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To know effects of elevated ozone (eO3) response to beech (Fagus crenata) and oak (Quercus mongolica var. crispula) saplings, focusing on shoot elongation and leaf nutrient characteristics, beech and oak were grown in a free-air ozone-enrichment system (concentration: 35 ppb (aO3) vs. 75 ppb (eO3)) for two years under different soils (volcanic ash soil vs. brown forest soil). This treatment resulted in lower O3 tolerance in beech than in oak. The ratio of 2nd shoot flush showed a characteristic response to eO3 fumigation in beech and oak. For beech, the ratio was less than 0.5, while for oak, it ranged between 0.4 and 0.6. There was no difference in the photosynthetic rate of oak, although the photosynthetic rate of beech was slightly lower under elevated O3. Principal component analysis provided a good summary of the traits of beech and oak in response to O3 enrichment and soil combination, with O3-enriched beech characterized by higher Ca and lower N, P, K and Amax in the 1st component and lower Mg and Mn in the 2nd component. O3 -treated oak in O3-enriched brown forest soils was characterized by high N, P, K and Amax, while the influence of the 2nd component was small. Increased soil acidification will increase soil Mn and Al concentrations and may reduce the photosynthetic activity of beech saplings in eO3.

The Shirakami Mountains are a World Natural Heritage site because of their vast beech (Fagus crenata) forests and high diversity of flora and fauna. As global warming progresses, the area suitable for F. crenata growth is expected to shift from low to higher elevations. However, no studies have examined the current elevational distribution of the species’ regeneration. Here, we investigated the amount of F. crenata regeneration in the Shirakami Mountains and compared this amount among different elevations. Our nine study sites covered forests ranging from 269 m to 662 m, which covers the low to middle parts of the elevational distribution of F. crenata. At each site, we established three 20 m × 20 m plots and conducted a tree census. We approximated the regeneration density as a function of the diameter at breast height (DBH) distribution of F. crenata at each site using a power function: density = a DBHb. We found that a and b decreased and increased, respectively, as elevation decreased. Generalized linear mixed model analysis also indicated that decreasing elevation significantly decreased the number of seedlings of F. crenata. These results suggest decreasing regeneration at low elevation. Although it is difficult to identify the main causes of this pattern, we predict that regeneration may become even poorer if climate warming continues. Long-term monitoring along elevational clines will help us track the detailed effects of global warming on the beech forests.

Identifikacija i klasifikacija drva u drvnoj industriji ključna je za trgovinu, proizvodnju i znanost o drvu. Tradicionalne metode identifikacije vrste drva složene su, dugotrajne i skupe te zahtijevaju stručnost s područja znanosti o drvu. Za bolju identifikaciju vrste drva tradicionalne su metode zamijenjene konvolucijskim neuronskim mrežama (CNN), odnosno alatom za duboko učenje. Za razliku od ranijih studija koje su se koristile unaprijed obučenim modelima, u ovoj je studiji predložena nova arhitektura dizajnirana upravo za skup podataka WOOD-AUTH kako bi se razvio novi 2D CNN model. Zbirka podataka obuhvaća vizualne prikaze visoke razlučivosti 12 različitih vrsta drva. Cilj je bio stvoriti jednostavniji i brži model kao alternativu dugotrajnim i složenim modelima klasifikacije drva. Za razliku od prethodnih istraživanja, u ovom je istraživanju primijenjena nova 2D CNN mreža koja se temelji na 12 vrsta drva. Visoka točnost i brzo vrijeme izračuna postignuti su korištenjem manjeg broja slojeva (tri sloja) konvolucijske neuronske mreže. Predloženim je modelom postignuta točnost od 94 %, preciznost od 87 %, opoziv od 81 %, F1 rezultat od 80 % i vrijeme izračuna od 112 minuta i 27 sekundi. Model 2D CNN pokazao se boljim od modela transfernog učenja u smislu epohe poduke. Primarna prednost modela jest njegova sposobnost postizanja visoke točnosti uz kraće vrijeme izračuna, čak i pri visokim epohama u usporedbi s drugim modelima. Prezentirani 2D CNN model dao je zadovoljavajuće rezultate za klasifikaciju vrste drva. The woodworking industry’s recognition and classification of timber is essential for trade, production and timber science. Traditional methods of identifying wood types are complex, time-consuming, costly and require expertise in wood science. Traditional techniques have been replaced by convolutional neural networks (CNNs), a deep learning tool to better identify wood species. In contrast to earlier studies that used pretrained models, a novel architecture designed explicitly for the WOOD-AUTH dataset was proposed in this study to develop a new 2D CNN model. The data collection encompasses high-level visual representations of 12 distinct types of timber. It is aimed to create a simpler and faster model as an alternative to time-consuming and heavy wood classification models. Compared to previous studies, this research worked with a newly structured 2D CNN network based on 12 wood species. High accuracy and fast computation time were achieved using fewer numbers (three layers) of the convolutional neural network. The proposed model achieved 94 % accuracy, 87 % precision, 81 % recall, 80 % F1 score and 112 minutes 27 seconds computation time. The 2D CNN model performed better than the transfer learning models regarding training epochs. The primary benefit of the model is its ability to achieve high accuracy with lower computation time, even at high epochs compared to other models. The introduced 2D CNN model produced satisfactory outcomes for wood species classification.

AbstractRice is the pillar of the Malagasy diet. This paper provides empirical insights into which rice attributes are preferred in rural Madagascar, taking both producers’ and consumers’ perspectives into account. We conducted an original, interview-based household survey for 596 randomly selected rice-producing households in Vakinankaratra region of Madagascar. As producers, the dominant preference was for high productivity, followed by early maturity, and drought resistance. As consumers, qualities such as cleanliness and purity were important, and sweetness was expressly preferred. We found that consumers’ preferences were diverse, so we employed hierarchical cluster analysis and two-step multiple comparisons for further understanding. Cluster analysis categorized households into four groups, characterized by the respondents’ nature of preferences as consumers: (I) quality, (II) cooking, (III) appearance and texture, and (IV) flavor. Though the increase in productivity remains important, understanding consumer preference is also critical because it is transmitted through markets and affects producers. In addition, we found several unique characteristics of the Malagasy people, such as they tend to prefer local, non-scented rice while many African people prefer imported rice with aroma. Rice preference varies among countries, and this study’s comprehensive findings on Malagasy rice preferences contribute to the development of targeted rice-breeding and marketing strategies in Madagascar.

Advances in sensor technology and computing performance has brought us into an era of digital forestry where a forest environment can be digitally replicated. At the same time, an increasing interest in the use of unmanned vehicles and other autonomous mobile systems (AMSs) in forest mapping and operations has emerged. However, a forest is an unstructured and rather complex environment for AMSs to operate in, and usually some kind of a priori information of traversability is required. The aim of this study was to assess forest traversability for AMSs using high-density airborne laser scanning (ALS) point clouds. It was assumed that such point clouds acquired from a helicopter flying at a low altitude can be used to characterise vegetation obstacles affecting forest traversability. A voxel-based vegetation occupancy analysis was carried out with the aim to detect open space to define traversable three-dimensional space. The experimental setup included seven sample plots (32×32 m) representing diverse boreal forest structures. Terrestrial laser scanning (TLS) was used for obtaining reference for vegetation occupancy. Comparison between ALS and TLS revealed an overall accuracy of 0.85–0.94 with a recall of 0.78–0.91 and a precision of 0.62–0.74 for ALS-based voxel classification for vegetation occupancy depending on forest structure. This implies that up to 91% of voxels assigned a classification »occupied« based on the TLS could be correctly classified using the ALS, while up to 74% of voxels assigned a classification »occupied« using the ALS were occupied based on the TLS. Density of low vegetation accounted for 83% of the variation in accuracy and precision. The feasibility of vegetation occupancy information to be used by an AMS for navigation was also demonstrated. It was assumed that the ALS data convey as sufficient information of AMS path planning as does the TLS data. The experiments showed that out of 1393 randomly generated paths based on empty space detected by the ALS, 72% were considered feasible when validated with the TLS data. The success rate in path planning varied from 0.54 to 0.92 between the sample plots and was seemingly affected by vegetation density that accounted for 53% of variation in success rate. Altogether, the demonstrated possibility to predefine forest traversability using remote sensing will support the use of AMSs in forestry.

The aim of the study was to provide a comprehensive overview of global long-distance road transportation of industrial roundwood. The study focused on the maximum gross vehicle weight (GVW) limits allowed with different timber truck configurations, typical payloads in timber trucking, the road transportation share of the total industrial roundwood long-distance transportation volume, and the average long-distance transportation distances and costs of industrial roundwood. The study was carried out as a questionnaire survey. The questionnaire was sent to timber transportation logistics experts and research scientists in the 30 countries with the largest industrial roundwood removals in Europe, as well as selected major forestry countries in the world (Argentina, Australia, Brazil, Canada, Chile, China, Japan, New Zealand, South Africa, Türkiye, the United States of America and Uruguay) in February 2022, and closed in May 2022. A total of 31 countries took part in the survey. The survey illustrated that timber trucking was the main long-distance transportation method of industrial roundwood in almost every country surveyed. Road transportation averaged 89% of the total industrial roundwood long-distance transportation volume. Timber truck configurations of 4 to 9 axles with GVW limits of around 30 tonnes to over 70 tonnes were most commonly used. The results indicated that higher GVW limits allowed significantly higher payloads in timber trucking, with the lowest payloads at less than 25 tonnes, and the highest payloads more than 45 tonnes. The average road transportation distance with industrial roundwood was 128 km, and the average long-distance transportation cost in timber trucking was €11.1 per tonne of timber transported. In the entire survey material, there was a direct relationship between transportation distance and transportation costs and an inverse relationship between maximum GVW limits and transportation costs. Consequently, in order to reduce transportation costs, it is essential to maximise payloads (within legal limits) and minimise haul distances. Several measures to increase cost- and energy-efficiency, and to reduce greenhouse gas emissions in road transportation logistics, are discussed in the paper. On the basis of the survey, it is recommended that up-to-date statistical data and novel research studies on the long-distance transportation of industrial roundwood be conducted in some countries in the future.

Scientific Forestry Management (SciFM), an advanced silvicultural practice, was implemented from 2014 in the protection-oriented community forests (CFs) of Nepal, to advance national prosperity. However, in 2021, deforestation and rampant corruption led to criticism and the program was suspended. New policy formulations and discussions are developing among different actors in forest management. We, therefore, conducted a study to determine how CF users perceived the economic, social, and environmental aspects of SciFM and how it affects their livelihoods. We surveyed 290 households, interviewed key informants and conducted group discussions in the Terai and Hilly regions, where SciFM was implemented in CFs. All the users disagreed with the cessation of SciFM. Implementation of SciFM in CFs with sufficient reserve funds had a positive impact on income. However, those with fewer reserve funds incurred losses due to the initial investment required. 80% of CF users supported SciFM due to its economic benefits; 17% agreed on both economic and social benefits, while a mere 3% valued it for all three merits including resource conservation. SciFM was driven by the desire for financial upliftment, unaware of the corruption involved in its implementation. Policymakers emphasize the importance of addressing corruption issues and providing government support to implement advanced silviculture practices that are easily comprehensible to local forest users. The study’s findings underscore the need for policymakers to regain trust and gain public acceptance and support for regulations and management initiatives. This can be achieved by fostering transparent communication, consistent reliable actions, and active listening to stakeholders’ concerns and feedback.

The values of the fractional distribution of aboveground (AGB) and belowground biomass (BGB), root/shoot ratios (R/S) and stemwood density were estimated for silver (Betula pendula) and downy birch (Betula pubescens) along site index and age gradients for Estonia. Biomass models were used, in which the diameter at breast height (D1.3, measured above root collar), tree height, age and site index served as independent variables. The models for AGB and BGB were elaborated based on the data of 117 and 30 model trees, respectively. Strong allometric relationships were revealed between D1.3 and total AGB (with foliage), stem mass and BGB. For trees with D1.3 < 15 cm, individual models predicted similar biomass patterns for both species, for larger trees, species-specific models are suggested. The relative share of stem mass in AGB was 80–90% for all site index and age classes, being significantly higher for silver birch. The average relative share of stembark, branches, leaves and the R/S, were significantly higher for downy birch. Mean oven-dry mass stemwood density for silver and downy birch was 0.573 g cm−3, there was no statistically significant difference between the species. The mean R/S ratio was 0.29 and 0.43 for silver and downy birch, respectively.