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.
2021
Abstract
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Abstract
Many greenkeepers and authorities are concerned about the environmental risks resulting from pesticide use on golf courses. We studied leaching and surface runoff of fungicides and metabolites during two winter seasons after fall application of boscalid, pyraclostrobin, prothioconazole, trifloxystrobin and fludioxonil in field lysimeters at NIBIO Landvik, Norway. The applications were made on creeping bentgrass greens (5% slope) that had been established from seed or sod (26 mm mat) on USGA‐spec. root zones amended with Sphagnum peat or garden compost, both with 0.3‐0.4% organic carbon in the root zone. The proportions of the winter precipitation recovered as surface and drainage water varied from 3 and 91% in 2016‐17 to 33 and 55% in 2017‐18 due to differences in soil freezing, rainfall intensity and snow and ice cover. Detections of fungicides and their metabolites in drainage water were mostly within the Environmental Risk Limits (ERLs) for aquatic organisms. In contrast, concentrations in surface runoff exceeded ERLs by up to 1000 times. Greens established from sod usually had higher fungicide losses in surface runoff but lower losses in drainage water than greens established from seed. Presumably because of higher microbial activity and a higher pH that made prothioconazole‐desthio more polar, fungicide and metabolite losses in drainage water were usually higher from greens containing compost that from greens containing peat. Leaching of fungicides and metabolites occurred even from frozen greens. The results are discussed in a practical context aiming for reduced environmental risks from spraying fungicides against turfgrass winter diseases.
Abstract
Sustainability learning is gaining popularity as an important field within sustainability research, where farm sustainability can be understood as a learning process. In this study, we seek to reveal the sustainability learning process of farmers, utilizing a framework distinguishing contextual factors (where? and when?), knowledge (what?), motivation (why?), and process (how?). The article presents a participatory inquiry mixed-methods approach, utilizing results from sustainability assessments on five farms with the SMART-farm tool as a unifying starting point for further discussions on sustainability learning in farmers' interviews and stakeholder workshops. Empirically the study is set in the horticultural production in Arctic Norway, where few studies on sustainability have been undertaken. The study shows how both the complexity of the concept of farm sustainability and contextual factors influence the sustainability learning process, for instance by giving rise to a vast number of conflicting issues while working toward farm sustainability. The sustainability learning process is found to be predominantly a social learning process. The theoretic contribution of the study lies in its novel framework that can be used to reveal important aspects of the sustainability learning process, as well as to contribute to the literature on how to proceed from sustainability assessments to implementation. A key finding from the study is that farmers will require continuous assistance in their processes toward farm sustainability, but for this to be possible, knowledge, sources of knowledge, and learning platforms for holistic sustainability need to be established.
Authors
Owusu Fordjour Aidoo Sarah Cunze Ritter Atoundem Guimapi Linda Arhin Fred Kormla Ablormeti Elizabeth Tettey Frank Dampare Yayra Afram Osei Bonsu Joshua Obeng Hanif Lutuf Matthew Dickinson Ndede YankeyAbstract
Coconut is recognized for its popularity in contributing to food and nutritional security. It generates income and helps to improve rural livelihood. However, these benefits are constrained by lethal yellowing disease (LYD). A clear understanding of climate suitable areas for disease invasion is essential for implementing quarantine measures. Therefore, we used a machine learning algorithm based on maximum entropy to model and map habitat suitability of LYD and coconut under current and future climate change scenarios using three Shared Socio-economic Pathways (SSPs) (1.26, 3.70 and 5.85) for three time periods (2041–2060, 2061–2080 and 2081–2100). Outside its current range, the model projected habitat suitability of LYD in Australia, Asia and South America. The distribution of coconut exceeded that of LYD. The area under the curve value of 0.98 was recorded for LYD, whereas 0.87 was obtained for the coconut model. The predictor variables that most influenced LYD projections were minimum temperature of the coldest month (88.4%) and precipitation of the warmest quarter (7.3%), whereas minimum temperature of the coldest month (85.9%) and temperature seasonality (8.7%) contributed most to the coconut model. Our study highlights potential climate suitable areas of LYD and coconut, and provides useful information for increasing quarantine measures and developing resistant or tolerant coconut varieties against the disease. Also, our study establishes an approach to model the climatic suitability for surveillance and monitoring of the disease, especially in areas that the disease has not been reported.
Authors
Alexandre Tisserant Marjorie Morales Otávio Cavalett Adam O'Toole Simon Weldon Daniel Rasse Francesco CherubiniAbstract
Limiting temperature rise below 2 °C requires large deployment of Negative Emission Technologies (NET) to capture and store atmospheric CO2. Compared to other types of NETs, biochar has emerged as a mature option to store carbon in soils while providing several co-benefits and limited trade-offs. Existing life-cycle assessment studies of biochar systems mostly focus on climate impacts from greenhouse gasses (GHGs), while other forcing agents, effects on soil emissions, other impact categories, and the implications of a large-scale national deployment are rarely jointly considered. Here, we consider all these aspects and quantify the environmental impacts of application to agricultural soils of biochar from forest residues available in Norway considering different scenarios (including mixing of biochar with synthetic fertilizers and bio-oil sequestration for long-term storage). All the biochar scenarios deliver negative emissions under a life-cycle perspective, ranging from -1.72 ± 0.45 tonnes CO2-eq. ha−1 yr−1 to -7.18 ± 0.67 tonnes CO2-eq. ha−1 yr−1 (when bio-oil is sequestered). Estimated negative emissions are robust to multiple climate metrics and a large range of uncertainties tested with a Monte-Carlo analysis. Co-benefits exist with crop yields, stratospheric ozone depletion and marine eutrophication, but potential trade-offs occur with tropospheric ozone formation, fine particulate formation, terrestrial acidification and ecotoxicity. At a national level, biochar has the potential to offset between 13% and 40% of the GHG emissions from the Norwegian agricultural sector. Overall, our study shows the importance of integrating emissions from the supply chain with those from agricultural soils to estimate mitigation potentials of biochar in specific regional contexts.
Abstract
Scots pine exhibits variations in ray anatomy, which are poorly understood. Some ray parenchyma cells develop thick and lignified cell walls before heartwood formation. We hypothesized that some stands and trees show high numbers of lignified and thick-walled parenchyma cells early in the sapwood. Therefore, a microscopic analysis of Scots pine sapwood from four different stands in Northern Europe was performed on Safranin — Astra blue-stained tangential micro sections from outer and inner sapwood areas. Significant differences in lignification and cell wall thickening of ray parenchyma cells were observed in the outer sapwood between all of the stands for the trees analyzed. On a single tree level, the relative lignification and cell wall thickening of ray parenchyma cells ranged from 4.3% to 74.3% in the outer sapwood. In the inner sapwood, lignification and cell wall thickening of ray parenchyma cells were more frequent. In some trees, however, the difference in lignification and cell wall thickening between inner and outer sapwood was small since early lignification, and cell wall thickening was already more common in the outer sapwood. Ray composition and number of rays per area were not significantly different within the studied material. However, only one Scottish tree had a significantly higher number of ray parenchyma cells per ray. The differences discovered in lignification and cell wall thickening in ray parenchyma cells early in the sapwood of Scots pine are relevant for wood utilization in general and impregnation treatments with protection agents in particular.
Abstract
In the Bramke valley (western Harz mountains, North Germany), three forested headwater catchments have been monitored since decades. A broad range of observables relevant to forestry, hydrology, hydrochemistry and ecosystem research allows to compare different approaches to environmental monitoring; each of them has its own set of relevant observables. The basic temporal resolution is daily for hydrometeorology and bi-weekly for streamwater chemistry; standing biomass of the Norway spruce stands is measured every couple of years. Tree growth (site index) has changed between and within rotation periods (of up to 129 years); changes in soil nutrient pools are typical variables used to explain this nonstationary forest growth when the spatial-temporal scales match. In hydrology, transport mechanisms of water and solutes through catchment soils are used to model and predict runoff and its chemistry. Given the homogeneity of the area in terms of geology, soils and topography as well as climate, differences between the catchments in the Bramke valley are mostly related to forestry variables. The catchments exhibit long-term changes and spatial gradients related to atmospheric deposition, management and changing climate. After providing a short multivariate summary of the dataset, we present several nonlinear metrics suitable to detect and quantify subtle changes and to describe different behavior, both between different variables from the same catchment, as well as for the same variable across catchments. Soil water potential and solution chemistry are further links between forestry and hydrology. However, at Lange Bramke, similar to other catchment studies, the evaluation of these data sets has not converged to a consistent, realistic model at the catchment scale. We hypothesize that this lack of model integration is due to theoretical rather than technical limits. A possible representation of these limits might be phrased in a category theory approach. How to cite: Hauhs, M., Meesenburg, H., and Lange, H.: Long-term monitoring of vegetation and hydrology in headwater catchments and the difficulties to embrace data-oriented and process-oriented approaches, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7684, https://doi.org/10.5194/egusphere-egu21-7684, 2021.
Abstract
Plant diseases may survive and be spread by infected seeds. In this study we monitored the longevity of 14 seed-borne pathogens in 9 crop species commonly grown in the Nordic countries, in addition to a sample of sclerotia of Sclerotinia sclerotiorum. The data from the first 30 years of a 100-year seed storage experiment located in a natural −3.5 °C environment (permafrost) in Svalbard, Norway, are presented. To date, the pathogens, tested by traditional seed health testing methods (freezing blotter, agar plates, growing on tests), have survived. Linear regression analyses showed that the seed infection percentages of Drechslera dictyoides in meadow fescue, Drechslera phlei in timothy, and Septoria nodorum in wheat were significantly reduced compared to the percentages at the start of the experiment (from 63% to 34%, from 70% to 65%, and from 15% to 1%, respectively), and that Phoma betae in beet had increased significantly (from 43% to 56%). No trends in the infection percentage were observed over the years in Drechslera spp. in barley (fluctuating between 30% and 64%) or in Alternaria brassicicola in cabbage (fluctuating between 82% and 99%), nor in pathogens with low seed infection percentages at the start of the experiment. A major part of the stored sclerotia was viable after 30 years. To avoid the spread of seed-borne diseases, it is recommended that gene banks implement routines that avoid the use of infected seeds.
Authors
Marion Lang Harald Albrecht Marlene Rudolph Johannes KollmannAbstract
A better understanding of regional differentiation and local adaptation of rare arable plants is essential for the development of suitable methods for the reintroduction of these species. We set up F1 and F2 greenhouse experiments with 4–12 source populations of five rare arable plant species to test for genetically based differentiation in biomass production and phenology in South Germany. For three species, i.e. Arnoseris minima, Consolida regalis and Teesdalia nudicaulis, reciprocal transplant experiments were performed in arable fields to investigate local adaptation in plant establishment as well as biomass production to the northern or southern regions of three seed transfer zones. We found low regional differentiation, but provenance-specific responses to drought stress in Legousia speculum-veneris biomass and A. minima phenology. Moreover, little evidence was identified for local adaptation, while significant differences were seen in the performance between the transplant sites and study years, indicating a high phenotypic variability. Our results suggest that the current seed zones are suitable for the seed transfer of rare arable plants in the study region. Thus, there is a low risk of maladaptation when using autochthonous seed sources within the seed zones, but a high extinction risk of these species and their respective ecosystem functions if no active restoration is done, including transplant measures.
Authors
Xinbing Wang Yuxin Miao Rui Dong Hainie Zha Tingting Xia Zhichao Chen Krzysztof Kusnierek Guohua Mi Hong Sun Minzan LiAbstract
Reliable and efficient in-season nitrogen (N) status diagnosis and recommendation methods are crucially important for the success of crop precision N management (PNM). The accuracy of these methods has been found to be influenced by soil properties, weather conditions, and crop management practices. It is important to effectively incorporate these variables to improve in-season N management. Machine learning (ML) methods are promising due to their capability of processing different types of data and modeling both linear and non-linear relationships. The objectives of this study were to (1) determine the potential improvement of in-season prediction of corn N nutrition index (NNI) and grain yield by combining soil, weather and management data with active sensor data using random forest regression (RFR) as compared with Lasso linear regression (LR) using similar data and simple regression (SR) models only using crop sensor data; and (2) to develop a new in-season side-dress N fertilizer recommendation strategy at eighth to ninth leaf stage (V8-V9) of corn developement using the RFR model. Twelve site-year experiments examining corn N rates and planting densities were conducted in Northeast China. The GreenSeeker sensor data and corn NNI were collected at V8-V9 stage, and grain yield was determined at the harvest stage (R6). The soil information was obtained at planting and the weather data was measured throughout the growing season. The results indicated that corn NNI and grain yield were better predicted by combining soil, weather and management information with GreenSeeker sensor data using RFR model (R2 = 0.86 and 0.79) and LR model (R2 = 0.85 and 0.76) as compared with only using GreenSeeker sensor data (R2 = 0.66 and 0.62–63) based on the test dataset. An innovative in-season side-dress N recommendation strategy was developed using the RFR grain yield prediction model to simulate corn grain yield responses to a series of side-dress N rates at V8-V9 stage. Based on these response curves, site-, and year-specific optimum side-dress N rates can be determined. The scenario analysis results indicated that this RFR model-based in-season N recommendation strategy could recommend side-dress N rates similar to those based on measured agronomic optimum N rate (AONR) or economic optimum N rate (EONR), with root mean square error (RMSE) of 17 kg ha−1 and relative error (RE) of 14–15 %. It is concluded that combining soil, weather and management information with crop sensor data using RFR can significantly improve both in-season corn NNI and grain yield prediction and N management, compared with the approach based only on crop sensor data. More studies are needed to further improve and evaluate this approach under diverse on-farm conditions.