Maamme perunalajikkeiden viljelylaajuus ja viljelyalueet

Vuoden 1950 maatalouslaskennan yhteydessä suoritettu otantatiedustelu perunalajikkeiden viljelylaajuuksista osoittaa, että myöskin Suomessa viljelty perunalajikkeisto on varsin kirjava, ja että huomattavan suurilla aloilla vielä viljellään tuntemattomia, sekalaisia ja maatiaislajikkeita. Yleisimmin viljelty perunalajikkeemme on Ruusulehti, mutta senkään viljelyala ei ole ollut suurempi kuin 1/8 maan koko peruna-alasta. Selvitettyjen lajikkeiden hallussa oleva viljelyala on ollut 2/3 koko peruna-alasta, joten kolmanneksella viljelyalasta vielä viljellään sekalaisia (10.7 %), tarkemmin määrittelemättömiä tai tuntemattomia (18.9 %) ja maatiaisia (3.6 %). Vastaisen lajikeohjauksen tulisi epäilemättä kohdistua juuri näiden ryhmien osuuden pienentämiseen. Niinikään olisi pyrittävä supistamaan myös vanhojen heikkosatoisten perunalajikkeiden viljelyä. Edelleen olisi lajikevalintaohjaus kohdistettava erityisesti pienikokoisiin viljelmiin, joiden perunalajikkeisto kaipaa eniten uusimista, kuten taulukosta 2 voidaan todeta. Toiselta puolen on lajikeohjausta suunniteltaessa ja toteutettaessa muistettava, että valtaosa maan perunasadosta tuotetaan yli 10 ha peltoa sisältäviltä viljelmiltä. Tulevaa lajikeohjausta ja sen tuloksellisuuden seuraamista silmällä pitäen tullaan vastaisuudessakin panemaan toimeen esitetynlaisia lajiketiedusteluja. Suunnitelmien mukaan tapahtuisivat tiedustelut joka viides vuosi

Can models for forest attributes based on airborne laser scanning be generalized for different silvicultural management systems?

In Finland, interest in continuous cover forestry (CCF) has increased rapidly in recent years. During those years CCF has been examined from various viewpoints but not from the perspective of forest inventories. This holds especially true for applications based on remote sensing. Conversely, airborne laser scanning (ALS) data have been widely used to predict forest characteristics such as size distribution and vertical forest structure, which are closely related to the forest information needs of CCF. In this study we used the area-based approach to predict a set of stand attributes from ALS data (5 pulses per m2) in a CCF forest management experiment in Katajama & BULL;ki, eastern Finland. In addition to the CCF stands, the experiment included shelterwood stands and untreated stands. The predicted attributes included volume, biomass, basal area, number of stems, mean diameter, Lorey's height, dominant height, standing dead wood volume, parameters of the theoretical stem diameter distribution model, understory height and number of understory stems. Our main aim was to test whether the same model could be used across different management systems. The accuracy of the attributes predicted for the CCF stands was compared with the predictions for the other management systems in the same experiment. We also compared and discussed our results in relation to the even-aged stand attribute predictions that were conducted by using separate operational forest data collected from sites surrounding Katajama & BULL;ki. The results showed that forest data from the different management systems could be combined into a single model of a stand attribute, i.e., ALS metrics were found to be suitable for comparing different management systems in regard to differences in forest structure. The accuracy of the predicted attributes in the CCF plots was comparable to that of the other management alternatives in the experiment. The accuracy was also comparable to that of even-aged forests. The results of this study were promising; the stand attributes of CCF-managed forests could be predicted analogously to those of other management systems. This indicates that for the purposes of forest inventories there may not be a need to stratify forest lands by management system. It should be noted, however, that the study area was relatively small, that the forest stands were harvested in the 1980 s, and that the attributes may not have been completely exhaustive for CCF

Single-step genomic evaluation of Russian dairy cattle using internal and external information

Genomic data are widely used in predicting the breeding values of dairy cattle. The accuracy of genomic prediction depends on the size of the reference population and how related the candidate animals are to it. For populations with limited numbers of progeny-tested bulls, the reference populations must include cows and data from external populations. The aim of this study was to implement state-of-the-art single-step genomic evaluations for milk and fat yield in Holstein and Russian Black & White cattle in the Leningrad region (LR, Russia), using only a limited number of genotyped animals. We complemented internal information with external pseudo-phenotypic and genotypic data of bulls from the neighbouring Danish, Finnish and Swedish Holstein (DFS) population. Three data scenarios were used to perform single-step GBLUP predictions in the LR dairy cattle population. The first scenario was based on the original LR reference population, which constituted 1,080 genotyped cows and 427 genotyped bulls. In the second scenario, the genotypes of 414 bulls related to the LR from the DFS population were added to the reference population. In the third scenario, LR data were further augmented with pseudo-phenotypic data from the DFS population. The inclusion of foreign information increased the validation reliability of the milk yield by up to 30%. Suboptimal data recording practices hindered the improvement of fat yield. We confirmed that the single-step model is suitable for populations with a low number of genotyped animals, especially when external information is integrated into the evaluations. Genomic prediction in populations with a low number of progeny-tested bulls can be based on data from genotyped cows and on the inclusion of genotypes and pseudo-phenotypes from the external population. This approach increased the validation reliability of the implemented single-step model in the milk yield, but shortcomings in the LR data recording scheme prevented improvements in fat yield.Peer reviewe

Variation of Absorption Angstrom Exponent in Aerosols From Different Emission Sources

The absorption Angstrom exponent (AAE) describes the spectral dependence of light absorption by aerosols. AAE is typically used to differentiate between different aerosol types for example., black carbon, brown carbon, and dust particles. In this study, the variation of AAE was investigated mainly in fresh aerosol emissions from different fuel and combustion types, including emissions from ships, buses, coal-fired power plants, and residential wood burning. The results were assembled to provide a compendium of AAE values from different emission sources. A dual-spot aethalometer (AE33) was used in all measurements to obtain the light absorption coefficients at seven wavelengths (370-950 nm). AAE(470/950) varied greatly between the different emission sources, ranging from -0.2 +/- 0.7 to 3.0 +/- 0.8. The correlation between the AAE(470/950) and AAE(370-950) results was good (R-2 = 0.95) and the mean bias error between these was 0.02. In the ship engine exhaust emissions, the highest AAE(470/950) values (up to 2.0 +/- 0.1) were observed when high sulfur content heavy fuel oil was used, whereas low sulfur content fuels had the lowest AAE(470/950) (0.9-1.1). In the diesel bus exhaust emissions, AAE(470/950) increased in the order of acceleration (0.8 +/- 0.1), deceleration (1.1 +/- 0.1), and steady driving (1.2 +/- 0.1). In the coal-fired power plant emissions, the variation of AAE(470/950) was substantial (from -0.1 +/- 2.1 to 0.9 +/- 1.6) due to the differences in the fuels and flue gas cleaning conditions. Fresh wood-burning derived aerosols had AAE(470/950) from 1.1 +/- 0.1 (modern masonry heater) to 1.4 +/- 0.1 (pellet boiler), lower than typically associated with wood burning, while the burn cycle phase affected AAE variation.Peer reviewe

Investigating the use of 3-D full-waveform inversion to characterize the host rock at a geological disposal site

The U.K. government has a policy to dispose of higher activity radioactive waste in a geological disposal facility (GDF), which will have multiple protective barriers to keep the waste isolated and to ensure no harmful quantities of radioactivity are able to reach the surface. Currently no specific GDF site in the United Kingdom has been chosen but, once it has, the site is likely to be investigated using seismic methods. In this study, we explore whether 3-D full-waveform inversion (FWI) of seismic data can be used to map changes in physical properties caused by the construction of the site, specifically tunnel-induced fracturing. We have built a synthetic model for a GDF located in granite at 1000 m depth below the surface, since granite is one of the candidate host rocks due to its high strength and low permeability and the GDF could be located at such a depth. We use an effective medium model to predict changes in P-wave velocity associated with tunnel-induced fracturing, within the spatial limits of an excavated disturbed zone (EdZ), modelled here as an increase in fracture density around the tunnel. We then generate synthetic seismic data using a number of different experimental geometries to investigate how they affect the performance of FWI in recovering subsurface P-wave velocity structure. We find that the location and velocity of the EdZ are recovered well, especially when data recorded on tunnel receivers are included in the inversion. Our findings show that 3-D FWI could be a useful tool for characterizing the subsurface and changes in fracture properties caused during construction, and make a suite of suggestions on how to proceed once a potential GDF site has been identified and the geological setting is known

Maamme perunalajikkeiden viljelylaajuus ja viljelyalueet

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