Browsing by discipline "Agroteknologia (maatalousteknologia)"

In recent times climate change, decrease of fossil fuels and increase of their price have greatly increased worldwide interest in renewable energy sources. In Finland, there has been a lot of concentration towards forest industry’s secondary produced wood basis biomass, that forest industry uses for its energy production. Forest industry’s waste water cleaning process creates different kinds of sludge, which are either reused or destroyed by burning or transporting to waste treatment plant. Especially reuse of bio sludge is difficult, and waste area placing in the future is impossible or at least economically too expensive. In practice, sludge is treated by burning, and by drying it becomes a bio fuel. The energy use is the best way to destroy waste sludge. Because of the high water consist of the sludge it must be dried before burning. Drying the sludge with secondary energy flow with waste heat from forest industry processes increases energy income from the burning process and replaces the use of fossil fuels. The goal of this research was to find out the most optimal mixture of bark and sludge by changing different drying parameters. The experimental work was started by building a laboratory size fixed bed dryer for the energy technology experiment hall, where drying was studied by blowing heated air through the fuel layer. The dried fuel material was a mixture of bark and sludge, or just bark or sludge at different masses, different percentage mixtures and different temperatures. Making the drying curves was based on weight changes. In the test rig were probes for controlling and setting the temperature as the experiment expected. The temperature and weight changes were recorded to computer during the experiment. The drying experiments showed that sludge-bark mixture dries well, when the percentage of the sludge mass doesn’t increase over 50 %. When the share of sludge is higher, drying is no longer effective, which is due to channelling of the air through the dried fuel material in the fixed bed dryer. When drying the bark, increase of the temperature from 50 °C to 70 °C was much more effective than from 70 °C to 90 °C, the difference in drying time was about doubled.

The aim of this study was to calculate energy consumption of Finnish domestic farms by source books and compare these results with the actual field energy consumption. Base of this study was on Finnish and foreign research, according to which the energy consumption of domestic farms was calculated. Information about actual energy consumption was taken from enquiry executed by Työtehoseura, which was part of project done by Työtehoseura, University of Jyväskylä and Finnish Ministry of Agriculture and Forestry: Biokaasu maataloudessa. The study composed of dairy, cattle, farrow-growing and feederpig farms. Stanchion barn calculated energy consumption in TE-center areas I-II was between 2523–3734 kWh/cowplace/year (avg. 3129kWh) and real energy consumption was 1987–9000 kWh/cowplace/year (mv.5264 kWh). In the TE-center area III the calculated energy consumption was 2585–3796 kWh/cowplace/year(mv.3191kWh) and the real energy consumption was 1630–5024 kWh/cowplace/year (mv.4566 kWh). Parloe stalls calculated energy consumption in the TE-center areas I-II was 2480–4139 kWh/cowplace/year (mv.3310kWh) and real energy consumption was 2631– 6943 kWh/cowplace/year (mv.4571 kWh). In the TE-center area III calculated energy consumption was 2555–4214 kWh/cowplace/year (mv.3385 kWh) and real energy consumption was 1513–7763 kWh/cowplace/year (avg.5134 kWh). Energy consumption of barns for Calfs from 2 weeks to 6 months in the TE-center areas I-II was 445-652 kWh/calf /year (avg.549 kWh) and real consumption was 390-1263 kWh/calf/year (avg. 774 kWh). in the TE-center area III calculated energy consumption was 541-748 kWh/calf/year (avg.645 kWh) and real consumption was 856-1316 kWh/calf/year (avg. 1065). Calculated total energy consumption of barns for Calf from 6 months to slaughter in the TE-center areas I-II was 1143-1745 kWh/calf/year (avg. 1444 kWh) and real consumption was 1630-2567 kWh/calf/year (avg.2065 kWh). In the TE-center III calculated energy consumption was 1392-1994 kWh/calf/year (avg.1693 kWh) and real consumption was 329-1327 kWh/calf/year (avg.689 kWh). In the farrow-growing pig hoconsumption in the TE-center areas I-II calculated energy consumption was 1197-1518 kWh/ pigplace/year (avg.1308 kWh) and real consumption was 1494-3331 kWh/pigplace /year (avg. 2233) TE-center area III calculated energy consumption was 1375-1695 kWh (avg.1327) The real energy consumption can not be found. Fattening feeder pig hoconsumptions calculated energy consumption was in TE-center areas I-II 213-296 kWh/pigplace /year (avg 255 kWh), real consumption was 154-1662 kWh/pigplace /year (avg.489 kWh). TE-center area III calculated energy consumption was 355-438 kWh/pigplace /year (avg 793), The real energy consumption can not be found. Domestic farms energy consumption calculation was succesful. Real energy consumption can not be calculated very precisely from Työtehoseura’s material. Työtehoseura’s material included en-ergy consumption that doesn`t directly relate to farming. Comparison between the calculated and real energy consumption was made only if real consumption was known. In the comparison some conformity with calculated and real energy consumption was found, but there was also big differences.

The heart rate of an individual varies all the time. This phenomenon is called heart rate variability. Both respiration and physical activity induce variations in heart rate. Heart rate variability can be assessed by studying electrical changes in the heart cycle. Electrical changes can be monitored by measuring ECG (electrocardiography). The main target of this study was to find out cow’s normal heart rate variability while they were awake, ruminating or sleeping. For this study, the heart rate of nine cows was recorded as well as their behavior during that time. Cows were monitored while they were sleeping, ruminating, standing or lying down. Four of the cows were from Finland and five of them from Sweden. From those nine cows, 543 one minute samples were obtained. This was the first time that cow’s heart rate was studied during their sleep cycle. The cows were not stressed in any way and their autonomic nervous system was not affected by drugs. One minute samples were analyzed. Samples were gathered by using a Matlab-based computer program, CowSS. All samples were checked visually, and all errors, for instance missing S-peaks or the errors caused by timing, were corrected. Statistical analysis was made by using a linear mixed effects model. According to the analysis, the best way to represent a cow’s heart rate variability is to use RMSSD-value. RMSSD-value describes the variations of adjacent intervals in different recordings. The result of this study show that during sleep the heart rate and the heart variability of cows are different from humans´. Cows´ sleep periods (NREM- and REM-sleep) are shorter and during REM-sleep the parasympathetic toning is stronger in cows. Cow is herbivore and prey for many predators, which may well explain the dominance of the parasympathetic system. Parasympathetic toning works faster than sympathetic toning, and this may give the cow a better chance to escape.

Leys productivity decreases over time in Finland. After three years the yield is usually low. Most of the finnish leys are renovated less than five years old. Leys suffer from winter, foraging and grazing and they become thinner. Grass height and density correlate relatively well to the yield. Leys which are thinned out can be fixed with overseeding. The object of this study was to find out how grass overseeding affects to the growth and assess how profitable it is with grass roller. One year field experiment was made. It’s goal was to test the features of the Kivi-Pekka grass roller. Effects to the growth were also studied but the significance was considered low because of the one year test. Overseeding field experiment was made in 3, 5 and 8 years old leys. Yield was cut two times. The profitability of the grass roller was estimated with calculations. Yields can be maintained with overseeding and new plants can be added to the growth. Overseeding succeed better with clover than grasses referring to the literature. The density of old growth and moisture are main features which affect to succes in overseeding. Seeds need warmth, space for growing, moisture and good soil contact to germinate properly. There were only minor differences between the overseeding techniques and results. Surface seeding without harrowing or rolling wasn’t very effective. There were no significant differences between the yields of different treatments in field experiment. Newly sown seeds probably lost in growth competition. Yield value, renovating costs and seed cost had major effect to the profitability of overseeding. According to the calculations grass rollers profitability increased when overseeded area increased and the age of leys become longer.

Hectolitre mass (HLM) measurement is one of the oldest and still one of the most widely used methods for assessing grain quality. It indicates the amount of flour available for extraction in milling grains and the feed value of feed grains. Grain quality tests are often performed on grain in its arrival condition. Should arriving grain be above storage moisture, grain drying will change the measurable grain quality, namely HLM, and the actual quality parameters will only be revealed after drying. Sampling grain after drying is not always possible due to practical reasons, for example drying grain in a co-operated or a continuous dryer or a high-capacity commercial dryer. Some grain buyers in Finland use a moisture conversion tables for HLM, which date back to the subsequently repealed national Grain Trade Act. The accuracy of these conversion tables is uncertain. The purpose of the study was to create new moisture conversion factors for HLM, with which high-moisture grain HLM could be reliably converted into postdrying values. Literary research investigated factors affecting grain HLM and factors that cause HLM change during mechanical grain drying. In the experimental part wheat, barley and oats samples, in pre- and post-drying state, were analysed. The amount of HLM change and factors contributing to it, as well as the factors affecting HLM per se, were studied. The results were compared with values found in literary and previous documented studies. It was concluded that the Finnish Grain Trade Act grain moisture conversion tables, along with other literary sources, underestimate the HLM change in all tested grains. In barley and oats the difference was almost threefold, in wheat around one third. The factor with the greatest influence to the HLM among measured parameters was grain moisture. The effect of moisture was the most significant in all grains. In barley the thousand kernel weight was also found to be a highly significant factor affecting the HLM.

The aim of this study was to design a small scale grain drying facility and measure the effectiveness of grain drying. The small scale dryer was built by Antti-Teollisuus, a Finnish grain dryer manufacturer, near their research, development and production plant. Measurements were carried out in the autumn of 2008. The dryer could be used as a continuous flow dryer or as a batch dryer. Dryer was instrumented with suitable sensors to measure air temperature, relative humidity of air and air velocity. This information was then used to solve the efficiency of drying. Measurements went reasonably well, although moisture content from grain was quite high when compared to results given by moisture meter that grinds grain and gives the results straight away. Dryer performance was approx. 50-75 kW, which is quite near to the results found in earlier studies on the same subject. Energy consumption 3-6 MJ/kg water removed from grain was very near the results in previous studies of grain drying whether the dryer was large scale or small scale. In general, a small scale dryer can be used to study the phenomenon in grain drying quite well. For future studies are left for example inspecting how the shape or distance between air ducts inside the drying cells can be altered or what effect does increasing or decreasing drying air temperature or air velocity have on drying. For improved efficiency the grain dryer should be insulated, this could also prevent some air leaks and ensure that the data measured by the sensors is even more accurate.