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.
2025
Abstract
Urban agriculture (UA) is increasingly recognized as a key component of sustainable cities. Commercial farmers in urban areas benefit from a large customer base, short transport distances, and access to diverse sales channels. However, high pressure on land resources makes it difficult for farmers and decision makers to find suitable areas for UA. This study ranks urban and peri-urban farmland areas based on their suitability for urban agriculture (UA) and identifies opportunities for extending the area for UA to currently unused farmland. Through collaboration with urban farmers, we identified four key themes and eleven criteria, which were weighted for two sales scenarios: on-farm and off-farm. We performed a GIS-based multi-criteria decision analysis (MCDA) and assessed suitability using the technique of order preference similarity to the ideal solution (TOPSIS) on 1 × 1 km grid cells. By overlaying the suitability maps with presumably unused farmland (PUF), we identified areas with high potential for extending UA. In the City of Bergen, 15.3 % (on-farm; off-farm=14 %) of the total farmland is both unused and highly suitable for UA, compared to only 2.8 % (on-farm; off-farm=2.4 %) in Oslo. Assessing the suitability of agricultural land for UA can support spatial planning, protect agricultural topsoil from urban expansion, and help achieve global, national, and local goals for urban farming and sustainable land use.
Abstract
Many countries have goals to reduce soil sealing of agricultural land to preserve food production capacity. To monitor progress, reliable data are needed to quantify soil sealing and changes over time. We examined the potential of the Imperviousness Classified Change (IMCC) 2015–2018 product provided by the Copernicus Land Monitoring Service (CLMS) to assess soil sealing in agricultural areas in Poland and Norway. We found very high overall accuracy due to the dominance of the area with no change. When we focused on areas classified as change, we found low user accuracy, with over-estimation of soil sealing. The producer accuracy was generally much higher, meaning that real cases of soil sealing were captured. This is better than under-estimation of soil sealing because it highlights areas where sealing may have occurred, allowing the user to carry out further control of this much smaller area, without having to assess the great expanse of unchanged area. We concluded that the datasets provide useful information for Europe. They are standardized and comparable across countries, which can enable comparison of the effects of policies intended to prevent soil sealing. Some distinctions between classes are not reliable, but the general information about increase or decrease is useful.
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Authors
Ioanna S. Panagea Paul Quataert María Alonso-Ayuso Teresa Gómez de la Bárcena Maarten De Boever Mariangela Diacono Anna Jacobs Johannes L. Jensen Felix Seidel Daria Seitz Heide Spiegel Thijs Vanden Nest Axel Don Greet RuysschaertAbstract
Sustainable land management can play an important role in climate change mitigation by reducing soil organic carbon (SOC)losses or even by sequestering C in soils. This can be achieved through practices that increase C inputs to the soil and/or improve the quality of these inputs, thereby facilitating the removal of atmospheric carbon dioxide (CO 2) and storing it in the soil asSOC. In this study, we investigated the potential of an increased share of legumes in crop rotations to enhance SOC accrual—defined as the increase in SOC stocks at a given land unit compared to the baseline scenario—using data from 30 mid-term(MTEs, 5–20 years) and long-term (LTEs, 20+ years) field experiments across Europe. Our findings indicate that increasing the proportion of forage legumes in rotations (based on 21 experiments and 39 paired comparisons) led to SOC accrual of up to13.25 Mg ha−1 (0.44 Mg ha−1 year−1), while grain legumes (based on nine experiments and 28 paired comparisons) resulted in a decrease in SOC stocks of up to 14.37 Mg ha−1 (−0.48 Mg ha−1 year−1) compared to the reference treatment. For forage legumes,the largest SOC gains were achieved at sites with the smallest reference SOC stocks and greater share of forage legumes in the rotation. Our observations suggested that the duration of crop growth of the forage legumes (annual vs. perennial) did not exert a significant impact on SOC stock increase, while pedoclimatic zone did. Positive effects on SOC stocks were more pronounced in the Atlantic climatic zone in contrast to the Mediterranean climatic zone. For grain legumes, larger SOC losses were observed with a greater share of grain legumes in the rotation. Overall, integrating forage legumes in cropping systems can enhance their sustainability and present a viable option for climate change mitigation. Finally, we present a regression equation to derive emission factors (EFs) for estimating SOC changes due to the increase of the share of forage legumes in a rotation, and another due to the increase of the share of grain legumes in the rotation. The first can be used to support the assessment of management impacts for the purpose of rewarding carbon farming and the estimation of a national-scale SOC accrual potential, while the second can be used for estimating national-scale SOC losses.
Abstract
The precise spatially explicit data on land cover and land use changes is one of the essential variables for enhancing the quantification of greenhouse gas emissions and removals, which is relevant for meeting the goal of the European economy and society to become climate-neutral by 2050. The accuracy of the machine learning models trained on remote-sensed data suffers from a lack of reliable training datasets and they are often site-specific. Therefore, in this study, we proposed a method that integrates the bi-temporal analysis of the combination of spectral indices that detects the potential changes, which then serve as reference data for the Random Forest classifier. In addition, we examined the transferability of the pre-trained model over time, which is an important aspect from the operational point of view and may significantly reduce the time required for the preparation of reliable and accurate training data. Two types of vegetation losses were identified: woody coverage converted to non-woody vegetation, and vegetated areas converted to sealed surfaces or bare soil. The vegetation losses were detected annually over the period 2018–2021 with an overall accuracy (OA) above 0.97 and a Kappa coefficient of 0.95 for all time intervals in the study regions in Poland and Norway. Additionally, the pre-trained model’s temporal transferability revealed an improvement of the OA by 5 percentage points and the macroF1-Score value by 12 percentage points compared to the original model.
Authors
Anders Bryn Rune Halvorsen Peter Horvath Lasse Torben Keetz Ida Marielle Mienna Trond Simensen Olav Skarpaas Ingrid Vesterdal Tjessem Joachim Paul Töpper Vigdis Vandvik Liv Guri Velle Catharina Caspara VloonAbstract
No abstract has been registered
Authors
H. Heinemann F. Durand-Maniclas F. Seidel F. Ciulla Teresa Gómez de la Bárcena M. Camenzind S. Corrado Z. Csűrös Zs. Czakó D. Eylenbosch Andrea Ficke C. Flamm J.M. Herrera V. Horáková A. Hund F. Lüddeke F. Platz B. Poós Daniel Rasse M. da Silva-Lopes M. Toleikiene A. Veršulienė M. Visse-Mansiaux K. Yu J. Hirte A. DonAbstract
Ensuring food security through sustainable practices while reducing greenhouse gas emissions are key challenges in modern agriculture. Utilising genetic variability within a crop species to identify varieties with higher root biomass carbon (C) could help address these challenges. It is thus crucial to quantify and understand intra-specific above- and belowground performance under varying environmental conditions. The study objectives were to: (a) quantify root biomass and depth distribution in different winter wheat varieties under various pedoclimatic conditions, (b) investigate the influence of variety and pedoclimatic conditions on the relationship between above- and belowground biomass production, and (c) assess whether optimised winter wheat variety selection can lead to both greater root biomass C and yield, boosting C accrual. Root biomass, root distribution to 1 m soil depth and root-to-shoot ratios were assessed in 10 different winter wheat varieties grown at 11 experimental sites covering a European climatic gradient from Spain to Norway. Median root biomass down to 1 m depth was 1.4 ± 0.7 Mg ha−1. The primary explanatory factor was site, accounting for 60% of the variation in root biomass production, while the genetic diversity between wheat varieties explained 9.5%. Precipitation had a significantly negative effect on total root biomass, especially in subsoil. Significant differences were also observed between varieties in root-to-shoot ratios and grain yield. The difference between the variety with the lowest root biomass and the one with the highest across sites was on average 0.9 Mg ha−1 which is an increase of 45%. Pedoclimatic conditions had a greater influence than variety, and determined the relationship's direction between root biomass and grain yield. A site-specific approach is, therefore, needed to realise the full potential for increased root biomass and yield offered by optimised variety selection. Summary The variability in root biomass among 10 winter wheat varieties was quantified in field trials. Root biomass differs significantly between varieties, but is mainly driven by site conditions. Root-to-shoot ratios decreased with increasing precipitation. Root biomass was 45% higher in the best performing variety compared to the worst performing one.
Authors
Chala Adugna Kufa Afework Bekele Anagaw Meshesha Atickem Desalegn Chala Diress Tsegaye Alemu Torbjørn Ergon Nils Christian Stenseth Dietmar ZinnerAbstract
Ethiopia is home to two subspecies of Colobus guereza, C. g. guereza and C. g. gallarum. Whereas C. g. guereza is listed as Least Concern by IUCN, the conservation status of C. g. gallarum is unclear, but according to a recent assessment, it will most likely be listed as Vulnerable, because of habitat loss due to agricultural expansion. We used climate data to model the habitat suitability for both taxa in a comparative study to identify suitable habitats within and outside of protected areas that may serve as Anthropocene refugia. Our ensemble models estimated 168,731 km2 as climatically suitable habitat for C. g. guereza and 69,542 km2 for C. g. gallarum with an overlap between the two taxa of 17.2 %. Areas that qualified as refugia, i.e., areas covered by forest, were 47,101 km2 (only 27.9 % of the total suitable habitat) and 8430 km2 (12.1 % of the suitable habitat) for C. g. guereza and C. g. gallarum, respectively. Of these, 39.8 % (C. g. guereza) and 53.7 % (C. g. gallarum) are within Ethiopia’s current protected area network. Given that potential Anthropocene refugia are found only partly within protected areas, conservation management should include this information when developing conservation strategies for both taxa. As the majority of suitable habitats for the two colobus taxa exist in non-forested regions, afforestation in these areas would be highly beneficial and is strongly recommended.