Adsorption of sugar beet herbicides to Finnish soils

Three sugar beet herbicides, ethofumesate, phenmedipham and metamitron, are currently used on conventional sugar beet cultivation, while new varieties of herbicide resistant (HR) sugar beet, tolerant of glyphosate or glufosinate-ammonium, are under field testing in Finland. Little knowledge has so far been available on the adsorption of these herbicides to Finnish soils. The adsorption of these five herbicides was studied using the batch equilibrium method in 21 soil samples collected from different depths. Soil properties like organic carbon content, texture, pH and partly the phosphorus and oxide content of the soils were tested against the adsorption coefficients of the herbicides. In general, the herbicides studied could be arranged according to their adsorption coefficients as follows: glyphosate>phenmedipham>ethofumesate approximately glufosinate-ammonium>metamitron, metamitron meaning the highest risk of leaching. None of the measured soil parameters could alone explain the adsorption mechanism of these five herbicides. The results can be used in model assessments of risk for leaching to ground water resulting from weed control of sugar beet in Finland.     

Spray charging of droplets in a wet scrubber

The experimental setup was designed to study the effect of natural charging of droplets during spraying (called spray electrification or spray charging) in a wet scrubber. The influence of nozzle material, liquid feed rate, and liquid composition on the specific charge (charge per unit volume of liquid) formed in spraying was measured. It was found that the nozzle material has no measurable effect on this specific charge. Increasing the liquid feed rate increases the specific charge significantly. Increasing solute (NaCl or NaOH) concentration increases the conductivity that decreases the formed specific charge. The same correlation between the specific charge and conductivity was also observed with real scrubbing liquids. It is proposed that the specific charge depends only on the value of liquid conductivity, not on liquid composition. The conductivity of the tested scrubbing liquids was high enough to suppress the formation of a high specific charge during spraying.     

Tropical Timber Rush in Peruvian Amazonia: Spatial Allocation of Forest Concessions in an Uninventoried Frontier

Land-use allocation has important implications for the conservation and management of tropical forests. Peru’s forestry regime has recently been reformed and more than 7 million ha has been assigned as forest concessions. This potentially has a drastic impact on the land-use practices and species composition of the assigned areas. Nevertheless, the environmental variation found within the concessions and the process applied to delimit them are poorly studied and documented. Thus, it is difficult to estimate the biological impacts of forestry activities in concessions or plan them sustainably. This paper reveals the characteristics of the current concession allocation in Loreto, Peruvian Amazonia, using environmental and access-related variables and compares the concessions to other major land-use assignments. The work draws on a number of data sets describing land-use, ecosystem diversity, and fluvial network in the region. According to our data, certain environment types such as relatively fertile Pebas soils are overrepresented in the concessions, while others, like floodplain forests, are underrepresented in comparison to other land-use assignments. Concessions also have less anthropogenic disturbance than other areas. Furthermore, concessions are located on average further from the river network than the other land-use assignments studied. We claim that forest classification based on productivity, soil fertility, accessibility, and biodiversity patterns is an achievable long-term goal for forest authorities in Peru, and in many other tropical countries. We present a rough design of a geographic information system incorporating environmental, logging, and access-related data that could be applied to approach this goal in Peru.     

Quantification of a Primary Production Model Using Two Versions of the Spectral Distribution of the Phytoplankton Absorption Coefficient

In a recent publication, we discussed and presented a semi-empirical phytoplankton primary production model. In the present paper, our main purpose is to determine how the algorithms of a primary production model change when different values of specific absorption coefficient by phytoplankton are used in the model. A new version of our earlier model was quantified for this purpose. Differences between the previous and the new models are as follows: (a) the spectra of the specific absorption coefficient of light by phytoplankton differ in the new model from those used in the previous model, and (b) the quantification of the new model brings about a change in the parameters of the algorithm for the quantum yield of carbon fixation. We compared the results of primary production profiles obtained by the new model with those measured in situ and also with the values obtained by the previous model. Due to an adequate choice of quantification parameters, both the old and new models give rather close values of phytoplankton primary production. In the present study, the computational algorithms of both models have been automated. The resulting programs calculate the temporal–spatial variability of phytoplankton primary production, providing hourly values from morning to evening, daily sums, and monthly sums. The input of a table of initial parameters and selected depths produces rapid calculations of the model's results, which are given as vertical profiles of primary production and areal values.     

Exploring human–nature interactions in national parks with social media photographs and computer vision

Understanding the activities and preferences of visitors is crucial for managing protected areas and planning conservation strategies. Conservation culturomics promotes the use of user-generated online content in conservation science. Geotagged social media content is a unique source of in situ information on human presence and activities in nature. Photographs posted on social media platforms are a promising source of information, but analyzing large volumes of photographs manually remains laborious. We examined the application of state-of-the-art computer-vision methods to studying human–nature interactions. We used semantic clustering, scene classification, and object detection to automatically analyze photographs taken in Finnish national parks by domestic and international visitors. Our results showed that human–nature interactions can be extracted from user-generated photographs with computer vision. The different methods complemented each other by revealing broad visual themes related to level of the data set, landscape photogeneity, and human activities. Geotagged photographs revealed distinct regional profiles for national parks (e.g., preferences in landscapes and activities), which are potentially useful in park management. Photographic content differed between domestic and international visitors, which indicates differences in activities and preferences. Information extracted automatically from photographs can help identify preferences among diverse visitor groups, which can be used to create profiles of national parks for conservation marketing and to support conservation strategies that rely on public acceptance. The application of computer-vision methods to automatic content analysis of photographs should be explored further in conservation culturomics, particularly in combination with rich metadata available on social media platforms.