Landholders will have different costs and benefits from transitioning to improved practices, even if similar operations are practiced, hence it is recommended that landholders that are willing to change management undertake their own research and analysis into the expected costs and benefits for their own soil types and property circumstances.

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In Queensland the subtropical strawberry ( Fragaria * ananassa) breeding program aims to combine traits into novel genotypes that increase production efficiency. The contribution of individual plant traits to cost and income under subtropical Queensland conditions was investigated, with the overall goal of improving the profitability of the industry through the release of new strawberry cultivars. The study involved specifying the production and marketing system using three cultivars of strawberry that are currently widely grown annually in southeast Queensland, developing methods to assess the economic impact of changes to the system, and identifying plant traits that influence outcomes from the system. From May through September P (price; $ punnet -1), V (monthly mass; tonne of fruit on the market) and M (calendar month; i.e. May=5) were found to be related ( r2=0.92) by the function (SE) P=4.741(0.469)-0.001630(0.0005) V-0.226(0.102) M using data from 2006 to 2010 for the Brisbane central market. Both income and cost elements in the gross margin were subject to sensitivity analysis. 'Harvesting' and 'Handling/Packing' 'Groups' of 'Activities' were the major contributors to variable costs (each >20%) in the gross margin analysis. Within the 'Harvesting Group', the 'Picking Activity' contributed most (>80%) with the trait 'display of fruit' having the greatest (33%) influence on the cost of the 'Picking Activity'. Within the 'Handling/Packing Group', the 'Packing Activity' contributed 50% of costs with the traits 'fruit shape', 'fruit size variation' and 'resistance to bruising' having the greatest (12-62%) influence on the cost of the 'Packing Activity'. Non-plant items (e.g. carton purchases) made up the other 50% of the costs within the 'Handling/Packing Group'. When any of the individual traits in the 'Harvesting' and 'Handling/Packing' groups were changed by one unit (on a 1-9 scale) the gross margin changed by up to 1%. Increasing yield increased the gross margin to a maximum (15% above present) at 1320 g plant -1 (94% above present). A 10% redistribution of total yield from September to May increased the gross margin by 23%. Increasing fruit size increased gross margin: a 75% increase in fruit size (to ~30 g) produced a 22% increase in the gross margin. The modified gross margin analysis developed in this study allowed simultaneous estimation of the gross margin for the producer and gross value of the industry. These parameters sometimes move in opposite directions.

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The recent emergence of heritable high level resistance to phosphine in stored grain pests is a serious concern among major grain growing countries around the world. Here we describe the genetics of phosphine resistance in the rust red flour beetle Tribolium castaneum (Herbst), a pest of stored grain as well as a genetic model organism. We investigated three field collected strains of T. castaneum viz., susceptible (QTC4), weakly resistant (QTC1012) and strongly resistant (QTC931) to phosphine. The dose-mortality responses of their test- and inter-cross progeny revealed that most resistance was conferred by a single major resistance gene in the weakly (3.2x) resistant strain. This gene was also found in the strongly resistant (431x) strain, together with a second major resistance gene and additional minor factors. The second major gene by itself confers only 12-206x resistance, suggesting that a strong synergistic epistatic interaction between the genes is responsible for the high level of resistance (431x) observed in the strongly resistant strain. Phosphine resistance is not sex linked and is inherited as an incompletely recessive, autosomal trait. The analysis of the phenotypic fitness response of a population derived from a single pair inter-strain cross between the susceptible and strongly resistant strains indicated the changes in the level of response in the strong resistance phenotype; however this effect was not consistent and apparently masked by the genetic background of the weakly resistant strain. The results from this work will inform phosphine resistance management strategies and provide a basis for the identification of the resistance genes.

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The aim of this review is to report changes in irrigated cotton water use from research projects and on-farm practice-change programs in Australia, in relation to both plant-based and irrigation engineering disciplines. At least 80% of the Australian cotton-growing area is irrigated using gravity surface-irrigation systems. This review found that, over 23 years, cotton crops utilise 6-7ML/ha of irrigation water, depending on the amount of seasonal rain received. The seasonal evapotranspiration of surface-irrigated crops averaged 729mm over this period. Over the past decade, water-use productivity by Australian cotton growers has improved by 40%. This has been achieved by both yield increases and more efficient water-management systems. The whole-farm irrigation efficiency index improved from 57% to 70%, and the crop water use index is >3kg/mm.ha, high by international standards. Yield increases over the last decade can be attributed to plant-breeding advances, the adoption of genetically modified varieties, and improved crop management. Also, there has been increased use of irrigation scheduling tools and furrow-irrigation system optimisation evaluations. This has reduced in-field deep-drainage losses. The largest loss component of the farm water balance on cotton farms is evaporation from on-farm water storages. Some farmers are changing to alternative systems such as centre pivots and lateral-move machines, and increasing numbers of these alternatives are expected. These systems can achieve considerable labour and water savings, but have significantly higher energy costs associated with water pumping and machine operation. The optimisation of interactions between water, soils, labour, carbon emissions and energy efficiency requires more research and on-farm evaluations. Standardisation of water-use efficiency measures and improved water measurement techniques for surface irrigation are important research outcomes to enable valid irrigation benchmarks to be established and compared. Water-use performance is highly variable between cotton farmers and farming fields and across regions. Therefore, site-specific measurement is important. The range in the presented datasets indicates potential for further improvement in water-use efficiency and productivity on Australian cotton farms.

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This study compared pregnancy rates (PRs) and costs per calf born after fixed-time artificial insemination (FTAI) or AI after estrus detection (i.e., estrus detection and AI, EDAI), before and after a single PGF2α treatment in Bos indicus (Brahman-cross) heifers. On Day 0, the body weight, body condition score, and presence of a CL (46% of heifers) were determined. The heifers were then alternately allocated to one of two FTAI groups (FTAI-1, n = 139) and (FTAI-2, n = 141) and an EDAI group (n = 273). Heifers in the FTAI groups received an intravaginal progesterone-releasing device (IPRD; 0.78 g of progesterone) and 1 mg of estradiol benzoate intramuscularly (im) on Day 0. Eight days later, the IPRD was removed and heifers received 500 μg of PGF2α and 300 IU of eCG im; 24 hours later, they received 1 mg estradiol benzoate im and were submitted to FTAI 30 to 34 hours later (54 and 58 hours after IPRD removal). Heifers in the FTAI-2 group started treatment 8 days after those in the FTAI-1 group. Heifers in the EDAI group were inseminated approximately 12 hours after the detection of estrus between Days 4 and 9 at which time the heifers that had not been detected in estrus received 500 μg of PGF2α im and EDAI continued until Day 13. Heifers in the FTAI groups had a higher overall PR (proportion pregnant as per the entire group) than the EDAI group (34.6% vs. 23.2%; P = 0.003), however, conception rate (PR of heifers submitted for AI) tended to favor the estrus detection group (34.6% vs. 44.1%; P = 0.059). The cost per AI calf born was estimated to be $267.67 and $291.37 for the FTAI and EDAI groups, respectively. It was concluded that in Brahman heifers typical of those annually mated in northern Australia FTAI compared with EDAI increases the number of heifers pregnant and reduces the cost per calf born.