Wheat and lentil crop loss and harvest difficulties doe to wild tomato (Solanum triflorum)

Non-Peer ReviewedCrop loss studies were conducted in wheat and lentil near Delisle, Laird, and Vonda, Saskatchewan in 1991 and 1992. Wheat yield and biomass were reduced at one of four sites . Lentil yield was reduced at three of six sites; while lentil biomass was reduced at four of six sites. Wild tomato was most competitive when it emerged early and at high density, and when the crop vigour was low. In the fall of 1992, data were collected on the effect of wild tomato on harvestability. Wheat harvestability is not affected by wild tomato. Wild tomato caused soil to adhere to the lentil seed (earth-tag), and increased the moisture content of the lentil sample. Wild tomato berry juice mixed with harvest debris and this mixture plugged the concaves and the augers of the combine. Wild tomato seed is being spread by harvest equipment

Establishment of short rotation forage crops

Non-Peer ReviewedForages could provide annual crop producers with a cash hay crop as a viable option in rotation. However, traditional perennial forage crop species that are difficult to establish and left in for many years are not the best option for short rotations of forage (1-3 years) and annual crops. Short-lived grass species that establish quickly and produce more forage for one to three years would provide traditional crop producers with a cash crop that would fit in their crop rotation system. New annual crops have not been tested as companion crops for establishment of grasses with high seedling vigour. The objective of the project is to determine the establishment success (risk) and first year production of fast-establishing forage grasses as affected by soil zone, companion crop, and legume associate

Liquid swine manure application to forage soil: effect on soil carbon and economic returns

Non-Peer ReviewedHog production has been on the increase in Saskatchewan for the past several years. This has lead to an increase in the environmental interest surrounding the application of liquid hog manure. The expected increase in swine production operations will lead to an increase in demand for suitable land area to properly dispose of this effluent within the economic transport distance of the collection site. Soil injection of liquid swine effluent into forage crops will produce two types of benefits: increased forage crop production and an increase in soil carbon. A three-year study was conducted to determine the effects on injecting different rates of swine effluent into three types of forage crops: alfalfa, Russian Wild Rye and brome-alfalfa The effluent treated plots were sampled to determine if there has been any significant increases in soil carbon compared to untreated control plots. The economic distance which hog manure can be transported depends on the cost of transport and application and the short-term returns to be realized from the additional yield produced by the fertilizing effect of the manure. Results showed that the yearly application of the low rate of liquid hog manure into the brome/alfalfa forage crop produced the greatest net return to the forage grower

Effect of oat and barley cover crop seeding rate on greenfeed and forage yield in northeast Saskatchewan

Non-Peer Reviewe

A summary of forage variety performance in Saskatchewan (1992-1996)

Non-Peer Reviewe

Shallow injection of hog manure into grassland

Non-Peer ReviewedWith the expected increase in hog production in Saskatchewan, concern has risen regarding the availability of sufficient land to utilize the manure resource to its full potential as a source of plant nutrients. Increases in inorganic fertilizer costs and concerns about the environmental effects of alternative manure disposal methods have stimulated interest in the use of livestock manure as a plant nutrient source (Hoff , Nelson and Sutton, 1981). Liquid hog manure is a valuable plant nutrient source, which is best perceived as a dilute multi-nutrient solution fertilizer blend. Therefore, application rates in the order of several thousand liters per hectare are required to meet the nutrient requirements of the crop versus 90 to 200 liters per hectare for concentrated commercial solution fertilizer blends (Schoenau, 1997). Ammonia-N losses from livestock manure have been reported to range from 10 to 90% when surface-applied to the soil (Vanderholm, 1975). Hoff, Nelson and Sutton, (1981) showed that 14 to 65% of the ammonia-N was lost over a 3.5 day period, when the hog manure was broadcast on the soil surface. However, if the hog manure was injected, the ammonia-N losses were reduced to 2.5% or less. Hoff, Nelson and Sutton, (1981) suggested that injecting hog manure into the soil would maximize the nutrient value of the hog manure as a fertilizer and would minimize surface water and atmospheric N contamination. There currently is little information on the plant and soil response to injection of hog manure into grasslands on the Canadian prairies. The objectives of the study are (1) to evaluate the practicality of shallow injection of hog manure into typical grassland stands in Saskatchewan (2) to determine the effect hog manure has on forage yield, forage quality and forage nitrate levels at various application rates (3) and to determine the effect on soil nutrients under various hog manure application rates

Prozessmodell für selektives Laserstrahlschmelzen: Ein Prozessmodell für das Qualitätsmanagement des Fertigungsprozesses

The research on Additive Manufacturing technologies (for example Selective Laser Melting) has been intensified due to a greater freedom in designing and manufacturing of mechanical workpieces. Thus, a reproducible process quality is required in order to assure the product quality. By means of process modelling and the definition of process indicators, process management is facilitated and process optimization is achieved

Intelligent pattern recognition of SLM machine energy data

Selective Laser Melting (SLM) is an additive manufacturing process, in which the research has been increasing over the past few years to meet customer-specific requirements. Different parameters from the process and the machine components have been monitored in order to obtain vital information such as productivity of the machine and quality of the manufactured workpiece. The monitoring of parameters related to energy is also realized, but the utilisation of such data is usually performed for determining basic information, for instance, from energy consumption. By applying machine learning algorithms on these data, it is possible to identify not only the steps of the manufacturing process, but also its behaviour patterns. Along with these algorithms, evidences regarding the conditions of components and anomalies can be detected in the acquired data. The results can be used to point out the process errors and component faults and can be adopted to analyse the energy efficiency of the SLM process by comparing energy consumption of one single layer during the manufacturing of different components. Moreover, the state of the manufacturing process and the machine can be determined automatically and applied to predict failures in order to launch appropriate counter measures

Intelligent pattern recognition of SLM machine energy data

Selective Laser Melting (SLM) is an additive manufacturing process, in which the research has been increasing over the past few years to meet customer-specific requirements. Different parameters from the process and the machine components have been monitored in order to obtain vital information such as productivity of the machine and quality of the manufactured workpiece. The monitoring of parameters related to energy is also realized, but the utilisation of such data is usually performed for determining basic information, for instance, from energy consumption. By applying machine learning algorithms on these data, it is possible to identify not only the steps of the manufacturing process, but also its behaviour patterns. Along with these algorithms, evidences regarding the conditions of components and anomalies can be detected in the acquired data. The results can be used to point out the process errors and component faults and can be adopted to analyse the energy efficiency of the SLM process by comparing energy consumption of one single layer during the manufacturing of different components. Moreover, the state of the manufacturing process and the machine can be determined automatically and applied to predict failures in order to launch appropriate counter measures