Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2026
Authors
Yasinta Beda Nzogela Joseph Maosa Marjolein Couvreur Mdili Katemani Charles Gervas Beartha Nguku Nessie Luambano Beatrice Kashando Danny Coyne John T. Jones Solveig Haukeland James Price Wim BertAbstract
No abstract has been registered
Authors
Mikhail MastepanovAbstract
The closed chamber method is widely used for measuring greenhouse gas fluxes (CO2, CH4, N2O) in natural and agricultural ecosystems. Automatic chambers are essential for long-term monitoring with high temporal resolution, but their production typically demands significant time, labor and expertise. While ready-to-use commercial solutions are available, many projects avoid them because of their high prices. We present a cost-effective and scalable alternative: modular automatic chambers built from off-the-shelf components. These chambers feature integrated valves and wireless controllers, enabling flexible deployment without the need for multiplexers. Systems can be easily expanded by adding more units. Our modular chambers have been successfully deployed in Arctic and subarctic field studies: north-eastern Greenland, natural wet tundra, two sites, 5 + 5 chambers, three summer seasons, CO2 and CH4 flux monitoring; northern Finland, natural boreal fen, 2–12 chambers, year-round measurements over four years, CO2 and CH4 fluxes; northern Norway, cultivated drained peatland, 30 chambers along a 300 m transect, four growing seasons (May–November), CO2, CH4, and N2O fluxes. Across all sites, the chambers demonstrated reliability, ease of construction, operation and maintenance. While further improvements are always possible, the current design offers a practical and accessible solution for the broader scientific community.
Abstract
The top‐of‐atmosphere (TOA) albedo controls the amount of solar energy absorbed by Earth and is influenced by the reflectivity of both the atmosphere and surface. With considerable changes in land use over the past few decades it is reasonable to question whether a perturbed surface albedo has influenced TOA albedo over the corresponding period. Here, we identify regions for which surface albedo changes have been the dominant driver of TOA albedo trends from 2001 to 2020 and examine the degree to which this relates to changes in snow cover, surface soil moisture, and vegetation density and greenness. We show that land surface albedo changes have been the dominant driver of TOA albedo trends in 10.0% of the global land area, within which surface albedo decreases have led to increases in absorbed solar radiation of 0.737 ± 4.984 Wm −2 from 2001 to 2020. This corresponds to global change in absorbed solar radiation of 0.019 ± 0.812 Wm −2 , which is equivalent to approximately 7.0% of the radiative forcing from anthropogenic CO 2 emissions from 2011 to 2019 (IPCC, 2021, https://doi.org/10.1017/9781009157896.009 ). Net TOA darkening above tundra and deserts constitutes 38.6% and 21.4%, respectively, to the radiative feedback identified, whereas temperate biomes induced net TOA brightening, corresponding to 22.3%. Collectively, changes in snow cover, vegetation density and greenness, and surface soil moisture drive 68.5% of the surface albedo changes. The importance of surface albedo in explaining TOA albedo trends for parts of the globe highlights the relevance of land surface changes in understanding Earth's energy imbalance.
Abstract
Accurate species identification is essential for conserving and managing plants that provide important ecosystem services and have ethnobotanical value. The Greyia tree genus ( G. sutherlandii , G. radlkoferi and G. flanaganii ) is endemic to South Africa and Eswatini, and certain genotypes have medicinal value for treating skin hyper‐pigmentation. However, distinguishing among species is difficult because of overlapping phenotypes and the limited resolution of standard DNA barcodes. To overcome these limitations, a robust molecular identification assay was developed using a two‐phase strategy. First, de novo SNP discovery using 3RAD sequencing identified 47,726 genome‐wide SNPs from two to three plants sampled from each species' core geographic range: G. radlkoferi in northern Limpopo, G. sutherlandii in eastern KwaZulu‐Natal, and G. flanaganii in the south‐eastern Eastern Cape. Principal component analysis and coancestry matrices revealed three discrete genetic clusters, supporting the recognition of the three species. Selecting a set of 200 SNPs with intermediate Fst values (0.2–0.5) resulted in optimal separation of the three clusters. This led to the final selection of a 23‐SNP panel that included five informative barcoding loci (ITS, trnL‐F , matK ). Second, the 23 SNPs were converted into allele‐specific fluorescent PCR assays (SNP Type) for genotyping on the BioMark HD platform. The panel was validated using genomic DNA from 17 individuals from the 3RAD population groups and successfully differentiated all three species. It was then applied to 73 trees sampled across a 1000‐km transect from the Eastern Cape to Limpopo. Genetic clustering (PCA, UPGMA and ADMIXTURE) assigned each tree to one of three species‐level groups matching their expected ranges. In a practical case study, the assay also identified the species origin of 33 Greyia trees of unknown provenance from production orchards. This study provides an efficient SNP‐based tool for accurate species identification, supporting conservation planning and the sustainable management of Greyia populations.
Abstract
Maximizing genetic response to selection while constraining inbreeding is a central challenge in breeding and conservation. Classic optimal contribution selection methods address this by managing average population coancestry. However, this often results in complex, nonlinear optimization problems that cannot be guaranteed to reach a global optimum. Furthermore, many applications require a stricter pairwise constraint to avoid immediate inbreeding in offspring. Here, we present a binary integer linear programming formulation to select an optimal subset of individuals under a strict maximum tolerable pairwise genomic relationship threshold. We construct a binary matrix indicating whether each pair exceeds this threshold. This reformulation transforms the problem from a complex nonlinear program into a binary integer linear program. While this formulation remains NP-hard, the linearity allows modern solvers to efficiently navigate the solution space and, when convergence is achieved within the imposed runtime and tolerance settings, certify global optimality, a key advantage over heuristic approaches. We demonstrate the method using two distinct datasets: a large Norway spruce breeding population and a conservation population of German Black Pied cattle. We explore the trade-offs between the selection response, the relationship threshold, and the maximum number of individuals that can be selected under the threshold. Although large, dense problem instances remain computationally demanding, our results show that typical applications can often be solved to proven global optimality in seconds, whereas denser instances may terminate with a remaining optimality gap. This method is a practical solution for breeders and conservation geneticists to select optimal subsets under a strict relationship threshold, enabling applications from maximizing gain in breeding populations to establishing genetic reserves for endangered species.
Abstract
Time and motion studies in forest operations benefit from video-based analysis, but manual annotation is time consuming. This pilot study aims to reduce analysis time by developing a deep-learning framework that classifies dashcam video into four work elements: crane out, cutting and processing, driving, and processing. Using a 3D ResNet-50 (PyTorchVideo) trained on manually annotated clips, the model achieved validation F1 = 0.88 and precision = 0.90, showing that spatiotemporal CNNs can capture rele-vant motion and appearance cues in forest environments. Overfitting indicates that more diverse data and better class balance are needed, but the approach shows clear potential to scale automated work-element monitoring and efficiency analysis.
Authors
Andony Melathopoulos Nicole Anderson Ramesh SagiliAbstract
No abstract has been registered
Authors
Siv Mari Aurdal Alexander T. Sentinella Gerald Schmilewski James Altland Jean Caron Paul Alexander Martine Holtkamp Bart Vandecasteele Beatrix W. AlsaniusAbstract
The term “sustainable growing media” is widely used in horticultural research, policy, and industry, yet its meaning remains ambiguous, inconsistently applied, and often unsupported by evidence, undermining the very decisions it is meant to guide. Materials are frequently characterised as sustainable based on single attributes, such as being peat-free, recycled, renewable or aligned with policy priorities, without demonstrating reliable horticultural performance, economic viability, social responsibility or reduced environmental impact within real production systems. This paper examines why defining sustainable growing media has proven persistently challenging and why a single, universal definition may be neither achievable nor even useful. Drawing on existing literature and policy initiatives, we analyse sustainability through its environmental, economic and social pillars, highlighting how narrow or assumption-based assessments obscure trade-offs and shift, rather than reduce, environmental and social burdens. We argue that sustainability in growing media is not an inherent material property but a context-dependent outcome that must be demonstrated within a defined production system. Rather than proposing a new definition, we outline eight minimum conditions for responsible use of the term, emphasising measurable impacts, functional performance, economic feasibility, and explicit treatment of trade-offs. Where such evidence is lacking, precise, verifiable descriptors should replace broad sustainability claims.
Abstract
Abstract Timothy ( Phleum pratense L.) is a perennial grass widely grown for livestock feed in temperate regions of the world. It is one of the primary forage grasses grown for seed in Scandinavia due to good cold tolerance and high‐quality feed characteristics. Changes to European Union requirements for use of organic seed in organic forage and livestock production are expected and will soon increase demand for organic timothy seed in the region. An inadequate nitrogen (N) supply is a key yield‐limiting challenge on many arable farms in Norway's main seed‐growing region. In this study, we investigated methods to produce organic timothy seed with limited access to manure, by intercropping seven legume species sown with timothy in the same row or in alternate rows. Timothy seed was harvested for two seed production years in three field experiments in southeastern Norway. Sowing annual legumes with timothy increased seed yield by 10%–25% in first‐year stands. Berseem clover ( Trifolium alexandrinum L.) and subterranean clover ( Trifolium subterraneum L.) sown in the same row with timothy provided the most consistent seed yield increases. Perennial legume intercrops decreased seed yield by 10%–36% in first‐year stands, but black medic ( Medicago lupulina L.) provided 21%–64% seed yield increases in second‐year stands. Seed yield increases in response to legume intercrops were attributable to panicle number and seed number but not seed weight. Timothy seed purity standards can be met if the legume has a seed size and shape that can be removed during seed cleaning.
Abstract
Errors in thematically detailed land-cover maps have large consequences for downstream applications. Moreover, simulation-based studies suggest that land-cover classifiers are sensitive to errors in reference data. We (1) quantified the expected error from field interpretation of land-cover types; (2) the sensitivity of classifiers to reference data errors; and (3) the error transferred from reference data to classifiers. Lastly, we (4) recommended strategies to reduce errors. The study area was mapped by 12 field interpreters divided into three equal-sized experience-level groups. The field-based land-cover maps were aggregated to three thematic resolutions and used to train 6804 land-cover classifiers by varying inputs, algorithm, and hyperparameter values. Separately from the first field campaign, four field interpreters classified validation data points, which were used to quantify error for each field interpreter and land-cover classifier, as the proportion of incorrectly classified validation points. We observed (1) generally high and varying levels of interpreter error; (2) a strong relationship between interpreter and classifier error; and (3) a net positive transfer of errors from reference data to classifiers. Because classifier error seems largely driven by interpreter error at the levels commonly observed in thematically detailed land-cover mapping, we (4) recommend strategies to reduce interpreter error before modelling.