Scotland's Rural College

This study investigates the use of shredded Pinus sylvestris (Scots pine) cones as a partial replacement for industrial wood chips in the surface layers of three-layer particleboards. Boards were manufactured with a target density of 700 kg/m3 and a thickness of 16 mm, bonded with urea-formaldehyde adhesive. Scots pine cone particles were introduced into the surface layers at substitution levels of 10-60% relative to wood chips, while the core layer consisted entirely of industrial chips. The preparation process included drying, milling, classification of cone particles, and hot pressing under controlled temperature and pressure conditions. Standardized tests according to EN requirements were conducted to determine mechanical properties, complemented by scanning electron microscopy (SEM) for structural evaluation. The results showed that all boards met EN 312 flexural strength requirements, with up to 15% improvement in surface screw pull-out resistance at the highest cone content. SEM analysis revealed that pine cone particles have a complex, entangled fibrous structure, unlike conventional chips, enhancing mechanical interlocking with the resin. This improved interfacial bonding contributes to better stress transfer and reduced porosity in the outer layers. Additionally, boards gained unique aesthetic features from the natural coloration of cones. These findings demonstrate that underutilized biomass can be successfully applied in sustainable, structurally sound, and visually appealing wood-based panels.

Enhancing soil organic carbon (SOC) is critical for climate mitigation and stable crop production, yet the effectiveness of different fertilization strategies varies widely across environmental and management contexts. To clarify these inconsistencies, empirical field data from major grain-producing regions of northern China were synthesized using meta-analysis, regression models, random forest algorithms, and partial least squares path modeling to systematically evaluate the impacts of chemical fertilization (CF), organic fertilization (OF), and combined organic-inorganic fertilization (COF) on SOC dynamics. Results showed that CF, OF, and COF increased SOC content by 13 %, 34 %, and 39 %, respectively, with long-term application (>20 years) further amplifying carbon sequestration. Pronounced spatial heterogeneity was observed. In Northeast China (NEC) with higher initial SOC, over 80 % of sites showed absolute SOC gains exceeding 10 g C kg- 1, with COF most effective. In Huanghuaihai Farming Region of China (HFR), characterized by lower baseline SOC, relative gains reached 63 %, and OF showed stronger effects. Across soil textures, OF consistently achieved the largest SOC improvements, and under nutrient-limited conditions, SOC enhancement followed the order OF > COF > CF. Test duration emerged as the dominant driver of SOC accumulation, while climate, nitrogen availability, and initial SOC modulated responses under different regimes. Structural equation modeling indicated that SOC mediated yield responses under CF, whereas direct soil and management effects dominated under OF and COF. These findings emphasize that fertilization management strategies should fully consider regional initial SOC levels and integrate carbon-enhancing practices within broader conservation-oriented farming systems to simultaneously enhance soil carbon sequestration, sustain crop productivity, and provide actionable evidence for promoting sustainable agricultural intensification and national carbon neutrality goals.

Under the current global biodiversity crisis, there is a need for automated and noninvasive monitoring techniques that can gather large amounts of data cost-effectively at various ecological scales, from local to large spatial scales. These data can then be analyzed to inform stakeholders and decision-makers. One such technique is passive acoustic monitoring, which is commonly coupled with automatic identification of animal species based on their sound. Automated sound analyses usually require the training of sound detection and identification algorithms. These algorithms are based on annotated acoustic datasets which mark the occurrence of sounds of species inside sound recordings. However, compiling large annotated acoustic datasets is time-consuming and requires experts, and therefore, they normally cover reduced spatial, temporal, and taxonomic scales. This data paper presents WABAD, the World Annotated Bird Acoustic Dataset for passive acoustic monitoring. WABAD is designed to provide the public, the research community, and conservation managers with a novel and globally representative annotated acoustic dataset. This database includes 5047 min of audio files annotated to species-level by local experts with the start and end time and the upper and lower frequencies of each identified bird vocalization in the recordings. The database has a wide taxonomic and spatial coverage, including information on 91,931 vocalizations from 1192 bird species recorded at 72 recording sites in 29 recording locations (mainly countries) and distributed across 13 biomes. WABAD can be used, for example, for developing and/or validating automatic species detection algorithms, answering ecological questions, such as assessing geographical variations on bird vocalizations, or comparing acoustic diversity indices with species-based diversity indices. The dataset is published under a Creative Commons Attribution 4.0 International license that permits redistribution and reuse on the condition that the original work is properly credited.

Large Language Models (LLMs) such as ChatGPT are transforming how scientists conduct and validate research, offering promise as tools to improve scientific reproducibility. However, computational reproducibility and error detection remain expensive and labor-intensive. We experimentally test how collaboration between researchers and LLM assistants influences the reproduction of quantitative social science findings across different levels of AI autonomy. We randomly assigned 288 researchers to 103 teams working under three conditions: human-only, AI-assisted (using ChatGPT as a collaborative tool), or AI-led (ChatGPT operating with minimal human oversight). Teams reproduced published results from leading social science journals, detected coding errors, and proposed robustness checks. Human-only and AI-assisted teams achieved comparable reproduction rates (94% vs. 91%) and performed similarly on most outcomes, except human-only teams identified significantly more major coding errors. Both substantially outperformed AI-led teams, which achieved only a 37% reproduction rate, detected fewer errors across all categories, proposed weaker robustness checks, and required more time. This autonomous approach, however, likely represents only a lower bound of AI capabilities. Despite rapid model advances, expert human judgment currently remains indispensable for reliable empirical verification. While AI assistance did not degrade most outcomes, it provided no measurable advantages and was associated with reduced detection of major errors. However, the 37% autonomous reproduction rate indicates that AI could provide value in settings where scale or cost constraints preclude human review of papers, even though general-purpose LLMs offer no immediate advantages for human-supervised verification.

Here, we describe AgMicrobiomeBase as an output of the UK Crop Microbiome Cryobank (UKCMCB) project, including details of the underlying meta-barcode sequence-based methods and three microbiome analysis case studies. The UKCMCB links genomic datasets and associated soil metadata with a cryobank collection of samples, for six economically significant crops: fava bean (Vicia faba), oil seed rape (Brassica napus), spring barley (Hordeum vulgare), spring oats (Avena sativa), spring wheat (Triticum aestivum) and sugar beet (Beta vulgaris). The crops were grown in nine agricultural soils from the UK, representing three major soil texture classes. The UKCMCB is a scalable sequence-based data catalogue linked to a cryo-preserved sample collection. The focus of this paper is the amplicon sequencing, associated bioinformatics workflows, and development of the project data catalogue. Short-read amplicon sequencing (16 S rRNA gene and ITS region) was implemented to describe the rhizosphere and bulk soil communities, for the multiple crop-soil combinations. Three case studies illustrate how different biological questions in phytobiome research can be addressed using this data resource. The three case studies illustrate how to (1) determine the impact of soil texture and location on microbiome composition, (2) determine a core microbiome for a single crop across different soil types, and (3) analyse a single genus, Fusarium within a single crop microbiome. The UKCMCB data catalogue AgMicroBiomeBase ( https://agmicrobiomebase.org/data ) links the sequence-based data with soil metadata and to cryopreserved samples. The UKCMCB provides baseline data and resources to enable researchers to assess the impact of soil type, location and crop type variables on crop soil microbiomes. The resource can be used to address biological questions and cross-study comparisons. Development of the UKCMCB will continue with the addition of metagenome and bacterial isolate genomic sequence data and has the potential to integrate additional data types including microbial phenotypes and synthetic microbial communities.