Afforestation using a range of tree species, in New Zealand: New Forest trial series establishment, site description, and initial data.

Global climate change and shift towards a bio-economy has heightened the need to design sustainable forestry systems that balance economic, environmental and social benefits. In New Zealand, production forests are dominated by planted Pinus radiata, which makes up 90 % of the planted forest area. There is very little data driven evidence in New Zealand to support diversifying across a range of tree species and what timber and non-timber benefits may be gained by diversifying tree species in New Zealand's production forests. The New Zealand New Forest Trial Series (NFTS) was designed and established in 2013 on marginal pastoral land to address the knowledge gap for how afforesting land with different trees species, both exotic and indigenous to New Zealand, across a climate range can deliver to both timber and non-timber benefits. These trials were planted with Cupressocyparis ovensii, Eucalyptus fastigata, Fraxinus excelsior, Nothofagus fusca (plus Leptospermum scoparium), Pinus radiata, Podocarpus totara and Sequoia sempervirens plus a control with no planting to monitor natural succession. The Before-After-Control-Impact (BACI) experiment design has collected pre-planting data describing the present vegetation and a range of soil properties, presented in this paper. This will allow the comparative monitoring of the changes that will occur through planting the various tree species on marginal land in different environments through time. With time the long-term trials will deliver data evidence on tree species survival when planted into marginal pastoral land, tree productivity and the flow of economic, environmental and social benefits from the new plantings. This knowledge will strengthen New Zealand's forestry sector confidence to make informed decisions to diversify tree species with changing climatic and social challenges.

Leaf nutrient traits of planted forests demonstrate a heightened sensitivity to environmental changes compared to natural forests.

Leaf nutrient content (nitrogen, phosphorus) and their stoichiometric ratio (N/P) as key functional traits can reflect plant survival strategies and predict ecosystem productivity responses to environmental changes. Previous research on leaf nutrient traits has primarily focused on the species level with limited spatial scale, making it challenging to quantify the variability and influencing factors of forest leaf nutrient traits on a macro scale. This study, based on field surveys and literature collected from 2005 to 2020 on 384 planted forests and 541 natural forests in China, investigates the differences in leaf nutrient traits between forest types (planted forests, natural forests) and their driving factors. Results show that leaf nutrient traits (leaf nitrogen content (LN), leaf phosphorus content (LP), and leaf N/P ratio) of planted forests are significantly higher than those of natural forests (P< 0.05). The impact of climatic and soil factors on the variability of leaf nutrient traits in planted forests is greater than that in natural forests. With increasing forest age, natural forests significantly increase in leaf nitrogen and phosphorus content, with a significant decrease in N/P ratio (P< 0.05). Climatic factors are key environmental factors dominating the spatial variability of leaf nutrient traits. They not only directly affect leaf nutrient traits of planted and natural forest communities but also indirectly through regulation of soil nutrients and stand factors, with their direct effects being more significant than their indirect effects.

Green deserts, but not always: A global synthesis of native woody species regeneration under tropical tree monocultures.

Tree monocultures constitute an increasing fraction of the global tree cover and are the dominant tree-growing strategy of forest landscape restoration commitments. Their advantages to produce timber are well known, but their value for biodiversity is highly controversial and context dependent. Therefore, understanding whether, and in which conditions, they can harbor native species regeneration is crucial. Here, we conducted meta-analyses based on a global survey of the literature and on a database created with local, unpublished studies throughout Brazil to evaluate the regeneration potential of native species under tree monocultures and the way management influences this regeneration. Native woody species regeneration under tree monocultures harbors a substantial fraction of the diversity (on average 40% and 68% in the global and Brazilian surveys, respectively) and abundance (on average 25% and 60% in the global and Brazilian surveys, respectively) of regeneration observed in natural forests. Plantations with longer rotation lengths, composed of native tree species, and located adjacent to forest remnants harbor more species. Pine plantations harbor more native individuals than eucalypt plantations, and the abundance of regenerating trees is higher in sites with higher mean temperatures. Species-area curves revealed that the number of woody species under pine and eucalypt plantations in Brazil is 606 and 598 species, respectively, over an aggregated sampled area of ca. 12 ha. We highlight that the understory of tree monocultures can harbor a considerable diversity of regenerating native species at the landscape and regional scales, but this diversity strongly depends on management. Long-rotation length and favorable location are key factors for woody regeneration success under tropical tree monocultures. Therefore, tree monocultures can play a role in forest landscape restoration and conservation, but only if they are planned and managed for achieving this purpose.

Forest Age Drives the Resource Utilization Indicators of Trees in Planted and Natural Forests in China.

Specific leaf area (SLA) and leaf dry matter content (LDMC) are key leaf functional traits commonly used to reflect tree resource utilization strategies and predict forest ecosystem responses to environmental changes. Previous research on tree resource utilization strategies (SLA and LDMC) primarily focused on the species level within limited spatial scales, making it crucial to quantify the spatial variability and driving factors of these strategies. Whether there are discrepancies in resource utilization strategies between trees in planted and natural forests, and the dominant factors and mechanisms influencing them, remain unclear. This study, based on field surveys and the literature from 2008 to 2020 covering 263 planted and 434 natural forests in China, using generalized additive models (GAMs) and structural equation models (SEMs), analyzes the spatial differences and dominant factors in tree resource utilization strategies between planted and natural forests. The results show that the SLA of planted forests is significantly higher than that of natural forests (p < 0.01), and LDMC is significantly lower (p < 0.0001), indicating a "faster investment-return" resource utilization strategy. As the mean annual high temperature (MAHT) and mean annual precipitation (MAP) steadily rise, trees have adapted their resource utilization strategies, transitioning from a "conservative" survival tactic to a "rapid investment-return" model. Compared to natural forests, planted forest trees exhibit stronger environmental plasticity and greater variability with forest age in their resource utilization strategies. Overall, forest age is the dominant factor influencing resource utilization strategies in both planted and natural forests, having a far greater direct impact than climatic factors (temperature, precipitation, and sunlight) and soil nutrient factors. Additionally, as forest age increases, both planted and natural forests show an increase in SLA and a decrease in LDMC, indicating a gradual shift towards more efficient resource utilization strategies.

Estimating deep soil water depletion and availability under planted forest on the Loess Plateau, China.

Deep soil water (DSW) plays a pivotal role in tree growth, susceptibility to drought-induced mortality, and belowground carbon and nutrient cycling. Assessing DSW depletion is essential for evaluating the resilience and sustainability of planted forests. But, due to the poor accessibility of deep soil layers, little is known about large scale DSW depletion. In this study, we leverage the concept that "plants are reliable indicators of deep soil water" to estimate DSW depletion in planted forests within the arid and semi-arid regions of the Chinese Loess Plateau (CLP). Our approach involves establishing a model that correlates forest age with DSW depletion. We then employ this model to estimate DSW depletion across the region, utilizing readily available data on the distribution of forest age and utilize the boundary models to consider the variability of DSW depletion estimated with forest age. Our results indicate that the model effectively estimates DSW depletion in planted forests, demonstrating a strong fit with an R2 of 0.71 and a low root mean square error (RMSE) of 332 mm. Notably, a substantial portion of the planted forest areas on the CLP has experienced DSW depletion from 800 mm to 1600 mm, and totaling 2.41 × 1010 m3 DSW depletion from 1995 to 2020 based on the general model. However, the available DSW in the existing planted forests on the CLP is estimated at only 1.73 × 1010 m3 by 2038. This suggests that there is potential risks and unsustainability for further afforestation efforts and carbon sequestration on the CLP under the current continuous afforestation measures. Our study holds significant implications for sustainable regional ecological management and quantifying water resources for carbon trading through afforestation.

Climate factors affect forest biomass allocation by altering soil nutrient availability and leaf traits.

Biomass in forests sequesters substantial amounts of carbon; although the contribution of aboveground biomass has been extensively studied, the contribution of belowground biomass remains understudied. Investigating the forest biomass allocation is crucial for understanding the impacts of global change on carbon allocation and cycling. Moreover, the question of how climate factors affect biomass allocation in natural and planted forests remains unresolved. Here, we addressed this question by collecting data from 384 planted forests and 541 natural forests in China. We evaluated the direct and indirect effects of climate factors on the belowground biomass proportion (BGBP). The average BGBP was 31.09% in natural forests and was significantly higher (38.75%) in planted forests. Furthermore, we observed a significant decrease in BGBP with increasing temperature and precipitation. Climate factors, particularly those affecting soil factors, such as pH, strongly affected the BGBP in natural and planted forests. Based on our results, we propose that future studies should consider the effects of forest type (natural or planted) and soil factors on BGBP.

CRISPR/Cas-mediated plant genome editing: outstanding challenges a decade after implementation.

The discovery of the CRISPR/Cas genome-editing system has revolutionized our understanding of the plant genome. CRISPR/Cas has been used for over a decade to modify plant genomes for the study of specific genes and biosynthetic pathways as well as to speed up breeding in many plant species, including both model and non-model crops. Although the CRISPR/Cas system is very efficient for genome editing, many bottlenecks and challenges slow down further improvement and applications. In this review we discuss the challenges that can occur during tissue culture, transformation, regeneration, and mutant detection. We also review the opportunities provided by new CRISPR platforms and specific applications related to gene regulation, abiotic and biotic stress response improvement, and de novo domestication of plants.

Tolerance of microorganisms to residual herbicides found in eucalyptus plantations.

Competition with weeds is one of the main factors that limit the development of forest species. Some herbicides used to control these plants have a residual effect on the soil. Bioremediation is an alternative to decontaminate these areas. The aim of this study was to evaluate the tolerance of Aspergillus niger, Penicillium pinophilum and Trichoderma sp. and its degrading potential on residual effect herbicides. The tolerance of Bacillus subtilis, Pseudomonas sp. and Azospirillum brasilense to herbicides was also evaluated. The herbicides used in this study were indaziflam, sulfentrazone, sulfentrazone + diuron, clomazone and glyphosate + s-metolachlor. The analysis of the tolerance and degradation potential of fungi was carried out in Czapek Dox medium and the growth was evaluated by determining the biomass. Bacterial tolerance analysis was performed in Luria Bertani medium and growth monitored by optical density. The data were applied to the Gompertz model to evaluate the behavior of bacteria. Bacterial growth parameters were not influenced by the presence of herbicides. All fungi were tolerant to the herbicides tested and there was an increase in the growth of Trichoderma sp. Thus, the analysis of the degrading potential was performed only for Trichoderma sp. in the presence of herbicides that potentiated its growth. In this analysis, there was no effect of herbicides on fungal growth; the fungus was unable to use the carbon present in the herbicide to enhance its growth; and there was no significant effect of nitrogen in the presence of the herbicide. It is concluded, therefore, that the tested residual herbicides do not interfere with the development of the evaluated microorganisms.

Moso bamboo (Phyllostachys edulis (Carrière) J. Houzeau) invasion affects soil microbial communities in adjacent planted forests in the Lijiang River basin, China.

Introduction: Moso bamboo (Phyllostachys edulis (Carrière) J. Houz.), the most widely distributed economic bamboo species in southern China, can easily invade adjacent communities due to its clonal reproduction. However, there is little information on the effects of its establishment and expansion to adjacent forest soil communities, particularly in planted forests. Methods: We investigated the relationships between soil properties and the microbial community during bamboo invasion under different slope directions (shady or sunny slope) and positions (bottom, middle, or top slope), in three typical stand types (bottom: pure moso bamboo, middle: mixed stands of moso bamboo and Masson pine (Pinus massoniana Lamb.), and top: pure Masson pine) in the Lijiang River Basin. This study aimed to explore the effects of key environmental factors on soil microbial composition, diversity, and abundance. Results and Discussion: The results showed that the abundance of Acidobacteria bacterium and Acidobacteria bacterium 13_2_20CM_58_27, and Verrucomicrobia bacterium decreased as the slope increased (p < 0.05), whereas the abundance of Alphaproteobacteria bacterium, Actinobacteria bacterium, Trebonia kvetii, and Bradyrhizobium erythrophlei increased as the slope increased (p < 0.05). However, the difference of slope direction on microbial communities was not significant. The pH, organic matter (OM) and total phosphorus (TP) were the key soil environmental factors; most microorganisms (Betaproteobacteria bacterium, Candidatus Eisenbacteria bacterium, Betaproteobacteria bacterium SCGC_AG - 212 - J23, Gemmatimonadetes bacterium, Actinobacteria bacterium 13_2_20CM_2_66_6, and Myxococcaceae bacterium) showed a positive relationship with pH and a negative relationship with OM and TP. Slope position significantly affected OM, calcium (Ca), total nitrogen (TN), available phosphorus (AP), hydrolyzed nitrogen (HN), pH, and microbial abundance and composition. Slope direction significantly affected TP and magnesium (Mg). The structural equations also indicated that slope position had an effect on microbial composition, abundance, and diversity. Slope position was negatively correlated with pH (r = -0.333, p = 0.034) and positively correlated with OM (r = 0.728, p < 0.001), TN (r = 0.538, p < 0.001) and Ca (r = 0.672, p < 0.001); pH was positively correlated with microbial composition (r = 0.634, p < 0.001), abundance (r = 0.553, p < 0.001) and diversity (r = 0.412, p = 0.002), TN was positively correlated with microbial composition (r = 0.220, p = 0.014) and abundance (r = 0.206, p = 0.013), and Ca was negatively correlated with microbial composition (r = -0.358, p = 0.003) and abundance (r = -0.317, p = 0.003). Slope position can also influence microbial composition (r = 0.452, p < 0.001) directly. In addition, slope direction had an indirect effect on microbial diversity through total potassium (TK). Therefore, we proposed that the different variations in microbial community during bamboo invasion could be related to the influence of invasion on the soil properties at different invasion stages.

Accelerator trial series in Pinus radiata stands in New Zealand: Trial establishment, site description and initial soil, forest floor and tree data.

Interest in establishing biological-based economies has created increasing and rapidly moving demand for wood and fibre from production forests. Meeting the global demand for timber supply will require investment and development across all components of the supply chain but will ultimately rely on the ability of the forestry sector to increase productivity without compromising the sustainability of plantation management. To address this issue in the context of New Zealand forestry, a trial series was established from 2015 to 2018 to accelerate plantation forest growth by exploring current and future limitations to timber productivity, then altering management practices to overcome these limits. The six sites in this Accelerator trial series were planted with a mix of 12 different types of Pinus radiata D. Don stock expressing various traits related to tree growth, health and wood quality. The planting stock included ten clones, a hybrid and a seed lot representing a widely planted tree stock used throughout New Zealand. At each trial site a range of treatments were applied, including a control. The treatments were designed to address the specific current and predicted limitations to productivity at each location, with consideration for environmental sustainability and impacts on wood quality. Additional site-specific treatments will be implemented across the approximately 30-year life span of each trial. Here we present data describing both the pre-harvest and time zero state of at each trial site. These data provide a baseline that will enable treatment responses to be holistically understood as the trial series matures. This comparison will determine if current tree productivity has been enhanced, and if improvements in site characteristics may also benefit future rotations. The Accelerator trials represent an ambitious research goal that will take planted forest productivity to a new level of enhanced long-term forest productivity without compromising the sustainable management of future forests.

Experimental Investigation on Interface Performance of UHPC-Strengthened NC Structure through Push-Out Tests.

Strengthening concrete structures with ultra-high performance concrete (UHPC) can both improve the bearing capacity of the original normal concrete (NC) structure and prolong the service life of the structure due to the high strength and durability of UHPC. The key to the synergistic work of the UHPC-strengthened layer and the original NC structures lies in the reliable bonding of their interfaces. In this research study, the shear performance of the UHPC-NC interface was investigated by the direct shear (push-out test) test method. The effects of different interface preparation methods (smoothing, chiseling, and planting straight and hooked rebars) and different aspect ratios of planted rebars on the failure mode and shear performance of the pushed-out specimens were studied. Seven groups of push-out specimens were tested. The results show that the interface preparation method can significantly affect the failure mode of the UHPC-NC interface, which is specifically divided into interface failure, planted rebar pull-out, and NC shear failure. The critical aspect ratio for the pull-out or anchorage of planted rebars in UHPC is around 2. The interface shear strength of straight-planted rebar interface preparation is significantly improved compared with that of the chiseled and smoothened interfaces, and as the embedding length of the planted rebar becomes longer, it first increases greatly and then tends to be stable when the rebar planted in UHPC is fully anchored. The shear stiffness of UHPC-NC increases with the increase of the aspect ratio of planted rebars. A design recommendation based on the experimental results is proposed. This research study supplements the theoretical basis of the interface design of UHPC-strengthened NC structures.

Combining planted drying beds to maturation ponds at pilot scale for a comprehensive treatment of faecal sludge in sub-Saharan Africa.

Hight birth rate in developing countries generates huge amounts of faecal sludge to treat at a given time. In sub-Saharan Africa, it is estimated that 95% of households are not connected to a sewerage system for excreta disposal, and faecal sludge treatment of plants is almost absent, thus the necessity of developing cost-effective technologies to contain their harmful effect. In response to this preoccupation, pilot scale experiments combining drying beds with maturation ponds were conducted in Yaounde (Cameroon) for the treatment of faecal sludge. Raw faecal sludge was applied once a week at a load of 200 kg DM/m2/yr. on a 1 m2 section drying bed planted with Echinochloa pyramidalis plant for dehydration. The leachate obtained was subsequently polished in two shallow maturation ponds (50 cm depth) in series, following a varying hydraulic retention time (HRT) of 4, 7, and 10 days. Results showed the set-up to be effective at 10 days HRT in reducing significantly (p < 0.05) nutrient and organic pollutants as well as faecal coliforms in the final effluent compared to 4 and 7 days. However, suspended solids removal remained inefficient due to high algal biomass. The performance of the system enabled the treatment to meet the requirements of the Cameroonian guidelines for discharge and reuse in non-restricted agriculture. These results constitute advancement towards a comprehensive treatment of faecal sludge with the perspective of reuse of the effluent. Validation of this treatment scheme at full scale is ongoing for its adoption and implementation in the Cameroon national sanitation strategy.

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The structural and functional characteristics of soil prokaryotic community are important for maintaining ecosystem functions. In this study, we examined the diversity and compositions, the key drivers, as well as functional characteristics of prokaryotic communities in the rhizosphere and non-rhizosphere soils of Picea asperata with different stand ages using high-throughput sequencing technique and bioinformatics methods. The results showed that ß-diversity of soil prokaryotic communities in both rhizosphere and non-rhizosphere showed significant differences among different stand ages, but no significant difference between rhizosphere and non-rhizosphere in the same stand age. In terms of community composition at the phylum level, the relative abundances of Proteobacteria and Rokubacteria showed an increasing trend with the increases of stand age, while the relative abundance of Actinobacteria showed a decreasing trend, but no significant difference was observed between 75 year-old planted forests (PF75) and natural forests (NF). The relative abundances of Firmicutes and Thaumarchaeota in the soil of the 25 year-old planted forests (PF25) were significantly higher than in other planted forests and NF. At the genus level, the relative abundances of RB41, Terrimonas and Acidibacter showed an increasing trend with the increases of stand age, and RB41 and Terrimonas in rhizosphere soil of PF75 were significantly higher than those in NF. Soil properties and vegetation characteristics jointly influenced the structure of soil prokaryotic communities, with herb layer coverage, soil pH, total phosphorus, and total nitrogen as major drivers. The functional characteristics of soil prokaryotic communities were significantly different among different stand ages. The relative abundances of functions involved in carbon and nitrogen cycle, e.g., cellulolysis and nitrification, decreased with the increases of stand age, whereas that of sulfate respiration involved in the sulfur cycle increased. We proposed that the structure and functional characteristics of soil prokaryotic communities could serve as important indicators of the development stages of P. asperata forests. In the later stages of plantation forest development, soil nutrient availability could be improved by mediating phosphorus-dissolving and nitrogen-enhancing microorganisms to maintain the stability of the plantation ecosystem.

[Crown shape model for planted greening tree species Pinus tabuliformis in Shenyang based on marginal regression.] / åŸºäºŽè¾¹é™ å›žå½’çš„æ²ˆé˜³å¸‚ç»¿åŒ–æ ‘ç§äººå·¥æ²¹æ¾å† å½¢æ¨¡æ‹Ÿ.

Developing outer crown profile prediction models of typical urban greening tree species will lay a foundation for the spatial allocation optimization of urban greening. In this study, Pinus tabuliformis, a typical greening tree species in Shenyang, was selected as the research object. Based on the Crown Window device, a total of 60 sample trees were selected to measure the crown shape, with power equation, segmented polynomial equation, and modified Kozak equation as the basic models. By introducing crown structure variables (the maximum crown radius) and neighbour competition variables (mean tree height, mean diameter at breast height, mean crown width, number for the neighbour trees, and mean crown contact height between sample trees and neighbour trees) through reparameterization, we constructed an outer crown shape model of P. tabuliformis that incorporates neighbour tree competition and maximum crown radius. The results showed that modified Kozak equation had the largest Ra2 and the smallest RMSE, as well as good stability. After introducing the maximum crown radius and the mean DBH of neighbour trees into the basic model through reparameterization, the Ra2 of the model increased by 0.0693 and the MSER was 14.4%. The maximum crown radius had a great influence on the crown shape, while the crown radius increased with the increases of the maximum crown radius. The influence of mean DBH of neighbour trees on crown shape was weaker than that of maximum crown radius. The upper part of crown increased and the lower part of crown decreased with increasing neighbour tree competition. In this study, the marginal regression outer crown profile model of P. tabuliformis coupled with neighbour tree competition and the maximum crown radius showed good goodness of fit and could reasonably simulate and predict the crown shape of planted P. tabuliformis.

Mapping forest and site quality of planted Chinese fir forest using sentinel images.

Normally, forest quality (FQ) and site quality (SQ) play an important role in evaluating actual and potential forest productivity. Traditionally, these assessment indices (FQ and SQ) are mainly based on forest parameters extracted from ground measurement (forest height, age, density, forest stem volume (FSV), and DBH), which is labor-intensive and difficult to access in certain remote forest areas. Recently, remote sensing images combined with a small number of samples were gradually applied to map forest parameters because of the various advantages of remote sensing technology, such as low cost, spatial coverage, and high efficiency. However, FQ and SQ related to forest parameters are rarely estimated using remote sensing images and machine learning models. In this study, the Sentinel images and ground samples of planted Chinese fir forest located in the ecological "green-core" area of Changzhutan urban cluster, were initially employed to explore the feasibility of mapping the FQ and SQ. And then, four types of alternative variables (backscattering coefficients (VV and VH), multi-spectral bands, vegetation indices, and texture characteristics) were extracted from Sentinel-1A and Sentinel-2A images, respectively. After selecting variables using a stepwise regression model, three machine learning models (SVR, RF, and KNN) were employed to estimate various forest parameters. Finally, the FQ of the study region was directly mapped by the weights sum of related factors extracted by the factor analysis method, and the SQ was also extracted using mapped forest height and age. The results illustrated that the accuracy of estimated forest parameters (DBH, H, and Age) was significantly higher than FSV, FCC, and Age and the largest and smallest rRMSEs were observed from FSV (0.38~0.40) and forest height (0.20~0.21), respectively. Using mapped forest parameters, it also resulted that the rRMSEs of estimated FQ and SQ were 0.19 and 0.15, respectively. Furthermore, after normalization and grading, the grades of forest quality were mainly concentrated in grades I, II, and III in the study region. Though the accuracy of mapping FQ and SQ is limited by the saturation phenomenon, it is significantly proved that using machine learning models and Sentinel images has great potential to indirectly map FQ and SQ.

Microcosm test for pesticide fate assessment in planted water filters: 13C,15N-labeled glyphosate as an example.

Planted filters are often used to remove pesticides from runoff water. However, the detailed fate of pesticides in the planted filters still remains elusive. This hampers an accurate assessment of environmental risks of the pesticides related to their fate and thereby development of proper mitigation strategies. In addition, a test system for the chemical fate analysis including plants and in particular for planted filters is not well established yet. Therefore, we developed a microcosm test to simulate the fate of pesticide in planted filters, and applied 2-13C,15N-glyphosate as a model pesticide. The fate of 2-13C,15N-glyphosate in the planted microcosms over 31 day-incubation period was balanced and compared with that in the unplanted microcosms. The mass balance of 2-13C,15N-glyphosate turnover included 13C mineralization, degradation products, and the 13C and 15N incorporation into the rhizosphere microbial biomass and plants. We observed high removal of glyphosate (> 88%) from the water mainly due to adsorption on gravel in both microcosms. More glyphosate was degraded in the planted microcosms with 4.1% of 13C being mineralized, 1.5% of 13C and 3.8% of 15N being incorporated into microbial biomass. In the unplanted microcosms, 1.1% of 13C from 2-13C,15N-glyphosate was mineralized, and only 0.2% of 13C and 0.1% of 15N were assimilated into microbial biomass. The total recovery of 13C and 15N was 81% and 85% in planted microcosms, and 91% and 93% in unplanted counterparts, respectively. The microcosm test was thus proven to be feasible for mass balance assessments of the fate of non-volatile chemicals in planted filters. The results of such studies could help better manage and design planted filters for pesticide removal.

Yield estimation of high-density cotton fields using low-altitude UAV imaging and deep learning.

BACKGROUND: China has a unique cotton planting pattern. Cotton is densely planted in alternating wide and narrow rows to increase yield in Xinjiang, China, causing the difficulty in the accurate estimation of cotton yield using remote sensing in such field with branches occluded and overlapped. RESULTS: In this study, unmanned aerial vehicle (UAV) imaging and deep convolutional neural networks (DCNN) were used to estimate densely planted cotton yield. Images of cotton fields were acquired by the UAV at an altitude of 5 m. Cotton bolls were manually harvested and weighed afterwards. Then, a modified DCNN model (CD-SegNet) was constructed for pixel-level segmentation of cotton boll images by reorganizing the encoder-decoder and adding dilated convolutions. Besides, linear regression analysis was employed to build up the relationship between cotton boll pixels ratio and cotton yield. Finally, the estimated yield for four cotton fields were verified by weighing harvested cotton. The results showed that CD-SegNet outperformed the other tested models, including SegNet, support vector machine (SVM), and random forest (RF). The average error in yield estimates of the cotton fields was as low as 6.2%. CONCLUSIONS: Overall, the estimation of densely planted cotton yields based on low-altitude UAV imaging is feasible. This study provides a methodological reference for cotton yield estimation in China.

Machine part data with part-of relations and part dissimilarities for planted partition generation.

Identifying relationships between entities in data is a central topic across various industries and businesses, from social networks to supply chain and heavy manufacturing industries. In this paper we present data from a database of machinery represented in terms of machine parts. The machine parts are originally organised in tree structures where the vertices are machine part types, and the edges are "part-of" relations. Hence, each tree represents a type of machinery broken down into its machine part constituent types. The data we present is the union over these trees, making up a directed acyclic graph describing the type hierarchy of the machine parts. The motivation for publishing the dataset is the following real-world industry problem: Each tree represents a mechanical design, and over time some designs have been copy-pasted with minor modifications. The new instances have been given new identifiers with no reference to where from they were copied. In hindsight, it is desirable to recover the copy-paste links to for interchange between essentially identical designs. However, telling which parts are copies of which other parts has turned out to be difficult. In particular, the metadata has a tendency of displaying higher similarities within a composite part than between a part and its copy. Due to non-disclosure, we cannot provide the metadata, but we provide element wise dissimilarities that are generated based on the metadata using classical methods such as Jaccard similarity on description texts, material types etc. The dissimilarities are obtained from a data science project in the company owning the data, trying to tackle the very problem of recovering the copy-paste links. Availability of labeled data on this data set is limited, so based on our in-depth knowledge of the problem domain, we present a data synthesisation method that can generate arbitrarily large problem instances of the copy-paste problem based on the sample data, that provides a realistic representation of the real world problem. The problems are presented as planted partitions of vertices of directed acyclic graphs with vertex dissimilarities, and thus constitutes a typical classification problem along the lines of graph- or network clustering. The type of industry data we present is usually company confidential, bound by intellectual property rights, and generally not available to scientists. We therefore publish this anonymised dataset to offer real world sample data and generated problem instances for researchers that are interested in this type of classification problems, and on which theories and algorithms can be tested. The data and the problem generation methodology are backed by a Python implementation, providing both data access and an API for parameterised problem generation. The data is also available as raw files.

Occupational accidents in native and planted forests in Brazil: 2007-2018.

BACKGROUND: Despite constant technological evolution, the forestry sector is still characterised as one with the highest risk of occupational accidents. There is no doubt that accidents penalise workers considerably, as well as the companies, insurers, and, consequently, the society, to which many of the costs are externalised, namely in cases where the worker is incapacitated and unable to work. OBJECTIVE: The aim of this work is to analyse data on occupational accidents in forestry operations in Brazil between 2007 and 2018 in both native and planted forests. METHODS: Data were made available by the Brazilian Statistical Directory of Accidents at Work. RESULTS: The results show that, although most accidents occurred in planted forests, mortality was higher in native forests. It was also found that, overall, the rate of serious accidents resulting in an incapacity to work and, consequently, sick leave is considerably high. CONCLUSIONS: These results highlight that integrating an occupational safety system into a forest management system is crucial for reducing work-related accidents in both native and planted forests. However, the success of this measure will only be effective if it is accompanied by a review of both the Brazilian labour policy and the Brazilian occupational safety management policy, to ensure compliance with legislation, mainly on the part of outsourced companies.

Phosphorus is the key soil indicator controlling productivity in planted Masson pine forests across subtropical China.

Soil physiochemical properties are critical to understanding forest productivity and carbon (C) finance schemes in terrestrial ecosystems. However, few studies have focused on the effects of the soil physiochemical properties on the productivity in planted forests. This study was therefore conducted at 113 sampling plots located in planted Masson pine forests across subtropical China to test what and how the aboveground net primary productivity (ANPP) would be explained by the soil physiochemical properties, stand attributes, and functional traits using regression analysis and structural equation modelling (SEM). Across subtropical China, the ANPP ranged from 1.79 to 14.04 Mg ha-1 year-1 among the plots, with an average value of 6.05 Mg ha-1 year-1. The variations in ANPP were positively related to the stand density, root phosphorus (P) content and soil total P content but were negatively related to the stand age, root C:P and N:P ratios. Among these factors, the combined effects of stand density, stand age and soil total P content explained 35% of the ANPP variations. The SEM results showed the indirect effect of the soil total P content via the root P content and C:P ratio on the ANPP and indirect effects of other soil properties (e.g., pH, clay, and bulk density) via the soil total P content and root functional traits (e.g., root P, C:P, and N:P) on the ANPP. By considering all possible variables and paths, the best-fitting SEM explained only 11-13% of the ANPP variations, which suggested that other factors may be more important in determining the productivity in planted forests. Overall, this study highlights that soil total P content should be used as a key soil indicator for determining the ANPP in planted Masson pine forests across subtropical China, and suggests that the root functional traits mediate the effects of soil properties on the ANPP.